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

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook was prepared by [Donne Martin](http://donnemartin.com). Source and license info is on [GitHub](https://github.com/donnemartin/data-science-ipython-notebooks)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# matplotlib-applied\n",
    "\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": "markdown",
   "metadata": {},
   "source": [
    "## Applying Matplotlib Visualizations to Kaggle: Titanic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prepare the titanic data to plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pylab as plt\n",
    "import seaborn\n",
    "\n",
    "# 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": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "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 = np.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 = np.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[np.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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar Plots, Histograms, subplot2grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11357ac50>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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KBcLCwqDT6SBJEnJycpCdnQ2lUomxY8ciPj7e1eWTg3za3wF1hztdXQa1AgxT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\nBMIUAACACf8PA3yXZdwarecAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113145b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1138e6f10>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AwEAENAAABiKgAQAwEAENAICBCGgAAAxEQAMAYCACGgAAAxHQAAAYiIAGAMBA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\nAQAw0P8DObo8PxWIIQIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1132fcd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the unique values of Embarked and its maximum\n",
    "family_sizes = np.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')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalized Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x113ccbc50>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/1dq1a7VgwQKtXLmy2P1v3bp12T54wMQ4bA5UUL/99pusVqsGDRqk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\nvAEAMJn/BxSJuuk2dCC9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113b53b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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UPPSpwtGniqc09qnqBXzxKpHw/uWex1/uUQwNDVVkZKSGDRsmp9OpkJAQ1ahRoyRKAQDA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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113c19f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rT5+nFBR0+019/0jyT6rasIbswdVu6vsAABSkTIX3oUO/6KuvNuv11xdKkg4c2K/p0yfr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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113cc3d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter Plots, subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x113f4d710>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ImssoB8RScLPnVEtMRETElpub7eQyc0zsQSNoLsMD8/ToLZbnrdC3X0RErs4d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\nAADDUNAAAAAMQ0EDAAAwzP8H7tN4VQuAfaYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113b53250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 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')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kernel Density Estimation Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x113175ed0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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o34O+MTwcZ0kxitOpdrWEEBcgzXr6JSUl3mF6cGdEcrlc6PV6SkpKsFhOrz4M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\nOO1qJkQz4ZlTN1r8S8HrGX7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XwkeN2aOv9SI+D2NMDCgK9oJ879B+Xav2z+Spm1qL+c58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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113175d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the unique values of Pclass:\n",
    "passenger_classes = np.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')"
   ]
  }
 ],
 "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
}