Arusarka_8_30.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--<img width=700px; src=\"../img/logoUPSayPlusCDS_990.png\"> -->\n",
    "\n",
    "<p style=\"margin-top: 3em; margin-bottom: 2em;\"><b><big><big><big><big>Introduction to Pandas</big></big></big></big></b></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "pd.options.display.max_rows = 8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Let's start with a showcase\n",
    "\n",
    "#### Case 1: titanic survival data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"titanic.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Starting from reading this dataset, to answering questions about this data in a few lines of code:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What is the age distribution of the passengers?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x207bc016e80>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Age'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**How does the survival rate of the passengers differ between sexes?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived\n",
       "Sex             \n",
       "female  0.742038\n",
       "male    0.188908"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Sex')[['Survived']].aggregate(lambda x: x.sum() / len(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Or how does it differ between the different classes?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x207be733940>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Zui42nwHekOS5AEmeM2DNpcB/J3k6C2fo9NY+v6rurKp9wP3AxiQ/BZysqj9l4cvSL6oPg9JTi2fouqj0vuD8fcA/Jnkc+Cpww6JlvwvcCXwd+BoLgQf4QO9Nz7DwH8NdwF7gjUkeY+HT/vav+g8hLcE3RSWpEb7kIkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1Ij/A9p0qu4gc8fmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('Pclass')['Survived'].aggregate(lambda x: x.sum() / len(x)).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the needed functionality for the above examples will be explained throughout this tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Case 2: air quality measurement timeseries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "AirBase (The European Air quality dataBase): hourly measurements of all air quality monitoring stations from Europe\n",
    "\n",
    "Starting from these hourly data for different stations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('20000101_20161231-NO2.csv', sep=';', skiprows=[1], na_values=['n/d'], index_col=0, parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BASCH</th>\n",
       "      <th>BONAP</th>\n",
       "      <th>PA18</th>\n",
       "      <th>VERS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01 01:00:00</th>\n",
       "      <td>108.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.0</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01 02:00:00</th>\n",
       "      <td>104.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01 03:00:00</th>\n",
       "      <td>97.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01 04:00:00</th>\n",
       "      <td>77.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01 05:00:00</th>\n",
       "      <td>79.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     BASCH  BONAP  PA18  VERS\n",
       "timestamp                                    \n",
       "2000-01-01 01:00:00  108.0    NaN  65.0  47.0\n",
       "2000-01-01 02:00:00  104.0   60.0  77.0  42.0\n",
       "2000-01-01 03:00:00   97.0   58.0  73.0  34.0\n",
       "2000-01-01 04:00:00   77.0   52.0  57.0  29.0\n",
       "2000-01-01 05:00:00   79.0   52.0  64.0  28.0"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "to answering questions about this data in a few lines of code:\n",
    "\n",
    "**Does the air pollution show a decreasing trend over the years?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x207be787d30>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['1999':].resample('M').mean().plot(ylim=[0,120])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x207bbfe2e10>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['1999':].resample('A').mean().plot(ylim=[0,100])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**What is the difference in diurnal profile between weekdays and weekend?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x207becdde80>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['weekday'] = data.index.weekday\n",
    "data['weekend'] = data['weekday'].isin([5, 6])\n",
    "data_weekend = data.groupby(['weekend', data.index.hour])['BASCH'].mean().unstack(level=0)\n",
    "data_weekend.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will come back to these example, and build them up step by step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Pandas: data analysis in python\n",
    "\n",
    "For data-intensive work in Python the [Pandas](http://pandas.pydata.org) library has become essential.\n",
    "\n",
    "What is `pandas`?\n",
    "\n",
    "* Pandas can be thought of as *NumPy arrays with labels* for rows and columns, and better support for heterogeneous data types, but it's also much, much more than that.\n",
    "* Pandas can also be thought of as `R`'s `data.frame` in Python.\n",
    "* Powerful for working with missing data, working with time series data, for reading and writing your data, for reshaping, grouping, merging your data, ...\n",
    "\n",
    "It's documentation: http://pandas.pydata.org/pandas-docs/stable/\n",
    "\n",
    "\n",
    "** When do you need pandas? **\n",
    "\n",
    "When working with **tabular or structured data** (like R dataframe, SQL table, Excel spreadsheet, ...):\n",
    "\n",
    "- Import data\n",
    "- Clean up messy data\n",
    "- Explore data, gain insight into data\n",
    "- Process and prepare your data for analysis\n",
    "- Analyse your data (together with scikit-learn, statsmodels, ...)\n",
    "\n",
    "<div class=\"alert alert-warning\">\n",
    "<b>ATTENTION!</b>: <br><br>\n",
    "\n",
    "Pandas is great for working with heterogeneous and tabular 1D/2D data, but not all types of data fit in such structures!\n",
    "<ul>\n",
    "<li>When working with array data (e.g. images, numerical algorithms): just stick with numpy</li>\n",
    "<li>When working with multidimensional labeled data (e.g. climate data): have a look at [xarray](http://xarray.pydata.org/en/stable/)</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. The pandas data structures: `DataFrame` and `Series`\n",
    "\n",
    "A `DataFrame` is a **tablular data structure** (multi-dimensional object to hold labeled data) comprised of rows and columns, akin to a spreadsheet, database table, or R's data.frame object. You can think of it as multiple Series object which share the same index.\n",
    "\n",
    "\n",
    "<img align=\"left\" width=50% src=\"img/schema-dataframe.svg\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "..           ...       ...     ...   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500   NaN        S  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Attributes of the DataFrame\n",
    "\n",
    "A DataFrame has besides a `index` attribute, also a `columns` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=891, step=1)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "To check the data types of the different columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "                ...   \n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "Length: 12, dtype: object"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "An overview of that information can be given with the `info()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Also a DataFrame has a `values` attribute, but attention: when you have heterogeneous data, all values will be upcasted:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 3, ..., 7.25, nan, 'S'],\n",
       "       [2, 1, 1, ..., 71.2833, 'C85', 'C'],\n",
       "       [3, 1, 3, ..., 7.925, nan, 'S'],\n",
       "       ...,\n",
       "       [889, 0, 3, ..., 23.45, nan, 'S'],\n",
       "       [890, 1, 1, ..., 30.0, 'C148', 'C'],\n",
       "       [891, 0, 3, ..., 7.75, nan, 'Q']], dtype=object)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Apart from importing your data from an external source (text file, excel, database, ..), one of the most common ways of creating a dataframe is from a dictionary of arrays or lists.\n",
    "\n",
    "Note that in the IPython notebook, the dataframe will display in a rich HTML view:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>population</th>\n",
       "      <th>area</th>\n",
       "      <th>capital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>11.3</td>\n",
       "      <td>30510</td>\n",
       "      <td>Brussels</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>France</td>\n",
       "      <td>64.3</td>\n",
       "      <td>671308</td>\n",
       "      <td>Paris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Germany</td>\n",
       "      <td>81.3</td>\n",
       "      <td>357050</td>\n",
       "      <td>Berlin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>16.9</td>\n",
       "      <td>41526</td>\n",
       "      <td>Amsterdam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>64.9</td>\n",
       "      <td>244820</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  population    area    capital\n",
       "0         Belgium        11.3   30510   Brussels\n",
       "1          France        64.3  671308      Paris\n",
       "2         Germany        81.3  357050     Berlin\n",
       "3     Netherlands        16.9   41526  Amsterdam\n",
       "4  United Kingdom        64.9  244820     London"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'country': ['Belgium', 'France', 'Germany', 'Netherlands', 'United Kingdom'],\n",
    "        'population': [11.3, 64.3, 81.3, 16.9, 64.9],\n",
    "        'area': [30510, 671308, 357050, 41526, 244820],\n",
    "        'capital': ['Brussels', 'Paris', 'Berlin', 'Amsterdam', 'London']}\n",
    "df_countries = pd.DataFrame(data)\n",
    "df_countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### One-dimensional data: `Series` (a column of a DataFrame)\n",
    "\n",
    "A Series is a basic holder for **one-dimensional labeled data**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      22.0\n",
       "1      38.0\n",
       "2      26.0\n",
       "3      35.0\n",
       "       ... \n",
       "887    19.0\n",
       "888     NaN\n",
       "889    26.0\n",
       "890    32.0\n",
       "Name: Age, Length: 891, dtype: float64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "age = df['Age']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Attributes of a Series: `index` and `values`\n",
    "\n",
    "The Series has also an `index` and `values` attribute, but no `columns`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=891, step=1)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can access the underlying numpy array representation with the `.values` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([22., 38., 26., 35., 35., nan, 54.,  2., 27., 14.])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age.values[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We can access series values via the index, just like for NumPy arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.0"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Unlike the NumPy array, though, this index can be something other than integers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Braund, Mr. Owen Harris</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heikkinen, Miss. Laina</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Futrelle, Mrs. Jacques Heath (Lily May Peel)</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Graham, Miss. Margaret Edith</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Johnston, Miss. Catherine Helen \"Carrie\"</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Behr, Mr. Karl Howell</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dooley, Mr. Patrick</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    PassengerId  Survived  \\\n",
       "Name                                                                        \n",
       "Braund, Mr. Owen Harris                                       1         0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...            2         1   \n",
       "Heikkinen, Miss. Laina                                        3         1   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)                  4         1   \n",
       "...                                                         ...       ...   \n",
       "Graham, Miss. Margaret Edith                                888         1   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                    889         0   \n",
       "Behr, Mr. Karl Howell                                       890         1   \n",
       "Dooley, Mr. Patrick                                         891         0   \n",
       "\n",
       "                                                    Pclass     Sex   Age  \\\n",
       "Name                                                                       \n",
       "Braund, Mr. Owen Harris                                  3    male  22.0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...       1  female  38.0   \n",
       "Heikkinen, Miss. Laina                                   3  female  26.0   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)             1  female  35.0   \n",
       "...                                                    ...     ...   ...   \n",
       "Graham, Miss. Margaret Edith                             1  female  19.0   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                 3  female   NaN   \n",
       "Behr, Mr. Karl Howell                                    1    male  26.0   \n",
       "Dooley, Mr. Patrick                                      3    male  32.0   \n",
       "\n",
       "                                                    SibSp  Parch  \\\n",
       "Name                                                               \n",
       "Braund, Mr. Owen Harris                                 1      0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...      1      0   \n",
       "Heikkinen, Miss. Laina                                  0      0   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)            1      0   \n",
       "...                                                   ...    ...   \n",
       "Graham, Miss. Margaret Edith                            0      0   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                1      2   \n",
       "Behr, Mr. Karl Howell                                   0      0   \n",
       "Dooley, Mr. Patrick                                     0      0   \n",
       "\n",
       "                                                              Ticket     Fare  \\\n",
       "Name                                                                            \n",
       "Braund, Mr. Owen Harris                                    A/5 21171   7.2500   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...          PC 17599  71.2833   \n",
       "Heikkinen, Miss. Laina                              STON/O2. 3101282   7.9250   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)                  113803  53.1000   \n",
       "...                                                              ...      ...   \n",
       "Graham, Miss. Margaret Edith                                  112053  30.0000   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                  W./C. 6607  23.4500   \n",
       "Behr, Mr. Karl Howell                                         111369  30.0000   \n",
       "Dooley, Mr. Patrick                                           370376   7.7500   \n",
       "\n",
       "                                                   Cabin Embarked  \n",
       "Name                                                               \n",
       "Braund, Mr. Owen Harris                              NaN        S  \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...   C85        C  \n",
       "Heikkinen, Miss. Laina                               NaN        S  \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)        C123        S  \n",
       "...                                                  ...      ...  \n",
       "Graham, Miss. Margaret Edith                         B42        S  \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"             NaN        S  \n",
       "Behr, Mr. Karl Howell                               C148        C  \n",
       "Dooley, Mr. Patrick                                  NaN        Q  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index('Name')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "Braund, Mr. Owen Harris                                22.0\n",
       "Cumings, Mrs. John Bradley (Florence Briggs Thayer)    38.0\n",
       "Heikkinen, Miss. Laina                                 26.0\n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)           35.0\n",
       "                                                       ... \n",
       "Graham, Miss. Margaret Edith                           19.0\n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                NaN\n",
       "Behr, Mr. Karl Howell                                  26.0\n",
       "Dooley, Mr. Patrick                                    32.0\n",
       "Name: Age, Length: 891, dtype: float64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age = df['Age']\n",
    "age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.0"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age['Dooley, Mr. Patrick']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "but with the power of numpy arrays. Many things you can do with numpy arrays, can also be applied on DataFrames / Series.\n",
    "\n",
    "Eg element-wise operations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "Braund, Mr. Owen Harris                                22000.0\n",
       "Cumings, Mrs. John Bradley (Florence Briggs Thayer)    38000.0\n",
       "Heikkinen, Miss. Laina                                 26000.0\n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)           35000.0\n",
       "                                                        ...   \n",
       "Graham, Miss. Margaret Edith                           19000.0\n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                   NaN\n",
       "Behr, Mr. Karl Howell                                  26000.0\n",
       "Dooley, Mr. Patrick                                    32000.0\n",
       "Name: Age, Length: 891, dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age * 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A range of methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.69911764705882"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fancy indexing, like indexing with a list or boolean indexing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "Goldschmidt, Mr. George B               71.0\n",
       "Connors, Mr. Patrick                    70.5\n",
       "Artagaveytia, Mr. Ramon                 71.0\n",
       "Barkworth, Mr. Algernon Henry Wilson    80.0\n",
       "Svensson, Mr. Johan                     74.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age[age > 70]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But also a lot of pandas specific methods, e.g."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Embarked'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>:\n",
    "\n",
    " <ul>\n",
    "  <li>What is the maximum Fare that was paid? And the median?</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "512.3292"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fare=df['Fare']\n",
    "Fare.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.4542"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fare.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>:\n",
    "\n",
    " <ul>\n",
    "  <li>Calculate the average survival ratio for all passengers (note: the 'Survived' column indicates whether someone survived (1) or not (0)).</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "clear_cell": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3838383838383838"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Survived=df['Survived']\n",
    "Survived.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Data import and export"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "A wide range of input/output formats are natively supported by pandas:\n",
    "\n",
    "* CSV, text\n",
    "* SQL database\n",
    "* Excel\n",
    "* HDF5\n",
    "* json\n",
    "* html\n",
    "* pickle\n",
    "* sas, stata\n",
    "* (parquet)\n",
    "* ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Very powerful csv reader:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Luckily, if we have a well formed csv file, we don't need many of those arguments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"titanic.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>: Read the `data/20000101_20161231-NO2.csv` file into a DataFrame `no2`\n",
    "<br><br>\n",
    "Some aspects about the file:\n",
    " <ul>\n",
    "  <li>Which separator is used in the file?</li>\n",
    "  <li>The second row includes unit information and should be skipped (check `skiprows` keyword)</li>\n",
    "  <li>For missing values, it uses the `'n/d'` notation (check `na_values` keyword)</li>\n",
    "  <li>We want to parse the 'timestamp' column as datetimes (check the `parse_dates` keyword)</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [],
   "source": [
    "no2=pd.read_csv(\"20000101_20161231-NO2.csv\",sep=';',skiprows=[1],na_values='n/d',parse_dates=['timestamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "clear_cell": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>BASCH</th>\n",
       "      <th>BONAP</th>\n",
       "      <th>PA18</th>\n",
       "      <th>VERS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-01-01 01:00:00</td>\n",
       "      <td>108.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.0</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-01-01 02:00:00</td>\n",
       "      <td>104.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000-01-01 03:00:00</td>\n",
       "      <td>97.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000-01-01 04:00:00</td>\n",
       "      <td>77.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149035</th>\n",
       "      <td>2016-12-31 20:00:00</td>\n",
       "      <td>73.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149036</th>\n",
       "      <td>2016-12-31 21:00:00</td>\n",
       "      <td>61.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149037</th>\n",
       "      <td>2016-12-31 22:00:00</td>\n",
       "      <td>57.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149038</th>\n",
       "      <td>2016-12-31 23:00:00</td>\n",
       "      <td>51.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>149039 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 timestamp  BASCH  BONAP  PA18  VERS\n",
       "0      2000-01-01 01:00:00  108.0    NaN  65.0  47.0\n",
       "1      2000-01-01 02:00:00  104.0   60.0  77.0  42.0\n",
       "2      2000-01-01 03:00:00   97.0   58.0  73.0  34.0\n",
       "3      2000-01-01 04:00:00   77.0   52.0  57.0  29.0\n",
       "...                    ...    ...    ...   ...   ...\n",
       "149035 2016-12-31 20:00:00   73.0   51.0  49.0  20.0\n",
       "149036 2016-12-31 21:00:00   61.0   51.0  48.0  16.0\n",
