docs/scales/scale_color_brewer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from ggplot import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `scale_color_brewer`\n",
    "`scale_color_brewer` applies color palettes to discrete color variables in your ggplots. It has 2 parameters:\n",
    "\n",
    "- `type` - type of palette to use ('diverging/div', 'qualitative/qual', 'sequential/seq')\n",
    "- `palette` - palette number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Jmj59unbs2GEyJgAAAHCSQBTyE7W0tKipqUljx45VMplULBaTdKy0J5NJw+kA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\n9P8B+x/rxuEaBMwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f43bdd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (284441529)>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(mtcars, aes(x='wt', y='mpg', color='factor(cyl)')) + geom_point()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3wgsvqKamZvCizlQqNXhR55lWyHO5nLLZbFlrL4fq6mr19fXZLqPkgsGg6uvr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\nAAAAwCICOQAAAGDR/wNKMjKUf//BqQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f43bed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (285303209)>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(mtcars, aes(x='wt', y='mpg', color='factor(cyl)')) + \\\n",
    "    geom_point() + \\\n",
    "    scale_color_brewer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lCjlGLRMcKxn/iQv+vssAAAAjjCUrGLX8ZdUK1y6Xk+7IryEHAAAYaRRyjFrG+PrWjLNu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\nRCEHAAAALKKQAwAAABZRyAEAAACL/g/7v6czWAeykgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11019b750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (285330073)>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(mtcars, aes(x='wt', y='mpg', color='factor(cyl)')) + \\\n",
    "    geom_point() + \\\n",
    "    scale_color_brewer(type='div')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CjjHLraqVa4IDp01QblWt5UQAAGAsYskKxiwnXq9samXpGnIAAIARRiHH2GUCA2vGWTcO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\n5AAAAIBFFHIAAADAIgo5AAAAYNH/B/trpvYTr8PCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1101b0bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (284723677)>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(mtcars, aes(x='wt', y='mpg', color='factor(cyl)')) + \\\n",
    "    geom_point() + \\\n",
    "    scale_color_brewer(type='div', palette=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UqFGj9Oyzz0qSWlpadMstt2j9+vXauXOnRowYoSVLlgx8TyQSUSQSOeex2tvb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ff738d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (285179185)>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(mtcars, aes(x='wt', y='mpg', color='factor(cyl)')) + \\\n",
    "    geom_point() + \\\n",
    "    scale_color_brewer(type='seq', palette=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rzZo1WrhwocLh8Gm3n/qpoqlUSplMpuz2aDSqQMBTb0kfv9/vyX3kT87Lq3OT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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1105ea310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (285608533)>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(mtcars, aes(x='wt', y='mpg', color='factor(cyl)')) + \\\n",
    "    geom_point() + \\\n",
    "    scale_color_brewer(type='qual')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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
}