varun_madhavan_8_10.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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115961940>"
      ]
     },
     "execution_count": 4,
     "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()\n",
    "#plt.hist(df['Age'].dropna())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**How does the survival rate of the passengers differ between sexes?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x104b38fd0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GSPKiBfZcBfxnkmczd+ZOb+/Lqmp/Ve0EZoG1SX4QOFZVfwR8ErhiP4VQlxbP3HVFqapDSd4H/G2S7wBfBm6ft+03gf3MBXw/c7EH+GDvBdMAnwEeAd4N/HySbzP3kbO/t+TfhNSBL6hKUoO8LCNJDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDfpfVeqzbSvdtiYAAAAASUVORK5CYII=\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": 7,
   "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": 8,
   "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": 8,
     "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115ab4c18>"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x119c40780>"
      ]
     },
     "execution_count": 10,
     "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x119a25198>"
      ]
     },
     "execution_count": 11,
     "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": 12,
   "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": 12,
     "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=891, step=1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "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": 15,
   "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": 15,
     "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": 16,
   "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": 17,
   "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": 17,
     "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": 18,
   "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": 18,
     "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": 19,
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=891, step=1)"
      ]
     },
     "execution_count": 21,
     "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": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([22., 38., 26., 35., 35., nan, 54.,  2., 27., 14.])"
      ]
     },
     "execution_count": 22,
     "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.0"
      ]
     },
     "execution_count": 23,
     "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": 24,
   "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index('Name')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age = df['Age']\n",
    "age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.0"
      ]
     },
     "execution_count": 26,
     "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": 27,
   "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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age * 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A range of methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.69911764705882"
      ]
     },
     "execution_count": 28,
     "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": 29,
   "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": 29,
     "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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 30,
     "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": 31,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "512.3292"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# code here (for maximum)\n",
    "df['Fare'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.4542"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# code here (for median)\n",
    "df['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": 33,
   "metadata": {
    "clear_cell": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deceased    549\n",
      "Survived    342\n",
      "Name: PassengerId, dtype: int64\n",
      "Survival ratio = 0.3838383838383838\n"
     ]
    }
   ],
   "source": [
    "# code here \n",
    "g_obj = df.groupby('Survived')['PassengerId'].count()\n",
    "g_obj.index = ['Deceased','Survived']\n",
    "print(g_obj)\n",
    "ratio = g_obj.Survived/g_obj.sum()\n",
    "print('Survival ratio = {}'.format(ratio))"
   ]
  },
  {
   "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": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"titanic.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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": 35,
     "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": 36,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [],
   "source": [
    "# code here \n",
    "# While checking this, you'll have to add 'data/' at the start of the filepath\n",
    "no2 = 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": 37,
   "metadata": {
    "clear_cell": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 20:00:00</th>\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>2016-12-31 21:00:00</th>\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>2016-12-31 22:00:00</th>\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>2016-12-31 23:00:00</th>\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 × 4 columns</p>\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",
       "...                    ...    ...   ...   ...\n",
       "2016-12-31 20:00:00   73.0   51.0  49.0  20.0\n",
       "2016-12-31 21:00:00   61.0   51.0  48.0  16.0\n",
       "2016-12-31 22:00:00   57.0   49.0  45.0  14.0\n",
       "2016-12-31 23:00:00   51.0   47.0  45.0  12.0\n",
       "\n",
       "[149039 rows x 4 columns]"
      ]
     },
     "execution_count": 37,
     "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": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<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",
       "  </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"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\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>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>2016-12-31 19:00:00</th>\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>2016-12-31 20:00:00</th>\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>2016-12-31 21:00:00</th>\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>2016-12-31 22:00:00</th>\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>2016-12-31 23:00:00</th>\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": [
       "                     BASCH  BONAP  PA18  VERS\n",
       "timestamp                                    \n",
       "2016-12-31 19:00:00   77.0   49.0  52.0  23.0\n",
       "2016-12-31 20:00:00   73.0   51.0  49.0  20.0\n",
       "2016-12-31 21:00:00   61.0   51.0  48.0  16.0\n",
       "2016-12-31 22:00:00   57.0   49.0  45.0  14.0\n",
       "2016-12-31 23:00:00   51.0   47.0  45.0  12.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "`info()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 149039 entries, 2000-01-01 01:00:00 to 2016-12-31 23:00:00\n",
      "Data columns (total 4 columns):\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: 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": 41,
   "metadata": {},
   "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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       "\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": 41,
     "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": 42,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115d349b0>"
      ]
     },
     "execution_count": 42,
     "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.plot(kind='box', ylim=[0,250])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x116752908>"
      ]
     },
     "execution_count": 43,
