commit 74d7d2355344e15fd8e67f3470aabcaff8c9132e Author: Super_JK Date: Sat Mar 5 21:59:46 2022 +0100 initial commit diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..485dee6 --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +.idea diff --git a/Lab3_Exercises.ipynb b/Lab3_Exercises.ipynb new file mode 100644 index 0000000..6b53c40 --- /dev/null +++ b/Lab3_Exercises.ipynb @@ -0,0 +1,859 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Lab3_Exercises", + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "**Import necessary libraries**" + ], + "metadata": { + "id": "jBfdU_joaqrm" + } + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import os\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.metrics import mean_squared_error, precision_score, recall_score, accuracy_score, confusion_matrix, \\\n", + " roc_auc_score, roc_curve, f1_score\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "from mpl_toolkits import mplot3d\n", + "%matplotlib notebook" + ], + "metadata": { + "id": "y90MCHqMa1j5" + }, + "execution_count": 70, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Load the 'Pokemon.csv' dataset as a pandas dataframe, change the Type 1 and Type 2 variables to categorical and replace 'Type 2' missing values (replace by the value of 'Type 1').**" + ], + "metadata": { + "id": "zx1zL4ura5ze" + } + }, + { + "cell_type": "code", + "source": [ + "file = 'data/Pokemon.csv'\n", + "\n", + "##Read dataframe##\n", + "\n", + "df = pd.read_csv(file)\n", + "print(df.head())" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LuZ28dR_bsXi", + "outputId": "4ccaf80f-2e27-45d0-dcce-c4c9f5910324" + }, + "execution_count": 71, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " # Name Type 1 Type 2 Total HP Attack Defense \\\n", + "0 1 Bulbasaur Grass Poison 318 45 49 49 \n", + "1 2 Ivysaur Grass Poison 405 60 62 63 \n", + "2 3 Venusaur Grass Poison 525 80 82 83 \n", + "3 3 VenusaurMega Venusaur Grass Poison 625 80 100 123 \n", + "4 4 Charmander Fire NaN 309 39 52 43 \n", + "\n", + " Sp. Atk Sp. Def Speed Generation Legendary \n", + "0 65 65 45 1 False \n", + "1 80 80 60 1 False \n", + "2 100 100 80 1 False \n", + "3 122 120 80 1 False \n", + "4 60 50 65 1 False \n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "##Change variables types##\n", + "\n", + "print(df.dtypes)\n", + "df.astype({'Type 1': 'category', 'Type 2': 'category'})" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 667 + }, + "id": "BRPoZ4YhbuLr", + "outputId": "4c6635e7-9c5d-4d1c-f81e-035d63c4e086" + }, + "execution_count": 72, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# int64\n", + "Name object\n", + "Type 1 object\n", + "Type 2 object\n", + "Total int64\n", + "HP int64\n", + "Attack int64\n", + "Defense int64\n", + "Sp. Atk int64\n", + "Sp. Def int64\n", + "Speed int64\n", + "Generation int64\n", + "Legendary bool\n", + "dtype: object\n" + ] + }, + { + "data": { + "text/plain": " # Name Type 1 Type 2 Total HP Attack Defense \\\n0 1 Bulbasaur Grass Poison 318 45 49 49 \n1 2 Ivysaur Grass Poison 405 60 62 63 \n2 3 Venusaur Grass Poison 525 80 82 83 \n3 3 VenusaurMega Venusaur Grass Poison 625 80 100 123 \n4 4 Charmander Fire NaN 309 39 52 43 \n.. ... ... ... ... ... .. ... ... \n795 719 Diancie Rock Fairy 600 50 100 150 \n796 719 DiancieMega Diancie Rock Fairy 700 50 160 110 \n797 720 HoopaHoopa Confined Psychic Ghost 600 80 110 60 \n798 720 HoopaHoopa Unbound Psychic Dark 680 80 160 60 \n799 721 Volcanion Fire Water 600 80 110 120 \n\n Sp. Atk Sp. Def Speed Generation Legendary \n0 65 65 45 1 False \n1 80 80 60 1 False \n2 100 100 80 1 False \n3 122 120 80 1 False \n4 60 50 65 1 False \n.. ... ... ... ... ... \n795 100 150 50 6 True \n796 160 110 110 6 True \n797 150 130 70 6 True \n798 170 130 80 6 True \n799 130 90 70 6 True \n\n[800 rows x 13 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
#NameType 1Type 2TotalHPAttackDefenseSp. AtkSp. DefSpeedGenerationLegendary
01BulbasaurGrassPoison3184549496565451False
12IvysaurGrassPoison4056062638080601False
23VenusaurGrassPoison525808283100100801False
33VenusaurMega VenusaurGrassPoison62580100123122120801False
44CharmanderFireNaN3093952436050651False
..........................................
795719DiancieRockFairy60050100150100150506True
796719DiancieMega DiancieRockFairy700501601101601101106True
797720HoopaHoopa ConfinedPsychicGhost6008011060150130706True
798720HoopaHoopa UnboundPsychicDark6808016060170130806True
799721VolcanionFireWater6008011012013090706True
\n