       "149037 2016-12-31 22:00:00   57.0   49.0  45.0  14.0\n",
       "149038 2016-12-31 23:00:00   51.0   47.0  45.0  12.0\n",
       "\n",
       "[149039 rows x 5 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Some useful methods:\n",
    "\n",
    "`head` and `tail`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>BASCH</th>\n",
       "      <th>BONAP</th>\n",
       "      <th>PA18</th>\n",
       "      <th>VERS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-01-01 01:00:00</td>\n",
       "      <td>108.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.0</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-01-01 02:00:00</td>\n",
       "      <td>104.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000-01-01 03:00:00</td>\n",
       "      <td>97.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            timestamp  BASCH  BONAP  PA18  VERS\n",
       "0 2000-01-01 01:00:00  108.0    NaN  65.0  47.0\n",
       "1 2000-01-01 02:00:00  104.0   60.0  77.0  42.0\n",
       "2 2000-01-01 03:00:00   97.0   58.0  73.0  34.0"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>BASCH</th>\n",
       "      <th>BONAP</th>\n",
       "      <th>PA18</th>\n",
       "      <th>VERS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>149034</th>\n",
       "      <td>2016-12-31 19:00:00</td>\n",
       "      <td>77.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149035</th>\n",
       "      <td>2016-12-31 20:00:00</td>\n",
       "      <td>73.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149036</th>\n",
       "      <td>2016-12-31 21:00:00</td>\n",
       "      <td>61.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149037</th>\n",
       "      <td>2016-12-31 22:00:00</td>\n",
       "      <td>57.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149038</th>\n",
       "      <td>2016-12-31 23:00:00</td>\n",
       "      <td>51.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 timestamp  BASCH  BONAP  PA18  VERS\n",
       "149034 2016-12-31 19:00:00   77.0   49.0  52.0  23.0\n",
       "149035 2016-12-31 20:00:00   73.0   51.0  49.0  20.0\n",
       "149036 2016-12-31 21:00:00   61.0   51.0  48.0  16.0\n",
       "149037 2016-12-31 22:00:00   57.0   49.0  45.0  14.0\n",
       "149038 2016-12-31 23:00:00   51.0   47.0  45.0  12.0"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "`info()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 149039 entries, 0 to 149038\n",
      "Data columns (total 5 columns):\n",
      "timestamp    149039 non-null datetime64[ns]\n",
      "BASCH        139949 non-null float64\n",
      "BONAP        136493 non-null float64\n",
      "PA18         142259 non-null float64\n",
      "VERS         143813 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 5.7 MB\n"
     ]
    }
   ],
   "source": [
    "no2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Getting some basic summary statistics about the data with `describe`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BASCH</th>\n",
       "      <th>BONAP</th>\n",
       "      <th>PA18</th>\n",
       "      <th>VERS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>139949.000000</td>\n",
       "      <td>136493.000000</td>\n",
       "      <td>142259.000000</td>\n",
       "      <td>143813.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>89.270098</td>\n",
       "      <td>64.001714</td>\n",
       "      <td>45.104211</td>\n",
       "      <td>27.613227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>36.772855</td>\n",
       "      <td>27.866767</td>\n",
       "      <td>23.212719</td>\n",
       "      <td>19.604953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>63.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>87.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>112.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>38.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>358.000000</td>\n",
       "      <td>345.000000</td>\n",
       "      <td>306.000000</td>\n",
       "      <td>197.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               BASCH          BONAP           PA18           VERS\n",
       "count  139949.000000  136493.000000  142259.000000  143813.000000\n",
       "mean       89.270098      64.001714      45.104211      27.613227\n",
       "std        36.772855      27.866767      23.212719      19.604953\n",
       "min         4.000000       0.000000       2.000000       0.000000\n",
       "25%        63.000000      44.000000      28.000000      13.000000\n",
       "50%        87.000000      62.000000      42.000000      22.000000\n",
       "75%       112.000000      81.000000      59.000000      38.000000\n",
       "max       358.000000     345.000000     306.000000     197.000000"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Quickly visualizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x207c3ac2e80>"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "no2['PA18'].plot(kind='box', ylim=[0,250])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x207c0f88908>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "no2[['BASCH','VERS']].plot(kind='hist', bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>: \n",
    "\n",
    " <ul>\n",
    "  <li>Plot the age distribution of the titanic passengers</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x207bbe84390>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Age'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default plot (when not specifying `kind`) is a line plot of all columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x207c007e438>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "no2['BASCH'].plot(figsize=(12,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This does not say too much .."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We can select part of the data (eg the latest 500 data points):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x207c00ddeb8>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jRZlYXJvn1sBux4QQAobtKsaaIsNyHL94zkUxtvoVY/JRh1IpAlYKTfaa7zIev2XYqOqKP7xk0JAP+joVxqwYMwxzreI4Av/uT0/gS6c3Cj+2fy1mKwWTES6MMxJUjDuWjbKmoKTKxaZSdG1f2QQmZ6XY7Vo4OFMG4GYZu/nC8BRjCaYVsFLk0nzn+IU4eYwXazqA8A1JNK4NQOYmuLbp/i3pdR3kHW4b7s3CkndeDVaMGYa5RulYNj716CoeeCb75NOxj82KMZMTIwtjSZI+JEnSZUmSHgl87dclSXpCkqSTkiR9XJKk+cD33idJ0ilJkp6UJOlfTerErxaCvqau6aCkKYUrxo2u5SubQM9KMShJIQ1dy4ZhObhh3iuM26b/O+qqDFWWYTqO36CXx+9v2g6qmvt7GbaryC94BWh7QFwbNd9lbT5sRxXjAUpwy7Nc0NRBbr5jGOZahSx0hl3cztlHH3gen3vyMg/4YHIjiWL8xwDeEvna3QBeIoS4A8BTAN4HAJIk3Q7g+wG82PuZ35UkScE+ph3Y0u9aNsqajFLhHmPLL8wA+IVinskUpARfP18B4DbgkV1B9+wjli1CVoqshblpCVT84SECHdP2ldn2gLg2v/kuY45x24wUvAOU4JbpqvV+kx4P+WAY5hqla/eaoovid+45hT/50nM9jzFbKZiMjCyMhRCfB3Al8rV/FkJQpXA/gMPef78NwMeEEF0hxDMATgF4VY7ne9UR9hjbU/IYh60UmmcnyHOBIIvAIa8w3g1Mt9MDcW2hjOOMxzdtxx9zbTkO2oaN+aoGIKAYm3bfSGgge45x27C9VAqv+W6QYty1UNUVv/mRFWOGYa5VSIwpsjhtGTaev9IC6TCsGDNZycNj/MMA/sn770MAzga+d877Wh+SJL1bkqQTkiSdWFtby+E0pkNw0ETHdKbkMY4237l/1jwXCCp4qTDeaZv+70ipFKYtQh7brIq1O266NzykbdpYqPY8xrYjPMU20Hyn5NN8R4oxPfeggpea9MjywYoxwzDXKpQ9X6Ri3DIsPL/R8v+fFWMmK5kKY0mSfg6ABeAj9KWYh8VWR0KIDwghXiGEeMXKykqW05gqUcW4rBbrMXaoOAzFtSn+OeUFFcY3BKwURsBKoSmkGAfGRWc8vmE7vpXB9JrvqiUFZU1Gx7Sx2TIgRC9DGOh5jLM23/VSKSiuLb7gbZuuskwNgLwoMwxzrUKCRFHroOMINz7UO95MWWXFmMlM6sJYkqQfBPBdAH5A9Myk5wAcCTzsMIAL6U/v6qdtBD3GjucxLk4x7lg2hEBINSXPbZ4LBCnBB2dLkCVgp235anlJVaB6cW1BxTjr8YNWCsNys5MrmoKKpqBl2FhvdAEAS3Xd/xlKpcjcfGfaKOuu+i9Lg/OJ3eEqqq/SF6mUMAzDXE1QgVpUj01w6BMAzFU0LoyZzKQqjCVJeguAnwbwPUKIVuBbfw/g+yVJKkmSdDOAWwE8kP00r16Chv+OF/FVpGJMhXlsYZxjZzApwbNlDTNlDbsdE1st92sLVc2Pawt5jDO8BpbtwAkU/PS8NHSjbdrYaBgAwopxHs13tiNcG4fmjtmu6erAyXeNroV6SYWquD7rIruxGYZhriZ8j3FB179obvx8VYPlCDgZdwyZaxt11AMkSfoogDcAWJYk6RyA/wA3haIE4G7JDZq9XwjxHiHEo5Ik/SWAx+BaLN4rhNjXlUKvCazXfFdkKkXHW4DKWr/HOE/VmpTgelnFbEXFdtvElaZbmC7WdGiKjIZlwegEptVlKBJpgaXmNyrMK7prpWgHFOPloGKcQ/Md3exUdPd1rJYUtAZYKRodCzNl9xxLqsyKMcMw1yymrxgXVRiHBYu5itucbdgOyvK+DsRiJsjIwlgI8faYL//hkMf/ZwD/OctJ7RWEEGHF2HI8xdguXDEOFsaTiGsjxbZeUrFYK2GjaWCr5RbG81W3MLa8BjlFlmA7ItPiSFtyFU8x3vGOXw4oxusxirEqk2Kc/nenxZai4oYpxrtdC3UqjDWl0KZLhmGYqwm/+W5ainHFFUkM2wldExlmHHjyXQY6AXWwY9reFLZiUynI51vR+pMZ8m6+0xQJZU3BSl3HesPAlaar4s5XNT+urdGx/Ol0WY5PPxu1UpR9j7GF9UYXqixhtqz5P0ceYytD80fH8Ipy7zWtlpTYUc+O46ZwzJQCinGB+dUMwzBXEyRoTMtKMUuKMQsUTAa4MM5A0PjfK9yKzTFuxxTGk2i+224bmPei0pZqJWw0uthsGZgtq9AUGZoq+6kUNIQji2JNW3LVqJVCU1DRFbRNBxuNLpbqOmS5F4ZCGc5mFsXYtLxju69pTVdjm+9aptv4OOMV5nrBMX0MwzBXE7TmF7UOto3+5juAC2MmGyOtFMxgaMtdktxcX8BNaHA9xsUuDOSHBSbTfHelaWDBG66xPKNjo2lgo2n46rAmS+haDpqGnVkx/qsTZ/H1s1sA4I9kDjbfVTQFF7baWG8YWKqVQj/rWykyKMb+a+rdbNRKKtZ2u32Po2mAdfYYMwzD9OLaCto5I4sbiVE0AIoLYyYLrBhngGwMs2XNn4xGcW15jEROAinGcc13eS4Omy3TH66xXC/BdgSeXW/6KrKmyNj2UiqWPM9v2sL8Eycv4iNffh5Az+e74zffybhhvoKzmy2s7nSwPBMpjMlKkUEx7t1seFYKXYn1GJOKXfetFMWOAmcYhrmaKLr5jtbqmxarAMLNdwyTFi6MM0D+JrpLBeAP+ACKWRziPMaTOP5mQB2mwvfU5Yb/NVWRsevZDZYyKsbBqLVajMf4jsNz6JgOHr+4g+WaHvrZnmKcQyqFNtxKsdvtV4x5QWaY4vjymQ38j4fcqPwPf/FZnLq8O+UzurahNb9oj/HNyzUAbKVg8oEL4wzQ3ep8JVAYe813QDF3rUWlUmy2DCzUSDF2/x0c0UzeXgA4uuTevactzINFLXmMKQGjpqu44/AcAMARGKIYp3/tW5Fs6EFxbWSlmPVTKdhKwTBF8qF/eQY/89cn8fTqLv7D3z+KvzpxbtqndE1TtMeY7Izfecf1+I6XXtcbCMUCBZMBLowHcP+ZDT8ndxAtT1mcq/ZUS7JSAMVMQRumGOd11yyE8KwUnsc4EI9GX6MkjHpJxWtvXcl0/KANgha6y57Hd6Gm4+hSzU+CWIooxpqc/aagl2McUIwNq88a04uwc1+DIr3lDMMAbdPta/iPn3gMAPyhQ8x0MCeQSvHAM1dw9kor9nskYnzHS6/H7/7AyyfSeM5ce3BhHIMQAu/80AP44H1nhj6uE6MYU/MdUJBibIbzfoFgXFs+ftedjgXbESGPMUEqsuIlQ7z+hSu+5zZtcRosjMuaAklyi1BFljBbViHLEl7qqcbBcwHyiWuLNt/NVzU4AtjwBpoQja7nMS5zXBvDTAMSBu57eh0AsN3mwniamDmPhBZC4Ef/5ATe9eGv+M8dpGXY0BXZv+aVuDBmcoAL4xi6lgPDcvD8RvxdKhHrMfbi2gCga06+SCJ1kxYEIJhKkc/isBmYcAe4NwJUCNPXHr2wDQB48+0HA3ft6X7/YFGrKZK/6C1UdXiTFv3CeKkeVozpvLIN+CArhVvwvuSQe6yHz2+HHkeKMU2+47g2himW6Bq71TYGPJIpArrmOCKbOEGsNbrYbpt4arWBP/zCM33fbxtWSBTSFU+UmtI6fGm749+sXQ1caRr44ql1fPHUOr58ZoNvGBLChXEMNP743GZ76OOoKJ2mx7hj2qhoil8wAvmnUlzx/L2kGMuy5BfEZKV44cFZAMAbXnAgc2FuB4paXZWh+UV473V+9c1LAIAbvW5kgoroLM13ncjNxksOzUGSgJNnw4UxvU9obDXHtTFMsXQtx7dbHV6oYLsdP6GSKQbT6q27eYgEZ9aaAIAb5sr47c+e8tOJiJZh+39/ANBU91oxLY/xd//2F/D79w7faS6Sn/7rk3jHH3wZ7/iDL+P7PnA//uIrz0/7lPYEXBjHQI1W57dGFMa+Yhz2GOsFeozbhh26YwZ6jXB5FcbU+LYQ8PMu+YWx+++ffusLcf/7vh1zVS1zYR7cMtMVGar3fMHX+Q0vXMF9P/VGHFuph35WkSVIUrbmO9sRUGTJHxxSL6k4vlLHw+e3Qo/b7Vio6YqvUnNcG8MUS8e08aYXHcS9P/kGvPrYErZbrBhPk+Dancf15/RaAwDwW2+/Ex/90VeHppwCbmEcVoypx6T4wthxBNZ2u3huo1n4sQex3ujiZYfn8LF3vxpAvx2QiYcL4xgos/ZK0/C7XuMgxXhuqh5jG2U1/GeUJMkNPM8plYJGPy8GCtMVLw2ClOOSquC6uTKA7IU5FaYAUC2pvgocPL4kSTgSUYsJTZYzNd9ZgeMTdxyaw0PntkMNeI2O5fuLARQ6CpxhGKBjOihrMm5aqmGuorHHeMoEr3l5KcZlTcbLb1zw7XNBWobl79gBk4kqTQodc/0qKj5bXRvXz1Xw6mNL0FU5NK13UpzbbOH2X/ykb6/ci3BhHEOwGD4/xE7RMmzoqhzayikVrRibNsoRxRgASkp+Y6nJY7wQsDL4inEkFQLwCnNFRjfljYFpC3zHS6/HH/3bV+LQfMUvtOOOFYciS7AzKcYO1GhhfHgOa7tdXNrp+F9rdC1/HDTg/u3Zw8UwxdGxbF+ImK+4g5amoRYyLsH1L4+18MxaAzcv1/3duyh9ivEUm++o6FyPmZI6LZqGhao3PbaiKYXUJE9fbqBl2Hj0ws7EjzUpuDCOoRnIrB3mMyZ/rx5QbMMe48nfnXW9c4jiKsbZjr/e6OKtv3UfvvzMBlRZ8tMmgF4aRNBf3Xf8DIpxVVPwxtsOAOglTSxU448VRVWk7IqxFF6IX3p4HgBw8lzvLninY4Zek5KqwHJELk0nDDMOD5/bxjs+eP9V1fhTBF1PMQaAOW99YNV4epghxTj7e/H0WhPHV2oDv982wx7jktd89+nHV/FDf/QAnAxN2ONCn72N5lVUGHd7inpZk3375yR439+cxF+eOIuNhiukrW53RvzE1Ys6+iHXHkHF+Nzm4GSKjml7ucW9D2Zo8l1BinFcYazloBg/+NwmHr+4g8cv7mBlphRq8Hv7N92IWw7Uff9vFF2VUys3luP4xTDQyyZeTKgYa4qcyWPsOAKKEi6MjyxWAPTylAFSjMNWCsDdThz0ujDMJPj62U188fQGLm53/Clg+x0hBDqW7Q83IkvbVsvsi3FkisHM0UrRtWyc22zhX995aOBjml0LRxZ6ljq69n7uyTUArngR7E2ZJKQYbzQMOI4YqHIXSdOwQ4rxJK0Unzh5EVeaBu68cQEAsLrLhfG+IqQYD2nAcwvjXiEsS66/tujJd7VS/58xi2L7B/edwUsOzflxZUDY3wsAx1fqOB5pfAsdP0NhbjkiZGUIxrUlQZWlTKkU0eMD8Js+dgNd0Y2Ohes9XzWA0GCXgtZihgHQK0J2O9eOWmrYDoRAX2HMivH0CO7UZS2Mn9towREYrhhHrBRaRNDYbBVYGHtqrOWIQgvyQZi2GzvbU4wnVxjbjsBux8LqThcb3mC01Z2rRzkfF5a1YiDFeKakDrVSdC0HJbU36a6kKn7jG1CUYuyExkETrpUi3fF/555T+Luvnw/d/c8ntDGEjp9hJHRQcSX1eDzFOH1hbMc035U1BboiYycQB7XbsUJWCt3bOeAGPKZo6LO+cw3FlXW89ZXWXypEtjnLeGoYOaZS0OTZAzPlgY9pRawUqiIjuHRfKbARLmhjGjU1twh6efyeYqwrE7Na7Xg3o5d3Olj3rBSXd/auYsyFcQwNTzF+wXUzQ5vv3MK4pxiT142sFWmbz8aBVOsormKbcvKcLWBYIrTIJS1K/eNnKMytSPNbL64tWXGuyFImn2+cxxgAZitqKEez0bX8cdBAQDHmyDamYKgIiea87mfoc1ZixfiqwbTy8xiT+qyr4bX4dz93Cp84eQFAf/Od+/heWbNZYGHcDhXG0785I4GPdpQrmjIxj/EWFca7Xax5dsNLXBjvL1qGBUkCbj1Qx/MDZrQD7gff9RhTYex+QAudfGfYqGj9f8YshanpODBtx1eMb1qq4oS4lb0AACAASURBVFU3L471HFk8zq5iHBxYMp5irCoSzAyKcZzHGHDtFHRn7Dii32OsTS8qiLm28Qvja6gopB05iqucD3iMmelg2o6/25ZVMSZxQ5XD17e/+MpZ3P3YKqyIVYDQA7uNmwXmWgeLzqtBMSZLaKgwnlBNQjejliPw1OouAGBttxsa1rWX4MI4hmbXRk13hzpcaRoD7zo7ZlQxdgvjQj3Gw1IpUt6x244IFcZ//EOvwg99y81jPUfa8chCCC9HOGCl8P47aVybJsuZFePoYgwAMxXNHwPdMMLjoIHeTgFHtjFFY/ge42vJSuGub7TuzrJiPHVMW6Cm52Mpo+uPFmlkni2763DLDFsFCD3QDF9oYRwoOjeuJsXYe33K2uSsFFuB1/nybheKLMER8P3Gew0ujGNoGRaquoJjnun/zHoj9nFdy/Y8xuGCmO5Yi/AYdwblGKf0+AohYNpUGLt3e9GGhiSUlHSpFHSHqYWsFBJUWcJMTJNhHKoiZbpTjfMYA8BsuWeloAIkHNfGVgpmOvge42vISkEeYyqMFVnCTFllxXiKGLbjZ7tnFQgGXX9myip22qav0EatFCVVhiK71wwaTlUEwev91aQYVwPNd50J1STRm1FqmNyrDXhcGMfQ9JIeKHXh9Fr8iMeu6YQGepDXTZbdAReTVIw7po1m10LXcgbHtWUoTA2vOAbCW1NJSdt8R01zQSuDpsiYr+qhuLhhqHLWHGMn3mMcsFL0hp70VOxSgU2XDBPEtHtWio5pXxN5xr7HOOApnato15Sd5GrDtB1fLJikYrzTMfuaywhdlXHjYhWLNT2kZE4aUow1RZqqx9hxBLbbZsBjTM13k5t8Fy2MX3yDO6Vwr/qMuTCOodV1FePDC+7UNZrXHqVj2SirvYEewdHMuipPtED6qf9+Ej/0R18BgNjCuKTKqe4OqTA1LcdvpIguTElI63G2fMW4d8z5ioYb5gd3JkdRM+YY2w7iFeOKih1PKaYtuqDvmW6M2GPMFE3Xb76z8L6/eRg/9pEHp3xGkyeqGANug+4WF8ZTw7Qd1D17WVorH0GRm2pEMZ6tqNhpW2h23bW4GvEYz1U0vOj6GSzW9EJTKajovGG+MlXF+JOPXsJrfvkzflFKr88km++2W9HCeBYAsLpHC2POMY6haViolVSoioyblmo4M0oxVsIeY8AtTCe5pX7qcgNPeib3uFSK+aqWymtHd+khK4WaojBO2Xxne8cMFqY/+50vGqvYzKoY25EBI8RMQDGmBTeYrexbaNhKwRRMsPnuwnan0Ilf06LnMQ7eROvsMZ4ipiWwWMtJMXaGeYxN3zITzbf/7XfcibKm4Mf//MGpNN8dmq9M1Vt79koLLcPG06uuoFeLDPgQQiTefU3KVttETVdQ0RWsNwy88LoZyNLejWxjxTgGt/nOfTMdX6nhzADFmOLayDoRXKDTNp8lZb3R6/iMU4znq+42khDjXSDpOU3b8RXf6LCLJGgprRS9xbB3zOV6CYfmK8mPrch4enUX3/Irn8X5IQNaBmEN8Rh3LQddy+5ZKQIRcpxKwUyLYPPd5Z0OOlO6OXvvRx7E79xzqpBj0e8YFAbmKlqh2+dMGNN2/F6QE89t4nW/dk+fmpj4uQbsWM6UNTQNG2sNt+haiMR4Hl6oYrleKlwx7phuz9GBmRKev9KeWhN20yvQKVHL9xjn1BQZx1bLxFxFw8FZd2f34GwZy/USLu7RsdBcGMfQNCxUvQ/3sZU6nttoxTaSdUzbL4Z0NTwauqJPbtvCcUToAx/XfLdY1WHabqTYOJDSGvQYp7JSpFWMyWMckwqRFEWWsNkycX6rjefW49X+UecQn2NM0+8sbLZMSFIvOxVgjzEzPegmdr3RxUbTmIrH2HYE7n58FV99brOQ43UjAz4AVx0LTi5lisWw3YFTsgR84el1PH+lhbObgyNPh0G2ujgrBQA8v+GKHoPSihaqeqGNmG3TzVR+2zccwnqjiz/4wpnCjh2k5V3zqTD2Uym8+mQSa8N228RcVfcL46WajpuWqnhuI93fftpwYRxDK6AYH1uuwXIEzkbyjIUQvmIMuItzUDGeKaljF6VJ2emYoclucYoxLRabY3blkjfXtB1Ytqucxqmno0jrMaZiPM7KkJSg2pxmyMrgVAq3CN5pm9hsGZiraKEJfUUOdmGYIHQT+py3TuXVff6+v3kYH//auUSPvbDlqmRFWRniFOOqrvpNR0zxmLYDXZVQUnuZuWkbvowBzd+0Dj+34Yoe85X4wU+LNR2bLaMwW5E7U0DBG287gLe8+Dq8/zNP49IUFNOm0SuMS6rsX6MovSPPBry//dp5/NzHH8Z228BcRcXB2TJkyb0pObZcH5jodbXDhXEMTcPytx9WZkoA+vMQ6UNLasVPvOlW/K+vOOJ/v1ZS/eaAvIka+2MLY2976cqY24rU8EA5xmmi2oD0cXGkGKexbxDBDGIz5TnEe4zd98ROx8KVptHnbfOtFNdAIgBzdUGfNfr85HHxe26jiY8+8Dx+/uOPJLrAU5NyUVYGv/kuulPHn7+pYdoCmiL7ayHQG008Ln7znRxVjL3C+EqrT5wIMl/V4YjiIgw7gYSon3rLC9ExHfzjwxcLOXYQ2jExLMcf7gH06oS8drIvbrfxcx9/GB/58vM4e6WN+YqO73vlEbzvrS+CLEs4fqCG9YaR2kozTbgwjiCEQMuwfcM6qRHR7XHy6VBh/G9ecxSvPNqbDlebgGJ8YauN3/r007i8GymM9f4/o68Yj1sYB1IpDNtJZaMAsijGtH2W/q0Z9FemacKzHAF5qJXCVYyj3rZejjErxkyxRK1ehuVkVsrufmzVe26B//QPj418PMVabreLUWxpSzhYhNV0BaYteMjOlDAt95oRVHnbKRV803YgSf0JQSRQPLfR7FuDgyzW3O9tFlSYtQ3brxeOrdRx64G6/xkqkuCOSTDKjs4t643jTsfEz338Ybzrj0/4fuZLOx3MVTR8w5F5/OjrjgEAji17cbd7UDXmwjhC13JgO8JXjAcVO9GpS1HqEyiMP/aVs/iNTz+FB565AqAXoh30NhOLVbJSjKsYu78neYxTF8YZPcZZFOMnLu76/23Y4y8CtiNij9+zUli40jT7RlT3Uin4oswUS9x7Luv78J8fW8Vt183gf3n5YXzuybWRj6cm5e32+E2/aaCdmaDHuOKt25Pq72CGQ2JK8GYlrefbtAU0We5LUKB1eL1hDJ2GSjt6RTXgdUw7ZKe86/aDeODZK4U3gwZf7+C4bLJSZPUYf/bxy/jIl5/HdtvEz37Hbf7X5yM3KccPeIXxZS6M9zy07UMh5b5vNNLlHdf4EcRtAsm3MH743BYA+IXxq25eAtA/+QdIvyiYQSuFJVJbKTRFhiMw9mhmM0MSBhEMFTet8S/QdmQkNUFNHzsdE1utfiuFJEm5xvTdf2Zjz+ZAMsUStzuTRRm60jRw4tkruOv2gzg4W0Kja438LFOspWmLQuwMbo9HuHAihaxlss+4aIQQMGwHuiKFFONWyveCZcfHZtI6DPQEoDhIuPjLr5zFE5d2Up3DOFDzHXHX7QdhOwL3PHl54scOElKMSwGbkW+lyHbD/NC5LZQ1Gff+5Bvw7tcdx42LVQC9HVXiiDcH4kyKBvhpw4VxhF5oOFkp3Jco2sziWykGKMauxzi/i4MQAifPbQMAHnx+E7IEfMdLr8NyvYQDng86yExZhSJLY3flBpvvsijGFPI+biPOoE7kcXjXa2/2/zvt9L+4wpxGnW63TddjHKNW5DnY5Uc/fAJ/+IVncnkuZn9jWE5oPDmQTRk6eW4LjgC+5ZZlv7mJhtsM4vRaw//cFJEG4Cp04fXXL4xZMS4c2xEQwhVFgruYWawUcdefYAE2P6QwPrJQRb2k4i9OnMV/+dSTqc5hHKj5jnjZ4XnUdAUPnd2e+LGDNI14xZhqmaw3rQ+f28aLb5jz7Y53HHan3EUVY1VxJxAOiru9muHCOELTH6PoKcZavGLsWykGKMZ1XYVhO5m8bpbt4FnvbuvCdgcbnvrbMR0s1nR8660rOPHzb/ILtiCyLGGhqo3ffBfIMTYdkWocNADcvOzeRT4z5t2i7ZBinP6t+QvfdTu+/ot3AUDKsdRObCpFTXdjiFZ3OuhaTp9iDLg7DHlYKRxHYLdrjW2FYa5NDMvBcj38fsxSGFMRvFzX/eJj2JbwbsfE5d0ubvcmXhWRTNExndDWNdDLbG1xZFvhBAdC6YHrYtqbFNOJ37Gs6ypok4B8xHEs1HQ8+At34RtvnC/kRil6oybLEqoltfCBT61uvMeYivYs64JlO3jkwrZfDAO9wnguJh3k+Erd7z3YS3BhHIFUXnpDDfIYJ1GM3edLv6X3sa+cxZt/4/PY7Zg4eda1UdCbb7nerxJHma/qKTzGVBgLdE07tWJ8fMX1Fw2aGjgIc0An8rjQecflT49iUFybJEmYrWh43stmjFuU06ZxRKH316Qi/5j9hWE7/ppAxUSWyDaa8Dhb1vw1Z1ixe/aKmyn7ssPzAApSjK1hijF/boqGdudcxTiHwtiKV4xlWfJ3R4Z5jAF3B6+qq4X0fbRNuy8hSlcmO+grjpBiXAoqxtmb706tNdAxnVBh/E2epfPIQrXv8UeXa36e8l5iZNUjSdKHJEm6LEnSI4GvLUqSdLckSU97/17wvi5JkvR+SZJOSZJ0UpKkb5zkyU+CVlQxVslKEfEYW/2NH0HISpClsHn0wjYM28FGw8DJ89vQFAlvvv0gAGCpPnxBAFz/1bge46CPsG3a0NR0BerhhSp0RfYjnJLiN99lSKUAMhbGIt5KAbgWFcqKjdvGK2n5eIzp/caFMZMEw3L8NeGwd4HKcgGkiKvZioY5b4t0a0hhTI+/ack9dhGKcdd0+tbfiu8xZsW4aEw/d1iC7uX6L1S11Dcp1oDYTKDXgBe3axclz76PYXQiHmM6dpEJKUIINLsWDs66N8m1oMc4h+a7k54t5A7vBhgAXnZkHl/8mW/Dy47M9z2+pqswvECDvUSS6uOPAbwl8rWfAfAZIcStAD7j/T8AvBXArd4/7wbw3/I5zeIgxZju/AbGtY1ovqM72mYG5aIXf2Ti0Qs7eMHBGbzoenerMolivFDTxlZuzMAbuNm1UlsaFFnC0eXq2NsotLimGSoShFQzI0Vcm23HK8aAuxD3FOPJWSmoqBnl62QYwP3cULg+7dZkydPeaVvQPeXPV4yHrCW0M0aj27fbk7cADVOMOZWieIKTUmfLGo4u1byBK2lTKQb3uJDPOFFhrOXX9zGMWMW44MLYsB1YjsBNi25iVSiVIocc40cvbKNeUnHzUi309Ru8z32UXo/W3vo8jqx6hBCfB3Al8uW3Afiw998fBvCvA1//E+FyP4B5SZKuz+tki4C2g+gPqsoSZCnOSjE8ro0U50aGwoZsCFttE5d3OrhhvoJjXkRbosK4qqcY8NH7PVuGndpjDCDV5Bu6s0ybhkFIkpQ6Ms4aYKUAgO975RH/PRLvMc5n64wK40ZB4fTM3sVxBExbYLGm46M/+mr88LccBRDO8x6X3Y6JmbIKSZL85rthKjDtbBxaqIx8bF50TDs03APoFQKTGq7EDIYSgDRFxs+89Ta8/+13oqorqQsx03agDRBmKMs4TpyIkpdYMQzHEeiYTp+1spQyzz8t5K2/0du5qer5WinWmwYOzJYgJxSuyjn4mqdB2qrnoBDiIgB4/z7gff0QgLOBx53zvrZnoElpdKfqRnAp/XFt1ijF2H1DpN0K326b/oS7bW8E8WJV99WgJFaKhZrrMR4nUzQ4arppWKmtFABw/EANz2+0xrIzkMc4q2IMuMV1nh5jAHj7K2/EN3hbRnGLclmT8cVT63jLb34+0505/SxbKZhR+KNzVRnfdGwJi97akMlj3LF8VY7+PWz3iXbaDsyUoQbScH7pE4/lmqzy+/eexi99wh024hYi8VYKnn5XPL7HWJVxZLGKFxycQVVXQp7XcbDs0VaKYc13hOvznez7geqBaSvGtEN91CuMg1YKRXbFoiyfje2WOXAEdxwkMO61bP+8m+/i3sWxVZkkSe+WJOmEJEkn1tZGh8cXhRm4yBBlTe6PayMrxcjmu3RvwmDEyVbLwGbTxHxNw+GFCv7j216M//nOwyOfY7Gqw3LEWMWVFbAetI30zXeAqxhbjhjLfN9TjLO/NTVVzt1jLMsS3v/9d+IXvuv22ML437/hFnzzLct44tIuzm2mbzqgO+xdtlIwI/ALY+8zQypqlhuznbaJWU+V0xQZ9ZI6QjF2v1crKZiraL4f+Z4nLuMLT+e3vt/71Br+9mvnAVCOMce1XS0EPcZERVdSx7UNm7xKWcbD4tqIkjb54pSKzUrkRk3PaQcxKfS+P7pcwy+97cX47pfdEPp+OaOtZLttxqZPDII+n9eKYrxKFgnv35RgfQ7AkcDjDgO4EPcEQogPCCFeIYR4xcrKSsrTyJ+gT4qIU4xpm3JQXFvWLb1gmsOFrQ4M28FiVYckSXjna47iurnyyOegjt1xGvAoxxhwi/osBWqayTd0/DwU49TT9+z4AR/EjUvVUFZykNe/YAXveb07EnOt0Y19TBJooW0Z9p5rXGCKhd7jdDPvN9lkUMl2OmYoL9YtdgevIw1PAKjpKuaqml9Et0071yJ1p2Nio2lgq2WgG5k0BvRuCrgwLp44UamWwWNs2YMHTJFinES9zMveNgy/MNb7UykKVYy7vfCAf/Oaozg4G64TKhmsLQCw1TYS3YwQg+ZAXO2krXr+HsAPev/9gwD+LvD1d3rpFK8GsE2Wi70CNWuFCmOt/4M1SjGm5ru0W+EUll9SZT/LOEmjQZDrvA/F+a124p8xg4qxmc1jfMuBOiQJeDwwonkUpFgP8paNg6ak83e5HuP0xyX/90YjfQNScPFiOwUzDL8wjijGWS5Gux3L93ECbmG8M0QxbnYtVHUFsixhrqL5jXpt087V1kA7KKfXmrEDPmRZQlVXQlmuTDHEiUpZCrFhzXdvfcl1+HevP5YovagIjzH9jtH3Y9FWCroJCTbdBaloSmYrxViKsZb9Jn0aJIlr+yiALwF4oSRJ5yRJeheAXwFwlyRJTwO4y/t/APhHAGcAnALwQQA/NpGzDnBxu40/vf+5sXy0wzAj25KAe6EZN64ta47xmbUmblyqYrGm49kNrzBO0GgQhBr1xkmGsJ3whzjLBLp6ScXxlTpOeqOsk+Arxhmb7wB3UTLTpFIMGAmdFCqM13NQjAG3EYphBhFVjEs5dIK7VoqIYjwilYLWvPlKQDE2claMvec9vdZAx+of8AG4dgqOaysew+oXlaq6kmnAx6DC95uOLeF9b31RoucpqTJsR4wcaZ6Fjm+liBbGSqHNd9HJvVHKGQpj2xHY6VhjFcZ0k15EKkiexN9WBBBCvH3At7495rECwHuzntQ4/MF9z+APv/AMVFnC2191Y+bn6zXf9QqzOMW4YzqQpcGDKHRv+k8jpb/q4nYbh+YrWNvt+oVxkkaDINfNllHVlbFGMkYLyaxe3zsOz+G+p9chhIAkjS52qflPy81KMf4iMMxjnIT5igZFljIpxsGFhBVjZhjRLeySKkOSsk6+MyOjdzWcGmKJanQtzJR6vs9Taw04jkDXcnJTb4UQfnzhmbUmuqbd5zEGsm8XM+mIU4yrupo6stSynZBfOS2lQANY1nz8QXSuFitFZA5DlIreL/IlhQSa8RRj7yZ9vynGVzv0Bvi1Tz4xdGRpUkzbgSSFPa4ltd+w3vUyNIcVe/WSmloxbho2Zsoq5iqavyU6jrcHcLcVb16ujaUYR++qMxfGh+awttvFpZ1OwuPnmEqhSmMrxkKIoakUSZBlCYs1PTfFOEvkH7P/6UasFJIkxe5yJX8+Gx3T8ZvvALcwHjbgI6gYk5WCLoZ5qbdBv/2ZYYqxln7dZdITt9uaNa4tbY5+ELp5mqSdgtbrqJUiTlSbJE3f6x+vGFe09H8P2jGar6ZRjLkwLhSyNGy2THz0gbMjHj0awxbQFDlU8A6KaxtkoyBqJSV1UdPqWqjqaujubHHMwhhww/7HUYytSKNX1jv2O7xos5Pntsc6fh539poyfioFXXizFuZLNR3rWTzGISsFX+SZwQRjsoi4JJ2k0PttJmClmPXsEYMsa82u7UdDzVY07HQs/yKdl5ViJ2Ap+tKZDdiOiM1zr5ay+SiZdPiKcSDis6orsByRLk9+SFzbONB1epKRbfQejx8JXdx7kaYMVgcoxqPSZYZBPzeWleIaa767auh6ysZLD83h7scuZX4+03b6Gs7iLjKdAdt4QWq66ndrj0vTsFEvqf7dmSwhtLWZlGMrNZzfaidWj6KFcVbF+PbrZ6HKUmKfMSnWWawMRJptLCunwnhlppRNMQ4UE7usfl3TtAxrqMpD7/FSsC9Cy7Jl6r7fKBILAOYrOgzLGXiB2+1afsMxqVWb3g6eYTmp/J2bTQNOYD2i87ppqYrdjoUb5sr43lcc6fu5LL5WJj1xjesVrwksjUppxFyL00AWo0n6XINpEEGKHglNN6PVAaEAR5dreO5KK1XSEe0YjaUY0+RgtlIUC80nv+v2g/ja2S2s7aYvRgDqhA0XRQMV45htvCBZrBQtw+3yJvvEnOdbHZfjK3UIATyznsxO0WelGKGKj6KsKbj14AwevbCT7Pi+YpxP8924jQ+Op4hlLcyX6yVsNNO/FztspWA8/v2fPYif/uuTA78fbb4DsnWfU4NbtPkOGDzRLmiloH8HPfbj2inaho3X/upn8d8fPNd3Xq88uggA+A/f8+JYL2VFSx8RxqQnmo4C9G6S0viM81OM3XOYZBMcXefrkfcjXYPyCgcYBdUNgybTHVuuwbAcnN9MnlRFpFGMSa1nxbhgKLLnrtsPQgjgM4+vZnq+uIiYuBzErun0jSONUiulazwwLAemLVAr9awU4yZSEJRMcSahz7iv+S4H5fboUhVnEw75II9xHt6yNFaKvBTjpZqO9d1sVgq6wHAqxbXNha02Hr84+MYyrjAuaUrqixFZFqLNdwAGZhmHC2N3XQzeGI6rGG63TTQNGw+d7e000Xm9/VVH8In/7bX4Vy++LvZnXcWYbyaLhgZ5BH22lQwDV4bFtY1DqQDFmHaG+wpjRYYQ/Tuxk6Jp2KEx0FH82QLrye2VxLa3AzRXGSfH+Noa8HHV0DZtlFUFt103g0PzFXzuyWxTlgxL9BfGWn8OYteyEynGaRIFgpErVBin8RcD7vQ5AIl9xpaTb/MdABxeqOD8VjvRXbPt9Dc/pkVTJJjWeAuSnVPz3/JMyRtukO4C3TZsLNZ0SBKnUlwtPHlpF6/55c/g8m6yRtK8aJs2zm0O/vwMmtaZdvtyp00e43COMdA/FvpHPnwCv/u5U2gErBR0YQ4pxmMWRqR2nw6sW3ReC1UdLzk0N/BnayW2UkwDP0M3MIa4msFKkVthrE3eY9zsWpAl9DWD0meyKDtFq2uFXv8ox5a9CNcxhm4R6TzG2TPVp8GeL4w7ptuZLEkSblqqZtq+BjyPsdqvGEfveDpmsua7NFYKP3Il0Hw3biIFUdEVzJRVbCScftfnMc5opQCAQ/MVdEwn0TmYTraotCBpMiR9K0cOijGA1Kpxx3JQ1RXUSyo3310lPLW6i4vbHZxaHf+ikoW24Q7J2ByQIxwdCQ243eBpu89phyJopaD/jtp6vn52C/c9tY6u5fhDBWp+Ydxbi8e9QaTHB3e6dmOU7DgqmspxbVOACuPgTmpvRHcKK4UzePLdOBSRStHwdkyiKVWlggvjUYrxYk3HfFXDmYTWyiBbLRNVXemrj4ahyBI0ReK4tqIJTj8qe9uHlu3gvR95EI+cT5aEECTWYxw3+c7qn7oUpV7SfDP8ONACUy0p/hbmuBnGQeYDI1pHYeWcYwwAhxeqAIBzCXxNtiNysVEArmI87oJEHuMsAz4AVzEGgPVmF6cu7+JHPnxirO2ktuG+v2bLGp5a3cU7P/TAWKO9mfyhv1+WUd9pCKqn7/6TE322im6cx1hXUl+M4qwUdU893u2G15GOaeOp1d3QY6qeYrXezKAYe4+/vNv1C+KdTr+SHQdZKYrydTIuLcNCRQv7W6sZrRR5pBMVkUoR3DEJohdQlAPu5/AdH7wf/3JqfeBwD8CNcjy2XBsrqYrYbo839Y4oqcqeG/CxrwpjajhZbxj4h4cv4oun18d+PsOK8xgrMKywgT5JXFu9pKDRtUKd1UnwO1z17B5jgKZWJVSMIwprHgHrhxYqAJDI8O9mV+akGGfwGGduvqt5hfFuF/c8sYZPP77qD2pJAjWV1ksqvnh6A59/ai3VjR6TH1QYZxncMi5CCL8w/oeTF/HPj63iy2c2Qo+Ja3rKEte203a3hYNZqP6I+4BiTOdGO0F1ryCOV4zTWSmAnmq80zZRUuWRaUAVXYEjJl+MMGFaht1XlJF6Oe7fXwgB0xa59Lj4VooJp1LENYIWZaV4+Pw2vnh6A3ccnsO//eajQx97fKU+1mwDYitlYVzWZFaMi4asFID7AeiYtn8BS9PNb8T4msqByTm94yaIa/M+KON2ZPe8WirmPaP7QkorBeBGLSVVjE1HhLIY87hjp8L43OboBjzbyacTGaCR0GPmGHuK+aCu3qQsz7h/r42mgfNb7g3BOIpv27RR0RRfhQMGJwJcy9iOwH/51JOhImxSUKE5KIav2bXwy//0eK6NX13LAd2Pf/IRN46yGSky4prvsgz42OmYmClroW3hGV8x7v1upi1CsU8132PsKcaBG4j22FaK3rmTzzg6jW8QWVRKJj1tw/Z3C4i0Vgp/AmouinF21XZtt4tf/9QTA68njVGFsT3Z9yI1qb7/7Xfiu192w9DHHlupY223i//9Y1/Dk5d2Ex8ji2LMzXcF07H6rRSk+smkkAAAIABJREFUNqTJf43LMS7FzPtONuDD/aCM6zMONt9dP1/Gm28/iNfesjzWcwSZqwyfWhXEsp3QXX8eC9NsWcNsWfULxGGYtshsYyC0VDnG+eQoL9dLUGQJ5zZb/g3BZjN5YetaKeTQ9lzSv+G1xDPrDfz2PacyN90mgRb3QYXxfU+v4/fvPYP7nh5/p2oQweKOpkdGmzGNuOa7DKNfW4bdNzmrpMrQFCkkNkTj4Prj2tIrxp1YxdgKTeMbRM1XKdmbXyQtw0ZVC/996FoybnSgn06Uo5Uii2r7mcdX8Tv3nMZXn9uM/X4zMBI9CNUSk969ePj8Nq6fK+PATHnkY1//ghXcdt0M/u7rF/B3Xz+f+BjbLXOsDGOirPVPDr7a2fuFsaesAT2VxC+MOxYaXQuffORi4uczbRGa3APEe5TcHONRHmNv+3HMwjioGGuKjA+88xVDu7BHMVfV/AzQUViOCHmn82h+AFyfcTKPcb/HOy1uXNt4NpaexzjbOWiKjJsWqziz1vR/780xRpaTRSjop0z6N5wGn31iNdNAk7SQilvEVh0dY5CVgm6AkkYjJiGuoIjuhMVaKdT0cW2G1d+ALElSXyNodMxrPaIYB5ttoyr3KPw+C13BmfXxFOMsEWFMepqG5b/2BFkpxu218ac55pRnD2TzGFPj68MDJrgGJz8GKar57uFz23hpwhrh9htm8cmfeB1WZkpj2cIyeYzZSlEs1KQEABXds1IYPSvF3339PN7zZw/i0nayiKW4iJje9Bb3zb3TMbHZNPzkgUGkVYwbvsd4eOGdFNdjPHicaxDLdjuBaUHKY/IQ4NopkniMLVvkEtUGpAtXz8tjDLgZ0mfWmv7vvZnCSvGSQ3N45dEFVDQlsU+8aJpdC+/68Al84PNnCj82fSaLiANqG8OtFLQjcjpFY8vgY/avHdH1xLQdyFJYXStr/Uk6SRkUk1Uvh+Mn+xRjrwgqqTJUWQoV0eNaKaj57taDMzh7xX1ddzpWaEz1IKhASTtciUlHO8ZjXNZkKLKERne8m3rLL4zzbL5Lv0aQqPHQgAmug6wURRTG220TZ9abeNmR+bF+bqmmj5XitdU2UqVjZel3mBZ7vjDuBCbQlVV3Ljt1Lze6Fq54d0RJ1br45jua3uIu1vc+uQbLEXjDC1eGPhct0OMrxsPnnY/LfEWD5YhECorluJ3A9BrksTABbpbxuc3WyCLVjejJ55jUODiOamzl5DEGXC/XqbWGb+m5MqZiXNEVvOf1x/FX7/lmzFWSJ4sUjZtR7UZ3FY3hF8bFKcbrAxVjt4BL0/E9CCrGD81X/K8F15NTl3fRMuw+hbesuWvhuB57ID6yEgBmSlq42B2gGEuS5BdI9Dxpm+8OzZf9oSK7bTORlaJe8qLluDAulLjmO0mSsFDVBkYNDuJq8xhTf8jDAxqgm8agVAryGE+uMHzUO6ekijGxMlPCWkLF2O3dclI237HHuFAcR8CwehPoSNklZW23Y/q+zKRZsINyjIHeB+vux1axVNNx540LQ59rxlugx91GGjXvfFz8cP4EhZVpuznCfmGcQ44x4F7Ym4Y9srizHCc3xZh+h3GKAztHxfj4Si3UnBQdjjCMdsAiBLiRe+P8fJGQheCR89uh37cIaIuukMI44DGOu8GjnYHTa83cosLoJvnOG+dR1RXceqDu55yvN7p4y2/eh4898HxfAVHJMHGqGyMOAK5iHJzCGM0KDm4lk3pWL6koqfLYucJtw4YsASv1ErZbFNeWzEpB9iMepV4s7jji/uJwvqqPvdtFN7x5NGJrigRJ6rf+jAOd/3Mbrb7fRQiBRmd4890kPbZPenGJt98wO9bPLdX0xE3LOymGexBlLX105LTY04Uxvdjka6L0CL8Y7lp+IZbUn2naor/5zrdS2DBtB/c8eRnfdtuBkQVc2i29UfPOx4UM89sJCivbU2z9wjinc0iaZWzZ+Q34SFUY5+QxBlzFmNAVOXEqheMIL22lV2jMXs2Ksfc3bRl2rmppEgpVjL1jdC0n1jN7brMFTZGw3TZzy5wm5fQHv/ko7v3JN+L6+Yo/fvbp1QYsR6Bp2H2NwLQWptnCjGtABoCZUryVgi6WwQQVUg7LqoxaSfWL+aS0DPfGcK6qY7drwXaE13w3+sJMyl2a5msmPXGKMeBObQ1+HqLRp3H0FOPs67AkSSip/bMIxuFK0/A/Y1HVuGs5sBwxNcWYdmNG5XtHWa6XBt7kR0kz9Y4oqdx8Vyi06Je9Nx8VEmSbaHQsX2Xb6SQtjPubv8pq7yLzwDNXsNuxcNftB0c+V9oFetT0mnGZ9RXjBJPnbFexJRtCXorx4YSRbVbOcW3AeP6unmKc/fem8ZsAcNv1M4ntPLSABwvj+au4MA7e7Dw0oDllUhTpMQ4eY303rLTsdEzsdCy84qZFAEg1WSoOUlrrJRUrMyXUA9M0g17maCFL7500yQxxDciAe+ENFsZ0o/Ci62dQ05VQfCWpZ2VdQUUbf0Rz27RR0VXMVzQI4arjhu0kuvj70XKsGBdK27D7mu8AYKGm+Yk8XcvG637tHnz4i88OfS4zR48xQA1gWTzGJr7p2BIA4GRkjWsO6Qmiz+UkPcYd04Ykjd8PtDxTQsd0En02SWxMl0rBinGh0MIcjGsDgK1mzz6xM7ZiHOMxDijGdz+2irIm41tvHe4vBtI3342adz4ulIWc5DXwm+/U/D3GQALFOMfJd/6iNMbdes9jnP34NH6zqis4tlxLXBiTElfReicxzvTCojm32caNi1XUdAUPD2hOmRR0wRk3DioNQVU62rRCqvnrXuCuC3kp5733Qm94Bq0nwfSLqP3r4Kwb25S06TjIIMXYtVIEC2P3tf+JN70AH333q0OPJeWwoimo6uOPp24bFiq67CtUdBMwquEZiB9GwkwWIQRaZrxivFDV/f6K+89cwaWdDp7dGC6QUGGc17XAVYyzpFIYOLpUxdGlKk5G1jiyPtZjdjOKGPDRMW2UVaVvHPUo6LOUJJmCdptTD/hgxbg4BhXG9CFsmzbWvQtYUvVgePOdg7sfW8Vrb1mJvTOOUtUVSFKKHOOcFeM57y4viUfVchyocrD5Lh/1dq6ioaYrCawU+U2+I9VrnOa7PBVjSZJwfKWOQ/MVLNZKiXOM/WIo8B6jZJGrkXNbbmH8kkNzOFnwdL5uwVaKBe+ztLYbvphQYfyqmxehq3JukW3B2DLAvdmmgu/MesO3/EQLY7oRTZIdHiVuDQTcprZQjrF3btfPlXHH4XBHPCVUUGGcRjGuaqqvUNGkruV6aeTPqoqMiqaMnYTApKdrObAdEXvdWqi5HmMhBO5+zB1SM6oxktZsPWbnIg0lLb2VwrIdbLdNLFR13HF4vk8xpt+lHhvX5olqE7RSdK3ekLNxWJ5xP0tJRtz7inFl/FQKHvBRMO2+wtjzGAeUuQtbFPUzhsc4psMbcLvuz2+1cdftBxI9lyRJqOnq2N3Rza6VW1Qb4G7DA8kmp5GVgS6MecW1SZKEwwvVkRfqXK0UivsaTstjDAA/89bb8IvffTsWqhoaXSuRckAFR8hKUdXRNu2rMg/y/GYLhxcquGG+Uui4ZAAw/Oa7AuLaTMf3ykcVY7II3bhYxWJVHyuzehj+zb+3HtQ9v64QAqfXGvgWb/BPdM26YT7ZDk0chu3EWqhmyioM2/Hfg1E1Owgl6lR0BVVdHdvS0fK25X3F+LKnGNeTXZij0XLMZGlHbuCCLFZ1mLbAbtfCpx+7DGC0WGTlrBjrSvrCeLttQghgoarhjsNzuLjdweXd3k4M+eenNRI6yRTeOJZrbmGcpAGPaqpUHuMMNyXTYk8Xxr7H2CuIK77H2Ox7zE47eSpFVCUlxfgfTl6EJAHfdttofzFRC3gCk9I07Nyi2gB3sVJlKVEqBTW/+R7jnApjwM0yTqYY5zX5zv0dxvMY5zP5jnjl0UV8660rWPC2rZJ0Z3diCo7ZMW5uWoaF//Mvvx5avCdFx7Sx3jBwaL7iKYPFFiO04BZxw9A1bdww71oU1qOK8VYbJVXGcl1PpZAOwleMtZ5i7Ah39+fcZht3HpnHTUvVWI/xykwpUXZ4FNN2UIprvoukPUSL9iA1vbeLl+b1oMFNPcXYLYyTKMZ0rjtspSiMljm4MKa/4X1PrQ+c3hiFFONcPcYpb56pnlio6f7OSHDQhz93YEiO8STXJ7dRO41i7F6TBsVPBtlpm5Ck8Rv8ADdG17AcOAUnFmVhTxfG3QFWirhBCkkUY8cRsTm69OY+v9XGN964gJWZZIsz4H5Yxo1ra+WsGEuSlNijatrhHOO81FvA3d49P6L5zs5RMdZSdASTxzgvxZhY9ArjJFnGnRgrBan+SXziD53dxt88eB73n7mS5lTHgm50Di9WUr3Xs+J7jAuYctYxbcyUNcyU1L5G1o2GgeV6CZIkoZLCUzuItmlDV2R/eAclPzx6YQdCAMcP1PHeN96C73/ljX0/e2i+gnNbwz9vcQy2UoSb2uh3jFOM/eY7TUn1elDCAd0QnhnDSgF4CRpcGBcGDXCpxFgpaO279ylXLT62UhspFvWa7/K0UqT7TNLuz2JNx4tvmIUshRvwmr6VYvBI6Il7jFNEuy76HuNkVorZspYqKSs6IG0vsKcK408+cgknnu1d7AdaKWKKhySFsenEd8IGRz+/6UXJ1WKgP+IoCS3Djr37zMJsRUsU1+beGEj+FlBeVgrAvVDvdKyhfwszx7g2Ur3MFKkUeRfGC97EoCtNA7sdE3/4hWcG3kFH39dAIIs6wd+QlOLtAiblkTXm8ELVbbIy7UKzjH2PcSEjoV1lpl7uL7p2u5avptT08ePJBuFO9ux9BsnHePK82wB0bLmG733FEXzvK4/0/ezhhNMmowxKpYiOuG+bdijzPEjNb76TQ4rx559aw1efG33D1jZtlANWivNbbdR0JVFvB8BWiqKhG+I4QYd2y048t4mqruCWlfrIG2iLdu5yU4zTb+dT1NxCVUetpOKWA/VQAx6tBXHXbFmWoMoSml0LH/j86Ykox13LCdUoSSmpCmbL6sBJnkG222aqRAogGB159dkAB7FnCuOnVnfx43/+IH7v3tP+18gmQYoF+WziLsxJmu98w390WzLgt0sS0xaklqIwbhr5KsZA8rgvsjLkPfkO6GUZD7tY2zmmUqRRjMljnFdxTizUeoXtpx5dxS994jE8dXk39rF00YgO+ACSWSlWve3KIlIs6FjXzZb9hqsi7RT0ty1mJLS7vV+P+Uw3Or3COFfFONKIS6/xo+d3AAA3ByIBoxxaqODCVmfsLUzTcnx/fhAax7zrWymcWLUYCHiMNddjTDcKv/SJx/Cbn3565Dm0DRtVzY2Ao2Msj7FT507p4+a7oqAbn7gbl0VPFDiz1sTNy7VENy2GlV+OMZAtro3sb1Tgv/ymBZx4dtNXgf3muwEN87oq454n1/D//uMTuPfJtVTnMAzXY5zumrlcL2E9Qeb6VstM5S8G8pk8WDR7ojAWQuDnP/4ILEfE5mj6HuPAhzLohVmq6Ym2oElZjH4YVcWd935suYZbDtTjfnQg7vbyuHFt+XqMAS/VIEGOsRuXlv/kO8C9UAPDG4JMx4GSl5Uiw+S73K0UAcV4zcvAHaT+xm3NjaMYX9oe/vx54sf4VDVUS5SdW5wyQHaqSVsphBDoWO6W5UwktgwAdrumXzjm4THe6Zh48PlNtMxwNiy9J564tIPFmj50Z+nwQhWG7STqOg/iNt/F5xgD8AvOtmkPVKp8j7Eejmu7tNNJNPykHfi96aYwSVQbEafqM5OjbbqvdWwqRbX3dzu2Uo+9sYxiDdi9TYs7ZCLdZ/KKlyZEiTTffttB7HYt3H9mA0BALR8QsVpSZZy94lqa8so3D+LuZKUT0pbrpb5M9ji22+kL42kpxhe323h6NV58GsWeKIxPrzXxgGehCF5waPs06jEGEPIBH16sJmrE8H1NMcXgjYtV/E93Hhr73JMsAkEMy4FhO7krxocWKjh1uTF6wIbtenwpJievO3YAuGHOy1bdGdwUZjsit2l7vea75IrZpDzG89VeZiR5ugYpunFdzhSTk0gx3i1OMd5qG1BkCTMl1Vczx70RzAIpxpNuvjNsB0K4a0y9rPUN7Wl0LL9oreRQGP/FA2fxvb/3JazvdkOqLL0nnt1o4ZCXPDGIwymSKYQQMAblGEesFB3TRkWPv4RUA3FtM2UNliOw3uhit2PF9oBEoVQKoHdTmNRfTOfKk++Kg4rDuOa7mbLqr6fHV2q+WDRs4lruAz40JfX0uc2WO/WOPoevvXUZZU3Gpx9fBeCu12VNHmj70AM2DkpXyZOuaYd2tcdhsaYnulHNVhh74+kLTlT65X98Au/98wdT/eyeKIy3PaVzJqK++rFWam/8KHEgUBgfWahgp22OHH1oDPkw3v1/vA7vfeMtY5/7uKkUtA2dZ44xALzn9cchQcL/8z8eG/o4ywk332k52RqAXuPQMCXHsgWUHEPdgXRWirwLY12VsVTTcXm343u6Bnm+GzGK8UxZhSTF++ejXPZuPJI8NivbbROzZRWSJPkXxbwUY8NyRhZR1Gk+CSuFEMJ/LXsJOIrX2BV+bRtdy39/11LEk0XZ6ZiwHIGnLzdCijEVxrYj/KziQSSdNhnEdgSEiF8D/VQK8hh71pI4SD0ra4qf5PHgc5sAwqlBg87BsHo2DbogL41RGM962/V7qRN+LzOsEVOWJV9tJcXYcsTQrXWyNebWb5JhLPFm08BiTfcHaJQ1Bd966wo+/dgqhHB3seMa74hglOIkFONuBsV4rqIlspnm4zEu1kqxutPxd2fHZU8UxvSHOzhXDl10/dG5OiUoyP4HabGmgz5TRxarsBwx8g8zyGNMz52mI3PcTn36XYd90NJweKGKH/+2W3D3Y6t4asj2gmm7iq3m2UfS/M6DqGgK5BEDTyynPy4vLVqG5ru8fM5BDsyWsbrT8eNxBirGXQuyhFDTlSxLWKrpeCTBAI1LBXqMt9uWr4bTezYvxfiD953BW3/rvqGP6foe4/zViM8/vY7X/Mpn8cx6M2TbirNS7AQ8xnlYKWhtW290QypccF0YpRhf733/4hjT73qDFeIn3wGBVApzcGEcVIypQH/w+S3/54ZZX9qR6C8qjFcSZhjTuQrRixFjJktrSJYv0NsxO7ZcS7ROWEPeh2nQMzTfXd7t+gkOxF23H8SF7Q4evbCDtd0uZmOm3vnHDtQTp3OaiBkki8fYjTUcfp0QQmRSjMljXLSVYrNlYLttpro53hOFMSkUB2dLfR7j6IzwSsBWUS+5KhuF3Y96AxhWvts3gGvIN2wncVwL+QHHiYRLyquPLQIYPg3LjUtzFeM8bRRAsoEnrmI8PY+xNSGPMeC+f1d3ur5iPMjz3eza3ns3fA4//Nqb8dknLuMz3hZeHEIIrO4MV6TzZKtl+JFa5IvPSzE+t9nGpZ3O0L8fqUCWI8b6OyfhmbUGbEfgxLNXeoWx2t9817VsGJaDmYCVgiaBpSW4XpRDVoref49SjGspFPxha2BJVaArcqD5bnBMFJ1nRVNwaN5tuiXFGMDQAShR9dH3GI+hGJPfm33GxTAsxxjo9Vgc86wUAIYKRr2R0Dkqxim28oUQePTCNl50/Wzo699+2wHIEvCJkxfxxVPr+KZjSwOfQw8M39hqmYmsC+OQNq4NcNOqWoY9dO1sdC3Yjkg19Q7oCTxFN99daZpwBNBIsXu3JwpjXzGecRVjskS0jf4Z4dQMQt622bKWOAM27+xEAIFFINkfh7ZuD8zmXxiTR2/YdDLTG8lc1ZWBalAW6uXhzYhxOdJpIbVhrOY777ETKYxnyriUQDEetDX3I689hlsP1PGrn3xi4DG226Zf3BShGO+0Tf/zRYVYXlFlVHwO2+oL2mTyViQ2vAvYyXPbIStFvayiZfRi6egCT38zKg7aGc4neBEPFhu1gMXqkJfyMghJksZuOqLXUx+wBtbLqt9814k0BgYhdaleVnFgpgRNkfBQIOIqUWHs/a5pPcYAeCx0QbS6rkg1SLlcntG9IUCqHzk4TCDxC+M8B3ykKMwubrvr9R2H50JfX6qX8PKbFvBH//IMmoaNNw9Jq6Lr0HWzrqUob9U47YAPwLUcAcPXWGrizuoxLjIlRgjhp4mkEYj2RGHcCFgp7IA3ye0Sj0586k3BmymrmK9qvqI1UjEe0nyXlnrElzeKS9u9+Ku8oQvLsNxCGsn8rtfejN/9gZfnfg610vCMV8t2cleMx1kQvR28iSnG640urjSHp0Y0OtbA8aKvf8HK0Lg7UotvmCsnSiHJylZgi81XjHMa8kEL6bAb2mDRl7eHjT4nJ89vB4auyH1qJJ1nL5WCXof0NwhBP2TwBlWWJf8GZJRiDLgXpXHe/1SQDNrCnq9qvne9bdp+f0eU4yt1/PY77sS3v+gAZFnCoflK6Dw2m4P/ptFR07QNn3QcNNBbd3n6XTG0vHi96C4X8X+9+YX4r++4E0BALBpyHRhma0xDSZVhWM7IPqMolFdME++CvOlFB9G1HFR1Ba85PlgxppsFesyZHAtjIQS6VjbFGBi+xpLAMpfSY3x8pY6qruCLpzdS/XwadruWv/ubRiDaE4UxdReveIUdbQ3G5WjSG6Siu1uecxXN9/6NWiTJi5rnUItoJ/coVne70BQpFHGTF1VdQVmTB066EUL4OcI3zFeGftjT4uY6Dy6cqDDPA923UiRfDPMeCR3k4FwZQvz/7L1pmCTnWSV6IiMiI3KtrL26q7rb6tbS2lqSLRvvsiXL2GCMWa6HB3jGDNwLeO4AAzMXGw/bw8wA88ydgeEZ4F4wY8xqwJhhMzDG13iRbXnTLllLt6Tu6qW69txjvz8i3sjIyFgz44sqVdf5I1VXVmZWVsT3vd95z3sOQB32KFeKMK1eWRLQUY1Q3RTpi29cqqGvmcx1Xd6hDCrYsgpWoMIz6kAbxhj/+qeew+8+8PxEr0/M/lOXmi6jIgu8K5loOWykOxcgDzPGqzs9fO8HH3S9ntPAW0T6WVm6NpYTFMaSUEh1DcS5AUyXi+5AZC+CMeY4Du84c9TVF9J7pQNnVALkYADZ70qRfE2sJxj0PcT4+OBnz+E3/uk59+uepkdajJ6ar+Llx6cBDK7faEkdMcbZJd8B6dv5j67uQihwOL1UG/keZRrcc+N8ZGFKhfHLT0yjyBfw5KVmqvcQBc2wYFrhTH0c/N7kQXAL4wkY43tutIcV8xqG9Q5tH9zCuK+hKglugdtRwjVuXo3xe990Cv/qzde7wvh4KUW2+exAeinFWrOPhZqc6dAbgeM427cwREpBJ6ystcVeVGNcOshHOQuMI6VgqjGuDXcB0kopgEHxGdampyLsxkV7IU/i3z0uTNMeyiApRTnjgI8kUgovs+otAD/2tVX8wxPhWuwk2Ggr4Di7+CYZgORIKbzvj/5b80kpHjy3hc89t4GverS1SeGVUvgP/1VJQF0WIgd+CLLIpyqM4+YspstF11Wipyafhl9xdMYnnUCSnSgphS/58a23LuJH77sBJ+eSe8hXJYfVP7RsY4J/eOIKPvKlC+7XHcUI1Rf7kWT4LmuNcVkczzHn0dVdnD5SC7zOT85X8b63nY51qyKC5uiUjPtvXcRHvnwB5zfTR7UHwW9ZmxZ1lzQM3yfIC/j4TLR0Kwr337KIqy0FjyYYHs8CXuebcfz892VhrBkmfvHjT7lVP6VKVXzDPf0Ag3mvlOK+mxfx1luXUC8lZIwZaIyT6Km8uNpUmOiLCbNVKVRKMfDwZXdZVIrhGmMvY50F+AKHApcup95g5GMMAIseecxiXYp0pQgziy/HtCFJo36DE0TDUmfcUnRY1qAdVxTsgc1ORsN3VBBHFfeqYboHIK+UYrOjTuybudlWcdcxu4VKZv6UfOd9f/RfYl9IG3txx978rqRwhSB4mS1/wVGRhFh9MUEWC6kkJlGWlQAwUxHddbkf4UrhBzHGNzrMW9QAEmmM6fdeqMn4iftvTEUWVH1hJIfIFj3NwOp21z10dSOs+/xwGeOI/VgzLYg8FyrNSAuv/Onjj13GPz4Zf2i2LAuPru7g9uVRGQXhvW86hduWp0K/DwwImsW6jJ/+5pshFDj8wt88keLdh4M+/3EioYFkUopHL+5irirhyNT48s57Ty+AL3D4xJNXxn6ONDiQjPHTV1r4rc+cwz88YX+IxKCVfcM9QaLzoLCPpMEDaoy+bhwQc5HEKxCwGT8W+mLCfLUYyhhrZvYHAz+q0qjVFUE3s/WuBOwNPtXwHfkYZ7Qge7HoOfCcmq9GJN8ZoVIKOmiF6Xg32ipqsuAW4Sy9jGmooeGR/ZSLwkTaWi9cjXFEcaPohtvio0JYM0zsdLWJNccbbQV3HZ9GTRLwyAWbMSa7NmCwsdOA18DH2JFSOFpwClxJAy8T7meDvvXOo/jfXrGS6HlsjXEaKYV9/Ye1ZqcrRWx1VTsJMCLgww/SQy83SpgqiZH+1K7GeIKQo1qCoaJDjI+easC0gBcd5rPV14bSZqOQRF6oG2a27lDyQP70G//0HD74uXOxP7PT1dDs6y7JMC6onlioSzgyVcK3v3wlM70trRPjBnwkmb96dHUXd6xMTXRIaZSLuGNlCl96fmvs50gD78F7nFmbia48juN+nOO4JziOe5zjuD/mOE7mOO46juMe5DjuWY7j/oTjuNRiWWJLyAy71bfN8/2yhCDGgjRt3gXbDXqIYQ7dwZMMb0hiq5Mu0Fea/SFmMWvMVqRQjbGesal6EKKG71wP4Qw//yJfSBfwYVoocGAiZZmtSp4EqCqa/WCPxbaiu215P0iuEPYZbjlm9KT7ZWnZFqQ9qzoa6ElBxvkA0OxFuFLoptsOvLTTwz88ccVdFCfRV3dVHV3VwFxVwsmFqtttokhoYDD70HaPg0/UAAAgAElEQVQZ44FdGwB3SPJqM73JvKIbHpeL4Wvhf3/DSXz/669L9Dy2xjgFY5xASqHqJpo9e7glMWPsWGYu1CTMVIqRIR/diLCIpCAy5FBKwQZ0TdEg2XZXTTwX47rXRNq1ZSepAwYyp1ZfR7OnJ4qQJx2838M4LYpOJsBcxSZGGmURPc1IPQgYBDr0js0Yxxwg24qOs+tt3L4SzYonwZmVBh6/2JzIxjIpvK43uTLGHMctA/hRAHdblnUbAB7AdwH4TwB+xbKsGwBsA/iBtM9NBSrFJ7YUHTVZHDDGzg2129NGtJi0KXkXVQr+iGNOso6hBODqAJO09LqqjlZfZyqlmKsVsdlRAwsyPWOLnCBQHGgQVAZSlqONEj7zzHpiOYWeoZTDD77AYd4pjk/MlmFZowuSZVmOlCJMYxztFUwbFBWrLBljOol7E5HscIvJi5Guw0gB0feOopvu7/rbnz2HH/r9r+I5Z92YpDAmS8PZahGnHF0sQP7ow/c0Fc3+Qpb8wseVUtx6tI5jMyXctDQ+YyWLfCpJSZycjPxoL+323OdPgpuWalhulHDX8Wk0ymKkXRulVY07BQ/Y91q5mC519BDJQffWWbcw1hIXkAJfgCwWIl0pFN0c8v+dFF4pRbOvJTq8kw5+esLC+Najdbz21KxLtsgiD8vKxte3PyFjXCnaWQ9hUorHL+7CsoA7Alw50uKOY1PoaYa7PrPEdle1DyNVaU/s2gQAJY7jBABlAJcB3Avgo873PwzgXWmflBZnYozbfQ01SfAUBXbO+sWd3shkNl0g/gU7SSSkpjvDZxlKKcpFHnyBi7WKAwbMkn9IK0vMVSUYztCUHyykDH5UJR6aYQUeUqh4zVLK8v63n8bZ9Q7+498+ic89uxE7FWuYFhhKrLFYlzBbKbryA//fQdFN6KYV4UpBh8PgTcUujEXXjJ2lxjiIMS6nTHoMg5fpi5oNUD2FMS24Dzuyh0kKY9Lhz1clnJwfFMYlD2M8kFLoEHnO7Uz5o7HHkVKouomFuozP/uS9eMWJmbF/D0ngx9IYh92DVCRc2klXGDfKRTzw/nvxihPTmCkXIzXGj67u4GWz5UTDhVGQJkg7O0Q0SO5ybr0Dy7Kw3VFTFZD+kBw/umr4nMU48EopWv2EjLFjKTgzoUPU973uOvz+D3yD+7Xrc55BZ62vTTZ8VyhwqElC6BpLdnVZMMak1X7U42eeFZ672hpa77c6GqbLRUyXxXwZY8uyLgL4vwGch10Q7wL4KoAdy7LoU14FsBz08xzH/SDHcV/hOO4r6+vrQ9+jAun8VheqbtpSCkkYFAWqgZ2uhq5qjMSiunZt/sI4AXPCgrHkOM6OXYxoBxPIUYCplCLCy9iVUjBkjAcTyaN/C9rExrWeCcKbTy/gHWeO4MNfeBHf+zsP4ksvRGucshz+C8INizWcnK+4Tg5+/RNtFuGuFDGMcUfDdKWImmwzAbsRzNykII10w1MYVzJijL1MeqSPsacwpgLwEbcwHr8o2vAyxvMDxlYSCigXeXCcx5Wib3e0SIPnH5YbT0phZnIfyGK6xK94KYX9WVNhPI7cYbpSjNQYP7a6i9szYKgkgU81eHuIZLAsyy2Mz250XM/YNAVkVOcQIJeLZJrlJKD1dK2pwDCtRGsUXaONCToXQaB7Jou4ctozxy2MAVtnHLbGPnW5haW6nCpcJwwUB/7oarbOFH3NwDf/2ufwu59/wf237Y5NEE2VxHxdKTiOmwbwrQCuA3AUQAXA2wMeGkjRWZb1W5Zl3W1Z1t3z8/ND3yPG2DAtnN/q2JpL2cMYK7o72LLim852XSmKYzDGDDTGgC2nSMIYrzktxKUphlIKxwt0PaAwzmP4Lsq+jgVjDAC/9l134bf/+d0AEOspa5jZRVIH4T+86zb8znte6baJ/adZ+lxCGeMYr+DtroqZchGFAoe6PN5pOSnouetexriYDWPslU+E3Tu6Yccu+/01yVptEh0f6fDnqhJOOoVxUSigULAn5b1DpGQnSfBv6G1FT611VXQjk8JYEvjYdc+LuIAPlzF25CHjDMhFaYzXWwou7fZxRwYMlSQeMsYsoOgm6LY6d7U9VgEZ5U4EOIzxBMOXftQ8cwj288evUdsZaYz9KDFgjCdZK+z6JPhvcWGrixOz49u0eVEocLhtuZ65ZdtuT4Oim3jq8sAfertrdzAaeTPGAN4C4HnLstYty9IAfAzAawE0HGkFAKwAuJT2iVVPIMOza210VQNVWXBPWh3VcK2Q/OlPpQBXCiBZW42FxhiwB/CSDN+R1dZ8la2UAgiOhR4M37FnjIMKBXeQIENtGWDfkC8/bjNQUUwVAOimyVRKIos8KpIwYIx9BcKAMQ7+DFzLwoDPr68Z6KqGW7zYWk62hbEsFoYdYKRsGGP6HESeC120qcPjL4zXPAztuIURdVRmKkWcmC2D44bZ0bosuve033eaL3BuYUk/kzbkQ9HMTO4D264tvcY4jBwgVvD8lr3+jlMYT5eL6GlGYGH02EWndRtjgZUE9prPNuDmWgRdTy+bLaOl6HhmzZYwpSkg46QUHTXcmWccSI6VJBXGim7GDoFtdVUUnQ5RlqA1IZvCOAvGWAglH4LkqpPgjpUGnrrUzLSTQ3voufWO+28kKayX8i+MzwN4NcdxZc7uId4H4EkAnwLwnc5j3gPgL9M+seb50B5xaPeqJLhxqMOM8fAfTQqTUgjxtkUsAj4A50SW4I/jtkUSWiCNg6hYaN1hjFkypokYYwZSjqmSCI4DtmIKRVtjzO73974fYHQ4jtjWOMY4aHiEGA6aDp+pRGs5J8VuVxspSsvF6GTDpCD97pGpUui9Q9dLPSKRKQ1b6gXZ3skiD1nkcWy6PGQNaW/sg+Q7v1UVsV3k25u6MNZNN61rEqQN+Iibs6iXRBQ44IuO3dQ4VlYUFPDCZmfke4+u7oLjEOsNmwTFBF3CQ6QHySgoROgxhwFMozGuSHxkZ6kb4eU+DqjLc3FncB/GHeCpHZ+VlzKBOkphIU1pMNAYj79W1ELqE1U3caXZH+nKT4LbV6agGiaecUJDsgAVvufW226HcKtjD4M2SsXcNcYPwh6y+xqAx5zn+i0A7wPwExzHPQdgFsDvpH1u1ZN68/AFOzWKBjHKjt3X6nYPlSIfsDEPIqG9SNJWG+jrsr0RkkopiLEVGTK2jZIIvsCNMMZ/+OCL+OZf+5z9+jlIKYIZY0djnEFB4IfAF2L9UwHSGLMvjMOM1TsxGmNJsK1/ghb1bRoWqdjPPVsJD3PJAjs9deT+y1pjfLQhh3Zb6HopFXn3mvUfiMfdfDY76pCu7tR8ZUgiUZWFoYAPf2FMj73ZKYzT6IxN07KDSzI4IMpiAf0U7IwSM2fBFzhMlURsdlTMVoojMx5JcGrBHmb0MjyEx1Z3cf18NRO20CZDDgvjrEEs5fXOoehxKoxTaIyrshgzfJetxth+TcFljIF4xna7q6X6nZKCrGSzWCcz0Rh7ul9eXNntw7KAlTHu8TCQu8UjGQ7gkXtIRzWw1lRgmhZ2uioajjtTW9FTZRkAtqvE2LAs6+cA/Jzvn88BeNUkz0u/xOkjNTx03v4Aveb5HcXAekvFynR55DT3zjuOolIURsTiSTXGWabtEGpyMimFbprMPHQJBcfGyL8o/Y/PPe/+/14N37FkjAGKs42TUrDVGBNsJrIwEo0bN3zHcZx7D/hBvxs5XszXiq5DAwustxTM14bvs7Ik2FZrEzLv5BF8tFHCExebgY9R3WFNHrLAQzN0vOLEND733Ib7mHGdKXqqPtRC/TdvvWnokFGTBZeND4rwpoP5aacwvpKCMSZiIBPGWOBhmBZ0w0x0X2sJ7sFpRyN8ZkzT/5fNVsBxA6svL5652sKdx6ZTP2cQDqUUbEAFJQ2l0jBVmuG7OClFW8lWYwwANUnEha1BYRynM952POGzRkm014pJXHMImWiMS0IgY7y6HSxXnQQr0yVMl0U8troLfEP845PAywifW29DN21np5Xpkks2NnuaazyQBPsy+Y42vLtPzLgnIq9HaFfVQ7Uvs1UJ737lsZF/TyalyDZthxA19Tn8+hbTopQQlIZ169HJW5dJUHEjskc/j0nNyuMwHeOfCrAfvvMiqM3Tjhm+o+952YZmX8OP/PFDbnuKFvPZioStjhJrUTcu1prKiLUgaaMnbRPS8N3RqRLaqh74O9D1UhQK7jXzypfZ1mZk6D9uLLTtozq4F29bnsKbblpwv/YP39Xk4M7VsZkyqpKQystY8RT8k4KK66SscdzwHTAogMZ1jpBFHsuN0ghj3NcMrG73cNLjGz0JDu3a2IDu7bmahEZZxEZbAV/gEiffAUQWRYW86ChnqDEGBuQaIcpHGbA1xmwY42E7x0lA69ukjHHQGrvqsOtZaow5jsPtKw1XIpsFvHvo2fW2u66cmq+O7ee/PwtjZ3F+xYkBc0A3HWmTLm53U51kkg3fWWwKY1lERzXcAI0w6IYJMYeirCSOepuSH+x3vHwFd2ZglRSGwfBd/oyxrbmNvkHyYowBBFrJxLlSAHbR5dUYf/WFbfz1I5fwJ1++AGDQ0pyrFmFaiD0MjAPLsnC11cfi1HBhHJfMlxTtvo6SyGO6UrSDUKKkN4IdGDBfk9w2PS3m41q2xR2Sl6dLWN3u4mqrj+2uhiMN/+dgb1SNchE3LdXw5RibQC8GQ6jZaIyB5OxUnF0bMOhITOIccWq+OsIYv7jZhWVhyDd6EqR15DhEMtC1VBJ5dw+eLoupOkRVSUBfMwNb3KpuQjMsBozx8JoaJ6XY6WqYrmRr1QZ4fIyzsGvTJrc4rclC4Bq7ut0Dx9lzHlnizPIUnllrZcKYA3ZhXOBsNcHZ9Y6bxnhyvuJa33olNEmwLwtjGgAJKozLRQFrzT6afT2Vvs1mSWM0xswY42Sx0LqZF2NcGFkUFN3A8Zky/su775gocSoOUcN3LDXGgF0w+qULfpg5aYwBO9krlDGO2BQqkjDkSkEn+69fsRljsk2ac2QOmwwG8LY6KjTDwqJPSkEdge6EA3hk0UiRpc2ehuc3OvjTr1xwH0PXS1EooOSwkLQm0IY97uS3ZliRB7Qzyw1ohoU/+8oqgNFkKDogzFSKeMvNi3jiUjPx4pzFZkeQhXSFseaZ7wgDadgnMf0/OV/BufXOEEtFhbLXN3oSSCk9nA+RDD1PZDfdb42UzCrt50H7AHXDstYY+xntKMbWcHSqk4Z7BGESu7ZPff3q0CG779g6TiL/DJt3ubjdw1Jdztw+9czKFAzTwhOXgiVyabHT1VAviTjpHLbPrndQkwXMVwdEydmUaXv7szA2TPAFDkcbJSw68cgUw1qReDcR74bF5AtoEr2ZppsoMhg8G8RCRxfGpHFmjaCYWNUwM78BgiDyBUhCIbIwZqYxdlwaorxtbcY4n9tiKsBKpqPokMVC5AGp7NMYkxYMsBd/OtzNVhwHklb2A3hkieYPo6H7NMmwaRRafR1VWXAlCs2+hj/44ov4yY8+6t7HrsaYL+DNpxfw9tuWcONiDXefmMY9N9re6ONKKVQ9+n444xSFf/TgeQCjLgq0+c2Ui7j/lkUAwD8+tZbotQcHxOykFEklBaphoRiz0b7+hnm8/bYlLEyQ0HlyvoqeZgxpr71MTxY4lFKwATGdpWLBdSxIW0BS5zBoT6RuWJauFMColCKqMG72NJhW+oI/CSaxa/v3f/skfv1Tz7lf27aOk+1XLvngW7NXt7tjDdfGgYY2z2+NDt+Og92ehkZJxC1H6njkwg6eXmvh5HwVHMdhviqhJgluzZgU+7IwVj0FIsUIehljwL64XntqLvFzSmL88J1qmJnGQRNqIReeH7rBNnWNIAujFk5Z3GBJETZ4kQdjrOhmZAvL1hgzefkRNJzCWDNMfOrpq7AsC23FcIvLMFSKwpBU4eL2gIn0DovM18LDXCYFxRwv+ApjYhMn9U9uKTpqkjDUbaEiihwevNfLB77pZvzQPadQkQR89L2vdWOUlTHbdXGHVBoiubjTw3VzlVF3HJF3dZfXL1Rxcq6CTzyZtDDOTkohjcEYxx1M33nHUfzm975iovd1am7UmeLcegdHp+TMmMKiUDhMvmOAnjvwNWCM00oOaE8MKoy7CeRk44DWVbqvo1whWIV72K9veyqPk3y3ttt3rSwB+76eRF8MwCUf/APdF3d6mQ7eEVwv/gw01oCtH54qF3HvzQto9nV86fktnHIO1xzH4eR8JXDQNwr7szDWB5KGN900jyNTsqvLoZPmG26YS3VBJLHuYTl8B0RH2wJ28lwujgjFAI1xTowxEB4HOmAA2QzfJSna8maMd7oa/vHJNfyLD30ZD13YwXpLiU2QIucHwup2z13svQwHMcZBYS6T4qobXz4spaDXj7PFi0PbGWhrlOzn2+qo7mtedYrygSZ99HohX89xdXxxsioaIgEG7LEXpxaquGmx5uou33jjPL70/FaiJL4so9Hpc0iqtbbXXvZr0MucwvhFD2t0dr3tpgxmgUO7NjZQXMbYqzFOK6Ww17gggsRljBlJKajTEVWYuZ7wDApjwO7apmWM24qOjmoMfWaKbk5cGA/Y+8G+aFkWruz2sZSxvhjwDB9m4HcP2IzxVEnEG26Yc9dMrxzr1Hw10BoyCvuyMPayFt/zDcfxwPvudVt7VCBTezIpkkgpVD0b71A/6nKy9rJuWPlIKYTRNKw8GeOKFBwCMXClYMcYA9FFW54a40ZZRE8z3DbPw+d38PjFXdx6tB75c7Zdm4cx3unhddfb3ZMZT1E9VRIhFDgmXsZXdu3n9LfTqaU66cBfq29boNEQ3aWdnivfWHMZ4/DrZTB0Nl5hFCelAAbDZ2cChlV/+J5T+Nsffb379dGGDEU3E0VDDzTGWSTf8c5zJmeMWZADfrhBQy37OrEsC+fWO5nJKIBDuzZW6HmG7+j+TFtABhVjBGKMs06co8J4yRkYjipMaUh7mtG8TbmYvjCmkCBvt9BmjCe7X0li4l2bepoB3bRSxXwnRVnMzpUDAHa7KholEeWigDfcYO+DXmebk/MVXN7tR0aQ+7F/C2NnU+I4bmjadaEmoSgUcO/phbAfD4QkFNDXzEjGRjUsJlIKageHRdsSdDOZ1+ikCErDshljNkytHzVPatjQe8hBYwwgMg1Oz4m1Bwbpd886Nmuf/PoarjT7gYWWFxUPY9zXDKy3FLz8+DSOz5SHrHUKBQ4zlSITxnit1cdspThSPFIy2sSMsaKjIgmYKomoSQJWt3uulIKsz6Kul1JKNwY/kkgK7nas4e4+Eey769XpunrvBH8LNYFlWlK4hXFijXE+naOiUHCCQuxDzup2Dy1Fd/WHWUASeGiGFRv9e4h06KmDUInjM2VIQiF1yz2oGCMMNMZZSyns56MuV5Rzzo4vRTRrlEQ+dTdrzVn3vJKHbKQUo7KWZk8f+l6WEPgCikIBXW3ygBPAkVI4e+nbbzsCADh9ZEAuEXv8fAqdcfa/dQbwSin8+K5XHcd9Ny+mMmsGBoMsqmGGMjGqbkBiUJS5A0RxUgojH7ZSFgsjTJqiGbkxxvWSrc30g9q4rAJOphOwmXbyXU5SCuf9PLNm658eeM6O2Q1qzXtRKfLoqDosy3KdDlamS/izH37NCMsyV2WTfne12R/RFwODZLStCRnjnma4wzfL0yU8cWnXLYTXWn1YluVewywY4yTWjW+8YQ7/+BNvxPULtdjncx1C2gqui/HpVbQsNcYkpUhu18bqYOrHbLXoXpufdAYT33DDfGbPT9eFqpsjSagsYJoWepqBolDIhXXfK9DvaGvoRXzix+9xWdikiNIYdxgxxlQYT5WKkIRRZyYv6H3VZTaMcakopGZMaa7De5joZ9DprUmj5gDE5LP6/cdhzINgmhaaPc1ltr/95cu441hjaI0ledbZ9XbiqPl9WRhrEZICWeRxbCZ9djddPIoeXhhrhjVxWyIINUkAxyVgjHNqY5b20JUCsJnSpy6PWrUojDdlGqSIYjN104Is5ssYP+cZDChwiJVSlCXbd7KvmVh1Bu+WG6URhwjAKT4Y2LWtNZURfTGBktEmQVc13GJmZbqEzzw7SLO72lTwz//Hl/BZ59+Crhm638fVGGsRh3MCx3GJimIAmHWuvSSHlEHMa4Y+xgklBXlJKQA6tNnX5ieeWsOp+UrsoSENBmu+kUth/KMfeQh/8+hlzFaKeOD9907M5O1X9DUDsmevOD6bfj8OKsYIxORmzRgTQVUvCSgX+cjClN5X1s4YhJI4KmeMA0nIVN10pV593QhNSU0KWbQPOd4uLsk+6yVGhbEYnN6aFi1Fh2kN9lJ7TR7uOp2YLYPj0jHG+/JYy6Kt7y6SEQwSK7akUOCcpKz4cAkhJ7u2ER/jHDXGjbIY6Ces6iaz1DvAvnk4DtiKKNrMXJPv7JtZ1U1Xy3bjYi12Kp88jjtOAiQArIQcFuerEhO7tivNPpYCCnHA1hlPIqUwTMtm+ZxrYblR8gRPcHhxs4PPn910Hx90zRQKHIpCYWxXCiXjgyJFZyeRUmSZfJd2+E5z7NrywJzDGO/2NDx4bgv337KU6fPT55fXAN7Tjo/4ZkdlEqqzX9BTJz9oBBVjBBrKYqUxrsuik6Abvja0FQ0lkWcmbaQE3zRY81gbEqve6usTyx04znbPaQdIKeoMpBSATe70MpBSUBfe7wrkhSzyqErCSJhWFPYlY6wy8BMeLJLhN4PGkDWty6OetUGvL+bQxpecsBPLslwdZNaFQBSmSnYSoJ+dUnSDKWPMFzg0SmIsY5xbwIfnZn7z6QV87GsXcXuCVo+bLqfoOHu1Db7AjQRtEOZq2UspdMPERlvBQshrNsrFIW/ltCCWt+wyxoOi/6alGh6+sAOvbDTsmikFaOmTwLIsR2Oc3XUwk4oxzjD5Thhn+C6f63+uKmGjtYFPP7MO3bRSD1THoZiADMkSzb7mMpHtvg6Mn3+yr9HTDPfQOi6oGItijLMO+CBmtS4TYxxemFHAECvIIp8oeOkX/vpJfOKpKzjuRMt73990pYidroap0uQ6aG/EPTBgjP1R91khjrFPgt/+zDl88uu2BCuqMAbs3y/N8N2+LIxZtPOSGN1HaZsnRakYv0kbJptIaj9kz2chizwsy2bosmCokoD0QLs9zZ1OB4gxZvv7z1UlrEcwqIZpoTBBilAaeCd+b16q4+e+ZQqvPjkb+3PU3nt2rY0/fPA87ju9EMpsVCUBim5CN7Ib7Lyw3YNlIVTSNFMR8fjF8aUU3mQtAEMDhbcvN/D4RVuG86HveyWeWWuFHuhksTCWlMIwLVhWdCxyWoh8AY2ymGgQMktXCrqf+glZU4XhGujHbEVCs6/jofPbKPKFiSKmg+CVUuSBlpPG+uzVdmCE+UFBL4OBL8Dxsw/yMVbtwjvrzt2J2TL+7VtvxDfetoSPfnU1OuDDCRhiBVtjG3+N/H9fX8NGS8WFrd4Qg04zJk3P4NkkqErC0DVLsk8yDsgaJXHywvg/fvwp9//jglgqkhA5bOnHvpRSsGBuE0kpGLKmRT7ebF4z8omE9k/s0xR8XlIKupH9DDprjTFgJ7XREEMQ8pKzAMOn8YW6hH/xuutw85FofTEwYFLe/7HHAAA/+y23hD7W/Vtn2E4eJJQFOwhMV4rY6kYnDEbBLYyd35Mm3htlESccPeNcVcKbbprHD91zKvR5bPeV9L93lq4QXsxWiqk0xlkcEseJhM5NSuEE0HzlhW2cmC1nvvZ550pYQzNMdFUDR5zAi7iU05cy+lo2mm1/MUboKDoTbS/HcfhX996AhZqMUszwV7tvBwyxQlJXir5m4s2n5+1AENVwO08dRUdPM6AaZiaWanVZ9Ekp2A/fpZWS+HHb8mCvjPsMwixiw7AvC2M1wUR4WiSRUrCcyJbE+HhS3TQh5uJK4URSOjdmloECSUCFsV/zkwdjvFCX3OS0IOQ5lc8XOFfDFTQ4F4abj9TxypdNY6Em4Ze+/fYhqYEfbtBFRp6RANwUoVMhnrPT5SJU3RybEfD6pAJw07UWa7I78HfHylRkbDH9/DhSCk23C/qs16C5qpSMMXbWqCyuw0KBQ5EfdaEJQxKbuqxAFnZPXNodMuTPCqQ9z6MwpkJ4uWHfx0FM6EFBPwMpBWAXXYE+xqqRuYzCj3JRiLQLs6UUbIpCwO4gJ1kf+7qBuarkdhJpzW0rhkssZcIYywJavuG7olBgNkDqD6kaB5pu4ciUjLfcvIjjMYYMVYl/6fsYs5A0JJJS7DFjrBt5Dd8ND+S4frA5F8Z++7o8GOOluoyrrT7MEG9T2w0hP4XRlHPSDRtkC8J8TcKf/fBr8fEfewPedddy5GMlMR1jmATn1juYrRRD21eThnx0XY2h/d5nKkWURB4Ldck9QNyeoO0uifxYTDkxxll7mie1zqPDWVa2hVKKCXiWcjI/KLLctJBpsAchTykFrWV0iAsaKssSn3r6Ki5sja/jnwSZSSlkIdjHWNEzH7zzo1zkI5PXWn1tYreHKCSRVgLOoKPI462O/p4OkB1Fd4mlRkZSCv/wHavBO8B2pZiUrOlpBr7huhl88D13x16PlWKwbCcM+7Iwttt5rIbvwjfKJBZN46IoFNwNN/T1M9SBRsEvpcibMaaCaqc3XDjloXNerMvQDCvUZ7ersl+UvaDI44UQ67NJMQh4yLYwjipkpl1bvPGKA2KM6b1zHIc3n57Hq0/O4qbFGlamS7jvdPygliwU0B9j8XWlFBkfUueqyaUUWR5SZTF5NLLGKOQoCMQYA2DDGOcopSDG+GhOUop/+Qdfw4ceeIHpa4SBirVJEaYx7qh65lZtfsQNf7VZa4xFO3xGi6gJLMuy7WVFHt942xKOz5TdhNO2omfOGHsPKa2+xkxGAWQzfNdN4Y5SlYIPYWHYt8N3WTOHA41xhAnyq08AACAASURBVJSCIWMsCQVsdeKkFPk4IvhZxL1ijHe7fsbYiBXRTwpqxa81+0ODf4C9EPU0w7VDywNTJRE1WWDWOhwcgrIrDs6utyMdBMh6btyQD2ISvAeU3/ieV7j//7n33ZvoeWSRH4u11hjdD7NVe9hM0Y3IA6D9/exeW0phW5enlGjO42rChjEmRw72hTFN8VPQBcvCuK8Z6GnGxBrN8V/fzIQxDnWlUAymjhBAvF1aS9GZM8aATQKEkXFeP/OFmozP/OSbXUeldn/AGE9loDGuycJQzkKzr6PGyMMYICnFZNevLelJ9jc6EMN3LNp5coyUwrZoYucKURQSaIyNfFLXaCBnoDEme6h8CkJq0ewESClYs9aU1hakM7Yjw5GrlOK6uQpuWkwWEjEOXI1xRlKKna6KzY6aiDEO8qpOAldjPOEBZWyNMUkpGGiMgehIciB7T3E5INAnDCy6dWGoFHn39wwb5JwEA/lcflKK6XIRlSKfip1KC2IKs5RHpUFPM1AqTn592rrWIFcKHRXGa3CpGD78ZpoW2gpbKYFbGEewpn53HmAQetJR9EQevklRkwSouuneK82exlxKEceYR8GyrFTd3ap8UOzaMneliJZSsHZmSOZKkY+HqHtI8GuMc2KKBL6AmiSMuFKoGbeQg0BaXq9ZOsGvbc0DP/2Om2GE6J2zgJyxxvjsup0edHIuvJAhjXFcARiGbsCGMA7GtWujNSLrwni26ngZt1QcmSqFPo7ap1khKAI+DGqOyXccx2GuKkHRzUw2dz9oPctDSuFNCqvK6fSMaUFMYZZdoDTISkrhLca8pExHMVBmlDhHoMIsaM/pagYsC0ylFPT5RckJ6DDrZeeLQgFFvoC2qkNypIhZdFmrbsFt/y1afc3Vy7MAHQy6qoGpUvr1RjVMmFZy8qQqCdAMK7ZbR9i3jDErKUVYgaAZdnHCqjgsCgmG73KyCqOLaURjzNgRwoupshggpWCvMaYEsiuBhXE2TGUaSALPdAJ7YNeVzSb6ghOreV0EY1x3EgbHTb/rZ8QYj2vXpjGya5ujwrgTrTPOWkohC3xi1pTlnEUQlhslnF5i0zFJMnCdFUgSUJcF1GRxaMI/axChkFUXKA0sy0Jfz6gwdjSs/kNEW2HPGJedQjCIsaX3U5XYamzDXp9Aa5c/Gr7iOCzs9jTwBS4T6Z//b9Hs68w8jIGB5ei4A3hBbHoU3LTYhJZt+5IxZqH1jWWMPZGzLCAJfLLhuxylFHQizZsxBuz2T5CUgjVjLPIFzFWLbu68F1QYs16U8wS1PLNijDedoi4s9Q6wbehq0rBmLQ26ARrjcSCPLaVgc0imzSeupZe1pEhKwRhrOfp4A8B/efcdzCLYpZSpf5Og2dNQ4Oy1w58iljVIorQXUgo7MTU4hj0tiKVsKzpmHZmRots2ZPMR60sWoIKqpxmYwnAB3HJT39gm39mvH36d0N9X9pFFtizAZrUbJTHWtjIJiB2nzoctpWB3MCCf6jS6Xy/Syu28EhTygo7CvqwAbK1vxq4UMXqzwQAaG7YwEWPM4PcOgntTqvb7cTXGjDwLg9Aoj0ZkqxkzZWFYqMm4uk+kFKwh+fTkk2Kro0HkudjBlJosuotsWhAb4N8Q0kIW+bEGr1hpjOm6irKJArJ3Z5EFPrFDiJFjJDoQnp6YBWgtiSMkskCzbw9rFQp21HEuGuMcmHA/0jJ1UaBizHuIoNmPNPaV44AIg6B1kXTPbJPv7OeOklK47jy+/ahStK8vzchOglTzHFIU3YCim0wPBu7BZEzGOC154j2EJcG+k1IYpsUkGtnVm4VslIPNkM2mkExKkY9d28DHeI8ZY99wVh7Dd4A9OR6UftfbAykFa7h2bRkO302Xi7EsRb0kotkbnw2Qxcl9fGXRtkhMq+Fm5dJCnYg4lkTJOOhmvibhakQMOsGyLKcw3nfbwlhIknaaFZo9DXWnSAlzW8gKVBjnwYT7QS4vWaSt0TDqusfC8KqzLrOyryT4LUu9IDkBy+Q7f5ZAEEIZY0lwpRRZOFIAw1IKVxbE0pUiwcEgCi55klRK8VIvjFnp+ygBKkxKoTDaDAlF3t6kw4IlXFeMHJPvSEqxJxrjUhG7nsLJsiyoRj6F8WJdwpXd0UKhk1ELfz9hoCfPpjjY6tiFcRzqsjARY5wFIzXu4KHK6JBMA0Vxm4GiG5keUpcbJWy0ldjPgQ4QeTLGLMFxXCI3oCzQ7Otu6znMnzcr7KXGeNOZG/BbXY4Dss70du9I4pYmCXQc+NNfvaDCkGnyXYK1SQnVGHsK44yKV5e9VzTmcdCAd/huMilF0r36JV8YD8z1s39rklAIlVJoDF8XGBTcYW09d1PKgbWVhAI4blAsKXvEGO/2VFiW/XtrhgXLysdL+ciUXSj4r4WBlGJfKozGgixka9e23VUxXYlfMG3GeLzCOKtIWFrY/ZKdOLBaC4p8AUKBi9UYdxXDHQ7KAisz9nT5xZ1e5ON0Zw3ic9QYs0bUmp8lmn3NHVaqSiJTKcXAlSL/wnjD6TyQw8okIB2xd97jyq5dJOdVGAcFAFFqIUspRZJDuz/oiFCVbJu7na6WSeodPSdgM8ZNlzFm9/tXEpIEYUgr6al6NMZJsO8KY01no+8DbEY0bviOZcAHEF4Y625hzH5T4jgOklDYU1eKRlmEZljuzT+wy2PP1pINzaWdYTlFULDESx2CU4xltYlud7VEwwt1WRy7ndzXkicaRYFcIDbb6