     "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'].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": 44,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1159fecc0>"
      ]
     },
     "execution_count": 44,
     "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": [
    "# code here\n",
    "df['Age'].plot(kind='hist', bins = 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default plot (when not specifying `kind`) is a line plot of all columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1166ac048>"
      ]
     },
     "execution_count": 45,
     "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.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": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11d561a58>"
      ]
     },
     "execution_count": 46,
     "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[-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": 47,
   "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": 48,
   "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": 48,
     "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": 49,
   "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": 49,
     "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": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
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       "      <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",
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       "      <th>11</th>\n",
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       "      <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",
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       "    <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",
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       "      <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": 50,
     "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": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.set_index('Name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26.55"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Bonnell, Miss. Elizabeth', 'Fare']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>Bonnell, Miss. Elizabeth</th>\n",
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       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>Andersson, Mr. Anders Johan</th>\n",
       "      <td>14</td>\n",
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       "      <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",
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       "</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": 53,
     "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": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "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": 54,
     "metadata": {},
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   ],
   "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": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc['Braund, Mr. Owen Harris', 'Survived'] = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2</td>\n",
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       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>71.2833</td>\n",
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       "      <td>C</td>\n",
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       "      <th>Heikkinen, Miss. Laina</th>\n",
       "      <td>3</td>\n",
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       "      <th>Futrelle, Mrs. Jacques Heath (Lily May Peel)</th>\n",
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       "      <td>53.1000</td>\n",
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       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>Dooley, Mr. Patrick</th>\n",
       "      <td>891</td>\n",
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       "      <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": 56,
     "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": 57,
   "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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Fare'] > 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>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>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>McCarthy, Mr. Timothy J</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fortune, Mr. Charles Alexander</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <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>Sage, Miss. Dorothy Edith \"Dolly\"</th>\n",
       "      <td>864</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Roebling, Mr. Washington Augustus II</th>\n",
       "      <td>868</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17590</td>\n",
       "      <td>50.4958</td>\n",
       "      <td>A24</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11751</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>D35</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</th>\n",
       "      <td>880</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11767</td>\n",
       "      <td>83.1583</td>\n",
       "      <td>C50</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>160 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    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": 58,
     "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": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"titanic.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.72664459161148"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Code here\n",
    "df[df['Sex'] == 'male']['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.915708812260537"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Code here\n",
    "df[df['Sex'] == 'female']['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.69911764705882"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Code here\n",
    "df['Age'].mean()"
   ]
  },
  {
   "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": 63,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Code here \n",
    "df[df['Age'] > 70]['PassengerId'].count() # The ['PassengerID'] could actually be any column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 passengers older than 70 were on the Titanic\n"
     ]
    }
   ],
   "source": [
    "# Code here\n",
    "print('{} passengers older than 70 were on the Titanic'.format(df[df['Age'] > 70]['PassengerId'].count()))"
   ]
  },
  {
   "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": 65,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
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       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  data\n",
       "0    A     0\n",
       "1    B     5\n",
       "2    C    10\n",
       "3    A     5\n",
       "..  ..   ...\n",
       "5    C    15\n",
       "6    A    10\n",
       "7    B    15\n",
       "8    C    20\n",
       "\n",
       "[9 rows x 2 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 66,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90"
      ]
     },
     "execution_count": 66,
     "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": 67,
   "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": 68,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "     data\n",
       "key      \n",
       "A      15\n",
       "B      30\n",
       "C      45"
      ]
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     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.groupby('key').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>45</td>\n",
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       "</table>\n",
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      "text/plain": [
       "     data\n",