800 rows × 13 columns

\n
" + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ] + }, + { + "cell_type": "code", + "source": [ + "##Check for any missing values, replace them accordingly or remove the associated rows###\n", + "\n", + "print(df.isna().sum())\n", + "df['Type 2'].fillna(df['Type 1'], inplace=True)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SGYJzwQsb6Le", + "outputId": "579a9ffb-9817-47e7-a5df-4fa7b0cbbf1d" + }, + "execution_count": 73, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# 0\n", + "Name 0\n", + "Type 1 0\n", + "Type 2 386\n", + "Total 0\n", + "HP 0\n", + "Attack 0\n", + "Defense 0\n", + "Sp. Atk 0\n", + "Sp. Def 0\n", + "Speed 0\n", + "Generation 0\n", + "Legendary 0\n", + "dtype: int64\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "print(df.isna().sum())" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "PaFw_zDvbinl", + "outputId": "781afbbd-098a-44ec-d7e0-838134a2d902" + }, + "execution_count": 74, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# 0\n", + "Name 0\n", + "Type 1 0\n", + "Type 2 0\n", + "Total 0\n", + "HP 0\n", + "Attack 0\n", + "Defense 0\n", + "Sp. Atk 0\n", + "Sp. Def 0\n", + "Speed 0\n", + "Generation 0\n", + "Legendary 0\n", + "dtype: int64\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Select the target variable y as 'HP' and the predictor as 'Attack', split the dataset in a train and a test set with a 0.8/0.2 ratio.**" + ], + "metadata": { + "id": "ZiupbAY9b-H2" + } + }, + { + "cell_type": "code", + "source": [ + "##Select a target variable y and a predictor x. Reshape the data x to [num_samples, 1]##\n", + "\n", + "y = df['HP'].values\n", + "x = df['Attack'].values\n", + "X = x[:, np.newaxis]\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.scatter(x,y)\n", + "ax.set_xlabel('Attack')\n", + "ax.set_ylabel('HP')\n", + "plt.show()\n", + "\n", + "##Define a training set and a validation set##\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, test_size=0.2, shuffle=True)\n", + "print((X_train.shape, y_train.shape), (X_test.shape, y_test.shape))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 296 + }, + "id": "sbGfkAWmcbRN", + "outputId": "3ba45f44-374b-45a7-bbbd-a098977e54e2" + }, + "execution_count": 75, + "outputs": [ + { + "data": { + "text/plain": "", + "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key_event(event, name);\n };\n }\n\n canvas_div.addEventListener(\n 'keydown',\n on_keyboard_event_closure('key_press')\n );\n canvas_div.addEventListener(\n 'keyup',\n on_keyboard_event_closure('key_release')\n );\n\n this._canvas_extra_style(canvas_div);\n this.root.appendChild(canvas_div);\n\n var canvas = (this.canvas = document.createElement('canvas'));\n canvas.classList.add('mpl-canvas');\n canvas.setAttribute('style', 'box-sizing: content-box;');\n\n this.context = canvas.getContext('2d');\n\n var backingStore =\n this.context.backingStorePixelRatio ||\n this.context.webkitBackingStorePixelRatio ||\n this.context.mozBackingStorePixelRatio ||\n this.context.msBackingStorePixelRatio ||\n this.context.oBackingStorePixelRatio ||\n this.context.backingStorePixelRatio ||\n 1;\n\n this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n 'canvas'\n ));\n rubberband_canvas.setAttribute(\n 'style',\n 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n );\n\n // Apply a ponyfill if ResizeObserver is not implemented by browser.\n if (this.ResizeObserver === undefined) {\n if (window.ResizeObserver !== undefined) {\n this.ResizeObserver = window.ResizeObserver;\n } else {\n var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n this.ResizeObserver = obs.ResizeObserver;\n }\n }\n\n this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n var nentries = entries.length;\n for (var i = 0; i < nentries; i++) {\n var entry = entries[i];\n var width, height;\n if (entry.contentBoxSize) {\n if (entry.contentBoxSize instanceof Array) {\n // Chrome 84 implements new version of spec.\n width = entry.contentBoxSize[0].inlineSize;\n height = entry.contentBoxSize[0].blockSize;\n } else {\n // Firefox implements old version of spec.\n width = entry.contentBoxSize.inlineSize;\n height = entry.contentBoxSize.blockSize;\n }\n } else {\n // Chrome <84 implements even older version of spec.\n width = entry.contentRect.width;\n height = entry.contentRect.height;\n }\n\n // Keep the size of the canvas and rubber band canvas in sync with\n // the canvas container.\n if (entry.devicePixelContentBoxSize) {\n // Chrome 84 implements new version of spec.\n canvas.setAttribute(\n 'width',\n entry.devicePixelContentBoxSize[0].inlineSize\n );\n canvas.setAttribute(\n 'height',\n entry.devicePixelContentBoxSize[0].blockSize\n );\n } else {\n canvas.setAttribute('width', width * fig.ratio);\n canvas.setAttribute('height', height * fig.ratio);\n }\n canvas.setAttribute(\n 'style',\n 'width: ' + width + 'px; height: ' + height + 'px;'\n );\n\n rubberband_canvas.setAttribute('width', width);\n rubberband_canvas.setAttribute('height', height);\n\n // And update the size in Python. We ignore the initial 0/0 size\n // that occurs as the element is placed into the DOM, which should\n // otherwise not happen due to the minimum size styling.\n if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n fig.request_resize(width, height);\n }\n }\n });\n this.resizeObserverInstance.observe(canvas_div);\n\n function on_mouse_event_closure(name) {\n return function (event) {\n return fig.mouse_event(event, name);\n };\n }\n\n rubberband_canvas.addEventListener(\n 'mousedown',\n on_mouse_event_closure('button_press')\n );\n rubberband_canvas.addEventListener(\n 'mouseup',\n on_mouse_event_closure('button_release')\n );\n rubberband_canvas.addEventListener(\n 'dblclick',\n on_mouse_event_closure('dblclick')\n );\n // Throttle sequential mouse events to 1 every 20ms.\n rubberband_canvas.addEventListener(\n 'mousemove',\n on_mouse_event_closure('motion_notify')\n );\n\n rubberband_canvas.addEventListener(\n 'mouseenter',\n on_mouse_event_closure('figure_enter')\n );\n rubberband_canvas.addEventListener(\n 'mouseleave',\n on_mouse_event_closure('figure_leave')\n );\n\n canvas_div.addEventListener('wheel', function (event) {\n if (event.deltaY < 0) {\n event.step = 1;\n } else {\n event.step = -1;\n }\n on_mouse_event_closure('scroll')(event);\n });\n\n canvas_div.appendChild(canvas);\n canvas_div.appendChild(rubberband_canvas);\n\n this.rubberband_context = rubberband_canvas.getContext('2d');\n this.rubberband_context.strokeStyle = '#000000';\n\n this._resize_canvas = function (width, height, forward) {\n if (forward) {\n canvas_div.style.width = width + 'px';\n canvas_div.style.height = height + 'px';\n }\n };\n\n // Disable right mouse context menu.\n this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n event.preventDefault();\n return false;\n });\n\n function set_focus() {\n canvas.focus();\n canvas_div.focus();\n }\n\n window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'mpl-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n continue;\n }\n\n var button = (fig.buttons[name] = document.createElement('button'));\n button.classList = 'mpl-widget';\n button.setAttribute('role', 'button');\n button.setAttribute('aria-disabled', 'false');\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n var icon_img = document.createElement('img');\n icon_img.src = '_images/' + image + '.png';\n icon_img.srcset = '_images/' + image + '_large.png 2x';\n icon_img.alt = tooltip;\n button.appendChild(icon_img);\n\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n var fmt_picker = document.createElement('select');\n fmt_picker.classList = 'mpl-widget';\n toolbar.appendChild(fmt_picker);\n this.format_dropdown = fmt_picker;\n\n for (var ind in mpl.extensions) {\n var fmt = mpl.extensions[ind];\n var option = document.createElement('option');\n option.selected = fmt === mpl.default_extension;\n option.innerHTML = fmt;\n fmt_picker.appendChild(option);\n }\n\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n // which will in turn request a refresh of the image.\n this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n properties['type'] = type;\n properties['figure_id'] = this.id;\n this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n if (!this.waiting) {\n this.waiting = true;\n this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n var format_dropdown = fig.format_dropdown;\n var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n var size = msg['size'];\n if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n fig._resize_canvas(size[0], size[1], msg['forward']);\n fig.send_message('refresh', {});\n }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n var x0 = msg['x0'] / fig.ratio;\n var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n var x1 = msg['x1'] / fig.ratio;\n var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n x0 = Math.floor(x0) + 0.5;\n y0 = Math.floor(y0) + 0.5;\n x1 = Math.floor(x1) + 0.5;\n y1 = Math.floor(y1) + 0.5;\n var min_x = Math.min(x0, x1);\n var min_y = Math.min(y0, y1);\n var width = Math.abs(x1 - x0);\n var height = Math.abs(y1 - y0);\n\n fig.rubberband_context.clearRect(\n 0,\n 0,\n fig.canvas.width / fig.ratio,\n fig.canvas.height / fig.ratio\n );\n\n fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n // Updates the figure title.\n fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n fig.rubberband_canvas.style.cursor = msg['cursor'];\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n // Request the server to send over a new figure.\n fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n for (var key in msg) {\n if (!(key in fig.buttons)) {\n continue;\n }\n fig.buttons[key].disabled = !msg[key];\n fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n if (msg['mode'] === 'PAN') {\n fig.buttons['Pan'].classList.add('active');\n fig.buttons['Zoom'].classList.remove('active');\n } else if (msg['mode'] === 'ZOOM') {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.add('active');\n } else {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.remove('active');\n }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Called whenever the canvas gets updated.\n this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n return function socket_on_message(evt) {\n if (evt.data instanceof Blob) {\n var img = evt.data;\n if (img.type !== 'image/png') {\n /* FIXME: We get \"Resource interpreted as Image but\n * transferred with MIME type text/plain:\" errors on\n * Chrome. But how to set the MIME type? It doesn't seem\n * to be part of the websocket stream */\n img.type = 'image/png';\n }\n\n /* Free the memory for the previous frames */\n if (fig.imageObj.src) {\n (window.URL || window.webkitURL).revokeObjectURL(\n fig.imageObj.src\n );\n }\n\n fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n img\n );\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n } else if (\n typeof evt.data === 'string' &&\n evt.data.slice(0, 21) === 'data:image/png;base64'\n ) {\n fig.imageObj.src = evt.data;\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n }\n\n var msg = JSON.parse(evt.data);\n var msg_type = msg['type'];\n\n // Call the \"handle_{type}\" callback, which takes\n // the figure and JSON message as its only arguments.\n try {\n var callback = fig['handle_' + msg_type];\n } catch (e) {\n console.log(\n \"No handler for the '\" + msg_type + \"' message type: \",\n msg\n );\n return;\n }\n\n if (callback) {\n try {\n // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n callback(fig, msg);\n } catch (e) {\n console.log(\n \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n e,\n e.stack,\n msg\n );\n }\n }\n };\n};\n\n// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function (e) {\n //this section is from http://www.quirksmode.org/js/events_properties.html\n var targ;\n if (!e) {\n e = window.event;\n }\n if (e.target) {\n targ = e.target;\n } else if (e.srcElement) {\n targ = e.srcElement;\n }\n if (targ.nodeType === 3) {\n // defeat Safari bug\n targ = targ.parentNode;\n }\n\n // pageX,Y are the mouse positions relative to the document\n var boundingRect = targ.getBoundingClientRect();\n var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n\n return { x: x, y: y };\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * https://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n return Object.keys(original).reduce(function (obj, key) {\n if (typeof original[key] !== 'object') {\n obj[key] = original[key];\n }\n return obj;\n }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n var canvas_pos = mpl.findpos(event);\n\n if (name === 'button_press') {\n this.canvas.focus();\n this.canvas_div.focus();\n }\n\n var x = canvas_pos.x * this.ratio;\n var y = canvas_pos.y * this.ratio;\n\n this.send_message(name, {\n x: x,\n y: y,\n button: event.button,\n step: event.step,\n guiEvent: simpleKeys(event),\n });\n\n /* This prevents the web browser from automatically changing to\n * the text insertion cursor when the button is pressed. We want\n * to control all of the cursor setting manually through the\n * 'cursor' event from matplotlib */\n event.preventDefault();\n return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n // Prevent repeat events\n if (name === 'key_press') {\n if (event.key === this._key) {\n return;\n } else {\n this._key = event.key;\n }\n }\n if (name === 'key_release') {\n this._key = null;\n }\n\n var value = '';\n if (event.ctrlKey && event.key !== 'Control') {\n value += 'ctrl+';\n }\n else if (event.altKey && event.key !== 'Alt') {\n value += 'alt+';\n }\n else if (event.shiftKey && event.key !== 'Shift') {\n value += 'shift+';\n }\n\n value += 'k' + event.key;\n\n this._key_event_extra(event, name);\n\n this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n return false;\n};\n\nmpl.figure.prototype.toolbar_button_onclick = function (name) {\n if (name === 'download') {\n this.handle_save(this, null);\n } else {\n this.send_message('toolbar_button', { name: name });\n }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n // Create a \"websocket\"-like object which calls the given IPython comm\n // object with the appropriate methods. Currently this is a non binary\n // socket, so there is still some room for performance tuning.\n var ws = {};\n\n ws.binaryType = comm.kernel.ws.binaryType;\n ws.readyState = comm.kernel.ws.readyState;\n function updateReadyState(_event) {\n if (comm.kernel.ws) {\n ws.readyState = comm.kernel.ws.readyState;\n } else {\n ws.readyState = 3; // Closed state.\n }\n }\n comm.kernel.ws.addEventListener('open', updateReadyState);\n comm.kernel.ws.addEventListener('close', updateReadyState);\n comm.kernel.ws.addEventListener('error', updateReadyState);\n\n ws.close = function () {\n comm.close();\n };\n ws.send = function (m) {\n //console.log('sending', m);\n comm.send(m);\n };\n // Register the callback with on_msg.\n comm.on_msg(function (msg) {\n //console.log('receiving', msg['content']['data'], msg);\n var data = msg['content']['data'];\n if (data['blob'] !== undefined) {\n data = {\n data: new Blob(msg['buffers'], { type: data['blob'] }),\n };\n }\n // Pass the mpl event to the overridden (by mpl) onmessage function.\n ws.onmessage(data);\n });\n return ws;\n};\n\nmpl.mpl_figure_comm = function (comm, msg) {\n // This is the function which gets called when the mpl process\n // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n var id = msg.content.data.id;\n // Get hold of the div created by the display call when the Comm\n // socket was opened in Python.\n var element = document.getElementById(id);\n var ws_proxy = comm_websocket_adapter(comm);\n\n function ondownload(figure, _format) {\n window.open(figure.canvas.toDataURL());\n }\n\n var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n\n // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n // web socket which is closed, not our websocket->open comm proxy.\n ws_proxy.onopen();\n\n fig.parent_element = element;\n fig.cell_info = mpl.find_output_cell(\"
\");\n if (!fig.cell_info) {\n console.error('Failed to find cell for figure', id, fig);\n return;\n }\n fig.cell_info[0].output_area.element.on(\n 'cleared',\n { fig: fig },\n fig._remove_fig_handler\n );\n};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n var width = fig.canvas.width / fig.ratio;\n fig.cell_info[0].output_area.element.off(\n 'cleared',\n fig._remove_fig_handler\n );\n fig.resizeObserverInstance.unobserve(fig.canvas_div);\n\n // Update the output cell to use the data from the current canvas.\n fig.push_to_output();\n var dataURL = fig.canvas.toDataURL();\n // Re-enable the keyboard manager in IPython - without this line, in FF,\n // the notebook keyboard shortcuts fail.\n IPython.keyboard_manager.enable();\n fig.parent_element.innerHTML =\n '';\n fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n fig.send_message('closing', msg);\n // fig.ws.close()\n};\n\nmpl.figure.prototype.push_to_output = function (_remove_interactive) {\n // Turn the data on the canvas into data in the output cell.\n var width = this.canvas.width / this.ratio;\n var dataURL = this.canvas.toDataURL();\n this.cell_info[1]['text/html'] =\n '';\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Tell IPython that the notebook contents must change.\n IPython.notebook.set_dirty(true);\n this.send_message('ack', {});\n var fig = this;\n // Wait a second, then push the new image to the DOM so\n // that it is saved nicely (might be nice to debounce this).\n setTimeout(function () {\n fig.push_to_output();\n }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'btn-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n var button;\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n continue;\n }\n\n button = fig.buttons[name] = document.createElement('button');\n button.classList = 'btn btn-default';\n button.href = '#';\n button.title = name;\n button.innerHTML = '';\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n // Add the status bar.\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message pull-right';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n\n // Add the close button to the window.\n var buttongrp = document.createElement('div');\n buttongrp.classList = 'btn-group inline pull-right';\n button = document.createElement('button');\n button.classList = 'btn btn-mini btn-primary';\n button.href = '#';\n button.title = 'Stop Interaction';\n button.innerHTML = '';\n button.addEventListener('click', function (_evt) {\n fig.handle_close(fig, {});\n });\n button.addEventListener(\n 'mouseover',\n on_mouseover_closure('Stop Interaction')\n );\n buttongrp.appendChild(button);\n var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n titlebar.insertBefore(buttongrp, titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n var fig = event.data.fig;\n if (event.target !== this) {\n // Ignore bubbled events from children.\n return;\n }\n fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n // this is important to make the div 'focusable\n el.setAttribute('tabindex', 0);\n // reach out to IPython and tell the keyboard manager to turn it's self\n // off when our div gets focus\n\n // location in version 3\n if (IPython.notebook.keyboard_manager) {\n IPython.notebook.keyboard_manager.register_events(el);\n } else {\n // location in version 2\n IPython.keyboard_manager.register_events(el);\n }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n // Check for shift+enter\n if (event.shiftKey && event.which === 13) {\n this.canvas_div.blur();\n // select the cell after this one\n var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n IPython.notebook.select(index + 1);\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n fig.ondownload(fig, null);\n};\n\nmpl.find_output_cell = function (html_output) {\n // Return the cell and output element which can be found *uniquely* in the notebook.\n // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n // IPython event is triggered only after the cells have been serialised, which for\n // our purposes (turning an active figure into a static one), is too late.\n var cells = IPython.notebook.get_cells();\n var ncells = cells.length;\n for (var i = 0; i < ncells; i++) {\n var cell = cells[i];\n if (cell.cell_type === 'code') {\n for (var j = 0; j < cell.output_area.outputs.length; j++) {\n var data = cell.output_area.outputs[j];\n if (data.data) {\n // IPython >= 3 moved mimebundle to data attribute of output\n data = data.data;\n }\n if (data['text/html'] === html_output) {\n return [cell, data, j];\n }\n }\n }\n }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n IPython.notebook.kernel.comm_manager.register_target(\n 'matplotlib',\n mpl.mpl_figure_comm\n );\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "
" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "((640, 1), (640,)) ((160, 1), (160,))\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Build a linear regression model and fit it to the training data.**" + ], + "metadata": { + "id": "QRsWArmDcj_Z" + } + }, + { + "cell_type": "code", + "source": [ + "##Define the linear regression model, and reshape the data x to [num_samples, 1]##\n", + "\n", + "model = LinearRegression(fit_intercept=True)\n", + "\n", + "##Fit the model to the training data##\n", + "\n", + "model.fit(X_train,y_train)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wofvXKaicjJB", + "outputId": "aefa4735-aa7b-4048-b260-8b81069b76b0" + }, + "execution_count": 76, + "outputs": [ + { + "data": { + "text/plain": "LinearRegression()" + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Get the model's parameters, and compute the coefficient of determination and the mean square error on both the training and test sets.**" + ], + "metadata": { + "id": "HZtL8vxjc8yb" + } + }, + { + "cell_type": "code", + "source": [ + "##Get the model's parameters##\n", + "\n", + "print('Model\\'s coefficients : {}'.format(model.coef_))\n", + "print('Model\\'s intercept : {}'.format(model.intercept_))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "FyO_-2aGeJCw", + "outputId": "2e3b287d-6e98-471d-be74-4aa34aa5eb24" + }, + "execution_count": 77, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model's coefficients : [0.33470968]\n", + "Model's intercept : 43.04881227026741\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "##Make predictions for both the training and the test sets##\n", + "\n", + "y_pred_train = model.predict(X_train)\n", + "y_pred_test = model.predict(X_test)\n", + "\n", + "##Compute th coefficient of determination and the mean square error on both sets##\n", + "\n", + "MSE_train = mean_squared_error(y_train, y_pred_train)\n", + "MSE_test = mean_squared_error(y_test, y_pred_test)\n", + "R2_train = model.score(X_train, y_train)\n", + "R2_test = model.score(X_test, y_test)\n", + "\n", + "print('MSE on training set : {}'.format(MSE_train))\n", + "print('R2 on training set : {}'.format(R2_train))\n", + "print('MSE on test set : {}'.format(MSE_test))\n", + "print('R2 on test set : {}'.format(R2_test))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EF08yODueNB9", + "outputId": "50d4762e-72f9-4ceb-bba4-6dee9f055804" + }, + "execution_count": 78, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE on training set : 572.9958107080326\n", + "R2 on training set : 0.17238405096381515\n", + "MSE on test set : 383.43260139243756\n", + "R2 on test set : 0.21113634820526395\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Plot the regression line**" + ], + "metadata": { + "id": "MAjY7FyheSwV" + } + }, + { + "cell_type": "code", + "source": [ + "##Generate predictions out of the fitted model##\n", + "\n", + "xfit = np.linspace(0,200)\n", + "xfit = xfit[:, np.newaxis]\n", + "yfit = model.predict(xfit)\n", + "\n", + "##Plot the regression line##\n", + "\n", + "_, ax = plt.subplots()\n", + "ax.scatter(x,y)\n", + "ax.set_xlabel('Attack')\n", + "ax.set_ylabel('HP')\n", + "ax.plot(xfit, yfit, label='Regression line', color='red')\n", + "ax.legend()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 279 + }, + "id": "PIZ5v8MDem9w", + "outputId": "8bfe3caa-0ec1-495c-b4ce-923e8b2b3549" + }, + "execution_count": 79, + "outputs": [ + { + "data": { + "text/plain": "", + "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key_event(event, name);\n };\n }\n\n canvas_div.addEventListener(\n 'keydown',\n on_keyboard_event_closure('key_press')\n );\n canvas_div.addEventListener(\n 'keyup',\n on_keyboard_event_closure('key_release')\n );\n\n this._canvas_extra_style(canvas_div);\n this.root.appendChild(canvas_div);\n\n var canvas = (this.canvas = document.createElement('canvas'));\n canvas.classList.add('mpl-canvas');\n canvas.setAttribute('style', 'box-sizing: content-box;');\n\n this.context = canvas.getContext('2d');\n\n var backingStore =\n this.context.backingStorePixelRatio ||\n this.context.webkitBackingStorePixelRatio ||\n this.context.mozBackingStorePixelRatio ||\n this.context.msBackingStorePixelRatio ||\n this.context.oBackingStorePixelRatio ||\n this.context.backingStorePixelRatio ||\n 1;\n\n this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n 'canvas'\n ));\n rubberband_canvas.setAttribute(\n 'style',\n 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n );\n\n // Apply a ponyfill if ResizeObserver is not implemented by browser.\n if (this.ResizeObserver === undefined) {\n if (window.ResizeObserver !== undefined) {\n this.ResizeObserver = window.ResizeObserver;\n } else {\n var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n this.ResizeObserver = obs.ResizeObserver;\n }\n }\n\n this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n var nentries = entries.length;\n for (var i = 0; i < nentries; i++) {\n var entry = entries[i];\n var width, height;\n if (entry.contentBoxSize) {\n if (entry.contentBoxSize instanceof Array) {\n // Chrome 84 implements new version of spec.\n width = entry.contentBoxSize[0].inlineSize;\n height = entry.contentBoxSize[0].blockSize;\n } else {\n // Firefox implements old version of spec.\n width = entry.contentBoxSize.inlineSize;\n height = entry.contentBoxSize.blockSize;\n }\n } else {\n // Chrome <84 implements even older version of spec.\n width = entry.contentRect.width;\n height = entry.contentRect.height;\n }\n\n // Keep the size of the canvas and rubber band canvas in sync with\n // the canvas container.\n if (entry.devicePixelContentBoxSize) {\n // Chrome 84 implements new version of spec.\n canvas.setAttribute(\n 'width',\n entry.devicePixelContentBoxSize[0].inlineSize\n );\n canvas.setAttribute(\n 'height',\n entry.devicePixelContentBoxSize[0].blockSize\n );\n } else {\n canvas.setAttribute('width', width * fig.ratio);\n canvas.setAttribute('height', height * fig.ratio);\n }\n canvas.setAttribute(\n 'style',\n 'width: ' + width + 'px; height: ' + height + 'px;'\n );\n\n rubberband_canvas.setAttribute('width', width);\n rubberband_canvas.setAttribute('height', height);\n\n // And update the size in Python. We ignore the initial 0/0 size\n // that occurs as the element is placed into the DOM, which should\n // otherwise not happen due to the minimum size styling.\n if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n fig.request_resize(width, height);\n }\n }\n });\n this.resizeObserverInstance.observe(canvas_div);\n\n function on_mouse_event_closure(name) {\n return function (event) {\n return fig.mouse_event(event, name);\n };\n }\n\n rubberband_canvas.addEventListener(\n 'mousedown',\n on_mouse_event_closure('button_press')\n );\n rubberband_canvas.addEventListener(\n 'mouseup',\n on_mouse_event_closure('button_release')\n );\n rubberband_canvas.addEventListener(\n 'dblclick',\n on_mouse_event_closure('dblclick')\n );\n // Throttle sequential mouse events to 1 every 20ms.\n rubberband_canvas.addEventListener(\n 'mousemove',\n on_mouse_event_closure('motion_notify')\n );\n\n rubberband_canvas.addEventListener(\n 'mouseenter',\n on_mouse_event_closure('figure_enter')\n );\n rubberband_canvas.addEventListener(\n 'mouseleave',\n on_mouse_event_closure('figure_leave')\n );\n\n canvas_div.addEventListener('wheel', function (event) {\n if (event.deltaY < 0) {\n event.step = 1;\n } else {\n event.step = -1;\n }\n on_mouse_event_closure('scroll')(event);\n });\n\n canvas_div.appendChild(canvas);\n canvas_div.appendChild(rubberband_canvas);\n\n this.rubberband_context = rubberband_canvas.getContext('2d');\n this.rubberband_context.strokeStyle = '#000000';\n\n this._resize_canvas = function (width, height, forward) {\n if (forward) {\n canvas_div.style.width = width + 'px';\n canvas_div.style.height = height + 'px';\n }\n };\n\n // Disable right mouse context menu.\n this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n event.preventDefault();\n return false;\n });\n\n function set_focus() {\n canvas.focus();\n canvas_div.focus();\n }\n\n window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'mpl-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n continue;\n }\n\n var button = (fig.buttons[name] = document.createElement('button'));\n button.classList = 'mpl-widget';\n button.setAttribute('role', 'button');\n button.setAttribute('aria-disabled', 'false');\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n var icon_img = document.createElement('img');\n icon_img.src = '_images/' + image + '.png';\n icon_img.srcset = '_images/' + image + '_large.png 2x';\n icon_img.alt = tooltip;\n button.appendChild(icon_img);\n\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n var fmt_picker = document.createElement('select');\n fmt_picker.classList = 'mpl-widget';\n toolbar.appendChild(fmt_picker);\n this.format_dropdown = fmt_picker;\n\n for (var ind in mpl.extensions) {\n var fmt = mpl.extensions[ind];\n var option = document.createElement('option');\n option.selected = fmt === mpl.default_extension;\n option.innerHTML = fmt;\n fmt_picker.appendChild(option);\n }\n\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n // which will in turn request a refresh of the image.\n this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n properties['type'] = type;\n properties['figure_id'] = this.id;\n this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n if (!this.waiting) {\n this.waiting = true;\n this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n var format_dropdown = fig.format_dropdown;\n var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n var size = msg['size'];\n if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n fig._resize_canvas(size[0], size[1], msg['forward']);\n fig.send_message('refresh', {});\n }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n var x0 = msg['x0'] / fig.ratio;\n var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n var x1 = msg['x1'] / fig.ratio;\n var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n x0 = Math.floor(x0) + 0.5;\n y0 = Math.floor(y0) + 0.5;\n x1 = Math.floor(x1) + 0.5;\n y1 = Math.floor(y1) + 0.5;\n var min_x = Math.min(x0, x1);\n var min_y = Math.min(y0, y1);\n var width = Math.abs(x1 - x0);\n var height = Math.abs(y1 - y0);\n\n fig.rubberband_context.clearRect(\n 0,\n 0,\n fig.canvas.width / fig.ratio,\n fig.canvas.height / fig.ratio\n );\n\n fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n // Updates the figure title.\n fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n fig.rubberband_canvas.style.cursor = msg['cursor'];\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n // Request the server to send over a new figure.\n fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n for (var key in msg) {\n if (!(key in fig.buttons)) {\n continue;\n }\n fig.buttons[key].disabled = !msg[key];\n fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n if (msg['mode'] === 'PAN') {\n fig.buttons['Pan'].classList.add('active');\n fig.buttons['Zoom'].classList.remove('active');\n } else if (msg['mode'] === 'ZOOM') {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.add('active');\n } else {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.remove('active');\n }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Called whenever the canvas gets updated.\n this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n return function socket_on_message(evt) {\n if (evt.data instanceof Blob) {\n var img = evt.data;\n if (img.type !== 'image/png') {\n /* FIXME: We get \"Resource interpreted as Image but\n * transferred with MIME type text/plain:\" errors on\n * Chrome. But how to set the MIME type? It doesn't seem\n * to be part of the websocket stream */\n img.type = 'image/png';\n }\n\n /* Free the memory for the previous frames */\n if (fig.imageObj.src) {\n (window.URL || window.webkitURL).revokeObjectURL(\n fig.imageObj.src\n );\n }\n\n fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n img\n );\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n } else if (\n typeof evt.data === 'string' &&\n evt.data.slice(0, 21) === 'data:image/png;base64'\n ) {\n fig.imageObj.src = evt.data;\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n }\n\n var msg = JSON.parse(evt.data);\n var msg_type = msg['type'];\n\n // Call the \"handle_{type}\" callback, which takes\n // the figure and JSON message as its only arguments.\n try {\n var callback = fig['handle_' + msg_type];\n } catch (e) {\n console.log(\n \"No handler for the '\" + msg_type + \"' message type: \",\n msg\n );\n return;\n }\n\n if (callback) {\n try {\n // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n callback(fig, msg);\n } catch (e) {\n console.log(\n \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n e,\n e.stack,\n msg\n );\n }\n }\n };\n};\n\n// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function (e) {\n //this section is from http://www.quirksmode.org/js/events_properties.html\n var targ;\n if (!e) {\n e = window.event;\n }\n if (e.target) {\n targ = e.target;\n } else if (e.srcElement) {\n targ = e.srcElement;\n }\n if (targ.nodeType === 3) {\n // defeat Safari bug\n targ = targ.parentNode;\n }\n\n // pageX,Y are the mouse positions relative to the document\n var boundingRect = targ.getBoundingClientRect();\n var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n\n return { x: x, y: y };\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * https://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n return Object.keys(original).reduce(function (obj, key) {\n if (typeof original[key] !== 'object') {\n obj[key] = original[key];\n }\n return obj;\n }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n var canvas_pos = mpl.findpos(event);\n\n if (name === 'button_press') {\n this.canvas.focus();\n this.canvas_div.focus();\n }\n\n var x = canvas_pos.x * this.ratio;\n var y = canvas_pos.y * this.ratio;\n\n this.send_message(name, {\n x: x,\n y: y,\n button: event.button,\n step: event.step,\n guiEvent: simpleKeys(event),\n });\n\n /* This prevents the web browser from automatically changing to\n * the text insertion cursor when the button is pressed. We want\n * to control all of the cursor setting manually through the\n * 'cursor' event from matplotlib */\n event.preventDefault();\n return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n // Prevent repeat events\n if (name === 'key_press') {\n if (event.key === this._key) {\n return;\n } else {\n this._key = event.key;\n }\n }\n if (name === 'key_release') {\n this._key = null;\n }\n\n var value = '';\n if (event.ctrlKey && event.key !== 'Control') {\n value += 'ctrl+';\n }\n else if (event.altKey && event.key !== 'Alt') {\n value += 'alt+';\n }\n else if (event.shiftKey && event.key !== 'Shift') {\n value += 'shift+';\n }\n\n value += 'k' + event.key;\n\n this._key_event_extra(event, name);\n\n this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n return false;\n};\n\nmpl.figure.prototype.toolbar_button_onclick = function (name) {\n if (name === 'download') {\n this.handle_save(this, null);\n } else {\n this.send_message('toolbar_button', { name: name });\n }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n // Create a \"websocket\"-like object which calls the given IPython comm\n // object with the appropriate methods. Currently this is a non binary\n // socket, so there is still some room for performance tuning.\n var ws = {};\n\n ws.binaryType = comm.kernel.ws.binaryType;\n ws.readyState = comm.kernel.ws.readyState;\n function updateReadyState(_event) {\n if (comm.kernel.ws) {\n ws.readyState = comm.kernel.ws.readyState;\n } else {\n ws.readyState = 3; // Closed state.\n }\n }\n comm.kernel.ws.addEventListener('open', updateReadyState);\n comm.kernel.ws.addEventListener('close', updateReadyState);\n comm.kernel.ws.addEventListener('error', updateReadyState);\n\n ws.close = function () {\n comm.close();\n };\n ws.send = function (m) {\n //console.log('sending', m);\n comm.send(m);\n };\n // Register the callback with on_msg.\n comm.on_msg(function (msg) {\n //console.log('receiving', msg['content']['data'], msg);\n var data = msg['content']['data'];\n if (data['blob'] !== undefined) {\n data = {\n data: new Blob(msg['buffers'], { type: data['blob'] }),\n };\n }\n // Pass the mpl event to the overridden (by mpl) onmessage function.\n ws.onmessage(data);\n });\n return ws;\n};\n\nmpl.mpl_figure_comm = function (comm, msg) {\n // This is the function which gets called when the mpl process\n // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n var id = msg.content.data.id;\n // Get hold of the div created by the display call when the Comm\n // socket was opened in Python.\n var element = document.getElementById(id);\n var ws_proxy = comm_websocket_adapter(comm);\n\n function ondownload(figure, _format) {\n window.open(figure.canvas.toDataURL());\n }\n\n var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n\n // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n // web socket which is closed, not our websocket->open comm proxy.\n ws_proxy.onopen();\n\n fig.parent_element = element;\n fig.cell_info = mpl.find_output_cell(\"
\");\n if (!fig.cell_info) {\n console.error('Failed to find cell for figure', id, fig);\n return;\n }\n fig.cell_info[0].output_area.element.on(\n 'cleared',\n { fig: fig },\n fig._remove_fig_handler\n );\n};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n var width = fig.canvas.width / fig.ratio;\n fig.cell_info[0].output_area.element.off(\n 'cleared',\n fig._remove_fig_handler\n );\n fig.resizeObserverInstance.unobserve(fig.canvas_div);\n\n // Update the output cell to use the data from the current canvas.\n fig.push_to_output();\n var dataURL = fig.canvas.toDataURL();\n // Re-enable the keyboard manager in IPython - without this line, in FF,\n // the notebook keyboard shortcuts fail.\n IPython.keyboard_manager.enable();\n fig.parent_element.innerHTML =\n '';\n fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n fig.send_message('closing', msg);\n // fig.ws.close()\n};\n\nmpl.figure.prototype.push_to_output = function (_remove_interactive) {\n // Turn the data on the canvas into data in the output cell.\n var width = this.canvas.width / this.ratio;\n var dataURL = this.canvas.toDataURL();\n this.cell_info[1]['text/html'] =\n '';\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Tell IPython that the notebook contents must change.\n IPython.notebook.set_dirty(true);\n this.send_message('ack', {});\n var fig = this;\n // Wait a second, then push the new image to the DOM so\n // that it is saved nicely (might be nice to debounce this).\n setTimeout(function () {\n fig.push_to_output();\n }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'btn-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n var button;\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n continue;\n }\n\n button = fig.buttons[name] = document.createElement('button');\n button.classList = 'btn btn-default';\n button.href = '#';\n button.title = name;\n button.innerHTML = '';\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n // Add the status bar.\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message pull-right';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n\n // Add the close button to the window.\n var buttongrp = document.createElement('div');\n buttongrp.classList = 'btn-group inline pull-right';\n button = document.createElement('button');\n button.classList = 'btn btn-mini btn-primary';\n button.href = '#';\n button.title = 'Stop Interaction';\n button.innerHTML = '';\n button.addEventListener('click', function (_evt) {\n fig.handle_close(fig, {});\n });\n button.addEventListener(\n 'mouseover',\n on_mouseover_closure('Stop Interaction')\n );\n buttongrp.appendChild(button);\n var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n titlebar.insertBefore(buttongrp, titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n var fig = event.data.fig;\n if (event.target !== this) {\n // Ignore bubbled events from children.\n return;\n }\n fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n // this is important to make the div 'focusable\n el.setAttribute('tabindex', 0);\n // reach out to IPython and tell the keyboard manager to turn it's self\n // off when our div gets focus\n\n // location in version 3\n if (IPython.notebook.keyboard_manager) {\n IPython.notebook.keyboard_manager.register_events(el);\n } else {\n // location in version 2\n IPython.keyboard_manager.register_events(el);\n }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n // Check for shift+enter\n if (event.shiftKey && event.which === 13) {\n this.canvas_div.blur();\n // select the cell after this one\n var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n IPython.notebook.select(index + 1);\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n fig.ondownload(fig, null);\n};\n\nmpl.find_output_cell = function (html_output) {\n // Return the cell and output element which can be found *uniquely* in the notebook.\n // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n // IPython event is triggered only after the cells have been serialised, which for\n // our purposes (turning an active figure into a static one), is too late.\n var cells = IPython.notebook.get_cells();\n var ncells = cells.length;\n for (var i = 0; i < ncells; i++) {\n var cell = cells[i];\n if (cell.cell_type === 'code') {\n for (var j = 0; j < cell.output_area.outputs.length; j++) {\n var data = cell.output_area.outputs[j];\n if (data.data) {\n // IPython >= 3 moved mimebundle to data attribute of output\n data = data.data;\n }\n if (data['text/html'] === html_output) {\n return [cell, data, j];\n }\n }\n }\n }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n IPython.notebook.kernel.comm_manager.register_target(\n 'matplotlib',\n mpl.mpl_figure_comm\n );\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "
" + }, + "metadata": {}, + "output_type": "display_data" + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Your turn !**" + ], + "metadata": { + "id": "J3OzPndKsEsA" + } + }, + { + "cell_type": "markdown", + "source": [ + "**1) Do the same as above, but now include 'Defense' as a second predictor.**" + ], + "metadata": { + "id": "PLEEhDHje0Zm" + } + }, + { + "cell_type": "code", + "source": [ + "##Select a target variable y and a predictor x. Reshape the data x to [num_samples, 1]##\n", + "\n", + "y = df['HP'].values\n", + "x1 = df['Attack'].values\n", + "x2 = df['Defense'].values\n", + "X = df[['Attack','Defense']].values\n", + "\n", + "fig = plt.figure()\n", + "ax = plt.axes(projection ='3d')\n", + "ax.scatter(x1,x2,y)\n", + "ax.set_xlabel('Attack')\n", + "ax.set_ylabel('Defense')\n", + "ax.set_zlabel('HP')\n", + "plt.show()\n", + "\n", + "##Define a training set and a validation set##\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, test_size=0.2, shuffle=True)\n", + "print((X_train.shape, y_train.shape), (X_test.shape, y_test.shape))" + ], + "metadata": { + "id": "YkbDiZxafwpD" + }, + "execution_count": 80, + "outputs": [ + { + "data": { + "text/plain": "", + "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key_event(event, name);\n };\n }\n\n canvas_div.addEventListener(\n 'keydown',\n on_keyboard_event_closure('key_press')\n );\n canvas_div.addEventListener(\n 'keyup',\n on_keyboard_event_closure('key_release')\n );\n\n this._canvas_extra_style(canvas_div);\n this.root.appendChild(canvas_div);\n\n var canvas = (this.canvas = document.createElement('canvas'));\n canvas.classList.add('mpl-canvas');\n canvas.setAttribute('style', 'box-sizing: content-box;');\n\n this.context = canvas.getContext('2d');\n\n var backingStore =\n this.context.backingStorePixelRatio ||\n this.context.webkitBackingStorePixelRatio ||\n this.context.mozBackingStorePixelRatio ||\n this.context.msBackingStorePixelRatio ||\n this.context.oBackingStorePixelRatio ||\n this.context.backingStorePixelRatio ||\n 1;\n\n this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n 'canvas'\n ));\n rubberband_canvas.setAttribute(\n 'style',\n 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n );\n\n // Apply a ponyfill if ResizeObserver is not implemented by browser.\n if (this.ResizeObserver === undefined) {\n if (window.ResizeObserver !== undefined) {\n this.ResizeObserver = window.ResizeObserver;\n } else {\n var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n this.ResizeObserver = obs.ResizeObserver;\n }\n }\n\n this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n var nentries = entries.length;\n for (var i = 0; i < nentries; i++) {\n var entry = entries[i];\n var width, height;\n if (entry.contentBoxSize) {\n if (entry.contentBoxSize instanceof Array) {\n // Chrome 84 implements new version of spec.\n width = entry.contentBoxSize[0].inlineSize;\n height = entry.contentBoxSize[0].blockSize;\n } else {\n // Firefox implements old version of spec.\n width = entry.contentBoxSize.inlineSize;\n height = entry.contentBoxSize.blockSize;\n }\n } else {\n // Chrome <84 implements even older version of spec.\n width = entry.contentRect.width;\n height = entry.contentRect.height;\n }\n\n // Keep the size of the canvas and rubber band canvas in sync with\n // the canvas container.\n if (entry.devicePixelContentBoxSize) {\n // Chrome 84 implements new version of spec.\n canvas.setAttribute(\n 'width',\n entry.devicePixelContentBoxSize[0].inlineSize\n );\n canvas.setAttribute(\n 'height',\n entry.devicePixelContentBoxSize[0].blockSize\n );\n } else {\n canvas.setAttribute('width', width * fig.ratio);\n canvas.setAttribute('height', height * fig.ratio);\n }\n canvas.setAttribute(\n 'style',\n 'width: ' + width + 'px; height: ' + height + 'px;'\n );\n\n rubberband_canvas.setAttribute('width', width);\n rubberband_canvas.setAttribute('height', height);\n\n // And update the size in Python. We ignore the initial 0/0 size\n // that occurs as the element is placed into the DOM, which should\n // otherwise not happen due to the minimum size styling.\n if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n fig.request_resize(width, height);\n }\n }\n });\n this.resizeObserverInstance.observe(canvas_div);\n\n function on_mouse_event_closure(name) {\n return function (event) {\n return fig.mouse_event(event, name);\n };\n }\n\n rubberband_canvas.addEventListener(\n 'mousedown',\n on_mouse_event_closure('button_press')\n );\n rubberband_canvas.addEventListener(\n 'mouseup',\n on_mouse_event_closure('button_release')\n );\n rubberband_canvas.addEventListener(\n 'dblclick',\n on_mouse_event_closure('dblclick')\n );\n // Throttle sequential mouse events to 1 every 20ms.\n rubberband_canvas.addEventListener(\n 'mousemove',\n on_mouse_event_closure('motion_notify')\n );\n\n rubberband_canvas.addEventListener(\n 'mouseenter',\n on_mouse_event_closure('figure_enter')\n );\n rubberband_canvas.addEventListener(\n 'mouseleave',\n on_mouse_event_closure('figure_leave')\n );\n\n canvas_div.addEventListener('wheel', function (event) {\n if (event.deltaY < 0) {\n event.step = 1;\n } else {\n event.step = -1;\n }\n on_mouse_event_closure('scroll')(event);\n });\n\n canvas_div.appendChild(canvas);\n canvas_div.appendChild(rubberband_canvas);\n\n this.rubberband_context = rubberband_canvas.getContext('2d');\n this.rubberband_context.strokeStyle = '#000000';\n\n this._resize_canvas = function (width, height, forward) {\n if (forward) {\n canvas_div.style.width = width + 'px';\n canvas_div.style.height = height + 'px';\n }\n };\n\n // Disable right mouse context menu.\n this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n event.preventDefault();\n return false;\n });\n\n function set_focus() {\n canvas.focus();\n canvas_div.focus();\n }\n\n window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'mpl-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n continue;\n }\n\n var button = (fig.buttons[name] = document.createElement('button'));\n button.classList = 'mpl-widget';\n button.setAttribute('role', 'button');\n button.setAttribute('aria-disabled', 'false');\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n var icon_img = document.createElement('img');\n icon_img.src = '_images/' + image + '.png';\n icon_img.srcset = '_images/' + image + '_large.png 2x';\n icon_img.alt = tooltip;\n button.appendChild(icon_img);\n\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n var fmt_picker = document.createElement('select');\n fmt_picker.classList = 'mpl-widget';\n toolbar.appendChild(fmt_picker);\n this.format_dropdown = fmt_picker;\n\n for (var ind in mpl.extensions) {\n var fmt = mpl.extensions[ind];\n var option = document.createElement('option');\n option.selected = fmt === mpl.default_extension;\n option.innerHTML = fmt;\n fmt_picker.appendChild(option);\n }\n\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n // which will in turn request a refresh of the image.\n this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n properties['type'] = type;\n properties['figure_id'] = this.id;\n this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n if (!this.waiting) {\n this.waiting = true;\n this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n var format_dropdown = fig.format_dropdown;\n var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n var size = msg['size'];\n if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n fig._resize_canvas(size[0], size[1], msg['forward']);\n fig.send_message('refresh', {});\n }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n var x0 = msg['x0'] / fig.ratio;\n var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n var x1 = msg['x1'] / fig.ratio;\n var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n x0 = Math.floor(x0) + 0.5;\n y0 = Math.floor(y0) + 0.5;\n x1 = Math.floor(x1) + 0.5;\n y1 = Math.floor(y1) + 0.5;\n var min_x = Math.min(x0, x1);\n var min_y = Math.min(y0, y1);\n var width = Math.abs(x1 - x0);\n var height = Math.abs(y1 - y0);\n\n fig.rubberband_context.clearRect(\n 0,\n 0,\n fig.canvas.width / fig.ratio,\n fig.canvas.height / fig.ratio\n );\n\n fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n // Updates the figure title.\n fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n fig.rubberband_canvas.style.cursor = msg['cursor'];\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n // Request the server to send over a new figure.\n fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n for (var key in msg) {\n if (!(key in fig.buttons)) {\n continue;\n }\n fig.buttons[key].disabled = !msg[key];\n fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n if (msg['mode'] === 'PAN') {\n fig.buttons['Pan'].classList.add('active');\n fig.buttons['Zoom'].classList.remove('active');\n } else if (msg['mode'] === 'ZOOM') {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.add('active');\n } else {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.remove('active');\n }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Called whenever the canvas gets updated.\n this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n return function socket_on_message(evt) {\n if (evt.data instanceof Blob) {\n var img = evt.data;\n if (img.type !== 'image/png') {\n /* FIXME: We get \"Resource interpreted as Image but\n * transferred with MIME type text/plain:\" errors on\n * Chrome. But how to set the MIME type? It doesn't seem\n * to be part of the websocket stream */\n img.type = 'image/png';\n }\n\n /* Free the memory for the previous frames */\n if (fig.imageObj.src) {\n (window.URL || window.webkitURL).revokeObjectURL(\n fig.imageObj.src\n );\n }\n\n fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n img\n );\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n } else if (\n typeof evt.data === 'string' &&\n evt.data.slice(0, 21) === 'data:image/png;base64'\n ) {\n fig.imageObj.src = evt.data;\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n }\n\n var msg = JSON.parse(evt.data);\n var msg_type = msg['type'];\n\n // Call the \"handle_{type}\" callback, which takes\n // the figure and JSON message as its only arguments.\n try {\n var callback = fig['handle_' + msg_type];\n } catch (e) {\n console.log(\n \"No handler for the '\" + msg_type + \"' message type: \",\n msg\n );\n return;\n }\n\n if (callback) {\n try {\n // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n callback(fig, msg);\n } catch (e) {\n console.log(\n \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n e,\n e.stack,\n msg\n );\n }\n }\n };\n};\n\n// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function (e) {\n //this section is from http://www.quirksmode.org/js/events_properties.html\n var targ;\n if (!e) {\n e = window.event;\n }\n if (e.target) {\n targ = e.target;\n } else if (e.srcElement) {\n targ = e.srcElement;\n }\n if (targ.nodeType === 3) {\n // defeat Safari bug\n targ = targ.parentNode;\n }\n\n // pageX,Y are the mouse positions relative to the document\n var boundingRect = targ.getBoundingClientRect();\n var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n\n return { x: x, y: y };\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * https://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n return Object.keys(original).reduce(function (obj, key) {\n if (typeof original[key] !== 'object') {\n obj[key] = original[key];\n }\n return obj;\n }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n var canvas_pos = mpl.findpos(event);\n\n if (name === 'button_press') {\n this.canvas.focus();\n this.canvas_div.focus();\n }\n\n var x = canvas_pos.x * this.ratio;\n var y = canvas_pos.y * this.ratio;\n\n this.send_message(name, {\n x: x,\n y: y,\n button: event.button,\n step: event.step,\n guiEvent: simpleKeys(event),\n });\n\n /* This prevents the web browser from automatically changing to\n * the text insertion cursor when the button is pressed. We want\n * to control all of the cursor setting manually through the\n * 'cursor' event from matplotlib */\n event.preventDefault();\n return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n // Prevent repeat events\n if (name === 'key_press') {\n if (event.key === this._key) {\n return;\n } else {\n this._key = event.key;\n }\n }\n if (name === 'key_release') {\n this._key = null;\n }\n\n var value = '';\n if (event.ctrlKey && event.key !== 'Control') {\n value += 'ctrl+';\n }\n else if (event.altKey && event.key !== 'Alt') {\n value += 'alt+';\n }\n else if (event.shiftKey && event.key !== 'Shift') {\n value += 'shift+';\n }\n\n value += 'k' + event.key;\n\n this._key_event_extra(event, name);\n\n this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n return false;\n};\n\nmpl.figure.prototype.toolbar_button_onclick = function (name) {\n if (name === 'download') {\n this.handle_save(this, null);\n } else {\n this.send_message('toolbar_button', { name: name });\n }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n // Create a \"websocket\"-like object which calls the given IPython comm\n // object with the appropriate methods. Currently this is a non binary\n // socket, so there is still some room for performance tuning.\n var ws = {};\n\n ws.binaryType = comm.kernel.ws.binaryType;\n ws.readyState = comm.kernel.ws.readyState;\n function updateReadyState(_event) {\n if (comm.kernel.ws) {\n ws.readyState = comm.kernel.ws.readyState;\n } else {\n ws.readyState = 3; // Closed state.\n }\n }\n comm.kernel.ws.addEventListener('open', updateReadyState);\n comm.kernel.ws.addEventListener('close', updateReadyState);\n comm.kernel.ws.addEventListener('error', updateReadyState);\n\n ws.close = function () {\n comm.close();\n };\n ws.send = function (m) {\n //console.log('sending', m);\n comm.send(m);\n };\n // Register the callback with on_msg.\n comm.on_msg(function (msg) {\n //console.log('receiving', msg['content']['data'], msg);\n var data = msg['content']['data'];\n if (data['blob'] !== undefined) {\n data = {\n data: new Blob(msg['buffers'], { type: data['blob'] }),\n };\n }\n // Pass the mpl event to the overridden (by mpl) onmessage function.\n ws.onmessage(data);\n });\n return ws;\n};\n\nmpl.mpl_figure_comm = function (comm, msg) {\n // This is the function which gets called when the mpl process\n // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n var id = msg.content.data.id;\n // Get hold of the div created by the display call when the Comm\n // socket was opened in Python.\n var element = document.getElementById(id);\n var ws_proxy = comm_websocket_adapter(comm);\n\n function ondownload(figure, _format) {\n window.open(figure.canvas.toDataURL());\n }\n\n var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n\n // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n // web socket which is closed, not our websocket->open comm proxy.\n ws_proxy.onopen();\n\n fig.parent_element = element;\n fig.cell_info = mpl.find_output_cell(\"
\");\n if (!fig.cell_info) {\n console.error('Failed to find cell for figure', id, fig);\n return;\n }\n fig.cell_info[0].output_area.element.on(\n 'cleared',\n { fig: fig },\n fig._remove_fig_handler\n );\n};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n var width = fig.canvas.width / fig.ratio;\n fig.cell_info[0].output_area.element.off(\n 'cleared',\n fig._remove_fig_handler\n );\n fig.resizeObserverInstance.unobserve(fig.canvas_div);\n\n // Update the output cell to use the data from the current canvas.\n fig.push_to_output();\n var dataURL = fig.canvas.toDataURL();\n // Re-enable the keyboard manager in IPython - without this line, in FF,\n // the notebook keyboard shortcuts fail.\n IPython.keyboard_manager.enable();\n fig.parent_element.innerHTML =\n '';\n fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n fig.send_message('closing', msg);\n // fig.ws.close()\n};\n\nmpl.figure.prototype.push_to_output = function (_remove_interactive) {\n // Turn the data on the canvas into data in the output cell.\n var width = this.canvas.width / this.ratio;\n var dataURL = this.canvas.toDataURL();\n this.cell_info[1]['text/html'] =\n '';\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Tell IPython that the notebook contents must change.\n IPython.notebook.set_dirty(true);\n this.send_message('ack', {});\n var fig = this;\n // Wait a second, then push the new image to the DOM so\n // that it is saved nicely (might be nice to debounce this).\n setTimeout(function () {\n fig.push_to_output();\n }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'btn-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n var button;\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n continue;\n }\n\n button = fig.buttons[name] = document.createElement('button');\n button.classList = 'btn btn-default';\n button.href = '#';\n button.title = name;\n button.innerHTML = '';\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n // Add the status bar.\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message pull-right';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n\n // Add the close button to the window.\n var buttongrp = document.createElement('div');\n buttongrp.classList = 'btn-group inline pull-right';\n button = document.createElement('button');\n button.classList = 'btn btn-mini btn-primary';\n button.href = '#';\n button.title = 'Stop Interaction';\n button.innerHTML = '';\n button.addEventListener('click', function (_evt) {\n fig.handle_close(fig, {});\n });\n button.addEventListener(\n 'mouseover',\n on_mouseover_closure('Stop Interaction')\n );\n buttongrp.appendChild(button);\n var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n titlebar.insertBefore(buttongrp, titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n var fig = event.data.fig;\n if (event.target !== this) {\n // Ignore bubbled events from children.\n return;\n }\n fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n // this is important to make the div 'focusable\n el.setAttribute('tabindex', 0);\n // reach out to IPython and tell the keyboard manager to turn it's self\n // off when our div gets focus\n\n // location in version 3\n if (IPython.notebook.keyboard_manager) {\n IPython.notebook.keyboard_manager.register_events(el);\n } else {\n // location in version 2\n IPython.keyboard_manager.register_events(el);\n }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n // Check for shift+enter\n if (event.shiftKey && event.which === 13) {\n this.canvas_div.blur();\n // select the cell after this one\n var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n IPython.notebook.select(index + 1);\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n fig.ondownload(fig, null);\n};\n\nmpl.find_output_cell = function (html_output) {\n // Return the cell and output element which can be found *uniquely* in the notebook.\n // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n // IPython event is triggered only after the cells have been serialised, which for\n // our purposes (turning an active figure into a static one), is too late.\n var cells = IPython.notebook.get_cells();\n var ncells = cells.length;\n for (var i = 0; i < ncells; i++) {\n var cell = cells[i];\n if (cell.cell_type === 'code') {\n for (var j = 0; j < cell.output_area.outputs.length; j++) {\n var data = cell.output_area.outputs[j];\n if (data.data) {\n // IPython >= 3 moved mimebundle to data attribute of output\n data = data.data;\n }\n if (data['text/html'] === html_output) {\n return [cell, data, j];\n }\n }\n }\n }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n IPython.notebook.kernel.comm_manager.register_target(\n 'matplotlib',\n mpl.mpl_figure_comm\n );\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "
" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "((640, 2), (640,)) ((160, 2), (160,))\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "##Define the linear regression model, and reshape the data x to [num_samples, 1]##\n", + "\n", + "model = LinearRegression(fit_intercept=True)\n", + "\n", + "##Fit the model to the training data##\n", + "\n", + "model.fit(X_train,y_train)\n", + "\n", + "##Get the model's parameters##\n", + "\n", + "print('Model\\'s coefficients : {}'.format(model.coef_))\n", + "print('Model\\'s intercept : {}'.format(model.intercept_))\n", + "\n", + "##Make predictions for both the training and the test sets##\n", + "\n", + "y_pred_train = model.predict(X_train)\n", + "y_pred_test = model.predict(X_test)\n", + "\n", + "##Compute th coefficient of determination and the mean square error on both sets##\n", + "\n", + "MSE_train = mean_squared_error(y_train, y_pred_train)\n", + "MSE_test = mean_squared_error(y_test, y_pred_test)\n", + "R2_train = model.score(X_train, y_train)\n", + "R2_test = model.score(X_test, y_test)\n", + "\n", + "print('MSE on training set : {}'.format(MSE_train))\n", + "print('R2 on training set : {}'.format(R2_train))\n", + "print('MSE on test set : {}'.format(MSE_test))\n", + "print('R2 on test set : {}'.format(R2_test))" + ], + "metadata": { + "id": "Q9Z0y3gxf2QS" + }, + "execution_count": 87, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model's coefficients : [0.00163605 0.00121004 0.00160545]\n", + "Model's intercept : -0.25421264450554726\n", + "MSE on training set : 0.06026369818477989\n", + "R2 on training set : 0.14762212864788704\n", + "MSE on test set : 0.07750258734530717\n", + "R2 on test set : 0.13886014060769825\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "##Generate predictions out of the fitted model##\n", + "\n", + "xfit1 = np.linspace(0,200)\n", + "xfit = np.column_stack((xfit1,xfit1))\n", + "#xfit = xfit[:, np.newaxis]\n", + "yfit = model.predict(xfit)\n", + "\n", + "\n", + "##Plot the regression line##\n", + "\n", + "fig = plt.figure()\n", + "ax = plt.axes(projection ='3d')\n", + "ax.scatter(x1,x2,y)\n", + "ax.set_xlabel('Attack')\n", + "ax.set_ylabel('Defense')\n", + "ax.set_zlabel('HP')\n", + "ax.plot(xfit1,xfit1, yfit, label='Regression line', color='red')\n", + "ax.legend()\n", + "plt.show()" + ], + "metadata": { + "id": "qCTnZI0vgrw4" + }, + "execution_count": 88, + "outputs": [ + { + "ename": "ValueError", + "evalue": "X has 2 features, but LinearRegression is expecting 3 features as input.", + "output_type": "error", + "traceback": [ + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mValueError\u001B[0m Traceback (most recent call last)", + "Input \u001B[0;32mIn [88]\u001B[0m, in \u001B[0;36m\u001B[0;34m\u001B[0m\n\u001B[1;32m 4\u001B[0m xfit \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39mcolumn_stack((xfit1,xfit1))\n\u001B[1;32m 5\u001B[0m \u001B[38;5;66;03m#xfit = xfit[:, np.newaxis]\u001B[39;00m\n\u001B[0;32m----> 6\u001B[0m yfit \u001B[38;5;241m=\u001B[39m \u001B[43mmodel\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpredict\u001B[49m\u001B[43m(\u001B[49m\u001B[43mxfit\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 9\u001B[0m \u001B[38;5;66;03m##Plot the regression line##\u001B[39;00m\n\u001B[1;32m 11\u001B[0m fig \u001B[38;5;241m=\u001B[39m plt\u001B[38;5;241m.\u001B[39mfigure()\n", + "File \u001B[0;32m~/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:362\u001B[0m, in \u001B[0;36mLinearModel.predict\u001B[0;34m(self, X)\u001B[0m\n\u001B[1;32m 348\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mpredict\u001B[39m(\u001B[38;5;28mself\u001B[39m, X):\n\u001B[1;32m 349\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m 350\u001B[0m \u001B[38;5;124;03m Predict using the linear model.\u001B[39;00m\n\u001B[1;32m 351\u001B[0m \n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 360\u001B[0m \u001B[38;5;124;03m Returns predicted values.\u001B[39;00m\n\u001B[1;32m 361\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[0;32m--> 362\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_decision_function\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[0;32m~/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:345\u001B[0m, in \u001B[0;36mLinearModel._decision_function\u001B[0;34m(self, X)\u001B[0m\n\u001B[1;32m 342\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_decision_function\u001B[39m(\u001B[38;5;28mself\u001B[39m, X):\n\u001B[1;32m 343\u001B[0m check_is_fitted(\u001B[38;5;28mself\u001B[39m)\n\u001B[0;32m--> 345\u001B[0m X \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_validate_data\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maccept_sparse\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mcsr\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mcsc\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mcoo\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mreset\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[1;32m 346\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m safe_sparse_dot(X, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcoef_\u001B[38;5;241m.\u001B[39mT, dense_output\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m) \u001B[38;5;241m+\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mintercept_\n", + "File \u001B[0;32m~/.local/lib/python3.10/site-packages/sklearn/base.py:585\u001B[0m, in \u001B[0;36mBaseEstimator._validate_data\u001B[0;34m(self, X, y, reset, validate_separately, **check_params)\u001B[0m\n\u001B[1;32m 582\u001B[0m out \u001B[38;5;241m=\u001B[39m X, y\n\u001B[1;32m 584\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m no_val_X \u001B[38;5;129;01mand\u001B[39;00m check_params\u001B[38;5;241m.\u001B[39mget(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mensure_2d\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mTrue\u001B[39;00m):\n\u001B[0;32m--> 585\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_check_n_features\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mreset\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mreset\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 587\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m out\n", + "File \u001B[0;32m~/.local/lib/python3.10/site-packages/sklearn/base.py:400\u001B[0m, in \u001B[0;36mBaseEstimator._check_n_features\u001B[0;34m(self, X, reset)\u001B[0m\n\u001B[1;32m 397\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m\n\u001B[1;32m 399\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m n_features \u001B[38;5;241m!=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mn_features_in_:\n\u001B[0;32m--> 400\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[1;32m 401\u001B[0m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mX has \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mn_features\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m features, but \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__class__\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__name__\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m 402\u001B[0m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mis expecting \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mn_features_in_\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m features as input.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m 403\u001B[0m )\n", + "\u001B[0;31mValueError\u001B[0m: X has 2 features, but LinearRegression is expecting 3 features as input." + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Let's now experiment with classification. Select the variables 'HP', 'Attack' and 'Defense' as predictors, and 'Legendary' as the target variable.**" + ], + "metadata": { + "id": "Zciw48DXgzuX" + } + }, + { + "cell_type": "markdown", + "source": [ + "**2) Apply the same methodology as above, but this time use the KNeighborsClassifier of the sklearn library to predict whether a Pokemon is legendary or not. Set the number of neighbors to 5.**" + ], + "metadata": { + "id": "lw9udYmhhX49", + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 83, + "outputs": [ + { + "data": { + "text/plain": "", + "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key_event(event, name);\n };\n }\n\n canvas_div.addEventListener(\n 'keydown',\n on_keyboard_event_closure('key_press')\n );\n canvas_div.addEventListener(\n 'keyup',\n on_keyboard_event_closure('key_release')\n );\n\n this._canvas_extra_style(canvas_div);\n this.root.appendChild(canvas_div);\n\n var canvas = (this.canvas = document.createElement('canvas'));\n canvas.classList.add('mpl-canvas');\n canvas.setAttribute('style', 'box-sizing: content-box;');\n\n this.context = canvas.getContext('2d');\n\n var backingStore =\n this.context.backingStorePixelRatio ||\n this.context.webkitBackingStorePixelRatio ||\n this.context.mozBackingStorePixelRatio ||\n this.context.msBackingStorePixelRatio ||\n this.context.oBackingStorePixelRatio ||\n this.context.backingStorePixelRatio ||\n 1;\n\n this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n 'canvas'\n ));\n rubberband_canvas.setAttribute(\n 'style',\n 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n );\n\n // Apply a ponyfill if ResizeObserver is not implemented by browser.\n if (this.ResizeObserver === undefined) {\n if (window.ResizeObserver !== undefined) {\n this.ResizeObserver = window.ResizeObserver;\n } else {\n var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n this.ResizeObserver = obs.ResizeObserver;\n }\n }\n\n this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n var nentries = entries.length;\n for (var i = 0; i < nentries; i++) {\n var entry = entries[i];\n var width, height;\n if (entry.contentBoxSize) {\n if (entry.contentBoxSize instanceof Array) {\n // Chrome 84 implements new version of spec.\n width = entry.contentBoxSize[0].inlineSize;\n height = entry.contentBoxSize[0].blockSize;\n } else {\n // Firefox implements old version of spec.\n width = entry.contentBoxSize.inlineSize;\n height = entry.contentBoxSize.blockSize;\n }\n } else {\n // Chrome <84 implements even older version of spec.\n width = entry.contentRect.width;\n height = entry.contentRect.height;\n }\n\n // Keep the size of the canvas and rubber band canvas in sync with\n // the canvas container.\n if (entry.devicePixelContentBoxSize) {\n // Chrome 84 implements new version of spec.\n canvas.setAttribute(\n 'width',\n entry.devicePixelContentBoxSize[0].inlineSize\n );\n canvas.setAttribute(\n 'height',\n entry.devicePixelContentBoxSize[0].blockSize\n );\n } else {\n canvas.setAttribute('width', width * fig.ratio);\n canvas.setAttribute('height', height * fig.ratio);\n }\n canvas.setAttribute(\n 'style',\n 'width: ' + width + 'px; height: ' + height + 'px;'\n );\n\n rubberband_canvas.setAttribute('width', width);\n rubberband_canvas.setAttribute('height', height);\n\n // And update the size in Python. We ignore the initial 0/0 size\n // that occurs as the element is placed into the DOM, which should\n // otherwise not happen due to the minimum size styling.\n if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n fig.request_resize(width, height);\n }\n }\n });\n this.resizeObserverInstance.observe(canvas_div);\n\n function on_mouse_event_closure(name) {\n return function (event) {\n return fig.mouse_event(event, name);\n };\n }\n\n rubberband_canvas.addEventListener(\n 'mousedown',\n on_mouse_event_closure('button_press')\n );\n rubberband_canvas.addEventListener(\n 'mouseup',\n on_mouse_event_closure('button_release')\n );\n rubberband_canvas.addEventListener(\n 'dblclick',\n on_mouse_event_closure('dblclick')\n );\n // Throttle sequential mouse events to 1 every 20ms.\n rubberband_canvas.addEventListener(\n 'mousemove',\n on_mouse_event_closure('motion_notify')\n );\n\n rubberband_canvas.addEventListener(\n 'mouseenter',\n on_mouse_event_closure('figure_enter')\n );\n rubberband_canvas.addEventListener(\n 'mouseleave',\n on_mouse_event_closure('figure_leave')\n );\n\n canvas_div.addEventListener('wheel', function (event) {\n if (event.deltaY < 0) {\n event.step = 1;\n } else {\n event.step = -1;\n }\n on_mouse_event_closure('scroll')(event);\n });\n\n canvas_div.appendChild(canvas);\n canvas_div.appendChild(rubberband_canvas);\n\n this.rubberband_context = rubberband_canvas.getContext('2d');\n this.rubberband_context.strokeStyle = '#000000';\n\n this._resize_canvas = function (width, height, forward) {\n if (forward) {\n canvas_div.style.width = width + 'px';\n canvas_div.style.height = height + 'px';\n }\n };\n\n // Disable right mouse context menu.\n this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n event.preventDefault();\n return false;\n });\n\n function set_focus() {\n canvas.focus();\n canvas_div.focus();\n }\n\n window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'mpl-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n continue;\n }\n\n var button = (fig.buttons[name] = document.createElement('button'));\n button.classList = 'mpl-widget';\n button.setAttribute('role', 'button');\n button.setAttribute('aria-disabled', 'false');\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n var icon_img = document.createElement('img');\n icon_img.src = '_images/' + image + '.png';\n icon_img.srcset = '_images/' + image + '_large.png 2x';\n icon_img.alt = tooltip;\n button.appendChild(icon_img);\n\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n var fmt_picker = document.createElement('select');\n fmt_picker.classList = 'mpl-widget';\n toolbar.appendChild(fmt_picker);\n this.format_dropdown = fmt_picker;\n\n for (var ind in mpl.extensions) {\n var fmt = mpl.extensions[ind];\n var option = document.createElement('option');\n option.selected = fmt === mpl.default_extension;\n option.innerHTML = fmt;\n fmt_picker.appendChild(option);\n }\n\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n // which will in turn request a refresh of the image.\n this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n properties['type'] = type;\n properties['figure_id'] = this.id;\n this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n if (!this.waiting) {\n this.waiting = true;\n this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n var format_dropdown = fig.format_dropdown;\n var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n var size = msg['size'];\n if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n fig._resize_canvas(size[0], size[1], msg['forward']);\n fig.send_message('refresh', {});\n }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n var x0 = msg['x0'] / fig.ratio;\n var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n var x1 = msg['x1'] / fig.ratio;\n var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n x0 = Math.floor(x0) + 0.5;\n y0 = Math.floor(y0) + 0.5;\n x1 = Math.floor(x1) + 0.5;\n y1 = Math.floor(y1) + 0.5;\n var min_x = Math.min(x0, x1);\n var min_y = Math.min(y0, y1);\n var width = Math.abs(x1 - x0);\n var height = Math.abs(y1 - y0);\n\n fig.rubberband_context.clearRect(\n 0,\n 0,\n fig.canvas.width / fig.ratio,\n fig.canvas.height / fig.ratio\n );\n\n fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n // Updates the figure title.\n fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n fig.rubberband_canvas.style.cursor = msg['cursor'];\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n // Request the server to send over a new figure.\n fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n for (var key in msg) {\n if (!(key in fig.buttons)) {\n continue;\n }\n fig.buttons[key].disabled = !msg[key];\n fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n if (msg['mode'] === 'PAN') {\n fig.buttons['Pan'].classList.add('active');\n fig.buttons['Zoom'].classList.remove('active');\n } else if (msg['mode'] === 'ZOOM') {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.add('active');\n } else {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.remove('active');\n }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Called whenever the canvas gets updated.\n this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n return function socket_on_message(evt) {\n if (evt.data instanceof Blob) {\n var img = evt.data;\n if (img.type !== 'image/png') {\n /* FIXME: We get \"Resource interpreted as Image but\n * transferred with MIME type text/plain:\" errors on\n * Chrome. But how to set the MIME type? It doesn't seem\n * to be part of the websocket stream */\n img.type = 'image/png';\n }\n\n /* Free the memory for the previous frames */\n if (fig.imageObj.src) {\n (window.URL || window.webkitURL).revokeObjectURL(\n fig.imageObj.src\n );\n }\n\n fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n img\n );\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n } else if (\n typeof evt.data === 'string' &&\n evt.data.slice(0, 21) === 'data:image/png;base64'\n ) {\n fig.imageObj.src = evt.data;\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n }\n\n var msg = JSON.parse(evt.data);\n var msg_type = msg['type'];\n\n // Call the \"handle_{type}\" callback, which takes\n // the figure and JSON message as its only arguments.\n try {\n var callback = fig['handle_' + msg_type];\n } catch (e) {\n console.log(\n \"No handler for the '\" + msg_type + \"' message type: \",\n msg\n );\n return;\n }\n\n if (callback) {\n try {\n // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n callback(fig, msg);\n } catch (e) {\n console.log(\n \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n e,\n e.stack,\n msg\n );\n }\n }\n };\n};\n\n// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function (e) {\n //this section is from http://www.quirksmode.org/js/events_properties.html\n var targ;\n if (!e) {\n e = window.event;\n }\n if (e.target) {\n targ = e.target;\n } else if (e.srcElement) {\n targ = e.srcElement;\n }\n if (targ.nodeType === 3) {\n // defeat Safari bug\n targ = targ.parentNode;\n }\n\n // pageX,Y are the mouse positions relative to the document\n var boundingRect = targ.getBoundingClientRect();\n var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n\n return { x: x, y: y };\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * https://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n return Object.keys(original).reduce(function (obj, key) {\n if (typeof original[key] !== 'object') {\n obj[key] = original[key];\n }\n return obj;\n }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n var canvas_pos = mpl.findpos(event);\n\n if (name === 'button_press') {\n this.canvas.focus();\n this.canvas_div.focus();\n }\n\n var x = canvas_pos.x * this.ratio;\n var y = canvas_pos.y * this.ratio;\n\n this.send_message(name, {\n x: x,\n y: y,\n button: event.button,\n step: event.step,\n guiEvent: simpleKeys(event),\n });\n\n /* This prevents the web browser from automatically changing to\n * the text insertion cursor when the button is pressed. We want\n * to control all of the cursor setting manually through the\n * 'cursor' event from matplotlib */\n event.preventDefault();\n return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n // Prevent repeat events\n if (name === 'key_press') {\n if (event.key === this._key) {\n return;\n } else {\n this._key = event.key;\n }\n }\n if (name === 'key_release') {\n this._key = null;\n }\n\n var value = '';\n if (event.ctrlKey && event.key !== 'Control') {\n value += 'ctrl+';\n }\n else if (event.altKey && event.key !== 'Alt') {\n value += 'alt+';\n }\n else if (event.shiftKey && event.key !== 'Shift') {\n value += 'shift+';\n }\n\n value += 'k' + event.key;\n\n this._key_event_extra(event, name);\n\n this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n return false;\n};\n\nmpl.figure.prototype.toolbar_button_onclick = function (name) {\n if (name === 'download') {\n this.handle_save(this, null);\n } else {\n this.send_message('toolbar_button', { name: name });\n }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n // Create a \"websocket\"-like object which calls the given IPython comm\n // object with the appropriate methods. Currently this is a non binary\n // socket, so there is still some room for performance tuning.\n var ws = {};\n\n ws.binaryType = comm.kernel.ws.binaryType;\n ws.readyState = comm.kernel.ws.readyState;\n function updateReadyState(_event) {\n if (comm.kernel.ws) {\n ws.readyState = comm.kernel.ws.readyState;\n } else {\n ws.readyState = 3; // Closed state.\n }\n }\n comm.kernel.ws.addEventListener('open', updateReadyState);\n comm.kernel.ws.addEventListener('close', updateReadyState);\n comm.kernel.ws.addEventListener('error', updateReadyState);\n\n ws.close = function () {\n comm.close();\n };\n ws.send = function (m) {\n //console.log('sending', m);\n comm.send(m);\n };\n // Register the callback with on_msg.\n comm.on_msg(function (msg) {\n //console.log('receiving', msg['content']['data'], msg);\n var data = msg['content']['data'];\n if (data['blob'] !== undefined) {\n data = {\n data: new Blob(msg['buffers'], { type: data['blob'] }),\n };\n }\n // Pass the mpl event to the overridden (by mpl) onmessage function.\n ws.onmessage(data);\n });\n return ws;\n};\n\nmpl.mpl_figure_comm = function (comm, msg) {\n // This is the function which gets called when the mpl process\n // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n var id = msg.content.data.id;\n // Get hold of the div created by the display call when the Comm\n // socket was opened in Python.\n var element = document.getElementById(id);\n var ws_proxy = comm_websocket_adapter(comm);\n\n function ondownload(figure, _format) {\n window.open(figure.canvas.toDataURL());\n }\n\n var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n\n // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n // web socket which is closed, not our websocket->open comm proxy.\n ws_proxy.onopen();\n\n fig.parent_element = element;\n fig.cell_info = mpl.find_output_cell(\"