dwxVEZrEMdxKBf52MU563CB5Yat413dTlYYHxTGGKDCeHzG+Ox6G3/04Hn36xc2Ovj9L7ww8rhmT3MZRtIYs7JhHPgYs2fCLcvCr3/qOaxu2/HTlNw4nwFjLAk8ZirFIYegtVYfRb7ghgSxQikBY8wy4EPyyRmD4Lrz+Io/rytFZhpjlzEe+COzZMzL4mRSirQOUnTIeekWxjQRzqBAlYTwYRxWEo7BazuFccgiTZuSmJO+zzuxT+9J4vNNfAMG7Afp5fJgjFemHQbNVyhk5Yaw3zCubVkQthNKKWoTSCm6qp6JlIIm3ZPEMHvBci2oSoIr2QlDq69lOvgSdr37YRhUGO+7bWFsRK35SfCnX76AD/zFY65TwUe+fAE/85dPjHh0tzxSCvrbjTtxH4edHAM+Lu708J//4Wn82z97BJZlYcM5ZE4nOBwnwWJ9eBD6alPBQl3KxGkhCt7kOT/yKIwHjHGUxtgceiyhKtmkQ7OfXWEsCbZb01OXW3jqchPAYN1ggUHAyfjkCZCCMS6SlCLZPbPvVkBWbA1AjHG0KwXL5Dvv6/ihO5txXlZJ3lSwgStFjoxxabjNzTpgxYtl54YnFoRAUoqDNHwHpEs+i4JpWraUIonGuGS3k8M09VHoZcQYz49ZGKtOgchiLYiLQtUME33NzHRTXqzLEArcyPXuh27muwblgUmlFHS4e97x76Yi7tx6e+RxAynFoC3NAu7wnR4+s5IVSBLyxXNb+J8PX8RGW8F0Wczs3lisS0NSirVmn7mMAoh2RbB9lHlmNoLe10/CGPs1xlWJR1e17dqmMgj3AOxu1r2nF/BPT1/Fxx+/gluP1iNDiCYFkU9xJIEXz11t49m1FgAviZU0Epp8jF+ijDGrwRfAYQ/Chu8YM8ZxhTEx5XlojIFhFnGvXCkAL2PM9vP3Yqkugy9wI5rLrmpAcIY0DxJksRCopUuLVl+HaSVji+qyAMtCYORrHLIavnOT5saUUrC4DipFPtJknhbuLBljvsDhSENOrjE+QFKKSYfvyFnlnJP4eMUtjDvuYwYRwsQY2/9l5Uyx63HzYT1YSNcjX+DwB188j8226nZissBiTR5KIb3S7LtDeSwRpfFt93Wm+mLAPnTzBS6SsAjTGHvDlU5FBC2lxf23LKHV1/HIhR285ebFzJ43CEmS//z4N3/2CH7qY48B8PgYJ9wnBL4ASSgkLoz33ZSRxpA5jNKbaYyLwyIfHTDisjU5bUqSMIjLVXQ7inpSe6w0IJuZUcaYPVsr8AUs1eURzWVXtZlK1m28vJEVY7zlbMgzCYfvAFt7mbbd19MMHM2AMa5IAkoij819JKUoF6MZY1Zt3OVGKbHGOC85Vx6QxHAyJAmIMSaGmIq4sxsDxril6LCswWGGiqo2o/Q77zBpkB7/xc0OTsxOVjDtdFUUCpx7Pd64WMPZ9TY4DLT7WWCxLmGjrUA3bKvSq00F99w4n9nzhyHKraelaExlFARZKLhZAkFQNAMcN1oLfdtdK7j39CIKXLY64NdfP+fGx99/C9vCuFDgUBL5xFIKRTfw5KVdt5vQG6O7W5WS+4vvuxWQlbk+4BTGIa0LdY8ZY93Id/DFrzHOmyWdcqUUdrGVJ2MM2HIKv+aypxoHTl8MwF3sJgXpKhuJ7NrGZ826GTHGgM0ap9YYM0zBtAdnwg8pA6uobDfmlelyYo3xQWKMJaEAdSIphf33OLtOUgr7Wjp7dcAYU7Q9rWlUVLFgjE3Twm5Pc4tT/4H3iUu7uOc//xM+8qXzQT+eGD/yxw/h3/3F4+61emZ5CjtdDc+tt7NljKdkmJbd1WkrOtqKnouUIsqtp60YqDIcPCOUitGERd/x9Q8iaqZKYubDcaUij3tPL+D4TBm3Hq1n+txBKBf5xIzx01da0AzLHaTuaQb4Apdqja68lAtjthpjPjQtiGVBDnjt2qLt4li9vh8lTyqYohu5pt4Bg+JqwBg7OuecCuOV6dKoxljLxiZsv8E+mU/OGFMgy0xCH2MAYw3gZeVKAdh+q5udlFIKwwTHsSkQ4xjjtiulyHbTW26UsNbqR4YMHVyN8fiHwpazPp1db6Oj6K406JyHMSaJylHH7Yau/atNJXMNcFu15UxLU8ScDd/XxGj/0t99PXWnxIv1loKL21239Xzm2BQAW/qWhSMFYbFm/x5rzb6r385DSiHwBYg8F1gY91UDpRzmbSQhOrK+rxmJAyyywi9/xxl89L2vyaVrWkpRGD+yugvALog7im5beorpurvk/5wE+68w3ivGOIeADyBKSpGfXRvgsIj63jHGNNywFxpjAFhplHCl2XcPJADQVfQDyhhnJKXokJQi2fAdgLG8jLNkjOeqRawnSH3zQjXs+4HF5lCR+MiBE3I/yLqVe2ymDMsCHr+0G/qYg6gxttf8yaUUz290XH3x0SkZ5ze77tpBB2ya4ieZ2E/++aP413/y8NivHQRipyky2d8JIpZ6t6fh//3MubFfRzVMNPu6e1A7s9xwvzebkSMFMPArXmv2XalPHowxYK+LQYRBL6eCVBajr82sZi3SoC6LWKjl8/lXJcFd7+Lw2OqO+/+bbdU+NKTcq2svZcaYrV1beCzzYBKdzaZAjg/hw3ekMc5v+I4WhawjaJOA4zg0SuJgwjpHVwrAbi2b1sBQHqBgiYNXGNvMxORSijRxsCSlaKZsJ1uW7W2d1d9hHMZY0y1mB8VyUUA3YnGmhTvr4Z/7b17EXLWIX/jrJ0NZTP2g2rVNKKVolEUouomHztub82tOzUE3LZzfsgviizs9cBzcKf6Fmoz//t134a7jDXz1xe3JfwkPaL1coMLY97vR9bNQk/D0ldbYr6PqJlp9zWXYblisunvyXC1LKYUT8tFS8Oln1lEUCrhjpRHzU9nA68zkRV/LpyCVRT4yeKmvm7kzxnni1HwVT68lu0YfXd11a4P1tjLWXh0nY/NiohWQ47gGx3Ef5Tju6xzHPcVx3Gs4jpvhOO4THMc96/x3Os1zspwIj3KlYO3lS79PnMaYVWHuh5dF3AvGGLB1UuTJmTtj7LA7Fzxyiq5moHQApRQ2MzFecfDQ+W18929/ET3VwHZXg8hzidhMsq56bHUH3/mbn3dlGHFQdBOWhdRsQBhmq0VsddRULW3NMCEyug4rkoCuZoS+H1Ya46myiA980814+MIO/uqRS4GPOWiR0ACcIKPxDoV9zYCqm7jzmF2oPfDcBgDgNadmAQDv/n++4IRf9LBYk4fWrnecOYo337SAizu9sWNvg7AzwhgP39d0/Zw+Uo+154uCZpho9mzGuCgUIIs8rnMG+rJkjGcrEvgChxc2OvjEk2t43alZN76XNUrF4MK0r+fDGIcV5u770IzciKK9wJmVKVzY6o14gvvR1ww8s9bCq0/a991mWxmLTc9TY/zfAPy9ZVmnAdwB4CkA7wfwScuybgDwSefrxHC1tgIDu7YIH+O8Aj5iXSlyKlDrsuBaESm6mVtB6sVUeRAbnKcrBQAcn7XTwF7YGGwePdX2rzxoKMUwE1H4zDMb+PzZTTy6uuOGeySRGFDx/BcPXcRXXtzGs1fbMT9hw00fzExKIcEwLfcAlgSq49LCApUiD8sKj+h2C2Mp++Gfb7trGXyBw3MhfwvNWYMOUiT0JNHkJKN4zclZFIUC/v7xKwCAt9y8gB9640lUZQEf+9oqVre7rje6F6fmqwAGHshZgA7yJx2bLn9h1e7rKHDADQtVXNzpwbLG0ziruomeZmCnq6Hm3MunFuzXzJIx5gsc7ju9gN/7wgs4v9XFWxi7IXgRNnvRU80R72AWkBMUxgeZMb59xdatP3YxXN4FAOe3ujCtwYF0o62O5XV/ZErGpZ1eIpJk7L8+x3F1AG8E8DsAYFmWalnWDoBvBfBh52EfBvCuNM/rFqjMNMbRjDGrDTGpj7GYE1szXSlit6dBN0yozvRr3pgqiSPJd3m9j6NTJchiYciov6NkN/S1nxC3AEfh4o69ET92cRdbCVPvAPuAVynyrpQiaXHi+lNmKKUA0oV8aAa7g2JZik5Faysa+ALHZGOmKPgwcuAgMsZz1SJaij7W9U/EwdKUjNedmkVPM1Ap8miUi/ipb7oZ3/HyFZzb6ODZtXZgShgVr17P40lxbr0NSSjg5JxddPvZ8LYTJ74yXUJfM2PZuDDQfnS52XcZXHrNLIfvAOBnv+UWV77D2j/XCzlkGF/RjFwImji3oLwkHXuF25ftwvjRCzuRjyM3nTNOIT0uY3xyvgpFN2P93IHJGOOTANYBfIjjuIc4jvsgx3EVAIuWZV0GAOe/C0E/zHHcD3Ic9xWO476yvr7u/rvC0pUiRkpR4NgxtgNXihi7tpwY42mPK4SiG3vCGHs1xk9ebjqxlOwnkgHbR/G6uSrOegrjLLWt+wmT2LXRQMzDF3bwyOqOu9knQd3jX5zUnYKmlLOStAxCPpIXxqphMnOHoY5EN0Tr1u7bhQ2rqfAoacFB1BjTepJWZw4MBiHrJRH337IEYHgw7PaVKViW/dzLjdHC+Lq5CjgOQ2vMpDi73sF1cxV3nfIX/M2+hposYmXa7ojFeVeHgQicyzs9tzD+ptuP4NvuWsaRqWyHs1amy/gP77oNP/D663IbvAMcKUMAY9zX8yFIpFjGOB/meq9Qk0WcnK/g0RjGmCRBp+arqMkCNsbUGFMHJ8n9OMmnLgB4OYDftCzrLgAdpJBNWJb1W5Zl3W1Z1t3z8wNDb5aSBlksQDWCYzQ1hpshEK8x1nK2SqL0su2u6jDG+ReENmOswrIsfOLJNbz++rlcGduT8xWc87Q5u6p+IO3aSE8+TluVTtefeHINa00lFaNT91iOEfsWB9oosmJKBrHQyQsjlpp7ur7CGONWX89cX+yFLIYPoxk5O+PkAfLc3UjpTAIMBkfrsoj7brb5nQWPldgZh/EC4BaiXsgij+VGKXPG+NR81V0n/ZKctnP9UKH+wmYHv/vA84mn/wF7AJYInMu7fVSdON1bjtbxK//sTibkzXe8YgU/845bMn/eKMhiYeTz0w0TmmFBzmE/TKIxPshSCgC4Y6WBR1ejGePVnR6KfAHzVQnzVQkbHXWszyZNB2eSK3wVwKplWQ86X38UdqG8xnHcEQBw/ns1zZNqjBljIJi1Za2zJZ/gsMLYcKUU+ZwQyYt2q6Ptoca4iGZfx5OXm1jd7jFP2/Hj1FwFF7a6UHQDhmmhr5kHlDG2da1p/VxN08KlnZ49vKeb4Asc7j0d2AAKBA3gAUi8MbuMcUYbAlnLbe0TKUVFio5CbTmtcFaITP8kjfEBk1IAwGZnjMLY6WbVZQGLdRnvOHMErzs1535/tiq5BWiQxhiw27dez+NJoOgGzm91cXK+4hZuYVIKej8feuAF/PxfP4lf+ruvJ34dklHQ8+U1DJc3gobvSFqRj8a4EJqrANiHnoMspQDsRMW1phLpL3xxu4ejDRmFAoe5qoSN1niM8WyliLosJLofx/7rW5Z1BcAFjuNucv7pPgBPAvgrAO9x/u09AP4yzfOytmsDEKgz1gy2zgwuYxwmpcidMbbZvK2OuqcaYwD4869eBMcB9+WoLwOAUwtVmBbw4mbXXSAPamEM2Nd9R9Hx5KVmop+72lKgGRbefJNdDN99YtrtNCQBhVQIBS6xbZvLGBezuR4HCYvJnQE0w2LWPXIZ45CNoM2YMY4KFTByTt/MA67GvJVeStH0SCkA4L9/98vxI/fdMPSYO5zgiyCNMQCcmq/g3Hpn7CE4L85v2kNIp+arrr3myPCdYl8/djKagIcd/eYfPXgeXzufzDpO8+1RB7UwDvIx7mc84xD5+rEBH2buwVt5g6w/o1IiV7d77kFvtlrEZkcd69DAcRxOLVSHUivDMOnq/yMA/pDjuEcB3AngFwH8MoD7OY57FsD9zteJMQj4YONKASCwlagyZk3p9wmzzdJy3pRIY7zTVfeMMW44G85fPnwRdx1rYD7DaeckoGGSc+tt11LpoNq1AbZ27nc//wLe9RsPJBpGIm3X228/gkqRxzvvPJrqdU/MlnFmZQozlWLi4bu+O4SZzYYg8AVUJQE7vXRSClZDuMQGhzPGWuapd14Q+x8EN2ToAGmMXY35WIzxQEoRhteemsNUSQzUGAPAiZkyuqox9hCcFxRLfXK+4kQFj+4nrb7uxhmTvONtty5hqS7j3/3F49BDiBkv/F3N6gFcE4FgKQN9nYeUgnyMww5NimYcaI0x4PW7D98fLu70sNKwr+W5qoQNGr4b47o8OZesgzPRp25Z1sOOTviMZVnvsixr27KsTcuy7rMs6wbnv1tpntN1h2CwONNmG7QxsGyfAvZppSgU3CALP/K2a6PCeMspjPdKYwzYwyt52vQQrnM0R2fXO5nbhO0n0CLfUw2cW+9A1U1cbcYXCqQvvuVIDV/4wH347lcdT/W6H/imm/EnP/ga2zIroZRi0MrM7u8w5RnyTAKWw3fUkYhijNlKKfhQZ56DqDEuFwWUi/xYjHGrb/t2RxUn3/2q43jg/feGXq/UIYhqmScFDQ3ZQ30cZGFUCtDyXD9UrL/rrqP4uW+5BU9dbuJ3P/9C7Ov4u5oHlTEOsrF0D+Y5FKQlx7oxrIucVwLfXoK6Y2HESV8zsN5Shhjjna4G1TDHkpmcWqhgranESvv23XHE3pQ4FBgwpwMv4QDGmPHwHQBIfHjyXt6McanIoyTy2Gqr6Kn6npxMvQlqb92DwrgqCVioSXh+o+Mm4pAG9CCB2oJ93XBZYIq3jQJNtB9tlFCXxdROCSJfQKnID3lmx4GmxLO8HqdKohulmwSawU5aVIlhjNuKnnnqnReSJwreD/0AaowBSj8cZ/jOZu+jrvtCITrwpujK9yaPZH9xs4P5muR2FILcZtqKhrpz/Zyar6Ak8njDDfN4221LuOfGefy3Tz4bK+sYYYwP4JoI2Oti38fY0ueZR0FKa0yQS4xumNBN68BrjEmmFEacXHLIGZIqHZ0adGaSJLD6QV1ib35BEPbdUZBlPnjUhZhH+lsxIpJ6kHyXX4E6XRbx3HobHdXAsYCpatYgxvi6uYprpZI3FuoStjqqm8w2Vcou1Wm/wJVSaAMPx7WEhfFspTixU0e9JGI7YSuZirYs14BGOSVjrLNnjMMSmJrMNcYFbLbj7NoOVmE8Wy1iM4UrCaHZ090ic1xIMTadabDb09yhaWDUn1wzTPQ10y3U/+Wbr8d3vmLFPYzdc+M8Pv3MOna6WuSswLXCGMsiD9NhbCV3mDH79Sfq9QHn0FQaLvLyHALcS9D9FaYxJnKGZEHvvPMopsoiLMvCG2+cD/yZKBxt2HaAccTQvrviOwynYEnIHswYW8xiYAmRhXHOw3eAbdn2tRftgYw0/rRZvj5gJ0mx8m2NfQ9lOzJ422EUZzKMO90vIClFR9FxZddeEJIUxhd3eqHT9mlQk0W8uJksntbV+GUspUiavAewtW6UhAL4AhcYE6zodgRxjaWUIpFd28HajOeqEi5spY9Hbva1IS/ucVCMGPhOC7+VX8kXUNF2igvqOEyVRJd8AAYezGutfmRhfK0M31Hx21e9hXF+jDG9RmAsNYN1cD+Cuh9hUgoqjGkfkkUe33jr0tiv594DMfvfvlsBx7HhSIooVwpVNyCxllIIhVDmQMvZrg2wi0ByC9gLxnauKuG/vvsOvPdN1+f+2oTpchHbXRVbDmNMbh0HCXQgfHGz6w5YJSmMN1pKJilXtpQiYfKdmv3GlJYx1gyL2bwBx3EoF3lXuuMF/Rtru7bQgI8DmHwH2JZtaXysCa2+Hjl4lwRRFqFp4ZfZSD5XBepChF0/i44H81rMfIGfvGHZwdhLBBWmPS17KVcY3MI84H6kv2seQ4B7CVdjHMIYP3FpFzVJwJGMgl9mK0UUuPj9b99d8R2V3fDJQGMcNHxnMb8ZihGR1DQtnCtj7LTlJKEQOlXNGt/+8pU9eV3CTMVhjJ1Wf9LI45cSaAF+9mrL/be4zRGwHUtuOVqf+PVp+M6yrNjOQF83IPJcpjrXujN8l+T1AfbzBpWiEDh8RwMhVYauFFHpnwdZY7zVUWCYVqrfrdnTsDChU06WjHG7r+P4zEDyZjuM2AXUL378KZdQCnM1ScqWjTDGB9WVwrGE9BbG+Uopgi33gEFXO48hwL2ELPKQhEIocfLYxV3cvjKV2cyZwBcwg6ET/wAAIABJREFUX5NeeoxxR2GXPjZwpQixa8tDYxzqSpH/RPh0eaDxZTHs+FJAoyyi1dex3lJQk4RcNd554dhMCSLP4e8fvwLAZtCSMMZbXTUTaUldFqEZVqKAERZpT41SEapuJo7FttcCdvdDoyxiuzvKYNJAXoWhh6q3mPJD34OuVR6YrRRhWnDnCJKi2dcyYIxJYzz58J2tPx+8H68P7oc//wJ+57PPAwhneCm1b203+t6n+5T0nwddSuFl3fOUMMjisK7Zi8FacDA/ey9s4iRYWvbU5SZuX5kK+KnxsViXY4mhfbcCdhSDmTPAwMc42K6NdVFUjHSlYGdTFwbSme3V4Nt+ABV+z290UoVXvJRQk0W8+uQsLjsb4p3HpnE1JiK3pxroa+ZYk79+UAJeEjlFXzMz35QGIR/J5BSsrRuXpoIX5jysopLYtfEHyK4NAOZq6WPBAWf4rjRZYZIpY6xowxrjIo++ZqKnGlB0E60YKYUk8Jgui1hrxTHG9nVA4SgspT17iSApBWm282BqiTEO0hiTrKp8QB1BvKjJQqArxdcvt6AZFu5YaWT6egs1+aXHGHdVloxxlMaYfchFnCtFgUOuzC0VhXsxeLdfQNKJs+vtA1sYA3DjtueqEk7MlnFltx9p20SM5kwG0pJaAhN3Qp+BqT0V90lDPlgfkhdDFmZ38IehrpDmHExz9G9PkdAHTWM8W7ELvM2UseA9zciQMZ6sMPY7TgB2YdXTDHc+ghClCU7CltEeReEoB9HCEhgwxl4rvYFdZJ6M8ei1QcO51wRjLIuBpMmjF3cBALcvZ80YvxSlFCo7xliOcKXII/2tKPChAR+aaeY+Dd4oHzLGdDi4vNt3pSUHEW9x4rZXpktYrEvoaQY+99wGnl1rBT6ekrqyOCxQS/aJS0189cXoWNq+ZmReGLqMcQIvY9O0mEZCA/bCvNFWRlLIBrpChoWxGF6oUST0QdMYz9fsa3g9RWFM9lGTDp5lxRi3A94PSSn8VohRPtiLdRlXY4oCKowPOmNM/u57pzEOr0c6JKU4oIcSL4KkFH/58EX81cMXMVMphsatj4vFuuy6UIVh3xXGXaYa42gpBWuNsRTDGIs5b0inl2qoyQLuOp5tq+KlBK9UIAt2dL/iaKOEN9wwh1ecmHaHcL7vQ1/Gz//1E4GPJ8Y4i2FEsrz6qY89hh/944ciH9vXDHfDygpUGO8kkFJQwcjykLxQl2Fao639gVUUu9emQ0dQoXZQXSkGjHFyKQUxWJPatblzLRMyxkGOExVJQFvRR/TqNSn8PS/WpVgPV5L13XGsgeVGCVMHlDAoBUopDPAFLpdZk0iNsfP3ZlUL7SfUZWEoie7ybg8/9pGH8eUXtvHGG+Yyt3JdSuBwsa8+ddO00NUMZsMnUZHQKmNdIeC4UkR4iObN1Ny4WMNjP/+Nub7mfoN3uOwgSykA4Pe+/1XgOA5fPLcJwL7mngvx9yXGeCYD+zpqR9sDJdHFSV8z2THGCQpjGnphyZItedwBlqYGizStDSzbuMQY93UDUxj+29IatFee4qwwVRIhFDhspGCMSfYzqZQiKvlut6fBMK1EA65BDPZCTcJOV3NnB2qygJ4aLUVarMtYb0U7dBB5844zR/DD95yKfW8vVcgBw3c91YTMuA5wX995nV5ACmbnGhq+q8niUDIqHWB/9Z/diW+982jmr0dDqFHYV4xxXzdgWeymYEWeA8cFL1Iaw7QrQnQkNPvXP8QovIzoQQz38IIKnuMzZXAcsNwohebG7zitpkwYY89m3lWNETsoL3qakfngC3UFkkgpOi5Tw644DbPNcofvGG7MUgRjrJnmgZNRAPbcRtr0O9qoJ2eMw6UrP/0/H8f/+YdfS/Q8dI96XSkWnUPV01dsOdT9tyziSEOOPNhQtyJKb51H12Q/wI65Bx53tKyAXYNk3bEKA71OP6AmcBnja0JKMTx8R92apanoa3lcLCZgjPfVld92LwY2hTHHcZCEwp4yxlFSijyt2g5hQxZ5t6WWhQPDSwFHGyV85v96M37mHbcAsB05/CDGeGrCwgAYLS7C4j8BNnZtVUkAX+ASMcYdGnphyBgPghaGC2PFjYFla9dmv9YoOWAY1oGTURBmK7au27KsEW13EAaF6IQaYz5cY3xltxfrEEEIklLQBv/0lRY4Dvj333obPvbe10U+z2ItPuSD9ijW0sK9xlRZxHe98jj+4MHzePJSE4C9/kg5hWrIQriUoqMaKAqFa4Isq8uiY6dpfw4kecti7wnCS64w7irsfTyDDO4ti/3ADRDtY6yZJoQD5h/6UgExxQdZY+zHsZkyTjluJOfWRwvj7a5qt6AzuCckoYBykXc39SjbNkU3Mx984TgOUyUxkSuFa5PEcA2arUrgC9xIcZKHh6o/+tYL3Ty4hfFcTcJGR8Wv/uOzuO+/ftotNMPgSikm3JwLBc626QxY99uKEdhGD0LLF/cMDA5YT11uYqokoiIJmI8JJEkS8qFdI4wxALzvbTehURLxy3//dQD2ASaP1DtgcG2EuVKwrIP2E6ijSNc4ERisiKrpshh76NtXVz6xNSwF53Yk6vBiRIsWyxYmEO1jrBsWxEPGeE9AMdAHXWPsx/HZMvgCh7ProzrjrU424R6AXZj+3ve/Cj/rMNTxjHH29+FUScRuL7oYAjw2SQwZY77AYb46ahk0sGtjKaWIYIxNK3dnnLwwVylio6Xgqy9u48XNLn71E89EPt6VUmQQhxyWeNpRdFfTHgfyKPYy2Is1u8jd7KiJD/VUWNNeGwTao64FtrJRLuLdrzyGB57bwG5XQ49BxyoKkjhajwB2h+BaGLwDBodPOoySjI8VY8xxHH7unbdEPmZfXfndHCxKJHFUSpFX64he2wjwENX3wK7tEDZIR3sQ46CjIAk8jk2XAhnjna6W6Yn97pfN4MSsHWcb5WfMamOaKomJks/yYIwBx0uzNcoY8wWO6TowsIgKYowPpsYYsBnjzY6Cs+ttFDjgQ59/AS9ujl73hGZfQ4HLZvjJ9o4OCnHQEzPGrl2bx3GiURZdVjfpvVqKcEIgaIYJjjt47iRhuP+WRRimhX965qrtipNjYVwS+RBXCna2tfsNNOBKncTdnoYiX2D6d/iebzgR+f19VYl1crAosaUUwxciJf2wZmxvWqrDMC08fGHUy1U7wPq+/Q63MM7AgeGlhlPz1XDGOOODgssMREgpWGiMAcemKiYKF8jPWH+hLo9E8yo6+4l4YoyDNmP9AK9Bs5Ui+pqJy7t9vPHGeRimhRc2u6GPb/Y0VCUhk8ClMMa4rehQDTNyGJXQ6mvgC9xQN4XjOFdOkbS7ExSD7IfiDIIfNHeSMNy50sB8TcL/enKN2foTBrLc86PDMOhsv4HSJQdSChX1krin19++KoxzYYwDFimXMWYsun/TTfMQeQ7/68m1ke/ph64UewbaVK41xhiwUw+f3+iMJKFtd9XMpSX+lpkflmUxiYQGgOVGGRd3epFpf4DXWJ/tpmQzxqOuFKw3ZckdvgsI+DAP7gAwhVUAgyStqOKw1dcn1hcTpIDZEs0w3b9BEjlFW9FRk4WRYoHkFEnXrkGoRXgxruompGtoLyoUOLzl5gV8+ul1NPt6bhpjwJbGBBXGXYZBZ/sN5LTyS3/3dXzsa6vY7WXbrRwH++rqJ8aYJVsT5EpBJ3bWjHFdFvHqk7P4RFBhfIA3pf2Ot922hB94/XXX5MHk2EwZim6OxMpud9XMkwD9QxZ+DFwZsv87LE+X0FWN2MQjsklivSkt1WXsdLUh5pbVocCLgZd7gGWlaR3YAWCKNwaAW486hbEWrrNt9rWJPYwJQYxxx1MMJZFTtPt6oLc2DdMlZYypY9CLkVKI18DgnRdvvGEebUXH2fV2roxxVRJcmYwXnWtIY3x8poy33LyAi9td/MEXX8ROV2OmL06KfXX1u4UxQ7YmSEqh6PlN4b7l5kWcW++MtK81w4R4QDel/Y5Xn5x1rcuuNVAqmDf8oKca6Gtm5oxxpSiA48KlFK4rA4PODcWKXtzuRT6uoxrgODbvwYsFp6BZ9+iM+7rBfACYDh1Bk/DGQdYYO4wxxwG3HKkDsMMcwtDs6W6Ld1JIAj/CGHtZwm7EIFxX1fHhz7+A7a4aWRg3EjLGHGfLMaI0xqrOPgV2v+HMMTv91bLYusL4UZWEQKKgqxoHNorbD1nk8cH3vBJvu20Jq9s9mzE+LIwHoDYmy8EXOWD4zrWnyWExeM2pWQDDpuLAoY/xIfYGcw6TttEaMMZXnRb/XCU+ISgNCgUONUlAM4QxpmKNhcH+csMujFe3w3WlgBNJL/KZaEujQAWNN55X0UxIeTHG15jGmArjlelBxHFUQdrsa0NhGpMgKPGUhjzt9xFepH766XX83F89gc8+uxHoqTzQGCd/r2EDXwTNsK4JqzYvjk7JmHWIgHylFGKIlEJnPgC837DcKONqS8HVlrLnMeT76urvqjr4Asc8+SlcY8z+46CTkP+UqB1gq6RD7F/MOgXDZkfB+c0uVre7LqtKLGuWqJfEeMaYwcZ0bNp2xLi4E8cY68wChrwICvlQdDZWdV4M7NquLY0xSQ1OzVcTOTO0+npmUgopINhpmDEOfx+kx9dNK7BQpwNWmvmIkshHyjdU3bzmrEM5jsOZFVtiw7pb5EVNFgKTRzuKwXzOYb+B9pv1lnIopfCioxgoF3mm04i2ZZrflSK/wph8JP2nRE03IR5QtuYQ+xfzTmG83lLw43/6MN73549i1Skel1kUxrIYOnzXYyilqJcEVCUBq3FSCsXIxVh/yQ1a8EgpNIP5pjxwpQiKhLbAH1A5V1Eo4ORcBS8/Po2iUIBQ4KIL0p6WmZSiGDDX0kkopfASKEGt9ZuWauALHE46YT1JIBf5SI2xnQJ7bbGVAHD7ii2nyCsSGnA0xoo+NBRsmBZ6mnHtMcae/aZR2ttB+H11JLHTXti+paDhuzwNzUsiD77AjQju+7oB+Rq7EQ6x96iXBIg8h82Oiuc3OihwHFa3e+A44MhU9oVxTY6SUrBLfuM4DivTpdjCuJuTTdJUyfag9TLGfc3EbJXtawu8XRQGB3yYB1ZKAQB/96/f4A4XlsTw4tAwLbQUtoxx0uE7L4FSDZBS3Hykjsd//htTFXNxUgpbY3xwr4MwnHHcSvLUGNdkAaZFLhT23zcvy8j9Bm+HciqjQ+m42Ff0QEc1UGY8DR4UCa3kyBhzHOcI7odZs76ar7H4IQ4B2NfjbEXC+a0utjoqNtoKnrrcxGJNZnI/REspyJWCzX2w3CjFaow7ORnrkwftiJQiB6YuiBwADrbGGLDXfhouLBXD5QRumEYGqXeAbQPq/7y9BW8nxjauKBQgiwW3u+NHWoYz6lAA2B3Ua01jDAB3HGtAKHC52nYGdZCpk8G6FtpvWKrL7v2ZdJiUFfbVkaSr5MQY+xYFLafkO0JVEtyIT0JfNw8L40PsCWarRTy6uuN+/YWzmzi9VGPyWnVZDLVr6+vsNMaAzUh86fmtyMd0VT23aPDFmjzCGOcx+COLo848gM2USjkOHu0lShFyApL6ZOpjHMkYh0sp2n0d02URf/R/vNrVE08KWeQj5Ruqfm0WxvM1CR//sTfg+Ew5t9ckeUyrr2PRNkvJxbZ2P0LgC1iqy7i40zvUGHuRB1sTGAmdI2MMOKbevuKgpxq5apsOcQjCXFXCha2BxKCt6Ez0xYAt3QhljFV2UgrA1rC1FB27Ecl7HdXIbUNanJJx1a8xzuFwLAmFa05j7EdJ5EM1xm5hnBljPDrXknT4rq3Y/sWn5quZ2XfJIh8d8HGNMsYAcONiLVcpBcl1vB3kbk4hQ/sRtO8culJ40MlFY8xDNy3oHl/JQcBHfoWxlzWzLFtsn+cNeYhDELzhBwQWjhSAwxgrOgxzNIFuwBizuQ/Is3m7o4Y+pqvkZ5O0WJNxpdl3B29yK4zF0dY+cPA1xl6UiuE6255rG5qVj/EoGdNWDPezjnOlqGakdSaUinygXR/BdqXYV6XBgUWQlGLAGF979cCKY6t5yBh70M2BNQ2yK8rTrg0YTKISWCZ+HeIQcSDtoshzuG7Onm5fbrBpJ9ZCXFkAj48xo+KQYkZ34hjjnJiaxbqErmq4n4Wim8wDPoBgORlw8DXGXpSL4Yxx1lr3MFeKqixAFguRet+2omfGXBNKMa95LTPGeYO6AN4O8kBjfO0xxkTI7HVhvK8+eWobsYS3MKb8AtWwGZu8vBtrsogXNgdDQMRQHGqMD7EXIMb4aKOE6xeqeH6jw44xdha8Zm809pOljzEwWGyjpBR5GusveizbqpJgF8Y5Mcb9a8zH2I+SyGO7E2MbmNF1KAk8VN2EZVmuFWnH2et4jhvSG/vR7uuutV9WSDR8d8gY5wIiCrwd5I567TLG77pr2RkI39vhu3119XcUnTlbQxuPV/NFjLHE53MhVn2m3rRIHRbGh9gLeFPByA+VmcbYaQsHeRn3GNq1AR7GuBsspVB0A5ph5cgY2wXP1WY/165RKGN8LWmMi0JocdjPeD0mMsYbC00kUJQ7BmAXTFmTRXLMa16LkdB7hZrkaIy9mnPl2mWMT85X8eP338g0yyIJ9s0nb5rWkJcfKwQZ3OctpfBrjN2F+Bo8IR5i70HpdyuNMt56yxLOXm0zm8yedorTrQCdL92TrOQEXrY6CO6GlNN9OF9z0u9a/QFbnpNdW5AziH4NaYzLEelvWftpu4WxbrqR3B3VJoFMy4odvssqmppQcjTmpmkFRp9fi5HQewUyG2gfMsb7Cvvm6qeLoca4MKbFzmufMxi+y0lK4bRN6T2wZsoOcYgozDlSiuXpEl5xYhoffM8rmQ3fLAYkvhEUzY5EZsUWkJRipxtcGHdyNtYnbfdmW3UZ4zzs0uSQgAfjGtIYl4rhlmX9jP8WxYC5lrYT+VsqCuiGMNemadnMcsYaY9pn+gGWfcDh8F2eEPgCykV+qINM0po8goYOEYx9c/V3lHwsSgaM8bCUosDZF2kecAX3zg2QdevuEIdIg+MzZSzWJdx9Ypr5aw0K4/7I93a6WubsmBeSwKMk8qEa47xtkih1cKOt5soY1yQh8DPQriWNcZEPtKwDsrcN9DLGBFtjzKNS5NEN0Ri3GZFFtM+EMeaHw3f5wj+M31J0FPnC4d9gDzHxJ89xHM9x3EMcx/2N8/V1HMc9yHHcsxzH/QnHcYlU1G3FXqiZ+xgLpDEeZozzPCHT5k/tk57qTOMftk4OsQeoySIe/MBb8Nrr55i/VqnIoy4LuBpQGJ/baOO62QrT12+UxVBXCpepySlxilIHN9sK89Q/L142V8Hl3f4IY2qYlps8ddBREnmohjlk20nImqgIZIz7tjVplDtG1gl8hJLLGI/+7pZlXbOR0HuFqjwc+NXsaXvu43utI4tq8McAPOX5+j8B+BXLsm4AsA3gB5I8SdthjFm7UtBwi3f4Tsk56YdaYy3nMNDLkS06xCH2Got1OVBKcXa9g1MLbAvjqZIYzxjn2MKcqxWx0VaYO3J4cWq+CgA4t94Z+nfdMCFcI8N3pCMPGsDr6wb4ApcZWUJkjJ8xJilF2BAgsYiZSymK4Yyx5jg0HbKV+aHmSwPdDXDsOUS+mOjq5zhuBcA3A/ig8zUH4F4AH3Ue8mEA70ryXK6pNXMphXNa1oYZ4zyncGvSsEWL60pRPFyMDnHwsVi3gy282O6o2OqoODlXZfraUYXxQNuX3wF1tiJhszOQUkg5HI7JeeTcxnBhbJjXjsZYjpAT9DUTcoaFIe0tRMZYloWOartNVCK0zrQ/ZE0WuYxxQEGed9jVIex6oO3RGO90NTQOC+M9xaRX/68C+EkAVGXOAtixLIvu9FUAy0meyD0dM7drG2WM886G90spWEfhHuIQ+wkLdWlESnFuow0AzBnjRlnEbtzwXY42SXNVCRstJVe7tuvmKuA44Nx6e+jfNdMCf4200KMY456WbdAU7TnEGHdUA6ZlM8GlIu+6ofhBA1ksXCmA4N89b4emQ4xqjA8Z473H2Fc/x3HvAHDVsqyvev854KGj2a/2z/8gx3Ff4TjuK+vr67kxxiRXULx2bTkPG/hjIGk6+HD47hDXApbqMq62FJieWOizTlt/LxnjZs++H7NOGovCXLWIDQ9jnMfhWBZ5LDdKI1KKa4kxprU2SN/b14xMmfsBY2zvOS9u2p/7semyrTHWDDcW3AvaHzLXGDudyWApxSFjnDf89q073UON8V5jkqv/dQDeyXHcCwA+AltC8asAGhzH0Z28AuBS0A9blvVblmXdbVnW3fPz825hnB9jvHfDd1VXSuFojP//9u49OK6zvOP499n7Sru62JLs2HLiS65OMAkkIZCBQFJDuLRJWyhkWqA0lH+YDh1a2gDtH3ToDJRpGWg7nTJDJ2HotPQSIAxlSCakDS0JkCEXxzGQWCGJL0lkW9eVdrWXt3+cc1YraWUSWXvOWuf3mdFYOrurPfK+e/Y5z3ne5w1WvtPkO4mBLX05ag3HqZaFNsbGS2SSiY6tuBcY6MkwOd9+gY+gjVuY2ZqhQpaFWoMTs94+hbUs/O7hAodbMsbOOT8wjkdAlD9NxrhSbazr65Bd1iI0OAncM9JLTyZFveGWLP4RmO1QKUUQ9Lf925UxDl1hWWDcblVQCdeaR79z7uPOuVHn3E7gPcD3nHO/DdwHvNO/2/uBb76U3xfW5LvV2rWFWmPcnHy3tMZYk+8kDrb0+QtbtJRTHB6f5bzNPR1vmdifT1OuNtrWV07NVylkU6G1bYTF5biPTHhLxIdRYwywZ7iXp0+UmpnKup+9j1vGuF3WdL5aX9fM/fIa47HxWcxg5+bexZKONvsx06muFJnVa4yb/bQVGIdmW3+e2UqNidICtXqDmUqNgXy0SyLHXSdG/58CHzWzp/Bqjr/8Uh5UqtRIWOczJu3atS3UHekQDwTZVIJ00hbbtVXrZFOJtqsQiWw0I216GY+NzzYnhXVS/2lWv5ucXwg9UxMsx310ch4IZ4EP8DLGcwt1jk95r0HND4zjU2PsBZvtJ9/V17WsbflVyrHxEtsH8uTSyWZgXGoXGFdqmK1/l5TTTb4Ls6RHPMG8irETs0z7MUF/Xot7RGldjsLOuf92zr3D/37MOXe1c+5859y7nHMr+zK1Meu3r+n0GtnZVLvJd3WyIWaJzIxCdvHySXlhfSd7iHSzrW1Wvzs2WWbHYGeWoW4VBL7t6oyjuIQZZIyPTniBcVgBybZ+7zUYn/Feg1rcMsZ+nW27VefKHcsYB6UUs+z2W+YFc2raLfIxW65RyKTWPWFyumy5MsbhC+ZVHB4vMemXlw30KGMcpa4Z/bOVWsfLKAASCSOTTCxr1+ZIp8L9QCjm0s3JFfPVusooJDaGi0tLKeYWasxX6wz52ztpwJ/U0m6Rj8m5avP2sAwvyxiHdRwIOh0EJ+f1ehAYd81HQkfl/SxsebV2betaY7wYGDcajrHxEnv8qyPBZ950eWVgPFOurnsPY2itr15Z11xRxjh0o4N50knj8Phs84RdNcbR6pqjYNDwPAzZVGJlu7aQZ+EO9maa2ZpytaGMscRGOplgpJjliJ8lPTHjZUk293Y+S9LMGLdp2RZFm6RN/t98fKrMtv4c6ZBKGRaXpff+H2oNL0iKzZLQza4UKwPScrXenDC3Hvrzafrzab792DGOT5eZr9abGePisg5FrTqVLAqywastbgIKjMOUSibYubmXsfFS84RdXSmi1TWB8WyYgXE6GWlXCoDdQ73NPqLrPdlDpNvtHl4c/ydK3gliKBljf1JL24zxfPgZ41QywaD/nH/+jr0dLyULNCcA+5nKZo1xTEopek6TNV33GuNUktveejEPjp3iL751EIA9Q17GeHlP+1azldq6T7wDr5Qvl06sUmMcXj9tWRQcD4O5D1rgI1pdM/pLlVpzRbhOy6YSS/sYh7zAB3izwo9NlZlbqPkH4q55KUQ6bs9wgcPjXleEE/6Vk6HeEALjXu8D51Rp6dQH5xxT81X6IvhAumCkyP69W7jxsq2hPedqgXFcaoyzqQRmMN8uY1xb31IKgHdfuYPX7t7Mdw++QCaV4KKtRWBl685W0+UahXVe3COQTydXqTEObwVGWbRnuMAzJ+eaV5FVShGtrpn6WKrUm7WHnZZNJ5qXjMCr/Qo7MA4upY2Nl5jX5DuJmd3DBabmq5wqLXCy5JVSDBU7X0rRl0tTzKU4OjHvBeWzCwwXs5SrDRZqjUjaJH31g68hYYSWLYbFSV/BJfy41RibmRccrjb5bp0Dw0TC+OoHX8PxqXmK2XTzUvnyxZ5azZarjA50pq93Pp1UxriL7B4uUGs4Dh6bBhQYR61rRn+YpRS5VHJJxrhaD7/GeM9wMBN11luCVKUUEiPB5KPD46VmxnhTCDXGANsH8hydnOfbB45z7We/x4sz5UgnvWRSiVB7J4NX551LJ5oBWTVmNcbgBYfLV75zzq37ktCBZMIYHexZUj9ayCzN3LfqVCkFQC6z+kkBqKd+2M4f8eKBB8dOht5LXVbqmv/90kI4XSnAyxgvmXwX8pLQAOdt7sHMzxirxlhiZk/zisksJ0sL9OVSoV2+HR3s4cjEPI8+N8lCrcHPnp9proYXdo1xlArZdPMSfj1mNcbgZWuXZ2oX6g2cC2/yWSKxtHVnq5ly5z4Tf3nGWJ9HYbpsWx/9+TTHp8rKFneB7gmMw+5K0ZoxroU/+S6XTrJjsIexEyXKCwqMJV62DeTJphKMnSgxPltpLnQRhtHBPEcn5hnzl+YdGy81u1TE6UOpr2Up2lo9XjXG4NVZL5/0FgSGYfbxLWRTze4ggXrDMbdQ70i7NmDVMpLFGuOuCQ1iIZVMcP3FI0C8jkHdqitGv3NeL+HQMsapZOQZY/Bmoh5+cZZyraFSComVZMLYNeSN/5MR7kjGAAARV0lEQVQRBMYzlRqPPDcJeFnryRj2Dy20BMaLS0J3xUdCKNplaqPo41tsk7kOfi52avJdpv3ku3LVKyvUKqzh2793CxCvq1bdqiuOgnXnHZTDCoxz6USzXZtzzlvgI4Kanj3DBZ4+UaJUqWnyncTOnuECT43PcmJ2obkCXBi2+xOagkl/h8dLsWys72Uql9YYx2VJaPBLSZYFpEEWNcxEResJSqAZGHfoMzGbSq7aqi6sZcllqTdcOEwmmYjVMahbdcU7oOFnK8IrpVjsY7xQj24JzCvOHWC+WqdSa6iUQmLninMHeObkHM+enAs5Y7y49HQunWBsfHaxlCJG2ZrWUoJ6zNq1gVdKsryEIYoa23aZ66D2u2OlFJn2NcaVmsr6olLIpvjUTZfy3teeF/WuxF53BMbNjHE4b8hsarG5+YIfIIfdlQLguguHmytdqZRC4uaGS7xLhwv1RrgZ48HFFlivv2CYY1Nljk+VSSYstF7q3aB18l1QYxy3yXfLA9JmV4YQs6bFXGpFH+PghKVTXSnyqyzwUak2VF8coVuuPpfX7RmKejdiryveAcHlq9ZMTiflWla+q/ofCGEtxdqqmEtzze7NAFrgQ2Jn11Bvs01RmBnjwZ50c+Wz/X5w/shzE/TlUqH2Eo5aMZdqlhIEGeMoSsqiUsh6GXPnJ2ZgMTAOM1FRzKZX1BgHr0snu1KstiS0MsYSd11xFJyer7G1L8el2/pCeT6vK8WyjHFEfRvf7Bfcq8ZY4iiYcDIUYsbYzNg+kGekmGXfjn4AHj86zUBP+It7RCmY9OWcW6wxjlHGuJhLU2u4ZpIEFpM02ZBrjJd3x5jpcMY4d5rJd1rcQ+KuK94Bs5Uav7J3JLRsjbfyXZAx9v6NImMM8JZLt3JOf66ZOROJk5sv385QIcPFW8M5KQ68bs9mrrtwmF1DvWzty7FQb7A3pBPzblHIpnAOSgt1JvyJiLEqJfGDzumWMoYoVn4rZFOUFurNrD20llJ0bknoSq3RnN8T6MSqfyJnm644CjacY//eraE9Xy6VpN5w1OqNZrYginZtACN9OR74+A2RPLdI1C7aWuShP9sf+vN+6qbLmt//4LbrqTsXq4ln0LIccbnGY0emyKeT7BrqjXivwhOcBMyWa4wUvW1BG8+w27WBlyAKOhIEkwI7WUoBXulET2bxObyJ4F2RLxOJTFe8AxJmXLN7U2jPF7SjqdQazYxxFJPvRCR6iYSRTiZiVV8Mi9nI2UqVA0enuHRbX6yWog2Cztb63khqjHMr92OmXCNhNGvh11sQ+C8vp1DGWKRLAuNdQ72hLQcLNJ+rUmu01Bh3xX+FiEgogozpxFyVg8em2Dc6EPEehSsISFs7UwSBYrjt2tL+fiyWdATLQXfqZC0I/JdPwCtXNflOpCuiwU6dFa8muFRUrtabfYzjNBtbRCQopXj42QnK1Qb7Rvsj3qNwFdoExsHck7DbtQFLJuDNVmodqy8Gb/IdLNZUB8rVhhb4kNiL5TugNWNcVcZYRGIoCMj+76mTALELjIvZoJRkZSlFmOUEzQB9SSlFtaMrwTZrjJdljCu1RqhXb0W6USyjwaCBeaVWp1JXYCwi8RMEXj96+hTFbIqdm+Mz8Q5aSykWSxjmq3UyyQSJECdiBiUtMysyxp0PjJeXUlSqdU2+k9iL5Tsg2yylaMkYq5RCRGIkyJjOV+v85qtHQw0Gu0FvdmUJw6nZBTb1htvPutCulKJc69hy0AD5jPd5t2LynRb4EIlnYBxcJqu01BgrYywicRIEXsPFLB9984UR7034MqkE2VRiSQnDCzMVtvSFtwojLHYHmW4z+a5TgnKJ1oxxveGo1p26UkjsdUUf47C1tmsr+QfFsCcAiohEKZkwfv/1u7juwhH6OjjRq5sVc6klJQwvTpc5d1NPqPvQm0mypS/Lo89NNrfNdHjyXT6zssY46OGsyXcSd/EMjFsm350qeWfpYV8+ExGJ2iffvjfqXYhUMZdeMvnu+ekyV+0Mr6c+eEuU33DJFr7x8NFmu7TZcjg1xq2BcXPVP109lZiL5TugtV3bxNwC2VQi1IbuIiISvUI21Zx8V67WmZyrhl5KAbB/7xbmFuo8MHaSmXKV+Wq9o8mafJsFPpodOfRZKDGnjHHJm2wRt1WvRETirpBNNSe9jc9UABjpy4W+H6/bs5neTJJ7nniBTT1eQLy7g8tzB6UU8y19jBUYi3himTFubdc2ObfAQI/KKERE4qaYSzVLKV6YLgOwNYLAOJtKcu35Q3z/yXEOj88CsHu40MHn87tSLKkxDn9xE5FuFMt3QDa9uOqPlzGO58QTEZE4K+RSnCwt8MSxaZ73A+MtEQTGAK86b5DnTs3z0DMTpBLGeZs7NwnQzMink8tqjP3Jd+pKITEXy1KK4Ix4rlJjYq7KtoF8xHskIiJhGy5mGZ+p8LYvfp83XDgMEEmNMcC+7d7Kg/914Djnbuoh3eHe+rl0YlmNsZcxVlcKibtYvgOyqSSbejMcny4zMbfAoEopRERi5w+uv4A7fu9qitkU9/98nEwqQX8+miuIl/lLck/OVdk93PlVCPPp5JJSinJNNcYiENPAGGD7QJ5nT84xNV9lUK3aRERip5BNcd2Fw1x3kZct3tqXi2widl8u3Zxwt6eD9cWBXGZpKUWl2a5NgbHEW2wD49HBPAePTeEcbOpRjbGISFzt37sFiK6MIrDPzxqHlTHWAh8iK8X2HTA6mGdizutfqYyxiEh8vfGiEVIJY6QYzcS7wL7RAaCzHSkCK0op1K5NBIjp5DvwSikCqjEWEYmv/nyaT998GeePdD4gPZ2bLt/GyVKFy3cMdPy58pkkpZZV/7TynYhnze8AM9thZveZ2SEzO2hmH/G3bzKze8zsSf/fwfXb3fUzOrjYCkfLQYuIxNt7rj6XK0NeDnq5zYUsH3vLxR3vSAFeZrh1gY+KJt+JAGdWSlED/sg5dwlwDfBhM9sL3Abc65y7ALjX/7nrbB9syRgrMBYRkRjJLasxPlWqkk5ac7lokbhac2DsnDvunPuJ//0McAjYDtwE3OHf7Q7g5jPdyU5YEhhr8p2IiMRIflkf4yMTc2wbyJNIRNOVQ6RbrMv1GjPbCVwB/BDY4pw7Dl7wDIys8pgPmdlDZvbQ+Pj4euzGy9KXS9OXS5FNJXSGLCIisZJPJ5u9iwGOTs4zOqjFrkTOODA2swLwn8AfOuemX+rjnHNfcs5d6Zy7cnh4+Ex3Y01GB3vY1JuJrG+liIhIFHKZ5LKM8TyjA51bhlrkbHFGgbGZpfGC4n92zt3pb37BzM7xbz8HePHMdrFz9m7rY9dQ5/tFioiIdJO+XJpKrUGpUqNcrTM+U1lSYigSV2tu12ZemvXLwCHn3N+03HQX8H7gM/6/3zyjPeygT998GQ3not4NERGRUF28tQjAwWPTDBW8CegqpRA5sz7G1wLvBQ6Y2SP+tk/gBcT/Zma3As8C7zqzXewctaUREZE4eoW/yt5jRya5yA+SW/v7i8TVmgNj59z/AqsV596w1t8rIiIinTVSzHFOf47HjkzRm/VCgdFNqjEW0RI3IiIiMbRvtJ8DR6c4OjFPMmFsKWaj3iWRyCkwFhERiaF9owM8faLEE8enOac/RyqEFfdEup3eBSIiIjH0iu1enfH9Px9XfbGI70wm34mIiMhZ6qqdm/jVV25jcm6Bd125I+rdEekKCoxFRERiKJ9J8re3XBH1boh0FZVSiIiIiIigwFhEREREBFBgLCIiIiICKDAWEREREQEUGIuIiIiIAAqMRUREREQABcYiIiIiIoACYxERERERQIGxiIiIiAigwFhEREREBFBgLCIiIiICKDAWEREREQEUGIuIiIiIAGDOuaj3ATMbB56Jej82gCHgRNQ7IV1H40La0biQ1WhsSDsbaVyc55wbbndDVwTGsj7M7CHn3JVR74d0F40LaUfjQlajsSHtxGVcqJRCRERERAQFxiIiIiIigALjjeZLUe+AdCWNC2lH40JWo7Eh7cRiXKjGWEREREQEZYxFRERERAAFxl3BzP7JzF40s8fb3PbHZubMbMj/ud/MvmVmj5rZQTP7QMt9zzWzu83skJk9YWY7/e27zOyHZvakmX3NzDL+9qz/81P+7TvD+HvlpVmPcWFmbzKzR1q+ymZ2s3+bxsVZah2PGX/lbztkZl80M/O3v9rMDvhjoHX7JjO7xx8z95jZYFh/s/xy6zguPmtmj/tf727ZrmPGWehljotBM/u6mT1mZj8ys8ta7nujmf3Mf51va9m+ocaFAuPucDtw4/KNZrYD2A8827L5w8ATzrlXAm8E/joYhMBXgM855y4BrgZe9Ld/Fvi8c+4CYAK41d9+KzDhnDsf+Lx/P+ket3OG48I5d59z7nLn3OXA9cAccLf/GI2Ls9ftnOHYMLPXAdcC+4DLgKuA6/zH/APwIeAC/yt4rtuAe/0xc6//s3SP2znzcfF24FXA5cBrgI+ZWZ//GB0zzk6389LHxSeAR5xz+4D3AV/w75sE/h54K7AXuMXM9vqP2VDjQoFxF3DO3Q+canPT54E/AVoLwR1Q9DM4Bf9xNX+Appxz9/i/c9Y5N+ff73rgP/zH3wHc7H9/k/8z/u03BJkhid56jItlj3sn8B2Ni7PfOo0NB+SADJAF0sALZnYO0Oece8B5k1C+Qvux0TpmpAus07jYC/yPc67mnCsBjwI36phx9nqZ42Iv3kkvzrmfAjvNbAtesu0p59yYc24B+Ffgpo04LhQYdykz+zXgqHPu0WU3/R1wCXAMOAB8xDnXAC4EJs3sTjN72Mw+55/hbQYmnXNBkHQE2O5/vx14DsC/fcq/v3SpNYyLVu8B/sX/XuNig3m5Y8M59wBwH3Dc//quc+4Q3ut/pOXxrWNji3PuOID/70in/h5ZH2s4ZjwKvNXMevzL628CdqBjxoZymnHxKPAb/n2uBs4DRml5jX3B67/hxkUq6h2QlcysB/gk8OY2N78FeATvDG0PcI+ZfR/vtXw9cAXeZZGvAb8L3NXmdwRnh+3O3NSmpEutZVw456b9x54DvAL4bvDr2vwOjYuz1BqPGSN4gdGof797zOwNwHyb36HX/yy0xmPG3WZ2FfADYBx4AC+TrGPGBvFLxsVngC+Y2SN4J0wPc/rXf8ONC2WMu9MeYBfwqJn9Au+D6ydmthX4AHCn8zwFPA1cjHeW9rB/maMGfAOvTuwEMGBmwUnQKF6GAP8xOwD82/tpf7lFusNaxkXgt4CvO+eq/s8aFxvLWsbGrwMP+mVXs8B3gGvwXv/Rlt/dOjaCUovgZOtFpJut6ZjhnPtLf27Cfrzg5kl0zNhIVh0Xzrlp59wH/Hkp7wOG8cZG8zX2Ba//hhsXCoy7kHPugHNuxDm30zm3E29wvco59zxeNvgGAL/u5yJgDPgxMGhmw/6vuR5vYoXDu1z6Tn/7+4Fv+t/f5f+Mf/v3nBpbd601jovALSyWUaBxsbGscWw8C1xnZikzS+NNvDvkl0jMmNk1fj3g+2g/NlrHjHShtYwLM0ua2WZ/+z68yZl365ixcZxuXJjZQMuE/g8C9/tXHn8MXOB3oMjglebdtSHHhXNOXxF/4QUsx4Eq3gC9ddntvwCG/O+34XUVOAA8DvxOy/32A4/5t90OZPztu4EfAU8B/w5k/e05/+en/Nt3R/1/oa+OjIudwFEgsezxGhdn6dd6jA0gCfwjcAh4Aviblsdf6d/3MF4tarAY1Ga8iTlP+v9uivr/Ql/rPi5y/nh4AngQuLzl8TpmnIVfL3NcvNZ/f/8UuBMYbLnf24Cf+8eFT27UcaGV70REREREUCmFiIiIiAigwFhEREREBFBgLCIiIiICKDAWEREREQEUGIuIiIiIAAqMRUREREQABcYiIiIiIoACYxERERERAP4flJQxI4HuHfwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "no2['BASCH'][-500:].plot(figsize=(12,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or we can use some more advanced time series features -> see further in this notebook!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Selecting and filtering data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "<b>ATTENTION!</b>: <br><br>\n",
