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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GbW8Z6klNjS6gHU2NLkCStKNpbm6mubm52/t3N1ytioj9MnNlROwPrK62twCj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\nQn26Z51/swAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1057fcf90>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Normalized Plots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "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')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<matplotlib.text.Text at 0x10aea5350>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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+FzNmZr55qJFJkiTNMtONeF1b/nl5ZV0CUf6p2ewmHPVSB/RwNECzXw/zfHRM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\nnk7w9PBiAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ad1ab50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3ZeaNq1pPk5SjAGoC81yNMFh3AKpo56rGf42IhRFxakTM7HhEkiRJk1Q79/Ea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\nggAuJN4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ad9a890>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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NhX8laearMM8181WY58Ojn+b6LwJPAhZSv3tPAe4ZYEySJEkz0mqnGiPi4sxc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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ae36ad0>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Scatter Plots, subplots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# 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')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.text.Text at 0x10afb98d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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NtF9mF46ZmeVcHzZ0hyk4O6NYKk+myZmk/iSLdn0nIpYlk94lIiIklawimzJl\nyvrHNTU11NTUdG2g3V6phN19B8zMrLSqqt40NDwDjE9LdqSqyn2VW6Ouro66uroOnSOzZk1JvYB7\ngT9GxFVp2QtATUS8LWk4cL+bNTvOMz2bmVlbVFVVsXbtlhTfN3r2XEFDQ0OWYXVL3aZZU0m28Evg\n+UJilir0Orws/e/vMghvM1QNXEnj6ulJGcViZmZ5t3btR2h631i71veNcsmqWXN/4GTgaUmz07Lv\nA/8M3C7pdOB14NhswtvcrGllmZmZGfi+ka1cjNZsCzdrtp2XbzIzs7bwfaPzeIUAK8kLn5uZWVsk\n943TaLx803W+b7SDkzMryQMCzMysLXzf6DzdZkCAlZsHBJiZWVtUA6OAc9Pno4E52YVTYZycVQR3\n7DQzs7ZYDbxI4z5nXlWmXJycVYQg+YdVcE5aZmZmVkoPmq8QMDGjWCqPk7OK0JsNC9iSPr4uu3DM\nzCznqoDGKwQ4ZSgfDwioAJ7p2czM2qJfv36sWtWb4vtG376rWblyZZZhdUseEGAl7bHHAcye/Rob\nBgEMYo899swyJDMzy7FVq7YEplHcrLlq1eTM4qk0PbIOwLre3LlvAfsAg9Ntn7TMzMzM8sY1ZxVh\nOTCTxvPV9MsuHDMzy7Xhw3uzYEHxAICJDB++ZWbxVBonZxXg/ffXkSRmE4rKvpdZPGZmlm/vvLMW\nGERxd5h33lmVYUSVxclZBUhmet50mZmZGcDatcuBVRS3uKxd60Fk5eLkrAKcdNKXmT69cfX0SScd\nnVk8ZmaWb716DWD16ssobnHp1eu87AKqME7OKsD1118PwM03J8twnHTS0evLzMzMmurfvz/19c3L\nrDycnFWI66+/HudjZmbWGmPHHtCsxWXsWLe4lIun0qgQtbW19Oo1lF69hlJbW5t1OGZmlmPz5y8j\nWez83HQbnZZZObjmrALU1tYyffpvKXTsLPwactOmmZmV8u67C2m68Pm772YYUIVxclYBbr75jzSd\nSuPmm891M6eZmbWgiuYLn3tN5nJxclYxmi5ga2ZmVtpWWw1pVZl1DSdnFWD06G2ZPftaiuerGT36\nY1mGZGZmOTZ58pk89NAECuuc9+t3HpMnT882qAqiiMg6hjaRFN0t5qwNGbIz9fVfBV5LS3akuvp3\nLF78SpZhmZlZjtXW1qbdYpL5Mt1PuX0kERFtmvndNWcV4L33FgLTKe7Y+d57XobDzMxKmzp1arOB\nZLvssgtlLD6PAAAgAElEQVQXXHBBtoFVCNecVQBpMHAVGzp2TgfOJmJJdkGZmVluJS0uF1F836iu\nvsQtLu3gmjNrQanp7DzFnZmZbYwHkmXFyVkFqK6G+vrGMz1XV/urNzOz0vbaa0dmzWo8kGyvvT6T\nZUgVxXfoijAY+Cpwd/r8DOB32YVjZma59uSTr9F0fswnn7wks3gqjdu2KsDKlStIluG4M91Gp2Vm\nZmbNrVmzulVl1jVcc1YR1gLnFD0/B2hT30QzM6sgW289iGXLGt83tt56eGbxVBonZxWgX7+BrFw5\nBihUSR9Ev35PZRmSmZnl2E477cKrrw4kWfQcYDd22mloliFVFDdrVoCxYw8AZgIXpdvMtMzMzKy5\nbbYZQDJa8/J0eyYts3JwzVkFmD9/GU07ds6ff3eL+5uZWWW7556HSAaPbRhIds89HkhWLk7OzMzM\nrJGlSxfTdGWZpUsbMoyosjg5qwBewNbMzNqioSFIErMJRWXfySyeSuPlmyrEIYccwqxZswE4+OA9\nmTlzZsYRmZlZXklbAbXAa2nJjsD1RLybWUzdVXuWb/KAgApQW1vLrFmPAdOAacya9Ri1tbUZR2Vm\nZnnVq9dS4FpgXLpdm5ZZObjmrAL06jWUhobLKV7AtqrqXNasWZhlWGZmllPSEOBKiu8bMImIxdkF\n1U255sxKiljbqjIzM7NEqUoQV4yUiwcEVICtt/4ICxY0nen5I5nFY2Zmefc+MLHo+URgeUaxVB4n\nZxXgk58cw4IFCyme6fmTn/RMz2Zm1pJBJEv/TUqfKy2zcnCfswowZMgQ6usbSCaiBZhIdXUVixe7\n74CZmTXn+0bnaU+fM9ecVYD6emi6QkB9/aSWdjczswq3dGkVjQcEwNKl57a4v3Wu3A0IkHS4pBck\nvSzpvKzjMTMzq0w/AIak2w8yjqWy5KpZU1JP4EXgYGAe8DhwQkTMKdrHzZptNGDAAJYv70Fx9XT/\n/utYtmxZlmGZmVlO9ejRg4gBFN83pGWsW7cuy7C6pc2hWfMzwCsR8TqApF8BRwFzNnaQbVzv3kOB\nMcAlackh9O79VIYRmZlZnkUMAn5CcbNmxHczi6fS5K1Zc1vgzaLnb6Vl1mFjgVfSbWzGsZiZWb6V\nquhpU+WPdUDeas7cXtkFJk06jQsvbDxfzaRJ7thpZmalSSuIaHzfkD7MLJ5Kk7fkbB4wouj5CJLa\ns0amTJmy/nFNTQ01NTVdHVe3dsEFFwBw5ZVJs+akSeeuLzMzM2vqj3+8i8MPPwq4MC35kD/+8a4s\nQ+o26urqqKur69A58jYgoIpkQMCXgPnAY3hAgJmZWdnNmDGDadOuAWDy5DM57LDDMo6oe2rPgIBc\nJWcAkr4MXAX0BH4ZET9u8rqTMzMzM+sWNovkbFOcnJmZmVl30Z7kLG+jNc3MzMwqmpMzMzMzsxxx\ncmZmZmaWI07OzMzMzHLEyZmZmZlZjjg5MzMzM8sRJ2dmZmZmOeLkzMzMzCxHnJyZmZmZ5YiTMzMz\nM7MccXJmZmZmliNOzszMzMxyxMmZmZmZWY44OTMzMzPLESdnZmZmZjni5MzMzMwsR5ycmZmZmeWI\nk7MKU1dXl3UI3ZavXcf4+nWMr1/7+dp1jK9f+Tk5qzD+R9Z+vnYd4+vXMb5+7edr1zG+fuXn5MzM\nzMwsR5ycmZmZmeWIIiLrGNpEUvcK2MzMzCpaRKgt+3e75MzMzMxsc+ZmTTMzM7MccXJmZmZmliNO\nzszMzMxyJPfJmaRqSTMlvSTpPkmDSuwzQtL9kp6T9KykiVnEmheSDpf0gqSXJZ3Xwj4/S1//q6Q9\nyx1jnm3q+kk6Kb1uT0v6i6Q9sogzr1rz95fut4+kBklfK2d8edbKf7s1kman/6+rK3OIudaKf7tb\nSfqTpKfS61ebQZi5JOm/JC2U9MxG9vF9owWbun5tvm9ERK434HLg3PTxecA/l9hnGDAmfdwfeBEY\nlXXsGV2vnsArwA5AL+CpptcCOAL4Q/r4s8AjWcedl62V129f4CPp48N9/dp2/Yr2+2/gXmB81nHn\nYWvl394g4Dlgu/T5VlnHnZetlddvCvDjwrUDFgNVWceehw04ENgTeKaF133f6Nj1a9N9I/c1Z8A4\nYHr6eDrw1aY7RMTbEfFU+ng5MAfYpmwR5stngFci4vWIWAP8CjiqyT7rr2lEPAoMkjS0vGHm1iav\nX0Q8HBHvp08fBbYrc4x51pq/P4BvA78G3ilncDnXmmt3InBnRLwFEBHvljnGPGvN9VsADEwfDwQW\nR0RDGWPMrYh4EFiykV1839iITV2/tt43ukNyNjQiFqaPFwIb/WOQtANJ9vpo14aVW9sCbxY9fyst\n29Q+TjASrbl+xU4H/tClEXUvm7x+krYluWlenRZ5Pp9Ea/72dgGq024cT0g6pWzR5V9rrt+1wCck\nzQf+CnynTLFtDnzf6DybvG9UlSmQjZI0k6RpsqkLip9ERGxsElpJ/Ul+jX8nrUGrRK290TWdEM83\nyESrr4OkLwBfB/bvunC6ndZcv6uA89N/z6L532Klas216wXsBXwJ2AJ4WNIjEfFyl0bWPbTm+v0j\n8FRE1Ej6GDBT0qciYlkXx7a58H2jg1p738hFchYRh7T0WtrBblhEvC1pOLCohf16AXcCN0XE77oo\n1O5gHjCi6PkIkl84G9tnu7TMWnf9SDtzXgscHhEbawqoNK25fp8GfpXkZWwFfFnSmoi4uzwh5lZr\nrt2bwLsRsRJYKekB4FOAk7PWXb/9gKkAEfGqpNeA3YAnyhJh9+b7Rge15b7RHZo17wYmpI8nAM0S\nr/TX9y+B5yPiqjLGlkdPALtI2kFSb+A4kmtY7G7gVABJnwPeK2o6rnSbvH6Stgd+A5wcEa9kEGOe\nbfL6RcROEbFjROxIUtP9TSdmQOv+7d4FHCCpp6QtSDpmP1/mOPOqNdfvBeBggLS/1G7A38oaZffl\n+0YHtPW+kYuas034Z+B2SacDrwPHAkjaBrg2Io4kqR48GXha0uz0uO9HxJ8yiDdTEdEg6VvADJLR\nS7+MiDmSvpG+/h8R8QdJR0h6BVgBnJZhyLnSmusH/AAYDFyd1v6siYjPZBVznrTy+lkJrfy3+4Kk\nPwFPA+tI/h/o5IxW/+39CLhO0l9JKifOjYj6zILOEUm3AgcBW0l6E7iYpBnd941W2NT1o433Da+t\naWZmZpYj3aFZ08zMzKxiODkzMzMzyxEnZ2ZmZmY54uTMzMzMLEecnJmZmZnliJMzMzMzsxxxcmZm\n3ZKkCyQ9K+mvkmZL6vBcc5LGSjqvk+Kr1CXkzKyDPM+ZmXU7kvYFpgEHRcQaSdVAn4hY0IpjqyKi\noQwxLouIAV39Pma2+XHNmZl1R8NI1phcAxAR9RGxQNLraaKGpL0l3Z8+niLpRkkPATdIeljS7oWT\nSaqT9GlJtZJ+LmmgpNeLXt9S0hvpskkfk/RHSU9IekDSbuk+O6bnfVrSpWW8Fma2mXFyZmbd0X3A\nCEkvSvo3SZ9PyzfWFPBx4EsRcSJwGxuWghsODIuI/yvsGBFLgack1aRFXwH+FBFrgWuAb0fE3sD3\ngH9P9/kp8G8RsQcwvzM+pJlVJidnZtbtRMQK4NPAmcA7wG2Sajd2CHB3RHyYPr8d+Lv08bHAHSWO\nuY1k8WyA49P36A/sB9yRruP7C5JaPNLyW9PHN7X1M5mZFXSHhc/NzJqJiHXA/wD/I+kZoBZoYMOP\nzr5NDvmg6Nj5khZLGk2SnH2j8FLR/vcAP5I0GNgL+G9gALAkIvbs5I9jZraea87MrNuRtKukXYqK\n9gReT7e907LxxYeUOM1twHnAwIh4tul+EbEceBz4GXBPJJYCr0n6uzQOSdojPeQvJDVsACe186OZ\nmTk5M7NuqT9wvaTnJP2VpD/ZxcA/AT+V9DhJLVqhJixo3h/t1yTNlrcXlTXd7zag0Eet4CTgdElP\nAc8C49Ly7wD/IOlpYJsS72dm1iqeSsPMzMwsR1xzZmZmZpYjTs7MzMzMcsTJmZmZmVmOODkzMzMz\nyxEnZ2ZmZmY54uTMzMzMLEecnJmZmZnliJMzMzMzsxxxcma2GZF0taQLM47hekmXZBmDNSZpe0nL\nJJVaxsrMcsbJmVkXk3SApP+V9F662PZDkvbe9JFtFxHfjIhLu+LcbQmDTly6KF2/8m+Snuusc27k\nvY6X9Kik5ZIWSnpE0je7+n07QtIOktZJ6tGkfH2SHBFvRMSA2MSSMJJqJT3YlfGa2aY5OTPrQpIG\nAvcCPwUGA9uSrP/4YTvOpSxqPiRVteewTgzh80Af4KNdldQCSJoMXAVcBgyNiKHAWcD+knq3cEye\n/x/aqUlyR2X192vWHeX5fyxmm4NdgYiI2yKxKiJmRsQzAJKmSLqxsHPTWhBJdZIulfQXYAXwvXRR\nb4qO+a6ku9LH62tLJM2RdGTRflWS3pE0Jn0+Ll04fImk+yV9vGjf1yWdmy7ivUxST0nnSXpL0lJJ\nL0j64kY+91aS7kv3rZO0fXref5N0RZP475Z09kbONQG4E7grfVx87I6SHkjfZ2Z6/uLr+bm01nKJ\npKckHVTqDSR9hCRp/mZE/CYiVgBExFMRcXJErC66vldL+oOk5UCNpFHpZ1wi6VlJY4vOWyfp9KLn\njWqm0u/625JeTb+bywsJjKSdJf1PWuP6jqRfbeQataRwrqZ/V7Xpey5NayVPTL//XwD7KmkCrS9c\nG0k3SFqU/l1cUBRjD0nT0vj+Julbm/j73UnSaZKeT9/7VUlnFl2PmvRv7Hvp+82X9FVJR0h6SUnN\n8/ntuA5m3UtEePPmrYs2YADwLnA9cDgwuMnrFwM3Fj3fAVgH9Eif1wGvA6NIfkwNBJYCOxcd8zhw\nbPr4OuCH6eOLgJuK9jsSeC59vCuwHPgS0BP4HvAyUJW+/jrwJElNXx9gN+ANYFj6+vbATi185uvT\nGA8AepPURj2YvrYPMA9Q+nwrkpv2R1s41xbA+8D+wCHAO0