docs/scales/scale_alpha_identity.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from ggplot import *\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `scale_alpha_identity`\n",
    "`scale_alpha_identity` applies the alpha values in your data to your ggplots."
   ]
  },
  {
   "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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>ze-alpha</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x  y  ze-alpha\n",
       "0  0  0      0.20\n",
       "1  1  1      0.30\n",
       "2  2  2      0.40\n",
       "3  3  3      0.90\n",
       "4  4  4      0.80\n",
       "5  5  5      0.65\n",
       "6  6  6      0.43\n",
       "7  7  7      0.55\n",
       "8  8  8      0.34\n",
       "9  9  9      0.65"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ze_alpha = [\n",
    "    0.2,\n",
    "    0.3,\n",
    "    0.4,\n",
    "    0.9,\n",
    "    0.8,\n",
    "    0.65,\n",
    "    0.43,\n",
    "    0.55,\n",
    "    0.34,\n",
    "    0.65\n",
    "]\n",
    "df = pd.DataFrame({\n",
    "        \"x\": range(10),\n",
    "        \"y\": range(10),\n",
    "        \"ze-alpha\": ze_alpha\n",
    "    })\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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AVJ2uMlFDQ0MIhUJah5EWVqtV6xCIMoIFNxERZZX+/n74/f6MXlNPBTdwtWuKXqc+TIRe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\nf4w4eAupYXI7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ff97b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (285185805)>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(df, aes(x='x', y='y', alpha='ze-alpha')) + \\\n",
    "    geom_point(size=7500) + \\\n",
    "    scale_alpha_identity()"
   ]
  },
  {
   "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
}