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

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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# matplotlib"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Setting Global Parameters\n",
      "* Basic Plots\n",
      "* Histograms\n",
      "* Two Histograms on the Same Plot\n",
      "* Scatter Plots\n",
      "* Applying Matplotlib Visualizations to Kaggle: Titanic\n",
      "* Bar Plots, Histograms, subplot2grid\n",
      "* Normalized Plots\n",
      "* Scatter Plots, subplots\n",
      "* Kernel Density Estimation Plots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "import pylab as plt\n",
      "import seaborn\n",
      "\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Setting Global Parameters"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# 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()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Basic Plots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "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()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAfYAAAFtCAYAAAD1Skg8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZPhlPUGVHj+8djI/ePAzSivq
       "text": [
        "<matplotlib.figure.Figure at 0x10bbb9910>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Histograms"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# 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()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(1000,)\n",
        "float64\n",
        "[ 1.44418744  0.46400374  1.25345292  1.97078609  0.80932915  0.09078554\n",
        "  0.66967121  0.15271056  0.57636656  0.84077082  1.35157266  0.29905241\n",
        "  1.38532784  1.80721741  0.4306797   0.43750271  0.6245115   1.06267641\n",
        "  1.51031708  1.38542507  1.2802048   1.48447563  1.36070319 -0.0184091\n",
        "  0.42067395  0.93246141  1.75653823  1.21452101  1.41877116  1.21999727]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAFVCAYAAAA+OJwpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAF0ZJREFUeJzt3XFs3Gd9x/G3S+NSz8Y0kcNGagUtS78qmmC0aGwMNelW\nqmWjLUOb0GAdrdjGRjVlDJXRwCptYitaSKVU66otlIUxiXXtSpeqaunWVU1bsTFYGUQNTyiUxNWq\nxuQcE9cEJ7H3h8/FdXz3O5995+fu935JVX33/O5+3ye/8338u+d3z9MzMzODJElaXeesdgGSJMlA\nliQpCwayJEkZMJAlScqAgSxJUgYMZEmSMnBu0QYR8T7guurN84E3Am8DdgPTwAHghpSS35+SJKlJ\nPUv5HnJE/DXwNeAqYFdKaX9E3AF8MaV0X4tqlCSp6zX8kXVEvBl4fUrp08ClKaX91aYHgStaUZwk\nSWWxlDHkHcCfVX/umXf/BDC4YhVJklRChWPIABHxauCilNJj1bum5zUPAMfrPX5mZmamp6en3iaS\nJHWTJYdeQ4EMXAY8Mu/2UxGxpRrQ2xa0nV1VTw+joyeWWlvXGBoaKG3/y9x3sP/2v7z9L3PfYbb/\nS9VoIF8EfHve7Q8DeyKiF3gauGfJe5YkSS9pKJBTSp9acPtbwNZWFCRJUhk5MYgkSRkwkCVJyoCB\nLElSBgxkSZIyYCBLkpQBA1mSpAwYyJIkZcBAliQpAwayJEkZMJAlScqAgSxJUgYMZEmSMtDoak+S\nlLWpqSlGRg7X3WZ4eCO9vb1tqkhaGgNZUlcYGTnM9p376Btcv2j75PhRdt94NZs2bW5zZVJjDGRJ\nXaNvcD39F2xY7TKkpjiGLElSBgxkSZIyYCBLkpQBA1mSpAwYyJIkZcBAliQpAwayJEkZMJAlScqA\ngSxJUgYMZEmSMmAgS5KUAQNZkqQMGMiSJGXAQJYkKQMGsiRJGTCQJUnKgIEsSVIGDGRJkjJgIEuS\nlAEDWZKkDBjIkiRl4NyiDSLiJuAqYA3w18CTwF5gGjgA3JBSmmlhjZIkdb26Z8gRsRX4+ZTSW4Gt\nwE8Cu4AdKaXLgB7gmhbXKElS1yv6yPpK4BsRcR9wP7APuDSltL/a/iBwRQvrkySpFIo+sh4ChoF3\nMHt2fD+zZ8VzJoDB1pQmSVJ5FAXy94CDKaXTwKGIOAlsmNc+ABxvZEdDQwPNVdglytz/Mvcd7H+7\n+j821l+4zdq1/W0/HmU+/mXuezOKAvkJYDtwa0S8FugDHomILSmlx4BtwCON7Gh09MSyCu1kQ0MD\npe1/mfsO9r+d/a9UJhrapp3Ho8zHv8x9h+b+GKkbyCmlByLisoj4MrPjzR8EvgvsiYhe4GngnqWX\nKkmS5iv82lNK6U8WuXvrypciSVJ5OTGIJEkZMJAlScqAgSxJUgYMZEmSMmAgS5KUAQNZkqQMGMiS\nJGXAQJYkKQMGsiRJGTCQJUnKgIEsSVIGDGRJkjJgIEuSlAEDWZKkDBjIkiRlwECWJCkDBrIkSRkw\nkCVJyoCBLElSBgxkSZIyYCBLkpSBc1e7AEmdb2pqipGRw2fdPzbWT6UyAcDw8EZ6e3vbXZrUMQxk\nScs2MnKY7Tv30Te4ftH2yfGj7L7xajZt2tzmyqTOYSBLWhF9g+vpv2DDapchdSzHkCVJyoBnyJJq\njgHP5xiw1FoGsiTHgKUMGMiSAMeApdXmGLIkSRkwkCVJyoCBLElSBgxkSZIyYCBLkpQBA1mSpAwY\nyJIkZcBAliQpAw1NDBIR/wOMV29+B7gF2AtMAweAG1JKM60oUJKkMigM5Ih4JUBK6fJ59+0DdqSU\n9kfEHcA1wH0tq1KSpC7XyBnyG4G+iPhidfuPAZeklPZX2x8ErsRAliSpaY0E8ovAzpTSnRGxGXho\nQfsEMLjilUkCXIlJKotGAvkQ8AxASulbEXEMeNO89gHgeNGTDA0NNFVgtyhz/8vcd1h+/w8dOlS4\nEtPnbnkPGzZc1PQ+xsb6C7dZu7a/Zl+W+/iVkEMNiynz67/MfW9GI4F8PfAG4IaIeC2zAfxwRGxJ\nKT0GbAMeKXqS0dETyyq0kw0NDZS2/2XuO6xM/yuVicKVmCqViWXtp1KZaGibWvtY7uNXQg41LFTm\n13+Z+w7N/THSSCDfCfx9RMyNGV8PHAP2REQv8DRwz5L3LEmSXlIYyCml08C1izRtXfFqJEkqKScG\nkSQpAwayJEkZMJAlScpAQ1NnSiq36TOnOXKk9neh67VJaoyBLKnQyYlj7LqrQt/g84u2H3vuIOsu\nvLjNVUndxUCW1JB634WeHH+hzdVI3ccxZEmSMmAgS5KUAT+yltRyRReFzam3SEbRIhteWKZOZyBL\narmii8JgdpGM3TdezaZNmxdtHxk5XHeRDS8sU6czkCW1RdECGct9Di8sU6dzDFmSpAwYyJIkZcBA\nliQpAwayJEkZMJAlScqAgSxJUgYMZEmSMmAgS5KUAQNZkqQMGMiSJGXAqTMlZaFoAYp2LB5RtIAF\n1F8AQ1oOA1lSFooWoGjH4hFFC1gULYAhLYeBLCkbOSwesRKLYEjNcAxZkqQMeIYsrbKicct2jJ1K\nWn0GsrTKisYt2zF2Kmn1GchSBnIYO5W0uhxDliQpAwayJEkZMJAlScqAgSxJUgYMZEmSMmAgS5KU\nAQNZkqQMGMiSJGWgoYlBImI98FXgl4BpYG/1/weAG1JKM60qUJKkMig8Q46INcDfAi8CPcCtwI6U\n0mXV29e0tEJJkkqgkTPkncAdwE3V25eklPZXf34QuBK4rwW1SVoBRYtXgAtYNGr6zOnCf6vh4Y30\n9va2qSJ1k7qBHBHXAaMppYcj4iZmz4h75m0yAQw2sqOhoYFma+wKZe5/mfsOxf0fG+tf9j7Wru2v\nuZ9Dhw7VXbwCyrOARb1/Jyg+FicnjrHrrgp9g88v2j45fpTP3fIeNmy46KX7yvz6L3Pfm1F0hnw9\nMBMRVwA/A3wWGJrXPgAcb2RHo6MnmiqwGwwNDZS2/2XuOzTW/0plYtn7qVQmau6nUpmou3gFlGcB\ni3r/TnPtRYr+Lefvo8yv/zL3HZr7Y6TuGHJKaUtKaWtK6XLga8BvAw9FxJbqJtuA/TWfQJIkNWSp\nyy/OAB8G9kREL/A0cM+KVyVJUsk0HMjVs+Q5W1e+FEnNKLrQyAu2pM6w1DNkSZkputCoLBdsSZ3O\nQJa6QL0LjcpywZbU6Zw6U5KkDBjIkiRlwECWJCkDBrIkSRkwkCVJyoCBLElSBgxkSZIyYCBLkpQB\nA1mSpAwYyJIkZcCpMyWVQtEiHOBCHFpdBrKkUihahANciEOry0CWVBr1FuEAF+LQ6nIMWZKkDBjI\nkiRlwECWJCkDBrIkSRkwkCVJyoCBLElSBgxkSZIyYCBLkpQBA1mSpAwYyJIkZcCpM6VlmJqaYmSk\n9oIEY2P9VCoTDA9vpLe3t42VSeo0BrK0DCMjh9m+cx99g+trbjM5fpTdN17Npk2b21iZpE5jIEvL\nVLRggSQ1wjFkSZIyYCBLkpQBA1mSpAwYyJIkZcBAliQpAwayJEkZ8GtPUotNnznNkSO1Jw+p1yap\nPAxkqcVOThxj110V+gafX7T92HMHWXfhxW2uSlJuCgM5Il4B7AEuAmaA3wd+COwFpoEDwA0ppZnW\nlSl1tnqTh0yOv9DmaiTlqJEx5HcA0ymltwEfB/4S2AXsSCldBvQA17SuREmSul9hIKeU/hX4QPXm\n64Ax4NKU0v7qfQ8CV7SkOkmSSqKhMeSU0pmI2Au8E/gN4O3zmieAwaLnGBoaaKa+rlHm/ndz38fG\n+le7BGVm7dr+l73mu/n1X6TMfW9Gwxd1pZSui4jXAF8GXjmvaQA4XvT40dETS6+uSwwNDZS2/93e\n90plYrVLUGYqlYmXXvPd/vqvp8x9h+b+GCn8yDoiro2Im6o3fwCcAb4SEVuq920D9i/6YEmS1JBG\nzpDvAfZGxGPAGmA78E1gT0T0
       "text": [
        "<matplotlib.figure.Figure at 0x10bbccf50>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Two Histograms on the Same Plot"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "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()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAeoAAAFVCAYAAAAg8ayaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJGlHWM8V9VnAUIzxRcA7gHcC
