{
 "cells": [
  {
   "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": null,
   "metadata": {},
   "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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x118cf7358>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ioCIiIiKSiIGKiIiISCIGKiIiIiKJGKiIiIiIJGKgIiIiIpKIgYqIiIhIIgYq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1189a7e10>"
      ]
     },
     "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": null,
   "metadata": {},
   "outputs": [],
   "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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1194800b8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gqCowfN177725jlu0aKGePXtq6NChHisKAACgqCowfB07dsz92hijH374QWlp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\ngIUIXwAAABYifAEAAFjo/wGf6OAJxs0W0QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118ced908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Li9Ott96qzp07a8SIESpZsqQVtQEAABQ5eYavm2++WR9++CE3VgUAAMgHOYav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\nIiK0cuVKzZkzRyNHjlRycrI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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11934deb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vevXqac6cOde0//rrr6pXr54qVaokT09PNW/eXAkJCUV23IoVK+q5557XqFHPKzw8VJMn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11947ab00>"
      ]
     },
     "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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')"
   ]
  }
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