{ "metadata": { "name": "", "signature": "sha256:40f7b684d8531a2e1f091d7f256c7b63a42e76b4230314fe97b3735c4a4600cd" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Draw a line in ggplot\n", "\n", "- **Author:** [Chris Albon](http://www.chrisalbon.com/), [@ChrisAlbon](https://twitter.com/chrisalbon)\n", "- **Date:** -\n", "- **Repo:** [Python 3 code snippets for data science](https://github.com/chrisalbon/code_py)\n", "- **Note:**" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Import required modules\n", "from ggplot import *\n", "%matplotlib inline\n", "import pandas as pd" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "# Create a dataset of battle data\n", "data = {'battle': ['Battle of Two Forks', 'Battle of Cochise', 'Battle of Bells', 'Battle of the Beach', 'Battle of Flatlander', 'Battle of Middorin', 'Battle of Massai', 'Battle of Monogrop', 'Battle of ', 'Battle of Sticks'], \n", " 'season': ['winter', 'fall', 'fall', 'fall', 'spring', 'winter', 'summer', 'winter', 'summer', 'summer'],\n", " 'terrain': ['mountains', 'mountains', 'mountains', 'beach', 'beach', 'plains', 'plains', 'city', 'city', 'city'],\n", " 'weather': ['rain', 'rain', 'cloudy', 'sunny', 'rain', 'rain', 'sunny', 'cloudy', 'rain', 'sunny'],\n", " 'victor': ['attacker', 'defender', 'attacker', 'defender', 'attacker', 'defender', 'attacker', 'defender', 'attacker', 'defender'],\n", " 'deaths_attacker': [425, 242, 323, 223, 783, 436, 324, 3321, 262, 843],\n", " 'deaths_defender': [423, 264, 1231, 23, 23, 42, 124, 631, 232, 213],\n", " 'wounded_attacker': [41, 214, 131, 12, 123, 124, 264, 311, 132, 623],\n", " 'wounded_defender': [14, 1424, 131, 12, 34, 124, 1124, 1431, 122, 2563],\n", " 'soldiers_attacker': [2532, 6346, 3341, 6732, 12563, 2356, 253, 5277, 2732, 6278],\n", " 'soldiers_defender': [37235, 2523, 2133, 1245, 2671, 7832, 2622, 3331, 2522, 26773]}\n", "df = pd.DataFrame(data)\n", "df" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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battledeaths_attackerdeaths_defenderseasonsoldiers_attackersoldiers_defenderterrainvictorweatherwounded_attackerwounded_defender
0 Battle of Two Forks 425 423 winter 2532 37235 mountains attacker rain 41 14
1 Battle of Cochise 242 264 fall 6346 2523 mountains defender rain 214 1424
2 Battle of Bells 323 1231 fall 3341 2133 mountains attacker cloudy 131 131
3 Battle of the Beach 223 23 fall 6732 1245 beach defender sunny 12 12
4 Battle of Flatlander 783 23 spring 12563 2671 beach attacker rain 123 34
5 Battle of Middorin 436 42 winter 2356 7832 plains defender rain 124 124
6 Battle of Massai 324 124 summer 253 2622 plains attacker sunny 264 1124
7 Battle of Monogrop 3321 631 winter 5277 3331 city defender cloudy 311 1431
8 Battle of 262 232 summer 2732 2522 city attacker rain 132 122
9 Battle of Sticks 843 213 summer 6278 26773 city defender sunny 623 2563
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ " battle deaths_attacker deaths_defender season \\\n", "0 Battle of Two Forks 425 423 winter \n", "1 Battle of Cochise 242 264 fall \n", "2 Battle of Bells 323 1231 fall \n", "3 Battle of the Beach 223 23 fall \n", "4 Battle of Flatlander 783 23 spring \n", "5 Battle of Middorin 436 42 winter \n", "6 Battle of Massai 324 124 summer \n", "7 Battle of Monogrop 3321 631 winter \n", "8 Battle of 262 232 summer \n", "9 Battle of Sticks 843 213 summer \n", "\n", " soldiers_attacker soldiers_defender terrain victor weather \\\n", "0 2532 37235 mountains attacker rain \n", "1 6346 2523 mountains defender rain \n", "2 3341 2133 mountains attacker cloudy \n", "3 6732 1245 beach defender sunny \n", "4 12563 2671 beach attacker rain \n", "5 2356 7832 plains defender rain \n", "6 253 2622 plains attacker sunny \n", "7 5277 3331 city defender cloudy \n", "8 2732 2522 city attacker rain \n", "9 6278 26773 city defender sunny \n", "\n", " wounded_attacker wounded_defender \n", "0 41 14 \n", "1 214 1424 \n", "2 131 131 \n", "3 12 12 \n", "4 123 34 \n", "5 124 124 \n", "6 264 1124 \n", "7 311 1431 \n", "8 132 122 \n", "9 623 2563 " ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "# scatterplot of attacker deaths vs defender deaths, with a line specified by slope and intercept\n", "ggplot(df, aes(x='deaths_attacker', y='deaths_defender')) + \\\n", " geom_point() + \\\n", " geom_abline(intercept=100, slope=0, colour=\"red\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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AABiHEgsAAADjUGIBAABgHEosAAAAjEOJBQAAgHEosQAAADAOJRYAAADGocQCAADAOJRY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\nPXvkcrnU1dXV50xfVH/uCwCcDEosgDP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