       "text": [
        "<matplotlib.figure.Figure at 0x10bfb2390>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Scatter Plots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(samples_1, samples_2, alpha=0.1);\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAFVCAYAAAA30zxTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3VuMJPme2PVvRGbc836tqr73zJmcs2d2WR8sI1YI1lrL\nsiUsC14REi8IyTzsAwLktWQtCASSpX0CIWQE5hnLQiC00spgmdvDrvccLzNnZ3J6pm91zfs1rpkZ\nwUNkVld1V3dXV1d3dXX/PtJoqrMyMyIjo+IX////9//9lSRJEEIIIcT7pV71DgghhBCfIgnAQggh\nxBWQACyEEEJcAQnAQgghxBWQACyEEEJcAQnAQgghxBXInudJrVbrXwL+y3a7/ZdbrdbnwD8AYuAb\n4N9vt9syl0kIIYR4A69tAbdarf8I+PuAsX7oD4Dfa7fb/yqgAH/z3e2eEEII8XE6Txf0D8C/SRps\nAX7ebrf/z/XPfwj8lXexY0IIIcTH7LUBuN1u/yNgeeIh5cTPc6B42TslhBBCfOzONQb8nPjEz3lg\n/LoXJEmSKIryuqcJIYQQH4vXBr2LBOBftlqtf63dbv9T4K8D//tr90JR6PVmF9jUp6Vez8txOgc5\nTucnx+p85Didnxyr86nX8699zpsE4E2m838A/P1Wq6UDfw78wzffNSGEEOLTdq4A3G63HwO/tf75\nAfDb726XhBBCiI+fFOIQQgghroAEYCGEEOIKSAAWQgghroAEYCGEEOIKSAAWQgghroAEYCGEEOIK\nSAAWQgghroAEYCGEEOIKSAAWQgghroAEYCGEEOIKSAAWQgghroAEYCGEEOIKSAAWQgghroAEYCGE\nEOIKSAAWQgghroAEYCGEEOIKSAAWQgghroAEYCGEEOIKSAAWQgghroAEYCGEEOIKSAAWQgghroAE\nYCGEEOIKSAAWQgghroAEYCGEEOIKSAAWQgghroAEYCGEEOIKZC/yolarpQL/HfAFEAP/brvdbl/m\njgkhhBAfs4u2gP8q4LTb7X8F+E+B//zydkkIIYT4+F00APtAsdVqKUARiC5vl4QQQoiP34W6oIH/\nBzCB74Aq8DcubY+EEEKIT4CSJMkbv6jVav0eaRf032m1WjeB/wP4qt1uv6wl/OYbEUIIIa4v5XVP\nuGgL2AGm659HgAZkXvWCXm92wU19Our1vBync5DjdH5vcqxcNwTAcYx3uUsfJDmnzk+O1fnU6/nX\nPueiAfjvAf9Dq9X6v0iD799ut9v+Bd9LCHHFOh2PJHEAmM9dmk37ivdIiI/fhQJwu90eA//GJe+L\nEOIKuG54HHwBksTBdYNPsiUsxPskhTiEEEKIKyABWIhPnOMYKIp7/G9FcaX1K8R7cNExYCHER6TZ\ntHHdAADHkfFfId4HCcBCCODTzH4W4ipJF7QQQghxBSQACyGEEFdAArAQQghxBSQACyGEEFdAArAQ\nQghxBSQACyGEEFdAArAQQghxBSQACyGEEFdAArAQQghxBSQACyGEEFdAArAQQghxBSQACyGEEFdA\nArAQQghxBSQACyGEEFdAArAQQghxBSQACyGEEFdAArAQQghxBSQACyGEEFdAArAQQghxBSQACyGE\nEFdAArAQQghxBSQACyGEEFcge9EXtlqtvw38DUAD/qt2u/0/XtpeCSGEEB+5C7WAW63WbwP/crvd\n/i3gt4H7l7hPQgghxEfvoi3gvwp83Wq1/megAPyHl7dLQgghxMfvogG4DtwC/nXS1u//Anx5WTsl\nhBBCfOyUJEne+EWtVuu/AHrtdvsP1v/+58Bfabfb/Ze85M03IoQQQlxfyuuecNEW8P8N/C7wB61W\nawdwgMGrXtDrzS64qU9HvZ6X43QOcpzOT47V+chxOj85VudTr+df+5wLJWG12+3/Dfhlq9X6Y9Lu\n57/VbrellSuEEEKc04WnIbXb7f/4MndECCGE+JRIIQ4hhBDiCkgAFkIIIa6ABGAhhBDiCkgAFkII\nIa6ABGAhhBDiCkgAFkIIIa6ABGAhhHhLrhviuuFV74a4Zi48D1gIIQR0Oh5J4gAwn7s0m/YV75G4\nLqQFLIQQF+S64XHwBUgSR1rC4twkAAshTvlQulM/lP0Q4l2RLmjxSdpc2B3HuOI9+bB8KN2pH8p+\nvI7jGMzn7vG+KoqL43yY+yo+PBKAxSfnulzc37ezu1OD936T8qHsx3k1mzauGwBI8BVvRLqgxSdF\nxuyuH8/78LuiHcf4YG8QxIdLArAQAkiDiKK4x/9Ou1Pff1A5uR+9nofrxsRxkU7He+/7IsS7JAFY\nfFI+lCDzLr1N8lKzaZPPB+TzwZV2zTebNqo6wXGy1OsWIL0V4uMjY8Dik3OdxuzeNFns+fHtej3/\nxtv8UG5IHMcgjvWr3g0h3hkJwOKT9KEEmVd502Sxj218WzKMxcdOArAQV+hlLdzrlgn8rlyn3goh\n3pSMAQtxRTodj9nMZDYzLyXB6GMd35YMY/GxkgAsxBV4XXfxRYPph5JE9b5dRtUsqbwl3jfpghbv\njFSbejsX7X791I73ZRRWOTxMeyPe5j2EeFPSAhbvxGV3r35sztvCPav7VVpqz1xG4ln6Hs8C7nVP\nXhPXh7SAxaWTBKLzuUgLV8poCvHxkBawEFfoTRKMPrZpRpfhMhLP0vd41kvzsSSviQ+ftIDFpZP5\nm9fPdR6vv4ypStvbNmHYf6v3EOJNSQAW74TM37y4lwXDd3Vj8zF0a1/GjcN1vPkQ19tbBeBWq9UA\n/hT4nXa7/f3l7JL4WMgF7c29Lhhe9o2NjNcLcXUuPAbcarU04L8F3Nc9Vwjxeucd45XCFC+SzHBx\nHb1NEtbfA/4b4PCS9kUI8Z6dTGLyvBDfH76z4P6ugqRMeRPX1YUCcKvV+neAXrvd/qP1Q8ql7ZEQ\nn6irKiXZbNp43ghQsKzKOwli7ypISma4uM6UJEne+EWtVuufAsn6v98E2sDfbLfbnZe85M03IsQn\n6n1nJLtuyGRyelvFYnhp23+X7/+u912It/DahumFAvBJrVbrnwD/3muSsJJeb/ZW2/kU1Ot5ruNx\net8Bw7Z1er0ZjmNc6+kzF3Hy857ns5/nnHLd8LgM44aqTi5trPldv//JxDVFuVgW93X927sKcqzO\np17PvzYASyEO8Vbe9/hbp+MxmRjMZibffDO4tmN/FxkPPXmsL/OzP9/13e/3iePi8Xu/7djt697/\nbX2qC1CI6++t5wG32+2/fBk7Iq6f9z2F5eT2PC8ijut4XoBtG9dq+sxF5t2e/uzh+rOH2Lb+Vp99\nE1g305tcN6RWqx3/vttVcF0V29bfao7wy97/On1vQlw2aQEL8R59SElDz/deOI6B54X0+1MgDfSX\nua8X7XJ+XQtcsqDFdSUBWFzY+87aPbk929ZR1R62bbyXbb+ty+zGtW1j/dl14GKf/awbgT/+4yOm\n0ya9nsF33w3W7+0db+cynPec2Ryv54Pr88fxQ7qhEeJNSSlK8Vbed8nJZtPGtkOiKGBrq/rW234f\nSVzPJwkpysXKSZ481ic/O2Rw3bfL/O33pyRJ2jVcrZr4voqqjmg0HDZ5ms/v60WP3evOmc3x8rwI\n142p19PHL6s7XIgPhQRg8dbed8sz7SqN3nrbbzIWe9Fgc1YLLZ8PgLe/aXEc48J1nM+qK21ZxePf\nW5aObRs0GmcHy7etH33WGscbzx8vzwtOPB4e/7wZO5aFP8R1JQFYfJReFzCfT+gC7aWtyHexWMHr\nAvmmq/XkuOnz+5HLZd4oCe75Y3K6Rb3FN9/0iOO0uamqPRqN6pn7etnJdyc/l+8Psax0ypJt67iu\nx2akLO0Ot154vSz8Ia4rCcDio/MmAbPX80mS9PeKMqDRSB/fBJO3DTbnaaE9Hxg7HY9uVyVJSiiK\nS6PhvSTYToDT82tf5mXH5OTn+OqrKt3uGOA4+J61f5fhZS1ey6rgeSNsu7zej5hcTlnvd/zS7vAP\nefxfiJeRACw+KucNmGn37Ygk
       "text": [
        "<matplotlib.figure.Figure at 0x10c222f90>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Applying Matplotlib Visualizations to Kaggle: Titanic"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Prepare the titanic data to plot:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_train = pd.read_csv('../data/titanic/train.csv')\n",
      "\n",
      "def clean_data(df):\n",
      "    \n",
      "    # Get the unique values of Sex\n",
      "    sexes = sort(df['Sex'].unique())\n",
      "    \n",
      "    # Generate a mapping of Sex from a string to a number representation    \n",
      "    genders_mapping = dict(zip(sexes, range(0, len(sexes) + 1)))\n",
      "\n",
      "    # Transform Sex from a string to a number representation\n",
      "    df['Sex_Val'] = df['Sex'].map(genders_mapping).astype(int)\n",
      "    \n",
      "    # Get the unique values of Embarked\n",
      "    embarked_locs = sort(df['Embarked'].unique())\n",
      "\n",
      "    # Generate a mapping of Embarked from a string to a number representation        \n",
      "    embarked_locs_mapping = dict(zip(embarked_locs, \n",
      "                                     range(0, len(embarked_locs) + 1)))\n",
      "    \n",
      "    # Transform Embarked from a string to dummy variables\n",
      "    df = pd.concat([df, pd.get_dummies(df['Embarked'], prefix='Embarked_Val')], axis=1)\n",
      "    \n",
      "    # Fill in missing values of Embarked\n",
      "    # Since the vast majority of passengers embarked in 'S': 3, \n",
      "    # we assign the missing values in Embarked to 'S':\n",
      "    if len(df[df['Embarked'].isnull()] > 0):\n",
      "        df.replace({'Embarked_Val' : \n",
      "                       { embarked_locs_mapping[nan] : embarked_locs_mapping['S'] \n",
      "                       }\n",
      "                   }, \n",