\");\n if (!fig.cell_info) {\n console.error('Failed to find cell for figure', id, fig);\n return;\n }\n fig.cell_info[0].output_area.element.on(\n 'cleared',\n { fig: fig },\n fig._remove_fig_handler\n );\n};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n var width = fig.canvas.width / fig.ratio;\n fig.cell_info[0].output_area.element.off(\n 'cleared',\n fig._remove_fig_handler\n );\n fig.resizeObserverInstance.unobserve(fig.canvas_div);\n\n // Update the output cell to use the data from the current canvas.\n fig.push_to_output();\n var dataURL = fig.canvas.toDataURL();\n // Re-enable the keyboard manager in IPython - without this line, in FF,\n // the notebook keyboard shortcuts fail.\n IPython.keyboard_manager.enable();\n fig.parent_element.innerHTML =\n '';\n fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n fig.send_message('closing', msg);\n // fig.ws.close()\n};\n\nmpl.figure.prototype.push_to_output = function (_remove_interactive) {\n // Turn the data on the canvas into data in the output cell.\n var width = this.canvas.width / this.ratio;\n var dataURL = this.canvas.toDataURL();\n this.cell_info[1]['text/html'] =\n '';\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Tell IPython that the notebook contents must change.\n IPython.notebook.set_dirty(true);\n this.send_message('ack', {});\n var fig = this;\n // Wait a second, then push the new image to the DOM so\n // that it is saved nicely (might be nice to debounce this).\n setTimeout(function () {\n fig.push_to_output();\n }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'btn-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n var button;\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n continue;\n }\n\n button = fig.buttons[name] = document.createElement('button');\n button.classList = 'btn btn-default';\n button.href = '#';\n button.title = name;\n button.innerHTML = '';\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n // Add the status bar.\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message pull-right';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n\n // Add the close button to the window.\n var buttongrp = document.createElement('div');\n buttongrp.classList = 'btn-group inline pull-right';\n button = document.createElement('button');\n button.classList = 'btn btn-mini btn-primary';\n button.href = '#';\n button.title = 'Stop Interaction';\n button.innerHTML = '';\n button.addEventListener('click', function (_evt) {\n fig.handle_close(fig, {});\n });\n button.addEventListener(\n 'mouseover',\n on_mouseover_closure('Stop Interaction')\n );\n buttongrp.appendChild(button);\n var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n titlebar.insertBefore(buttongrp, titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n var fig = event.data.fig;\n if (event.target !== this) {\n // Ignore bubbled events from children.\n return;\n }\n fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n // this is important to make the div 'focusable\n el.setAttribute('tabindex', 0);\n // reach out to IPython and tell the keyboard manager to turn it's self\n // off when our div gets focus\n\n // location in version 3\n if (IPython.notebook.keyboard_manager) {\n IPython.notebook.keyboard_manager.register_events(el);\n } else {\n // location in version 2\n IPython.keyboard_manager.register_events(el);\n }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n // Check for shift+enter\n if (event.shiftKey && event.which === 13) {\n this.canvas_div.blur();\n // select the cell after this one\n var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n IPython.notebook.select(index + 1);\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n fig.ondownload(fig, null);\n};\n\nmpl.find_output_cell = function (html_output) {\n // Return the cell and output element which can be found *uniquely* in the notebook.\n // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n // IPython event is triggered only after the cells have been serialised, which for\n // our purposes (turning an active figure into a static one), is too late.\n var cells = IPython.notebook.get_cells();\n var ncells = cells.length;\n for (var i = 0; i < ncells; i++) {\n var cell = cells[i];\n if (cell.cell_type === 'code') {\n for (var j = 0; j < cell.output_area.outputs.length; j++) {\n var data = cell.output_area.outputs[j];\n if (data.data) {\n // IPython >= 3 moved mimebundle to data attribute of output\n data = data.data;\n }\n if (data['text/html'] === html_output) {\n return [cell, data, j];\n }\n }\n }\n }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n IPython.notebook.kernel.comm_manager.register_target(\n 'matplotlib',\n mpl.mpl_figure_comm\n );\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "
" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "((640, 3), (640,)) ((160, 3), (160,))\n" + ] + } + ], + "source": [ + "from matplotlib import cm\n", + "\n", + "##Select a target variable y and a predictor x. Reshape the data x to [num_samples, 1]##\n", + "\n", + "x1 = df['Attack'].values\n", + "x2 = df['Defense'].values\n", + "x3 = df['HP'].values\n", + "X = df[['Attack','Defense','HP']].values\n", + "y = df['Legendary'].values\n", + "\n", + "fig = plt.figure()\n", + "ax = plt.axes(projection ='3d')\n", + "ax.scatter(x1,x2,x3)\n", + "ax.set_xlabel('Attack')\n", + "ax.set_ylabel('Defense')\n", + "ax.set_zlabel('HP')\n", + "img = ax.scatter(x1, x2, x3, c=y, cmap='YlOrRd', alpha=1)\n", + "\n", + "plt.show()\n", + "\n", + "##Define a training set and a validation set##\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, test_size=0.2, shuffle=True)\n", + "print((X_train.shape, y_train.shape), (X_test.shape, y_test.shape))" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 84, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "R2 on training set : 0.9390625\n", + "R2 on test set : 0.90625\n" + ] + } + ], + "source": [ + "##Define the linear regression model, and reshape the data x to [num_samples, 1]##\n", + "\n", + "model = KNeighborsClassifier()\n", + "\n", + "##Fit the model to the training data##\n", + "\n", + "model.fit(X_train,y_train)\n", + "\n", + "##Make predictions for both the training and the test sets##\n", + "\n", + "y_pred_train = model.predict(X_train)\n", + "y_pred_test = model.predict(X_test)\n", + "\n", + "##Compute th coefficient of determination and the mean square error on both sets##\n", + "\n", + "\n", + "R2_train = model.score(X_train, y_train)\n", + "R2_test = model.score(X_test, y_test)\n", + "\n", + "print('R2 on training set : {}'.format(R2_train))\n", + "print('R2 on test set : {}'.format(R2_test))" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 85, + "outputs": [ + { + "data": { + "text/plain": "", + "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key_event(event, name);\n };\n }\n\n canvas_div.addEventListener(\n 'keydown',\n on_keyboard_event_closure('key_press')\n );\n canvas_div.addEventListener(\n 'keyup',\n on_keyboard_event_closure('key_release')\n );\n\n this._canvas_extra_style(canvas_div);\n this.root.appendChild(canvas_div);\n\n var canvas = (this.canvas = document.createElement('canvas'));\n canvas.classList.add('mpl-canvas');\n canvas.setAttribute('style', 'box-sizing: content-box;');\n\n this.context = canvas.getContext('2d');\n\n var backingStore =\n this.context.backingStorePixelRatio ||\n this.context.webkitBackingStorePixelRatio ||\n this.context.mozBackingStorePixelRatio ||\n this.context.msBackingStorePixelRatio ||\n this.context.oBackingStorePixelRatio ||\n this.context.backingStorePixelRatio ||\n 1;\n\n this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n 'canvas'\n ));\n rubberband_canvas.setAttribute(\n 'style',\n 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n );\n\n // Apply a ponyfill if ResizeObserver is not implemented by browser.\n if (this.ResizeObserver === undefined) {\n if (window.ResizeObserver !== undefined) {\n this.ResizeObserver = window.ResizeObserver;\n } else {\n var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n this.ResizeObserver = obs.ResizeObserver;\n }\n }\n\n this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n var nentries = entries.length;\n for (var i = 0; i < nentries; i++) {\n var entry = entries[i];\n var width, height;\n if (entry.contentBoxSize) {\n if (entry.contentBoxSize instanceof Array) {\n // Chrome 84 implements new version of spec.\n width = entry.contentBoxSize[0].inlineSize;\n height = entry.contentBoxSize[0].blockSize;\n } else {\n // Firefox implements old version of spec.\n width = entry.contentBoxSize.inlineSize;\n height = entry.contentBoxSize.blockSize;\n }\n } else {\n // Chrome <84 implements even older version of spec.\n width = entry.contentRect.width;\n height = entry.contentRect.height;\n }\n\n // Keep the size of the canvas and rubber band canvas in sync with\n // the canvas container.\n if (entry.devicePixelContentBoxSize) {\n // Chrome 84 implements new version of spec.\n canvas.setAttribute(\n 'width',\n entry.devicePixelContentBoxSize[0].inlineSize\n );\n canvas.setAttribute(\n 'height',\n entry.devicePixelContentBoxSize[0].blockSize\n );\n } else {\n canvas.setAttribute('width', width * fig.ratio);\n canvas.setAttribute('height', height * fig.ratio);\n }\n canvas.setAttribute(\n 'style',\n 'width: ' + width + 'px; height: ' + height + 'px;'\n );\n\n rubberband_canvas.setAttribute('width', width);\n rubberband_canvas.setAttribute('height', height);\n\n // And update the size in Python. We ignore the initial 0/0 size\n // that occurs as the element is placed into the DOM, which should\n // otherwise not happen due to the minimum size styling.\n if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n fig.request_resize(width, height);\n }\n }\n });\n this.resizeObserverInstance.observe(canvas_div);\n\n function on_mouse_event_closure(name) {\n return function (event) {\n return fig.mouse_event(event, name);\n };\n }\n\n rubberband_canvas.addEventListener(\n 'mousedown',\n on_mouse_event_closure('button_press')\n );\n rubberband_canvas.addEventListener(\n 'mouseup',\n on_mouse_event_closure('button_release')\n );\n rubberband_canvas.addEventListener(\n 'dblclick',\n on_mouse_event_closure('dblclick')\n );\n // Throttle sequential mouse events to 1 every 20ms.\n rubberband_canvas.addEventListener(\n 'mousemove',\n on_mouse_event_closure('motion_notify')\n );\n\n rubberband_canvas.addEventListener(\n 'mouseenter',\n on_mouse_event_closure('figure_enter')\n );\n rubberband_canvas.addEventListener(\n 'mouseleave',\n on_mouse_event_closure('figure_leave')\n );\n\n canvas_div.addEventListener('wheel', function (event) {\n if (event.deltaY < 0) {\n event.step = 1;\n } else {\n event.step = -1;\n }\n on_mouse_event_closure('scroll')(event);\n });\n\n canvas_div.appendChild(canvas);\n canvas_div.appendChild(rubberband_canvas);\n\n this.rubberband_context = rubberband_canvas.getContext('2d');\n this.rubberband_context.strokeStyle = '#000000';\n\n this._resize_canvas = function (width, height, forward) {\n if (forward) {\n canvas_div.style.width = width + 'px';\n canvas_div.style.height = height + 'px';\n }\n };\n\n // Disable right mouse context menu.\n this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n event.preventDefault();\n return false;\n });\n\n function set_focus() {\n canvas.focus();\n canvas_div.focus();\n }\n\n window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'mpl-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n continue;\n }\n\n var button = (fig.buttons[name] = document.createElement('button'));\n button.classList = 'mpl-widget';\n button.setAttribute('role', 'button');\n button.setAttribute('aria-disabled', 'false');\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n var icon_img = document.createElement('img');\n icon_img.src = '_images/' + image + '.png';\n icon_img.srcset = '_images/' + image + '_large.png 2x';\n icon_img.alt = tooltip;\n button.appendChild(icon_img);\n\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n var fmt_picker = document.createElement('select');\n fmt_picker.classList = 'mpl-widget';\n toolbar.appendChild(fmt_picker);\n this.format_dropdown = fmt_picker;\n\n for (var ind in mpl.extensions) {\n var fmt = mpl.extensions[ind];\n var option = document.createElement('option');\n option.selected = fmt === mpl.default_extension;\n option.innerHTML = fmt;\n fmt_picker.appendChild(option);\n }\n\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n // which will in turn request a refresh of the image.\n this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n properties['type'] = type;\n properties['figure_id'] = this.id;\n this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n if (!this.waiting) {\n this.waiting = true;\n this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n var format_dropdown = fig.format_dropdown;\n var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n var size = msg['size'];\n if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n fig._resize_canvas(size[0], size[1], msg['forward']);\n fig.send_message('refresh', {});\n }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n var x0 = msg['x0'] / fig.ratio;\n var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n var x1 = msg['x1'] / fig.ratio;\n var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n x0 = Math.floor(x0) + 0.5;\n y0 = Math.floor(y0) + 0.5;\n x1 = Math.floor(x1) + 0.5;\n y1 = Math.floor(y1) + 0.5;\n var min_x = Math.min(x0, x1);\n var min_y = Math.min(y0, y1);\n var width = Math.abs(x1 - x0);\n var height = Math.abs(y1 - y0);\n\n fig.rubberband_context.clearRect(\n 0,\n 0,\n fig.canvas.width / fig.ratio,\n fig.canvas.height / fig.ratio\n );\n\n fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n // Updates the figure title.\n fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n fig.rubberband_canvas.style.cursor = msg['cursor'];\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n // Request the server to send over a new figure.\n fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n for (var key in msg) {\n if (!(key in fig.buttons)) {\n continue;\n }\n fig.buttons[key].disabled = !msg[key];\n fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n if (msg['mode'] === 'PAN') {\n fig.buttons['Pan'].classList.add('active');\n fig.buttons['Zoom'].classList.remove('active');\n } else if (msg['mode'] === 'ZOOM') {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.add('active');\n } else {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.remove('active');\n }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Called whenever the canvas gets updated.\n this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n return function socket_on_message(evt) {\n if (evt.data instanceof Blob) {\n var img = evt.data;\n if (img.type !== 'image/png') {\n /* FIXME: We get \"Resource interpreted as Image but\n * transferred with MIME type text/plain:\" errors on\n * Chrome. But how to set the MIME type? It doesn't seem\n * to be part of the websocket stream */\n img.type = 'image/png';\n }\n\n /* Free the memory for the previous frames */\n if (fig.imageObj.src) {\n (window.URL || window.webkitURL).revokeObjectURL(\n fig.imageObj.src\n );\n }\n\n fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n img\n );\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n } else if (\n typeof evt.data === 'string' &&\n evt.data.slice(0, 21) === 'data:image/png;base64'\n ) {\n fig.imageObj.src = evt.data;\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n }\n\n var msg = JSON.parse(evt.data);\n var msg_type = msg['type'];\n\n // Call the \"handle_{type}\" callback, which takes\n // the figure and JSON message as its only arguments.\n try {\n var callback = fig['handle_' + msg_type];\n } catch (e) {\n console.log(\n \"No handler for the '\" + msg_type + \"' message type: \",\n msg\n );\n return;\n }\n\n if (callback) {\n try {\n // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n callback(fig, msg);\n } catch (e) {\n console.log(\n \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n e,\n e.stack,\n msg\n );\n }\n }\n };\n};\n\n// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function (e) {\n //this section is from http://www.quirksmode.org/js/events_properties.html\n var targ;\n if (!e) {\n e = window.event;\n }\n if (e.target) {\n targ = e.target;\n } else if (e.srcElement) {\n targ = e.srcElement;\n }\n if (targ.nodeType === 3) {\n // defeat Safari bug\n targ = targ.parentNode;\n }\n\n // pageX,Y are the mouse positions relative to the document\n var boundingRect = targ.getBoundingClientRect();\n var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n\n return { x: x, y: y };\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * https://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n return Object.keys(original).reduce(function (obj, key) {\n if (typeof original[key] !== 'object') {\n obj[key] = original[key];\n }\n return obj;\n }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n var canvas_pos = mpl.findpos(event);\n\n if (name === 'button_press') {\n this.canvas.focus();\n this.canvas_div.focus();\n }\n\n var x = canvas_pos.x * this.ratio;\n var y = canvas_pos.y * this.ratio;\n\n this.send_message(name, {\n x: x,\n y: y,\n button: event.button,\n step: event.step,\n guiEvent: simpleKeys(event),\n });\n\n /* This prevents the web browser from automatically changing to\n * the text insertion cursor when the button is pressed. We want\n * to control all of the cursor setting manually through the\n * 'cursor' event from matplotlib */\n event.preventDefault();\n return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n // Prevent repeat events\n if (name === 'key_press') {\n if (event.key === this._key) {\n return;\n } else {\n this._key = event.key;\n }\n }\n if (name === 'key_release') {\n this._key = null;\n }\n\n var value = '';\n if (event.ctrlKey && event.key !== 'Control') {\n value += 'ctrl+';\n }\n else if (event.altKey && event.key !== 'Alt') {\n value += 'alt+';\n }\n else if (event.shiftKey && event.key !== 'Shift') {\n value += 'shift+';\n }\n\n value += 'k' + event.key;\n\n this._key_event_extra(event, name);\n\n this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n return false;\n};\n\nmpl.figure.prototype.toolbar_button_onclick = function (name) {\n if (name === 'download') {\n this.handle_save(this, null);\n } else {\n this.send_message('toolbar_button', { name: name });\n }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n // Create a \"websocket\"-like object which calls the given IPython comm\n // object with the appropriate methods. Currently this is a non binary\n // socket, so there is still some room for performance tuning.\n var ws = {};\n\n ws.binaryType = comm.kernel.ws.binaryType;\n ws.readyState = comm.kernel.ws.readyState;\n function updateReadyState(_event) {\n if (comm.kernel.ws) {\n ws.readyState = comm.kernel.ws.readyState;\n } else {\n ws.readyState = 3; // Closed state.\n }\n }\n comm.kernel.ws.addEventListener('open', updateReadyState);\n comm.kernel.ws.addEventListener('close', updateReadyState);\n comm.kernel.ws.addEventListener('error', updateReadyState);\n\n ws.close = function () {\n comm.close();\n };\n ws.send = function (m) {\n //console.log('sending', m);\n comm.send(m);\n };\n // Register the callback with on_msg.\n comm.on_msg(function (msg) {\n //console.log('receiving', msg['content']['data'], msg);\n var data = msg['content']['data'];\n if (data['blob'] !== undefined) {\n data = {\n data: new Blob(msg['buffers'], { type: data['blob'] }),\n };\n }\n // Pass the mpl event to the overridden (by mpl) onmessage function.\n ws.onmessage(data);\n });\n return ws;\n};\n\nmpl.mpl_figure_comm = function (comm, msg) {\n // This is the function which gets called when the mpl process\n // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n var id = msg.content.data.id;\n // Get hold of the div created by the display call when the Comm\n // socket was opened in Python.\n var element = document.getElementById(id);\n var ws_proxy = comm_websocket_adapter(comm);\n\n function ondownload(figure, _format) {\n window.open(figure.canvas.toDataURL());\n }\n\n var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n\n // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n // web socket which is closed, not our websocket->open comm proxy.\n ws_proxy.onopen();\n\n fig.parent_element = element;\n fig.cell_info = mpl.find_output_cell(\"
\");\n if (!fig.cell_info) {\n console.error('Failed to find cell for figure', id, fig);\n return;\n }\n fig.cell_info[0].output_area.element.on(\n 'cleared',\n { fig: fig },\n fig._remove_fig_handler\n );\n};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n var width = fig.canvas.width / fig.ratio;\n fig.cell_info[0].output_area.element.off(\n 'cleared',\n fig._remove_fig_handler\n );\n fig.resizeObserverInstance.unobserve(fig.canvas_div);\n\n // Update the output cell to use the data from the current canvas.\n fig.push_to_output();\n var dataURL = fig.canvas.toDataURL();\n // Re-enable the keyboard manager in IPython - without this line, in FF,\n // the notebook keyboard shortcuts fail.\n IPython.keyboard_manager.enable();\n fig.parent_element.innerHTML =\n '';\n fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n fig.send_message('closing', msg);\n // fig.ws.close()\n};\n\nmpl.figure.prototype.push_to_output = function (_remove_interactive) {\n // Turn the data on the canvas into data in the output cell.\n var width = this.canvas.width / this.ratio;\n var dataURL = this.canvas.toDataURL();\n this.cell_info[1]['text/html'] =\n '';\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Tell IPython that the notebook contents must change.\n IPython.notebook.set_dirty(true);\n this.send_message('ack', {});\n var fig = this;\n // Wait a second, then push the new image to the DOM so\n // that it is saved nicely (might be nice to debounce this).\n setTimeout(function () {\n fig.push_to_output();\n }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'btn-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n var button;\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n continue;\n }\n\n button = fig.buttons[name] = document.createElement('button');\n button.classList = 'btn btn-default';\n button.href = '#';\n button.title = name;\n button.innerHTML = '';\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n // Add the status bar.\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message pull-right';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n\n // Add the close button to the window.\n var buttongrp = document.createElement('div');\n buttongrp.classList = 'btn-group inline pull-right';\n button = document.createElement('button');\n button.classList = 'btn btn-mini btn-primary';\n button.href = '#';\n button.title = 'Stop Interaction';\n button.innerHTML = '';\n button.addEventListener('click', function (_evt) {\n fig.handle_close(fig, {});\n });\n button.addEventListener(\n 'mouseover',\n on_mouseover_closure('Stop Interaction')\n );\n buttongrp.appendChild(button);\n var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n titlebar.insertBefore(buttongrp, titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n var fig = event.data.fig;\n if (event.target !== this) {\n // Ignore bubbled events from children.\n return;\n }\n fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n // this is important to make the div 'focusable\n el.setAttribute('tabindex', 0);\n // reach out to IPython and tell the keyboard manager to turn it's self\n // off when our div gets focus\n\n // location in version 3\n if (IPython.notebook.keyboard_manager) {\n IPython.notebook.keyboard_manager.register_events(el);\n } else {\n // location in version 2\n IPython.keyboard_manager.register_events(el);\n }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n // Check for shift+enter\n if (event.shiftKey && event.which === 13) {\n this.canvas_div.blur();\n // select the cell after this one\n var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n IPython.notebook.select(index + 1);\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n fig.ondownload(fig, null);\n};\n\nmpl.find_output_cell = function (html_output) {\n // Return the cell and output element which can be found *uniquely* in the notebook.\n // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n // IPython event is triggered only after the cells have been serialised, which for\n // our purposes (turning an active figure into a static one), is too late.\n var cells = IPython.notebook.get_cells();\n var ncells = cells.length;\n for (var i = 0; i < ncells; i++) {\n var cell = cells[i];\n if (cell.cell_type === 'code') {\n for (var j = 0; j < cell.output_area.outputs.length; j++) {\n var data = cell.output_area.outputs[j];\n if (data.data) {\n // IPython >= 3 moved mimebundle to data attribute of output\n data = data.data;\n }\n if (data['text/html'] === html_output) {\n return [cell, data, j];\n }\n }\n }\n }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n IPython.notebook.kernel.comm_manager.register_target(\n 'matplotlib',\n mpl.mpl_figure_comm\n );\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "
" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + } + ], + "source": [ + "##Generate predictions out of the fitted model##\n", + "#xf = (np.random.standard_normal(200)+2)/4*200\n", + "#yf = (np.random.standard_normal(200)+2)/4*200\n", + "#zf = (np.random.standard_normal(200)+2)/4*200\n", + "xfit = []\n", + "xf = []\n", + "yf = []\n", + "zf = []\n", + "for x in range(0, 200,20):\n", + " for y in range(0, 200,20):\n", + " for z in range(0, 200,20):\n", + " xfit.append([x,y,z])\n", + " xf.append(x)\n", + " yf.append(y)\n", + " zf.append(z)\n", + "#xfit = np.column_stack((xf,yf,zf))\n", + "\n", + "yfit = model.predict(xfit)\n", + "\n", + "##Plot the regression line##\n", + "\n", + "fig = plt.figure()\n", + "ax = plt.axes(projection ='3d')\n", + "#ax.scatter(x1,x2,x3)\n", + "ax.set_xlabel('Attack')\n", + "ax.set_ylabel('Defense')\n", + "ax.set_zlabel('HP')\n", + "img = ax.scatter(xf,yf ,zf , c=yfit, cmap='YlOrRd', alpha=0.7)\n", + "ax.legend()\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "**3) Create functions that, given arrays of true class labels and predicted lables, compute :**\n", + "\n", + "* **a) The accuracy**\n", + "* **b) The recall score**\n", + "* **c) The precision score**\n", + "* **d) The confusion matrix**\n", + "* **e) The F1-score**\n", + "\n", + "**Compare the result of your implementation of these metrics on predictions for the training and test sets to the methods of the sklearn library.**\n", + "\n" + ], + "metadata": { + "id": "WbwwdoxDjFeQ" + } + }, + { + "cell_type": "code", + "source": [ + "" + ], + "metadata": { + "id": "oyaDNpPInZ-V" + }, + "execution_count": 85, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "" + ], + "metadata": { + "id": "g8Uj7kRondQ7" + }, + "execution_count": 85, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/TPML.iml b/TPML.iml new file mode 100644 index 0000000..ad3c0a3 --- /dev/null +++ b/TPML.iml @@ -0,0 +1,9 @@ + + + + + + + + + \ No newline at end of file diff --git a/data/Pokemon.csv b/data/Pokemon.csv new file mode 100644 index 0000000..8948acc --- /dev/null +++ b/data/Pokemon.csv @@ -0,0 +1,801 @@ +#,Name,Type 1,Type 2,Total,HP,Attack,Defense,Sp. Atk,Sp. Def,Speed,Generation,Legendary +1,Bulbasaur,Grass,Poison,318,45,49,49,65,65,45,1,False +2,Ivysaur,Grass,Poison,405,60,62,63,80,80,60,1,False +3,Venusaur,Grass,Poison,525,80,82,83,100,100,80,1,False +3,VenusaurMega Venusaur,Grass,Poison,625,80,100,123,122,120,80,1,False +4,Charmander,Fire,,309,39,52,43,60,50,65,1,False +5,Charmeleon,Fire,,405,58,64,58,80,65,80,1,False +6,Charizard,Fire,Flying,534,78,84,78,109,85,100,1,False +6,CharizardMega Charizard X,Fire,Dragon,634,78,130,111,130,85,100,1,False +6,CharizardMega Charizard Y,Fire,Flying,634,78,104,78,159,115,100,1,False +7,Squirtle,Water,,314,44,48,65,50,64,43,1,False +8,Wartortle,Water,,405,59,63,80,65,80,58,1,False +9,Blastoise,Water,,530,79,83,100,85,105,78,1,False +9,BlastoiseMega Blastoise,Water,,630,79,103,120,135,115,78,1,False +10,Caterpie,Bug,,195,45,30,35,20,20,45,1,False +11,Metapod,Bug,,205,50,20,55,25,25,30,1,False +12,Butterfree,Bug,Flying,395,60,45,50,90,80,70,1,False +13,Weedle,Bug,Poison,195,40,35,30,20,20,50,1,False +14,Kakuna,Bug,Poison,205,45,25,50,25,25,35,1,False +15,Beedrill,Bug,Poison,395,65,90,40,45,80,75,1,False +15,BeedrillMega Beedrill,Bug,Poison,495,65,150,40,15,80,145,1,False +16,Pidgey,Normal,Flying,251,40,45,40,35,35,56,1,False +17,Pidgeotto,Normal,Flying,349,63,60,55,50,50,71,1,False +18,Pidgeot,Normal,Flying,479,83,80,75,70,70,101,1,False +18,PidgeotMega Pidgeot,Normal,Flying,579,83,80,80,135,80,121,1,False +19,Rattata,Normal,,253,30,56,35,25,35,72,1,False +20,Raticate,Normal,,413,55,81,60,50,70,97,1,False +21,Spearow,Normal,Flying,262,40,60,30,31,31,70,1,False +22,Fearow,Normal,Flying,442,65,90,65,61,61,100,1,False +23,Ekans,Poison,,288,35,60,44,40,54,55,1,False +24,Arbok,Poison,,438,60,85,69,65,79,80,1,False +25,Pikachu,Electric,,320,35,55,40,50,50,90,1,False +26,Raichu,Electric,,485,60,90,55,90,80,110,1,False +27,Sandshrew,Ground,,300,50,75,85,20,30,40,1,False +28,Sandslash,Ground,,450,75,100,110,45,55,65,1,False +29,Nidoran♀,Poison,,275,55,47,52,40,40,41,1,False +30,Nidorina,Poison,,365,70,62,67,55,55,56,1,False +31,Nidoqueen,Poison,Ground,505,90,92,87,75,85,76,1,False +32,Nidoran♂,Poison,,273,46,57,40,40,40,50,1,False +33,Nidorino,Poison,,365,61,72,57,55,55,65,1,False +34,Nidoking,Poison,Ground,505,81,102,77,85,75,85,1,False +35,Clefairy,Fairy,,323,70,45,48,60,65,35,1,False +36,Clefable,Fairy,,483,95,70,73,95,90,60,1,False +37,Vulpix,Fire,,299,38,41,40,50,65,65,1,False +38,Ninetales,Fire,,505,73,76,75,81,100,100,1,False +39,Jigglypuff,Normal,Fairy,270,115,45,20,45,25,20,1,False +40,Wigglytuff,Normal,Fairy,435,140,70,45,85,50,45,1,False +41,Zubat,Poison,Flying,245,40,45,35,30,40,55,1,False +42,Golbat,Poison,Flying,455,75,80,70,65,75,90,1,False +43,Oddish,Grass,Poison,320,45,50,55,75,65,30,1,False +44,Gloom,Grass,Poison,395,60,65,70,85,75,40,1,False +45,Vileplume,Grass,Poison,490,75,80,85,110,90,50,1,False +46,Paras,Bug,Grass,285,35,70,55,45,55,25,1,False +47,Parasect,Bug,Grass,405,60,95,80,60,80,30,1,False +48,Venonat,Bug,Poison,305,60,55,50,40,55,45,1,False +49,Venomoth,Bug,Poison,450,70,65,60,90,75,90,1,False +50,Diglett,Ground,,265,10,55,25,35,45,95,1,False +51,Dugtrio,Ground,,405,35,80,50,50,70,120,1,False +52,Meowth,Normal,,290,40,45,35,40,40,90,1,False +53,Persian,Normal,,440,65,70,60,65,65,115,1,False +54,Psyduck,Water,,320,50,52,48,65,50,55,1,False +55,Golduck,Water,,500,80,82,78,95,80,85,1,False +56,Mankey,Fighting,,305,40,80,35,35,45,70,1,False +57,Primeape,Fighting,,455,65,105,60,60,70,95,1,False +58,Growlithe,Fire,,350,55,70,45,70,50,60,1,False +59,Arcanine,Fire,,555,90,110,80,100,80,95,1,False +60,Poliwag,Water,,300,40,50,40,40,40,90,1,False +61,Poliwhirl,Water,,385,65,65,65,50,50,90,1,False +62,Poliwrath,Water,Fighting,510,90,95,95,70,90,70,1,False +63,Abra,Psychic,,310,25,20,15,105,55,90,1,False +64,Kadabra,Psychic,,400,40,35,30,120,70,105,1,False +65,Alakazam,Psychic,,500,55,50,45,135,95,120,1,False +65,AlakazamMega Alakazam,Psychic,,590,55,50,65,175,95,150,1,False +66,Machop,Fighting,,305,70,80,50,35,35,35,1,False +67,Machoke,Fighting,,405,80,100,70,50,60,45,1,False +68,Machamp,Fighting,,505,90,130,80,65,85,55,1,False +69,Bellsprout,Grass,Poison,300,50,75,35,70,30,40,1,False +70,Weepinbell,Grass,Poison,390,65,90,50,85,45,55,1,False +71,Victreebel,Grass,Poison,490,80,105,65,100,70,70,1,False +72,Tentacool,Water,Poison,335,40,40,35,50,100,70,1,False +73,Tentacruel,Water,Poison,515,80,70,65,80,120,100,1,False +74,Geodude,Rock,Ground,300,40,80,100,30,30,20,1,False +75,Graveler,Rock,Ground,390,55,95,115,45,45,35,1,False +76,Golem,Rock,Ground,495,80,120,130,55,65,45,1,False +77,Ponyta,Fire,,410,50,85,55,65,65,90,1,False +78,Rapidash,Fire,,500,65,100,70,80,80,105,1,False +79,Slowpoke,Water,Psychic,315,90,65,65,40,40,15,1,False +80,Slowbro,Water,Psychic,490,95,75,110,100,80,30,1,False +80,SlowbroMega Slowbro,Water,Psychic,590,95,75,180,130,80,30,1,False +81,Magnemite,Electric,Steel,325,25,35,70,95,55,45,1,False +82,Magneton,Electric,Steel,465,50,60,95,120,70,70,1,False +83,Farfetch'd,Normal,Flying,352,52,65,55,58,62,60,1,False +84,Doduo,Normal,Flying,310,35,85,45,35,35,75,1,False +85,Dodrio,Normal,Flying,460,60,110,70,60,60,100,1,False +86,Seel,Water,,325,65,45,55,45,70,45,1,False +87,Dewgong,Water,Ice,475,90,70,80,70,95,70,1,False +88,Grimer,Poison,,325,80,80,50,40,50,25,1,False +89,Muk,Poison,,500,105,105,75,65,100,50,1,False +90,Shellder,Water,,305,30,65,100,45,25,40,1,False +91,Cloyster,Water,Ice,525,50,95,180,85,45,70,1,False +92,Gastly,Ghost,Poison,310,30,35,30,100,35,80,1,False +93,Haunter,Ghost,Poison,405,45,50,45,115,55,95,1,False +94,Gengar,Ghost,Poison,500,60,65,60,130,75,110,1,False +94,GengarMega Gengar,Ghost,Poison,600,60,65,80,170,95,130,1,False +95,Onix,Rock,Ground,385,35,45,160,30,45,70,1,False +96,Drowzee,Psychic,,328,60,48,45,43,90,42,1,False +97,Hypno,Psychic,,483,85,73,70,73,115,67,1,False +98,Krabby,Water,,325,30,105,90,25,25,50,1,False +99,Kingler,Water,,475,55,130,115,50,50,75,1,False +100,Voltorb,Electric,,330,40,30,50,55,55,100,1,False +101,Electrode,Electric,,480,60,50,70,80,80,140,1,False +102,Exeggcute,Grass,Psychic,325,60,40,80,60,45,40,1,False +103,Exeggutor,Grass,Psychic,520,95,95,85,125,65,55,1,False +104,Cubone,Ground,,320,50,50,95,40,50,35,1,False +105,Marowak,Ground,,425,60,80,110,50,80,45,1,False +106,Hitmonlee,Fighting,,455,50,120,53,35,110,87,1,False +107,Hitmonchan,Fighting,,455,50,105,79,35,110,76,1,False +108,Lickitung,Normal,,385,90,55,75,60,75,30,1,False +109,Koffing,Poison,,340,40,65,95,60,45,35,1,False +110,Weezing,Poison,,490,65,90,120,85,70,60,1,False +111,Rhyhorn,Ground,Rock,345,80,85,95,30,30,25,1,False +112,Rhydon,Ground,Rock,485,105,130,120,45,45,40,1,False +113,Chansey,Normal,,450,250,5,5,35,105,50,1,False +114,Tangela,Grass,,435,65,55,115,100,40,60,1,False +115,Kangaskhan,Normal,,490,105,95,80,40,80,90,1,False +115,KangaskhanMega Kangaskhan,Normal,,590,105,125,100,60,100,100,1,False +116,Horsea,Water,,295,30,40,70,70,25,60,1,False +117,Seadra,Water,,440,55,65,95,95,45,85,1,False +118,Goldeen,Water,,320,45,67,60,35,50,63,1,False +119,Seaking,Water,,450,80,92,65,65,80,68,1,False +120,Staryu,Water,,340,30,45,55,70,55,85,1,False +121,Starmie,Water,Psychic,520,60,75,85,100,85,115,1,False +122,Mr. Mime,Psychic,Fairy,460,40,45,65,100,120,90,1,False +123,Scyther,Bug,Flying,500,70,110,80,55,80,105,1,False +124,Jynx,Ice,Psychic,455,65,50,35,115,95,95,1,False +125,Electabuzz,Electric,,490,65,83,57,95,85,105,1,False +126,Magmar,Fire,,495,65,95,57,100,85,93,1,False +127,Pinsir,Bug,,500,65,125,100,55,70,85,1,False +127,PinsirMega Pinsir,Bug,Flying,600,65,155,120,65,90,105,1,False +128,Tauros,Normal,,490,75,100,95,40,70,110,1,False +129,Magikarp,Water,,200,20,10,55,15,20,80,1,False +130,Gyarados,Water,Flying,540,95,125,79,60,100,81,1,False +130,GyaradosMega Gyarados,Water,Dark,640,95,155,109,70,130,81,1,False +131,Lapras,Water,Ice,535,130,85,80,85,95,60,1,False +132,Ditto,Normal,,288,48,48,48,48,48,48,1,False +133,Eevee,Normal,,325,55,55,50,45,65,55,1,False +134,Vaporeon,Water,,525,130,65,60,110,95,65,1,False +135,Jolteon,Electric,,525,65,65,60,110,95,130,1,False +136,Flareon,Fire,,525,65,130,60,95,110,65,1,False +137,Porygon,Normal,,395,65,60,70,85,75,40,1,False +138,Omanyte,Rock,Water,355,35,40,100,90,55,35,1,False +139,Omastar,Rock,Water,495,70,60,125,115,70,55,1,False +140,Kabuto,Rock,Water,355,30,80,90,55,45,55,1,False +141,Kabutops,Rock,Water,495,60,115,105,65,70,80,1,False +142,Aerodactyl,Rock,Flying,515,80,105,65,60,75,130,1,False +142,AerodactylMega Aerodactyl,Rock,Flying,615,80,135,85,70,95,150,1,False +143,Snorlax,Normal,,540,160,110,65,65,110,30,1,False +144,Articuno,Ice,Flying,580,90,85,100,95,125,85,1,True +145,Zapdos,Electric,Flying,580,90,90,85,125,90,100,1,True +146,Moltres,Fire,Flying,580,90,100,90,125,85,90,1,True +147,Dratini,Dragon,,300,41,64,45,50,50,50,1,False +148,Dragonair,Dragon,,420,61,84,65,70,70,70,1,False +149,Dragonite,Dragon,Flying,600,91,134,95,100,100,80,1,False +150,Mewtwo,Psychic,,680,106,110,90,154,90,130,1,True +150,MewtwoMega Mewtwo X,Psychic,Fighting,780,106,190,100,154,100,130,1,True +150,MewtwoMega Mewtwo Y,Psychic,,780,106,150,70,194,120,140,1,True +151,Mew,Psychic,,600,100,100,100,100,100,100,1,False +152,Chikorita,Grass,,318,45,49,65,49,65,45,2,False +153,Bayleef,Grass,,405,60,62,80,63,80,60,2,False +154,Meganium,Grass,,525,80,82,100,83,100,80,2,False +155,Cyndaquil,Fire,,309,39,52,43,60,50,65,2,False +156,Quilava,Fire,,405,58,64,58,80,65,80,2,False +157,Typhlosion,Fire,,534,78,84,78,109,85,100,2,False +158,Totodile,Water,,314,50,65,64,44,48,43,2,False +159,Croconaw,Water,,405,65,80,80,59,63,58,2,False +160,Feraligatr,Water,,530,85,105,100,79,83,78,2,False +161,Sentret,Normal,,215,35,46,34,35,45,20,2,False +162,Furret,Normal,,415,85,76,64,45,55,90,2,False +163,Hoothoot,Normal,Flying,262,60,30,30,36,56,50,2,False +164,Noctowl,Normal,Flying,442,100,50,50,76,96,70,2,False +165,Ledyba,Bug,Flying,265,40,20,30,40,80,55,2,False +166,Ledian,Bug,Flying,390,55,35,50,55,110,85,2,False +167,Spinarak,Bug,Poison,250,40,60,40,40,40,30,2,False +168,Ariados,Bug,Poison,390,70,90,70,60,60,40,2,False +169,Crobat,Poison,Flying,535,85,90,80,70,80,130,2,False +170,Chinchou,Water,Electric,330,75,38,38,56,56,67,2,False +171,Lanturn,Water,Electric,460,125,58,58,76,76,67,2,False +172,Pichu,Electric,,205,20,40,15,35,35,60,2,False +173,Cleffa,Fairy,,218,50,25,28,45,55,15,2,False +174,Igglybuff,Normal,Fairy,210,90,30,15,40,20,15,2,False +175,Togepi,Fairy,,245,35,20,65,40,65,20,2,False +176,Togetic,Fairy,Flying,405,55,40,85,80,105,40,2,False +177,Natu,Psychic,Flying,320,40,50,45,70,45,70,2,False +178,Xatu,Psychic,Flying,470,65,75,70,95,70,95,2,False +179,Mareep,Electric,,280,55,40,40,65,45,35,2,False +180,Flaaffy,Electric,,365,70,55,55,80,60,45,2,False +181,Ampharos,Electric,,510,90,75,85,115,90,55,2,False +181,AmpharosMega Ampharos,Electric,Dragon,610,90,95,105,165,110,45,2,False +182,Bellossom,Grass,,490,75,80,95,90,100,50,2,False +183,Marill,Water,Fairy,250,70,20,50,20,50,40,2,False +184,Azumarill,Water,Fairy,420,100,50,80,60,80,50,2,False +185,Sudowoodo,Rock,,410,70,100,115,30,65,30,2,False +186,Politoed,Water,,500,90,75,75,90,100,70,2,False +187,Hoppip,Grass,Flying,250,35,35,40,35,55,50,2,False +188,Skiploom,Grass,Flying,340,55,45,50,45,65,80,2,False +189,Jumpluff,Grass,Flying,460,75,55,70,55,95,110,2,False +190,Aipom,Normal,,360,55,70,55,40,55,85,2,False +191,Sunkern,Grass,,180,30,30,30,30,30,30,2,False +192,Sunflora,Grass,,425,75,75,55,105,85,30,2,False +193,Yanma,Bug,Flying,390,65,65,45,75,45,95,2,False +194,Wooper,Water,Ground,210,55,45,45,25,25,15,2,False +195,Quagsire,Water,Ground,430,95,85,85,65,65,35,2,False +196,Espeon,Psychic,,525,65,65,60,130,95,110,2,False +197,Umbreon,Dark,,525,95,65,110,60,130,65,2,False +198,Murkrow,Dark,Flying,405,60,85,42,85,42,91,2,False +199,Slowking,Water,Psychic,490,95,75,80,100,110,30,2,False +200,Misdreavus,Ghost,,435,60,60,60,85,85,85,2,False +201,Unown,Psychic,,336,48,72,48,72,48,48,2,False +202,Wobbuffet,Psychic,,405,190,33,58,33,58,33,2,False +203,Girafarig,Normal,Psychic,455,70,80,65,90,65,85,2,False +204,Pineco,Bug,,290,50,65,90,35,35,15,2,False +205,Forretress,Bug,Steel,465,75,90,140,60,60,40,2,False +206,Dunsparce,Normal,,415,100,70,70,65,65,45,2,False +207,Gligar,Ground,Flying,430,65,75,105,35,65,85,2,False +208,Steelix,Steel,Ground,510,75,85,200,55,65,30,2,False +208,SteelixMega Steelix,Steel,Ground,610,75,125,230,55,95,30,2,False +209,Snubbull,Fairy,,300,60,80,50,40,40,30,2,False +210,Granbull,Fairy,,450,90,120,75,60,60,45,2,False +211,Qwilfish,Water,Poison,430,65,95,75,55,55,85,2,False +212,Scizor,Bug,Steel,500,70,130,100,55,80,65,2,False +212,ScizorMega Scizor,Bug,Steel,600,70,150,140,65,100,75,2,False +213,Shuckle,Bug,Rock,505,20,10,230,10,230,5,2,False +214,Heracross,Bug,Fighting,500,80,125,75,40,95,85,2,False +214,HeracrossMega Heracross,Bug,Fighting,600,80,185,115,40,105,75,2,False +215,Sneasel,Dark,Ice,430,55,95,55,35,75,115,2,False +216,Teddiursa,Normal,,330,60,80,50,50,50,40,2,False +217,Ursaring,Normal,,500,90,130,75,75,75,55,2,False +218,Slugma,Fire,,250,40,40,40,70,40,20,2,False +219,Magcargo,Fire,Rock,410,50,50,120,80,80,30,2,False +220,Swinub,Ice,Ground,250,50,50,40,30,30,50,2,False +221,Piloswine,Ice,Ground,450,100,100,80,60,60,50,2,False +222,Corsola,Water,Rock,380,55,55,85,65,85,35,2,False +223,Remoraid,Water,,300,35,65,35,65,35,65,2,False +224,Octillery,Water,,480,75,105,75,105,75,45,2,False +225,Delibird,Ice,Flying,330,45,55,45,65,45,75,2,False +226,Mantine,Water,Flying,465,65,40,70,80,140,70,2,False +227,Skarmory,Steel,Flying,465,65,80,140,40,70,70,2,False +228,Houndour,Dark,Fire,330,45,60,30,80,50,65,2,False +229,Houndoom,Dark,Fire,500,75,90,50,110,80,95,2,False +229,HoundoomMega Houndoom,Dark,Fire,600,75,90,90,140,90,115,2,False +230,Kingdra,Water,Dragon,540,75,95,95,95,95,85,2,False +231,Phanpy,Ground,,330,90,60,60,40,40,40,2,False +232,Donphan,Ground,,500,90,120,120,60,60,50,2,False +233,Porygon2,Normal,,515,85,80,90,105,95,60,2,False +234,Stantler,Normal,,465,73,95,62,85,65,85,2,False +235,Smeargle,Normal,,250,55,20,35,20,45,75,2,False +236,Tyrogue,Fighting,,210,35,35,35,35,35,35,2,False +237,Hitmontop,Fighting,,455,50,95,95,35,110,70,2,False +238,Smoochum,Ice,Psychic,305,45,30,15,85,65,65,2,False +239,Elekid,Electric,,360,45,63,37,65,55,95,2,False +240,Magby,Fire,,365,45,75,37,70,55,83,2,False +241,Miltank,Normal,,490,95,80,105,40,70,100,2,False +242,Blissey,Normal,,540,255,10,10,75,135,55,2,False +243,Raikou,Electric,,580,90,85,75,115,100,115,2,True +244,Entei,Fire,,580,115,115,85,90,75,100,2,True +245,Suicune,Water,,580,100,75,115,90,115,85,2,True +246,Larvitar,Rock,Ground,300,50,64,50,45,50,41,2,False +247,Pupitar,Rock,Ground,410,70,84,70,65,70,51,2,False +248,Tyranitar,Rock,Dark,600,100,134,110,95,100,61,2,False +248,TyranitarMega Tyranitar,Rock,Dark,700,100,164,150,95,120,71,2,False +249,Lugia,Psychic,Flying,680,106,90,130,90,154,110,2,True +250,Ho-oh,Fire,Flying,680,106,130,90,110,154,90,2,True +251,Celebi,Psychic,Grass,600,100,100,100,100,100,100,2,False +252,Treecko,Grass,,310,40,45,35,65,55,70,3,False +253,Grovyle,Grass,,405,50,65,45,85,65,95,3,False +254,Sceptile,Grass,,530,70,85,65,105,85,120,3,False +254,SceptileMega Sceptile,Grass,Dragon,630,70,110,75,145,85,145,3,False +255,Torchic,Fire,,310,45,60,40,70,50,45,3,False +256,Combusken,Fire,Fighting,405,60,85,60,85,60,55,3,False +257,Blaziken,Fire,Fighting,530,80,120,70,110,70,80,3,False +257,BlazikenMega Blaziken,Fire,Fighting,630,80,160,80,130,80,100,3,False +258,Mudkip,Water,,310,50,70,50,50,50,40,3,False +259,Marshtomp,Water,Ground,405,70,85,70,60,70,50,3,False +260,Swampert,Water,Ground,535,100,110,90,85,90,60,3,False +260,SwampertMega Swampert,Water,Ground,635,100,150,110,95,110,70,3,False +261,Poochyena,Dark,,220,35,55,35,30,30,35,3,False +262,Mightyena,Dark,,420,70,90,70,60,60,70,3,False +263,Zigzagoon,Normal,,240,38,30,41,30,41,60,3,False +264,Linoone,Normal,,420,78,70,61,50,61,100,3,False +265,Wurmple,Bug,,195,45,45,35,20,30,20,3,False +266,Silcoon,Bug,,205,50,35,55,25,25,15,3,False +267,Beautifly,Bug,Flying,395,60,70,50,100,50,65,3,False +268,Cascoon,Bug,,205,50,35,55,25,25,15,3,False +269,Dustox,Bug,Poison,385,60,50,70,50,90,65,3,False +270,Lotad,Water,Grass,220,40,30,30,40,50,30,3,False +271,Lombre,Water,Grass,340,60,50,50,60,70,50,3,False +272,Ludicolo,Water,Grass,480,80,70,70,90,100,70,3,False +273,Seedot,Grass,,220,40,40,50,30,30,30,3,False +274,Nuzleaf,Grass,Dark,340,70,70,40,60,40,60,3,False +275,Shiftry,Grass,Dark,480,90,100,60,90,60,80,3,False +276,Taillow,Normal,Flying,270,40,55,30,30,30,85,3,False +277,Swellow,Normal,Flying,430,60,85,60,50,50,125,3,False +278,Wingull,Water,Flying,270,40,30,30,55,30,85,3,False +279,Pelipper,Water,Flying,430,60,50,100,85,70,65,3,False +280,Ralts,Psychic,Fairy,198,28,25,25,45,35,40,3,False +281,Kirlia,Psychic,Fairy,278,38,35,35,65,55,50,3,False +282,Gardevoir,Psychic,Fairy,518,68,65,65,125,115,80,3,False +282,GardevoirMega Gardevoir,Psychic,Fairy,618,68,85,65,165,135,100,3,False +283,Surskit,Bug,Water,269,40,30,32,50,52,65,3,False +284,Masquerain,Bug,Flying,414,70,60,62,80,82,60,3,False +285,Shroomish,Grass,,295,60,40,60,40,60,35,3,False +286,Breloom,Grass,Fighting,460,60,130,80,60,60,70,3,False +287,Slakoth,Normal,,280,60,60,60,35,35,30,3,False +288,Vigoroth,Normal,,440,80,80,80,55,55,90,3,False +289,Slaking,Normal,,670,150,160,100,95,65,100,3,False +290,Nincada,Bug,Ground,266,31,45,90,30,30,40,3,False +291,Ninjask,Bug,Flying,456,61,90,45,50,50,160,3,False +292,Shedinja,Bug,Ghost,236,1,90,45,30,30,40,3,False +293,Whismur,Normal,,240,64,51,23,51,23,28,3,False +294,Loudred,Normal,,360,84,71,43,71,43,48,3,False +295,Exploud,Normal,,490,104,91,63,91,73,68,3,False +296,Makuhita,Fighting,,237,72,60,30,20,30,25,3,False +297,Hariyama,Fighting,,474,144,120,60,40,60,50,3,False +298,Azurill,Normal,Fairy,190,50,20,40,20,40,20,3,False +299,Nosepass,Rock,,375,30,45,135,45,90,30,3,False +300,Skitty,Normal,,260,50,45,45,35,35,50,3,False +301,Delcatty,Normal,,380,70,65,65,55,55,70,3,False +302,Sableye,Dark,Ghost,380,50,75,75,65,65,50,3,False +302,SableyeMega Sableye,Dark,Ghost,480,50,85,125,85,115,20,3,False +303,Mawile,Steel,Fairy,380,50,85,85,55,55,50,3,False +303,MawileMega Mawile,Steel,Fairy,480,50,105,125,55,95,50,3,False +304,Aron,Steel,Rock,330,50,70,100,40,40,30,3,False +305,Lairon,Steel,Rock,430,60,90,140,50,50,40,3,False +306,Aggron,Steel,Rock,530,70,110,180,60,60,50,3,False +306,AggronMega Aggron,Steel,,630,70,140,230,60,80,50,3,False +307,Meditite,Fighting,Psychic,280,30,40,55,40,55,60,3,False +308,Medicham,Fighting,Psychic,410,60,60,75,60,75,80,3,False +308,MedichamMega Medicham,Fighting,Psychic,510,60,100,85,80,85,100,3,False +309,Electrike,Electric,,295,40,45,40,65,40,65,3,False +310,Manectric,Electric,,475,70,75,60,105,60,105,3,False +310,ManectricMega Manectric,Electric,,575,70,75,80,135,80,135,3,False +311,Plusle,Electric,,405,60,50,40,85,75,95,3,False +312,Minun,Electric,,405,60,40,50,75,85,95,3,False +313,Volbeat,Bug,,400,65,73,55,47,75,85,3,False +314,Illumise,Bug,,400,65,47,55,73,75,85,3,False +315,Roselia,Grass,Poison,400,50,60,45,100,80,65,3,False +316,Gulpin,Poison,,302,70,43,53,43,53,40,3,False +317,Swalot,Poison,,467,100,73,83,73,83,55,3,False +318,Carvanha,Water,Dark,305,45,90,20,65,20,65,3,False +319,Sharpedo,Water,Dark,460,70,120,40,95,40,95,3,False +319,SharpedoMega Sharpedo,Water,Dark,560,70,140,70,110,65,105,3,False +320,Wailmer,Water,,400,130,70,35,70,35,60,3,False +321,Wailord,Water,,500,170,90,45,90,45,60,3,False +322,Numel,Fire,Ground,305,60,60,40,65,45,35,3,False +323,Camerupt,Fire,Ground,460,70,100,70,105,75,40,3,False +323,CameruptMega Camerupt,Fire,Ground,560,70,120,100,145,105,20,3,False +324,Torkoal,Fire,,470,70,85,140,85,70,20,3,False +325,Spoink,Psychic,,330,60,25,35,70,80,60,3,False +326,Grumpig,Psychic,,470,80,45,65,90,110,80,3,False +327,Spinda,Normal,,360,60,60,60,60,60,60,3,False +328,Trapinch,Ground,,290,45,100,45,45,45,10,3,False +329,Vibrava,Ground,Dragon,340,50,70,50,50,50,70,3,False +330,Flygon,Ground,Dragon,520,80,100,80,80,80,100,3,False +331,Cacnea,Grass,,335,50,85,40,85,40,35,3,False +332,Cacturne,Grass,Dark,475,70,115,60,115,60,55,3,False +333,Swablu,Normal,Flying,310,45,40,60,40,75,50,3,False +334,Altaria,Dragon,Flying,490,75,70,90,70,105,80,3,False +334,AltariaMega Altaria,Dragon,Fairy,590,75,110,110,110,105,80,3,False +335,Zangoose,Normal,,458,73,115,60,60,60,90,3,False +336,Seviper,Poison,,458,73,100,60,100,60,65,3,False +337,Lunatone,Rock,Psychic,440,70,55,65,95,85,70,3,False +338,Solrock,Rock,Psychic,440,70,95,85,55,65,70,3,False +339,Barboach,Water,Ground,288,50,48,43,46,41,60,3,False +340,Whiscash,Water,Ground,468,110,78,73,76,71,60,3,False +341,Corphish,Water,,308,43,80,65,50,35,35,3,False +342,Crawdaunt,Water,Dark,468,63,120,85,90,55,55,3,False +343,Baltoy,Ground,Psychic,300,40,40,55,40,70,55,3,False +344,Claydol,Ground,Psychic,500,60,70,105,70,120,75,3,False +345,Lileep,Rock,Grass,355,66,41,77,61,87,23,3,False +346,Cradily,Rock,Grass,495,86,81,97,81,107,43,3,False +347,Anorith,Rock,Bug,355,45,95,50,40,50,75,3,False +348,Armaldo,Rock,Bug,495,75,125,100,70,80,45,3,False +349,Feebas,Water,,200,20,15,20,10,55,80,3,False +350,Milotic,Water,,540,95,60,79,100,125,81,3,False +351,Castform,Normal,,420,70,70,70,70,70,70,3,False +352,Kecleon,Normal,,440,60,90,70,60,120,40,3,False +353,Shuppet,Ghost,,295,44,75,35,63,33,45,3,False +354,Banette,Ghost,,455,64,115,65,83,63,65,3,False +354,BanetteMega Banette,Ghost,,555,64,165,75,93,83,75,3,False +355,Duskull,Ghost,,295,20,40,90,30,90,25,3,False +356,Dusclops,Ghost,,455,40,70,130,60,130,25,3,False +357,Tropius,Grass,Flying,460,99,68,83,72,87,51,3,False +358,Chimecho,Psychic,,425,65,50,70,95,80,65,3,False +359,Absol,Dark,,465,65,130,60,75,60,75,3,False +359,AbsolMega Absol,Dark,,565,65,150,60,115,60,115,3,False +360,Wynaut,Psychic,,260,95,23,48,23,48,23,3,False +361,Snorunt,Ice,,300,50,50,50,50,50,50,3,False +362,Glalie,Ice,,480,80,80,80,80,80,80,3,False +362,GlalieMega Glalie,Ice,,580,80,120,80,120,80,100,3,False +363,Spheal,Ice,Water,290,70,40,50,55,50,25,3,False +364,Sealeo,Ice,Water,410,90,60,70,75,70,45,3,False +365,Walrein,Ice,Water,530,110,80,90,95,90,65,3,False +366,Clamperl,Water,,345,35,64,85,74,55,32,3,False +367,Huntail,Water,,485,55,104,105,94,75,52,3,False +368,Gorebyss,Water,,485,55,84,105,114,75,52,3,False +369,Relicanth,Water,Rock,485,100,90,130,45,65,55,3,False +370,Luvdisc,Water,,330,43,30,55,40,65,97,3,False +371,Bagon,Dragon,,300,45,75,60,40,30,50,3,False +372,Shelgon,Dragon,,420,65,95,100,60,50,50,3,False +373,Salamence,Dragon,Flying,600,95,135,80,110,80,100,3,False +373,SalamenceMega Salamence,Dragon,Flying,700,95,145,130,120,90,120,3,False +374,Beldum,Steel,Psychic,300,40,55,80,35,60,30,3,False +375,Metang,Steel,Psychic,420,60,75,100,55,80,50,3,False +376,Metagross,Steel,Psychic,600,80,135,130,95,90,70,3,False +376,MetagrossMega Metagross,Steel,Psychic,700,80,145,150,105,110,110,3,False +377,Regirock,Rock,,580,80,100,200,50,100,50,3,True +378,Regice,Ice,,580,80,50,100,100,200,50,3,True +379,Registeel,Steel,,580,80,75,150,75,150,50,3,True +380,Latias,Dragon,Psychic,600,80,80,90,110,130,110,3,True +380,LatiasMega Latias,Dragon,Psychic,700,80,100,120,140,150,110,3,True +381,Latios,Dragon,Psychic,600,80,90,80,130,110,110,3,True +381,LatiosMega Latios,Dragon,Psychic,700,80,130,100,160,120,110,3,True +382,Kyogre,Water,,670,100,100,90,150,140,90,3,True +382,KyogrePrimal Kyogre,Water,,770,100,150,90,180,160,90,3,True +383,Groudon,Ground,,670,100,150,140,100,90,90,3,True +383,GroudonPrimal Groudon,Ground,Fire,770,100,180,160,150,90,90,3,True +384,Rayquaza,Dragon,Flying,680,105,150,90,150,90,95,3,True +384,RayquazaMega Rayquaza,Dragon,Flying,780,105,180,100,180,100,115,3,True +385,Jirachi,Steel,Psychic,600,100,100,100,100,100,100,3,True +386,DeoxysNormal Forme,Psychic,,600,50,150,50,150,50,150,3,True +386,DeoxysAttack Forme,Psychic,,600,50,180,20,180,20,150,3,True +386,DeoxysDefense Forme,Psychic,,600,50,70,160,70,160,90,3,True +386,DeoxysSpeed Forme,Psychic,,600,50,95,90,95,90,180,3,True +387,Turtwig,Grass,,318,55,68,64,45,55,31,4,False +388,Grotle,Grass,,405,75,89,85,55,65,36,4,False +389,Torterra,Grass,Ground,525,95,109,105,75,85,56,4,False +390,Chimchar,Fire,,309,44,58,44,58,44,61,4,False +391,Monferno,Fire,Fighting,405,64,78,52,78,52,81,4,False +392,Infernape,Fire,Fighting,534,76,104,71,104,71,108,4,False +393,Piplup,Water,,314,53,51,53,61,56,40,4,False +394,Prinplup,Water,,405,64,66,68,81,76,50,4,False +395,Empoleon,Water,Steel,530,84,86,88,111,101,60,4,False +396,Starly,Normal,Flying,245,40,55,30,30,30,60,4,False +397,Staravia,Normal,Flying,340,55,75,50,40,40,80,4,False +398,Staraptor,Normal,Flying,485,85,120,70,50,60,100,4,False +399,Bidoof,Normal,,250,59,45,40,35,40,31,4,False +400,Bibarel,Normal,Water,410,79,85,60,55,60,71,4,False +401,Kricketot,Bug,,194,37,25,41,25,41,25,4,False +402,Kricketune,Bug,,384,77,85,51,55,51,65,4,False +403,Shinx,Electric,,263,45,65,34,40,34,45,4,False +404,Luxio,Electric,,363,60,85,49,60,49,60,4,False +405,Luxray,Electric,,523,80,120,79,95,79,70,4,False +406,Budew,Grass,Poison,280,40,30,35,50,70,55,4,False +407,Roserade,Grass,Poison,515,60,70,65,125,105,90,4,False +408,Cranidos,Rock,,350,67,125,40,30,30,58,4,False +409,Rampardos,Rock,,495,97,165,60,65,50,58,4,False +410,Shieldon,Rock,Steel,350,30,42,118,42,88,30,4,False +411,Bastiodon,Rock,Steel,495,60,52,168,47,138,30,4,False +412,Burmy,Bug,,224,40,29,45,29,45,36,4,False +413,WormadamPlant Cloak,Bug,Grass,424,60,59,85,79,105,36,4,False +413,WormadamSandy Cloak,Bug,Ground,424,60,79,105,59,85,36,4,False +413,WormadamTrash Cloak,Bug,Steel,424,60,69,95,69,95,36,4,False +414,Mothim,Bug,Flying,424,70,94,50,94,50,66,4,False +415,Combee,Bug,Flying,244,30,30,42,30,42,70,4,False +416,Vespiquen,Bug,Flying,474,70,80,102,80,102,40,4,False +417,Pachirisu,Electric,,405,60,45,70,45,90,95,4,False +418,Buizel,Water,,330,55,65,35,60,30,85,4,False +419,Floatzel,Water,,495,85,105,55,85,50,115,4,False +420,Cherubi,Grass,,275,45,35,45,62,53,35,4,False +421,Cherrim,Grass,,450,70,60,70,87,78,85,4,False +422,Shellos,Water,,325,76,48,48,57,62,34,4,False +423,Gastrodon,Water,Ground,475,111,83,68,92,82,39,4,False +424,Ambipom,Normal,,482,75,100,66,60,66,115,4,False +425,Drifloon,Ghost,Flying,348,90,50,34,60,44,70,4,False +426,Drifblim,Ghost,Flying,498,150,80,44,90,54,80,4,False +427,Buneary,Normal,,350,55,66,44,44,56,85,4,False +428,Lopunny,Normal,,480,65,76,84,54,96,105,4,False +428,LopunnyMega Lopunny,Normal,Fighting,580,65,136,94,54,96,135,4,False +429,Mismagius,Ghost,,495,60,60,60,105,105,105,4,False +430,Honchkrow,Dark,Flying,505,100,125,52,105,52,71,4,False +431,Glameow,Normal,,310,49,55,42,42,37,85,4,False +432,Purugly,Normal,,452,71,82,64,64,59,112,4,False +433,Chingling,Psychic,,285,45,30,50,65,50,45,4,False +434,Stunky,Poison,Dark,329,63,63,47,41,41,74,4,False +435,Skuntank,Poison,Dark,479,103,93,67,71,61,84,4,False +436,Bronzor,Steel,Psychic,300,57,24,86,24,86,23,4,False +437,Bronzong,Steel,Psychic,500,67,89,116,79,116,33,4,False +438,Bonsly,Rock,,290,50,80,95,10,45,10,4,False +439,Mime Jr.,Psychic,Fairy,310,20,25,45,70,90,60,4,False +440,Happiny,Normal,,220,100,5,5,15,65,30,4,False +441,Chatot,Normal,Flying,411,76,65,45,92,42,91,4,False +442,Spiritomb,Ghost,Dark,485,50,92,108,92,108,35,4,False +443,Gible,Dragon,Ground,300,58,70,45,40,45,42,4,False +444,Gabite,Dragon,Ground,410,68,90,65,50,55,82,4,False +445,Garchomp,Dragon,Ground,600,108,130,95,80,85,102,4,False +445,GarchompMega Garchomp,Dragon,Ground,700,108,170,115,120,95,92,4,False +446,Munchlax,Normal,,390,135,85,40,40,85,5,4,False +447,Riolu,Fighting,,285,40,70,40,35,40,60,4,False +448,Lucario,Fighting,Steel,525,70,110,70,115,70,90,4,False +448,LucarioMega Lucario,Fighting,Steel,625,70,145,88,140,70,112,4,False +449,Hippopotas,Ground,,330,68,72,78,38,42,32,4,False +450,Hippowdon,Ground,,525,108,112,118,68,72,47,4,False +451,Skorupi,Poison,Bug,330,40,50,90,30,55,65,4,False +452,Drapion,Poison,Dark,500,70,90,110,60,75,95,4,False +453,Croagunk,Poison,Fighting,300,48,61,40,61,40,50,4,False +454,Toxicroak,Poison,Fighting,490,83,106,65,86,65,85,4,False +455,Carnivine,Grass,,454,74,100,72,90,72,46,4,False +456,Finneon,Water,,330,49,49,56,49,61,66,4,False +457,Lumineon,Water,,460,69,69,76,69,86,91,4,False +458,Mantyke,Water,Flying,345,45,20,50,60,120,50,4,False +459,Snover,Grass,Ice,334,60,62,50,62,60,40,4,False +460,Abomasnow,Grass,Ice,494,90,92,75,92,85,60,4,False +460,AbomasnowMega Abomasnow,Grass,Ice,594,90,132,105,132,105,30,4,False +461,Weavile,Dark,Ice,510,70,120,65,45,85,125,4,False +462,Magnezone,Electric,Steel,535,70,70,115,130,90,60,4,False +463,Lickilicky,Normal,,515,110,85,95,80,95,50,4,False +464,Rhyperior,Ground,Rock,535,115,140,130,55,55,40,4,False +465,Tangrowth,Grass,,535,100,100,125,110,50,50,4,False +466,Electivire,Electric,,540,75,123,67,95,85,95,4,False +467,Magmortar,Fire,,540,75,95,67,125,95,83,4,False +468,Togekiss,Fairy,Flying,545,85,50,95,120,115,80,4,False +469,Yanmega,Bug,Flying,515,86,76,86,116,56,95,4,False +470,Leafeon,Grass,,525,65,110,130,60,65,95,4,False +471,Glaceon,Ice,,525,65,60,110,130,95,65,4,False +472,Gliscor,Ground,Flying,510,75,95,125,45,75,95,4,False +473,Mamoswine,Ice,Ground,530,110,130,80,70,60,80,4,False +474,Porygon-Z,Normal,,535,85,80,70,135,75,90,4,False +475,Gallade,Psychic,Fighting,518,68,125,65,65,115,80,4,False +475,GalladeMega Gallade,Psychic,Fighting,618,68,165,95,65,115,110,4,False +476,Probopass,Rock,Steel,525,60,55,145,75,150,40,4,False +477,Dusknoir,Ghost,,525,45,100,135,65,135,45,4,False +478,Froslass,Ice,Ghost,480,70,80,70,80,70,110,4,False +479,Rotom,Electric,Ghost,440,50,50,77,95,77,91,4,False +479,RotomHeat Rotom,Electric,Fire,520,50,65,107,105,107,86,4,False +479,RotomWash Rotom,Electric,Water,520,50,65,107,105,107,86,4,False +479,RotomFrost Rotom,Electric,Ice,520,50,65,107,105,107,86,4,False +479,RotomFan Rotom,Electric,Flying,520,50,65,107,105,107,86,4,False +479,RotomMow Rotom,Electric,Grass,520,50,65,107,105,107,86,4,False +480,Uxie,Psychic,,580,75,75,130,75,130,95,4,True +481,Mesprit,Psychic,,580,80,105,105,105,105,80,4,True +482,Azelf,Psychic,,580,75,125,70,125,70,115,4,True +483,Dialga,Steel,Dragon,680,100,120,120,150,100,90,4,True +484,Palkia,Water,Dragon,680,90,120,100,150,120,100,4,True +485,Heatran,Fire,Steel,600,91,90,106,130,106,77,4,True +486,Regigigas,Normal,,670,110,160,110,80,110,100,4,True +487,GiratinaAltered Forme,Ghost,Dragon,680,150,100,120,100,120,90,4,True +487,GiratinaOrigin Forme,Ghost,Dragon,680,150,120,100,120,100,90,4,True +488,Cresselia,Psychic,,600,120,70,120,75,130,85,4,False +489,Phione,Water,,480,80,80,80,80,80,80,4,False +490,Manaphy,Water,,600,100,100,100,100,100,100,4,False +491,Darkrai,Dark,,600,70,90,90,135,90,125,4,True +492,ShayminLand Forme,Grass,,600,100,100,100,100,100,100,4,True +492,ShayminSky Forme,Grass,Flying,600,100,103,75,120,75,127,4,True +493,Arceus,Normal,,720,120,120,120,120,120,120,4,True +494,Victini,Psychic,Fire,600,100,100,100,100,100,100,5,True +495,Snivy,Grass,,308,45,45,55,45,55,63,5,False +496,Servine,Grass,,413,60,60,75,60,75,83,5,False +497,Serperior,Grass,,528,75,75,95,75,95,113,5,False +498,Tepig,Fire,,308,65,63,45,45,45,45,5,False +499,Pignite,Fire,Fighting,418,90,93,55,70,55,55,5,False +500,Emboar,Fire,Fighting,528,110,123,65,100,65,65,5,False +501,Oshawott,Water,,308,55,55,45,63,45,45,5,False +502,Dewott,Water,,413,75,75,60,83,60,60,5,False +503,Samurott,Water,,528,95,100,85,108,70,70,5,False +504,Patrat,Normal,,255,45,55,39,35,39,42,5,False +505,Watchog,Normal,,420,60,85,69,60,69,77,5,False +506,Lillipup,Normal,,275,45,60,45,25,45,55,5,False +507,Herdier,Normal,,370,65,80,65,35,65,60,5,False +508,Stoutland,Normal,,500,85,110,90,45,90,80,5,False +509,Purrloin,Dark,,281,41,50,37,50,37,66,5,False +510,Liepard,Dark,,446,64,88,50,88,50,106,5,False +511,Pansage,Grass,,316,50,53,48,53,48,64,5,False +512,Simisage,Grass,,498,75,98,63,98,63,101,5,False +513,Pansear,Fire,,316,50,53,48,53,48,64,5,False +514,Simisear,Fire,,498,75,98,63,98,63,101,5,False +515,Panpour,Water,,316,50,53,48,53,48,64,5,False +516,Simipour,Water,,498,75,98,63,98,63,101,5,False +517,Munna,Psychic,,292,76,25,45,67,55,24,5,False +518,Musharna,Psychic,,487,116,55,85,107,95,29,5,False +519,Pidove,Normal,Flying,264,50,55,50,36,30,43,5,False +520,Tranquill,Normal,Flying,358,62,77,62,50,42,65,5,False +521,Unfezant,Normal,Flying,488,80,115,80,65,55,93,5,False +522,Blitzle,Electric,,295,45,60,32,50,32,76,5,False +523,Zebstrika,Electric,,497,75,100,63,80,63,116,5,False +524,Roggenrola,Rock,,280,55,75,85,25,25,15,5,False +525,Boldore,Rock,,390,70,105,105,50,40,20,5,False +526,Gigalith,Rock,,515,85,135,130,60,80,25,5,False +527,Woobat,Psychic,Flying,313,55,45,43,55,43,72,5,False +528,Swoobat,Psychic,Flying,425,67,57,55,77,55,114,5,False +529,Drilbur,Ground,,328,60,85,40,30,45,68,5,False +530,Excadrill,Ground,Steel,508,110,135,60,50,65,88,5,False +531,Audino,Normal,,445,103,60,86,60,86,50,5,False +531,AudinoMega Audino,Normal,Fairy,545,103,60,126,80,126,50,5,False +532,Timburr,Fighting,,305,75,80,55,25,35,35,5,False +533,Gurdurr,Fighting,,405,85,105,85,40,50,40,5,False +534,Conkeldurr,Fighting,,505,105,140,95,55,65,45,5,False +535,Tympole,Water,,294,50,50,40,50,40,64,5,False +536,Palpitoad,Water,Ground,384,75,65,55,65,55,69,5,False +537,Seismitoad,Water,Ground,509,105,95,75,85,75,74,5,False +538,Throh,Fighting,,465,120,100,85,30,85,45,5,False +539,Sawk,Fighting,,465,75,125,75,30,75,85,5,False +540,Sewaddle,Bug,Grass,310,45,53,70,40,60,42,5,False +541,Swadloon,Bug,Grass,380,55,63,90,50,80,42,5,False +542,Leavanny,Bug,Grass,500,75,103,80,70,80,92,5,False +543,Venipede,Bug,Poison,260,30,45,59,30,39,57,5,False +544,Whirlipede,Bug,Poison,360,40,55,99,40,79,47,5,False +545,Scolipede,Bug,Poison,485,60,100,89,55,69,112,5,False +546,Cottonee,Grass,Fairy,280,40,27,60,37,50,66,5,False +547,Whimsicott,Grass,Fairy,480,60,67,85,77,75,116,5,False +548,Petilil,Grass,,280,45,35,50,70,50,30,5,False +549,Lilligant,Grass,,480,70,60,75,110,75,90,5,False +550,Basculin,Water,,460,70,92,65,80,55,98,5,False +551,Sandile,Ground,Dark,292,50,72,35,35,35,65,5,False +552,Krokorok,Ground,Dark,351,60,82,45,45,45,74,5,False +553,Krookodile,Ground,Dark,519,95,117,80,65,70,92,5,False +554,Darumaka,Fire,,315,70,90,45,15,45,50,5,False +555,DarmanitanStandard Mode,Fire,,480,105,140,55,30,55,95,5,False +555,DarmanitanZen Mode,Fire,Psychic,540,105,30,105,140,105,55,5,False +556,Maractus,Grass,,461,75,86,67,106,67,60,5,False +557,Dwebble,Bug,Rock,325,50,65,85,35,35,55,5,False +558,Crustle,Bug,Rock,475,70,95,125,65,75,45,5,False +559,Scraggy,Dark,Fighting,348,50,75,70,35,70,48,5,False +560,Scrafty,Dark,Fighting,488,65,90,115,45,115,58,5,False +561,Sigilyph,Psychic,Flying,490,72,58,80,103,80,97,5,False +562,Yamask,Ghost,,303,38,30,85,55,65,30,5,False +563,Cofagrigus,Ghost,,483,58,50,145,95,105,30,5,False +564,Tirtouga,Water,Rock,355,54,78,103,53,45,22,5,False +565,Carracosta,Water,Rock,495,74,108,133,83,65,32,5,False +566,Archen,Rock,Flying,401,55,112,45,74,45,70,5,False +567,Archeops,Rock,Flying,567,75,140,65,112,65,110,5,False +568,Trubbish,Poison,,329,50,50,62,40,62,65,5,False +569,Garbodor,Poison,,474,80,95,82,60,82,75,5,False +570,Zorua,Dark,,330,40,65,40,80,40,65,5,False +571,Zoroark,Dark,,510,60,105,60,120,60,105,5,False +572,Minccino,Normal,,300,55,50,40,40,40,75,5,False +573,Cinccino,Normal,,470,75,95,60,65,60,115,5,False +574,Gothita,Psychic,,290,45,30,50,55,65,45,5,False +575,Gothorita,Psychic,,390,60,45,70,75,85,55,5,False +576,Gothitelle,Psychic,,490,70,55,95,95,110,65,5,False +577,Solosis,Psychic,,290,45,30,40,105,50,20,5,False +578,Duosion,Psychic,,370,65,40,50,125,60,30,5,False +579,Reuniclus,Psychic,,490,110,65,75,125,85,30,5,False +580,Ducklett,Water,Flying,305,62,44,50,44,50,55,5,False +581,Swanna,Water,Flying,473,75,87,63,87,63,98,5,False +582,Vanillite,Ice,,305,36,50,50,65,60,44,5,False +583,Vanillish,Ice,,395,51,65,65,80,75,59,5,False +584,Vanilluxe,Ice,,535,71,95,85,110,95,79,5,False +585,Deerling,Normal,Grass,335,60,60,50,40,50,75,5,False +586,Sawsbuck,Normal,Grass,475,80,100,70,60,70,95,5,False +587,Emolga,Electric,Flying,428,55,75,60,75,60,103,5,False +588,Karrablast,Bug,,315,50,75,45,40,45,60,5,False +589,Escavalier,Bug,Steel,495,70,135,105,60,105,20,5,False +590,Foongus,Grass,Poison,294,69,55,45,55,55,15,5,False +591,Amoonguss,Grass,Poison,464,114,85,70,85,80,30,5,False +592,Frillish,Water,Ghost,335,55,40,50,65,85,40,5,False +593,Jellicent,Water,Ghost,480,100,60,70,85,105,60,5,False +594,Alomomola,Water,,470,165,75,80,40,45,65,5,False +595,Joltik,Bug,Electric,319,50,47,50,57,50,65,5,False +596,Galvantula,Bug,Electric,472,70,77,60,97,60,108,5,False +597,Ferroseed,Grass,Steel,305,44,50,91,24,86,10,5,False +598,Ferrothorn,Grass,Steel,489,74,94,131,54,116,20,5,False +599,Klink,Steel,,300,40,55,70,45,60,30,5,False +600,Klang,Steel,,440,60,80,95,70,85,50,5,False +601,Klinklang,Steel,,520,60,100,115,70,85,90,5,False +602,Tynamo,Electric,,275,35,55,40,45,40,60,5,False +603,Eelektrik,Electric,,405,65,85,70,75,70,40,5,False +604,Eelektross,Electric,,515,85,115,80,105,80,50,5,False +605,Elgyem,Psychic,,335,55,55,55,85,55,30,5,False +606,Beheeyem,Psychic,,485,75,75,75,125,95,40,5,False +607,Litwick,Ghost,Fire,275,50,30,55,65,55,20,5,False +608,Lampent,Ghost,Fire,370,60,40,60,95,60,55,5,False +609,Chandelure,Ghost,Fire,520,60,55,90,145,90,80,5,False +610,Axew,Dragon,,320,46,87,60,30,40,57,5,False +611,Fraxure,Dragon,,410,66,117,70,40,50,67,5,False +612,Haxorus,Dragon,,540,76,147,90,60,70,97,5,False +613,Cubchoo,Ice,,305,55,70,40,60,40,40,5,False +614,Beartic,Ice,,485,95,110,80,70,80,50,5,False +615,Cryogonal,Ice,,485,70,50,30,95,135,105,5,False +616,Shelmet,Bug,,305,50,40,85,40,65,25,5,False +617,Accelgor,Bug,,495,80,70,40,100,60,145,5,False +618,Stunfisk,Ground,Electric,471,109,66,84,81,99,32,5,False +619,Mienfoo,Fighting,,350,45,85,50,55,50,65,5,False +620,Mienshao,Fighting,,510,65,125,60,95,60,105,5,False +621,Druddigon,Dragon,,485,77,120,90,60,90,48,5,False +622,Golett,Ground,Ghost,303,59,74,50,35,50,35,5,False +623,Golurk,Ground,Ghost,483,89,124,80,55,80,55,5,False +624,Pawniard,Dark,Steel,340,45,85,70,40,40,60,5,False +625,Bisharp,Dark,Steel,490,65,125,100,60,70,70,5,False +626,Bouffalant,Normal,,490,95,110,95,40,95,55,5,False +627,Rufflet,Normal,Flying,350,70,83,50,37,50,60,5,False +628,Braviary,Normal,Flying,510,100,123,75,57,75,80,5,False +629,Vullaby,Dark,Flying,370,70,55,75,45,65,60,5,False +630,Mandibuzz,Dark,Flying,510,110,65,105,55,95,80,5,False +631,Heatmor,Fire,,484,85,97,66,105,66,65,5,False +632,Durant,Bug,Steel,484,58,109,112,48,48,109,5,False +633,Deino,Dark,Dragon,300,52,65,50,45,50,38,5,False +634,Zweilous,Dark,Dragon,420,72,85,70,65,70,58,5,False +635,Hydreigon,Dark,Dragon,600,92,105,90,125,90,98,5,False +636,Larvesta,Bug,Fire,360,55,85,55,50,55,60,5,False +637,Volcarona,Bug,Fire,550,85,60,65,135,105,100,5,False +638,Cobalion,Steel,Fighting,580,91,90,129,90,72,108,5,True +639,Terrakion,Rock,Fighting,580,91,129,90,72,90,108,5,True +640,Virizion,Grass,Fighting,580,91,90,72,90,129,108,5,True +641,TornadusIncarnate Forme,Flying,,580,79,115,70,125,80,111,5,True +641,TornadusTherian Forme,Flying,,580,79,100,80,110,90,121,5,True +642,ThundurusIncarnate Forme,Electric,Flying,580,79,115,70,125,80,111,5,True +642,ThundurusTherian Forme,Electric,Flying,580,79,105,70,145,80,101,5,True +643,Reshiram,Dragon,Fire,680,100,120,100,150,120,90,5,True +644,Zekrom,Dragon,Electric,680,100,150,120,120,100,90,5,True +645,LandorusIncarnate Forme,Ground,Flying,600,89,125,90,115,80,101,5,True +645,LandorusTherian Forme,Ground,Flying,600,89,145,90,105,80,91,5,True +646,Kyurem,Dragon,Ice,660,125,130,90,130,90,95,5,True +646,KyuremBlack Kyurem,Dragon,Ice,700,125,170,100,120,90,95,5,True +646,KyuremWhite Kyurem,Dragon,Ice,700,125,120,90,170,100,95,5,True +647,KeldeoOrdinary Forme,Water,Fighting,580,91,72,90,129,90,108,5,False +647,KeldeoResolute Forme,Water,Fighting,580,91,72,90,129,90,108,5,False +648,MeloettaAria Forme,Normal,Psychic,600,100,77,77,128,128,90,5,False +648,MeloettaPirouette Forme,Normal,Fighting,600,100,128,90,77,77,128,5,False +649,Genesect,Bug,Steel,600,71,120,95,120,95,99,5,False +650,Chespin,Grass,,313,56,61,65,48,45,38,6,False +651,Quilladin,Grass,,405,61,78,95,56,58,57,6,False +652,Chesnaught,Grass,Fighting,530,88,107,122,74,75,64,6,False +653,Fennekin,Fire,,307,40,45,40,62,60,60,6,False +654,Braixen,Fire,,409,59,59,58,90,70,73,6,False +655,Delphox,Fire,Psychic,534,75,69,72,114,100,104,6,False +656,Froakie,Water,,314,41,56,40,62,44,71,6,False +657,Frogadier,Water,,405,54,63,52,83,56,97,6,False +658,Greninja,Water,Dark,530,72,95,67,103,71,122,6,False +659,Bunnelby,Normal,,237,38,36,38,32,36,57,6,False +660,Diggersby,Normal,Ground,423,85,56,77,50,77,78,6,False +661,Fletchling,Normal,Flying,278,45,50,43,40,38,62,6,False +662,Fletchinder,Fire,Flying,382,62,73,55,56,52,84,6,False +663,Talonflame,Fire,Flying,499,78,81,71,74,69,126,6,False +664,Scatterbug,Bug,,200,38,35,40,27,25,35,6,False +665,Spewpa,Bug,,213,45,22,60,27,30,29,6,False +666,Vivillon,Bug,Flying,411,80,52,50,90,50,89,6,False +667,Litleo,Fire,Normal,369,62,50,58,73,54,72,6,False +668,Pyroar,Fire,Normal,507,86,68,72,109,66,106,6,False +669,Flabébé,Fairy,,303,44,38,39,61,79,42,6,False +670,Floette,Fairy,,371,54,45,47,75,98,52,6,False +671,Florges,Fairy,,552,78,65,68,112,154,75,6,False +672,Skiddo,Grass,,350,66,65,48,62,57,52,6,False +673,Gogoat,Grass,,531,123,100,62,97,81,68,6,False +674,Pancham,Fighting,,348,67,82,62,46,48,43,6,False +675,Pangoro,Fighting,Dark,495,95,124,78,69,71,58,6,False +676,Furfrou,Normal,,472,75,80,60,65,90,102,6,False +677,Espurr,Psychic,,355,62,48,54,63,60,68,6,False +678,MeowsticMale,Psychic,,466,74,48,76,83,81,104,6,False +678,MeowsticFemale,Psychic,,466,74,48,76,83,81,104,6,False +679,Honedge,Steel,Ghost,325,45,80,100,35,37,28,6,False +680,Doublade,Steel,Ghost,448,59,110,150,45,49,35,6,False +681,AegislashBlade Forme,Steel,Ghost,520,60,150,50,150,50,60,6,False +681,AegislashShield Forme,Steel,Ghost,520,60,50,150,50,150,60,6,False +682,Spritzee,Fairy,,341,78,52,60,63,65,23,6,False +683,Aromatisse,Fairy,,462,101,72,72,99,89,29,6,False +684,Swirlix,Fairy,,341,62,48,66,59,57,49,6,False +685,Slurpuff,Fairy,,480,82,80,86,85,75,72,6,False +686,Inkay,Dark,Psychic,288,53,54,53,37,46,45,6,False +687,Malamar,Dark,Psychic,482,86,92,88,68,75,73,6,False +688,Binacle,Rock,Water,306,42,52,67,39,56,50,6,False +689,Barbaracle,Rock,Water,500,72,105,115,54,86,68,6,False +690,Skrelp,Poison,Water,320,50,60,60,60,60,30,6,False +691,Dragalge,Poison,Dragon,494,65,75,90,97,123,44,6,False +692,Clauncher,Water,,330,50,53,62,58,63,44,6,False +693,Clawitzer,Water,,500,71,73,88,120,89,59,6,False +694,Helioptile,Electric,Normal,289,44,38,33,61,43,70,6,False +695,Heliolisk,Electric,Normal,481,62,55,52,109,94,109,6,False +696,Tyrunt,Rock,Dragon,362,58,89,77,45,45,48,6,False +697,Tyrantrum,Rock,Dragon,521,82,121,119,69,59,71,6,False +698,Amaura,Rock,Ice,362,77,59,50,67,63,46,6,False +699,Aurorus,Rock,Ice,521,123,77,72,99,92,58,6,False +700,Sylveon,Fairy,,525,95,65,65,110,130,60,6,False +701,Hawlucha,Fighting,Flying,500,78,92,75,74,63,118,6,False +702,Dedenne,Electric,Fairy,431,67,58,57,81,67,101,6,False +703,Carbink,Rock,Fairy,500,50,50,150,50,150,50,6,False +704,Goomy,Dragon,,300,45,50,35,55,75,40,6,False +705,Sliggoo,Dragon,,452,68,75,53,83,113,60,6,False +706,Goodra,Dragon,,600,90,100,70,110,150,80,6,False +707,Klefki,Steel,Fairy,470,57,80,91,80,87,75,6,False +708,Phantump,Ghost,Grass,309,43,70,48,50,60,38,6,False +709,Trevenant,Ghost,Grass,474,85,110,76,65,82,56,6,False +710,PumpkabooAverage Size,Ghost,Grass,335,49,66,70,44,55,51,6,False +710,PumpkabooSmall Size,Ghost,Grass,335,44,66,70,44,55,56,6,False +710,PumpkabooLarge Size,Ghost,Grass,335,54,66,70,44,55,46,6,False +710,PumpkabooSuper Size,Ghost,Grass,335,59,66,70,44,55,41,6,False +711,GourgeistAverage Size,Ghost,Grass,494,65,90,122,58,75,84,6,False +711,GourgeistSmall Size,Ghost,Grass,494,55,85,122,58,75,99,6,False +711,GourgeistLarge Size,Ghost,Grass,494,75,95,122,58,75,69,6,False +711,GourgeistSuper Size,Ghost,Grass,494,85,100,122,58,75,54,6,False +712,Bergmite,Ice,,304,55,69,85,32,35,28,6,False +713,Avalugg,Ice,,514,95,117,184,44,46,28,6,False +714,Noibat,Flying,Dragon,245,40,30,35,45,40,55,6,False +715,Noivern,Flying,Dragon,535,85,70,80,97,80,123,6,False +716,Xerneas,Fairy,,680,126,131,95,131,98,99,6,True +717,Yveltal,Dark,Flying,680,126,131,95,131,98,99,6,True +718,Zygarde50% Forme,Dragon,Ground,600,108,100,121,81,95,95,6,True +719,Diancie,Rock,Fairy,600,50,100,150,100,150,50,6,True +719,DiancieMega Diancie,Rock,Fairy,700,50,160,110,160,110,110,6,True +720,HoopaHoopa Confined,Psychic,Ghost,600,80,110,60,150,130,70,6,True +720,HoopaHoopa Unbound,Psychic,Dark,680,80,160,60,170,130,80,6,True +721,Volcanion,Fire,Water,600,80,110,120,130,90,70,6,True diff --git a/data/titanic.xls b/data/titanic.xls new file mode 100644 index 0000000..6e26913 Binary files /dev/null and b/data/titanic.xls differ diff --git a/eda-pandas.ipynb b/eda-pandas.ipynb new file mode 100644 index 0000000..646f1d2 --- /dev/null +++ b/eda-pandas.ipynb @@ -0,0 +1,2060 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "pythontutorial.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3.7.4 64-bit ('base': conda)" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4-final" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "dzNng6vCL9eP" + }, + "source": [ + "# Machine Learning 2020-2021 - UMONS \n", + "# Exploratory Data Analysis with Pandas (Tutorial) \n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "image/png": { + "width": 600, + "height": 300 + } + }, + "execution_count": 1 + } + ], + "source": [ + "from IPython.display import Image\n", + "# Load image from local storage\n", + "Image(filename = \"dspipeline.png\", width = 600, height = 300)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": " pclass survived name sex \\\n0 1 1 Allen, Miss. Elisabeth Walton female \n1 1 1 Allison, Master. Hudson Trevor male \n2 1 0 Allison, Miss. Helen Loraine female \n3 1 0 Allison, Mr. Hudson Joshua Creighton male \n4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female \n\n age sibsp parch ticket fare cabin embarked boat body \\\n0 29.0000 0 0 24160 211.3375 B5 S 2 NaN \n1 0.9167 1 2 113781 151.5500 C22 C26 S 11 NaN \n2 2.0000 1 2 113781 151.5500 C22 C26 S NaN NaN \n3 30.0000 1 2 113781 151.5500 C22 C26 S NaN 135.0 \n4 25.0000 1 2 113781 151.5500 C22 C26 S NaN NaN \n\n home.dest \n0 St Louis, MO \n1 Montreal, PQ / Chesterville, ON \n2 Montreal, PQ / Chesterville, ON \n3 Montreal, PQ / Chesterville, ON \n4 Montreal, PQ / Chesterville, ON ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
pclasssurvivednamesexagesibspparchticketfarecabinembarkedboatbodyhome.dest
011Allen, Miss. Elisabeth Waltonfemale29.00000024160211.3375B5S2NaNSt Louis, MO
111Allison, Master. Hudson Trevormale0.916712113781151.5500C22 C26S11NaNMontreal, PQ / Chesterville, ON
210Allison, Miss. Helen Lorainefemale2.000012113781151.5500C22 C26SNaNNaNMontreal, PQ / Chesterville, ON
310Allison, Mr. Hudson Joshua Creightonmale30.000012113781151.5500C22 C26SNaN135.0Montreal, PQ / Chesterville, ON
410Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female25.000012113781151.5500C22 C26SNaNNaNMontreal, PQ / Chesterville, ON
\n
" + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "#data = pd.read_csv(\"data/titanic-data.csv\")\n", + "\n", + "raw_data = pd.read_excel(\"data/titanic.xls\")\n", + "\n", + "raw_data.head()\n" + ] + }, + { + "source": [ + "1. ** pclass:** Socio-economic class (1 = Upper class; 2 = Middle class; 3 = Lower class)\n", + "2. ** survived:** Outcome of survival (0 = No; 1 = Yes)\n", + "3. ** name:** Name of passenger\n", + "4. ** sex: **Sex of the passenger\n", + "5. ** age:** Age of the passenger (Some entries contain NaN)\n", + "6. ** sibsp:** Number of siblings and spouses of the passenger aboard\n", + "7. ** parch:** Number of parents and children of the passenger aboard\n", + "8. ** ticket:** Ticket number of the passenger\n", + "9. ** fare: **Fare paid by the passenger\n", + "10. ** cabin** Cabin number of the passenger (Some entries contain NaN)\n", + "11. ** embarked:** Port of embarkation of the passenger (C = Cherbourg; Q = Queenstown; S = Southampton)\n", + "12. ..." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(1309, 14)" + ] + }, + "metadata": {}, + "execution_count": 3 + } + ], + "source": [ + "raw_data.shape\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\nRangeIndex: 1309 entries, 0 to 1308\nData columns (total 14 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 pclass 1309 non-null int64 \n 1 survived 1309 non-null int64 \n 2 name 1309 non-null object \n 3 sex 1309 non-null object \n 4 age 1046 non-null float64\n 5 sibsp 1309 non-null int64 \n 6 parch 1309 non-null int64 \n 7 ticket 1309 non-null object \n 8 fare 1308 non-null float64\n 9 cabin 295 non-null object \n 10 embarked 1307 non-null object \n 11 boat 486 non-null object \n 12 body 121 non-null float64\n 13 home.dest 745 non-null object \ndtypes: float64(3), int64(4), object(7)\nmemory usage: 143.3+ KB\n" + ] + } + ], + "source": [ + "raw_data.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['pclass', 'survived', 'name', 'sex', 'age', 'sibsp', 'parch', 'ticket',\n", + " 'fare', 'cabin', 'embarked', 'boat', 'body', 'home.dest'],\n", + " dtype='object')" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ], + "source": [ + "raw_data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "pclass int64\n", + "survived int64\n", + "name object\n", + "sex object\n", + "age float64\n", + "sibsp int64\n", + "parch int64\n", + "ticket object\n", + "fare float64\n", + "cabin object\n", + "embarked object\n", + "boat object\n", + "body float64\n", + "home.dest object\n", + "dtype: object" + ] + }, + "metadata": {}, + "execution_count": 6 + } + ], + "source": [ + "raw_data.dtypes\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " pclass survived name sex \\\n", + "0 1 1 Allen, Miss. Elisabeth Walton female \n", + "1 1 1 Allison, Master. Hudson Trevor male \n", + "2 1 0 Allison, Miss. Helen Loraine female \n", + "3 1 0 Allison, Mr. Hudson Joshua Creighton male \n", + "4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female \n", + "\n", + " age sibsp parch ticket fare cabin embarked boat body \\\n", + "0 29.0000 0 0 24160 211.3375 B5 S 2 NaN \n", + "1 0.9167 1 2 113781 151.5500 C22 C26 S 11 NaN \n", + "2 2.0000 1 2 113781 151.5500 C22 C26 S NaN NaN \n", + "3 30.0000 1 2 113781 151.5500 C22 C26 S NaN 135.0 \n", + "4 25.0000 1 2 113781 151.5500 C22 C26 S NaN NaN \n", + "\n", + " home.dest \n", + "0 St Louis, MO \n", + "1 Montreal, PQ / Chesterville, ON \n", + "2 Montreal, PQ / Chesterville, ON \n", + "3 Montreal, PQ / Chesterville, ON \n", + "4 Montreal, PQ / Chesterville, ON " + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
pclasssurvivednamesexagesibspparchticketfarecabinembarkedboatbodyhome.dest
011Allen, Miss. Elisabeth Waltonfemale29.00000024160211.3375B5S2NaNSt Louis, MO
111Allison, Master. Hudson Trevormale0.916712113781151.5500C22 C26S11NaNMontreal, PQ / Chesterville, ON
210Allison, Miss. Helen Lorainefemale2.000012113781151.5500C22 C26SNaNNaNMontreal, PQ / Chesterville, ON
310Allison, Mr. Hudson Joshua Creightonmale30.000012113781151.5500C22 C26SNaN135.0Montreal, PQ / Chesterville, ON
410Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female25.000012113781151.5500C22 C26SNaNNaNMontreal, PQ / Chesterville, ON
\n
" + }, + "metadata": {}, + "execution_count": 7 + } + ], + "source": [ + "raw_data.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " pclass survived name \\\n", + "1209 3 0 Skoog, Miss. Margit Elizabeth \n", + "1210 3 0 Skoog, Mr. Wilhelm \n", + "1211 3 0 Skoog, Mrs. William (Anna Bernhardina Karlsson) \n", + "1212 3 0 Slabenoff, Mr. Petco \n", + "1213 3 0 Slocovski, Mr. Selman Francis \n", + "... ... ... ... \n", + "1304 3 0 Zabour, Miss. Hileni \n", + "1305 3 0 Zabour, Miss. Thamine \n", + "1306 3 0 Zakarian, Mr. Mapriededer \n", + "1307 3 0 Zakarian, Mr. Ortin \n", + "1308 3 0 Zimmerman, Mr. Leo \n", + "\n", + " sex age sibsp parch ticket fare cabin embarked \\\n", + "1209 female 2.0 3 2 347088 27.9000 NaN S \n", + "1210 male 40.0 1 4 347088 27.9000 NaN S \n", + "1211 female 45.0 1 4 347088 27.9000 NaN S \n", + "1212 male NaN 0 0 349214 7.8958 NaN S \n", + "1213 male NaN 0 0 SOTON/OQ 392086 8.0500 NaN S \n", + "... ... ... ... ... ... ... ... ... \n", + "1304 female 14.5 1 0 2665 14.4542 NaN C \n", + "1305 female NaN 1 0 2665 14.4542 NaN C \n", + "1306 male 26.5 0 0 2656 7.2250 NaN C \n", + "1307 male 27.0 0 0 2670 7.2250 NaN C \n", + "1308 male 29.0 0 0 315082 7.8750 NaN S \n", + "\n", + " boat body home.dest \n", + "1209 NaN NaN NaN \n", + "1210 NaN NaN NaN \n", + "1211 NaN NaN NaN \n", + "1212 NaN NaN NaN \n", + "1213 NaN NaN NaN \n", + "... ... ... ... \n", + "1304 NaN 328.0 NaN \n", + "1305 NaN NaN NaN \n", + "1306 NaN 304.0 NaN \n", + "1307 NaN NaN NaN \n", + "1308 NaN NaN NaN \n", + "\n", + "[100 rows x 14 columns]" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
pclasssurvivednamesexagesibspparchticketfarecabinembarkedboatbodyhome.dest
120930Skoog, Miss. Margit Elizabethfemale2.03234708827.9000NaNSNaNNaNNaN
121030Skoog, Mr. Wilhelmmale40.01434708827.9000NaNSNaNNaNNaN
121130Skoog, Mrs. William (Anna Bernhardina Karlsson)female45.01434708827.9000NaNSNaNNaNNaN
121230Slabenoff, Mr. PetcomaleNaN003492147.8958NaNSNaNNaNNaN
121330Slocovski, Mr. Selman FrancismaleNaN00SOTON/OQ 3920868.0500NaNSNaNNaNNaN
.............................................
130430Zabour, Miss. Hilenifemale14.510266514.4542NaNCNaN328.0NaN
130530Zabour, Miss. ThaminefemaleNaN10266514.4542NaNCNaNNaNNaN
130630Zakarian, Mr. Mapriededermale26.50026567.2250NaNCNaN304.0NaN
130730Zakarian, Mr. Ortinmale27.00026707.2250NaNCNaNNaNNaN
130830Zimmerman, Mr. Leomale29.0003150827.8750NaNSNaNNaNNaN
\n

