{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "[Go back to the main page](https://leonpalafox.github.io/MLClass/)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data Science Tutorial" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial is designed to show the students of PTYS 595B how ti install and play with basic python libraries.\n", "\n", "The data for the GOT example is in: https://github.com/leonpalafox/MLClass/tree/master/Chapter1Introduction/data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Programming requeriments" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Python Programming Language.\n", " - Drive Stick (https://www.python.org/downloads/, Version 2.7)\n", " - Canopy (https://www.enthought.com/academic-subscriptions/index.html)\n", " \n", "- Packages (Canopy data manager or PIP (https://pip.pypa.io/en/stable/))\n", " - Pandas for data processing (http://pandas.pydata.org/)\n", " - Matplotlib, Numpy and Scipy (http://www.scipy.org/install.html)\n", " - Scikit-Learn (http://scikit-learn.org/stable/install.html)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Python examples" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np #Means that we will import all the functions from the numpy package\n", "import pylab as plt\n", "import pandas as pd\n", "%matplotlib inline #Don't use this command in canopy or in python" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n" ] } ], "source": [ "a = 1\n", "b = 2\n", "c = a + b\n", "print c" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n" ] } ], "source": [ "m = np.zeros((10,10))\n", "print m" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", " [ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", " [ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", " [ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", " [ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", " [ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", " [ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", " [ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", " [ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", " [ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]\n" ] } ], "source": [ "m = np.ones((10,10))\n", "print m" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. 1.1 1.2 1.3 1.4\n", " 1.5 1.6 1.7 1.8 1.9 2. 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9\n", " 3. 3.1]\n" ] } ], "source": [ "t = np.arange(0,3.15,0.1)\n", "print t" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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9uwMDwOgosHhx9m3bRJSS2JZxkbKP6YhSaWwELl0C7t3jaqEsaW8HvvrVbNt0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\n0js60gKtAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x =np.sin(2*np.pi*t)\n", "plt.plot(t, x)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t = np.arange(0,3.15,0.01)\n", "x =np.sin(2*np.pi*t)\n", "plt.plot(t, x)\n", "#plt.show() Use this command in canopy" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = pd.read_csv('data/5kings_battles_v1.csv') #windows or mac, be careful \\/" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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nameyearbattle_numberattacker_kingdefender_kingattacker_1attacker_2attacker_3attacker_4defender_1...major_deathmajor_captureattacker_sizedefender_sizeattacker_commanderdefender_commandersummerlocationregionnote
0Battle of the Golden Tooth2981Joffrey/Tommen BaratheonRobb StarkLannisterNaNNaNNaNTully...1.00.015000.04000.0Jaime LannisterClement Piper, Vance1.0Golden ToothThe WesterlandsNaN
1Battle at the Mummer's Ford2982Joffrey/Tommen BaratheonRobb StarkLannisterNaNNaNNaNBaratheon...1.00.0NaN120.0Gregor CleganeBeric Dondarrion1.0Mummer's FordThe RiverlandsNaN
2Battle of Riverrun2983Joffrey/Tommen BaratheonRobb StarkLannisterNaNNaNNaNTully...0.01.015000.010000.0Jaime Lannister, Andros BraxEdmure Tully, Tytos Blackwood1.0RiverrunThe RiverlandsNaN
3Battle of the Green Fork2984Robb StarkJoffrey/Tommen BaratheonStarkNaNNaNNaNLannister...1.01.018000.020000.0Roose Bolton, Wylis Manderly, Medger Cerwyn, H...Tywin Lannister, Gregor Clegane, Kevan Lannist...1.0Green ForkThe RiverlandsNaN
4Battle of the Whispering Wood2985Robb StarkJoffrey/Tommen BaratheonStarkTullyNaNNaNLannister...1.01.01875.06000.0Robb Stark, Brynden TullyJaime Lannister1.0Whispering WoodThe RiverlandsNaN
\n", "

5 rows × 25 columns