      "                   inplace=True)\n",
      "    \n",
      "    # Fill in missing values of Fare with the average Fare\n",
      "    if len(df[df['Fare'].isnull()] > 0):\n",
      "        avg_fare = df['Fare'].mean()\n",
      "        df.replace({ None: avg_fare }, inplace=True)\n",
      "    \n",
      "    # To keep Age in tact, make a copy of it called AgeFill \n",
      "    # that we will use to fill in the missing ages:\n",
      "    df['AgeFill'] = df['Age']\n",
      "\n",
      "    # Determine the Age typical for each passenger class by Sex_Val.  \n",
      "    # We'll use the median instead of the mean because the Age \n",
      "    # histogram seems to be right skewed.\n",
      "    df['AgeFill'] = df['AgeFill'] \\\n",
      "                        .groupby([df['Sex_Val'], df['Pclass']]) \\\n",
      "                        .apply(lambda x: x.fillna(x.median()))\n",
      "            \n",
      "    # Define a new feature FamilySize that is the sum of \n",
      "    # Parch (number of parents or children on board) and \n",
      "    # SibSp (number of siblings or spouses):\n",
      "    df['FamilySize'] = df['SibSp'] + df['Parch']\n",
      "    \n",
      "    return df\n",
      "\n",
      "df_train = clean_data(df_train)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Bar Plots, Histograms, subplot2grid"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Size of matplotlib figures that contain subplots\n",
      "figsize_with_subplots = (10, 10)\n",
      "\n",
      "# Set up a grid of plots\n",
      "fig = plt.figure(figsize=figsize_with_subplots) \n",
      "fig_dims = (3, 2)\n",
      "\n",
      "# Plot death and survival counts\n",
      "plt.subplot2grid(fig_dims, (0, 0))\n",
      "df_train['Survived'].value_counts().plot(kind='bar', \n",
      "                                         title='Death and Survival Counts',\n",
      "                                         color='r',\n",
      "                                         align='center')\n",
      "\n",
      "# Plot Pclass counts\n",
      "plt.subplot2grid(fig_dims, (0, 1))\n",
      "df_train['Pclass'].value_counts().plot(kind='bar', \n",
      "                                       title='Passenger Class Counts')\n",
      "\n",
      "# Plot Sex counts\n",
      "plt.subplot2grid(fig_dims, (1, 0))\n",
      "df_train['Sex'].value_counts().plot(kind='bar', \n",
      "                                    title='Gender Counts')\n",
      "plt.xticks(rotation=0)\n",
      "\n",
      "# Plot Embarked counts\n",
      "plt.subplot2grid(fig_dims, (1, 1))\n",
      "df_train['Embarked'].value_counts().plot(kind='bar', \n",
      "                                         title='Ports of Embarkation Counts')\n",
      "\n",
      "# Plot the Age histogram\n",
      "plt.subplot2grid(fig_dims, (2, 0))\n",
      "df_train['Age'].hist()\n",