    "\n",
    "One of pandas' basic features is the labeling of rows and columns, but this makes indexing also a bit more complex compared to numpy. <br><br> We now have to distuinguish between:\n",
    "\n",
    " <ul>\n",
    "  <li>selection by **label**</li>\n",
    "  <li>selection by **position**</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"titanic.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `df[]` provides some convenience shortcuts "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "For a DataFrame, basic indexing selects the columns.\n",
    "\n",
    "Selecting a single column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      22.0\n",
       "1      38.0\n",
       "2      26.0\n",
       "3      35.0\n",
       "       ... \n",
       "887    19.0\n",
       "888     NaN\n",
       "889    26.0\n",
       "890    32.0\n",
       "Name: Age, Length: 891, dtype: float64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "or multiple columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19.0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>NaN</td>\n",
       "      <td>23.4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32.0</td>\n",
       "      <td>7.7500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age     Fare\n",
       "0    22.0   7.2500\n",
       "1    38.0  71.2833\n",
       "2    26.0   7.9250\n",
       "3    35.0  53.1000\n",
       "..    ...      ...\n",
       "887  19.0  30.0000\n",
       "888   NaN  23.4500\n",
       "889  26.0  30.0000\n",
       "890  32.0   7.7500\n",
       "\n",
       "[891 rows x 2 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Age', 'Fare']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "But, slicing accesses the rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass                                  Name  \\\n",
       "10           11         1       3       Sandstrom, Miss. Marguerite Rut   \n",
       "11           12         1       1              Bonnell, Miss. Elizabeth   \n",
       "12           13         0       3        Saundercock, Mr. William Henry   \n",
       "13           14         0       3           Andersson, Mr. Anders Johan   \n",
       "14           15         0       3  Vestrom, Miss. Hulda Amanda Adolfina   \n",
       "\n",
       "       Sex   Age  SibSp  Parch     Ticket     Fare Cabin Embarked  \n",
       "10  female   4.0      1      1    PP 9549  16.7000    G6        S  \n",
       "11  female  58.0      0      0     113783  26.5500  C103        S  \n",
       "12    male  20.0      0      0  A/5. 2151   8.0500   NaN        S  \n",
       "13    male  39.0      1      5     347082  31.2750   NaN        S  \n",
       "14  female  14.0      0      0     350406   7.8542   NaN        S  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[10:15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Systematic indexing with `loc` and `iloc`\n",
    "\n",
    "When using `[]` like above, you can only select from one axis at once (rows or columns, not both). For more advanced indexing, you have some extra attributes:\n",
    "    \n",
    "* `loc`: selection by label\n",
    "* `iloc`: selection by position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.set_index('Name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26.55"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Bonnell, Miss. Elizabeth', 'Fare']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bonnell, Miss. Elizabeth</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.550</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saundercock, Mr. William Henry</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>8.050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andersson, Mr. Anders Johan</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.275</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                PassengerId  Survived  Pclass     Sex   Age  \\\n",
       "Name                                                                          \n",
       "Bonnell, Miss. Elizabeth                 12         1       1  female  58.0   \n",
       "Saundercock, Mr. William Henry           13         0       3    male  20.0   \n",
       "Andersson, Mr. Anders Johan              14         0       3    male  39.0   \n",
       "\n",
       "                                SibSp  Parch     Ticket    Fare Cabin Embarked  \n",
       "Name                                                                            \n",
       "Bonnell, Miss. Elizabeth            0      0     113783  26.550  C103        S  \n",
       "Saundercock, Mr. William Henry      0      0  A/5. 2151   8.050   NaN        S  \n",
       "Andersson, Mr. Anders Johan         1      5     347082  31.275   NaN        S  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Bonnell, Miss. Elizabeth':'Andersson, Mr. Anders Johan', :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Selecting by position with `iloc` works similar as indexing numpy arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Braund, Mr. Owen Harris</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Survived  Pclass\n",
       "Name                                                                \n",
       "Braund, Mr. Owen Harris                                    0       3\n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...         1       1"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:2,1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The different indexing methods can also be used to assign data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc['Braund, Mr. Owen Harris', 'Survived'] = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Braund, Mr. Owen Harris</th>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heikkinen, Miss. Laina</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Futrelle, Mrs. Jacques Heath (Lily May Peel)</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Graham, Miss. Margaret Edith</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Johnston, Miss. Catherine Helen \"Carrie\"</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Behr, Mr. Karl Howell</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dooley, Mr. Patrick</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    PassengerId  Survived  \\\n",
       "Name                                                                        \n",
       "Braund, Mr. Owen Harris                                       1       100   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...            2         1   \n",
       "Heikkinen, Miss. Laina                                        3         1   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)                  4         1   \n",
       "...                                                         ...       ...   \n",
       "Graham, Miss. Margaret Edith                                888         1   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                    889         0   \n",
       "Behr, Mr. Karl Howell                                       890         1   \n",
       "Dooley, Mr. Patrick                                         891         0   \n",
       "\n",
       "                                                    Pclass     Sex   Age  \\\n",
       "Name                                                                       \n",
       "Braund, Mr. Owen Harris                                  3    male  22.0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...       1  female  38.0   \n",
       "Heikkinen, Miss. Laina                                   3  female  26.0   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)             1  female  35.0   \n",
       "...                                                    ...     ...   ...   \n",
       "Graham, Miss. Margaret Edith                             1  female  19.0   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                 3  female   NaN   \n",
       "Behr, Mr. Karl Howell                                    1    male  26.0   \n",
       "Dooley, Mr. Patrick                                      3    male  32.0   \n",
       "\n",
       "                                                    SibSp  Parch  \\\n",
       "Name                                                               \n",
       "Braund, Mr. Owen Harris                                 1      0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...      1      0   \n",
       "Heikkinen, Miss. Laina                                  0      0   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)            1      0   \n",
       "...                                                   ...    ...   \n",
       "Graham, Miss. Margaret Edith                            0      0   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                1      2   \n",
       "Behr, Mr. Karl Howell                                   0      0   \n",
       "Dooley, Mr. Patrick                                     0      0   \n",
       "\n",
       "                                                              Ticket     Fare  \\\n",
       "Name                                                                            \n",
       "Braund, Mr. Owen Harris                                    A/5 21171   7.2500   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...          PC 17599  71.2833   \n",
       "Heikkinen, Miss. Laina                              STON/O2. 3101282   7.9250   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)                  113803  53.1000   \n",
       "...                                                              ...      ...   \n",
       "Graham, Miss. Margaret Edith                                  112053  30.0000   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                  W./C. 6607  23.4500   \n",
       "Behr, Mr. Karl Howell                                         111369  30.0000   \n",
       "Dooley, Mr. Patrick                                           370376   7.7500   \n",
       "\n",
       "                                                   Cabin Embarked  \n",
       "Name                                                               \n",
       "Braund, Mr. Owen Harris                              NaN        S  \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...   C85        C  \n",
       "Heikkinen, Miss. Laina                               NaN        S  \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)        C123        S  \n",
       "...                                                  ...      ...  \n",
       "Graham, Miss. Margaret Edith                         B42        S  \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"             NaN        S  \n",
       "Behr, Mr. Karl Howell                               C148        C  \n",
       "Dooley, Mr. Patrick                                  NaN        Q  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boolean indexing (filtering)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Often, you want to select rows based on a certain condition. This can be done with 'boolean indexing' (like a where clause in SQL) and comparable to numpy. \n",
    "\n",
    "The indexer (or boolean mask) should be 1-dimensional and the same length as the thing being indexed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "Braund, Mr. Owen Harris                                False\n",
       "Cumings, Mrs. John Bradley (Florence Briggs Thayer)     True\n",
       "Heikkinen, Miss. Laina                                 False\n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)            True\n",
       "                                                       ...  \n",
       "Graham, Miss. Margaret Edith                           False\n",
       "Johnston, Miss. Catherine Helen \"Carrie\"               False\n",
       "Behr, Mr. Karl Howell                                  False\n",
       "Dooley, Mr. Patrick                                    False\n",
       "Name: Fare, Length: 891, dtype: bool"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Fare'] > 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
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    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
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       "      <th>Sage, Miss. Dorothy Edith \"Dolly\"</th>\n",
       "      <td>864</td>\n",
       "      <td>0</td>\n",
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       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
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       "      <td>CA. 2343</td>\n",
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       "      <th>Roebling, Mr. Washington Augustus II</th>\n",
       "      <td>868</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</th>\n",
       "      <td>872</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>S</td>\n",
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       "      <td>11767</td>\n",
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       "      <td>C</td>\n",
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       "<p>160 rows × 11 columns</p>\n",
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      ],
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       "                                                    PassengerId  Survived  \\\n",
       "Name                                                                        \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...            2         1   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)                  4         1   \n",
       "McCarthy, Mr. Timothy J                                       7         0   \n",
       "Fortune, Mr. Charles Alexander                               28         0   \n",
       "...                                                         ...       ...   \n",
       "Sage, Miss. Dorothy Edith \"Dolly\"                           864         0   \n",
       "Roebling, Mr. Washington Augustus II                        868         0   \n",
       "Beckwith, Mrs. Richard Leonard (Sallie Monypeny)            872         1   \n",
       "Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)               880         1   \n",
       "\n",
       "                                                    Pclass     Sex   Age  \\\n",
       "Name                                                                       \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...       1  female  38.0   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)             1  female  35.0   \n",
       "McCarthy, Mr. Timothy J                                  1    male  54.0   \n",
       "Fortune, Mr. Charles Alexander                           1    male  19.0   \n",
       "...                                                    ...     ...   ...   \n",
       "Sage, Miss. Dorothy Edith \"Dolly\"                        3  female   NaN   \n",
       "Roebling, Mr. Washington Augustus II                     1    male  31.0   \n",
       "Beckwith, Mrs. Richard Leonard (Sallie Monypeny)         1  female  47.0   \n",
       "Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)            1  female  56.0   \n",
       "\n",
       "                                                    SibSp  Parch    Ticket  \\\n",
       "Name                                                                         \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...      1      0  PC 17599   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)            1      0    113803   \n",
       "McCarthy, Mr. Timothy J                                 0      0     17463   \n",
       "Fortune, Mr. Charles Alexander                          3      2     19950   \n",
       "...                                                   ...    ...       ...   \n",
       "Sage, Miss. Dorothy Edith \"Dolly\"                       8      2  CA. 2343   \n",
       "Roebling, Mr. Washington Augustus II                    0      0  PC 17590   \n",
       "Beckwith, Mrs. Richard Leonard (Sallie Monypeny)        1      1     11751   \n",
       "Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)           0      1     11767   \n",
       "\n",
       "                                                        Fare        Cabin  \\\n",
       "Name                                                                        \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...   71.2833          C85   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)         53.1000         C123   \n",
       "McCarthy, Mr. Timothy J                              51.8625          E46   \n",
       "Fortune, Mr. Charles Alexander                      263.0000  C23 C25 C27   \n",
       "...                                                      ...          ...   \n",
       "Sage, Miss. Dorothy Edith \"Dolly\"                    69.5500          NaN   \n",
       "Roebling, Mr. Washington Augustus II                 50.4958          A24   \n",
       "Beckwith, Mrs. Richard Leonard (Sallie Monypeny)     52.5542          D35   \n",
       "Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)        83.1583          C50   \n",
       "\n",
       "                                                   Embarked  \n",
       "Name                                                         \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...        C  \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)              S  \n",
       "McCarthy, Mr. Timothy J                                   S  \n",
       "Fortune, Mr. Charles Alexander                            S  \n",
       "...                                                     ...  \n",
       "Sage, Miss. Dorothy Edith \"Dolly\"                         S  \n",
       "Roebling, Mr. Washington Augustus II                      S  \n",
       "Beckwith, Mrs. Richard Leonard (Sallie Monypeny)          S  \n",
       "Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)             C  \n",
       "\n",
       "[160 rows x 11 columns]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Fare'] > 50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>:\n",
    "\n",
    " <ul>\n",
    "  <li>Based on the titanic data set, select all rows for male passengers and calculate the mean age of those passengers. Do the same for the female passengers</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"titanic.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.72664459161148"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Sex']=='male']['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.915708812260537"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Sex']=='female']['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "clear_cell": true,
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>:\n",
    "\n",
    " <ul>\n",
    "  <li>Based on the titanic data set, how many passengers older than 70 were on the Titanic?</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Age']>70].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "clear_cell": true,
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. The group-by operation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some 'theory': the groupby operation (split-apply-combine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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       "   key  data\n",
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       "1    B     5\n",
       "2    C    10\n",
       "3    A     5\n",
       "..  ..   ...\n",
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       "6    A    10\n",
       "7    B    15\n",
       "8    C    20\n",
       "\n",
       "[9 rows x 2 columns]"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df = pd.DataFrame({'key':['A','B','C','A','B','C','A','B','C'],\n",
    "                   'data': [0, 5, 10, 5, 10, 15, 10, 15, 20]})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recap: aggregating functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When analyzing data, you often calculate summary statistics (aggregations like the mean, max, ...). As we have seen before, we can easily calculate such a statistic for a Series or column using one of the many available methods. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['data'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, in many cases your data has certain groups in it, and in that case, you may want to calculate this statistic for each of the groups.\n",
    "\n",
    "For example, in the above dataframe `df`, there is a column 'key' which has three possible values: 'A', 'B' and 'C'. When we want to calculate the sum for each of those groups, we could do the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A 15\n",
      "B 30\n",
      "C 45\n"
     ]
    }
   ],
   "source": [
    "for key in ['A', 'B', 'C']:\n",
    "    print(key, df[df['key'] == key]['data'].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This becomes very verbose when having multiple groups. You could make the above a bit easier by looping over the different values, but still, it is not very convenient to work with.\n",
    "\n",
    "What we did above, applying a function on different groups, is a \"groupby operation\", and pandas provides some convenient functionality for this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Groupby: applying functions per group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The \"group by\" concept: we want to **apply the same function on subsets of your dataframe, based on some key to split the dataframe in subsets**\n",
    "\n",
    "This operation is also referred to as the \"split-apply-combine\" operation, involving the following steps:\n",
    "\n",
    "* **Splitting** the data into groups based on some criteria\n",
    "* **Applying** a function to each group independently\n",
    "* **Combining** the results into a data structure\n",
    "\n",
    "<img src=\"img/splitApplyCombine.png\">\n",
    "\n",