\n", "
" ], "text/plain": [ " name year battle_number \\\n", "0 Battle of the Golden Tooth 298 1 \n", "1 Battle at the Mummer's Ford 298 2 \n", "2 Battle of Riverrun 298 3 \n", "3 Battle of the Green Fork 298 4 \n", "4 Battle of the Whispering Wood 298 5 \n", "\n", " attacker_king defender_king attacker_1 attacker_2 \\\n", "0 Joffrey/Tommen Baratheon Robb Stark Lannister NaN \n", "1 Joffrey/Tommen Baratheon Robb Stark Lannister NaN \n", "2 Joffrey/Tommen Baratheon Robb Stark Lannister NaN \n", "3 Robb Stark Joffrey/Tommen Baratheon Stark NaN \n", "4 Robb Stark Joffrey/Tommen Baratheon Stark Tully \n", "\n", " attacker_3 attacker_4 defender_1 ... major_death major_capture \\\n", "0 NaN NaN Tully ... 1.0 0.0 \n", "1 NaN NaN Baratheon ... 1.0 0.0 \n", "2 NaN NaN Tully ... 0.0 1.0 \n", "3 NaN NaN Lannister ... 1.0 1.0 \n", "4 NaN NaN Lannister ... 1.0 1.0 \n", "\n", " attacker_size defender_size \\\n", "0 15000.0 4000.0 \n", "1 NaN 120.0 \n", "2 15000.0 10000.0 \n", "3 18000.0 20000.0 \n", "4 1875.0 6000.0 \n", "\n", " attacker_commander \\\n", "0 Jaime Lannister \n", "1 Gregor Clegane \n", "2 Jaime Lannister, Andros Brax \n", "3 Roose Bolton, Wylis Manderly, Medger Cerwyn, H... \n", "4 Robb Stark, Brynden Tully \n", "\n", " defender_commander summer location \\\n", "0 Clement Piper, Vance 1.0 Golden Tooth \n", "1 Beric Dondarrion 1.0 Mummer's Ford \n", "2 Edmure Tully, Tytos Blackwood 1.0 Riverrun \n", "3 Tywin Lannister, Gregor Clegane, Kevan Lannist... 1.0 Green Fork \n", "4 Jaime Lannister 1.0 Whispering Wood \n", "\n", " region note \n", "0 The Westerlands NaN \n", "1 The Riverlands NaN \n", "2 The Riverlands NaN \n", "3 The Riverlands NaN \n", "4 The Riverlands NaN \n", "\n", "[5 rows x 25 columns]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we want to search only for elements that had two attackers" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 NaN\n", "1 NaN\n", "2 NaN\n", "3 NaN\n", "4 Tully\n", "5 Tully\n", "6 NaN\n", "7 NaN\n", "8 NaN\n", "9 NaN\n", "10 NaN\n", "11 NaN\n", "12 NaN\n", "13 Greyjoy\n", "14 Tully\n", "15 NaN\n", "16 NaN\n", "17 NaN\n", "18 NaN\n", "19 NaN\n", "20 NaN\n", "21 NaN\n", "22 NaN\n", "23 NaN\n", "24 NaN\n", "25 Bolton\n", "26 NaN\n", "27 Thenns\n", "28 NaN\n", "29 NaN\n", "30 Karstark\n", "31 NaN\n", "32 NaN\n", "33 NaN\n", "34 NaN\n", "35 Frey\n", "36 Lannister\n", "37 Karstark\n", "Name: attacker_2, dtype: object" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['attacker_2']" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4 Tully\n", "5 Tully\n", "13 Greyjoy\n", "14 Tully\n", "25 Bolton\n", "27 Thenns\n", "30 Karstark\n", "35 Frey\n", "36 Lannister\n", "37 Karstark\n", "Name: attacker_2, dtype: object" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['attacker_2'].dropna()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 False\n", "1 False\n", "2 False\n", "3 False\n", "4 True\n", "5 True\n", "6 False\n", "7 False\n", "8 False\n", "9 False\n", "10 False\n", "11 False\n", "12 False\n", "13 True\n", "14 True\n", "15 False\n", "16 False\n", "17 False\n", "18 False\n", "19 False\n", "20 False\n", "21 False\n", "22 False\n", "23 False\n", "24 False\n", "25 True\n", "26 False\n", "27 True\n", "28 False\n", "29 False\n", "30 True\n", "31 False\n", "32 False\n", "33 False\n", "34 False\n", "35 True\n", "36 True\n", "37 True\n", "Name: attacker_2, dtype: bool" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.notnull(df['attacker_2'])" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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nameyearbattle_numberattacker_kingdefender_kingattacker_1attacker_2attacker_3attacker_4defender_1...major_deathmajor_captureattacker_sizedefender_sizeattacker_commanderdefender_commandersummerlocationregionnote
4Battle of the Whispering Wood2985Robb StarkJoffrey/Tommen BaratheonStarkTullyNaNNaNLannister...1.01.01875.06000.0Robb Stark, Brynden TullyJaime Lannister1.0Whispering WoodThe RiverlandsNaN
5Battle of the Camps2986Robb StarkJoffrey/Tommen BaratheonStarkTullyNaNNaNLannister...0.00.06000.012625.0Robb Stark, Tytos Blackwood, Brynden TullyLord Andros Brax, Forley Prester1.0RiverrunThe RiverlandsNaN
13Sack of Winterfell29914Joffrey/Tommen BaratheonRobb StarkBoltonGreyjoyNaNNaNStark...1.00.0618.02000.0Ramsay Snow, Theon GreyjoyRodrik Cassel, Cley Cerwyn, Leobald Tallhart1.0WinterfellThe NorthSince House Bolton betrays the Starks for Hous...
14Battle of Oxcross29915Robb StarkJoffrey/Tommen BaratheonStarkTullyNaNNaNLannister...1.01.06000.010000.0Robb Stark, Brynden TullyStafford Lannister, Roland Crakehall, Antario ...1.0OxcrossThe WesterlandsNaN
25The Red Wedding29926Joffrey/Tommen BaratheonRobb StarkFreyBoltonNaNNaNStark...1.01.03500.03500.0Walder Frey, Roose Bolton, Walder RiversRobb Stark1.0The TwinsThe RiverlandsThis observation refers to the battle against ...
27Battle of Castle Black30028Stannis BaratheonMance RayderFree folkThennsGiantsNaNNight's Watch...1.01.0100000.01240.0Mance Rayder, Tormund Giantsbane, Harma Dogshe...Stannis Baratheon, Jon Snow, Donal Noye, Cotte...0.0Castle BlackBeyond the WallNaN
30Retaking of Deepwood Motte30031Stannis BaratheonBalon/Euron GreyjoyBaratheonKarstarkMormontGloverGreyjoy...0.00.04500.0200.0Stannis Baratheon, Alysane MormotAsha Greyjoy0.0Deepwood MotteThe NorthNaN
35Siege of Riverrun30036Joffrey/Tommen BaratheonRobb StarkLannisterFreyNaNNaNTully...0.00.03000.0NaNDaven Lannister, Ryman Fey, Jaime LannisterBrynden Tully0.0RiverrunThe RiverlandsNaN
36Siege of Raventree30037Joffrey/Tommen BaratheonRobb StarkBrackenLannisterNaNNaNBlackwood...0.01.01500.0NaNJonos Bracken, Jaime LannisterTytos Blackwood0.0RaventreeThe RiverlandsNaN
37Siege of Winterfell30038Stannis BaratheonJoffrey/Tommen BaratheonBaratheonKarstarkMormontGloverBolton...NaNNaN5000.08000.0Stannis BaratheonRoose Bolton0.0WinterfellThe NorthNaN
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10 rows × 25 columns

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" ], "text/plain": [ " name year battle_number \\\n", "4 Battle of the Whispering Wood 298 5 \n", "5 Battle of the Camps 298 6 \n", "13 Sack of Winterfell 299 14 \n", "14 Battle of Oxcross 299 15 \n", "25 The Red Wedding 299 26 \n", "27 Battle of Castle Black 300 28 \n", "30 Retaking of Deepwood Motte 300 31 \n", "35 Siege of Riverrun 300 36 \n", "36 Siege of Raventree 300 37 \n", "37 Siege of Winterfell 300 38 \n", "\n", " attacker_king defender_king attacker_1 attacker_2 \\\n", "4 Robb Stark Joffrey/Tommen Baratheon Stark Tully \n", "5 Robb Stark Joffrey/Tommen Baratheon Stark Tully \n", "13 Joffrey/Tommen Baratheon Robb Stark Bolton Greyjoy \n", "14 Robb Stark Joffrey/Tommen Baratheon Stark Tully \n", "25 Joffrey/Tommen Baratheon Robb Stark Frey Bolton \n", "27 Stannis Baratheon Mance Rayder Free folk Thenns \n", "30 Stannis Baratheon Balon/Euron Greyjoy Baratheon Karstark \n", "35 Joffrey/Tommen Baratheon Robb Stark Lannister Frey \n", "36 Joffrey/Tommen Baratheon Robb Stark Bracken Lannister \n", "37 Stannis Baratheon Joffrey/Tommen Baratheon Baratheon Karstark \n", "\n", " attacker_3 attacker_4 defender_1 \\\n", "4 NaN NaN Lannister \n", "5 NaN NaN Lannister \n", "13 NaN NaN Stark \n", "14 NaN NaN Lannister \n", "25 NaN NaN Stark \n", "27 Giants NaN Night's Watch \n", "30 Mormont Glover Greyjoy \n", "35 NaN NaN Tully \n", "36 NaN NaN Blackwood \n", "37 Mormont Glover Bolton \n", "\n", " ... major_death \\\n", "4 ... 