      "plt.title('Age Histogram')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<matplotlib.text.Text at 0x10bf7ba50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAlMAAAJZCAYAAACN2rCOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XmcXFWZ8PFfyEpMJ5M4jYzKoCI+4oJCRAUVgiLICOIy\njo4rqODCIDOijkZ0NhBHwHlBGVSiBhX1HRgXlBeBicimiCCOMugDKGJcRqLdZCFItn7/OLehaLq7\nOrlVqa7q3/fzySdVt26fe+pW1+nnnvucc6YNDQ0hSZKkbbNDpysgSZLUzQymJEmSajCYkiRJqsFg\nSpIkqQaDKUmSpBoMpiRJkmqY0ekK9KqIeBTwM+BH1aYdgI3AGZn5uZplXwq8MjMHIuIXwEsz8wd1\nypzgcb8BnJ+Z547y2ruBvwamAdOBbwJLM3Nji459EXBCZv50G352OfDjzDx9lNceAZwM7A1sAf4I\nfDAzL6xX4zHr8gHgh+0qX2qnUdo1KN/5MzLzMx2p1HbQrJ2IiC3An2bmQBvrMAd4H/BC7m9nP5+Z\nH27T8V4IPD0z/6Ed5fcae6baa31m7lX9ewrwUuADEfHSmuUeRPkyAQw1PG63oerfA0TEy4EXA8/M\nzKcCTwMeD/xjqw6cmS/clkCqMla9+4HvAJdl5p5V3Y8BzomI5217bcf1XGBmm8qWtofGdm0v4C+A\n0yPiyZ2uWDt0qJ0YWYdpwFeBPko7uxewBHhJRPxzmw67D7CoTWX3HHumtqPM/GXVM/Eu4MsRMQv4\nV2B/ylXGjcDbM3NtRBwGvBeYBewEnJuZH4iI4au/b1VXDgBvjoi9qv0+l5knjjz2OOUtoVxx/Qx4\nEjAbODYzvx0RDwfOBf4MWAn86Rhvbeeq/nOBezPz3oj4G6C/OvZyGnqGGp9XPWvXAntSgq8TM3PP\nar8/AX4OPAb4IfAy4B3ADxrKegulUflr4P8Az6A0ONOAN2Xmd6o6jhZwvg24MjPPG96QmT+KiJcB\nd1XlPwf4cPXeNlT1uyQijgRelpmHV/vd97x6f6uBJwO7AD8FXgkcCSwGTo2IzcDvgdOrczcEnJKZ\nXx7jHEuTUmb+JiJuBXaPiJ8DZwO7U/4QrwVelZm3VBeR76P07GwG3pWZV42zfQFwBqVdmgmsqF7b\nHBF/BE4Bng88nNIzdkZETAdOBQ6nfAevA/bIzAOblHcvJVh5SlXfxp7+8dqJwcZzEREPadX7H3Ga\n96dcoB6amUNVHQYi4rXAo6pjP7I69q6U9u7czDyt6k38cWb2Vfvd97xqt15SHXd3Shv3OmAe8GZg\nekTcBZwFfBZ4aFWfizLzA+g+9kxtfz+i/JEFeA+wMTMXV1c7vwU+VL32DuB1mbkPsC/w3ohYlJlH\nVa8fmJm/qh7fU+33dOCEqkv6PtVVzajlVbs8HTgtM/cGPsX9PUpnAd/JzCdRGpQY4z2dSwk+/jci\nvhMRpwF/npnXV6+P7BlqfD5E+WI/ITP/A5gXEYur1/4a+EZm3tXwM+cAr28o60jgk5QgaufMfGZm\nPpHyxX/PGPUdthi4ZuTGzLw6M2+KiIcC51MC3KdUx/181Rg1szdwCLAHpbH/y8w8C7geeGdmfpVy\nnj+SmU8D3gAcOIFypUklIvYFHgt8D3gBMJCZ+2ZmAN8H/qba9cPAW6s26P3AAU22/xtwffX92Jty\ncfaO6rVZwKrMfDbwl8CHImI28KZq3ydS2rnHcH9bM155M4ELM/Pxo6RMjNdO/M+Iza18/42eBlw7\nHEg11OG2zPyv6ul5wIrqYvRZwGsi4hWjlDXS/sDfZOaTq/f5rsz8HvBx4EuZ+X7gaOBnmbkYeA4l\ncO6bQNlThj1T298QsL56fBiwICKeXz2fBfyuenw4cHhEvJryB3ka8BBgtHvyXwDIzN9FxO8oPU+/\nHn4xM4ciYqzyAO7IzOEciBspAQrA86gam8y8PSIuG+0NZeYa4JCIeDQlIFgCXBQR/56ZwwHNeLci\nG6/CPlUd/wbgKOCdI/a9AphTBVz3AP2Z+S2AiHh/RLyV0oAuAdaMc0woV4LTx3n9GcBtmfl9gMy8\nOSKuqcoebx2mIeCbw/liEfFjRu8u/w/grOqz+S/K1ak02e0YETdWj2dQelhflZm/Bv4zIm6PiOMo\nAdYSyi0ygC8BX63yHy+j9CCNt/0wYJ+IeOPwcSnf2WFfq/6/kdKj/hDKLcdzM3MDQER8Anj7BMsb\n2Rs0rFk7cZ/MbOX7b7R5vDpUPWL7UVJAyMw1VQ/5oZSe//HckJm/qR7/gJKOAqXNHm63Lwb+X0T8\nOaWtek9mrm1S7pRiz9T2tw8PTEp/e0PuwTOAv6q+GD8EnkoJKt5FSV4fKyBpTPJ+UA7VBMq7Z4yf\nH+KBvyObRjt4RPx9ROybmbdn5qcz83WUL/GxY9Rp9ogi1jU8/gzlHDwFWJCZVzbuWF2ZfYrSFX0k\nsKyqwwuBiygN31cpV1XNfr+vBZ45yvt5c0T8HaOf7+mUPyAj39OsEfv9seHxqHltmflJSi/lZZRe\nrB9FxPwmdZY67Z6GnKknZ+aBmXkJQHUxs4zynT4P+CLV97BKP3gWpXf2SOC7ETFtrO3Vz/1lQ/v4\nTO4PjKBqtxp6a6ZR2rXG731jsNSsvMZ2qFGzdqJxWyvf/8g67BMRD2jTImKfiPgs9wc+jT830bbq\nnhHPR2urrgceTbkL8CjguqpHUhWDqe0oIh4HnEjJkwG4BDguImZVX5KPAx+kXNH0Ae/PzIsoVzez\nuf/KZDMP/kKMZ/cm5Y3lm5REy+H78WMlW86hdLM35lQ9gRK4AayidFNT7fPssQ5YXSF9D/gE5Zbe\naJYDRwAvpwRfUK7Ivp6Zn6iO+xLuf39jBaGfAJZExKuGG6+qx+ufKAHvtWVT7FO99kRKF/e3q/f0\npIiYHREzKD2JjY36WDZRfXZVL9deWUZHvhn4k+qf1K0OBpZnGdl3C/AiYIeImB4RtwMPqb6jx1Jy\ngGZWeZMP2k5pH98REdOi5Jd+hZJuMJYhygXVa6o2dQYlOBkOqLa2vGHN2olh01r0/vdgxF2jzLyW\nknv5kep2JhHxMOBjwM8zcx2lvTq2em0B8FrKhdpqYFZE7FEV95IJvGcogenMqrwPUf5+fA34W+B/\nKH9XVPE2X3s1docPD6d9T2ZeXG37F+A0Sjf1DtX/7wDuBr4B/CQifku5j309Jci6HfgycFVEvHiC\n9fjvccrbwINvWQ0/Pxb4TETcDPyqKmc0/1K9v6sjYrg36zrgr6rXPwqcFxE/BX4BXN6kvudQcpUO\nH+3F6nbmDcD0zPzfavPHgS9U53uQcgvghKrxG3U0X2YORknA/zCwNMrw5ruBN2TmCrhvpOJHI2Ju\n9R6PzMzbqobxCkoD99vqPQ3nwo16vMrXgdMiYibwbuCMiDipKvsfM/OXTc6N1Gnj3eI+DfhkRLwO\n+AOll/jQKsn7bynf0Y2U3/c3ZOaGiDh+jO1vpySM/4jyR/0yynd1tDoMP19Oye28kdI7dDv397xs\nTXn3mUg7wf3f+Va8/6Ny9CllXka52L4hIjZRLhaX5/1TvryakjZwFOWC7fPVhdrw1DUXR8SdlLa1\nMWd1rHzWFZSBUvdWxz23Slm4l3Kn44tjnbOpaNrQ0HjfC0mSukOU/NOdhkfeRcQZlKkc3tvZmqnX\nNQ2mIuK9lB6CmZQuxWso0f8W4CbKMPqhiDiacktoE3BSdTtJkjoqIn5AudUBZaqNU7AN60lRpnNZ\nThmEM4PSg/JWk6XVbuMGU1XX5jsy80VVEvO7KUnMp2fmlRFxNuU+9LXApZQhpDsCVwNPGx5RIUmd\nEGXW6O9U034Mb7uQMhWIbZiklmiWM3Uw8OOI+CownzIK7I0NI6wurvbZDFxT3efdGBG3USZhvH6U\nMiVpe3kKMDciLqG0d+8D9rYNk9RKzYKpfsoMzodR5u75Og8cqbQWWEAJtFaPsl2SOulu4NTM/FRE\n7E4ZodrINkxSbc2Cqd8DP8nM
       "text": [
        "<matplotlib.figure.Figure at 0x10be60210>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get the unique values of Embarked and its maximum\n",
      "family_sizes = sort(df_train['FamilySize'].unique())\n",
      "family_size_max = max(family_sizes)\n",
      "\n",
      "df1 = df_train[df_train['Survived'] == 0]['FamilySize']\n",
      "df2 = df_train[df_train['Survived'] == 1]['FamilySize']\n",
      "plt.hist([df1, df2], \n",
      "         bins=family_size_max + 1, \n",
      "         range=(0, family_size_max), \n",
      "         stacked=True)\n",
      "plt.legend(('Died', 'Survived'), loc='best')\n",
      "plt.title('Survivors by Family Size')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.text.Text at 0x10bf0cb10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAegAAAFeCAYAAABOw4xkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nq8nvaUBLknYolcoeXHXVtTz8
       "text": [
        "<matplotlib.figure.Figure at 0x10bf60910>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Normalized Plots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pclass_xt = pd.crosstab(df_train['Pclass'], df_train['Survived'])\n",
      "\n",
      "# Normalize the cross tab to sum to 1:\n",
      "pclass_xt_pct = pclass_xt.div(pclass_xt.sum(1).astype(float), axis=0)\n",
      "\n",
      "pclass_xt_pct.plot(kind='bar', \n",
      "                   stacked=True, \n",
      "                   title='Survival Rate by Passenger Classes')\n",
      "plt.xlabel('Passenger Class')\n",
      "plt.ylabel('Survival Rate')\n",
      "\n",
      "# Plot survival rate by Sex\n",
      "females_df = df_train[df_train['Sex'] == 'female']\n",
      "females_xt = pd.crosstab(females_df['Pclass'], df_train['Survived'])\n",
      "females_xt_pct = females_xt.div(females_xt.sum(1).astype(float), axis=0)\n",
      "females_xt_pct.plot(kind='bar', \n",
      "                    stacked=True, \n",
      "                    title='Female Survival Rate by Passenger Class')\n",
      "plt.xlabel('Passenger Class')\n",
      "plt.ylabel('Survival Rate')\n",
      "\n",
      "# Plot survival rate by Pclass\n",
      "males_df = df_train[df_train['Sex'] == 'male']\n",
      "males_xt = pd.crosstab(males_df['Pclass'], df_train['Survived'])\n",
      "males_xt_pct = males_xt.div(males_xt.sum(1).astype(float), axis=0)\n",
      "males_xt_pct.plot(kind='bar', \n",
      "                  stacked=True, \n",
      "                  title='Male Survival Rate by Passenger Class')\n",
      "plt.xlabel('Passenger Class')\n",
      "plt.ylabel('Survival Rate')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<matplotlib.text.Text at 0x10d1a4a90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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       "text": [
        "<matplotlib.figure.Figure at 0x10c536d90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAAFxCAYAAABa5SD+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkV/mO985iXnz5nHJJT/m7LMv
       "text": [
        "<matplotlib.figure.Figure at 0x10c288f10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAAFxCAYAAABa5SD+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n2v+11arsfEuShtiIGjafOfOv
       "text": [
        "<matplotlib.figure.Figure at 0x10ced51d0>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Scatter Plots, subplots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Set up a grid of plots\n",
      "fig, axes = plt.subplots(2, 1, figsize=figsize_with_subplots)\n",
      "\n",
      "# Histogram of AgeFill segmented by Survived\n",
      "df1 = df_train[df_train['Survived'] == 0]['Age']\n",
      "df2 = df_train[df_train['Survived'] == 1]['Age']\n",
      "max_age = max(df_train['AgeFill'])\n",
      "\n",
      "axes[1].hist([df1, df2], \n",
      "             bins=max_age / 10, \n",
      "             range=(1, max_age), \n",
      "             stacked=True)\n",
      "axes[1].legend(('Died', 'Survived'), loc='best')\n",
      "axes[1].set_title('Survivors by Age Groups Histogram')\n",
      "axes[1].set_xlabel('Age')\n",
      "axes[1].set_ylabel('Count')\n",
      "\n",
      "# Scatter plot Survived and AgeFill\n",
      "axes[0].scatter(df_train['Survived'], df_train['AgeFill'])\n",
      "axes[0].set_title('Survivors by Age Plot')\n",
      "axes[0].set_xlabel('Survived')\n",
      "axes[0].set_ylabel('Age')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<matplotlib.text.Text at 0x10bf60690>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAmgAAAJoCAYAAADS7x1JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXXdN4KGHDmP48OFVzqw22INW
       "text": [
        "<matplotlib.figure.Figure at 0x10c536250>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Kernel Density Estimation Plots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get the unique values of Pclass:\n",
      "passenger_classes = sort(df_train['Pclass'].unique())\n",
      "\n",
      "for pclass in passenger_classes:\n",
      "    df_train.AgeFill[df_train.Pclass == pclass].plot(kind='kde')\n",
      "plt.title('Age Density Plot by Passenger Class')\n",
      "plt.xlabel('Age')\n",
      "plt.legend(('1st Class', '2nd Class', '3rd Class'), loc='best')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "<matplotlib.legend.Legend at 0x10d4ad3d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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       "text": [
        "<matplotlib.figure.Figure at 0x10c2dae10>"
       ]
      }
     ],
     "prompt_number": 15
    }
   ],
   "metadata": {}
  }
 ]
}