    "Similar to SQL `GROUP BY`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of doing the manual filtering as above\n",
    "\n",
    "\n",
    "    df[df['key'] == \"A\"].sum()\n",
    "    df[df['key'] == \"B\"].sum()\n",
    "    ...\n",
    "\n",
    "pandas provides the `groupby` method to do exactly this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     data\n",
       "key      \n",
       "A      15\n",
       "B      30\n",
       "C      45"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     data\n",
       "key      \n",
       "A      15\n",
       "B      30\n",
       "C      45"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key').aggregate(np.sum)  # 'sum'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And many more methods are available. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key\n",
       "A    15\n",
       "B    30\n",
       "C    45\n",
       "Name: data, dtype: int64"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key')['data'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Application of the groupby concept on the titanic data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We go back to the titanic passengers survival data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"titanic.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>:\n",
    "\n",
    " <ul>\n",
    "  <li>Calculate the average age for each sex again, but now using groupby.</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "clear_cell": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    27.915709\n",
       "male      30.726645\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Sex')['Age'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>:\n",
    "\n",
    " <ul>\n",
    "  <li>Calculate the average survival ratio for all passengers.</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "clear_cell": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3838383838383838"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Survived'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>:\n",
    "\n",
    " <ul>\n",
    "  <li>Calculate this survival ratio for all passengers younger that 25 (remember: filtering/boolean indexing).</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "clear_cell": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4244604316546763"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Age']<25]['Survived'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>:\n",
    "\n",
    " <ul>\n",
    "  <li>What is the difference in the survival ratio between the sexes?</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "clear_cell": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5531300709799203"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.abs(df[df['Sex']=='male']['Survived'].mean()-df[df['Sex']=='female']['Survived'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>:\n",
    "\n",
    " <ul>\n",
    "  <li>Or how does it differ between the different classes? Make a bar plot visualizing the survival ratio for the 3 classes.</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x207b96205c0>"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Zui42nwHekOS5AEmeM2DNpcB/J3k6C2fo9NY+v6rurKp9wP3AxiQ/BZysqj9l4cvSL6oPg9JTi2fouqj0vuD8fcA/Jnkc+Cpww6JlvwvcCXwd+BoLgQf4QO9Nz7DwH8NdwF7gjUkeY+HT/vav+g8hLcE3RSWpEb7kIkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1Ij/A9p0qu4gc8fmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('Pclass')['Survived'].mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>:\n",
    "\n",
    " <ul>\n",
    "  <li>Make a bar plot to visualize the average Fare payed by people depending on their age. The age column is devided is separate classes using the `pd.cut` function as provided below.</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "clear_cell": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "df['AgeClass'] = pd.cut(df['Age'], bins=np.arange(0,90,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "clear_cell": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x207b4a75c18>"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('AgeClass')['Fare'].mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "\n",
    "## Further reading\n",
    "\n",
    "* Pandas documentation: http://pandas.pydata.org/pandas-docs/stable/\n",
    "\n",
    "\n",
    "*  Blog\n",
    "\n",
    "  * https://tomaugspurger.github.io/modern-1.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make a simple logistic Regression Model\n",
    "* The model would be predicting whether a person with a certain attributes would survive or not\n",
    "* Train with 70 percent of the data and take the y column as Survived or not\n",
    "* You **CAN NOT** use any library to make the logistic regression model, you can use only numpy and pandas. The model has to be built from the scratch\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import *\n",
    "\n",
    "pd.options.display.max_rows = 8\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "df = pd.read_csv(\"titanic.csv\")\n",
    "\n",
    "\n",
    "age_by_pclass_sex = df.groupby(['Sex', 'Pclass']).median()['Age']\n",
    "\n",
    "# Filling the missing values in Age with the medians of Sex and Pclass groups\n",
    "df['Age'] = df.groupby(['Sex', 'Pclass'])['Age'].apply(lambda x: x.fillna(x.median()))\n",
    "\n",
    "\n",
    "#The two people with missing 'embarked' feature had embarked from Southampton (available online)\n",
    "df['Embarked'] = df['Embarked'].fillna('S')\n",
    "\n",
    "\n",
    "med_fare = df.groupby(['Pclass', 'Parch', 'SibSp']).Fare.median()[3][0][0]\n",
    "# Filling the missing value in Fare with the median Fare of 3rd class alone passenger\n",
    "df['Fare'] = df['Fare'].fillna(med_fare)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TPM6RUEMby9fCoSiKIiL/B+pDo7LMpQwOQn3AVwsA/QHMALC2FuOwHKNMRPpAnYm6VUR2APgC6vX1g/qV8R74z4O87Mk2j2uaOYw6DCAcwCio4XR7q7bdAawUkc3mdiVQZ50OB/CdoiiW2Y/ZIrIPauiVC6AR1GtUAeCft3l6jc0PrALU+xwKNUj7E9TZ2f1vow7uDhEpArAHwBmotYOHQp0B+4FVu31QH8KXIiLfQA28/2WZhVxLmQD+JSLLAZyH+r57DMAH5vIhFisBTAbwkYgshXqNnoT9f7f+CvUhf2NFpAzAFQCXFEX5l522UBTlCxHZCCAZani9HUAg1BnL5QDG38X51ZcnRaQE6vXxgfp+eALq5/tFRVFSLQ0VRSkVkf8LNeQ+bP4MHoX6PoiA+hn8bwC7zJ/X/we1hu73IrIK6vvfE2pJhc+gzla/jqIoJSLyONSfUf8UkSGKonwI9TO0W0Q+MvdTCPUXQGOgzpbeW8fXhYiIiOgPgeEuERHR/TUJahj1NNRw6wzUMGs/1GDUogzAW1Droj4GwAA1EPsYwAJFUSx1MT+AGs49CzW8vAo18HpSUZTNls4URTkjIrEAXjIfdwjUMOsM1BBmY21OxhwADYBaP/gZAD2hhrr/DWAs/vNQJkv7AyISAzXEfQLqA8qKAZyEGup+WZtx2BzjqPkYowAMgDpz0QD16/4ZUK/V/95kf5OI9AawyNzWFWoY9SzUcg3W4W4mgC0AHgEwGIAWwGmoDy77q1W7v0KtnTwegAfUsH4f1HuZeZun1g7/CWBLoT4EKxNqWYcNtg+wuoF3oL73RkGdKVkANegfpyjKv63avQU1+H8S6j3SQA2y7ybc/RhqAD4DasmOSwDmmpcaiqKcEJEkqNdwrnmMH0B9CNghm7ZGEUkG8D/mMTsB2A31Fwg3MhjqbOWhUO9LqXmfVxVF+fkuzq++vGP+swJqbejDUM9rtb2604qifG6u0z0d6s8BP6hB6zGoNcGzrNruN7d9Fer7ZjTUhwt+D+DrGw3I6pcsWwGsN89uT4d6D7sDSIJ6r85Brfn8+h3W1iYiIiIiM1G/4UlERERUt0TkZwAOiqJE1PdYiIiIiIiIfo9Yc5eIiIjuiojo7azrDaAN1JIIREREREREdA9w5i4RERHdFRFZACAGwL+hfi28HdQ6wlcBtFMU5Ww9Do+IiIiIiOh3i+EuERER3RUR6QW1fmdrqPVkL0OtefqqoihH63NsREREREREv2cMd4mIiIiIiIiIiIgaINbcJSIiIiIiIiIiImqAGO4SERERERERERERNUAMd4mIiIiIiIiIiIgaIIa7RERERERERERERA0Qw10iIiIiIiIiIiKiBojhLhEREREREREREVEDxHCXiIiIiIiIiIiIqAFiuEtERERERERERETUADHcJSIiIiIiIiIiImqAGO4SERERERERERERNUAMd4mIiIiIiIiIiIgaIIa7RERERERERERERA0Qw10iIiIiIiIiIiKiBojhLhEREREREREREVEDxHCXiIiIiIiIiIiIqAFiuEtERERERERERETUADHcJSIiIiIiIiIiImqAGO4SERERERERERERNUAMd4mIiIiIiIiIiIgaIIa7RERERERERERERA0Qw10iIiIiIiIiIiKiBojhLhEREREREREREVED1ODCXRFpISLvikimiJhEZJedNiIiL4vIGRExisgeEWlnp11rEflSRMpEJFdE/iIi2vtyIkRERERERERERER3ocGFuwAeAtALQI55sWc6gFcBvA6gL4ASADtFJNDSQES8AOwEoADoB+AvACYDeO2ejZyIiIiIiIiIiIiojoiiKPU9hjsiIhpFUarNf98EwFdRlEestjsDuAjgr4qi/MW8zhXASQDvKooy07xuBoBpAJooinLVvG4agDkAAi3riIiIiIiIiIiIiB5EDW7mriXYvYlOANwBbLTapxTAJwB6WrXrCeBzmxD3nwD0ALrVzWiJiIiIiIiIiIiI7o0GF+7ehggAJgBHbNZnm7dZtztk3UBRlNMAymzaERERERERERERET1wfo/hrheAEkVRTDbrCwG4iIijVbsrdvYvNG8jIiIiIiIiIiIiemDp6nsA94i9QsJiZ9uN2tktRCwiIwGMBABXV9fYiAhO8CUiIiIiIiIiqis//PBDvqIofvU9DqKG4vcY7hYCcBMRrc3sXU8AZYqiVFq187Szvwfsz+iFoigrAawEgA4dOigZGRl1N+p69Cl21PcQ/vB6IbFO+uG9rH+8l78PdXUfK75cUCf9UO05xs+ok37Wba6TbqiWhgyou754L+tXXd1L3sf6x3v5+1CXP1/579f6VVf/fn0QiMip+h4DUUPyeyzLcAiAFkALm/W2NXYPwaa2roiEAHC1aUdERERERERERET0wPk9hrvfALgK4CnLChFxAdAXQLpVu3QAPUTEzWrdQABGALvvwziJiIiIiIiIiIiIaq3BlWUwB7W9zC8bA3AXkSfNrz9VFKVMRBYCeFVECqHOwp0ENcj+m1VXKwCMB7BFRF4H0AzAHACLFUW5eu/PhIiIiIiIiIiIiKj2Gly4C8AfQKrNOsvrMAAnASyEGubOAOADIANAgqIoFy07KIpSKCLxAFIAfAK1zu4SqAEvERERERERERER0QOtwYW7iqKcBCC3aKMAmGdebtbuVwCP1tngiIiIiIiIiIiIiO6T32PNXSIiIiIiIiIiIqLfvQY3c5eIiIiIiIiIiP6Yfvzxxx46nW62oiiB4KRF+n2rFpELVVVVr7Vv3/7zGzViuEtERERERERERA+8H3/8sYeTk1NK06ZNK/R6faFGo1Hqe0xE90p1dbUYjUaPkydPpvz4448v3Cjg5W84iIiIiIiIiIjogafT6WY3bdq0wtXV1chgl37vNBqN4urqamzatGmFTqebfcN293NQREREREREREREtaEoSqBery+v73EQ3U96vb7cXIbELoa7RERERERERETUEGg4Y5f+aMzv+RtmuAx3iYiIiIiIiIiIiBoghrtERERERERERET3waRJk4JEJNay+Pv7R/Xo0aP5wYMHnep7bA2R0WiUWbNmBURGRrbW6/Uxer0+pk2bNpGLFi3yLS8vFwBYtmyZj4jEFhUV1WsOWl5eLqNGjQqOjY1t5ezs3F5EYuuiX11ddEJERERERERERFQfTDBFa6G97xmXCaYqLbSZd7qfwWAwbd269QgAHDlyxGnevHlBiYmJ4dnZ2Qfd3d2r636kv08lJSXSrVu38JycHP2IESMude3atRgAvv76a8P8+fMbG41GzauvvnqpvsdpUVJSotmwYYNvVFRUaUxMTMm+ffvc6qJfhrtERERERERERNRgaaHVfYod9/24vZBYq1xNp9Mp8fHxpQAQHx9fGhYWVvH444+32rRpk8ewYcMK63aUDVtJSYkYDAa7dZYnTpzY+Ndff3XZs2dPdseOHWsetJeUlFQ8ZcqUS1lZWc73b6S35uvra7py5coBjUaD+fPn+9VVuMuyDERERERERERERPWkc+fOpQBw4sQJRwDYuXOn66OPPtrC398/Sq/Xx0RERLR+5513vK33yc/P1w4cOLCJv79/lJOTU/tGjRq1TU5ObmLZfuzYMYdevXo18/b2jnZ2dm4fEhLSZsKECUHWfXz++eeGjh07ttLr9TGenp7tkpOTmxQWFtZkhZZyBt9//72+U6dOLfV6fUxYWNhDf//73z2t+6mursaECROCvL29ow0GQ8xTTz3VdOXKlV4iEnv48GFHS7uysjIZPXp0cGBgYJSjo2P7Vq1atf7www89rPtq3Lhx2xEjRgRPnTq1UUBAQJSnp2eMvWtWXFysWb9+vd+QIUPyrINdi4CAAFNCQkLpja752LFjG4eHh7d2cXGJCQgIiHriiSfCTp8+fV1Yv379eo+HHnooUq/Xx7i7u7eLioqKSEtLM1i2L1myxLdFixYPOTs7t/fy8oru2LFjq4yMjJsGyhpN3UexnLlLRERERERERERUT3JycpwAIDAwsBIAjh8/7vjnP/+5ZNSoUXl6vb567969hvHjxzfVaDQYNWrUZQAYM2ZMSEZGhuu8efPOBAUFVZ46dcpx7969NTNBBw8eHFZeXq5ZunTpKS8vL9PRo0cdDx06pLds37Fjh2vfvn3DExISrqxdu/Z4fn6+9rXXXgt+5plntJ999tlx6/ENGTIk7Nlnn82fMmXKxZSUFP/hw4c3i4uL+7l58+aVADB37lz/lJSURuPGjTvftWvXko8++shz9uzZwbbn2bt37+aZmZmuL730Um7Lli2vffjhh16DBw9uERIS8munTp2MlnZbt271btmyZfnixYtPVVVVib1r9vXXX7sYjUZNr169rtbmmufl5TlMnjz5QnBwcMWlS5ccli5dGtC9e/dWhw8fPqjT6XDw4EGnoUOHNh82bNilBQsWnDUajZqMjAyXgoICHQCkp6cbpk6dGjp16tTczp07lxYVFWm++eYbQ2FhobY247kbDHeJiIiIiIiIiIjuo8rKSgBAdna205gxY0JdXV2r+/TpUwwAI0eOrCnNUF1djZ49exafO3fOcc2aNb6WcPfAgQOuw4cPzxsxYkRN27Fjx162/D0rK8v1/fffPz5o0KAie8efMWNGcExMTElaWlpNkBsSElLZr1+/8P379ztbz4YdO3bsxRdffLEAADp16lQaHBzcbvPmzZ7Tpk3Lq6qqQkpKSuCgQYPy3nrrrVwA6N+//9Vu3bq1uHDhQs2s3W3btrnt2rXLY/v27Yd79+5dYmnXoUMH57lz5zZKT0+/LlDeuXPnERcXF7vlGADg9OnTDgDQrFmzaze90DeQmpp60vL3qqoqPPLIIyXNmjWL+uKLLww9e/Ys+f77711cXV2r33333bOWdgMHDqy5lt9++61rq1atjAsWLLhgWTd48GC71/peY1kGIiIiIiIiIiKi++TKlSs6R0fHWEdHx9jo6Og2Z86ccVq9evWxJk2aVAJAXl6edujQoSFBQUFtLe02bNjge+LEiZqv/Ldu3bosJSUlYOHChX5ZWVlOtseIjIwsmz17dvCyZct8jhw54mi9rbi4WJOZmWno379/YWVlJSxLYmJiiU6nU/bt2+dq3b5v3741s2MDAwNN3t7elWfPnnUAgGPHjjnm5+c7JCUlXbHep3fv3tcFnTt27HD39fWtTExMLLE+Zrdu3a5mZWVdd7xOnToV3yzYtSZid2LvLW3cuNE9JiYmws3NrZ2Dg0Nss2bNogAgOzvbGQDat29fVlJSou3fv3/TLVu2uF+9evW6DLV9+/Zl2dnZLs8991xIenq6oby8vHYDqQMMd4mIiIiIiIiIiO4Tg8Fg2r17d/aePXuyjx8/nnX27Nmfn3766ZoANTk5ueknn3zi/cILL1zYsmVLzu7du7Ofeuqp/IqKipoAcdWqVad79OhxZdGiRUHR0dFtmjRp0mblypVelu2bNm06HhUVVTpz5syQ8PDwthEREa23bdvmBqj1ek0mE6ZPnx5qCY8dHR1j9Xp9+6qqKjlz5sx1YbCvr6/J+rWDg4NSXl6uAQBLyBsQEFBl3cbf37/S+nVBQYEuPz/fwfp4jo6OsYsXLw66ePGi4832tSc0NLSmhMWt2travXu3y6BBg1o0atSoYsWKFSd27tx56MsvvzwEAJaQNjo6+tq6deuOnjp1yunpp59u6e/v365v375hubm5OkB9aNvSpUtP7tu3z9C7d+9Wvr6+7YYMGRJqGwLfDyzLQEREREREREREdJ/odDqla9euZfa2lZWVye7duz3nz59/etq0aXmW9cuWLbtuZqivr69p7dq1ZwCc+e677/Tz588PHDNmTLPY2NiDsbGx5WFhYZWbN28+aTKZsGvXLtc5c+YEJScntzhx4kSWj4+PSUQwadKk3CeeeOI3pQQswentCA4OrgSAixcvXpcxXrp0ycH6tZeXV5W/v39lamrq0Vv1eTuzcePi4kr1en11enq6R1JSUvHtjhcAUlNTvby9vau2b99+3PKAs5ycnN+ExMnJyUXJyclFBQUF2tTUVI+XX345ZOTIkaHbt28/DgDjxo0rGDduXEFubq5u/fr1nrNmzWQXVzwAACAASURBVApxd3c3vf322+fuZDx3i+EuERERERERERHRA8BoNGpMJhOcnJyqLesKCws1O3fu9BQRu6UKHn74YeOyZcvOfvzxx96//PKLc2xsbE29XK1Wi/j4+FJFUXITEhIijh496hQXF1cWHR1dmpOT49y1a9fzdzPe5s2bV/j6+lZ+9NFHngMGDKiZfZyWluZh3S4hIaH4vffeC3Rzc6uOiYkp/21Pd8ZgMCiDBw/O++CDD/xGjhyZb33OgDo7+cCBA86PPfZYqe2+RqNRo9PpFEuwCwCrV6/2vtGxfHx8TKNHj768Z88et4yMDFfb7UFBQVVTp07N37Ztm5f1Q+vuF4a7REREREREREREDwAfHx9TmzZtyt54440gDw8Pk0ajwaJFiwINBoOptLS0Jo2MjY1t1adPnytRUVFGEcF7773nq9frq+Pi4koLCgq0jz76aMtnnnmmICIiory8vFzz1ltvBfj6+la2a9fOCAALFy4827dv3/CkpCQ8+eSThe7u7qaTJ086pqene7z++uvnoqKibutBZTqdDs8///zFuXPnBvv5+VV16dKlZOvWrZ6HDx92AQBLgJqUlHQ1Li6uqGfPnuHjx48/37Zt2/KioiLtTz/9pC8vL9csX778jme7Llmy5NyPP/7o2r1794iRI0de7NKlSwmgPuxs1apV/uPHj79gL9xNTEy8unr1av9hw4aF9OvX78pXX31lSE1N9bFu8+abb/ru27fP0KNHj6KQkJDKQ4cOOaelpXkNGDCgAAAmTpwYdPnyZW337t2L/fz8qn744QeX/fv3u73yyitnbY9nbePGje6lpaXazMxMFwBYs2aNFwB07ty5NDw8vOJOrwHAcJeIiIiIiIiIiOiBsWHDhuPDhw9vMmbMmDBPT8+q55577lJZWZl2zZo1fpY2HTp0KN2wYYPPokWLnDQajdK6deuyLVu2HGnevHml0WiUyMhI44oVKwIuXrzo4OzsXN2uXbvStLS0IwaDQQGAHj16lHz22WeHZ8+eHTR69Ogwk8mEoKCgiu7duxc1bty46saj+61Zs2ZdLCws1K5du9Zv5cqVAfHx8VcmTpx4fvr06aFeXl4mQA1509PTj7388suNVqxYEXD+/HlHDw8PU2RkZNnzzz9/qTbXyWAwKF999VXOggUL/Ddu3OizfPnyQABo3rx5+QsvvHBh8uTJefb2GzhwYNHPP/989v333w/YsGGDb0xMTOknn3xyJCoqqo2lTUxMjDEtLc1z5syZIUVFRTpfX9/KQYMG5S1evDgXAP70pz+VLlu2LODjjz/2Lisr0zZq1Khi8uTJuTNnzrzpuUycOLFJbm5uTQmIYcOGNQOApUuXngwPDy+ozXUQRbmth8+RjQ4dOigZGRn1PYw68Sl21PcQ/vB6IbFO+uG9rH+8l78PdXUfK75cUCf9UO05xs+ok37Wba6TbqiWhgyou754L+tXXd1L3sf6x3v5+1CXP1/579f6VVf/fn0QiMgPiqJ0sLctMzPzZHR0dL7tehNM0Vpo7/sERhNMVVpoM+/3cRuCgQMHNtm7d697bm7uz/U9lt+DzMxM3+jo6Kb2tnHmLhERERERERERNVj1FbBqoa2Pwz5w9u/f77xu3Trvzp07l2i1WqSlpXls2rTJ91YlCqhuMNwlIiIiIiIiIiKiWnFzc6vet2+fYc2aNf5Go1ETFBRU8corr5ydM2fOxfoe2x8Bw10iIiIiIiIiIiKqlYiIiIrvvvsup77H8UeluXUTIiIiIiIiIiIiInrQMNwlIiIiIiIiIiIiaoAY7hIRERERERERERE1QAx3iYiIiIiIiIiIiBoghrtEREREREREREREDRDDXSIiIiIiIiIiIqIGiOEuERERERERERHRfTBp0qQgEYm1LP7+/lE9evRofvDgQaf6HltDZDQaZdasWQGRkZGt9Xp9jF6vj2nTpk3kokWLfMvLywUAli1b5iMisUVFRfWag+7evdvlySefbBoaGtpGr9fHNG3atM3kyZMblZWVyd30q6urARIREREREREREdHNGQwG09atW48AwJEjR5zmzZsXlJiYGJ6dnX3Q3d29ur7H11CUlJRIt27dwnNycvQjRoy41LVr12IA+Prrrw3z589vbDQaNa+++uql+h6nxfr1671PnjzpNGHChAutWrW6duDAAf3rr78e9Msvv7h8/vnnx2rbL8NdIiIiIiIiIiJqsBRTVbRodfc941JMVVWi1WXe6X46nU6Jj48vBYD4+PjSsLCwiscff7zVpk2bPIYNG1ZY9yNtuEpKSsRgMCj2tk2cOLHxr7/+6rJnz57sjh07llvWJyUlFU+ZMuVSVlaW8/0b6a3NmTPnQlBQUJXldZ8+fYqdnZ2rp06d2iQnJ8cxPDy8ojb9MtwlIiIiIiIiIqIGS7Q6XcWXC+77cR3jZ9RJrta5c+dSADhx4oQjAOzcudN1/vz5jX755ReX4uJibZMmTa5NmDDhwpgxYy5b9snPz9c+//zzwf/+9789ioqKdN7e3pXdunW7+s9//vMUABw7dsxh3LhxIfv27XMrKyvT+vn5VfTv3//y0qVLcy19fP7554aZM2c2/uWXX1ycnJyUxx9/vPCdd9454+XlVQ2o5QwmTJjQ9Lvvvvv1xRdfDP7pp58MgYGBFXPmzDn37LPPXrH0U11djYkTJwZ98MEHfhUVFZqePXsWJiQkFI0aNarZoUOHfm7VqlUFAJSVlcmkSZMab9261fvy5cu6sLCw8r/85S/nBg4cWGTpq3Hjxm179epV6OnpafrHP/7hV1BQoKuqqvrR9poVFxdr1q9f7zdkyJA862DXIiAgwJSQkFB6o2s+duzYxjt37vQ4e/ask5ubm+nhhx8uTklJORMaGloTvq5fv95j/vz5QcePH3d2cHBQmjZtWr5gwYKzvXv3LgGAJUuW+C5fvjzg7NmzTnq93tSiRYvyd95551SHDh1+Mx4AsA52LTp27FgGAOfPn9cx3CUiIiIiIiIiImpgcnJynAAgMDCwEgCOHz/u+Oc//7lk1KhReXq9vnrv3r2G8ePHN9VoNBg1atRlABgzZkxIRkaG67x5884EBQVVnjp1ynHv3r1ulj4HDx4cVl5erlm6dOkpLy8v09GjRx0PHTqkt2zfsWOHa9++fcMTEhKurF279nh+fr72tddeC37mmWe0n3322XHr8Q0ZMiTs2WefzZ8yZcrFlJQU/+HDhzeLi4v7uXnz5pUAMHfuXP+UlJRG48aNO9+1a9eSjz76yHP27NnBtufZu3fv5pmZma4vvfRSbsuWLa99+OGHXoMHD24REhLya6dOnYyWdlu3bvVu2bJl+eLFi09VVVXZrUf79ddfuxiNRk2vXr2u1uaa5+XlOUyePPlCcHBwxaVLlxyWLl0a0L1791aHDx8+qNPpcPDgQaehQ4c2HzZs2KUFCxacNRqNmoyMDJeCggIdAKSnpxumTp0aOnXq1NzOnTuXFhUVab755htDYWGh9k7GsWfPHoNGo0Hbtm3tBsK3g+EuERERERERERHRfVRZWQkAyM7OdhozZkyoq6trdZ8+fYoBYOTIkTWlGaqrq9GzZ8/ic+fOOa5Zs8bXEu4eOHDAdfjw4XkjRoyoaTt27Niamb1ZWVmu77///vFBgwbVzIq1NmPGjOCYmJiStLS0miA3JCSksl+/fuH79+93tp4NO3bs2IsvvvhiAQB06tSpNDg4uN3mzZs9p02blldVVYWUlJTAQYMG5b311lu5ANC/f/+r3bp1a3HhwgVHSx/btm1z27Vrl8f27dsPW2a+9u/f/2qHDh2c586d2yg9Pf26QHnnzp1HXFxc7JZjAIDTp087AECzZs2u3fRC30BqaupJy9+rqqrwyCOPlDRr1izqiy++MPTs2bPk+++/d3F1da1+9913z1raWc8w/vbbb11btWplXLBgwQXLusGDB9u91jc5B92SJUsaJSUlFXh7e9e61nK9PiWOiIiIiIiIiIjoj+TKlSs6R0fHWEdHx9jo6Og2Z86ccVq9evWxJk2aVAJAXl6edujQoSFBQUFtLe02bNjge+LEiZoasq1bty5LSUkJWLhwoV9WVpaT7TEiIyPLZs+eHbxs2TKfI0eOOFpvKy4u1mRmZhr69+9fWFlZCcuSmJhYotPplH379rlat+/bt2/N7NjAwECTt7d35dmzZx0A4NixY475+fkOSUlJV6z36d2793VB544dO9x9fX0rExMTS6yP2a1bt6tZWVnXHa9Tp07FNwt2rYnYndh7Sxs3bnSPiYmJcHNza+fg4BDbrFmzKADIzs52BoD27duXlZSUaPv37990y5Yt7levXr0uQ23fvn1Zdna2y3PPPReSnp5uKC8vv6OBlJeXS//+/Zu7uLhUr1ix4kytTsKM4S4REREREREREdF9YjAYTLt3787es2dP9vHjx7POnj3789NPP10ToCYnJzf95JNPvF944YULW7Zsydm9e3f2U089lV9RUVETIK5atep0jx49rixatCgoOjq6TZMmTdqsXLnSy7J906ZNx6OiokpnzpwZEh4e3jYiIqL1tm3b3AC1Xq/JZML06dNDLeGxo6NjrF6vb19VVSVnzpy5Lgz29fU1Wb92cHBQysvLNQBgCXkDAgKuqyfr7+9faf26oKBAl5+f72B9PEdHx9jFixcHXbx40fFm+9oTGhpaU8LiVm1t7d6922XQoEEtGjVqVLFixYoTO3fuPPTll18eAtTQFQCio6OvrVu37uipU6ecnn766Zb+/v7t+vbtG5abm6sD1Ie2LV269OS+ffsMvXv3buXr69tuyJAhobYhsD3V1dV48sknw44ePar/5JNPjvj5+Zlutc/NsCwDERERERERERHRfaLT6ZSuXbuW2dtWVlYmu3fv9pw/f/7padOm5VnWL1u27LqZob6+vqa1a9eeAXDmu+++08+fPz9wzJgxzWJjYw/GxsaWh4WFVW7evPmkyWTCrl27XOfMmROUnJzc4sSJE1k+Pj4mEcGkSZNyn3jiid+UErAEp7cjODi4EgAuXrx4XcZ46dIlB+vXXl5eVf7+/pWpqalHb9Xn7czGjYuLK9Xr9dXp6ekeSUlJxbc7XgBITU318vb2rtq+fftxjUbNYnNycn4TEicnJxclJycXFRQUaFNTUz1efvnlkJEjR4Zu3779OACMGzeuYNy4cQW5ubm69evXe86aNSvE3d3d9Pbbb5+72fGHDx8esnPnTs9t27blxMTE1LrWrgXDXSIiIiIiIiIiogeA0WjUmEwmODk51dRgLSws1OzcudNTROyWKnj44YeNy5YtO/vxxx97//LLL86xsbE1gaFWq0V8fHypoii5CQkJEUePHnWKi4sri46OLs3JyXHu2rXr+bsZb/PmzSt8fX0rP/roI88BAwbUzD5OS0vzsG6XkJBQ/N577wW6ublV10WgaTAYlMGDB+d98MEHfiNHjsy3PmdAnZ184MAB58cee6zUdl+j0ajR6XSKJdgFgNWrV3vf6Fg+Pj6m0aNHX96zZ49bRkaGq+32oKCgqqlTp+Zv27bNy/qhdfbMmDEj8O9//7v/qlWrjvXo0aPktk72FhjuEhERERERERERPQB8fHxMbdq0KXvjjTeCPDw8TBqNBosWLQo0GAym0tLSmjQyNja2VZ8+fa5ERUUZRQTvvfeer16vr46LiystKCjQPvrooy2feeaZgoiIiPLy8nLNW2+9FeDr61vZrl07IwAsXLjwbN++fcOTkpLw5JNPFrq7u5tOnjzpmJ6e7vH666+fi4qKuq0Hlel0Ojz//PMX586dG+zn51fVpUuXkq1bt3oePnzYBQAsAWpSUtLVuLi4op49e4aPHz/+fNu2bcuLioq0P/30k768vFyzfPnym852tWfJkiXnfvzxR9fu3btHjBw58mKXLl1KAPVhZ6tWrfIfP378BXvhbmJi4tXVq1f7Dxs2LKRfv35XvvrqK0NqaqqPdZs333zTd9++fYYePXoUhYSEVB46dMg5LS3Na8CAAQUAMHHixKDLly9ru3fvXuzn51f1ww8/uOzfv9/tlVdeOWt7PIsVK1Z4L1y4sPGAAQMKQkJCKr/88suaoDgyMvJaUFBQ1Y32vRmGu0RERERERERERA+IDRs2HB8+fHiTMWPGhHl6elY999xzl8rKyrRr1qzxs7Tp0KFD6YYNG3wWLVrkpNFolNatW5dt2bLlSPPmzSuNRqNERkYaV6xYEXDx4kUHZ2fn6nbt2pWmpaUdMRgMCgD06NGj5LPPPjs8e/bsoNGjR4eZTCYEBQVVdO/evahx48Z3FDLOmjXrYmFhoXbt2rV+K1euDIiPj78yceLE89OnTw/18vIyAWrIm56efuzll19utGLFioDz5887enh4mCIjI8uef/75S7W5TgaDQfnqq69yFixY4L9x40af5cuXBwJA8+bNy1944YULkydPzrO338CBA4t+/vnns++//37Ahg0bfGNiYko/+eSTI1FRUW0sbWJiYoxpaWmeM2fODCkqKtL5+vpWDho0KG/x4sW5APCnP/2pdNmyZQEff/yxd1lZmbZRo0YVkydPzp05c+YNz+WLL75wB4DNmzf7bN68+boweenSpSfHjx9fUJvrIIpyWw+fIxsdOnRQMjIy6nsYdeJT7KjvIfzh9UJinfTDe1n/eC9/H+rqPlZ8uaBO+qHac4yfUSf9rNtcJ91QLQ0ZUHd98V7Wr7q6l7yP9Y/38vehLn++8t+v9auu/v36IBCRHxRF6WBvW2Zm5sno6Oh82/WKqSpatLr7PoFRMVVViVaXeb+P2xAMHDiwyd69e91zc3N/ru+x/B5kZmb6RkdHN7W3jTN3iYiIiIiIiIiowaqvgFW0jNUAYP/+/c7r1q3z7ty5c4lWq0VaWprHpk2bfG9WooDqDt+FREREREREREREVCtubm7V+/btM6xZs8bfaDRqgoKCKl555ZWzc+bMuVjfY/sjYLhLREREdBMmk4IhA6S+h/GHZjIp0Gp5D4iIiIgeRBERERXfffddTn2P44+K4S4RERHRTWi1wvrJ9ayuaicTEREREf3eaOp7AERERERERERERER05xjuEhERERERERERETVADHeJiIiIiIiIiIiIGiCGu0REREREREREREQNEMNdIiIiIiIiIiIiogaI4S4RERERERERERFRA8Rwl4iIiIiIiIiI6D6YNGlSkIjEWhZ/f/+oHj16ND948KBTfY+tITIajTJr1qyAyMjI1nq9Pkav18e0adMmctGiRb7l5eUCAMuWLfMRkdiioqJ6zUEzMjKcu3Tp0tLf3z/K0dGxfaNGjdoOHDiwyalTpxzupl9dXQ2QiIiIiIiIiIjofqsyIVqnvf8ZV5UJVTotMu90P4PBYNq6desRADhy5IjTvHnzghITE8Ozs7MPuru7V9f9SH+fSkpKpFu3buE5OTn6ESNGXOratWsxAHz99deG+fPnNzYajZpXX331Un2P06KwsFAbGhp6bfDgwQXBwcGVx44dc3zjjTeCHn/8cdesrKxfHRxql/Ey3CUiIiIiIiIiogZLp4Vu3eb7f9whA2qXq+l0OiU+Pr4UAOLj40vDwsIqHn/88VabNm3yGDZsWGHdjrJhKykpEYPBoNjbNnHixMa//vqry549e7I7duxYblmflJRUPGXKlEtZWVnO92+kt5aQkFCakJBQar0uNDS0sn///i2/++47l7i4uLLa9MuyDERERERERERERPWkc+fOpQBw4sQJRwDYuXOn66OPPtrC398/Sq/Xx0RERLR+5513vK33yc/P1w4cOLCJv79/lJOTU/tGjRq1TU5ObmLZfuzYMYdevXo18/b2jnZ2dm4fEhLSZsKECUHWfXz++eeGjh07ttLr9TGenp7tkpOTmxQWFtZkhZZyBt9//72+U6dOLfV6fUxYWNhDf//73z2t+6mursaECROCvL29ow0GQ8xTTz3VdOXKlV4iEnv48GFHS7uysjIZPXp0cGBgYJSjo2P7Vq1atf7www89rPtq3Lhx2xEjRgRPnTq1UUBAQJSnp2eMvWtWXFysWb9+vd+QIUPyrINdi4CAAJNtkGpt7NixjcPDw1u7uLjEBAQERD3xxBNhp0+fvi6sX79+vcdDDz0UqdfrY9zd3dtFRUVFpKWlGSzblyxZ4tuiRYuHnJ2d23t5eUV37NixVUZGxh0Fyn5+flUAcO3aNbmT/axx5i4REREREREREVE9ycnJcQKAwMDASgA4fvy445///OeSUaNG5en1+uq9e/caxo8f31Sj0WDUqFGXAWDMmDEhGRkZrvPmzTsTFBRUeerUKce9e/e6WfocPHhwWHl5uWbp0qWnvLy8TEePHnU8dOiQ3rJ9x44drn379g1PSEi4snbt2uP5+fna1157LfiZZ57RfvbZZ8etxzdkyJCwZ599Nn/KlCkXU1JS/IcPH94sLi7u5+bNm1cCwNy5c/1TUlIajRs37nzXrl1LPvroI8/Zs2cH255n7969m2dmZrq+9NJLuS1btrz24Ycfeg0ePLhFSEjIr506dTJa2m3dutW7ZcuW5YsXLz5VVVVlN/T8+uuvXYxGo6ZXr15Xa3PN8/LyHCZPnnwhODi44tKlSw5Lly4N6N69e6vDhw8f1Ol0OHjwoNPQoUObDxs27NKCBQvOGo1GTUZGhktBQYEOANLT0w1Tp04NnTp1am7nzp1Li4qKNN98842hsLBQe6tjm0wmVFZWyuHDhx1feumlxm3atCl75JFHbhhE3wrDXSIiIiIiIiIiovuosrISAJCdne00ZsyYUFdX1+o+ffoUA8DIkSNrSjNUV1ejZ8+exefOnXNcs2aNryXcPXDggOvw4cPzRowYUdN27Nixly1/z8rKcn3//fePDxo0qMje8WfMmBEcExNTkpaWVhPkhoSEVPbr1y98//79ztazYceOHXvxxRdfLACATp06lQYHB7fbvHmz57Rp0/KqqqqQkpISOGjQoLy33norFwD69+9/tVu3bi0uXLhQM2t327Ztbrt27fLYvn374d69e5dY2nXo0MF57ty5jdLT068LlHfu3HnExcXFbjkGADh9+rQDADRr1uzaTS/0DaSmpp60/L2qqgqPPPJISbNmzaK++OILQ8+ePUu+//57F1dX1+p33333rKXdwIEDa67lt99+69qqVSvjggULLljWDR482O61tvXII4+0/Oqrr9wB4KGHHir7/PPPj2i1t8yEb4hlGYiIiIiIiIiIiO6TK1eu6BwdHWMdHR1jo6Oj25w5c8Zp9erVx5o0aVIJAHl5edqhQ4eGBAUFtbW027Bhg++JEydqvvLfunXrspSUlICFCxf6ZWVlOdkeIzIysmz27NnBy5Yt8zly5Iij9bbi4mJNZmamoX///oWVlZWwLImJiSU6nU7Zt2+fq3X7vn371syODQwMNHl7e1eePXvWAQCOHTvmmJ+f75CUlHTFep/evXtfF3Tu2LHD3dfXtzIxMbHE+pjdunW7mpWVdd3xOnXqVHyzYNeaSO2qGWzcuNE9JiYmws3NrZ2Dg0Nss2bNogAgOzvbGQDat29fVlJSou3fv3/TLVu2uF+9evW6DLV9+/Zl2dnZLs8991xIenq6oby8/LYH8vbbb5/+8ssvDy1fvvxEaWmpJjExsWVZWRnLMhARET1IFFMlHONn1Pcw/vAUUyVEW7unzhIRERER3QsGg8GUlpaWIyIIDg6ubNKkSaVG85/sMDk5uemBAwcMkydPzm3Tpk25p6enKSUlxe+LL76oqXW7atWq01OmTAlatGhR0IwZM0JDQ0OvvfLKK+css343bdp0fMqUKY1nzpwZMmHCBG2rVq2Mr7/++pl+/foV5+fna00mE6ZPnx46ffr0UNvxnTlz5row2NfX12T92sHBQSkvL9cAgCXkDQgIqLJu4+/vX2n9uqCgQJefn+/g6OgYa3s821mrtvvaExoaWlPCIioq6o5m7+7evdtl0KBBLRITE69MmTLlfGBgYJWIID4+PsIS0kZHR19bt27d0TfffDPw6aefbqnT6ZSEhITCd99990xQUFBVUlJS8dKlS0+uWLHCf82aNf4uLi7VSUlJBW+//fZZd3f36psdv23bttcAXHv00UdLH3vssZKIiIi2K1eu9LbMjr5TDHeJiIjuAdE64FPsqO9h/OH10ibW9xCIiIiIiK6j0+mUrl27ltnbVlZWJrt37/acP3/+6WnTpuVZ1i9btuy6mZ2+vr6mtWvXngFw5rvvvtPPnz8/cMyYMc1iY2MPxsbGloeFhVVu3rz5pMlkwq5du1znzJkTlJyc3OLEiRNZPj4+JhHBpEmTcp944onflBKwBKe3Izg4uBIALl68eF3GeOnSpetmWHh5eVX5+/tXpqamHr1Vn7czGzcuLq5Ur9dXp6eneyQlJRXf7ngBIDU11cvb27tq+/btxy2hek5OjqNtu+Tk5KLk5OSigoICbWpqqsfLL78cMnLkyNDt27cfB4Bx48YVjBs3riA3N1e3fv16z1mzZoW4u7ub3n777XO3O5bw8PAKDw+PquPHj/9m9vXtYrhLRERERERERET0ADAajRqTyQQnJ6ea2Z+FhYWanTt3eoqI3VIFDz/8sHHZsmVnP/74Y+9ffvnFOTY2tqZerlarRXx8fKmiKLkJCQkRR48edYqLiyuLjo4uzcnJce7atev5uxlv8+bNK3x9fSs/+ugjzwEDBtSUb0hLS/OwbpeQkFD83nvvBbq5uVXHxMSU/7anO2MwGJTBgwfnffDBB34jR47Mtz5nAMjPz9ceOHDA+bHHHvvNg8qMRqNGp9Mp1rOlV69e7X2jY/n4+JhGjx59ec+ePW4ZGRmuttuDgoKqpk6dmr9t2zYv64fW3Y7MzEynK1eu6MLCwiruZD9rDHeJiIiIiIiIiIgeAD4+PqY2bdqUvfHGG0EeHh4mjUaDRYsWbzZkogAAIABJREFUBRoMBlNpaWlNGhkbG9uqT58+V6Kioowigvfee89Xr9dXx8XFlRYUFGgfffTRls8880xBREREeXl5ueatt94K8PX1rWzXrp0RABYuXHi2b9++4UlJSXjyyScL3d3dTSdPnnRMT0/3eP3118/dbqkDnU6H559//uLcuXOD/fz8qrp06VKydetWz8OHD7sAgCVATUpKuhoXF1fUs2fP8PHjx59v27ZteVFRkfann37Sl5eXa5YvX37bs10tlixZcu7HH3907d69e8TIkSMvdunSpQRQH3a2atUq//Hjx1+wF+4mJiZeXb16tf+wYcNC+vXrd+Wrr74ypKam+li3efPNN3337dtn6NGjR1FISEjloUOHnNPS0rwGDBhQAAATJ04Munz5srZ79+7Ffn5+VT/88IPL/v373V555ZWztsezGDlyZLBOp1P+67/+q9TLy8t08OBB57/97W+BISEh15577rnLN9rvVhjuEhERERERERERPSA2bNhwfPjw4U3GjBkT5unpWfXcc89dKisr065Zs8bP0qZDhw6lGzZs8Fm0aJGTRqNRWrduXbZly5YjzZs3rzQajRIZGWlcsWJFwMWLFx2cnZ2r27VrV5qWlnbEYDAoANCjR4+Szz777PDs2bODRo8eHWYymRAUFFTRvXv3osaNG1fdeHS/NWvWrIuFhYXatWvX+q1cuTIgPj7+ysSJE89Pnz491MvLywSoIW96evqxl19+udGKFSsCzp8/7+jh4WGKjIwse/755y/V5joZDAblq6++ylmwYIH/xo0bfZb/f/buNNrSsj4T/vXnFOMrjqBAgjgHhxZROm0g2K0YoySEaGKTVtvY5o2tK9i20sYJVJxQk+gbRKM4Ju2QaNq0KBgVYjQO2OAclYjYKGpAlEnGgqr/++HsSo7Hs089Vex9qp7i91trr/08933vXVetzYdaF/e6n9e/fp8kufvd7379sccee/Fxxx136UqfO+aYY6786le/+r23vOUtd3rPe96z18EHH3zNBz/4wfPvf//732/TmoMPPvi6008//bbHH3/8/ldeeeW6vfba68bHPe5xl77mNa/5QZL84i/+4jUnn3zynU477bTbX3vttQv77rvv+uOOO+4Hxx9//NS/yyGHHHLNG9/4xju9613v2nv9+vW1zz77rD/yyCMvf8lLXnLx5s7pXU11D3r4HMsccsghfe65527rGDPhTMht78jM5kxIv+W257fcMfgddxyz+i3Xn3XSTL6HrTPLhxO+83/N7KvYCk/4rdl8j99x2/Nb7hhm9Tsm/t2zrc3q3zzbg6r6fHcfstLcl7/85QsPOuigHy0fv2lDDlq3sPYbGG/akJvWLeTLa/3njsExxxxzwD/8wz/c+gc/+MFXt3WWHcGXv/zlvQ466KC7rDRn5y4AAAAAo7WtCtZ1C9viT93+nHPOObu9853vvP1hhx129cLCQk4//fTb/PVf//Veqx1RwOwodwEAAACArbLnnntuPPvss2/19re//Y7XXXfdTvvtt9/6F7zgBd978YtffMm2znZLoNwFAAAAALbKgQceuP5zn/vcN7d1jluqnTa/BAAAAACA7Y1yFwAAAABghJS7AAAAAIzBxo0bN9a2DgFrafLf/MZp88pdAAAAALZ7VXXxddddt9u2zgFr6brrrtutqi6eNq/cBQAAAGC7d9NNN5144YUX7nLNNdfsbgcvO7qNGzfWNddcs/uFF164y0033XTitHXr1jIUAAAAAGyNBz7wgR/5whe+cOwFF1zwou7eJzYtsmPbWFUX33TTTSc+8IEP/Mi0RcpdAAAAAEZhUnJNLbrglsb/4QAAAAAAGCHlLgAAAADACCl3AQAAAABGSLkLAAAAADBCyl0AAAAAgBFS7gIAAAAAjJByFwAAAABghJS7AAAAAAAjpNwFAAAAABgh5S4AAAAAwAgpdwEAAAAARki5CwAAAAAwQspdAAAAAIARUu4CAAAAAIyQchcAAAAAYISUuwAAAAAAI6TcBQAAAAAYIeUuAAAAAMAIKXcBAAAAAEZIuQsAAAAAMELKXQAAAACAEdphy92q+p2q+kJVXV1V36+qv6iq/Zatqap6flVdVFXXVdUnq+oB2yozAAAAAMBQO2S5W1W/keQ9ST6T5Ogkz0nykCQfqqqlf+fnJjkhyauSHJXk6iRnVtU+a5sYAAAAAGDLrNvWAebkcUm+0N3HbhqoqquSfCDJLyT5RlXtlsVy96TuPmWy5rNJLkxybJLj1zo0AAAAAMBQO+TO3SQ7J7ly2dgVk/eavB+a5NZJ3rtpQXdfk+SDSR4174AAAAAAADfHjlruvi3J4VX1xKq6dVXdK8nLkny8u78+WXNgkg1Jzl/22W9M5gAAAAAAtls7ZLnb3acneVKSU7O4g/efkiwkecySZbdLcnV3b1j28cuT7FFVu6xBVAAAAACArbJDlrtV9dAkb0zyp0kemuR3ktw+yd9U1cKSpb3Sx6fNVdVTqurcqjr30ksvnXFqAAAAAIDhdtQHqv1JktO6+zmbBqrqS0nOS3J0kvdncYfunlW1sGz37m2TXNvdNy7/0u4+NYu7gXPIIYesVAwDAAAAAKyJHXLnbhbPzP3S0oHu/qck1yW5+2TovCwe1XCPFT573rwDAgAAAADcHDtqufudJA9cOlBV906ye5ILJ0OfSXJVkscuWbNHkqOSfHhNUgIAAAAAbKUd9ViGNyZ5bVX9IItF7Z2SvDCLxe4ZSdLd11fVK5OcUFWXZ3G37rOyWHi/bluEBgAAAAAYakctd09Osj7J05I8NckVST6V5Hndfc2Sda/MYpn7vCR3SHJukl/p7kvWNi4AAAAAwJbZIcvd7u4kfzZ5bW7dyycvAAAAAIDR2FHP3AUAAAAA2KEpdwEAAAAARki5CwAAAAAwQjvkmbsAALPSG27MLkc8b1vHuEXrDTemFnbe1jEAAGC7o9wFAFhFLeycM/LRbR3jFu3IhUds6wgAALBdciwDAAAAAMAIKXcBAAAAAEZIuQsAAAAAMELKXQAAAACAEVLuAgAAAACMkHIXAAAAAGCElLsAAAAAACOk3AUAAAAAGCHlLgAAAADACCl3AQAAAABGSLkLAAAAADBCyl0AAAAAgBFS7gIAAAAAjJByFwAAAABghJS7AAAAAAAjpNwFAAAAABgh5S4AAAAAwAgpdwEAAAAARki5CwAAAAAwQspdAAAAAIARUu4CAAAAAIyQchcAAAAAYISUuwAAAAAAI6TcBQAAAAAYIeUuAAAAAMAIKXcBAAAAAEZIuQsAAAAAMELKXQAAAACAEVLuAgAAAACMkHIXAAAAAGCElLsAAAAAACOk3AUAAAAAGCHlLgAAAADACCl3AQAAAABGSLkLAAAAADBCyl0AAAAAgBFS7gIAAAAAjJByFwAAAABghJS7AAAAAAAjpNwFAAAAABgh5S4AAAAAwAgpdwEAAAAARki5CwAAAAAwQspdAAAAAIARUu4CAAAAAIyQchcAAAAAYIS2uNytRftV1bp5BAIAAAAAYPMGl7tVdWRVfS7J9Um+m+T+k/FTq+oJc8oHAAAAAMAKBpW7VfXEJKclOS/JU5Z97vwkvzf7aAAAAAAATDN05+4LkvxRd/9ukncum/takvvMNBUAAAAAAKsaWu4ekORjU+auT3Lr2cQBAAAAAGCIoeXuRUkOnjJ3SJJvzSYOAAAAAABDDC1335rkRZMHp+0+GauqOiLJHyZ58zzCAQAAAACwsnUD170qyf5J/jzJhsnYZ5IsJHlTd588h2wAAAAAAEwxqNzt7k7yB1X1miQPT3KHJJcl+bvu/uYc8wEAAAAAsIKhO3eTJN19QZIL5pQFAAAAAICBBpW7VfWQVaY3JrkqyT919w0zSQUAAAAAwKqG7tz9+yS95L6W3SfJ9VX1liTP6u4NAQAAAABgboaWuw9P8tYkZyQ5LcmlSfZOcnSSI5Mcl+TeSZ6f5OokL5h5UgAAAAAA/sXQcvfYJH/e3S9eNv6Rqnpxkid191FVtS7Jk6LcBQAAAACYq50GrntEkk9Nmft0kodOrj+ZZN+bGwoAAAAAgNUNLXcvS/IbU+Z+YzKfJHskufLmhgIAAAAAYHVDj2V4dZKTq+ouST6Ynz1z9+mTdQ9Ncs5sIwIAAAAAsNygcre7T6mq7yd5XpLXTz53U5IvJXlMd//vydKTkqyfR1AAAAAAAP7V0J276e6/SfI3VbVTFnftXtrdG5et+dGM8wEAAAAAsILB5e4mk0L3kjlkAQAAAABgoMHl7uS83SckuVeS3ZbPd/d/nFkqAAAAAABWNajcraoHJflEkouyWO5+JcltktwlyfeSfGtO+QAAAAAAWMFOA9f9UZL/leR+SSrJ73X33ZL8cpJO8ur5xAMAAAAAYCVDy90HJHl3kk0PUNstSbr7M0lOTPLK2UcDAAAAAGCaoeVuJ1nf3Z3kh0kOWDJ3UZJ7zjoYAAAAAADTDS13v57k7pPrzyZ5ZlXds6oOSPKHSS6YRzgAAAAAAFY26IFqSU7Nv+7WfX6SjyY5b3J/TZLfnnEuAAAAAABWMajc7e7/ueT6G1V17yS/lGT3JGd39w/nlA8AAAAAgBUMOpahqp5YVXfYdN/dV3f3x7r7tCQ3VdUT55YQAAAAAICfMfTM3bfnX8/cXe6uk3kAAAAAANbI0HK3Vpm7Q5KrZpAFAAAAAICBpp65W1VHJzl6ydAJVXXpsmW7JTk8yTlzyAYAAAAAwBSrPVDtjkn+zZL7uyfZZ9ma9Uk+muRlM84FAAAAAMAqppa73f3mJG9Okqr6eJKndfd5axUMAAAAAIDpVtu5+y+6+6HzDgIAAAAAwHCDyt0kqar9kvx6kp/P4lm7S3V3P2eWwQAAAAAAmG5QuVtVj07yniQLSX6YxbN2l+okyl0AAAAAgDUydOfuK7L44LQndfdlc8wDAAAAAMAAQ8vd/ZM8XbELAAAAALB92Gngus8k+YV5BgEAAAAAYLihO3efleRdVXV1ko8luWL5gu6+dpbBAAAAAACYbmi5+5XJ+9uz+PC0lSzc/DgAAAAAAAwxtNx9cqaXugAAsN3bsKHzhN+qbR3jFm3Dhs7Cgt8AAGBWBpW73f2OOecAAIC5WliorD/rpG0d4xZtlyOet60jAADsUIbu3E2SVNV9kjwoyf5J3tbdF1fVPZJc0t0/mUdAAAAAAAB+1k5DFlXVrarqvUn+Mclbkrw0yX6T6VckedF84m29qlpXVc+tqvOr6oaq+l5VvXbZmqqq51fVRVV1XVV9sqoesK0yAwAAAAAMNajcTfKaJIcmOSLJnkmWHpR1RpJHzjjXLLw9yX9L8sdJHpHkuUmuW7bmuUlOSPKqJEcluTrJmVW1zxrmBAAAAADYYkOPZXhMkmd098eramHZ3HeSHDDbWDdPVT0yye8kOai7vz5lzW5ZLHdP6u5TJmOfTXJhkmOTHL82aQEAAAAAttzQnbu7J/nxlLk9k2yYTZyZeXKSv5tW7E4cmuTWSd67aaC7r0nywSSPmm88AAAAAICbZ2i5e06SJ06Z++0kn5lNnJn5d0m+WVWnVNVVVXVtVb2/qvZbsubALJbS5y/77DcmcwAAAAAA262hxzIcn8WzaM9M8r4kneTIqnpmFsvdh8wp39baJ8mTknw5i8cz7Jnk1Un+pqoe3N2d5HZJru7u5buOL0+yR1Xt0t3r1zAzAAAAAMBgg3budvensvgwtV2TnJLFB6qdmORuSR7e3efMLeHWqcnr6O4+o7v/Ksl/TvKLSR62ZF1P+eyKc1X1lKo6t6rOvfTSS2edGQAAAABgsKHHMqS7P93dh2fxnNqfT7Jndx/W3Z+eW7qtd3mSr3b30nOCP5VkfZL7LFmz5woPiLttkmu7+8blX9rdp3b3Id19yN577z2P3AAAAAAAgwwqd6tqz6raN0m6+7ru/kF3XzuZ27eqbjXPkFvhG1PGK8nGyfV5SRaS3GPZmgMncwAAAAAA262hO3ffmuQlU+ZenOQtM0kzOx9Kcv+q2mvJ2EOS7JzFc3iTxYfAXZXksZsWVNUeSY5K8uE1ygkAAAAAsFWGlrsPSXL6lLkzsv09UO3UJD9O8sGqOqqqHpfkfyY5c3J+cLr7+iSvTPL8qvqDqjoiiw+L2ynJ67ZRbgAAAACAQdYNXHebJNdOmbs+ye1mE2c2uvuqqnpYkpOT/GUWz9r9QJJnLlv6yiyWuc9Lcock5yb5le6+ZA3jAgAAAABssaHl7vlJfi3JR1eYOzLJBTNLNCPd/a0sZlttTSd5+eQFAAAAADAaQ8vd1yV5Y1WtT/KOJP+cZN8kv5vkD5I8bS7pAAAAAABY0aByt7vfXFV3yuLxBc9aMnV9kuO7+83zCAcAAAAAwMqG7txNd7+sql6X5JeyeD7tj5N8truvnFc4AAAAAABWttlyt6p2S3Jakld0998n+dt5hwIAAAAAYHU7bW5Bd1+f5N8mWZh/HAAAAAAAhthsuTtxWpLfnGcQAAAAAACGG3rm7keS/FFV7ZvkjCSXJOmlC7r7jBlnAwAAAABgiqHl7jsn74+ZvJbrOLYBAAAAAGDNDC137zrXFAAAAAAAbJFB5W53f2feQQAAAAAAGG7oA9VSVbtW1dOq6q1V9dGquudk/Jiquvf8IgIAAAAAsNygnbtVda8kH0tymySfT/Ifkuw5mT48ya8leeIc8gEAAAAAsIKhO3dPTvLdJHdJ8qtJasncJ5L88mxjAQAAAACwmqEPVDs8yWO7+4qqWlg2d0mSfWcbCwAAAACA1QzduXt9kt2nzP1ckitmEwcAAAAAgCGGlrsfS/L8qrrNkrGuql2TPD3JGTNPBgAAAADAVEOPZXh2kk8n+VYWi95O8sIk902yS5LHzCUdAAAAAAArGrRzt7svSnJQkjdm8aFqF2TxnN33JXlQd188r4AAAAAAAPysoTt3092XJzlh8gIAAAAAYBtatdytqvsn+f0s7ta9OMn7u/vDa5ALAAAAAIBVTD2WoaoeluTcJI9PsneSI5N8qKqOW6NsAAAAAABMsdqZuycm+USS/bv7wUn2T3JKkhdV1aCzegEAAAAAmI/VStr7JnlNd1+TJN29McnLk9wqyQFrkA0AAAAAgClWK3dvm+THy8Y23d9uPnEAAAAAABhi1QeqJblrVV295H5h8n63qrp+6cLu/vpMkwEAAAAAMNXmyt13Txl/b5KeXNfkemHKWgAAAAAAZmy1cveha5YCAAAAAIAtMrXc7e5PrGUQAAAAAACGW+2BagAAAAAAbKeUuwAAAAAAI6TcBQAAAAAYIeUuAAAAAMAIKXcBAAAAAEZoULlbVfeuqgcvud+9ql5RVf+7qp4+v3gAAAAAAKxk6M7dNyQ5asn9Hyd5RpLdkryqqp4962AAAAAAAEw3tNy9X5LPJklV7ZzkCUn+e3c/Msnzkzx5PvEAAAAAAFjJ0HL3/0ly1eT6wZP790/uv5DkgBnnAgAAAABgFUPL3W9nsdRNkkcn+WJ3/3hyv1eSn8w6GAAAAAAA060buO61Sf6sqh6b5OAk/2XJ3H9I8pUZ5wIAAAAAYBWDyt3ufmtVnZ/k3yZ5bneftWT6siT/3zzCAQAAAACwsqE7d9Pdn0zyyRXGXzzLQAAAAAAAbN6gM3er6vCqOnrJ/V5V9e6q+lJV/UlV7Ty/iAAAAAAALDf0gWqvTnK/Jfd/muSIJGcneVKSE2cbCwAAAACA1Qwtd38hyeeTpKr2SPLoJM/o7qcm+cMkx8wnHgAAAAAAKxla7u6S5PrJ9WFZPKv39Mn9N5PsO+NcAAAAAACsYmi5e16SR06uH5/ks939k8n9fkkum3UwAAAAAACmWzdw3UuSvK+qfi/JbZIcvWTukUm+OOtgAAAAAABMN6jc7e7TqureSQ5O8tXu/uaS6c8m+co8wgEAAAAAsLKhO3fT3d9O8u0Vxk+daSIAAAAAADZrcLlbVZXFh6ndK8luy6a7u/9slsEAAAAAAJhuULlbVXdK8ndJ7p2kk9RkqpcsU+4CAAAAAKyRnQau+5MkVyTZP4vF7r9LcpckJyQ5P4u7eQEAAAAAWCNDj2X490mekeSfJ/fV3d9N8oqq2inJG5L86hzyAQAAAACwgqE7d2+b5NLu3pjkqiR3XDL3mSSHzjoYAAAAAADTDS13/2+SfSfXX0vy+CVzRyW5bJahAAAAAABY3dBjGU5P8ogk703ysiQfqKrvJbkxyZ2TPGc+8QAAAAAAWMmgcre7n7fk+sNVdWiSRyfZPcnHuvvDc8oHAAAAAMAKhu7c/SndfW6Sc2ecBQAAAACAgaaWu1W1x5Z8UXdfe/PjAAAAAAAwxGo7d69O0lvwXQs3MwsAAAAAAAOtVu4+OVtW7gIAAAAAsEamlrvd/Y41zAEAAAAAwBbYadpEVe1cVU+vqgevsubBkzW7zCceAAAAAAArWe1YhicleUGSA1dZ840k709SSU6eXSwAAAAAAFYzdedukt9P8rruvmLagu6+MskpSR4/62AAAAAAAEy3Wrl73ySfHfAdZ0/WAgAAAACwRlYrd3vNUgAAAAAAsEVWK3e/meSwAd9x2GQtAAAAAABrZLVy991JnllV9562YDL335O8c9bBAAAAAACYbt0qcycnOTrJ/6mqP0vykSTfzeJxDXdO8qtJnpbki0leN+ecAAAAAAAsMbXc7e71VfUrSV6exRL3uCXTleSaJG9Kcnx33zjXlAAAAAAA/JTVdu6mu69PclxVHZ/kQUl+bjL1/STnTuYBAAAAAFhjq5a7m3T3dUk+NecsAAAAAAAMtNoD1QAAAAAA2E4pdwEAAAAARki5CwAAAAAwQspdAAAAAIAR2my5W1W7VdUNVfWbaxEIAAAAAIDN22y5293XJ/lhkpvmHwcAAAAAgCGGHsvwpiT/rap2nmcYAAAAAACGWTdw3W2T3C/JhVV1VpJLkvSS+e7u58w6HAAAAAAAKxta7v5Wkhsm14evMN9JlLsAAAAAAGtkULnb3XeddxAAAAAAAIYbeuYuAAAAAADbkcHlblXdv6r+qqouqKobquqBk/GXV9Wj5hcRAAAAAIDlBpW7k/L280n2SfIXSXZeMn1DkqfPPhoAAAAAANMM3bl7UpJ3dPe/T/LyZXNfSvKAmaYCAAAAAGBVQ8vdA5P81eS6l81dleT2M0sEAAAAAMBmDS13f5jkblPm7pvku7OJAwAAAADAEEPL3b9M8pKq+uUlY11V90rynCTvmnkyAAAAAACmWjdw3QlJ7pPkE0kunox9IIsPWPtoklfMPhoAAAAAANMMKne7+4Ykv15VRyQ5IsleSS5LclZ3f2yO+QAAAAAAWMHQnbtJku4+K8lZc8oCAAAAAMBAg8rdqrrzKtMbk1zV3VfNJhIAAAAAAJszdOfuhUl6tQVV9d0kJ3f3a29uKAAAAAAAVje03H1cklcl+cckpyW5NMneSY5Ocr8sPlDtkCSvrqooeAEAAAAA5mungesenuS07v617n5Td79/8n5kkg8kObS7/98kr0/y1HmF3RpV9XNVdXVVdVXdasl4VdXzq+qiqrquqj5ZVQ/YllkBAAAAAIYaWu4+Nosl7kpOy+IO3iT5cJIDbm6oGfujJFevMP7cJCdkcUfyUZM1Z1bVPmuYDQAAAABgqwwtd69PctiUucMm80lSSa65uaFmpaoOT/LIJH+8bHy3LJa7J3X3Kd19ZhYL7E5y7JoHBQAAAADYQkPP3D01yQlVdYckH8xPn7n71CQvn6w7NMmXZx1ya1TVQpLXJXlJkiuWTR+a5NZJ3rtpoLuvqaoPJnlUkuPXKicAAAAAwNYYVO529wlVdVmSZ2dxZ2tncZfuxUmeveQBan+V5G3zCLoVnppktyyeA/z4ZXMHJtmQ5Pxl499Icsz8owEAAAAA3DxDd+6mu19bVX+aZP8k+2Sx2L2ouzcuWfO12UfccpMdxi9N8oTuvrGqli+5XZKru3vDsvHLk+xRVbt09/oVvvcpSZ6SJHe+851nHxwAAAAAYKChZ+4mSbp7Y3d/p7s/N3nfuPlPbRMvT/K57j5jlTW9wlitMpfuPrW7D+nuQ/bee++bmxEAAAAAYKsN3rlbVfsl+fUkP5/F4w6W6u5+ziyDba2qum+SJyd5SFXddjK8x+T9NlW1IYs7dPesqoVlu3dvm+Ta7r5x7RIDAAAAAGy5QeVuVT06yXuSLCT5YZLlRxZ0ku2i3E1yzyQ7J/nsCnPfS/LWJO/O4t/lHkn+acn8gUnOm3dAAAAAAICba+jO3Vck+WiSJ3X3ZXPMMwufSvLQZWOPzGL5fGSSbyf5TpKrkjw2ycuSpKr2SHJUklPXLCkAAAAAwFYaWu7un+TpIyh2090/SvL3S8eq6i6Ty3/o7qsnY69MckJVXZ7F3brPyuIZxK9bq6wAAAAAAFtraLn7mSS/kOTMOWZZa6/MYpn7vCR3SHJukl/p7ku2aSoAAAAAgAGGlrvPSvKuqro6yceSXLF8QXdfO8tgs9Td70jyjmVjneTlkxcAAAAAwKgMLXe/Mnl/exYfnraShZsfBwAAAACAIYaWu0/O9FIXAAAAAIA1NqjcnRxrAAAAAADAdmLozt0kSVXdJ8mDkuyf5G3dfXFV3SPJJd39k3kEBAAAAADgZw0qd6vqVkneluS3k9w4+dzfJrk4ySuSfDfJ/5hTRgAAAAAAltlp4LrXJDk0yRFJ9kxSS+bOSPLIGecCAAAAAGAVQ49leEySZ3T3x6tqYdncd5IcMNtYAAAAAACsZujO3d2T/HjK3J5JNswmDgAAAAAAQwwtd89J8sQpc7+d5DOziQMAAAAAwBBDj2U4PsmZVXVmkvcl6SRHVtUzs1juPmRO+QAAAAAAWMGgnbvd/aksPkxt1ySnZPGBaicmuVuSh3f3OXNLCAAAAADAzxi6czfd/ekkh1fV7klul+SK7r52bskAAAAAAJjfOlWZAAAWmklEQVRq6Jm7/6K7r+vuHyTZtaoeUFW7ziEXAAAAAACrGFTuVtWJVfXKJfcPS/LdJJ9P8u2quu+c8gEAAAAAsIKhO3cfn+S8Jfd/kuRTSQ6bjJ8041wAAAAAAKxiaLm7X5JvJ0lV7Z/koCQv6u6zk7wmyYPnEw8AAAAAgJUMLXd/kuQ2k+uHJbm8u//P5P76JHvMOhgAAAAAANOtG7juE0meW1Ubk/yPJB9YMnevJBfNOhgAAAAAANMN3bn7zCQ3JPnLJFckecGSuScm+eSMcwEAAAAAsIpBO3e7+/tZPI5hJb+axaMZAAAAAABYI0N37v6Mqjqwqn4zya26e/0MMwEAAAAAsBmDyt2qelNVvXHJ/TFJvprk/UnOq6pD55QPAAAAAIAVDN25+8j89Lm6L03yniT7JfnI5B4AAAAAgDUytNy9Y5KLkqSq7pnkHkle3d0XJzk1ycHziQcAAAAAwEqGlruXJbnT5PrhSS7u7n+c3FeShVkHAwAAAABgunUD1304yUuq6k5J/jDJe5fM3S/JhTPOBQAAAADAKobu3D0uydlJnprFs3dfuGTu0Un+dsa5AAAAAABYxaCdu919ZZInT5k7fKaJAAAAAADYrKE7dwEAAAAA2I4MPXM3VXVMkt9Pcq8kuy2f7+47zjAXAAAAAACrGLRzt6oel+TPk3wryc8nOS3JhyafvyrJKfMKCAAAAADAzxp6LMOzk7w0yR9M7t/Q3U9OctckP0py7RyyAQAAAAAwxdBy955JPt3dG5JsSHLrJOnunyR5VZJj5xMPAAAAAICVDC13r0yy6+T6+0nuvWSuktxhlqEAAAAAAFjd0AeqnZvk/kk+ksXzdl9YVTclWZ/khUk+N594AAAAAACsZGi5e1KSAybXL5xcvyHJQpJzkjxl9tEAAAAAAJhmULnb3WcnOXtyfUWSo6tq1yS7dvdVc8wHAAAAAMAKVi13q2r3JEcmuUuSf05yVndfkiTdfUOSG+YdEAAAAACAnzW13K2quyU5M4vF7iZXVdV/7O6PzjsYAAAAAADT7bTK3KuTbExyeJI9ktw3yReTvGkNcgEAAAAAsIrVyt1fSnJ8d3+6u6/v7m8k+a9J7lxV+65NPAAAAAAAVrJaubtvkm8vG7sgSSXZZ26JAAAAAADYrNXK3STpNUkBAAAAAMAWmfpAtYmPVNVNK4yftXy8u+84u1gAAAAAAKxmtXL3xDVLAQAAAADAFpla7na3chcAAIC52LCh84Tfqm0d4xZtw4bOwoLfAGDMNncsAwAAAMzcwkJl/VknbesYt2i7HPG8bR0BgJtpcw9UAwAAAABgO6TcBQAAAAAYIeUuAAAAAMAIKXcBAAAAAEZIuQsAAAAAMELKXQAAAACAEVLuAgAAAACMkHIXAAAAAGCElLsAAAAAACOk3AUAAAAAGCHlLgAAAADACCl3AQAAAABGSLkLAAAAADBCyl0AAAAAgBFS7gIAAAAAjJByFwAAAABghJS7AAAAAAAjpNwFAAAAABgh5S4AAAAAwAgpdwEAAAAARki5CwAAAAAwQspdAAAAAIARUu4CAAAAAIyQchcAAAAAYISUuwAAAAAAI6TcBQAAAAAYIeUuAAAAAMAIKXcBAAAAAEZIuQsAAAAAMELKXQAAAACAEVLuAgAAAACMkHIXAAAAAGCElLsAAAAAACOk3AUAAAAAGCHlLgAAAADACCl3AQAAAABGSLkLAAAAADBCyl0AAAAAgBFS7gIAAAAAjJByFwAAAABghJS7AAAAAAAjpNwFAAAAABgh5S4AAAAAwAgpdwEAAAAARki5CwAAAAAwQspdAAAAAIARUu4CAAAAAIyQchcAAAAAYIR2yHK3qh5bVadV1fer6uqq+nxV/acV1v1+VZ1fVddP1hyxLfICAAAAAGypHbLcTfKsJFcneWaS30jy8STvrqqnb1pQVb+T5I1J/iLJo5J8LcmHqup+ax8XAAAAAGDLrNvWAebkqO7+0ZL7v6uq/bJY+r5uMnZikj/v7pcmSVV9IsnBSZ6b5AlrGRYAAAAAYEvtkDt3lxW7m3wxyR2TpKruluReSd675DMbk7wvi7t4AQAAAAC2aztkuTvFoUm+Prk+cPJ+3rI130hy+6rae81SAQAAAABshVtEuTt5UNrRSV4/Gbrd5P2KZUsvXzYPAAAAALBd2uHL3aq6S5J3J/lAd79j2XQvXz5lfNN3PaWqzq2qcy+99NJZxgQAAAAA2CI7dLlbVbdP8uEk381PPyRt0w7d2y77yKb75Tt6kyTdfWp3H9Ldh+y9t5MbAAAAAIBtZ4ctd6tqjyQfSrJLkl/r7muWTG86a/fAZR87MMll3W1bLgAAAACwXdshy92qWpfkfUnumeRR3f3DpfPd/e0k30zy2CWf2Wly/+E1jAoAAAAAsFXWbesAc/KGJEcmeUaS21fVg5fMfbG7b0jy4iTvrKoLk3w6ye9msQx+3NpGBQAAAADYcjtqufuIyfufrjB31yQXdvd7qupWSZ6T5IQkX0vy6939j2uUEQAAAABgq+2Q5W5332XgujcnefN80wAAAAAAzN4OeeYuAAAAAMCOTrkLAAAAADBCyl0AAAAAgBFS7gIAAAAAjJByFwAAAABghJS7AAAAAAAjpNwFAAAAABgh5S4AAAAAwAgpdwEAAAAARki5CwAAAAAwQspdAAAAAIARUu4CAAAAAIyQchcAAAAAYISUuwAAAAAAI6TcBQAAAAAYIeUuAAAAAMAIKXcBAAAAAEZIuQsAAAAAMELKXQAAAACAEVLuAgAAAACMkHIXAAAAAGCElLsAAAAAACOk3AUAAAAAGCHlLgAAAADACCl3AQAAAABGSLkLAAAAADBCyl0AAAAAgBFS7gIAAAAAjJByFwAAAABghJS7AAAAAAAjpNwFAAAAABgh5S4AAAAAwAgpdwEAAAAARki5CwAAAAAwQspdAAAAAIARUu4CAAAAAIyQchcAAAAAYISUuwAAAAAAI6TcBQAAAAAYIeUuAAAAAMAIKXcBAAAAAEZIuQsAAAAAMELKXQAAAACAEVLuAgAAAACMkHIXAAAAAGCElLsAAAAAACOk3AUAAAAAGCHlLgAAAADACCl3AQAAAABGSLkLAAAAADBCyl0AAAAAgBFS7gIAAAAAjJByFwAAAABghJS7AAAAAAAjpNwFAAAAABgh5S4AAAAAwAgpdwEAAAAARki5CwAAAAAwQspdAAAAAIARUu4CAAAAAIyQchcAAAAAYISUuwAAAAAAI6TcBQAAAAAYIeUuAAAAAMAIKXcBAAAAAEZIuQsAAAAAMELKXeD/b+/eY2Q96zqAf3/0tLS0pSXFgIqAAYVUowRKpFyEUloOaAM2gDSRoDFWikZKMSRcjFUpBhQoKabQcGkowaKk2FahNy5trGBaiAYlKCUqTbGNLb1QLuX08POPmQPDZttzds/uvvvsfj7Jm5l5Z3b2O3ky78x8553nBQAAAGBAyl0AAAAAgAEpdwEAAAAABqTcBQAAAAAY0I6pAwAAALD99O5dOej4100dY1vr3btSBxw4dQwA9oNyFwAAgA1XBxyYj+eKqWNsa88/4MSpIwCwn0zLAAAAAAAwIOUuAAAAAMCAlLsAAAAAAANS7gIAAAAADEi5CwAAAAAwIOUuAAAAAMCAlLsAAAAAAANS7gIAAAAADEi5CwAAAAAwIOUuAAAAAMCAlLsAAAAAAANS7gIAAAAADEi5CwAAAAAwIOUuAAAAAMCAlLsAAAAAAANS7gIAAAAADEi5CwAAAAAwIOUuAAAAAMCAlLsAAAAAAANS7gIAAAAADEi5CwAAAAAwIOUuAAAAAMCAlLsAAAAAAANS7gIAAAAADGhbl7tVdXRVfbKqvl1VX6+qP62qA6bOBQAAAACwNzumDjCVqnpIkquSfCnJC5I8JsnbMiu83zhhNAAAAACAvdq25W6SVyQ5JMnJ3X1Xkiur6sFJzqyqt87XAQAAAABsStt5WobnJbl8SYl7YWaF7zOniQQAAAAAsG+2c7n7+CRfXlzR3V9L8u35dQAAAAAAm9Z2LncfkuSOZdbfPr8OAAAAAGDT2s5z7iZJL7Ou7mN9qurUJKfOL95dVf+xXsFYkYcmuXXqEKwJY7k1GMetw1huHcZya9gC4/j6qQNsFsOP5cumDrB5DD+WJDGOm82jpg4AI9nO5e7tSY5cZv0RWX6P3nT3eUnOW89QrFxVXd/dx0ydg/1nLLcG47h1GMutw1huDcZx6zCWW4ex3BqMIzCy7Twtw5ezZG7dqvqpJIdmyVy8AAAAAACbzXYudz+R5LlVdfjCul9P8p0kV08TCQAAAABg32zncvfdSe5JclFVPWc+n+6ZSd7e3XdNmoyVMlXG1mEstwbjuHUYy63DWG4NxnHrMJZbh7HcGowjMKzqXvbYYdtCVR2d5F1Jjs1snt33Jjmzu3dPGgwAAAAAYC+2dbkLAAAAADCq7TwtA4Ormf+qqq6qx06dh5WrqjPn47dn+XZVfXE+TQoDqqqTq+pTVXVHVd1TVf9ZVW+qqodOnY29W/Kc/H5V3V5V11XVWVX18Knzse+W2b4uLr8xdT5W5n7G86qps7EyVfXCqrqiqm6rqu9V1U1VdWFVPW3qbOzdwnPxK/dx/Q3z68/c4Gis0P28Ri4uz5o6J8C+2DF1ANgPxyZ59Pz8S5O8aboo7Ic7k+ycnz80yUlJ3lNVd3f3h6eLxUpV1duSnJ7kA0nekeSuJEcneUWSn0vya9OlYwUWn5NHJHliktOSnFpVO7v785MlY6UWx3LRDRsdhDWx3HjeOUUQVqeq3pHkD5J8MMm5SW5L8qjM3sf+Y1U9tru/OmFE9s13k/x0VR3T3dfvWVlVT85sPL87WTJW4tiF84ck+VRmnyf/YWH9lzY0EcAqKXcZ2SlJvpXk3+bnlbtjure7P7dw+ZNV9dQkL0yi3B1EVZ2U5Iwkv93d71+46uqqOi/JidMkYxWWPicvr6pzk1yT5CNV9Thz0w9j6VgyNuM5sKp6QWZfgP5Wd5+/5OoL5q+j39nwYKzGt5J8IbNS/vqF9S/NrCB80hShWJnF7WlVHTY/+1XbWWBEpmVgSFV1QJIXJ7kkyfuTHF1VvzBtKtbQN5McOHUIVuTVSb6wpNhNknT37u7+xASZWCPdfUeS1yZ5TJITJo4DMKLTk1y3TLGbJOnuS7v76xsbif1wYZKXVFUls+nikrxkvh4ANpRyl1E9O8nDMnsD9dEkuzLbe5cBVdWO+fLg+VyQz0zysalzsW+q6sAkT01y2dRZWFefTnJvkqdMHYR9t7B9/cEydSZWb5nxrKkzsXfz592xSa6YOgtr5qLMPos8fX75GUl+LN6/AjAB5S6jOiXJHUku6+5vJLkyyUt9yBnSUZmV87symzvwgiR/1d0fnDQVK3FUkgcm+drUQVg/3X1Pklsz+zDLGBa3rz9YqurRE2Zi9ZYbz+MnTcS+2vM6eePiyvnBgZX1A5r/ouWyzKZiyPz0svl6ANhQyl2GU1UPzOzATB/r7u/NV/91ZgdXs0fZeO5M8uT58vQkr0ry8qr640lTsRo9dQDWneJhLIvb18XFT7/HtNx4/vOkidhXe7adS18nX5MfLet/byNDsd8uTPKi+WeTF8WUDABMxE/zGNHzkhyZ5ONVdeR83WeS3JPZHr2fnSgXq3Pv4pGGk1w7/5n/m6vqnPme2Wxut2X2/Hvk1EFYP1V1cGZ7n90ydRb22dLtK2MznuO6NbPXyUcsWX9BZu9hk+S6jQzEmrgkyXuTnJXk0CSXThsHgO3KnruMaM/cun+b5Pb5cmNmP3d7yfxga4ztS0kOyuzgTWxy3b0rybVJnjt1FtbVcZl9KewLNIAV6O57M9t2nrhk/S3dfb3Sfkzd/a0kf5/ZQWUvnV8GgA2n3GUoVXVYkl/NbBqG45YsZ2Q2F+RxkwVkrfz8/PTG+70Vm8nZSY6pqpcvvaKqHlBVOyfIxBqZ/0riLUluSHLVxHEARnR2kl+qqpdNHYQ1dW5me+y+e+ogAGxfpmVgNC9I8qAk7+zuH5lnrqquTfKGzPbsVT6MY0dV7Zkr+aAkT0ryxiQXd/fN08ViJbr70qp6e5L3VdXTklyc5O4kj0/yiiT/ndmBR9j8Fp+Th2f2nDwts23vzu7ePVkyVmpxLBfd2N03bXga2Ma6++KqOjvJ+VV1XGaF4K2ZTXdzwvxmd0+Vj9Xp7s/kh1NrAMAklLuM5pQkX1la7Cazn4ZX1d8kOaWqXjk/sjub3xH54c+8dyX5n8z2fnjTZIlYle5+TVX9U5LfT/LhJIdkVupekuQvJ4zGyux5TnaSuzLbW/dDSc7xhctwFrevi/4otrGw4br71VV1TZJXJnlfZl+g/V9mz9Pnd/cnpswHAIypuh3cHAAAAABgNObcBQAAAAAYkHIXAAAAAGBAyl0AAAAAgAEpdwEAAAAABqTcBQAAAAAYkHIXAAAAAGBAyl0AAAAAgAEpdwEAAAAABqTcBQAAAAAYkHIXAAAAAGBAyl0AAAAAgAEpdwEAAAAABqTcBQAAAAAYkHIXAAAAAGBAyl0AAAAAgAEpdwEAAAAABqTcBQAAAAAYkHIXAAAAAGBAyl0AAAAAgAEpdwEAAAAABqTcBQAAAAAYkHIXAAAAAGBAyl0AgE2qqs6sqp4v36+q26vquqo6q6oevg7/7/yqun6t7xcAAFgfO6YOAADA/bozyc75+SOSPDHJaUlOraqd3f35yZIBAACTUu4CAGxu93b35xYuX15V5ya5JslHqupx3b17omwAAMCETMsAADCY7r4jyWuTPCbJCUlSVQdX1Vur6saquqeq/rWqnr/0b6vqd6rqi1X13aq6pao+WlVHLPd/quqgqrqoqr5WVY9d1wcFAACsmHIXAGBMn05yb5KnzC9/NMlvJnlzkpOSXJfkkqp6wp4/qKo3JnlPkquTvDCz6R3uTHLY0juvqoOTfCzJLyZ5RnffsF4PBAAAWB3TMgAADKi776mqW5M8rKqOT/IrSZ7V3VfPb3JFVf1skjckeXFVHZnk9UnO7u4zFu7qoqX3XVUPSnJJkkck+eXuvmk9HwsAALA69twFABhXzU+fk+TmJNdW1Y49S5JPJjlmfptjkxyS5AN7uc9Dk1yW5GFJnqnYBQCAzcueuwAAA5pPm3BUkluS/GSShyfZtcxN9xxs7aj56f/u5a5/IsnPJDmru29Zg6gAAMA6Ue4CAIzpuMzey302ybOT3JTZPLr35bb56Y8nufV+bveVJO9Mcn5V3dzd565BVgAAYB0odwEABjOfP/ctSW5IclWSTvKaJHd395fv488+m+Q7SV6e5A/v7/67+4KqOizJu6rqm939oTULDwAArBnlLgDA5rajqp4yP394kiclOS3Jg5Ls7O7dVXVlksuTXFlVb0ny70kenOQJSQ7u7td19x1V9WdJzqqqg5J8PMkDMzsQ258snVu3u8+dF7wfqKq7u/vvNuCxAgAAK6DcBQDY3I7IbK/bTnJXZnvrfijJOd19c5J0d1fVyUlen+T0JI9M8o0k/5LknD131N1/XlXfSPKqJL+b5PYk1yT55nL/uLv/oqoOT3JhVZ3U3Veuz0MEAABWo7p76gwAAAAAAKzQA6YOAAAAAADAyil3AQAAAAAGpNwFAAAAABiQchcAAAAAYEDKXQAAAACAASl3AQAAAAAGpNwFAAAAABiQchcAAAAAYEDKXQAAAACAAf0/oNTXcKGhQuUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating Deck column from the first letter of the Cabin column (M stands for Missing)\n",
    "df['Deck'] = df['Cabin'].apply(lambda s: s[0] if pd.notnull(s) else 'M')\n",
    "\n",
    "df_all_decks = df.groupby(['Deck', 'Pclass']).count().drop(columns=['Survived', 'Sex', 'Age', 'SibSp', 'Parch', \n",
    "                                                                        'Fare', 'Embarked', 'Cabin', 'PassengerId', 'Ticket']).rename(columns={'Name': 'Count'}).transpose()\n",
    "\n",
    "def get_pclass_dist(df):\n",
    "    \n",
    "    # Creating a dictionary for every passenger class count in every deck\n",
    "    deck_counts = {'A': {}, 'B': {}, 'C': {}, 'D': {}, 'E': {}, 'F': {}, 'G': {}, 'M': {}, 'T': {}}\n",
    "    decks = df.columns.levels[0]    \n",
    "    \n",
    "    for deck in decks:\n",
    "        for pclass in range(1, 4):\n",
    "            try:\n",
    "                count = df[deck][pclass][0]\n",
    "                deck_counts[deck][pclass] = count \n",
    "            except KeyError:\n",
    "                deck_counts[deck][pclass] = 0\n",
    "                \n",
    "    df_decks = pd.DataFrame(deck_counts)    \n",
    "    deck_percentages = {}\n",
    "\n",
    "    # Creating a dictionary for every passenger class percentage in every deck\n",
    "    for col in df_decks.columns:\n",
    "        deck_percentages[col] = [(count / df_decks[col].sum()) * 100 for count in df_decks[col]]\n",
    "        \n",
    "    return deck_counts, deck_percentages\n",
    "\n",
    "def display_pclass_dist(percentages):\n",
    "    \n",
    "    df_percentages = pd.DataFrame(percentages).transpose()\n",
    "    deck_names = ('A', 'B', 'C', 'D', 'E', 'F', 'G', 'M', 'T')\n",
    "    bar_count = np.arange(len(deck_names))  \n",
    "    bar_width = 0.85\n",
    "    \n",
    "    pclass1 = df_percentages[0]\n",
    "    pclass2 = df_percentages[1]\n",
    "    pclass3 = df_percentages[2]\n",
    "    \n",
    "    plt.figure(figsize=(20, 10))\n",
    "    plt.bar(bar_count, pclass1, color='#b5ffb9', edgecolor='white', width=bar_width, label='Passenger Class 1')\n",
    "    plt.bar(bar_count, pclass2, bottom=pclass1, color='#f9bc86', edgecolor='white', width=bar_width, label='Passenger Class 2')\n",
    "    plt.bar(bar_count, pclass3, bottom=pclass1 + pclass2, color='#a3acff', edgecolor='white', width=bar_width, label='Passenger Class 3')\n",
    "\n",
    "    plt.xlabel('Deck', size=15, labelpad=20)\n",
    "    plt.ylabel('Passenger Class Percentage', size=15, labelpad=20)\n",
    "    plt.xticks(bar_count, deck_names)    \n",
    "    plt.tick_params(axis='x', labelsize=15)\n",
    "    plt.tick_params(axis='y', labelsize=15)\n",
    "    \n",
    "    plt.legend(loc='upper left', bbox_to_anchor=(1, 1), prop={'size': 15})\n",
    "    plt.title('Passenger Class Distribution in Decks', size=18, y=1.05)   \n",
    "    \n",
    "    plt.show()    \n",
    "\n",
    "all_deck_count, all_deck_per = get_pclass_dist(df_all_decks)\n",
    "display_pclass_dist(all_deck_per)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Passenger in the T deck is changed to A\n",
    "idx = df[df['Deck'] == 'T'].index\n",
    "df.loc[idx, 'Deck'] = 'A'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_all_decks_survived = df.groupby(['Deck', 'Survived']).count().drop(columns=['Sex', 'Age', 'SibSp', 'Parch', 'Fare', \n",
    "                                                                                   'Embarked', 'Pclass', 'Cabin', 'PassengerId', 'Ticket']).rename(columns={'Name':'Count'}).transpose()\n",
    "\n",
    "def get_survived_dist(df):\n",
    "    \n",
    "    # Creating a dictionary for every survival count in every deck\n",
    "    surv_counts = {'A':{}, 'B':{}, 'C':{}, 'D':{}, 'E':{}, 'F':{}, 'G':{}, 'M':{}}\n",
    "    decks = df.columns.levels[0]    \n",
    "\n",
    "    for deck in decks:\n",
    "        for survive in range(0, 2):\n",
    "            surv_counts[deck][survive] = df[deck][survive][0]\n",
    "            \n",
    "    df_surv = pd.DataFrame(surv_counts)\n",
    "    surv_percentages = {}\n",
    "\n",
    "    for col in df_surv.columns:\n",
    "        surv_percentages[col] = [(count / df_surv[col].sum()) * 100 for count in df_surv[col]]\n",
    "        \n",
    "    return surv_counts, surv_percentages\n",
    "\n",
    "def display_surv_dist(percentages):\n",
    "    \n",
    "    df_survived_percentages = pd.DataFrame(percentages).transpose()\n",
    "    deck_names = ('A', 'B', 'C', 'D', 'E', 'F', 'G', 'M')\n",
    "    bar_count = np.arange(len(deck_names))  \n",
    "    bar_width = 0.85    \n",
    "\n",
    "    not_survived = df_survived_percentages[0]\n",
    "    survived = df_survived_percentages[1]\n",
    "    \n",
    "    plt.figure(figsize=(20, 10))\n",
    "    plt.bar(bar_count, not_survived, color='#b5ffb9', edgecolor='white', width=bar_width, label=\"Not Survived\")\n",
    "    plt.bar(bar_count, survived, bottom=not_survived, color='#f9bc86', edgecolor='white', width=bar_width, label=\"Survived\")\n",
    " \n",
    "    plt.xlabel('Deck', size=15, labelpad=20)\n",
    "    plt.ylabel('Survival Percentage', size=15, labelpad=20)\n",
    "    plt.xticks(bar_count, deck_names)    \n",
    "    plt.tick_params(axis='x', labelsize=15)\n",
    "    plt.tick_params(axis='y', labelsize=15)\n",
    "    \n",
    "    plt.legend(loc='upper left', bbox_to_anchor=(1, 1), prop={'size': 15})\n",
    "    plt.title('Survival Percentage in Decks', size=18, y=1.05)\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "all_surv_count, all_surv_per = get_survived_dist(df_all_decks_survived)\n",
    "display_surv_dist(all_surv_per)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "A, B and C decks are labeled as ABC because all of them have only 1st class passengers.\n",
    "D and E decks are labeled as DE because both of them have similar passenger class distribution and same survival rate.\n",
    "F and G decks are labeled as FG because of the previous reasons.\n",
