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

{
 "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": 7,
   "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": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10d8a2d90>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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RnqlbgNsAMvPWiPgDsFfD6/OBu4A1QF/D9j5gcKxCFy6cy4wZ07e6wq0wODivI8edLBYt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\nkiRV8P8AUrk88f84hHIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ce4af10>"
      ]
     },
     "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": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10dd37850>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XM3zdq58ccpsX1jw7CpVs37Fzr7Ge9Q33+Lbh9h/fNtz+499zz49GoZJNdt11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\nJEkZMqAlScqQAS1JUoYMaEmSMvTfCghK2bXcxIIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cb96650>"
      ]
     },
     "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": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10ca8e490>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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G2Xrrbfjc575ET0/POm3X8JYkqUH23fcvGTNmzDpv1/CWJKliDG9JkirGE9YkSRuMrvb+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\nliSpYgxvSZIqxvC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      "text/plain": [
       "<matplotlib.figure.Figure at 0x100532f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cded610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Mb0mSCmN4S5JUGMNbkqTCGN6SJBXG8JYkqTCGtyRJhTG8JUkqjOEtSVJhDG9J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\nvCVJKsz/BzBpRWazCFqKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ded4b50>"
      ]
     },
     "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": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10c4cc150>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H9uudl19+iS22uJHW1nEANDScx6OPHsI666xb5cxWPs3NTXXLf9bbuZNAjfCK0JKk3hg9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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e046810>"
      ]
     },
     "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": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10c93f450>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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S9IVwQNeYvp0t/UxZqy+ESIwEfSEcEGpsxOXx2JKC19K16Y507wsh4iRBXwgH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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e0a96d0>"
      ]
     },
     "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
}