docs/geoms/geom_rect.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from ggplot import *\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>w</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   w   x  y  z\n",
       "0  4   2  1  1\n",
       "1  2   5  1  1\n",
       "2  2   7  1  2\n",
       "3  2   9  1  2\n",
       "4  4  12  1  3"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([{\"x\":2,\"y\":1,\"z\":\"1\",\"w\":4},{\"x\":5,\"y\":1,\"z\":\"1\",\"w\":2},{\"x\":7,\"y\":1,\"z\":\"2\",\"w\":2},{\"x\":9,\"y\":1,\"z\":\"2\",\"w\":2},{\"x\":12,\"y\":1,\"z\":\"3\",\"w\":4},{\"x\":2,\"y\":2,\"z\":\"3\",\"w\":4},{\"x\":5,\"y\":2,\"z\":\"4\",\"w\":2},{\"x\":7,\"y\":2,\"z\":\"4\",\"w\":2},{\"x\":9,\"y\":2,\"z\":\"5\",\"w\":2},{\"x\":12,\"y\":2,\"z\":\"5\",\"w\":4}])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ggw/mlFNOGdLjC0wAAA7pggsuyKpVqw77PE+RAwBwSBs2bMj555+fz3zmM4d1nsAEAGCA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105014e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (273683505)>\n"
     ]
    }
   ],
   "source": [
    "p = ggplot(df, aes(xmin='x - w / 2', xmax='x + w / 2', ymin='y', ymax='y + 1', fill='z')) + geom_rect()\n",
    "print p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BCQDAfp1zzjlZsWLFAZ/nKXIAAPZr3bp1Ofvss/Pnf/7nB3SewAQAYIATTzwxr776atau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\nKIEJAEBR/w/EZrJz6eWjtgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1112b03d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (286440725)>\n"
     ]
    }
   ],
   "source": [
    "p = ggplot(df, aes(xmin='x - w / 2', xmax='x + w / 2', ymin='y', ymax='y + 1', fill='z')) + geom_rect(color='black')\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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i586d0dvbG5lMJlavXh3nnXfehP327NkTBw4ciFwuFxs3bozm5uZTEhgAgPI2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\nlAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110b41bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (285769545)>\n"
     ]
    }
   ],
   "source": [
    "p = ggplot(df, aes(xmin='x - w / 2', xmax='x + w / 2', ymin='y', ymax='y + 1', fill='z')) + \\\n",
    "    geom_rect(color='black') + \\\n",
    "    facet_wrap('w')\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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