data-science-ipython-notebooks/matplotlib/matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Credits: Some content forked from [Parallel Machine Learning with scikit-learn and IPython](https://github.com/ogrisel/parallel_ml_tutorial) by Olivier Grisel\n",
    "\n",
    "* Setting Global Parameters\n",
    "* Basic Plots\n",
    "* Histograms\n",
    "* Two Histograms on the Same Plot\n",
    "* Scatter Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pylab as plt\n",
    "import seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting Global Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Set the global default size of matplotlib figures\n",
    "plt.rc('figure', figsize=(10, 5))\n",
    "\n",
    "# Set seaborn aesthetic parameters to defaults\n",
    "seaborn.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfYAAAFtCAYAAAD1Skg8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZPhlPUGVHj+8djI/ePA
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c45bad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 2, 10)\n",
    "\n",
    "plt.plot(x, x, 'o-', label='linear')\n",
    "plt.plot(x, x ** 2, 'x-', label='quadratic')\n",
    "\n",
    "plt.legend(loc='best')\n",
    "plt.title('Linear vs Quadratic progression')\n",
    "plt.xlabel('Input')\n",
    "plt.ylabel('Output');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000,)\n",
      "float64\n",
      "[ 0.6806888   0.72202042  1.40490113  1.13979846  0.5729488   1.32584077\n",
      "  0.61635621  0.60340336  1.29453467  0.69841457  0.6975998   0.72315991\n",
      "  0.66912189  1.03420801  0.62283168  0.38582511  0.89488414  1.4802518\n",
      "  1.43819256  0.98605861  0.60402232  1.03820507  0.35598796  1.32901087\n",
      "  1.03194436  1.3374366   1.82526334  1.26614489  1.20061661  0.86344001]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAFVCAYAAAA+OJwpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZABogEAGgAYIZABogEA
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c4c1510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gaussian, mean 1, stddev .5, 1000 elements\n",
    "samples = np.random.normal(loc=1.0, scale=0.5, size=1000)\n",
    "print(samples.shape)\n",
    "print(samples.dtype)\n",
    "print(samples[:30])\n",
    "plt.hist(samples, bins=50);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Two Histograms on the Same Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeoAAAFVCAYAAAAg8ayaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLgIuBpaBK2KMB1rUbkm
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c736a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "samples_1 = np.random.normal(loc=1, scale=.5, size=10000)\n",
    "samples_2 = np.random.standard_t(df=10, size=10000)\n",
    "bins = np.linspace(-3, 3, 50)\n",
    "\n",
    "# Set an alpha and use the same bins since we are plotting two hists\n",
    "plt.hist(samples_1, bins=bins, alpha=0.5, label='samples 1')\n",
    "plt.hist(samples_2, bins=bins, alpha=0.5, label='samples 2')\n",
    "plt.legend(loc='upper left');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeYAAAFVCAYAAAA6zUwUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIvWqGuKzMqxXlV1dPm/
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c514950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(samples_1, samples_2, alpha=0.1);\n",
    "plt.show()"
   ]
  }
 ],
 "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": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}