CvotcfBi4HqtJ93gduSF/bNr32h6fP\nD06fb1XifQ4H1hSu+0a+z+uB94B9i77fV4Dz0xi+kH72XdLX7we+XnR8beFapM/XAX8GBgEjgBeB\n09PXbgW+nz7uDezXQkyFv5meJWK9pOnfFbBlep0KMQ4Fdk8fTyiOLy27AfhtetzINMavp6+dBTwH\nbJN+hlnAWlr++60CjgB2TF//fPr975k+r0m/hwtJ/i7/Pv3Obk7ff3fgA2Bk1v+2vXnrys01Z2Zd\nKCKWkSQpAVwLLJJ0l6St01021cwTwPURMSci1kXEUpIapBMAJO1CkjjdXXRM4Zy3AuMk9U2fn5iW\nARwH3BsRf46ItcAVQD9gv6L3/VlEzIuID0luuH2AT0jqFUkfpr9tJO57I+KhSGqcLiCpjdk2Ih4n\nSQy+lO53PHB/RLzTwnm+BiyNiL8A/52WHZl+9u2BvYEfRERDuk/xdTgZ+ENE/AkgImYBT5AkB01t\nBbwbEesKBUU1bh9IOqBo399FxMPp4zHAlhHxz2kM95M0Y5+4kWvT1GUR8V5EvEmSyJ6Qlq8Gdkiv\n2+qI+N9NnOfdNN4lkpak52mpWXMdMFpSv4hYGBHPFz528U6SepL8rXw/IlZExFxgGnBKusuxwFUR\nMT8i3gN+3OQcTf9+GyLiDxHxGkBEPADcBxxYdMwaYGr6d3kbUJ2+x4o0zudJrrvZZsvJmVkXi4gX\nIuK0iBgBfJKkluGqNpzizSbPb2HDDfxE4LcRsarE+74CzCFJ0LYAxqbHAgwnqQkr7Bvp+2xb6n3T\nc50NTAEWSrpV0vAW4g3graJjVwD1JJ8bkpqYk9PHJwM30rIJwG/S86wFfseGps1tgPomn/0tNiQH\nI4FjmiQs+wPDSrzPYpKm2PX/T4yI/SJicPpaobzRZ0tjaPr9zC36rK1RfPwbRceem36Wx9Lm0tM2\ncZ4hETG4sJF8182S//T7OI6k1mu+pHsl7dbCObcCepF8puIYC38nw5vEX3xtChpdH0lfVjLQYnH6\nnRwBDCnaZXH69wiwMv3vwqLXV5LUoplttpycmZVRRLwITCdJ0iBp0tmiaJdSiUPT2o9ZJJ3jP0VS\n83RL80PWu5UkkTsKeL6otms+SfICJJ21SZrV5rX0vhFxa0QcmB4XJB3nWzKi6Nz9SWo/5qdFNwFH\npfF/nCThakbSdsAXgQmSFkhaQFJTc4SkamABUC2pX5P3LcT9BkmT8eCibUBEXF7i7R4mGaTx1Y18\npoLi6zIfGFHog5UayYbruILGiUSp73f7Jo/nAaQ1WmdGxLbAN4B/l7RTK+LbpIi4LyIOTeN5gaRW\nF5r/rb1LUpO1Q5MYC0nYAoq+6yaP179d4YGkPiT9By8Htk6TyD/QuQNIzLo9J2dmXUjSbpImSdo2\nfT6CJFkqNIs9BXxe0oi0U/r3S52m+ElErAHuIGmKHAzMbGlf4FfAYSS1JDcXld8OHCnpi5J6AZOB\nVUDJpjNJu6b79iFJYlaRNHWW3J0kgSqMcrwEeDgiCknHWyTNizcAv06bTUs5hSRx2BX4VLrtSpIY\nnJg2sT0BTJHUS9K+wFeKjr8JGCvpUCUDGvqmHc63pYm0Se6fSBKg8ZIGpJ3dx9A4uWp6fR8h6QN1\nbhpDTRpDofP+U8DXJPWTtDNwOs2dI2lQ+rcxkaQpD0nHpAkqJP3cgqQ5srVKJjyStpZ0lKQtSRKv\nFWz4LhcC26V/E4XaytuBqZL6SxoJfJfk2pK+9h1J20gaBJxH8wSvOI7e6fYusE7Sl4FD2/CZzCqC\nkzOzrrUM+CzwqJLRfQ8DT5MkQ0TETJKb8dMkHfvvofnNrVS/oVtI+m3dUdxPiibTJ0TE2yQJ177p\n+xTKXyJpUvw5SSf7I4GxEdHQwufoQ9Kf6B2S2pKtKJ1IFmK4mWSww2JgTzY0YxZMB0az8SbNU4F/\nj4hFRdtCkhGFp6b7nJR+tsUkSeBtJH21CkngUcA/AotIatIm08L/9yLiX4BJJM2Jb6fbL9LnhWS6\n6fVdQ9Jc/GWSa/OvwCnp9QX4SRrPQpLBGjfR/Pu8C/g/YDZJf7VfpuV7A49IWpbuMzEiXm/hWpX6\nG2k6lUbhcQ+SBGseyXU7ECjM5fZnkg7+b0talJZ9mySB+xvwIMl3e1362rUkfcaeTj/D74G1Jf4m\nkwdJH8yJJEldPckPlbs28VlyMx2IWbkURkx1/omTX4E3AFuT/OO6JiJ+JmkKyQicQgfgf4yIP6bH\nfB/4OsmvuIkRcV+XBGdmmZJ0IMlI0pGb3Llt572NpPn2nzrzvF1F0jqSkbcbG1zRbaQ1YVdHxA5Z\nx2LWnbVncsnWWgN8NyKeSvuc/J+kmSSJ2pURcWXxzpJ2J+mkujtJZ9NZknZt8gvMzLq5tMnsbDb0\nc+rIufYGlgCvkTTfjgN+1NHzWuukI4G/SFJ7NpSktvQ3mQZlthnosmbNiHg7Ip5KHy8nGTVW6OtR\nqi/EUcCtEbEmrbp/BfhMV8VnZuUnaRRJMjWUto1YbckwkrnElpE0IZ4VEX/thPOWS3dvshPJCN56\nknnxngN+kGVAZpuDrqw5W0/SDiT9Th4hGcr+bUmnknTmnZx2xt0mfb3gLRoP6zezbi4i5gD9O/F8\n95L00+qWIqJn1jF0RESsxD+izTpdlydnaZPmr4HvRMRySVcDP0xfvoRkQsNSI5igxK9KSd39l6aZ\nmZlVkIho03QxXTpaM+1bcidJx9/fAaQjriKdZPA/2fCrax6N58jZjsZzLq0XOVhaIW/bxRdfnHkM\nedt8TXxdfF18XXxNfF2y3tqjy5KzdFLGX5KMnLqqqLx4VvGjgWfSx3cDx0vqLWlHYBfgsa6Kz8zM\nzCyPurJZc3+SuY2eljQ7LftH4IR0YscgGWH1DYCIeF7S7STrpjUA/y/am3KamZmZdVNdlpxFxEOU\nrpn740aO+REeBt8uNTU1WYeQO74mpfm6lObrUpqvS3O+JqX5unSeLpuEtqtIcoWamZmZdQuSiDYO\nCCjLVBpmZmZWHkmXb8tCZ1UeOTkzMzPbzLiFqfw6Myn2wudmZmZmOeLkzMzMzCxHnJyZmZmZ5YiT\nMzMzM8vcN7/5TS699NJ2HVtbW8tFF13UyRFlxwMCzMzMrMvtsMMOLFq0iKqqKnr27Mnuu+/Oqaee\nyplnnokkrr766nafW9JmNUrVNWdmZmabuULy0pVba2K49957Wbp0KW+88Qbnn38+l112Gaeffnqn\nfMbNaYSqkzMzM7OKEF24tc2AAQMYO3Yst912G9OnT+e5555r1jR57733MmbMGAYPHsz+++/PM888\ns/612bNns9deezFw4ECOP/54Vq1a1eYY8szJmZmZmWVin332YbvttuPBBx9sVPs2e/ZsTj/9dK69\n9lrq6+v5xje+wbhx41izZg2rV6/mq1/9KhMmTGDJkiUcc8wx3HnnnW7WNDMzM+sM22yzDfX19cCG\niVyvueYavvGNb7DPPvsgiVNPPZU+ffrw8MMP88gjj9DQ0MB3vvMdevbsyfjx49lnn32y/AidzgMC\nzMzMLDPz5s2jurq6UdncuXO54YYb+PnPf76+bM2aNSxYsICIYNttt220/8iRI93nzMzMzKyjHn/8\ncebNm8eBBx7YqHz77bfnggsuYMmSJeu35cuXc9xxxzF8+HDmzZvXaP+5c+e6WdNsc1SO0UxZb2Zm\nWSrUbi1dupR7772XE044gVNOOYVPfOITRMT618844wx+8Ytf8NhjjxERrFixgt///vcsX76c/fbb\nj6qqKn72s5+xZs0afvOb3/D4449n+bE6nZs1zYpNyTqALjQl6wDMrNKNHTuWqqoqevTowSc+8Qkm\nT57MWWedBTSeq+zTn/401157Ld/61rd4+eWX6devHwceeCAHHXQQvXr14je/+Q1nnHEGF154IUcc\ncQTjx4/P8mN1OnW3NlpJ0d1itu5B0uadwEzZvOYBMrPSJDX7t16OmvNK//9LqeteVN6mL8A1Z2Zm\nZpu5Sk+cuhv3OTMzMzPLESdnZmZmZjni5MzMzMwsR5ycmZmZmeWIkzMzMzOzHHFyZmZmZpYjTs7M\nzMzMcsTJmZmZmW1WvvnNb3LppZd2+nmnTJnCKaec0unnbcrJmZmZmZXFQw89xH777cegQYMYMmQI\nBxxwAE888USnv8/VV1/NhRde2OnnLdcaxV4hwMzMbDOXh+Wbli5dyle+8hX+4z/+g2OPPZYPP/yQ\nBx98kD59+rTrfcqVKGXByZmZmVklmJLtuV966SUkcdxxxwHQt29fDjnkkOTwKVN49dVXufHGGwF4\n/fXX2WmnnWhoaKBHjx7U1NRwwAEHcP/99zN79mymTJnCHXfcweOPP77+/D/5yU+oq6vjrrvuora2\nlhEjRnDJJZcwatQorrjiCo488kgAGhoaGD58ODNnzmTMmDE88sgjTJo0iTlz5jBy5Eh++tOfctBB\nBwHw2muvUVtby+zZs/nc5z7Hbrvt1okXrWVu1jQzM7Mut9tuu9GzZ09qa2v505/+xJIlS9a/1ppa\nsJtuuon//M//ZPny5Zx11lm8+OKLvPLKK+tfv+WWWzjppJPWn69wzhNPPJFbb711/X4zZsxg6623\nZsyYMcybN4+vfOUr/OAHP2DJkiVcccUVjB8/nsWLF68/dp999mHx4sVcdNFFTJ8+vSw1dk7OzMzM\nrMsNGDCAhx56CEmcccYZbL311hx11FEsWrRok02ikqitrWXUqFH06NGDgQMHctRRR61Pul5++WVe\nfPFFxo0bt/6YwjlPOOEE7r77blatWgUkSdwJJ5wAJAnfEUccweGHHw7AwQcfzN57783vf/973njj\nDZ544gkuueQSevXqxYEHHsjYsWPLsoi8kzMzMzMri49//ONcd911vPnmmzz77LPMnz+fs88+u1W1\nUSNGjGj0vLhG7JZbbuHoo4+mb9++zY7beeedGTVqFHfffTcffPAB99xzDyeeeCIAc+fO5Y477mDw\n4MHrt7/85S+8/fbbzJ8/n8GDB9OvX7/15xo5cmRHPn6ruc+ZmZmZld1uu+3GhAkTuOaaa9hrr734\n4IMP1r/29ttvN9u/aQJ38MEH88477/DXv/6VX/3qV1x11VUtvtcJJ5zArbfeytq1a9l9993Zaaed\nANh+++055ZRTuOaaa5odM3fuXJYsWcIHH3zAFltssb6sZ8+e7fq8beGaMzMzM+tyL774IldeeSXz\n5s0D4M033+TWW29l3333ZcyYMTzwwAO8+eabvP/++/z4xz9udnzT5sRevXpxzDHHcM4557BkyZL1\ngwtK7Xv88cczY8YMfvGLX6zvlwZw8sknc