    "M deck doesn't need to be grouped with other decks because it is very different from others and has the lowest survival rate.\n",
    "\"\"\"\n",
    "\n",
    "df['Deck'] = df['Deck'].replace(['A', 'B', 'C'], 'ABC')\n",
    "df['Deck'] = df['Deck'].replace(['D', 'E'], 'DE')\n",
    "df['Deck'] = df['Deck'].replace(['F', 'G'], 'FG')\n",
    "\n",
    "\n",
    "# Dropping the Cabin feature\n",
    "df.drop(['Cabin'], inplace=True, axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "Input=df.to_numpy()\n",
    "Input=Input[:,[1,2,4,5,6,7,9,10,11]]\n",
    "X=Input[:,1:]\n",
    "Y=Input[:,0]\n",
    "Y=Y.reshape(891,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replacing 'male' and 'female' by 1 and 0 respectively\n",
    "K=(X[:,1]=='male')\n",
    "for i in range(0,X.shape[0]):\n",
    "    X[i,1]=K[i]\n",
    "\n",
    "\n",
    "#Replacing 'S' by 1, 'C' by 2, and 'Q by 3'\n",
    "mask=(X[:,6]=='S')\n",
    "X[:,6][mask]=1\n",
    "mask=(X[:,6]=='C')\n",
    "X[:,6][mask]=2\n",
    "mask=(X[:,6]=='Q')\n",
    "X[:,6][mask]=3\n",
    "\n",
    "\n",
    "\n",
    "#Replacing 'ABC' by 1, 'DE' by 2, 'FG by 3', and 'M' by 4\n",
    "mask=(X[:,7]=='ABC')\n",
    "X[:,7][mask]=1\n",
    "mask=(X[:,7]=='DE')\n",
    "X[:,7][mask]=2\n",
    "mask=(X[:,7]=='FG')\n",
    "X[:,7][mask]=3\n",
    "mask=(X[:,7]=='M')\n",
    "X[:,7][mask]=4\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#Casting into dtype=float64\n",
    "X=X.astype(np.float64)\n",
    "Y=Y.astype(np.float64)\n",
    "\n",
    "\n",
    "#Not considering examples in which some feature is missing\n",
    "for i in range (0,X.shape[1]):\n",
    "    mask=np.isnan(X[:,i])\n",
    "    X=X[~mask]\n",
    "    Y=Y[~mask]\n",
    "    \n",
    "    \n",
    "#Normalizing the matrix\n",
    "X=(X-X.mean(axis=0))/(np.sqrt(X.var(axis=0)))\n",
    "\n",
    "\n",
    "X_train=X[0:624,:]\n",
    "X_test=X[625:,:]\n",
    "Y_train=Y[0:624]\n",
    "Y_test=Y[625:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#No. of examples\n",
    "m_train=X_train.shape[0]\n",
    "m_test=X_test.shape[0]\n",
    "#No. of features\n",
    "n_x=X.shape[1]\n",
    "\n",
    "\n",
    "#Transposing for easier calculation\n",
    "X_train=X_train.T\n",
    "X_test=X_test.T\n",
    "Y_train=Y_train.T\n",
    "Y_test=Y_test.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(z):\n",
    "\n",
    "    s = 1/(1+np.exp(-z))\n",
    "        \n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize_parameters(dim):\n",
    "\n",
    "    w = np.random.randn(dim,1)*0.01\n",
    "    b = 0\n",
    "\n",
    "    \n",
    "    return w, b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def propagate(w, b, X, Y):\n",
    "\n",
    "    \n",
    "    m = X.shape[1]\n",
    "    \n",
    "    # FORWARD PROPAGATION (FROM X TO COST)\n",
    "    A = sigmoid((np.dot(w.T,X)+b))                                    # compute activation\n",
    "    cost = -(1/m)*np.sum(np.multiply(Y,np.log(A))+np.multiply((1-Y),np.log(1-A)))                        # compute cost    \n",
    "    \n",
    "    \n",
    "    # BACKWARD PROPAGATION (TO FIND GRAD)\n",
    "    dw = (1/m)*np.dot(X,(A-Y).T)\n",
    "    db = (1/m)*np.sum(A-Y)\n",
    "\n",
    "    \n",
    "    grads = {\"dw\": dw,\n",
    "             \"db\": db}\n",
    "    \n",
    "    return grads, cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimize(w, b, X, Y, num_iterations, learning_rate, print_cost = False):\n",
    "\n",
    "    \n",
    "    costs = []\n",
    "    \n",
    "    for i in range(num_iterations):\n",
    "        \n",
    "        \n",
    "        # Cost and gradient calculation\n",
    "        grads, cost = propagate(w,b,X,Y)\n",
    "        \n",
    "        # Retrieve derivatives from grads\n",
    "        dw = grads[\"dw\"]\n",
    "        db = grads[\"db\"]\n",
    "        \n",
    "        # update rule\n",
    "        w -=learning_rate*dw\n",
    "        b -=learning_rate*db\n",
    "        \n",
    "        # Record the costs\n",
    "        if i % 10000 == 0:\n",
    "            costs.append(cost)\n",
    "        \n",
    "        # Print the cost every 10000 training iterations\n",
    "        if print_cost and i % 10000 == 0:\n",
    "            print (\"Cost after iteration %i: %f\" %(i, cost))\n",
    "    \n",
    "    params = {\"w\": w,\n",
    "              \"b\": b}\n",
    "    \n",
    "    grads = {\"dw\": dw,\n",
    "             \"db\": db}\n",
    "    \n",
    "    return params, grads, costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(w, b, X):\n",
    "\n",
    "    m = X.shape[1]\n",
    "    Y_prediction = np.zeros((1,m))\n",
    "    w = w.reshape(X.shape[0], 1)\n",
    "    \n",
    "    # Compute vector \"A\" predicting the probabilities of survival\n",
    "    A = sigmoid(np.dot(w.T,X)+b)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    for i in range(A.shape[1]):\n",
    "        \n",
    "        # Convert probabilities A[0,i] to actual predictions p[0,i]\n",
    "        if(A[0,i]<=0.5):\n",
    "            Y_prediction[0,i]=0\n",
    "        else:\n",
    "            Y_prediction[0,i]=1\n",
    "    \n",
    "    \n",
    "    return Y_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model(X_train, Y_train, X_test, Y_test, num_iterations = 2000, learning_rate = 0.5, print_cost = False):\n",
    "\n",
    "    \n",
    "    # initialize parameter\n",
    "    w, b = initialize_parameters(X_train.shape[0])\n",
    "\n",
    "    # Gradient descent\n",
    "    parameters, grads, costs = optimize(w, b, X_train, Y_train, num_iterations, learning_rate, print_cost)\n",
    "    \n",
    "    # Retrieve parameters w and b from dictionary \"parameters\"\n",
    "    w = parameters[\"w\"]\n",
    "    b = parameters[\"b\"]\n",
    "    \n",
    "    # Predict test/train set examples\n",
    "    Y_prediction_test = predict(w, b, X_test)\n",
    "    Y_prediction_train = predict(w, b, X_train)\n",
    "\n",
    "\n",
    "    # Print train/test Errors\n",
    "    print(\"train accuracy: {} %\".format(100 - np.mean(np.abs(Y_prediction_train - Y_train)) * 100))\n",
    "    print(\"test accuracy: {} %\".format(100 - np.mean(np.abs(Y_prediction_test - Y_test)) * 100))\n",
    "\n",
    "    \n",
    "    fpr, tpr, thresholds = roc_curve(Y_test.T, Y_prediction_test.T)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "\n",
    "    plt.title('Receiver Operating Characteristic')\n",
    "    plt.plot(fpr, tpr, label='AUC = %0.4f'% roc_auc)\n",
    "    plt.legend(loc='lower right')\n",
    "    plt.plot([0,1],[0,1],'r--')\n",
    "    plt.xlim([-0.001, 1])\n",
    "    plt.ylim([0, 1.001])\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.show()\n",
    "    \n",
    "\n",
    "    \n",
    "    d = {\"costs\": costs,\n",
    "         \"Y_prediction_test\": Y_prediction_test, \n",
    "         \"Y_prediction_train\" : Y_prediction_train, \n",
    "         \"w\" : w, \n",
    "         \"b\" : b,\n",
    "         \"learning_rate\" : learning_rate,\n",
    "         \"num_iterations\": num_iterations}\n",
    "    \n",
    "    return d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.692556\n",
      "Cost after iteration 10000: 0.448748\n",
      "Cost after iteration 20000: 0.448748\n",
      "Cost after iteration 30000: 0.448748\n",
      "Cost after iteration 40000: 0.448748\n",
      "Cost after iteration 50000: 0.448748\n",
      "Cost after iteration 60000: 0.448748\n",
      "Cost after iteration 70000: 0.448748\n",
      "Cost after iteration 80000: 0.448748\n",
      "Cost after iteration 90000: 0.448748\n",
      "train accuracy: 80.76923076923077 %\n",
      "test accuracy: 80.82706766917293 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = model(X_train, Y_train, X_test, Y_test, num_iterations = 100000, learning_rate = 0.5, print_cost = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make a linear Regression model\n",
    "* The model would be predicting The age of a given passenger using all the other known attributes of the person\n",
    "* Train with 70 percent of the data and take the y column as Age\n",
    "* You **CAN NOT** use any library to make the logistic regression model, you can use only numpy and pandas. The model has to be built from the scratch\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "pd.options.display.max_rows = 8\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "df = pd.read_csv(\"titanic.csv\")\n",
    "\n",
    "\n",
    "age_by_pclass_sex = df.groupby(['Sex', 'Pclass']).median()['Age']\n",
    "\n",
    "# Filling the missing values in Age with the medians of Sex and Pclass groups\n",
    "df['Age'] = df.groupby(['Sex', 'Pclass'])['Age'].apply(lambda x: x.fillna(x.median()))\n",
    "\n",
    "\n",
    "#The two people with missing 'embarked' feature had embarked from Southampton (available online)\n",
    "df['Embarked'] = df['Embarked'].fillna('S')\n",
    "\n",
    "\n",
    "med_fare = df.groupby(['Pclass', 'Parch', 'SibSp']).Fare.median()[3][0][0]\n",
    "# Filling the missing value in Fare with the median Fare of 3rd class alone passenger\n",
    "df['Fare'] = df['Fare'].fillna(med_fare)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating Deck column from the first letter of the Cabin column (M stands for Missing)\n",
    "df['Deck'] = df['Cabin'].apply(lambda s: s[0] if pd.notnull(s) else 'M')\n",
    "\n",
    "df_all_decks = df.groupby(['Deck', 'Pclass']).count().drop(columns=['Survived', 'Sex', 'Age', 'SibSp', 'Parch', \n",
    "                                                                        'Fare', 'Embarked', 'Cabin', 'PassengerId', 'Ticket']).rename(columns={'Name': 'Count'}).transpose()\n",
    "\n",
    "def get_pclass_dist(df):\n",
    "    \n",
    "    # Creating a dictionary for every passenger class count in every deck\n",
    "    deck_counts = {'A': {}, 'B': {}, 'C': {}, 'D': {}, 'E': {}, 'F': {}, 'G': {}, 'M': {}, 'T': {}}\n",
    "    decks = df.columns.levels[0]    \n",
    "    \n",
    "    for deck in decks:\n",
    "        for pclass in range(1, 4):\n",
    "            try:\n",
    "                count = df[deck][pclass][0]\n",
    "                deck_counts[deck][pclass] = count \n",
    "            except KeyError:\n",
    "                deck_counts[deck][pclass] = 0\n",
    "                \n",
    "    df_decks = pd.DataFrame(deck_counts)    \n",
    "    deck_percentages = {}\n",
    "\n",
    "    # Creating a dictionary for every passenger class percentage in every deck\n",
    "    for col in df_decks.columns:\n",
    "        deck_percentages[col] = [(count / df_decks[col].sum()) * 100 for count in df_decks[col]]\n",
    "        \n",
    "    return deck_counts, deck_percentages\n",
    "\n",
    "def display_pclass_dist(percentages):\n",
    "    \n",
    "    df_percentages = pd.DataFrame(percentages).transpose()\n",
    "    deck_names = ('A', 'B', 'C', 'D', 'E', 'F', 'G', 'M', 'T')\n",
    "    bar_count = np.arange(len(deck_names))  \n",
    "    bar_width = 0.85\n",
    "    \n",
    "    pclass1 = df_percentages[0]\n",
    "    pclass2 = df_percentages[1]\n",
    "    pclass3 = df_percentages[2]\n",
    "    \n",
    "    plt.figure(figsize=(20, 10))\n",
    "    plt.bar(bar_count, pclass1, color='#b5ffb9', edgecolor='white', width=bar_width, label='Passenger Class 1')\n",
    "    plt.bar(bar_count, pclass2, bottom=pclass1, color='#f9bc86', edgecolor='white', width=bar_width, label='Passenger Class 2')\n",
    "    plt.bar(bar_count, pclass3, bottom=pclass1 + pclass2, color='#a3acff', edgecolor='white', width=bar_width, label='Passenger Class 3')\n",
    "\n",
    "    plt.xlabel('Deck', size=15, labelpad=20)\n",
    "    plt.ylabel('Passenger Class Percentage', size=15, labelpad=20)\n",
    "    plt.xticks(bar_count, deck_names)    \n",
    "    plt.tick_params(axis='x', labelsize=15)\n",
    "    plt.tick_params(axis='y', labelsize=15)\n",
    "    \n",
    "    plt.legend(loc='upper left', bbox_to_anchor=(1, 1), prop={'size': 15})\n",
    "    plt.title('Passenger Class Distribution in Decks', size=18, y=1.05)   \n",
    "    \n",
    "    plt.show()    \n",
    "\n",
    "all_deck_count, all_deck_per = get_pclass_dist(df_all_decks)\n",
    "display_pclass_dist(all_deck_per)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Passenger in the T deck is changed to A\n",
    "idx = df[df['Deck'] == 'T'].index\n",
    "df.loc[idx, 'Deck'] = 'A'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_all_decks_survived = df.groupby(['Deck', 'Survived']).count().drop(columns=['Sex', 'Age', 'SibSp', 'Parch', 'Fare', \n",
    "                                                                                   'Embarked', 'Pclass', 'Cabin', 'PassengerId', 'Ticket']).rename(columns={'Name':'Count'}).transpose()\n",
    "\n",
    "def get_survived_dist(df):\n",
    "    \n",
    "    # Creating a dictionary for every survival count in every deck\n",
    "    surv_counts = {'A':{}, 'B':{}, 'C':{}, 'D':{}, 'E':{}, 'F':{}, 'G':{}, 'M':{}}\n",
    "    decks = df.columns.levels[0]    \n",
    "\n",
    "    for deck in decks:\n",
    "        for survive in range(0, 2):\n",
    "            surv_counts[deck][survive] = df[deck][survive][0]\n",
    "            \n",
    "    df_surv = pd.DataFrame(surv_counts)\n",
    "    surv_percentages = {}\n",
    "\n",
    "    for col in df_surv.columns:\n",
    "        surv_percentages[col] = [(count / df_surv[col].sum()) * 100 for count in df_surv[col]]\n",
    "        \n",
    "    return surv_counts, surv_percentages\n",
    "\n",
    "def display_surv_dist(percentages):\n",
    "    \n",
    "    df_survived_percentages = pd.DataFrame(percentages).transpose()\n",
    "    deck_names = ('A', 'B', 'C', 'D', 'E', 'F', 'G', 'M')\n",
    "    bar_count = np.arange(len(deck_names))  \n",
    "    bar_width = 0.85    \n",
    "\n",
    "    not_survived = df_survived_percentages[0]\n",
    "    survived = df_survived_percentages[1]\n",
    "    \n",
    "    plt.figure(figsize=(20, 10))\n",
    "    plt.bar(bar_count, not_survived, color='#b5ffb9', edgecolor='white', width=bar_width, label=\"Not Survived\")\n",
    "    plt.bar(bar_count, survived, bottom=not_survived, color='#f9bc86', edgecolor='white', width=bar_width, label=\"Survived\")\n",
    " \n",
    "    plt.xlabel('Deck', size=15, labelpad=20)\n",
    "    plt.ylabel('Survival Percentage', size=15, labelpad=20)\n",
    "    plt.xticks(bar_count, deck_names)    \n",
    "    plt.tick_params(axis='x', labelsize=15)\n",
    "    plt.tick_params(axis='y', labelsize=15)\n",
    "    \n",
    "    plt.legend(loc='upper left', bbox_to_anchor=(1, 1), prop={'size': 15})\n",
    "    plt.title('Survival Percentage in Decks', size=18, y=1.05)\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "all_surv_count, all_surv_per = get_survived_dist(df_all_decks_survived)\n",
    "display_surv_dist(all_surv_per)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "A, B and C decks are labeled as ABC because all of them have only 1st class passengers.\n",
    "D and E decks are labeled as DE because both of them have similar passenger class distribution and same survival rate.\n",
    "F and G decks are labeled as FG because of the previous reasons.\n",
    "M deck doesn't need to be grouped with other decks because it is very different from others and has the lowest survival rate.\n",
    "\"\"\"\n",
    "\n",
    "df['Deck'] = df['Deck'].replace(['A', 'B', 'C'], 'ABC')\n",
    "df['Deck'] = df['Deck'].replace(['D', 'E'], 'DE')\n",
    "df['Deck'] = df['Deck'].replace(['F', 'G'], 'FG')\n",
    "\n",
    "\n",
    "# Dropping the Cabin feature\n",
    "df.drop(['Cabin'], inplace=True, axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [],
   "source": [
    "Input=df.to_numpy()\n",
    "Input=Input[:,[1,2,4,5,6,7,9,10,11]]\n",
    "X=Input[:,[0,1,2,4,5,6,7,8]]\n",
    "Y=Input[:,3]\n",
    "Y=Y.reshape(891,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replacing 'male' and 'female' by 1 and 0 respectively\n",
    "K=(X[:,2]=='male')\n",
    "for i in range(0,X.shape[0]):\n",
    "    X[i,2]=K[i]\n",
    "\n",
    "\n",
    "#Replacing 'S' by 1, 'C' by 2, and 'Q by 3'\n",
    "mask=(X[:,6]=='S')\n",
    "X[:,6][mask]=1\n",
    "mask=(X[:,6]=='C')\n",
    "X[:,6][mask]=2\n",
    "mask=(X[:,6]=='Q')\n",
    "X[:,6][mask]=3\n",
    "\n",
    "\n",
    "\n",
    "#Replacing 'ABC' by 1, 'DE' by 2, 'FG by 3', and 'M' by 4\n",
    "mask=(X[:,7]=='ABC')\n",
    "X[:,7][mask]=1\n",
    "mask=(X[:,7]=='DE')\n",
    "X[:,7][mask]=2\n",
    "mask=(X[:,7]=='FG')\n",
    "X[:,7][mask]=3\n",
    "mask=(X[:,7]=='M')\n",
    "X[:,7][mask]=4\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#Casting into dtype=float64\n",
    "X=X.astype(np.float64)\n",
    "Y=Y.astype(np.float64)\n",
    "\n",
    "\n",
    "#Not considering examples in which some feature is missing\n",
    "for i in range (0,X.shape[1]):\n",
    "    mask=np.isnan(X[:,i])\n",
    "    X=X[~mask]\n",
    "    Y=Y[~mask]\n",
    "    \n",
    "    \n",
    "#Normalizing the matrix\n",
    "X=(X-X.mean(axis=0))/(np.sqrt(X.var(axis=0)))\n",
    "Y=(Y-Y.mean(axis=0))/(np.sqrt(Y.var(axis=0)))\n",
    "\n",
    "\n",
    "X_train=X[0:624,:]\n",
    "X_test=X[625:,:]\n",
    "Y_train=Y[0:624]\n",
    "Y_test=Y[625:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [],
   "source": [
    "#No. of examples\n",
    "m_train=X_train.shape[0]\n",
    "m_test=X_test.shape[0]\n",
    "#No. of features\n",
    "n_x=X.shape[1]\n",
    "\n",
    "\n",
    "#Transposing for easier calculation\n",
    "X_train=X_train.T\n",
    "X_test=X_test.T\n",
    "Y_train=Y_train.T\n",
    "Y_test=Y_test.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize_parameters(dim):\n",
    "    \"\"\"   \n",
    "    Argument:\n",
    "    dim -- size of the w vector we want (or number of parameters in this case)\n",
    "    \n",
    "    Returns:\n",
    "    w -- initialized vector of shape (dim, 1)\n",
    "    b -- initialized scalar (corresponds to the bias)\n",
    "    \"\"\"\n",
    "    \n",
    "    w = np.random.randn(dim,1)*0.01\n",
    "    b = 0\n",
    "\n",
    "    \n",
    "    return w, b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [],
   "source": [
    "def propagate(w, b, X, Y):\n",
    "\n",
    "    \n",
    "    m = X.shape[1]\n",
    "    \n",
    "    # FORWARD PROPAGATION (FROM X TO COST)\n",
    "    A = (np.dot(w.T,X)+b)                                    # compute activation\n",
    "    cost = (1/(2*m))*(np.sum(np.square(A-Y)))                         # compute cost    \n",
    "    \n",
    "    \n",
    "    # BACKWARD PROPAGATION (TO FIND GRAD)\n",
    "    dw = (1/m)*np.dot(X,(A-Y).T)\n",
    "    db = (1/m)*np.sum(A-Y)\n",
    "\n",
    "    \n",
    "    grads = {\"dw\": dw,\n",
    "             \"db\": db}\n",
    "    \n",
    "    return grads, cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimize(w, b, X, Y, num_iterations, learning_rate, print_cost = False):\n",
    "\n",
    "    \n",
    "    costs = []\n",
    "    \n",
    "    for i in range(num_iterations):\n",
    "        \n",
    "        \n",
    "        # Cost and gradient calculation\n",
    "        grads, cost = propagate(w,b,X,Y)\n",
    "        \n",
    "        # Retrieve derivatives from grads\n",
    "        dw = grads[\"dw\"]\n",
    "        db = grads[\"db\"]\n",
    "        \n",
    "        # update rule\n",
    "        w -=learning_rate*dw\n",
    "        b -=learning_rate*db\n",
    "        \n",
    "        # Record the costs\n",
    "        if i % 10000 == 0:\n",
    "            costs.append(cost)\n",
    "        \n",
    "        # Print the cost every 10000 training iterations\n",
    "        if print_cost and i % 10000 == 0:\n",
    "            print (\"Cost after iteration %i: %f\" %(i, cost))\n",
    "    \n",
    "    params = {\"w\": w,\n",
    "              \"b\": b}\n",
    "    \n",
    "    grads = {\"dw\": dw,\n",
    "             \"db\": db}\n",
    "    \n",
    "    return params, grads, costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(w, b, X):\n",
    " \n",
    "    \n",
    "    m = X.shape[1]\n",
    "    Y_prediction = np.zeros((1,m))\n",
    "    w = w.reshape(X.shape[0], 1)\n",
    "    \n",
    "    # Compute vector predicting the age\n",
    "    Y_prediction =np.dot(w.T,X)+b\n",
    "    \n",
    "    \n",
    "    return Y_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model(X_train, Y_train, X_test, Y_test, num_iterations = 2000, learning_rate = 0.5, print_cost = False):\n",
    "\n",
    "    \n",
    "    \n",
    "    # initialize parameters\n",
    "    w, b = initialize_parameters(X_train.shape[0])\n",
    "\n",
    "    # Gradient descent\n",
    "    parameters, grads, costs = optimize(w, b, X_train, Y_train, num_iterations, learning_rate, print_cost)\n",
    "    \n",
    "    # Retrieve parameters w and b from dictionary \"parameters\"\n",
    "    w = parameters[\"w\"]\n",
    "    b = parameters[\"b\"]\n",
    "    \n",
    "    # Predict test/train set examples\n",
    "    Y_prediction_test = predict(w, b, X_test)\n",
    "    Y_prediction_train = predict(w, b, X_train)\n",
    "    \n",
    "    #print(Y_prediction_train)\n",
    "    #print(Y_train)\n",
    "    \n",
    "    y=Y_test.reshape(Y_test.shape[1],)\n",
    "    predictions=Y_prediction_test.reshape(Y_prediction_test.shape[1],)\n",
    "    from sklearn.metrics import mean_absolute_error\n",
    "    mape = mean_absolute_error(y,predictions)\n",
    "    \n",
    "    print(\"Mape = {} %\".format(mape*100))\n",
    "\n",
    "\n",
    "    \n",
    "    d = {\"costs\": costs,\n",
    "         \"Y_prediction_test\": Y_prediction_test, \n",
    "         \"Y_prediction_train\" : Y_prediction_train, \n",
    "         \"w\" : w, \n",
    "         \"b\" : b,\n",
    "         \"learning_rate\" : learning_rate,\n",
    "         \"num_iterations\": num_iterations}\n",
    "    \n",
    "    return d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.492926\n",
      "Cost after iteration 10000: 0.347070\n",
      "Cost after iteration 20000: 0.346488\n",
      "Cost after iteration 30000: 0.346464\n",
      "Cost after iteration 40000: 0.346463\n",
      "Cost after iteration 50000: 0.346463\n",
      "Cost after iteration 60000: 0.346463\n",
      "Cost after iteration 70000: 0.346463\n",
      "Cost after iteration 80000: 0.346463\n",
      "Cost after iteration 90000: 0.346463\n",
      "Mape = 70.0594210739707 %\n"
     ]
    }
   ],
   "source": [
    "d = model(X_train, Y_train, X_test, Y_test, num_iterations = 100000, learning_rate = 0.0007, print_cost = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26.48343679473262 %\n",
      "0.6833391497414723\n"
     ]
    }
   ],
   "source": [
    "#For checking whether I had chosen or scaled the features wrong\n",
    "x=X_test.T\n",
    "y=Y_test.reshape(Y_test.shape[1],)\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(x,y)\n",
    "accuracy = regressor.score(x,y)\n",
    "predictions = regressor.predict(x)\n",
    "mape = mean_absolute_error(y,predictions)\n",
    "print(100*accuracy,'%')\n",
    "print(mape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Submission rules\n",
    "* Report the AUC score of the logistic regression model and the MAPE score of the Linear Regression model on the test data\n",
    "* AUC score as .4 and MAPE as 5 percent then **name_4_95.ipynb** should be the name of your submission file \n",
    "* All submissions Are to be made as pull requests on the Business Club IIT Kharagpur's github account on the Summer assignment 1 repository (https://github.com/business-club-iit-kharagpur)"
   ]
  }
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