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",
      "* 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"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Prepare 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": 2
    },
    {
     "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))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "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": 4,
       "text": [
        "<matplotlib.text.Text at 0x10a5bfe10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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yyM+Tnr9D/ntpfn8Z6T4620jaDziA9GDXzuU29nXqqacWj8Evb/e5fpWgdCfup+X3O5Iu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\nLrkhZWZmZtYlN6TMzMzMuvT/AW38TvxXDWRnAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1057ff910>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "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": 5,
       "text": [
        "<matplotlib.text.Text at 0x10a896c50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x1057fcf90>"
       ]
      }
     ],
     "prompt_number": 5
    }
   ],
   "metadata": {}
  }
 ]
}