88993Dfffexdu1aVq1aRV1dHfPmzWPkyJHsvffeXHzx\nxaxZs4aHHnqIe++9tzMvSYucnJmZmVmXGzBgAI8++iif/exn6d+/P/vuuy977LEH06ZN4+CDD+a4\n445jjz32YJ999mHs2LHNaspKNX2eeOKJ/PnPf+aYY46hR48ejfYt3n/YsGHst99+PPzww+tHiwJs\nt9123HXXXfzoRz9i6623Zvvtt2fatGmsW7cOSJpLH330Uaqrq/nhD3/IhAkTOvuylKRydGzrTJKi\nu8Vs3YOkrh1qnrUpm56HyMy6P0nN/q3nYZ6zzV2p615U3qYvwH3OzMzMNnOVnjh1N27WNDMzM8sR\nJ2dmZmZmOeLkzMzMzCxHnJyZmZmZ5UiXJWeSRki6X9Jzkp79/+3de4xmdX3H8fcHEFBRthSzIKBs\nWlHW2rJaVwQtg6UGSQu2tiC90dbaJmq99ApNY8cmVWljxdhgk1oQidCiVCu2tSzotCZNsVSM4IKX\nxqmswizxfolmgW//eM7Cw+wwzMKcOb+d834lT+ac33lm8t1vnt357O9cfkle3Y0flmRbks8muTbJ\nhqnvuSDJ55LcluSFfdUmSZLUqj5nznYBr6uqpwMnAq9McjxwPrCtqo4Dru/2SbIZOAfYDJwOXJzE\nmcpp5wQAABLPSURBVD1JkjQqvT1Ko6ruBO7str+d5FbgKOBM4JTubZcBc0wC2lnAlVW1C5hP8nlg\nK/BffdUoSdJ6tBbPNVN/1uQ5Z0mOBbYANwAbq2qhO7QAbOy2n8gDg9gOJmFOkiStkM802/f1ftow\nySHA1cBrqupb08e6R/0v9ynyEyZJkkal15mzJI9iEswur6oPdMMLSY6oqjuTHAns7Ma/BBwz9e1H\nd2N7mJ2dvW97ZmaGmZmZVa5ckiRp783NzTE3N/eIfkZva2tmcsL7MuArVfW6qfG/6MYuTHI+sKGq\nzu9uCLiCyXVmRwHXAT+8eCFN19ZUX1xbU5K02lpbW/Nk4JeBTyW5qRu7AHgzcFWSlwHzwNkAVbU9\nyVXAduBu4BWmMEmSNDa9zZz1xZkz9cWZM0nSans4M2c+R0ySJKkhhjNJkqSGGM4kSZIaYjiTJElq\niOFMkiSpIYYzSZKkhhjOJEmSGmI4kyRJaojhTJIkqSGGM0mSpIYYziRJkhpiOJMkSWqI4UySJKkh\nhjNJkqSGGM4kSZIaYjiTJElqiOFMkiSpIYYzSZKkhhjOJEmSGmI4kyRJaojhTJIkqSGGM0mSpIYY\nziRJkhpiOJMkSWqI4UySJKkhhjNJkqSGGM4kSZIaYjiTJElqiOFMkiSpIYYzSZKkhhjOJEmSGmI4\nkyRJaojhTJIkqSGGM0mSpIYYziRJkhpiOJMkSWqI4UySJKkhhjNJkqSGGM4kSZIaYjiTJElqiOFM\nkiSpIYYzSZKkhvQazpJckmQhyc1TY7NJdiS5qXu9aOrYBUk+l+S2JC/sszZJkqQW9T1zdilw+qKx\nAv6qqrZ0r38FSLIZOAfY3H3PxUmc2ZMkSaNyQJ8/vKo+luTYJQ5libGzgCurahcwn+TzwFbgv/qr\nUNJykqX+qq4fVTV0CZK0h17D2TJ+J8mvAjcCv1dVXweeyAOD2A7gqCGKkzRldugCejI7dAGStLQh\nThu+A9gEnADcAbxlmff631pJkjQqaz5zVlU7d28neSdwTbf7JeCYqbce3Y3tYXZ29r7tmZkZZmZm\nVrtMSZKkvTY3N8fc3Nwj+hnp+5qL7pqza6rqGd3+kVV1R7f9OuDZVfWL3Q0BVzC5zuwo4Drgh2tR\ngUkWD0mrIsn6PtU1u/fXWK3rnsx6zZmk/iWhqvbqAt5eZ86SXAmcAhye5HbgT4GZJCcwOWX5BeC3\nAapqe5KrgO3A3cArTGGSJGlsep85W23OnKkv6/3ORHDm7AFmnTmT1L/mZs6kfc96/mW9/sOnJK0H\nPuRVkiSpIYYzSZKkhhjOJEmSGmI4kyRJaojhTJIkqSGGM0mSpIYYziRJkhrykOEsyfOWGDu5n3Ik\nSZLGbSUzZ29fYuyvV7sQSZIkLbNCQJLnAicBT0jyu9z/ePHH4elQSZKkXiy3fNOBTILY/t3X3b4J\n/HyfRUmSJI3Vg4azqvp34N+TvKuq5teuJEmSpPFaycLnByX5W+DYqfdXVb2gt6okSZJGaiXh7L3A\nO4B3Avd0Y9VbRZIkSSO2knC2q6re0XslkiRJWtFdl9ckeWWSI5MctvvVe2WSJEkjtJKZs19jchrz\n9xeNb1r1aiRJkkbuIcNZVR27BnVIkiSJFYSzJOexxA0AVfXuXiqSJEkasZWc1nw294ezRwMvAD4B\nGM4kSZJW2UpOa75qej/JBuAfeqtIkiRpxB7OGpnfxZsBJEmSerGSa86umdrdD9gMXNVbRZIkSSO2\nkmvO3tJ9LeBu4ItVdXt/JUmSJI3XQ57WrKo54Dbg8cAPAN/vuSZJkqTReshwluRs4AbgF4CzgY8n\n+YW+C5MkSRqjlZzW/BPg2VW1EyDJE4DrmSyILkmSpFW0krs1A9w1tf+VbkySJEmrbCUzZx8G/i3J\nFUxC2TnAv/ZalSRJ0kg9aDhL8hRgY1X9QZKXACd3h/4TuGItilO/kvU9AVq1x6pjkiQ1b7mZs4uA\nCwCq6mrgaoAkPwq8FfiZ3qtT/2