docs/plotting/ggplot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from ggplot import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `ggplot`\n",
    "`ggplot` is the base layer or object that you use to define\n",
    "the components of your chart (x and y axis, shapes, colors, etc.).\n",
    "You can combine it with layers (or geoms) to make complex graphics\n",
    "with minimal effort. `ggplot` has 2 parameters:\n",
    "\n",
    "- (1) What data is going to be used for your plot _(the data frame being used)_\n",
    "- (2) How that data is going to be displayed _(the aesthetics)_\n",
    "\n",
    "You can add layers to `ggplot` to build up your plot. For the example below we'll start with a `ggplot` that has the `x` aesthetic defined as the `price` column from the `diamonds` dataset. We'll then add a `geom_histogram` to it and then finally add `scale_x_continuous` to customize the x-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p = ggplot(aes(x='price'), data=diamonds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsUAAAIACAYAAABq7hEfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f53ba10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (284507049)>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p + geom_histogram()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsUAAAIACAYAAABq7hEfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110167d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (284507049)>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p + geom_histogram() + scale_x_continuous(\"Price ($)\", breaks=[0, 10000, 20000])"
   ]
  },
  {
   "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
}