1.0 \n", "5 ... 0.0 \n", "13 ... 1.0 \n", "14 ... 1.0 \n", "25 ... 1.0 \n", "27 ... 1.0 \n", "30 ... 0.0 \n", "35 ... 0.0 \n", "36 ... 0.0 \n", "37 ... NaN \n", "\n", " major_capture attacker_size defender_size \\\n", "4 1.0 1875.0 6000.0 \n", "5 0.0 6000.0 12625.0 \n", "13 0.0 618.0 2000.0 \n", "14 1.0 6000.0 10000.0 \n", "25 1.0 3500.0 3500.0 \n", "27 1.0 100000.0 1240.0 \n", "30 0.0 4500.0 200.0 \n", "35 0.0 3000.0 NaN \n", "36 1.0 1500.0 NaN \n", "37 NaN 5000.0 8000.0 \n", "\n", " attacker_commander \\\n", "4 Robb Stark, Brynden Tully \n", "5 Robb Stark, Tytos Blackwood, Brynden Tully \n", "13 Ramsay Snow, Theon Greyjoy \n", "14 Robb Stark, Brynden Tully \n", "25 Walder Frey, Roose Bolton, Walder Rivers \n", "27 Mance Rayder, Tormund Giantsbane, Harma Dogshe... \n", "30 Stannis Baratheon, Alysane Mormot \n", "35 Daven Lannister, Ryman Fey, Jaime Lannister \n", "36 Jonos Bracken, Jaime Lannister \n", "37 Stannis Baratheon \n", "\n", " defender_commander summer \\\n", "4 Jaime Lannister 1.0 \n", "5 Lord Andros Brax, Forley Prester 1.0 \n", "13 Rodrik Cassel, Cley Cerwyn, Leobald Tallhart 1.0 \n", "14 Stafford Lannister, Roland Crakehall, Antario ... 1.0 \n", "25 Robb Stark 1.0 \n", "27 Stannis Baratheon, Jon Snow, Donal Noye, Cotte... 0.0 \n", "30 Asha Greyjoy 0.0 \n", "35 Brynden Tully 0.0 \n", "36 Tytos Blackwood 0.0 \n", "37 Roose Bolton 0.0 \n", "\n", " location region \\\n", "4 Whispering Wood The Riverlands \n", "5 Riverrun The Riverlands \n", "13 Winterfell The North \n", "14 Oxcross The Westerlands \n", "25 The Twins The Riverlands \n", "27 Castle Black Beyond the Wall \n", "30 Deepwood Motte The North \n", "35 Riverrun The Riverlands \n", "36 Raventree The Riverlands \n", "37 Winterfell The North \n", "\n", " note \n", "4 NaN \n", "5 NaN \n", "13 Since House Bolton betrays the Starks for Hous... \n", "14 NaN \n", "25 This observation refers to the battle against ... \n", "27 NaN \n", "30 NaN \n", "35 NaN \n", "36 NaN \n", "37 NaN \n", "\n", "[10 rows x 25 columns]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[pd.notnull(df['attacker_2'])]" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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nameyearbattle_numberattacker_kingdefender_kingattacker_1attacker_2attacker_3attacker_4defender_1...major_deathmajor_captureattacker_sizedefender_sizeattacker_commanderdefender_commandersummerlocationregionnote
0Battle of the Golden Tooth2981Joffrey/Tommen BaratheonRobb StarkLannisterNaNNaNNaNTully...1.00.015000.04000.0Jaime LannisterClement Piper, Vance1.0Golden ToothThe WesterlandsNaN