aHLqAns0MXIEnSw7PcNWcbq+pTiwe7MVcIkCRJ6sFy4WzDMscO\nXu1CJEmStHw4uzHJby0eTPJy4H/6K0mSJGm8lrvm7LXA+5P8EveHsWcBBwE/23dhkiRJY/Sg4ayq\n7kxyEnAq8CNM1tb8UFV9ZK2KkyRJGptln3NWk2cRfKR7SZIkqWcrWSFAkiRJa8RwJkmS1BDDmSRJ\nUkMMZ5IkSQ3pNZwluSTJQpKbp8YOS7ItyWeTXJtkw9SxC5J8LsltSV7YZ22SJEkt6nvm7FLg9EVj\n5wPbquo44PpunySbgXOAzd33XJzEmT1JkjQqvYafqvoY8LVFw2cCl3XblwEv7rbPAq6sql1VNQ98\nHtjaZ32SJEmtGWJmamNVLXTbC8DGbvuJwI6p9+0AjlrLwiRJkoa27ENo+1ZVlaSWe8tSg7Ozs/dt\nz8zMMDMzs7qFSZIkPQxzc3PMzc09op8xRDhbSHJEtzzUkcDObvxLwDFT7zu6G9vDdDiTJElqxeJJ\noze84Q17/TOGOK35QeC8bvs84ANT4y9NcmCSTcBTgI8PUJ8kSdJgep05S3IlcApweJLbgdcDbwau\nSvIyYB44G6Cqtie5CtgO3A28olvbU5IkaTR6DWdVde6DHDrtQd7/RuCN/VUkSZLUNp8jJkmS1BDD\nmSRJUkMMZ5IkSQ0xnEmSJDXEcCZJktQQw5kkSVJDDGeSJEkNMZxJkiQ1xHAmSZLUEMOZJElSQ3pd\nvkmS1pskQ5fQK5c0loZnOJOkvTU7dAE9mR26AElgOJP0UGaHLkCSxsVwJukhrNfTXOv79KSkfZc3\nBEiSJDXEcCZJktQQw5kkSVJDDGeSJEkNMZxJkiQ1xHAmSZLUEMOZJElSQwxnkiRJDTGcSZIkNcRw\nJkmS1BCXbxq72aELkCRJ0wxno+e6iZIktcTTmpIkSQ0xnEmSJDXEcCZJktQQw5kkSVJDDGeSJEkN\nMZxJkiQ1xHAmSZLUEMOZJElSQwxnkiRJDTGcSZIkNcRwJkmS1BDDmSRJUkNGsfD52972NnZ8ecfQ\nZfQihN/49d/gaU972tClSJKkVTCKcHbR31zE/OPn4XFDV7L6Dr7lYGZOmTGcSZK0TowinAGwBThy\n6CJW30ELBw1dgiRJWkWDhbMk88A3gXuAXVW1NclhwD8ATwbmgbOr6utD1ShJkrTWhrwhoICZqtpS\nVVu7sfOBbVV1HHB9ty9JkjQaQ5/WzKL9M4FTuu3LgDkMaJJaMzt0AZLWs6Fnzq5LcmOSl3djG6tq\nodteADYOU5okLafW6UtSC4acOTu5qu5I8gRgW5Lbpg9WVSXxXwtJkjQqg4Wzqrqj+3pXkvcDW4GF\nJEdU1Z1JjgR2LvW9s7Oz923PzMwwMzPTf8GSJEkPYW5ujrm5uUf0M1K19pNTSR4D7F9V30ryWOBa\n4A3AacBXqurCJOcDG6rq/EXfW3tb86bjNzF/yvy6fJTGoe87lCsuvIIzzjhjr783Cev3VEbY28/J\n+u4H2JPF9r4fYE8k7Z0kVNXia+yXNdTM2Ubg/ZN/5DgAeE9VXZvkRuCqJC+je5TGQPVJkiQNYpBw\nVlVfAE5YYvyrTGbPJEmSRsmFzyVJkhpiOJMkSWqI4UySJKkhhjNJkqSGDL18kyRpH9fdeb9u+XgR\nrTXDmSTpkZsduoCezA5dgMbI05qSJEkNMZxJkiQ1xHAmSZLUEMOZJElSQwxnkiRJDTGcSZIkNcRw\nJkmS1BDDmSRJUkMMZ5IkSQ0xnEmSJDXEcCZJktQQw5kkSVJDDGeSJEkNOWDoAtbCvffcC98DvjN0\nJavvnl33sGvXrqHLkCRJq2QU4WznHXfBe4auoh/frm9zyy23cNZZZw1diiRJWgWjCGdHHH4c8/Pv\nAk4YupRVd+ihZ7Bly5ahy5A0drNDFyCtH6MIZ5KkvtXQBfQkQxegEfKGAEmSpIYYziRJkhpiOJMk\nSWqI4UySJKkhhjNJkqSGGM4kSZIaYjiTJElqiM85kyRplSXr+/loVev1uXZtMJxJktSH2aEL6Mns\n0AWsf57WlCRJaojhTJIkqSGe1pQkqQ+zQxegfZXhTJKkXqzXi+bX980OLfC0piRJUkMMZ5IkSQ0x\nnEmSJDXEcCZJktQQw5kkSVJDDGeSJEkNae5RGklOBy4C9gfeWVUXDlySJEl6hFxvdOWamjlLsj/w\n18DpwGbg3CTHD1vVvmFubm7oEppjT5ZmX5ZmX5ZmX/ZkT5ZmX1ZPU+EM2Ap8vqrmq2oX8PfAWQPX\ntE/wL8We7MnS7MvS7MvS7Mue7MnSVtaXWqev1dVaODsKuH1qf0c3JkmSNAqtXXPWy1oX++8Phxzy\navbb79A+fvygvv/9G4cuQZIkraKs5gVsj1SSE4HZqjq9278AuHf6poAk7RQsSZL0EKpqr+6GaC2c\nHQB8BvhJ4MvAx4Fzq+rWQQuTJElaI02d1qyqu5O8Cvg3Jo/S+DuDmSRJGpOmZs4kSZLGrrW7NR9U\nktOT3Jbkc0n+aOh6hpLkkiQLSW6eGjssybYkn01ybZINQ9Y4hCTHJPlokk8nuSXJq7vx0fYmycFJ\nbkjyySTbk7ypGx9tT6Yl2T/JTUmu6fZH35ck80k+1fXl492YfUk2JHlfklu7v0vPGXNfkjy1+4zs\nfn0jyavH3JPdklzQ/R66OckVSQ56OH3ZJ8KZD6d9gEuZ9GHa+cC2qjoOuL7bH5tdwOuq6unAicAr\nu8/IaHtTVd8DTq2qE4AfBU5N8jxG3JNFXgNs5/67xO3LpBczVbWlqrZ2Y/YF3gb8S1Udz+Tv0m2M\nuC9V9ZnuM7IFeBbwXeD9jLgnAEmOBV4OPLOqnsHk8qyX8jD6sk+EM3w47X2q6mPA1xYNnwlc1m1f\nBrx4TYtqQFXdWVWf7La/DdzK5Bl5o+5NVX232zyQyT8UX2PkPQFIcjRwBvBOYPddVKPvS2fxXWWj\n7kuSQ4HnV9UlMLk2uqq+wcj7MuU0Jr+fb8eefJPJRMFjuhscH8Pk5sa97su+Es58OO3yNlbVQre9\nAGwcspihdf972QLcwMh7k2S/JJ9k8mf/aFV9mpH3pPNW4A+Ae6fG7Mtk5uy6JDcmeXk3Nva+bALu\nSnJpkk8k+dskj8W+7PZS4Mpue9Q9qaqvAm8BvsgklH29qrbxMPqyr4Qz71pYoZrc4THafiU5BLga\neE1VfWv62Bh7U1X3dqc1jwZ+Ismpi46PridJfhrYWVU3secsETDOvnRO7k5VvYjJpQHPnz440r4c\nADwTuLiqngl8h0WnpUbaF5IcCPwM8N7Fx8bYkyQ/BLwWOBZ4InBIkl+efs9K+7KvhLMvAcdM7R/D\nZPZMEwtJjgBIciSwc+B6BpHkUUyC2eVV9YFu2N4A3WmYf2ZyfcjYe3IScGaSLzD5H/8LklyOfaGq\n7ui+3sXkGqKt2JcdwI6q+u9u/31MwtqdI+8LTEL8/3SfF/Cz8uPAf1bVV6rqbuAfgefyMD4r+0o4\nuxF4SpJju6R+DvDBgWtqyQeB87rt84APLPPedSlJgL8DtlfVRVOHRtubJIfvvisoyaOBnwJuYsQ9\nAaiqP66qY6pqE5NTMh+pql9h5H1J8pgkj+u2Hwu8ELiZkfelqu4Ebk9yXDd0GvBp4BpG3JfOudx/\nShNG/llhcqPIiUke3f1OOo3JTUd7/VnZZ55zluRFwEXc/3DaNw1c0iCSXAmcAhzO5Nz164F/Aq4C\nngTMA2dX1deHqnEI3V2I/wF8ivunjC9gssrEKHuT5BlMLj7dr3tdXlV/meQwRtqTxZKcAvxeVZ05\n9r4k2cRktgwmp/LeU1VvGntfAJL8GJObRw4E/hf4dSa/i0bbly7A/x+wafclJH5WIMkfMglg9wKf\nAH4TeBx72Zd9JpxJkiSNwb5yWlOSJGkUDGeSJEkNMZxJkiQ1xHAmSZLUEMOZJElSQwxnkiRJDTGc\nSRqFJC9Ocm+Spw5diyQtx3AmaSzOBT7UfZWkZhnOJK17SQ4BngO8isnybyTZL8nFSW5Ncm2Sf07y\nku7Ys5LMJbkxyYd3r4snSWvBcCZpDM4CPlxVXwTuSvJM4OeAJ1fV8cCvMFmguJI8Cng78JKq+nHg\nUuDPB6pb0ggdMHQBkrQGzgXe2m2/t9s/gMl6d1TVQpKPdsefCjwduG6ydjH7A19e02oljZrhTNK6\n1i3GfCrwI0mKSdgqJot850G+7dNVddIalShJD+BpTUnr3c8D766qY6tqU1U9CfgC8FXgJZnYCMx0\n7/8M8IQkJwIkeVSSzUMULmmcDGeS1ruXMpklm3Y1cASwA9gOXA58AvhGVe1iEuguTPJJ4CYm16NJ\n0ppIVQ1dgyQNIsljq+o7SX4QuAE4qap2Dl2XpHHzmjNJY/ahJBuAA4E/M5hJaoEzZ5IkSQ3xmjNJ\nkqSGGM4kSZIaYjiTJElqiOFMkiSpIYYzSZKkhhjOJEmSGvL/au/2J79qpQcAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a8a17d0>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Kernel Density Estimation Plots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get the unique values of Pclass:\n",
      "passenger_classes = 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')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<matplotlib.legend.Legend at 0x10c9afad0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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89thjZ1y/d+9eOnfuzFlnnUWVKlXo0qULW7ZsyTn/3nvv0bBhw5xWwYkTJwKQ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\nJO3YTKlUKVMBuXUr9OsnqycIkVWkeVQIZ9GpE/j7w6efAnD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       "text": [
        "<matplotlib.figure.Figure at 0x108498c50>"
       ]
      }
     ],
     "prompt_number": 8
    }
   ],
   "metadata": {}
  }
 ]
}