100 rows × 14 columns

\n
" + }, + "metadata": {}, + "execution_count": 8 + } + ], + "source": [ + "raw_data.tail(100)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " pclass survived age sibsp parch \\\n", + "count 1309.000000 1309.000000 1046.000000 1309.000000 1309.000000 \n", + "mean 2.294882 0.381971 29.881135 0.498854 0.385027 \n", + "std 0.837836 0.486055 14.413500 1.041658 0.865560 \n", + "min 1.000000 0.000000 0.166700 0.000000 0.000000 \n", + "25% 2.000000 0.000000 21.000000 0.000000 0.000000 \n", + "50% 3.000000 0.000000 28.000000 0.000000 0.000000 \n", + "75% 3.000000 1.000000 39.000000 1.000000 0.000000 \n", + "max 3.000000 1.000000 80.000000 8.000000 9.000000 \n", + "\n", + " fare body \n", + "count 1308.000000 121.000000 \n", + "mean 33.295479 160.809917 \n", + "std 51.758668 97.696922 \n", + "min 0.000000 1.000000 \n", + "25% 7.895800 72.000000 \n", + "50% 14.454200 155.000000 \n", + "75% 31.275000 256.000000 \n", + "max 512.329200 328.000000 " + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
pclasssurvivedagesibspparchfarebody
count1309.0000001309.0000001046.0000001309.0000001309.0000001308.000000121.000000
mean2.2948820.38197129.8811350.4988540.38502733.295479160.809917
std0.8378360.48605514.4135001.0416580.86556051.75866897.696922
min1.0000000.0000000.1667000.0000000.0000000.0000001.000000
25%2.0000000.00000021.0000000.0000000.0000007.89580072.000000
50%3.0000000.00000028.0000000.0000000.00000014.454200155.000000
75%3.0000001.00000039.0000001.0000000.00000031.275000256.000000
max3.0000001.00000080.0000008.0000009.000000512.329200328.000000
\n
" + }, + "metadata": {}, + "execution_count": 9 + } + ], + "source": [ + "raw_data.describe()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " pclass survived name sex age sibsp parch ticket fare \\\n", + "0 False False False False False False False False False \n", + "1 False False False False False False False False False \n", + "2 False False False False False False False False False \n", + "3 False False False False False False False False False \n", + "4 False False False False False False False False False \n", + "... ... ... ... ... ... ... ... ... ... \n", + "1304 False False False False False False False False False \n", + "1305 False False False False True False False False False \n", + "1306 False False False False False False False False False \n", + "1307 False False False False False False False False False \n", + "1308 False False False False False False False False False \n", + "\n", + " cabin embarked boat body home.dest \n", + "0 False False False True False \n", + "1 False False False True False \n", + "2 False False True True False \n", + "3 False False True False False \n", + "4 False False True True False \n", + "... ... ... ... ... ... \n", + "1304 True False True False True \n", + "1305 True False True True True \n", + "1306 True False True False True \n", + "1307 True False True True True \n", + "1308 True False True True True \n", + "\n", + "[1309 rows x 14 columns]" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
pclasssurvivednamesexagesibspparchticketfarecabinembarkedboatbodyhome.dest
0FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalse
1FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalse
2FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueTrueFalse
3FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalse
4FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueTrueFalse
.............................................
1304FalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseTrueFalseTrue
1305FalseFalseFalseFalseTrueFalseFalseFalseFalseTrueFalseTrueTrueTrue
1306FalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseTrueFalseTrue
1307FalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseTrueTrueTrue
1308FalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseTrueTrueTrue
\n

1309 rows × 14 columns

\n
" + }, + "metadata": {}, + "execution_count": 10 + } + ], + "source": [ + "raw_data.isnull()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "pclass 0\n", + "survived 0\n", + "name 0\n", + "sex 0\n", + "age 263\n", + "sibsp 0\n", + "parch 0\n", + "ticket 0\n", + "fare 1\n", + "cabin 1014\n", + "embarked 2\n", + "boat 823\n", + "body 1188\n", + "home.dest 564\n", + "dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 11 + } + ], + "source": [ + "raw_data.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data_copy = raw_data.copy()" + ] + }, + { + "source": [ + "# Pandas Series and DataFrame\n", + "\n", + "- https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html\n", + "- https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n" + ] + } + ], + "source": [ + "print(type(raw_data[\"age\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n" + ] + } + ], + "source": [ + "print(type( raw_data[ [\"age\"] ] ))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n" + ] + } + ], + "source": [ + "print(type(raw_data[[\"age\", \"sex\"] ]))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " pclass survived name sex \\\n", + "0 1 1 Allen, Miss. Elisabeth Walton female \n", + "1 1 1 Allison, Master. Hudson Trevor male \n", + "2 1 0 Allison, Miss. Helen Loraine female \n", + "3 1 0 Allison, Mr. Hudson Joshua Creighton male \n", + "4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female \n", + "\n", + " age sibsp parch ticket fare cabin embarked boat body \\\n", + "0 29.0000 0 0 24160 211.3375 B5 S 2 NaN \n", + "1 0.9167 1 2 113781 151.5500 C22 C26 S 11 NaN \n", + "2 2.0000 1 2 113781 151.5500 C22 C26 S NaN NaN \n", + "3 30.0000 1 2 113781 151.5500 C22 C26 S NaN 135.0 \n", + "4 25.0000 1 2 113781 151.5500 C22 C26 S NaN NaN \n", + "\n", + " home.dest \n", + "0 St Louis, MO \n", + "1 Montreal, PQ / Chesterville, ON \n", + "2 Montreal, PQ / Chesterville, ON \n", + "3 Montreal, PQ / Chesterville, ON \n", + "4 Montreal, PQ / Chesterville, ON " + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
pclasssurvivednamesexagesibspparchticketfarecabinembarkedboatbodyhome.dest
011Allen, Miss. Elisabeth Waltonfemale29.00000024160211.3375B5S2NaNSt Louis, MO
111Allison, Master. Hudson Trevormale0.916712113781151.5500C22 C26S11NaNMontreal, PQ / Chesterville, ON
210Allison, Miss. Helen Lorainefemale2.000012113781151.5500C22 C26SNaNNaNMontreal, PQ / Chesterville, ON
310Allison, Mr. Hudson Joshua Creightonmale30.000012113781151.5500C22 C26SNaN135.0Montreal, PQ / Chesterville, ON
410Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female25.000012113781151.5500C22 C26SNaNNaNMontreal, PQ / Chesterville, ON
\n
" + }, + "metadata": {}, + "execution_count": 19 + } + ], + "source": [ + "raw_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=1309, step=1)" + ] + }, + "metadata": {}, + "execution_count": 20 + } + ], + "source": [ + "raw_data.index" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " pclass survived sex \\\n", + "name \n", + "Allen, Miss. Elisabeth Walton 1 1 female \n", + "Allison, Master. Hudson Trevor 1 1 male \n", + "Allison, Miss. Helen Loraine 1 0 female \n", + "Allison, Mr. Hudson Joshua Creighton 1 0 male \n", + "Allison, Mrs. Hudson J C (Bessie Waldo Daniels) 1 0 female \n", + "... ... ... ... \n", + "Zabour, Miss. Hileni 3 0 female \n", + "Zabour, Miss. Thamine 3 0 female \n", + "Zakarian, Mr. Mapriededer 3 0 male \n", + "Zakarian, Mr. Ortin 3 0 male \n", + "Zimmerman, Mr. Leo 3 0 male \n", + "\n", + " age sibsp parch \\\n", + "name \n", + "Allen, Miss. Elisabeth Walton 29.0000 0 0 \n", + "Allison, Master. Hudson Trevor 0.9167 1 2 \n", + "Allison, Miss. Helen Loraine 2.0000 1 2 \n", + "Allison, Mr. Hudson Joshua Creighton 30.0000 1 2 \n", + "Allison, Mrs. Hudson J C (Bessie Waldo Daniels) 25.0000 1 2 \n", + "... ... ... ... \n", + "Zabour, Miss. Hileni 14.5000 1 0 \n", + "Zabour, Miss. Thamine NaN 1 0 \n", + "Zakarian, Mr. Mapriededer 26.5000 0 0 \n", + "Zakarian, Mr. Ortin 27.0000 0 0 \n", + "Zimmerman, Mr. Leo 29.0000 0 0 \n", + "\n", + " ticket fare cabin \\\n", + "name \n", + "Allen, Miss. Elisabeth Walton 24160 211.3375 B5 \n", + "Allison, Master. Hudson Trevor 113781 151.5500 C22 C26 \n", + "Allison, Miss. Helen Loraine 113781 151.5500 C22 C26 \n", + "Allison, Mr. Hudson Joshua Creighton 113781 151.5500 C22 C26 \n", + "Allison, Mrs. Hudson J C (Bessie Waldo Daniels) 113781 151.5500 C22 C26 \n", + "... ... ... ... \n", + "Zabour, Miss. Hileni 2665 14.4542 NaN \n", + "Zabour, Miss. Thamine 2665 14.4542 NaN \n", + "Zakarian, Mr. Mapriededer 2656 7.2250 NaN \n", + "Zakarian, Mr. Ortin 2670 7.2250 NaN \n", + "Zimmerman, Mr. Leo 315082 7.8750 NaN \n", + "\n", + " embarked boat body \\\n", + "name \n", + "Allen, Miss. Elisabeth Walton S 2 NaN \n", + "Allison, Master. Hudson Trevor S 11 NaN \n", + "Allison, Miss. Helen Loraine S NaN NaN \n", + "Allison, Mr. Hudson Joshua Creighton S NaN 135.0 \n", + "Allison, Mrs. Hudson J C (Bessie Waldo Daniels) S NaN NaN \n", + "... ... ... ... \n", + "Zabour, Miss. Hileni C NaN 328.0 \n", + "Zabour, Miss. Thamine C NaN NaN \n", + "Zakarian, Mr. Mapriededer C NaN 304.0 \n", + "Zakarian, Mr. Ortin C NaN NaN \n", + "Zimmerman, Mr. Leo S NaN NaN \n", + "\n", + " home.dest \n", + "name \n", + "Allen, Miss. Elisabeth Walton St Louis, MO \n", + "Allison, Master. Hudson Trevor Montreal, PQ / Chesterville, ON \n", + "Allison, Miss. Helen Loraine Montreal, PQ / Chesterville, ON \n", + "Allison, Mr. Hudson Joshua Creighton Montreal, PQ / Chesterville, ON \n", + "Allison, Mrs. Hudson J C (Bessie Waldo Daniels) Montreal, PQ / Chesterville, ON \n", + "... ... \n", + "Zabour, Miss. Hileni NaN \n", + "Zabour, Miss. Thamine NaN \n", + "Zakarian, Mr. Mapriededer NaN \n", + "Zakarian, Mr. Ortin NaN \n", + "Zimmerman, Mr. Leo NaN \n", + "\n", + "[1309 rows x 13 columns]" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
pclasssurvivedsexagesibspparchticketfarecabinembarkedboatbodyhome.dest
name
Allen, Miss. Elisabeth Walton11female29.00000024160211.3375B5S2NaNSt Louis, MO
Allison, Master. Hudson Trevor11male0.916712113781151.5500C22 C26S11NaNMontreal, PQ / Chesterville, ON
Allison, Miss. Helen Loraine10female2.000012113781151.5500C22 C26SNaNNaNMontreal, PQ / Chesterville, ON
Allison, Mr. Hudson Joshua Creighton10male30.000012113781151.5500C22 C26SNaN135.0Montreal, PQ / Chesterville, ON
Allison, Mrs. Hudson J C (Bessie Waldo Daniels)10female25.000012113781151.5500C22 C26SNaNNaNMontreal, PQ / Chesterville, ON
..........................................
Zabour, Miss. Hileni30female14.500010266514.4542NaNCNaN328.0NaN
Zabour, Miss. Thamine30femaleNaN10266514.4542NaNCNaNNaNNaN
Zakarian, Mr. Mapriededer30male26.50000026567.2250NaNCNaN304.0NaN
Zakarian, Mr. Ortin30male27.00000026707.2250NaNCNaNNaNNaN
Zimmerman, Mr. Leo30male29.0000003150827.8750NaNSNaNNaNNaN
\n

1309 rows × 13 columns

\n
" + }, + "metadata": {}, + "execution_count": 21 + } + ], + "source": [ + "raw_data.set_index(\"name\")" + ] + }, + { + "source": [ + "## Data cleaning" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "pclass 0\n", + "survived 0\n", + "name 0\n", + "sex 0\n", + "age 263\n", + "sibsp 0\n", + "parch 0\n", + "ticket 0\n", + "fare 1\n", + "cabin 1014\n", + "embarked 2\n", + "boat 823\n", + "body 1188\n", + "home.dest 564\n", + "dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 22 + } + ], + "source": [ + "raw_data.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "pclass 0\n", + "survived 0\n", + "name 0\n", + "sex 0\n", + "age 263\n", + "sibsp 0\n", + "parch 0\n", + "ticket 0\n", + "fare 1\n", + "cabin 1014\n", + "embarked 2\n", + "boat 823\n", + "body 1188\n", + "home.dest 564\n", + "dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 23 + } + ], + "source": [ + "raw_data.isna().sum()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "pclass 1309\n", + "survived 1309\n", + "name 1309\n", + "sex 1309\n", + "age 1046\n", + "sibsp 1309\n", + "parch 1309\n", + "ticket 1309\n", + "fare 1308\n", + "cabin 295\n", + "embarked 1307\n", + "boat 486\n", + "body 121\n", + "home.dest 745\n", + "dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 24 + } + ], + "source": [ + "raw_data.notna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "pclass 0.000000\n", + "survived 0.000000\n", + "name 0.000000\n", + "sex 0.000000\n", + "sibsp 0.000000\n", + "parch 0.000000\n", + "ticket 0.000000\n", + "fare 0.000764\n", + "embarked 0.001528\n", + "age 0.200917\n", + "home.dest 0.430863\n", + "boat 0.628724\n", + "cabin 0.774637\n", + "body 0.907563\n", + "dtype: float64" + ] + }, + "metadata": {}, + "execution_count": 25 + } + ], + "source": [ + "(raw_data.isna().sum()/raw_data.shape[0]).sort_values(ascending=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 26 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n\n \n \n \n \n 2021-04-23T16:55:47.836121\n image/svg+xml\n \n \n Matplotlib v3.3.2, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "import seaborn as sns\n", + "sns.heatmap(raw_data.isna())" + ] + }, + { + "source": [ + "### Dealing with missing values\n", + "\n", + "- Remove missing values: \n", + " - remove rows/columns with missing columns\n", + " - drop rows/columns by percentage of missing values \n", + " - see pd.dropna(axis = , thresh = )\n", + "- Impute missing values:\n", + " - Filling with generic values\n", + " - Filling with central tendencies\n", + " - see pd.fillna(), pd.bfill(), pd.ffill(), etc\n", + "\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 False\n", + "1 False\n", + "2 False\n", + "3 False\n", + "4 False\n", + " ... \n", + "1304 False\n", + "1305 False\n", + "1306 False\n", + "1307 False\n", + "1308 False\n", + "Name: embarked, Length: 1309, dtype: bool" + ] + }, + "metadata": {}, + "execution_count": 27 + } + ], + "source": [ + "raw_data[\"embarked\"].isna()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "2" + ] + }, + "metadata": {}, + "execution_count": 28 + } + ], + "source": [ + "raw_data[\"embarked\"].isnull().sum() # only 2 missing values" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 S\n", + "1 S\n", + "2 S\n", + "3 S\n", + "4 S\n", + " ..\n", + "1304 C\n", + "1305 C\n", + "1306 C\n", + "1307 C\n", + "1308 S\n", + "Name: embarked, Length: 1309, dtype: object" + ] + }, + "metadata": {}, + "execution_count": 29 + } + ], + "source": [ + "raw_data[\"embarked\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "S 914\n", + "C 270\n", + "Q 123\n", + "Name: embarked, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 30 + } + ], + "source": [ + "raw_data[\"embarked\"].value_counts()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "raw_data['embarked'].fillna('S', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " pclass survived sex age\n", + "0 1 1 female 29.0000\n", + "1 1 1 male 0.9167\n", + "2 1 0 female 2.0000\n", + "3 1 0 male 30.0000\n", + "4 1 0 female 25.0000" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
pclasssurvivedsexage
011female29.0000
111male0.9167
210female2.0000
310male30.0000
410female25.0000
\n
" + }, + "metadata": {}, + "execution_count": 32 + } + ], + "source": [ + "data = raw_data.drop(['name', 'sibsp', 'parch', 'ticket', 'fare', 'cabin', 'embarked', 'boat', 'body', 'home.dest'], axis=1)\n", + "data.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "263" + ] + }, + "metadata": {}, + "execution_count": 33 + } + ], + "source": [ + "data[\"age\"].isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "output_type": "error", + "ename": "AssertionError", + "evalue": "", + "traceback": [ + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mAssertionError\u001B[0m Traceback (most recent call last)", + "\u001B[0;32m\u001B[0m in \u001B[0;36m\u001B[0;34m\u001B[0m\n\u001B[0;32m----> 1\u001B[0;31m \u001B[0;32massert\u001B[0m \u001B[0mdata\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'age'\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mnotnull\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mall\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m", + "\u001B[0;31mAssertionError\u001B[0m: " + ] + } + ], + "source": [ + "assert data['age'].notnull().all()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# data = data.dropna(axis=0)\n", + "data.dropna(axis=0, inplace = True)\n", + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "assert data['age'].notnull().all()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "data['sex'] = data['sex'].astype('category')\n", + "data['pclass'] = data['pclass'].astype('category')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " pclass survived sex age\n", + "0 1 1 female 29.0000\n", + "1 1 1 male 0.9167\n", + "2 1 0 female 2.0000\n", + "3 1 0 male 30.0000\n", + "4 1 0 female 25.0000\n", + "5 3 0 male 45.5000\n", + "6 3 0 female 14.5000\n", + "7 3 0 male 26.5000\n", + "8 3 0 male 27.0000\n", + "9 3 0 male 29.0000" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
pclasssurvivedsexage
011female29.0000
111male0.9167
210female2.0000
310male30.0000
410female25.0000
530male45.5000
630female14.5000
730male26.5000
830male27.0000
930male29.0000
\n
" + }, + "metadata": {}, + "execution_count": 38 + } + ], + "source": [ + "data1 = data.head()\n", + "data2 = data.tail()\n", + "conc_data_row = pd.concat([data1,data2],axis = 0,ignore_index =True) \n", + "conc_data_row" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " age sex\n", + "0 29.0000 female\n", + "1 0.9167 male\n", + "2 2.0000 female\n", + "3 30.0000 male\n", + "4 25.0000 female" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
agesex
029.0000female
10.9167male
22.0000female
330.0000male
425.0000female
\n
" + }, + "metadata": {}, + "execution_count": 39 + } + ], + "source": [ + "data1 = data['age'].head()\n", + "data2= data['sex'].head()\n", + "conc_data_col = pd.concat([data1,data2], axis = 1) \n", + "conc_data_col" + ] + }, + { + "source": [ + "## Indexing and filtering" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 29.0000\n", + "1 0.9167\n", + "2 2.0000\n", + "3 30.0000\n", + "4 25.0000\n", + "5 48.0000\n", + "6 63.0000\n", + "7 39.0000\n", + "8 53.0000\n", + "Name: age, dtype: float64" + ] + }, + "metadata": {}, + "execution_count": 40 + } + ], + "source": [ + "# indexing using square brackets\n", + "data[\"age\"][0:9]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 29.0000\n", + "1 0.9167\n", + "2 2.0000\n", + "3 30.0000\n", + "4 25.0000\n", + "5 48.0000\n", + "6 63.0000\n", + "7 39.0000\n", + "8 53.0000\n", + "Name: age, dtype: float64" + ] + }, + "metadata": {}, + "execution_count": 41 + } + ], + "source": [ + "# using column attribute and row label\n", + "data.age[0:9]" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 1\n", + "1 1\n", + "2 0\n", + "3 0\n", + "4 0\n", + "5 1\n", + "6 1\n", + "7 0\n", + "8 1\n", + "9 0\n", + "Name: survived, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 42 + } + ], + "source": [ + "# using loc accessor\n", + "data.loc[0:9,\"survived\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " age sex\n", + "0 29.0000 female\n", + "1 0.9167 male\n", + "2 2.0000 female\n", + "3 30.0000 male\n", + "4 25.0000 female\n", + "5 48.0000 male\n", + "6 63.0000 female\n", + "7 39.0000 male\n", + "8 53.0000 female\n", + "9 71.0000 male" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
agesex
029.0000female
10.9167male
22.0000female
330.0000male
425.0000female
548.0000male
663.0000female
739.0000male
853.0000female
971.0000male
\n
" + }, + "metadata": {}, + "execution_count": 43 + } + ], + "source": [ + "data.loc[0:9,[\"age\", \"sex\"]]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " pclass survived\n", + "0 1 1\n", + "1 1 1\n", + "2 1 0\n", + "3 1 0\n", + "4 1 0\n", + "5 1 1\n", + "6 1 1\n", + "7 1 0\n", + "8 1 1\n", + "9 1 0" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
pclasssurvived
011
111
210
310
410
511
611
710
811
910
\n
" + }, + "metadata": {}, + "execution_count": 44 + } + ], + "source": [ + "data.iloc[0:10, 0:2]" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " survived pclass\n", + "0 1 1\n", + "1 1 1\n", + "2 0 1\n", + "3 0 1\n", + "4 0 1\n", + "... ... ...\n", + "1301 0 3\n", + "1304 0 3\n", + "1306 0 3\n", + "1307 0 3\n", + "1308 0 3\n", + "\n", + "[1046 rows x 2 columns]" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
survivedpclass
011
111
201
301
401
.........
130103
130403
130603
130703
130803
\n

1046 rows × 2 columns

\n
" + }, + "metadata": {}, + "execution_count": 45 + } + ], + "source": [ + "# Selecting only some columns\n", + "data[[\"survived\",\"pclass\"]]" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " pclass survived sex age\n", + "9 1 0 male 71.0\n", + "14 1 1 male 80.0\n", + "61 1 1 female 76.0\n", + "135 1 0 male 71.0\n", + "727 3 0 male 70.5\n", + "1235 3 0 male 74.0" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
pclasssurvivedsexage
910male71.0
1411male80.0
6111female76.0
13510male71.0
72730male70.5
123530male74.0
\n
" + }, + "metadata": {}, + "execution_count": 46 + } + ], + "source": [ + "# Creating boolean series\n", + "mask = data.age > 70\n", + "data[mask]\n", + "data[data.age > 70]" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(6, 4)" + ] + }, + "metadata": {}, + "execution_count": 47 + } + ], + "source": [ + "first_mask = data.age > 70\n", + "second_mask = data.survived == 1\n", + "data[first_mask & second_mask]\n", + "\n", + "data[data.age > 70].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " pclass survived sex age\n", + "0 1 1 female 29.0000\n", + "1 1 1 male 0.9167\n", + "2 1 0 female 2.0000\n", + "3 1 0 male 30.0000\n", + "4 1 0 female 25.0000" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
pclasssurvivedsexage
011female29.0000
111male0.9167
210female2.0000
310male30.0000
410female25.0000
\n
" + }, + "metadata": {}, + "execution_count": 48 + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 Allen, Miss. Elisabeth Walton\n", + "1 Allison, Master. Hudson Trevor\n", + "2 Allison, Miss. Helen Loraine\n", + "3 Allison, Mr. Hudson Joshua Creighton\n", + "4 Allison, Mrs. Hudson J C (Bessie Waldo Daniels)\n", + " ... \n", + "1304 Zabour, Miss. Hileni\n", + "1305 Zabour, Miss. Thamine\n", + "1306 Zakarian, Mr. Mapriededer\n", + "1307 Zakarian, Mr. Ortin\n", + "1308 Zimmerman, Mr. Leo\n", + "Name: name, Length: 1309, dtype: object" + ] + }, + "metadata": {}, + "execution_count": 49 + } + ], + "source": [ + "raw_data.name" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "1 Allison, Master. Hudson Trevor\n", + "2 Allison, Miss. Helen Loraine\n", + "94 Dodge, Master. Washington\n", + "273 Spedden, Master. Robert Douglas\n", + "339 Becker, Master. Richard F\n", + " ... \n", + "1209 Skoog, Miss. Margit Elizabeth\n", + "1230 Strom, Miss. Telma Matilda\n", + "1240 Thomas, Master. Assad Alexander\n", + "1256 Touma, Master. Georges Youssef\n", + "1257 Touma, Miss. Maria Youssef\n", + "Name: name, Length: 82, dtype: object" + ] + }, + "metadata": {}, + "execution_count": 50 + } + ], + "source": [ + "# Filtering column based on others\n", + "raw_data.name[raw_data.age<10]" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 14.50000\n", + "1 0.45835\n", + "2 1.00000\n", + "3 15.00000\n", + "4 12.50000\n", + " ... \n", + "1301 22.75000\n", + "1304 7.25000\n", + "1306 13.25000\n", + "1307 13.50000\n", + "1308 14.50000\n", + "Name: age, Length: 1046, dtype: float64" + ] + }, + "metadata": {}, + "execution_count": 51 + } + ], + "source": [ + "def div(n):\n", + " return n/2\n", + "data.age.apply(div)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 14.50000\n", + "1 0.45835\n", + "2 1.00000\n", + "3 15.00000\n", + "4 12.50000\n", + " ... \n", + "1301 22.75000\n", + "1304 7.25000\n", + "1306 13.25000\n", + "1307 13.50000\n", + "1308 14.50000\n", + "Name: age, Length: 1046, dtype: float64" + ] + }, + "metadata": {}, + "execution_count": 52 + } + ], + "source": [ + "data.age.apply(lambda n : n/2)\n" + ] + }, + { + "source": [ + "# Data visualization and summaries" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 53 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n\n \n \n \n \n 2021-04-23T16:55:58.233357\n image/svg+xml\n \n \n Matplotlib v3.3.2, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "data['age'].hist()" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 54 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n\n \n \n \n \n 2021-04-23T16:55:59.382860\n image/svg+xml\n \n \n Matplotlib v3.3.2, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD1CAYAAACrz7WZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAM2klEQVR4nO3df6jd913H8edrSY3Tirb0NoQkXfpHQFN1rcY4Kchch4l2mP5TyEAXpJB/IlYUJPGf4R+B+o/oHxYWtBr8FcJ0JHTgDNEyRG13u9V1aRca15pekjVZVWqZZCR7+0e+hWN6bu5J7j05zbvPB5TzPZ/z+Z7zvtzm2cM399ymqpAk9fKBWQ8gSVp5xl2SGjLuktSQcZekhoy7JDVk3CWpodWzHgDgrrvuqk2bNs16DEm6pTz//PPfqqq5cY+9J+K+adMm5ufnZz2GJN1SkvzHYo95WUaSGjLuktSQcZekhoy7JDVk3CWpoYninuS1JC8meSHJ/LB2Z5LjSV4Zbu8Y2b8/yekkp5Jsn9bwkqTxrued+89V1f1VtXW4vw84UVWbgRPDfZJsAXYB9wE7gCeTrFrBmSVJS1jOZZmdwKHh+BDwyMj64aq6WFWvAqeBbct4HUnSdZr0Q0wF/H2SAj5TVQeBtVV1DqCqziW5e9i7HvjXkXMXhrX3jE37Pj/rEabqtScenvUIkmZs0rg/WFVnh4AfT/L1a+zNmLV3/e+ekuwB9gDcc889E44hSZrERJdlqurscHse+BxXLrO8kWQdwHB7fti+AGwcOX0DcHbMcx6sqq1VtXVubuyvRpAk3aAl457k+5P8wDvHwM8DXwOOAbuHbbuBo8PxMWBXkjVJ7gU2A8+t9OCSpMVNcllmLfC5JO/s/6uq+rskXwKOJHkMOAM8ClBVJ5McAV4CLgF7q+ryVKaXJI21ZNyr6hvAh8esvwk8tMg5B4ADy55OknRD/ISqJDVk3CWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDRl3SWrIuEtSQ8Zdkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktSQcZekhoy7JDVk3CWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGJo57klVJvpLk6eH+nUmOJ3lluL1jZO/+JKeTnEqyfRqDS5IWdz3v3B8HXh65vw84UVWbgRPDfZJsAXYB9wE7gCeTrFqZcSVJk5go7kk2AA8DfzyyvBM4NBwfAh4ZWT9cVRer6lXgNLBtRaaVJE1k0nfufwD8NvDdkbW1VXUOYLi9e1hfD7w+sm9hWJMk3SRLxj3JJ4DzVfX8hM+ZMWs15nn3JJlPMn/hwoUJn1qSNIlJ3rk/CPxSkteAw8DHkvwF8EaSdQDD7flh/wKwceT8DcDZq5+0qg5W1daq2jo3N7eML0GSdLUl415V+6tqQ1Vt4spflP5DVf0ycAzYPWzbDRwdjo8Bu5KsSXIvsBl4bsUnlyQtavUyzn0COJLkMeAM8ChAVZ1McgR4CbgE7K2qy8ueVJI0seuKe1U9AzwzHL8JPLTIvgPAgWXOJkm6QX5CVZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDRl3SWrIuEtSQ8Zdkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktSQcZekhoy7JDVk3CWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDRl3SWrIuEtSQ8ZdkhpaMu5JvjfJc0n+LcnJJL87rN+Z5HiSV4bbO0bO2Z/kdJJTSbZP8wuQJL3bJO/cLwIfq6oPA/cDO5J8BNgHnKiqzcCJ4T5JtgC7gPuAHcCTSVZNYXZJ0iKWjHtd8fZw97bhnwJ2AoeG9UPAI8PxTuBwVV2sqleB08C2lRxaknRtE11zT7IqyQvAeeB4VT0LrK2qcwDD7d3D9vXA6yOnLwxrVz/nniTzSeYvXLiwjC9BknS1ieJeVZer6n5gA7AtyY9eY3vGPcWY5zxYVVurauvc3NxEw0qSJnNdPy1TVf8NPMOVa+lvJFkHMNyeH7YtABtHTtsAnF3uoJKkyU3y0zJzSX5oOP4g8HHg68AxYPewbTdwdDg+BuxKsibJvcBm4LkVnluSdA2rJ9izDjg0/MTLB4AjVfV0kn8BjiR5DDgDPApQVSeTHAFeAi4Be6vq8nTGlySNs2Tcq+qrwANj1t8EHlrknAPAgWVPJ0m6IX5CVZIamuSyjPSesmnf52c9wlS99sTDsx5BDfjOXZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDRl3SWrIuEtSQ8ZdkhryE6qSbho/XXzz+M5dkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktSQcZekhoy7JDVk3CWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNbRk3JNsTPKPSV5OcjLJ48P6nUmOJ3lluL1j5Jz9SU4nOZVk+zS/AEnSu03yzv0S8FtV9SPAR4C9SbYA+4ATVbUZODHcZ3hsF3AfsAN4MsmqaQwvSRpvybhX1bmq+vJw/D/Ay8B6YCdwaNh2CHhkON4JHK6qi1X1KnAa2LbCc0uSruG6rrkn2QQ8ADwLrK2qc3DlPwDA3cO29cDrI6ctDGuSpJtk4rgnuR34G+A3quqta20ds1Zjnm9Pkvkk8xcuXJh0DEnSBCaKe5LbuBL2v6yqvx2W30iybnh8HXB+WF8ANo6cvgE4e/VzVtXBqtpaVVvn5uZudH5J0hiT/LRMgD8BXq6q3x956BiwezjeDRwdWd+VZE2Se4HNwHMrN7IkaSmrJ9jzIPArwItJXhjWfgd4AjiS5DHgDPAoQFWdTHIEeIkrP2mzt6our/TgkqTFLRn3qvonxl9HB3hokXMOAAeWMZckaRn8hKokNWTcJakh4y5JDRl3SWrIuEtSQ8Zdkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktSQcZekhoy7JDVk3CWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDRl3SWrIuEtSQ8Zdkhoy7pLUkHGXpIaWjHuSp5KcT/K1kbU7kxxP8spwe8fIY/uTnE5yKsn2aQ0uSVrcJO/c/wzYcdXaPuBEVW0GTgz3SbIF2AXcN5zzZJJVKzatJGkiS8a9qr4I/OdVyzuBQ8PxIeCRkfXDVXWxql4FTgPbVmZUSdKkbvSa+9qqOgcw3N49rK8HXh/ZtzCsSZJuopX+C9WMWauxG5M9SeaTzF+4cGGFx5Ck97cbjfsbSdYBDLfnh/UFYOPIvg3A2XFPUFUHq2prVW2dm5u7wTEkSePcaNyPAbuH493A0ZH1XUnWJLkX2Aw8t7wRJUnXa/VSG5L8NfBR4K4kC8CngSeAI0keA84AjwJU1ckkR4CXgEvA3qq6PKXZJUmLWDLuVfXJRR56aJH9B4ADyxlKkrQ8fkJVkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktSQcZekhoy7JDVk3CWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDRl3SWrIuEtSQ8Zdkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGppa3JPsSHIqyekk+6b1OpKkd5tK3JOsAv4I+AVgC/DJJFum8VqSpHeb1jv3bcDpqvpGVX0HOAzsnNJrSZKusnpKz7seeH3k/gLw06MbkuwB9gx3305yakqzvBfcBXzrZr1Yfu9mvdL7ht+/W1f3792HFntgWnHPmLX6f3eqDgIHp/T67ylJ5qtq66zn0I3x+3frej9/76Z1WWYB2DhyfwNwdkqvJUm6yrTi/iVgc5J7k3wPsAs4NqXXkiRdZSqXZarqUpJfA74ArAKeqqqT03itW8T74vJTY37/bl3v2+9dqmrpXZKkW4qfUJWkhoy7JDVk3CWpIeM+BUm2Jfmp4XhLkt9M8ouznkvqLskPJ3koye1Xre+Y1Uyz4l+orrAkn+bK79RZDRznyidznwE+Dnyhqg7MbjotR5Jfrao/nfUcGi/JrwN7gZeB+4HHq+ro8NiXq+onZjjeTWfcV1iSF7nyL9Ya4JvAhqp6K8kHgWer6sdnOZ9uXJIzVXXPrOfQeMOfvZ+pqreTbAI+C/x5Vf1hkq9U1QOznfDmmtavH3g/u1RVl4FvJ/n3qnoLoKr+N8l3ZzyblpDkq4s9BKy9mbPouq2qqrcBquq1JB8FPpvkQ4z/lSitGfeV950k31dV3wZ+8p3FJD8IGPf3vrXAduC/rloP8M83fxxdh28mub+qXgAY3sF/AngK+LGZTjYDxn3l/WxVXQSoqtGY3wbsns1Iug5PA7e/E4hRSZ656dPoenwKuDS6UFWXgE8l+cxsRpodr7lLUkP+KKQkNWTcJakh4y5JDRl3SWrIuEtSQ/8Hwo0HngRrJSYAAAAASUVORK5CYII=\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "data['pclass'].value_counts().plot.bar()" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 55 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n\n \n \n \n \n 2021-04-23T16:56:01.040199\n image/svg+xml\n \n \n Matplotlib v3.3.2, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "raw_data.plot(kind = \"scatter\",x=\"age\",y = \"fare\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 56 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n\n \n \n \n \n 2021-04-23T16:56:02.216707\n image/svg+xml\n \n \n Matplotlib v3.3.2, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "data.boxplot(column='age',by = 'sex')" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " survived age\n", + "sex \n", + "female 0.752577 28.687071\n", + "male 0.205167 30.585233" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
survivedage
sex
female0.75257728.687071
male0.20516730.585233
\n
" + }, + "metadata": {}, + "execution_count": 57 + } + ], + "source": [ + "data.groupby(['sex']).mean()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " survived age\n", + "sex pclass \n", + "female 1 0.962406 37.037594\n", + " 2 0.893204 27.499191\n", + " 3 0.473684 22.185307\n", + "male 1 0.350993 41.029250\n", + " 2 0.145570 30.815401\n", + " 3 0.169054 25.962273" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
survivedage
sexpclass
female10.96240637.037594
20.89320427.499191
30.47368422.185307
male10.35099341.029250
20.14557030.815401
30.16905425.962273
\n
" + }, + "metadata": {}, + "execution_count": 58 + } + ], + "source": [ + "data.groupby(['sex', 'pclass']).mean()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "2 261\n", + "1 284\n", + "3 501\n", + "Name: pclass, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 59 + } + ], + "source": [ + "data['pclass'].value_counts().sort_values()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "1 15\n", + "2 33\n", + "3 106\n", + "Name: pclass, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 60 + } + ], + "source": [ + "data[data['age'] < 18]['pclass'].value_counts().sort_values()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " pclass survived sex age cat_ages\n", + "0 1 1 female 29.0000 20-30 ans\n", + "1 1 1 male 0.9167 <20 ans\n", + "2 1 0 female 2.0000 <20 ans\n", + "3 1 0 male 30.0000 20-30 ans\n", + "4 1 0 female 25.0000 20-30 ans" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
pclasssurvivedsexagecat_ages
011female29.000020-30 ans
111male0.9167<20 ans
210female2.0000<20 ans
310male30.000020-30 ans
410female25.000020-30 ans
\n
" + }, + "metadata": {}, + "execution_count": 61 + } + ], + "source": [ + "def category_ages(age):\n", + " if age <= 20:\n", + " return '<20 ans'\n", + " elif (age > 20) & (age <= 30):\n", + " return '20-30 ans'\n", + " elif (age > 30) & (age <= 40):\n", + " return '30-40 ans'\n", + " else:\n", + " return '+40 ans'\n", + "\n", + "data['cat_ages'] = data['age'].map(category_ages)\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " pclass survived sex age cat_ages age_new\n", + "0 1 1 female 29.0000 20-30 ans 0.290000\n", + "1 1 1 male 0.9167 <20 ans 0.009167\n", + "2 1 0 female 2.0000 <20 ans 0.020000\n", + "3 1 0 male 30.0000 20-30 ans 0.300000\n", + "4 1 0 female 25.0000 20-30 ans 0.250000" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
pclasssurvivedsexagecat_agesage_new
011female29.000020-30 ans0.290000
111male0.9167<20 ans0.009167
210female2.0000<20 ans0.020000
310male30.000020-30 ans0.300000
410female25.000020-30 ans0.250000
\n
" + }, + "metadata": {}, + "execution_count": 62 + } + ], + "source": [ + "data[\"age_new\"] = data[\"age\"]/100\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " pclass survived sex age cat_ages age_new income\n", + "0 1 1 female 29.0000 20-30 ans 0.290000 0\n", + "1 1 1 male 0.9167 <20 ans 0.009167 0\n", + "2 1 0 female 2.0000 <20 ans 0.020000 0\n", + "3 1 0 male 30.0000 20-30 ans 0.300000 0\n", + "4 1 0 female 25.0000 20-30 ans 0.250000 0" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
pclasssurvivedsexagecat_agesage_newincome
011female29.000020-30 ans0.2900000
111male0.9167<20 ans0.0091670
210female2.0000<20 ans0.0200000
310male30.000020-30 ans0.3000000
410female25.000020-30 ans0.2500000
\n
" + }, + "metadata": {}, + "execution_count": 63 + } + ], + "source": [ + "data[\"income\"] = 0 # broadcasting\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 0\n", + "1 1\n", + "2 0\n", + "3 1\n", + "4 0\n", + " ..\n", + "1301 1\n", + "1304 0\n", + "1306 1\n", + "1307 1\n", + "1308 1\n", + "Length: 1046, dtype: int8" + ] + }, + "metadata": {}, + "execution_count": 64 + } + ], + "source": [ + "data['sex'].astype('category').cat.codes\n" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 65 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n\n \n \n \n \n 2021-04-23T16:56:10.721430\n image/svg+xml\n \n \n Matplotlib v3.3.2, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "import seaborn as sns\n", + "sns.countplot(x='sex', hue='survived', data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 66 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n\n \n \n \n \n 2021-04-23T16:56:33.066829\n image/svg+xml\n \n \n Matplotlib v3.3.2, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "sns.catplot(x=\"embarked\", col=\"survived\",\n", + " data=raw_data, kind=\"count\",\n", + " height=4, aspect=.7)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n\n \n \n \n \n 2021-04-23T16:56:46.182717\n image/svg+xml\n \n \n Matplotlib v3.3.2, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "g = sns.countplot(x='embarked', hue='survived', data=raw_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n\n \n \n \n \n 2021-04-23T16:57:03.373964\n image/svg+xml\n \n \n Matplotlib v3.3.2, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "g = sns.countplot(x='embarked', hue='pclass', data=raw_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " pclass survived name sex \\\n", + "0 1 1 Allen, Miss. Elisabeth Walton female \n", + "1 1 1 Allison, Master. Hudson Trevor male \n", + "2 1 0 Allison, Miss. Helen Loraine female \n", + "3 1 0 Allison, Mr. Hudson Joshua Creighton male \n", + "4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female \n", + "5 1 1 Anderson, Mr. Harry male \n", + "6 1 1 Andrews, Miss. Kornelia Theodosia female \n", + "7 1 0 Andrews, Mr. Thomas Jr male \n", + "8 1 1 Appleton, Mrs. Edward Dale (Charlotte Lamson) female \n", + "9 1 0 Artagaveytia, Mr. Ramon male \n", + "\n", + " age sibsp parch ticket fare cabin embarked boat body \\\n", + "0 29.0000 0 0 24160 211.3375 B5 S 2 NaN \n", + "1 0.9167 1 2 113781 151.5500 C22 C26 S 11 NaN \n", + "2 2.0000 1 2 113781 151.5500 C22 C26 S NaN NaN \n", + "3 30.0000 1 2 113781 151.5500 C22 C26 S NaN 135.0 \n", + "4 25.0000 1 2 113781 151.5500 C22 C26 S NaN NaN \n", + "5 48.0000 0 0 19952 26.5500 E12 S 3 NaN \n", + "6 63.0000 1 0 13502 77.9583 D7 S 10 NaN \n", + "7 39.0000 0 0 112050 0.0000 A36 S NaN NaN \n", + "8 53.0000 2 0 11769 51.4792 C101 S D NaN \n", + "9 71.0000 0 0 PC 17609 49.5042 NaN C NaN 22.0 \n", + "\n", + " home.dest familysize \n", + "0 St Louis, MO 1 \n", + "1 Montreal, PQ / Chesterville, ON 4 \n", + "2 Montreal, PQ / Chesterville, ON 4 \n", + "3 Montreal, PQ / Chesterville, ON 4 \n", + "4 Montreal, PQ / Chesterville, ON 4 \n", + "5 New York, NY 1 \n", + "6 Hudson, NY 2 \n", + "7 Belfast, NI 1 \n", + "8 Bayside, Queens, NY 3 \n", + "9 Montevideo, Uruguay 1 " + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
pclasssurvivednamesexagesibspparchticketfarecabinembarkedboatbodyhome.destfamilysize
011Allen, Miss. Elisabeth Waltonfemale29.00000024160211.3375B5S2NaNSt Louis, MO1
111Allison, Master. Hudson Trevormale0.916712113781151.5500C22 C26S11NaNMontreal, PQ / Chesterville, ON4
210Allison, Miss. Helen Lorainefemale2.000012113781151.5500C22 C26SNaNNaNMontreal, PQ / Chesterville, ON4
310Allison, Mr. Hudson Joshua Creightonmale30.000012113781151.5500C22 C26SNaN135.0Montreal, PQ / Chesterville, ON4
410Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female25.000012113781151.5500C22 C26SNaNNaNMontreal, PQ / Chesterville, ON4
511Anderson, Mr. Harrymale48.0000001995226.5500E12S3NaNNew York, NY1
611Andrews, Miss. Kornelia Theodosiafemale63.0000101350277.9583D7S10NaNHudson, NY2
710Andrews, Mr. Thomas Jrmale39.0000001120500.0000A36SNaNNaNBelfast, NI1
811Appleton, Mrs. Edward Dale (Charlotte Lamson)female53.0000201176951.4792C101SDNaNBayside, Queens, NY3
910Artagaveytia, Mr. Ramonmale71.000000PC 1760949.5042NaNCNaN22.0Montevideo, Uruguay1
\n
" + }, + "metadata": {}, + "execution_count": 69 + } + ], + "source": [ + "def add_family(df):\n", + " df['familysize'] = df['sibsp'] + df['parch'] + 1 \n", + " return df\n", + "\n", + "new_data = add_family(raw_data)\n", + "new_data.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n\n \n \n \n \n 2021-04-23T16:57:05.046694\n image/svg+xml\n \n \n Matplotlib v3.3.2, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "sns.countplot(x=\"familysize\", hue=\"survived\",\n", + " data=new_data);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ] +} \ No newline at end of file diff --git a/eda_pokemon_22.ipynb b/eda_pokemon_22.ipynb new file mode 100644 index 0000000..d0404e5 --- /dev/null +++ b/eda_pokemon_22.ipynb @@ -0,0 +1,860 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "eda-pokemon-22.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4-final" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "dzNng6vCL9eP" + }, + "source": [ + "# Machine Learning 2020-2021 - UMONS \n", + "# Exploratory Data Analysis of the Pokemon dataset\n" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "id": "dU4S-rek_gQe" + }, + "outputs": [], + "source": [ + "import pandas as pd #importing all the important packages\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "plt.style.use('fivethirtyeight')" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "id": "hsFUdKmC_gQf", + "outputId": "30a3b6ed-68a6-4286-8adf-847edcdd6a4b" + }, + "outputs": [ + { + "data": { + "text/plain": " # Name Type 1 Type 2 Total HP Attack Defense \\\n0 1 Bulbasaur Grass Poison 318 45 49 49 \n1 2 Ivysaur Grass Poison 405 60 62 63 \n2 3 Venusaur Grass Poison 525 80 82 83 \n3 3 VenusaurMega Venusaur Grass Poison 625 80 100 123 \n4 4 Charmander Fire NaN 309 39 52 43 \n5 5 Charmeleon Fire NaN 405 58 64 58 \n6 6 Charizard Fire Flying 534 78 84 78 \n7 6 CharizardMega Charizard X Fire Dragon 634 78 130 111 \n8 6 CharizardMega Charizard Y Fire Flying 634 78 104 78 \n9 7 Squirtle Water NaN 314 44 48 65 \n\n Sp. Atk Sp. Def Speed Generation Legendary \n0 65 65 45 1 False \n1 80 80 60 1 False \n2 100 100 80 1 False \n3 122 120 80 1 False \n4 60 50 65 1 False \n5 80 65 80 1 False \n6 109 85 100 1 False \n7 130 85 100 1 False \n8 159 115 100 1 False \n9 50 64 43 1 False ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
#NameType 1Type 2TotalHPAttackDefenseSp. AtkSp. DefSpeedGenerationLegendary
01BulbasaurGrassPoison3184549496565451False
12IvysaurGrassPoison4056062638080601False
23VenusaurGrassPoison525808283100100801False
33VenusaurMega VenusaurGrassPoison62580100123122120801False
44CharmanderFireNaN3093952436050651False
55CharmeleonFireNaN4055864588065801False
66CharizardFireFlying534788478109851001False
76CharizardMega Charizard XFireDragon63478130111130851001False
86CharizardMega Charizard YFireFlying63478104781591151001False
97SquirtleWaterNaN3144448655064431False
\n
" + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('data/Pokemon.csv') #read the csv file and save it into a variable\n", + "df.head(n=10) #print the first 10 rows of the table\n" + ] + }, + { + "source": [ + "Your turn! Make some exploratory data analysis and cleaning of the Pokemon dataset " + ], + "cell_type": "markdown", + "metadata": { + "id": "Ux0TXMe0_gQg" + } + }, + { + "source": [ + "- #: ID for each pokemon\n", + "- Name: Name of each pokemon\n", + "- Type 1: Each pokemon has a type, this determines weakness/resistance to attacks\n", + "- Type 2: Some pokemon are dual type and have 2\n", + "- Total: sum of all stats that come after this, a general guide to how strong a pokemon is\n", + "- HP: hit points, or health, defines how much damage a pokemon can withstand before fainting\n", + "- Attack: the base modifier for normal attacks (eg. Scratch, Punch)\n", + "- Defense: the base damage resistance against normal attacks\n", + "- SP Atk: special attack, the base modifier for special attacks (e.g. fire blast, bubble beam)\n", + "- SP Def: the base damage resistance against special attacks\n", + "- Speed: determines which pokemon attacks first each round" + ], + "cell_type": "markdown", + "metadata": { + "id": "QMr1DM5q_gQg" + } + }, + { + "cell_type": "markdown", + "source": [ + "1) Print the general information of the Pokemon dataset. " + ], + "metadata": { + "id": "eLriTZtKBDTP" + } + }, + { + "cell_type": "code", + "source": [ + "df.info()" + ], + "metadata": { + "id": "yv1vhgCkAepn" + }, + "execution_count": 71, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 800 entries, 0 to 799\n", + "Data columns (total 13 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 # 800 non-null int64 \n", + " 1 Name 800 non-null object\n", + " 2 Type 1 800 non-null object\n", + " 3 Type 2 414 non-null object\n", + " 4 Total 800 non-null int64 \n", + " 5 HP 800 non-null int64 \n", + " 6 Attack 800 non-null int64 \n", + " 7 Defense 800 non-null int64 \n", + " 8 Sp. Atk 800 non-null int64 \n", + " 9 Sp. Def 800 non-null int64 \n", + " 10 Speed 800 non-null int64 \n", + " 11 Generation 800 non-null int64 \n", + " 12 Legendary 800 non-null bool \n", + "dtypes: bool(1), int64(9), object(3)\n", + "memory usage: 75.9+ KB\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "2) Get the shape of the dataframe. " + ], + "metadata": { + "id": "s1pHx3oR6w5G" + } + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XVRlqBq2_gQh", + "outputId": "85424ccb-868e-4f46-b4f7-f762a5a73c13" + }, + "outputs": [ + { + "data": { + "text/plain": "(800, 13)" + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "markdown", + "source": [ + "3) Drop the \"#' column and set the dataframe index to the 'Name' column. " + ], + "metadata": { + "id": "VuqDhanYR0v4" + } + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 238 + }, + "id": "nr5scIgK_gQi", + "outputId": "aec8fb97-4162-476c-b924-0648aa221541" + }, + "outputs": [ + { + "data": { + "text/plain": " Type 1 Type 2 Total HP Attack Defense Sp. Atk \\\nName \nBulbasaur Grass Poison 318 45 49 49 65 \nIvysaur Grass Poison 405 60 62 63 80 \nVenusaur Grass Poison 525 80 82 83 100 \nVenusaurMega Venusaur Grass Poison 625 80 100 123 122 \nCharmander Fire NaN 309 39 52 43 60 \n... ... ... ... .. ... ... ... \nDiancie Rock Fairy 600 50 100 150 100 \nDiancieMega Diancie Rock Fairy 700 50 160 110 160 \nHoopaHoopa Confined Psychic Ghost 600 80 110 60 150 \nHoopaHoopa Unbound Psychic Dark 680 80 160 60 170 \nVolcanion Fire Water 600 80 110 120 130 \n\n Sp. Def Speed Generation Legendary \nName \nBulbasaur 65 45 1 False \nIvysaur 80 60 1 False \nVenusaur 100 80 1 False \nVenusaurMega Venusaur 120 80 1 False \nCharmander 50 65 1 False \n... ... ... ... ... \nDiancie 150 50 6 True \nDiancieMega Diancie 110 110 6 True \nHoopaHoopa Confined 130 70 6 True \nHoopaHoopa Unbound 130 80 6 True \nVolcanion 90 70 6 True \n\n[800 rows x 11 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Type 1Type 2TotalHPAttackDefenseSp. AtkSp. DefSpeedGenerationLegendary
Name
BulbasaurGrassPoison3184549496565451False
IvysaurGrassPoison4056062638080601False
VenusaurGrassPoison525808283100100801False
VenusaurMega VenusaurGrassPoison62580100123122120801False
CharmanderFireNaN3093952436050651False
....................................
DiancieRockFairy60050100150100150506True
DiancieMega DiancieRockFairy700501601101601101106True
HoopaHoopa ConfinedPsychicGhost6008011060150130706True
HoopaHoopa UnboundPsychicDark6808016060170130806True
VolcanionFireWater6008011012013090706True
\n

800 rows × 11 columns

\n
" + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_ = df.drop('#',axis=1).set_index('Name')\n", + "df_" + ] + }, + { + "cell_type": "markdown", + "source": [ + "4) Check if there are any missing values in the dataframe, and count them per column. For the categorical variables, replace the missing values by 'None'. For the remaining variables, drop the complete row containing the missing value. Check that the dataframe does not contain missing values anymore. " + ], + "metadata": { + "id": "DmeOC9UQaEo1" + } + }, + { + "cell_type": "code", + "source": [ + "df_.isnull().sum()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8V7lioYdaNf9", + "outputId": "58b810aa-a87a-4209-e3c7-03eb3360e3ce" + }, + "execution_count": 74, + "outputs": [ + { + "data": { + "text/plain": "Type 1 0\nType 2 386\nTotal 0\nHP 0\nAttack 0\nDefense 0\nSp. Atk 0\nSp. Def 0\nSpeed 0\nGeneration 0\nLegendary 0\ndtype: int64" + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ] + }, + { + "cell_type": "code", + "source": [ + "df_ = df_.fillna('None')\n", + "df_.isna().sum()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1N41x3Lu8nAR", + "outputId": "8269f452-2535-413c-c0a3-99b4a15d2718" + }, + "execution_count": 75, + "outputs": [ + { + "data": { + "text/plain": "Type 1 0\nType 2 0\nTotal 0\nHP 0\nAttack 0\nDefense 0\nSp. Atk 0\nSp. Def 0\nSpeed 0\nGeneration 0\nLegendary 0\ndtype: int64" + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "5) Change the data types of 'Type 1' and 'Type 2' and 'Generation' to categorical data." + ], + "metadata": { + "id": "hJiIx80ZFODG" + } + }, + { + "cell_type": "code", + "source": [ + "df_['Type 1'] = df_['Type 1'].astype('category')\n", + "df_['Type 2'] = df_['Type 2'].astype('category')\n", + "df_['Generation'] = df_['Generation'].astype('category')\n", + "df_.info()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "N3QXeTJOEWp6", + "outputId": "1280f24f-8daa-41d3-bdcc-9a0ce4909e87" + }, + "execution_count": 76, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 800 entries, Bulbasaur to Volcanion\n", + "Data columns (total 11 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Type 1 800 non-null category\n", + " 1 Type 2 800 non-null category\n", + " 2 Total 800 non-null int64 \n", + " 3 HP 800 non-null int64 \n", + " 4 Attack 800 non-null int64 \n", + " 5 Defense 800 non-null int64 \n", + " 6 Sp. Atk 800 non-null int64 \n", + " 7 Sp. Def 800 non-null int64 \n", + " 8 Speed 800 non-null int64 \n", + " 9 Generation 800 non-null category\n", + " 10 Legendary 800 non-null bool \n", + "dtypes: bool(1), category(3), int64(7)\n", + "memory usage: 54.7+ KB\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "6) Get general statistics (mean, standard deviation,...) for the numerical attributes of the dataset. " + ], + "metadata": { + "id": "sfk3xosFZr3p" + } + }, + { + "cell_type": "code", + "source": [ + "df_.describe()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 300 + }, + "id": "ovW_8SlcZ1Q6", + "outputId": "aa1d60e7-2fb7-48c8-87ef-324b8f4ca775" + }, + "execution_count": 77, + "outputs": [ + { + "data": { + "text/plain": " Total HP Attack Defense Sp. Atk Sp. Def \\\ncount 800.00000 800.000000 800.000000 800.000000 800.000000 800.000000 \nmean 435.10250 69.258750 79.001250 73.842500 72.820000 71.902500 \nstd 119.96304 25.534669 32.457366 31.183501 32.722294 27.828916 \nmin 180.00000 1.000000 5.000000 5.000000 10.000000 20.000000 \n25% 330.00000 50.000000 55.000000 50.000000 49.750000 50.000000 \n50% 450.00000 65.000000 75.000000 70.000000 65.000000 70.000000 \n75% 515.00000 80.000000 100.000000 90.000000 95.000000 90.000000 \nmax 780.00000 255.000000 190.000000 230.000000 194.000000 230.000000 \n\n Speed \ncount 800.000000 \nmean 68.277500 \nstd 29.060474 \nmin 5.000000 \n25% 45.000000 \n50% 65.000000 \n75% 90.000000 \nmax 180.000000 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
TotalHPAttackDefenseSp. AtkSp. DefSpeed
count800.00000800.000000800.000000800.000000800.000000800.000000800.000000
mean435.1025069.25875079.00125073.84250072.82000071.90250068.277500
std119.9630425.53466932.45736631.18350132.72229427.82891629.060474
min180.000001.0000005.0000005.00000010.00000020.0000005.000000
25%330.0000050.00000055.00000050.00000049.75000050.00000045.000000
50%450.0000065.00000075.00000070.00000065.00000070.00000065.000000
75%515.0000080.000000100.00000090.00000095.00000090.00000090.000000
max780.00000255.000000190.000000230.000000194.000000230.000000180.000000
\n
" + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "7) For the categorical variables, count the number of values per category, as well as the count of co-occurences." + ], + "metadata": { + "id": "wTEEsbxsatcg" + } + }, + { + "cell_type": "code", + "source": [ + "df_[[\"Type 1\",\"Type 2\",\"Generation\"]].value_counts()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tLqsNREPa9rP", + "outputId": "d792c370-2bde-4a63-ee65-99f8a55d7de6" + }, + "execution_count": 78, + "outputs": [ + { + "data": { + "text/plain": "Type 1 Type 2 Generation\nWater None 1 19\nNormal None 3 14\n 1 13\n 4 12\nWater None 3 12\n ..\nFire Rock 2 1\n Steel 4 1\nNormal Ground 6 1\nFire Water 6 1\nWater Steel 4 1\nLength: 293, dtype: int64" + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "8) Get all the attributes of 'Bulbasaur'" + ], + "metadata": { + "id": "jyAToWqlbrJt" + } + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IHcc1OnP_gQj", + "outputId": "5d5ec6ae-13e6-4270-82a5-f0035fc3a005" + }, + "outputs": [ + { + "data": { + "text/plain": " Type 1 Type 2 Total HP Attack Defense Sp. Atk Sp. Def Speed \\\nName \nBulbasaur Grass Poison 318 45 49 49 65 65 45 \n\n Generation Legendary \nName \nBulbasaur 1 False ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Type 1Type 2TotalHPAttackDefenseSp. AtkSp. DefSpeedGenerationLegendary
Name
BulbasaurGrassPoison3184549496565451False
\n
" + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_[df_.index == 'Bulbasaur']" + ] + }, + { + "cell_type": "markdown", + "source": [ + "9) Sort the dataframe by increasing values of 'Attack' and decreasing values of 'Defense' (i.e. if two Pokemons have the same value for 'Attack', the one with higher 'Defense' should appear first). " + ], + "metadata": { + "id": "rnP-WJCAueWI" + } + }, + { + "cell_type": "code", + "source": [ + "df_.sort_values(by=['Attack','Defense'], ascending=[True,False])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 238 + }, + "id": "21AqqLSNulmH", + "outputId": "08f2548f-baae-41ff-bc16-c46cf3f94ab4" + }, + "execution_count": 80, + "outputs": [ + { + "data": { + "text/plain": " Type 1 Type 2 Total HP Attack Defense \\\nName \nChansey Normal None 450 250 5 5 \nHappiny Normal None 220 100 5 5 \nShuckle Bug Rock 505 20 10 230 \nMagikarp Water None 200 20 10 55 \nBlissey Normal None 540 255 10 10 \n... ... ... ... ... ... ... \nGroudonPrimal Groudon Ground Fire 770 100 180 160 \nRayquazaMega Rayquaza Dragon Flying 780 105 180 100 \nDeoxysAttack Forme Psychic None 600 50 180 20 \nHeracrossMega Heracross Bug Fighting 600 80 185 115 \nMewtwoMega Mewtwo X Psychic Fighting 780 106 190 100 \n\n Sp. Atk Sp. Def Speed Generation Legendary \nName \nChansey 35 105 50 1 False \nHappiny 15 65 30 4 False \nShuckle 10 230 5 2 False \nMagikarp 15 20 80 1 False \nBlissey 75 135 55 2 False \n... ... ... ... ... ... \nGroudonPrimal Groudon 150 90 90 3 True \nRayquazaMega Rayquaza 180 100 115 3 True \nDeoxysAttack Forme 180 20 150 3 True \nHeracrossMega Heracross 40 105 75 2 False \nMewtwoMega Mewtwo X 154 100 130 1 True \n\n[800 rows x 11 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Type 1Type 2TotalHPAttackDefenseSp. AtkSp. DefSpeedGenerationLegendary
Name
ChanseyNormalNone4502505535105501False
HappinyNormalNone220100551565304False
ShuckleBugRock50520102301023052False
MagikarpWaterNone2002010551520801False
BlisseyNormalNone540255101075135552False
....................................
GroudonPrimal GroudonGroundFire77010018016015090903True
RayquazaMega RayquazaDragonFlying7801051801001801001153True
DeoxysAttack FormePsychicNone6005018020180201503True
HeracrossMega HeracrossBugFighting6008018511540105752False
MewtwoMega Mewtwo XPsychicFighting7801061901001541001301True
\n

800 rows × 11 columns

\n
" + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "10) Create a dataframe containing all Pokemons of type 1 'Psychic' having more than 100 in 'Attack', less than 40 in 'Defense' and more than 45 in Speed." + ], + "metadata": { + "id": "qxOAc1B_mDUr" + } + }, + { + "cell_type": "code", + "source": [ + "mask = df_['Type 1'] == 'Psychic'\n", + "mask1 = df_['Attack'] > 100\n", + "mask2 = df_['Defense'] < 40\n", + "mask3 = df_['Speed'] > 45\n", + "\n", + "#df_[ df_['Type 1'] == 'Psychic'][ df_['Attack'] > 100][ df_['Defense'] < 40][ df_['Speed'] > 45]\n", + "df_[(mask) & mask1 & mask2 & mask3]" + ], + "metadata": { + "id": "OI07YycPoP9u", + "pycharm": { + "name": "#%%\n" + } + }, + "execution_count": 81, + "outputs": [ + { + "data": { + "text/plain": " Type 1 Type 2 Total HP Attack Defense Sp. Atk \\\nName \nDeoxysAttack Forme Psychic None 600 50 180 20 180 \n\n Sp. Def Speed Generation Legendary \nName \nDeoxysAttack Forme 20 150 3 True ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Type 1Type 2TotalHPAttackDefenseSp. AtkSp. DefSpeedGenerationLegendary
Name
DeoxysAttack FormePsychicNone6005018020180201503True
\n
" + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "11) Create two new columns, 'AttackAll' and 'DefenseAll' which take the sum of Attack and Sp. Attack, and the sum of 'Defense' and 'Sp. Defense' respectively." + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 238 + }, + "id": "6Mur9aCQp0o9", + "outputId": "0e3c39f3-ab17-41c1-e1a5-5011f73478d9", + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 82, + "outputs": [ + { + "data": { + "text/plain": " Type 1 Type 2 Total HP Attack Defense Sp. Atk \\\nName \nBulbasaur Grass Poison 318 45 49 49 65 \nIvysaur Grass Poison 405 60 62 63 80 \nVenusaur Grass Poison 525 80 82 83 100 \nVenusaurMega Venusaur Grass Poison 625 80 100 123 122 \nCharmander Fire None 309 39 52 43 60 \n... ... ... ... .. ... ... ... \nDiancie Rock Fairy 600 50 100 150 100 \nDiancieMega Diancie Rock Fairy 700 50 160 110 160 \nHoopaHoopa Confined Psychic Ghost 600 80 110 60 150 \nHoopaHoopa Unbound Psychic Dark 680 80 160 60 170 \nVolcanion Fire Water 600 80 110 120 130 \n\n Sp. Def Speed Generation Legendary AttackAll \\\nName \nBulbasaur 65 45 1 False 114 \nIvysaur 80 60 1 False 142 \nVenusaur 100 80 1 False 182 \nVenusaurMega Venusaur 120 80 1 False 222 \nCharmander 50 65 1 False 112 \n... ... ... ... ... ... \nDiancie 150 50 6 True 200 \nDiancieMega Diancie 110 110 6 True 320 \nHoopaHoopa Confined 130 70 6 True 260 \nHoopaHoopa Unbound 130 80 6 True 330 \nVolcanion 90 70 6 True 240 \n\n DefenseAll \nName \nBulbasaur 114 \nIvysaur 143 \nVenusaur 183 \nVenusaurMega Venusaur 243 \nCharmander 93 \n... ... \nDiancie 300 \nDiancieMega Diancie 220 \nHoopaHoopa Confined 190 \nHoopaHoopa Unbound 190 \nVolcanion 210 \n\n[800 rows x 13 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Type 1Type 2TotalHPAttackDefenseSp. AtkSp. DefSpeedGenerationLegendaryAttackAllDefenseAll
Name
BulbasaurGrassPoison3184549496565451False114114
IvysaurGrassPoison4056062638080601False142143
VenusaurGrassPoison525808283100100801False182183
VenusaurMega VenusaurGrassPoison62580100123122120801False222243
CharmanderFireNone3093952436050651False11293
..........................................
DiancieRockFairy60050100150100150506True200300
DiancieMega DiancieRockFairy700501601101601101106True320220
HoopaHoopa ConfinedPsychicGhost6008011060150130706True260190
HoopaHoopa UnboundPsychicDark6808016060170130806True330190
VolcanionFireWater6008011012013090706True240210
\n

800 rows × 13 columns

\n
" + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_['AttackAll'] = df_['Attack'] + df_['Sp. Atk']\n", + "df_['DefenseAll'] = df_['Defense'] + df_['Sp. Def']\n", + "df_" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "12) Write a generic function taking the ratio of two values 'a' and 'b'. Use this function to create a new column 'AtkOverDef' giving the ratio of 'AttackAll' over 'DefenseAll' for each Pokemon. " + ], + "metadata": { + "id": "ZCNx4IaIq3o-" + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "source": [ + "def ratio(a,b):\n", + " return a/b\n", + "\n", + "df_['AtkOverDef'] = ratio(df_['AttackAll'],df_['DefenseAll'])\n", + "df_" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 238 + }, + "id": "Gp9fpMMIsD_n", + "outputId": "29f64251-638f-4e74-c362-a73be8d5c012" + }, + "execution_count": 83, + "outputs": [ + { + "data": { + "text/plain": " Type 1 Type 2 Total HP Attack Defense Sp. Atk \\\nName \nBulbasaur Grass Poison 318 45 49 49 65 \nIvysaur Grass Poison 405 60 62 63 80 \nVenusaur Grass Poison 525 80 82 83 100 \nVenusaurMega Venusaur Grass Poison 625 80 100 123 122 \nCharmander Fire None 309 39 52 43 60 \n... ... ... ... .. ... ... ... \nDiancie Rock Fairy 600 50 100 150 100 \nDiancieMega Diancie Rock Fairy 700 50 160 110 160 \nHoopaHoopa Confined Psychic Ghost 600 80 110 60 150 \nHoopaHoopa Unbound Psychic Dark 680 80 160 60 170 \nVolcanion Fire Water 600 80 110 120 130 \n\n Sp. Def Speed Generation Legendary AttackAll \\\nName \nBulbasaur 65 45 1 False 114 \nIvysaur 80 60 1 False 142 \nVenusaur 100 80 1 False 182 \nVenusaurMega Venusaur 120 80 1 False 222 \nCharmander 50 65 1 False 112 \n... ... ... ... ... ... \nDiancie 150 50 6 True 200 \nDiancieMega Diancie 110 110 6 True 320 \nHoopaHoopa Confined 130 70 6 True 260 \nHoopaHoopa Unbound 130 80 6 True 330 \nVolcanion 90 70 6 True 240 \n\n DefenseAll AtkOverDef \nName \nBulbasaur 114 1.000000 \nIvysaur 143 0.993007 \nVenusaur 183 0.994536 \nVenusaurMega Venusaur 243 0.913580 \nCharmander 93 1.204301 \n... ... ... \nDiancie 300 0.666667 \nDiancieMega Diancie 220 1.454545 \nHoopaHoopa Confined 190 1.368421 \nHoopaHoopa Unbound 190 1.736842 \nVolcanion 210 1.142857 \n\n[800 rows x 14 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Type 1Type 2TotalHPAttackDefenseSp. AtkSp. DefSpeedGenerationLegendaryAttackAllDefenseAllAtkOverDef
Name
BulbasaurGrassPoison3184549496565451False1141141.000000
IvysaurGrassPoison4056062638080601False1421430.993007
VenusaurGrassPoison525808283100100801False1821830.994536
VenusaurMega VenusaurGrassPoison62580100123122120801False2222430.913580
CharmanderFireNone3093952436050651False112931.204301
.............................................
DiancieRockFairy60050100150100150506True2003000.666667
DiancieMega DiancieRockFairy700501601101601101106True3202201.454545
HoopaHoopa ConfinedPsychicGhost6008011060150130706True2601901.368421
HoopaHoopa UnboundPsychicDark6808016060170130806True3301901.736842
VolcanionFireWater6008011012013090706True2402101.142857
\n

800 rows × 14 columns

\n
" + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "13) Change the column names to upper cases, and remove the '.' in the column names, as well as blanks." + ], + "metadata": { + "id": "YJfaI8VKv64S" + } + }, + { + "cell_type": "code", + "source": [ + "df_.columns = df_.columns.str.upper().str.replace(\" \",\"\").str.replace(\".\",\"\")\n", + "df_" + ], + "metadata": { + "id": "oemote1Dwiil", + "pycharm": { + "name": "#%%\n" + } + }, + "execution_count": 85, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_4197/1336013445.py:1: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n", + " df_.columns = df_.columns.str.upper().str.replace(\" \",\"\").str.replace(\".\",\"\")\n" + ] + }, + { + "data": { + "text/plain": " TYPE1 TYPE2 TOTAL HP ATTACK DEFENSE SPATK \\\nName \nBulbasaur Grass Poison 318 45 49 49 65 \nIvysaur Grass Poison 405 60 62 63 80 \nVenusaur Grass Poison 525 80 82 83 100 \nVenusaurMega Venusaur Grass Poison 625 80 100 123 122 \nCharmander Fire None 309 39 52 43 60 \n... ... ... ... .. ... ... ... \nDiancie Rock Fairy 600 50 100 150 100 \nDiancieMega Diancie Rock Fairy 700 50 160 110 160 \nHoopaHoopa Confined Psychic Ghost 600 80 110 60 150 \nHoopaHoopa Unbound Psychic Dark 680 80 160 60 170 \nVolcanion Fire Water 600 80 110 120 130 \n\n SPDEF SPEED GENERATION LEGENDARY ATTACKALL \\\nName \nBulbasaur 65 45 1 False 114 \nIvysaur 80 60 1 False 142 \nVenusaur 100 80 1 False 182 \nVenusaurMega Venusaur 120 80 1 False 222 \nCharmander 50 65 1 False 112 \n... ... ... ... ... ... \nDiancie 150 50 6 True 200 \nDiancieMega Diancie 110 110 6 True 320 \nHoopaHoopa Confined 130 70 6 True 260 \nHoopaHoopa Unbound 130 80 6 True 330 \nVolcanion 90 70 6 True 240 \n\n DEFENSEALL ATKOVERDEF \nName \nBulbasaur 114 1.000000 \nIvysaur 143 0.993007 \nVenusaur 183 0.994536 \nVenusaurMega Venusaur 243 0.913580 \nCharmander 93 1.204301 \n... ... ... \nDiancie 300 0.666667 \nDiancieMega Diancie 220 1.454545 \nHoopaHoopa Confined 190 1.368421 \nHoopaHoopa Unbound 190 1.736842 \nVolcanion 210 1.142857 \n\n[800 rows x 14 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
TYPE1TYPE2TOTALHPATTACKDEFENSESPATKSPDEFSPEEDGENERATIONLEGENDARYATTACKALLDEFENSEALLATKOVERDEF
Name
BulbasaurGrassPoison3184549496565451False1141141.000000
IvysaurGrassPoison4056062638080601False1421430.993007
VenusaurGrassPoison525808283100100801False1821830.994536
VenusaurMega VenusaurGrassPoison62580100123122120801False2222430.913580
CharmanderFireNone3093952436050651False112931.204301
.............................................
DiancieRockFairy60050100150100150506True2003000.666667
DiancieMega DiancieRockFairy700501601101601101106True3202201.454545
HoopaHoopa ConfinedPsychicGhost6008011060150130706True2601901.368421
HoopaHoopa UnboundPsychicDark6808016060170130806True3301901.736842
VolcanionFireWater6008011012013090706True2402101.142857
\n

800 rows × 14 columns

\n
" + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "14) Plot an histogram of the different 'TYP1' categories. The figure must be 8 inch wide and 4 inch high. \n", + "Use the matplotlib.pyplot library and the countplot method from the seaborn librabry. The counts should appear in increasing order." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 304 + }, + "id": "Ykf0mI1J_gQk", + "outputId": "4da7416a-c3de-49d2-db86-95a03e3df593" + }, + "outputs": [], + "source": [ + "df_['Type 1'].hist()" + ] + }, + { + "cell_type": "markdown", + "source": [ + "15) Do the same as above, but for the 'TYP2' categories. " + ], + "metadata": { + "id": "Sd7rlq5RxgNa" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 306 + }, + "id": "PcWnQOBh_gQk", + "outputId": "4928077c-9051-42db-e2a2-8dc86e96f21e" + }, + "outputs": [], + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "source": [ + "16) Plot the densities of the variables 'Attack', 'Defenses' and 'Speed' onto three separates plots. Use the displot method of the library seaborn. " + ], + "metadata": { + "id": "HJQnPxOy0Cr0" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "57Gmsp9x_gQm", + "outputId": "38abfdbf-81ea-43be-b7ba-56735d7f002b" + }, + "outputs": [], + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "source": [ + "17) Plot the density of the variable 'ATTACK' for Legendary and non Legendary pokemons. The two densities should appear on different facets of the same plot. " + ], + "metadata": { + "id": "8fZ4qb_N1QCH" + } + }, + { + "cell_type": "code", + "source": [ + "" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 378 + }, + "id": "BV6m-Z380uDl", + "outputId": "c147bd2e-c7fa-4ba4-c593-a049f7064fa2" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "18a) Generate a scatter plot of the variable 'Defense' on the y-axis, and the variable 'Attack' on the x-axis. Also, plot a simple linear regression model between the two variables on the same plot. Use the method lmplot of the library seaborn. \n", + "\n", + "18b) Do the same, but with the variable 'Total' in the y-axis. " + ], + "metadata": { + "id": "IwSqLytJ12_K" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "ubOms2Hk_gQm", + "outputId": "d32debc7-e03e-4540-b384-071a02b6cceb" + }, + "outputs": [], + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "source": [ + "19) Create an histogram of the variable 'GENERATION'. The counts should appear in increasing order. " + ], + "metadata": { + "id": "Ufr2rxpd2xgh" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 304 + }, + "id": "Qk7RrmFt_gQm", + "outputId": "c905a67a-6c74-444c-95fe-474f3b639af2" + }, + "outputs": [], + "source": [ + "\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "20) Generate a boxplot of the variable 'TOTAL'. Use the method boxplot from the library seaborn. " + ], + "metadata": { + "id": "YszwDTeJ3HyN" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 283 + }, + "id": "lCREREg7_gQn", + "outputId": "375ecf14-716f-40d3-8c13-1e1b319deb8b" + }, + "outputs": [], + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "source": [ + "21) Generate one boxplot of the variable 'TOTAL' per category of the variable 'GENERATION'. All boxplots must appear on the same plot. " + ], + "metadata": { + "id": "PZeGkhZu3Y8i" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 323 + }, + "id": "_kQ2saC6_gQn", + "outputId": "253874dc-2728-4c40-88a1-f4c58e984ebd" + }, + "outputs": [], + "source": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-ev_yh3g_gQn" + }, + "outputs": [], + "source": [ + "" + ] + } + ] +} \ No newline at end of file diff --git a/eda_pokemon_sol_22.ipynb b/eda_pokemon_sol_22.ipynb new file mode 100644 index 0000000..7be1f8d --- /dev/null +++ b/eda_pokemon_sol_22.ipynb @@ -0,0 +1,1041 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "eda_pokemon_sol_22.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4-final" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "dzNng6vCL9eP" + }, + "source": [ + "# Machine Learning 2020-2021 - UMONS \n", + "# Exploratory Data Analysis of the Pokemon dataset\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "dU4S-rek_gQe" + }, + "outputs": [], + "source": [ + "import pandas as pd #importing all the important packages\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "plt.style.use('fivethirtyeight')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "id": "hsFUdKmC_gQf", + "outputId": "05723cd3-5387-44f9-d7ae-c10b97d3491d" + }, + "outputs": [ + { + "data": { + "text/plain": " # Name Type 1 Type 2 Total HP Attack Defense \\\n0 1 Bulbasaur Grass Poison 318 45 49 49 \n1 2 Ivysaur Grass Poison 405 60 62 63 \n2 3 Venusaur Grass Poison 525 80 82 83 \n3 3 VenusaurMega Venusaur Grass Poison 625 80 100 123 \n4 4 Charmander Fire NaN 309 39 52 43 \n5 5 Charmeleon Fire NaN 405 58 64 58 \n6 6 Charizard Fire Flying 534 78 84 78 \n7 6 CharizardMega Charizard X Fire Dragon 634 78 130 111 \n8 6 CharizardMega Charizard Y Fire Flying 634 78 104 78 \n9 7 Squirtle Water NaN 314 44 48 65 \n\n Sp. Atk Sp. Def Speed Generation Legendary \n0 65 65 45 1 False \n1 80 80 60 1 False \n2 100 100 80 1 False \n3 122 120 80 1 False \n4 60 50 65 1 False \n5 80 65 80 1 False \n6 109 85 100 1 False \n7 130 85 100 1 False \n8 159 115 100 1 False \n9 50 64 43 1 False ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
#NameType 1Type 2TotalHPAttackDefenseSp. AtkSp. DefSpeedGenerationLegendary
01BulbasaurGrassPoison3184549496565451False
12IvysaurGrassPoison4056062638080601False
23VenusaurGrassPoison525808283100100801False
33VenusaurMega VenusaurGrassPoison62580100123122120801False
44CharmanderFireNaN3093952436050651False
55CharmeleonFireNaN4055864588065801False
66CharizardFireFlying534788478109851001False
76CharizardMega Charizard XFireDragon63478130111130851001False
86CharizardMega Charizard YFireFlying63478104781591151001False
97SquirtleWaterNaN3144448655064431False
\n
" + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('data/Pokemon.csv') #read the csv file and save it into a variable\n", + "df.head(n=10) #print the first 10 rows of the table\n" + ] + }, + { + "source": [ + "Your turn! Make some exploratory data analysis and cleaning of the Pokemon dataset " + ], + "cell_type": "markdown", + "metadata": { + "id": "Ux0TXMe0_gQg" + } + }, + { + "source": [ + "- #: ID for each pokemon\n", + "- Name: Name of each pokemon\n", + "- Type 1: Each pokemon has a type, this determines weakness/resistance to attacks\n", + "- Type 2: Some pokemon are dual type and have 2\n", + "- Total: sum of all stats that come after this, a general guide to how strong a pokemon is\n", + "- HP: hit points, or health, defines how much damage a pokemon can withstand before fainting\n", + "- Attack: the base modifier for normal attacks (eg. Scratch, Punch)\n", + "- Defense: the base damage resistance against normal attacks\n", + "- SP Atk: special attack, the base modifier for special attacks (e.g. fire blast, bubble beam)\n", + "- SP Def: the base damage resistance against special attacks\n", + "- Speed: determines which pokemon attacks first each round" + ], + "cell_type": "markdown", + "metadata": { + "id": "QMr1DM5q_gQg" + } + }, + { + "cell_type": "markdown", + "source": [ + "1) Print the general information of the Pokemon dataset. " + ], + "metadata": { + "id": "eLriTZtKBDTP" + } + }, + { + "cell_type": "code", + "source": [ + "df.info()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yv1vhgCkAepn", + "outputId": "50fb7dfd-8b26-4566-cd2d-723730fc1cf3" + }, + "execution_count": 11, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 800 entries, 0 to 799\n", + "Data columns (total 13 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 # 800 non-null int64 \n", + " 1 Name 800 non-null object\n", + " 2 Type 1 800 non-null object\n", + " 3 Type 2 414 non-null object\n", + " 4 Total 800 non-null int64 \n", + " 5 HP 800 non-null int64 \n", + " 6 Attack 800 non-null int64 \n", + " 7 Defense 800 non-null int64 \n", + " 8 Sp. Atk 800 non-null int64 \n", + " 9 Sp. Def 800 non-null int64 \n", + " 10 Speed 800 non-null int64 \n", + " 11 Generation 800 non-null int64 \n", + " 12 Legendary 800 non-null bool \n", + "dtypes: bool(1), int64(9), object(3)\n", + "memory usage: 75.9+ KB\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "2) Get the shape of the dataframe. " + ], + "metadata": { + "id": "s1pHx3oR6w5G" + } + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XVRlqBq2_gQh", + "outputId": "85424ccb-868e-4f46-b4f7-f762a5a73c13" + }, + "outputs": [ + { + "data": { + "text/plain": "(800, 13)" + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "markdown", + "source": [ + "3) Drop the \"#' column and set the dataframe index to the 'Name' column. " + ], + "metadata": { + "id": "VuqDhanYR0v4" + } + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 238 + }, + "id": "nr5scIgK_gQi", + "outputId": "ad876841-fc3f-430d-f3a7-6c2a11eff76c" + }, + "outputs": [ + { + "data": { + "text/plain": " Type 1 Type 2 Total HP Attack Defense Sp. Atk \\\nName \nBulbasaur Grass Poison 318 45 49 49 65 \nIvysaur Grass Poison 405 60 62 63 80 \nVenusaur Grass Poison 525 80 82 83 100 \nVenusaurMega Venusaur Grass Poison 625 80 100 123 122 \nCharmander Fire NaN 309 39 52 43 60 \n\n Sp. Def Speed Generation Legendary \nName \nBulbasaur 65 45 1 False \nIvysaur 80 60 1 False \nVenusaur 100 80 1 False \nVenusaurMega Venusaur 120 80 1 False \nCharmander 50 65 1 False ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Type 1Type 2TotalHPAttackDefenseSp. AtkSp. DefSpeedGenerationLegendary
Name
BulbasaurGrassPoison3184549496565451False
IvysaurGrassPoison4056062638080601False
VenusaurGrassPoison525808283100100801False
VenusaurMega VenusaurGrassPoison62580100123122120801False
CharmanderFireNaN3093952436050651False
\n
" + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.drop(['#'], axis = 1, inplace = True) #drop the '#' column. \n", + "df.set_index('Name', inplace=True) #Set 'Name' as index.\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "source": [ + "4) Check if there are any missing values in the dataframe, and count them per column. For the categorical variables, replace the missing values by 'None'. For the remaining variables, drop the complete row containing the missing value. Check that the dataframe does not contain missing values anymore. " + ], + "metadata": { + "id": "DmeOC9UQaEo1" + } + }, + { + "cell_type": "code", + "source": [ + "#Count missing values \n", + "df.isna().sum()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8V7lioYdaNf9", + "outputId": "58b810aa-a87a-4209-e3c7-03eb3360e3ce" + }, + "execution_count": 14, + "outputs": [ + { + "data": { + "text/plain": "Type 1 0\nType 2 386\nTotal 0\nHP 0\nAttack 0\nDefense 0\nSp. Atk 0\nSp. Def 0\nSpeed 0\nGeneration 0\nLegendary 0\ndtype: int64" + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ] + }, + { + "cell_type": "code", + "source": [ + "#Replace missing values for the categorical variable 'Type 2' with 'None'.\n", + "df['Type 2'].fillna('None', inplace=True)\n", + "#Drop the complete row that contains at least one missing value for the remaining variables. \n", + "df.dropna(axis=0, inplace=True)\n", + "#Check that the dataframe does not contain any more missing values. \n", + "assert ~df.isna().values.any()\n", + "df.isna().sum()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1N41x3Lu8nAR", + "outputId": "8269f452-2535-413c-c0a3-99b4a15d2718" + }, + "execution_count": 15, + "outputs": [ + { + "data": { + "text/plain": "Type 1 0\nType 2 0\nTotal 0\nHP 0\nAttack 0\nDefense 0\nSp. Atk 0\nSp. Def 0\nSpeed 0\nGeneration 0\nLegendary 0\ndtype: int64" + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "5) Change the data types of 'Type 1' and 'Type 2' and 'Generation' to categorical data." + ], + "metadata": { + "id": "hJiIx80ZFODG" + } + }, + { + "cell_type": "code", + "source": [ + "df = df.astype({'Type 1': 'category', 'Type 2': 'category', 'Generation' : 'category'})\n", + "df.info()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "N3QXeTJOEWp6", + "outputId": "259240ad-95d5-485c-f110-6d9a061dde5f" + }, + "execution_count": 16, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 800 entries, Bulbasaur to Volcanion\n", + "Data columns (total 11 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Type 1 800 non-null category\n", + " 1 Type 2 800 non-null category\n", + " 2 Total 800 non-null int64 \n", + " 3 HP 800 non-null int64 \n", + " 4 Attack 800 non-null int64 \n", + " 5 Defense 800 non-null int64 \n", + " 6 Sp. Atk 800 non-null int64 \n", + " 7 Sp. Def 800 non-null int64 \n", + " 8 Speed 800 non-null int64 \n", + " 9 Generation 800 non-null category\n", + " 10 Legendary 800 non-null bool \n", + "dtypes: bool(1), category(3), int64(7)\n", + "memory usage: 54.7+ KB\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "6) Get general statistics (mean, standard deviation,...) for the numerical attributes of the dataset. " + ], + "metadata": { + "id": "sfk3xosFZr3p" + } + }, + { + "cell_type": "code", + "source": [ + "df.describe()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 300 + }, + "id": "ovW_8SlcZ1Q6", + "outputId": "aa1d60e7-2fb7-48c8-87ef-324b8f4ca775" + }, + "execution_count": 17, + "outputs": [ + { + "data": { + "text/plain": " Total HP Attack Defense Sp. Atk Sp. Def \\\ncount 800.00000 800.000000 800.000000 800.000000 800.000000 800.000000 \nmean 435.10250 69.258750 79.001250 73.842500 72.820000 71.902500 \nstd 119.96304 25.534669 32.457366 31.183501 32.722294 27.828916 \nmin 180.00000 1.000000 5.000000 5.000000 10.000000 20.000000 \n25% 330.00000 50.000000 55.000000 50.000000 49.750000 50.000000 \n50% 450.00000 65.000000 75.000000 70.000000 65.000000 70.000000 \n75% 515.00000 80.000000 100.000000 90.000000 95.000000 90.000000 \nmax 780.00000 255.000000 190.000000 230.000000 194.000000 230.000000 \n\n Speed \ncount 800.000000 \nmean 68.277500 \nstd 29.060474 \nmin 5.000000 \n25% 45.000000 \n50% 65.000000 \n75% 90.000000 \nmax 180.000000 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
TotalHPAttackDefenseSp. AtkSp. DefSpeed
count800.00000800.000000800.000000800.000000800.000000800.000000800.000000
mean435.1025069.25875079.00125073.84250072.82000071.90250068.277500
std119.9630425.53466932.45736631.18350132.72229427.82891629.060474
min180.000001.0000005.0000005.00000010.00000020.0000005.000000
25%330.0000050.00000055.00000050.00000049.75000050.00000045.000000
50%450.0000065.00000075.00000070.00000065.00000070.00000065.000000
75%515.0000080.000000100.00000090.00000095.00000090.00000090.000000
max780.00000255.000000190.000000230.000000194.000000230.000000180.000000
\n
" + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "7) For the categorical variables, count the number of values per category, as well as the count of co-occurences." + ], + "metadata": { + "id": "wTEEsbxsatcg" + } + }, + { + "cell_type": "code", + "source": [ + "df['Type 1'].value_counts()\n", + "df['Type 2'].value_counts()\n", + "df['Generation'].value_counts()\n", + "df[{'Type 1', 'Type 2', 'Generation'}].value_counts()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tLqsNREPa9rP", + "outputId": "d792c370-2bde-4a63-ee65-99f8a55d7de6" + }, + "execution_count": 18, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_11630/368321180.py:4: FutureWarning: Passing a set as an indexer is deprecated and will raise in a future version. Use a list instead.\n", + " df[{'Type 1', 'Type 2', 'Generation'}].value_counts()\n" + ] + }, + { + "data": { + "text/plain": "Generation Type 2 Type 1 \n1 None Water 19\n3 None Normal 14\n1 None Normal 13\n3 None Water 12\n4 None Normal 12\n ..\n Flying Ground 1\n Water 1\n Ghost Electric 1\n Ice 1\n Fairy Psychic 1\nLength: 293, dtype: int64" + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "8) Get all the attributes of 'Bulbasaur'" + ], + "metadata": { + "id": "jyAToWqlbrJt" + } + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IHcc1OnP_gQj", + "outputId": "5d5ec6ae-13e6-4270-82a5-f0035fc3a005" + }, + "outputs": [ + { + "data": { + "text/plain": "Type 1 Grass\nType 2 Poison\nTotal 318\nHP 45\nAttack 49\nDefense 49\nSp. Atk 65\nSp. Def 65\nSpeed 45\nGeneration 1\nLegendary False\nName: Bulbasaur, dtype: object" + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.loc[\"Bulbasaur\"]" + ] + }, + { + "cell_type": "markdown", + "source": [ + "9) Sort the dataframe by increasing values of 'Attack' and decreasing values of 'Defense' (i.e. if two Pokemons have the same value for 'Attack', the one with higher 'Defense' should appear first). " + ], + "metadata": { + "id": "rnP-WJCAueWI" + } + }, + { + "cell_type": "code", + "source": [ + "df = df.sort_values(by=['Attack', 'Defense'], ascending=[True, False])\n", + "df.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 238 + }, + "id": "21AqqLSNulmH", + "outputId": "e15ed048-91d4-40b2-ec71-0b2a32b3afdf" + }, + "execution_count": 20, + "outputs": [ + { + "data": { + "text/plain": " Type 1 Type 2 Total HP Attack Defense Sp. Atk Sp. Def Speed \\\nName \nChansey Normal None 450 250 5 5 35 105 50 \nHappiny Normal None 220 100 5 5 15 65 30 \nShuckle Bug Rock 505 20 10 230 10 230 5 \nMagikarp Water None 200 20 10 55 15 20 80 \nBlissey Normal None 540 255 10 10 75 135 55 \n\n Generation Legendary \nName \nChansey 1 False \nHappiny 4 False \nShuckle 2 False \nMagikarp 1 False \nBlissey 2 False ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Type 1Type 2TotalHPAttackDefenseSp. AtkSp. DefSpeedGenerationLegendary
Name
ChanseyNormalNone4502505535105501False
HappinyNormalNone220100551565304False
ShuckleBugRock50520102301023052False
MagikarpWaterNone2002010551520801False
BlisseyNormalNone540255101075135552False
\n
" + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "10) Create a dataframe containing all Pokemons of type 1 'Psychic' having more than 100 in 'Attack', less than 40 in 'Defense' and more than 45 in Speed." + ], + "metadata": { + "id": "qxOAc1B_mDUr" + } + }, + { + "cell_type": "code", + "source": [ + "sub_df = df[(df['Type 1'] == 'Psychic') & (df['Attack'] > 100) & (df['Defense'] < 80) & (df['Speed'] > 45)]\n", + "sub_df.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 238 + }, + "id": "vOT7IlvPmk9L", + "outputId": "07df5887-1887-4931-8934-c4c0a29a90e0" + }, + "execution_count": 21, + "outputs": [ + { + "data": { + "text/plain": " Type 1 Type 2 Total HP Attack Defense Sp. Atk \\\nName \nHoopaHoopa Confined Psychic Ghost 600 80 110 60 150 \nAzelf Psychic None 580 75 125 70 125 \nGallade Psychic Fighting 518 68 125 65 65 \nMewtwoMega Mewtwo Y Psychic None 780 106 150 70 194 \nDeoxysNormal Forme Psychic None 600 50 150 50 150 \n\n Sp. Def Speed Generation Legendary \nName \nHoopaHoopa Confined 130 70 6 True \nAzelf 70 115 4 True \nGallade 115 80 4 False \nMewtwoMega Mewtwo Y 120 140 1 True \nDeoxysNormal Forme 50 150 3 True ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Type 1Type 2TotalHPAttackDefenseSp. AtkSp. DefSpeedGenerationLegendary
Name
HoopaHoopa ConfinedPsychicGhost6008011060150130706True
AzelfPsychicNone5807512570125701154True
GalladePsychicFighting518681256565115804False
MewtwoMega Mewtwo YPsychicNone780106150701941201401True
DeoxysNormal FormePsychicNone6005015050150501503True
\n
" + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "11) Create two new columns, 'AttackAll' and 'DefenseAll' which take the sum of Attack and Sp. Attack, and the sum of 'Defense' and 'Sp. Defense' respectively. " + ], + "metadata": { + "id": "OI07YycPoP9u" + } + }, + { + "cell_type": "code", + "source": [ + "df['AttackAll'] = df['Attack'] + df['Sp. Atk']\n", + "df['DefenseAll'] = df['Defense'] + df['Sp. Def']\n", + "df.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 238 + }, + "id": "6Mur9aCQp0o9", + "outputId": "a5aa4596-07be-4a3a-d1d2-3f5b1f0f07ce" + }, + "execution_count": 22, + "outputs": [ + { + "data": { + "text/plain": " Type 1 Type 2 Total HP Attack Defense Sp. Atk Sp. Def Speed \\\nName \nChansey Normal None 450 250 5 5 35 105 50 \nHappiny Normal None 220 100 5 5 15 65 30 \nShuckle Bug Rock 505 20 10 230 10 230 5 \nMagikarp Water None 200 20 10 55 15 20 80 \nBlissey Normal None 540 255 10 10 75 135 55 \n\n Generation Legendary AttackAll DefenseAll \nName \nChansey 1 False 40 110 \nHappiny 4 False 20 70 \nShuckle 2 False 20 460 \nMagikarp 1 False 25 75 \nBlissey 2 False 85 145 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Type 1Type 2TotalHPAttackDefenseSp. AtkSp. DefSpeedGenerationLegendaryAttackAllDefenseAll
Name
ChanseyNormalNone4502505535105501False40110
HappinyNormalNone220100551565304False2070
ShuckleBugRock50520102301023052False20460
MagikarpWaterNone2002010551520801False2575
BlisseyNormalNone540255101075135552False85145
\n
" + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "12) Write a generic function taking the ratio of two values 'a' and 'b'. Use this function to create a new column 'AtkOverDef' giving the ratio of 'AttackAll' over 'DefenseAll' for each Pokemon. " + ], + "metadata": { + "id": "ZCNx4IaIq3o-" + } + }, + { + "cell_type": "code", + "source": [ + "def ratio(a,b):\n", + " return a/b \n", + "\n", + "df['AttkOverDef'] = ratio(df['AttackAll'], df['DefenseAll'])\n", + "#Divisions by 0 are automatically mapped ton 'inf'. \n", + "df.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 238 + }, + "id": "Gp9fpMMIsD_n", + "outputId": "4f7ed15f-769d-46ab-8c37-98652e29f3b0" + }, + "execution_count": 23, + "outputs": [ + { + "data": { + "text/plain": " Type 1 Type 2 Total HP Attack Defense Sp. Atk Sp. Def Speed \\\nName \nChansey Normal None 450 250 5 5 35 105 50 \nHappiny Normal None 220 100 5 5 15 65 30 \nShuckle Bug Rock 505 20 10 230 10 230 5 \nMagikarp Water None 200 20 10 55 15 20 80 \nBlissey Normal None 540 255 10 10 75 135 55 \n\n Generation Legendary AttackAll DefenseAll AttkOverDef \nName \nChansey 1 False 40 110 0.363636 \nHappiny 4 False 20 70 0.285714 \nShuckle 2 False 20 460 0.043478 \nMagikarp 1 False 25 75 0.333333 \nBlissey 2 False 85 145 0.586207 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Type 1Type 2TotalHPAttackDefenseSp. AtkSp. DefSpeedGenerationLegendaryAttackAllDefenseAllAttkOverDef
Name
ChanseyNormalNone4502505535105501False401100.363636
HappinyNormalNone220100551565304False20700.285714
ShuckleBugRock50520102301023052False204600.043478
MagikarpWaterNone2002010551520801False25750.333333
BlisseyNormalNone540255101075135552False851450.586207
\n
" + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "13) Change the column names to upper cases, and remove the '.' in the column names, as well as blanks. " + ], + "metadata": { + "id": "YJfaI8VKv64S" + } + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 292 + }, + "id": "I89576fk_gQk", + "outputId": "a5460673-406d-47d8-e376-e1e6d3220c77" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_11630/3854021541.py:1: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n", + " df.columns = df.columns.str.upper().str.replace('.', '').str.replace(' ', '') #change into upper case\n" + ] + }, + { + "data": { + "text/plain": " TYPE1 TYPE2 TOTAL HP ATTACK DEFENSE SPATK SPDEF SPEED \\\nName \nChansey Normal None 450 250 5 5 35 105 50 \nHappiny Normal None 220 100 5 5 15 65 30 \nShuckle Bug Rock 505 20 10 230 10 230 5 \nMagikarp Water None 200 20 10 55 15 20 80 \nBlissey Normal None 540 255 10 10 75 135 55 \n\n GENERATION LEGENDARY ATTACKALL DEFENSEALL ATTKOVERDEF \nName \nChansey 1 False 40 110 0.363636 \nHappiny 4 False 20 70 0.285714 \nShuckle 2 False 20 460 0.043478 \nMagikarp 1 False 25 75 0.333333 \nBlissey 2 False 85 145 0.586207 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
TYPE1TYPE2TOTALHPATTACKDEFENSESPATKSPDEFSPEEDGENERATIONLEGENDARYATTACKALLDEFENSEALLATTKOVERDEF
Name
ChanseyNormalNone4502505535105501False401100.363636
HappinyNormalNone220100551565304False20700.285714
ShuckleBugRock50520102301023052False204600.043478
MagikarpWaterNone2002010551520801False25750.333333
BlisseyNormalNone540255101075135552False851450.586207
\n
" + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns = df.columns.str.upper().str.replace('.', '').str.replace(' ', '') #change into upper case\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "source": [ + "14) Plot an histogram of the different 'TYPE1' categories. The figure must be 8 inch wide and 4 inch high. \n", + "Use the matplotlib.pyplot library and the countplot method from the seaborn librabry. The counts should appear in increasing order." + ], + "metadata": { + "id": "oemote1Dwiil" + } + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 304 + }, + "id": "Ykf0mI1J_gQk", + "outputId": "4da7416a-c3de-49d2-db86-95a03e3df593" + }, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": "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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(16,4)) # this creates a figure 8 inch wide, 4 inch high\n", + "sns.countplot(x='TYPE1', data=df, order = df.TYPE1.value_counts().index)\n", + "#sns.countplot(x='TYP1', data=df)\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "15) Do the same as above, but for the 'TYPE2' categories. " + ], + "metadata": { + "id": "Sd7rlq5RxgNa" + } + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 306 + }, + "id": "PcWnQOBh_gQk", + "outputId": "4928077c-9051-42db-e2a2-8dc86e96f21e" + }, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": "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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(16,4)) # this creates a figure 8 inch wide, 4 inch high\n", + "sns.countplot(x='TYPE2', data=df, order = df.TYPE2.value_counts().index)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "source": [ + "16) Plot the densities of the variables 'Attack', 'Defenses' and 'Speed' onto three separates plots. Use the displot method of the library seaborn. " + ], + "metadata": { + "id": "HJQnPxOy0Cr0" + } + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "57Gmsp9x_gQm", + "outputId": "38abfdbf-81ea-43be-b7ba-56735d7f002b" + }, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": "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\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "
", + "image/png": "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\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "
", + "image/png": "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\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "
", + "image/png": "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\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "
", + "image/png": "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\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "
", + "image/png": "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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.displot(df['ATTACK'], color='red',kind='kde')\n", + "sns.displot(df['DEFENSE'], color='blue', kind='kde')\n", + "sns.displot(df['SPEED'], color='green', kind='kde')\n", + "#OR#\n", + "sns.displot(df['ATTACK'], color='red',kde=True)\n", + "sns.displot(df['DEFENSE'], color='blue', kde=True)\n", + "sns.displot(df['SPEED'], color='green', kde=True)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "17) Plot the density of the variable 'ATTACK' for Legendary and non Legendary pokemons. The two densities should appear on different facets of the same plot. " + ], + "metadata": { + "id": "8fZ4qb_N1QCH" + } + }, + { + "cell_type": "code", + "source": [ + "sns.displot(data=df, x='ATTACK', color='red',kind='kde', col='LEGENDARY')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 378 + }, + "id": "BV6m-Z380uDl", + "outputId": "c147bd2e-c7fa-4ba4-c593-a049f7064fa2" + }, + "execution_count": 28, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": "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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "18a) Generate a scatter plot of the variable 'Defense' on the y-axis, and the variable 'Attack' on the x-axis. Also, plot a simple linear regression model between the two variables on the same plot. Use the method lmplot of the library seaborn. \n", + "\n", + "18b) Do the same, but with the variable 'Total' in the y-axis. " + ], + "metadata": { + "id": "IwSqLytJ12_K" + } + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "ubOms2Hk_gQm", + "outputId": "d32debc7-e03e-4540-b384-071a02b6cceb" + }, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": "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\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "
", + "image/png": "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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.lmplot(x='ATTACK', y='DEFENSE', data=df, height=7)\n", + "sns.lmplot(x='ATTACK', y='TOTAL', data=df, height=7)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "19) Create an histogram of the variable 'GENERATION'. The counts should appear in increasing order. " + ], + "metadata": { + "id": "Ufr2rxpd2xgh" + } + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 304 + }, + "id": "Qk7RrmFt_gQm", + "outputId": "c905a67a-6c74-444c-95fe-474f3b639af2" + }, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": "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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "plt.figure(figsize=(16,4)) # this creates a figure 8 inch wide, 4 inch high\n", + "sns.countplot(x='GENERATION', data=df, order = df.GENERATION.value_counts().index)\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "20) Generate a boxplot of the variable 'TOTAL'. Use the method boxplot from the library seaborn. " + ], + "metadata": { + "id": "YszwDTeJ3HyN" + } + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 283 + }, + "id": "lCREREg7_gQn", + "outputId": "375ecf14-716f-40d3-8c13-1e1b319deb8b" + }, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": "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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.boxplot(y='TOTAL', data=df)\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "21) Generate one boxplot of the variable 'TOTAL' per category of the variable 'GENERATION'. All boxplots must appear on the same plot. " + ], + "metadata": { + "id": "PZeGkhZu3Y8i" + } + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 323 + }, + "id": "_kQ2saC6_gQn", + "outputId": "253874dc-2728-4c40-88a1-f4c58e984ebd" + }, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": "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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.boxplot(x='GENERATION', y='TOTAL', data=df)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "id": "-ev_yh3g_gQn" + }, + "outputs": [], + "source": [ + "" + ] + } + ] +} \ No newline at end of file diff --git a/main.py b/main.py new file mode 100644 index 0000000..1079a2c --- /dev/null +++ b/main.py @@ -0,0 +1,2 @@ +import pandas as pd +print("d")