1Battle at the Mummer's Ford2982Joffrey/Tommen BaratheonRobb StarkLannisterNaNNaNNaNBaratheon...1.00.0NaN120.0Gregor CleganeBeric Dondarrion1.0Mummer's FordThe RiverlandsNaN
2Battle of Riverrun2983Joffrey/Tommen BaratheonRobb StarkLannisterNaNNaNNaNTully...0.01.015000.010000.0Jaime Lannister, Andros BraxEdmure Tully, Tytos Blackwood1.0RiverrunThe RiverlandsNaN
3Battle of the Green Fork2984Robb StarkJoffrey/Tommen BaratheonStarkNaNNaNNaNLannister...1.01.018000.020000.0Roose Bolton, Wylis Manderly, Medger Cerwyn, H...Tywin Lannister, Gregor Clegane, Kevan Lannist...1.0Green ForkThe RiverlandsNaN
4Battle of the Whispering Wood2985Robb StarkJoffrey/Tommen BaratheonStarkTullyNaNNaNLannister...1.01.01875.06000.0Robb Stark, Brynden TullyJaime Lannister1.0Whispering WoodThe RiverlandsNaN
\n", "

5 rows × 25 columns

\n", "
" ], "text/plain": [ " name year battle_number \\\n", "0 Battle of the Golden Tooth 298 1 \n", "1 Battle at the Mummer's Ford 298 2 \n", "2 Battle of Riverrun 298 3 \n", "3 Battle of the Green Fork 298 4 \n", "4 Battle of the Whispering Wood 298 5 \n", "\n", " attacker_king defender_king attacker_1 attacker_2 \\\n", "0 Joffrey/Tommen Baratheon Robb Stark Lannister NaN \n", "1 Joffrey/Tommen Baratheon Robb Stark Lannister NaN \n", "2 Joffrey/Tommen Baratheon Robb Stark Lannister NaN \n", "3 Robb Stark Joffrey/Tommen Baratheon Stark NaN \n", "4 Robb Stark Joffrey/Tommen Baratheon Stark Tully \n", "\n", " attacker_3 attacker_4 defender_1 ... major_death major_capture \\\n", "0 NaN NaN Tully ... 1.0 0.0 \n", "1 NaN NaN Baratheon ... 1.0 0.0 \n", "2 NaN NaN Tully ... 0.0 1.0 \n", "3 NaN NaN Lannister ... 1.0 1.0 \n", "4 NaN NaN Lannister ... 1.0 1.0 \n", "\n", " attacker_size defender_size \\\n", "0 15000.0 4000.0 \n", "1 NaN 120.0 \n", "2 15000.0 10000.0 \n", "3 18000.0 20000.0 \n", "4 1875.0 6000.0 \n", "\n", " attacker_commander \\\n", "0 Jaime Lannister \n", "1 Gregor Clegane \n", "2 Jaime Lannister, Andros Brax \n", "3 Roose Bolton, Wylis Manderly, Medger Cerwyn, H... \n", "4 Robb Stark, Brynden Tully \n", "\n", " defender_commander summer location \\\n", "0 Clement Piper, Vance 1.0 Golden Tooth \n", "1 Beric Dondarrion 1.0 Mummer's Ford \n", "2 Edmure Tully, Tytos Blackwood 1.0 Riverrun \n", "3 Tywin Lannister, Gregor Clegane, Kevan Lannist... 1.0 Green Fork \n", "4 Jaime Lannister 1.0 Whispering Wood \n", "\n", " region note \n", "0 The Westerlands NaN \n", "1 The Riverlands NaN \n", "2 The Riverlands NaN \n", "3 The Riverlands NaN \n", "4 The Riverlands NaN \n", "\n", "[5 rows x 25 columns]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = df[pd.notnull(df['attacker_2'])]" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df['region'].value_counts().plot(kind='bar')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Go back to the main page](https://leonpalafox.github.io/MLClass/)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }