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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       "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",
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       "text": [
        "<matplotlib.figure.Figure at 0x1057fcf90>"
       ]
      }
     ],
     "prompt_number": 5
    }
   ],
   "metadata": {}
  }
 ]
}