       "key      \n",
       "A      15\n",
       "B      30\n",
       "C      45"
      ]
     },
     "execution_count": 69,
     "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": 70,
   "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": 70,
     "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": 71,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"titanic.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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",
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       "\n",
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       "\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>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": 72,
     "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": 73,
   "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": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Code here\n",
    "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": 74,
   "metadata": {
    "clear_cell": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survival ratio = 0.3838383838383838\n"
     ]
    }
   ],
   "source": [
    "# Code here\n",
    "g_obj = df.groupby('Survived')['PassengerId'].count()\n",
    "ratio = g_obj[1]/g_obj.sum()\n",
    "print('Survival ratio = {}'.format(ratio))"
   ]
  },
  {
   "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": 75,
   "metadata": {
    "clear_cell": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survival ratio for all passengers younger that 25 = 42.45%\n"
     ]
    }
   ],
   "source": [
    "# Code here\n",
    "survive_25 = df[df['Age'] < 25].groupby('Survived')['PassengerId'].count()\n",
    "ratio_25 = survive_25[1]/survive_25.sum()\n",
    "print('Survival ratio for all passengers younger that 25 = {0:.2f}%'.format(100*ratio_25))"
   ]
  },
  {
   "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": 76,
   "metadata": {
    "clear_cell": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.89% of males survived\n",
      "74.20% of females survived\n"
     ]
    }
   ],
   "source": [
    "# Code here\n",
    "g_obj1 = df.groupby(['Survived','Sex'])['PassengerId'].count()\n",
    "\n",
    "male_survival_ratio = g_obj1[1,'male']/g_obj1[:,'male'].sum()\n",
    "print('{0:.2f}% of males survived'.format(male_survival_ratio*100))\n",
    "\n",
    "female_survival_ratio = g_obj1[1,'female']/g_obj1[:,'female'].sum()\n",
    "print('{0:.2f}% of females survived'.format(female_survival_ratio*100))"
   ]
  },
  {
   "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": 77,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 3 artists>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/BcPLMssoyVOA41X1DuCvgGcCXwXOJzKaQ5If6F7LOOtS4C5gtHuxlSSPTvL0qvoqM5+H9JJu/2OSfDfwj8ArkowkGQVeCHz8IQ77aWAsySXd9vYhf1k6T1V1H3Bf990YwM+s5DzLybgvr5cDn+q+nX8G8CfdTwZ8pHvxzhdUF+9xwLuTHE1yJzPXxncz85lH13Yvqh1m5vo6wM8Br+/W/jMz18vfB9wJ3AF8EPi1qvrCuQ7YXc7ZAdzavYj3xSX5ynS+fh7Y2/29u2Cug/oOVUlqkGfuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDfpfajy4kE3yjGAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code here \n",
    "classes = df.groupby(['Pclass','Survived'])['PassengerId'].count()\n",
    "\n",
    "survival_ratio_by_class = []\n",
    "for i in range(1,4):\n",
    "    survival_ratio_by_class.append(classes[i,1]/(classes[i,1]+classes[i,0]))\n",
    "\n",
    "x_labels = ['First', 'Second', 'Third']\n",
    "plt.bar(x_labels,survival_ratio_by_class) # y axis is the fraction of people from a class who survived"
   ]
  },
  {
   "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": 78,
   "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": 79,
   "metadata": {
    "clear_cell": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 8 artists>"
      ]
     },
     "execution_count": 79,
     "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": [
    "# Code here \n",
    "x_labels = ['(0, 10]','(10, 20]','(20, 30]', '(30, 40]', '(40, 50]', '(50, 60]', '(60, 70]', '(70, 80]']\n",
    "fare_groups = df.groupby('AgeClass')['PassengerId'].count()\n",
    "plt.bar(x_labels,fare_groups)"
   ]
  },
  {
   "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": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Code here, Refer to google for any doubts firsthand there is precedence for this task\n",
    "df = pd.read_csv(\"titanic.csv\") # Add 'data/' to the start of the filepath for Windows/Colab\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/Applications/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n",
      "/Applications/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  import sys\n",
      "/Applications/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "/Applications/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "df['Male'] = np.zeros([len(df),1])\n",
    "df['Female'] = np.zeros([len(df),1])\n",
    "df['Male'][df['Sex'] == 'male'] = 1\n",
    "df['Female'][df['Sex'] == 'female'] = 1\n",
    "df = df.drop('Sex', axis = 1)\n",
    "df['C'] = df['Q'] = df['S'] = np.zeros([len(df),1])\n",
    "df['C'][df['Embarked'] == 'C'] = 1\n",
    "df['Q'][df['Embarked'] == 'Q'] = 2\n",
    "df['S'][df['Embarked'] == 'S'] = 3\n",
    "df = df.drop('Embarked', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
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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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Male</th>\n",
       "      <th>Female</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass   Age  SibSp     Fare  Male  Female    C    Q    S\n",
       "1       1.0     1.0  38.0    1.0  71.2833   0.0     1.0  1.0  0.0  0.0\n",
       "2       1.0     3.0  26.0    0.0   7.9250   0.0     1.0  0.0  0.0  3.0\n",
       "3       1.0     1.0  35.0    1.0  53.1000   0.0     1.0  0.0  0.0  3.0\n",
       "4       0.0     3.0  35.0    0.0   8.0500   1.0     0.0  0.0  0.0  3.0\n",
       "5       0.0     3.0  28.0    0.0   8.4583   1.0     0.0  0.0  2.0  0.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.fillna(df.median())\n",
    "df = df.drop(0) #ffill will not fill NaN values in the first row \n",
    "df = df.drop(['PassengerId','Name','Ticket','Cabin','Parch'],axis=1)\n",
    "df = df.astype(float)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['First'] = df['Second'] = df['Third'] = np.zeros([len(df),1])\n",
    "df['First'][df['Pclass'] == 1] = 1\n",
    "df['Second'][df['Pclass'] == 2] = 1\n",
    "df['Third'][df['Pclass'] == 3] = 1\n",
    "df = df.drop('Pclass', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Male</th>\n",
       "      <th>Female</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.472229</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.139136</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.321438</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.015469</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.434531</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.103644</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.434531</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.015713</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.346569</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.016510</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived       Age  SibSp      Fare  Male  Female    C    Q    S  First  \\\n",
       "1       1.0  0.472229  0.125  0.139136   0.0     1.0  1.0  0.0  0.0    1.0   \n",
       "2       1.0  0.321438  0.000  0.015469   0.0     1.0  0.0  0.0  1.0    0.0   \n",
       "3       1.0  0.434531  0.125  0.103644   0.0     1.0  0.0  0.0  1.0    1.0   \n",
       "4       0.0  0.434531  0.000  0.015713   1.0     0.0  0.0  0.0  1.0    0.0   \n",
       "5       0.0  0.346569  0.000  0.016510   1.0     0.0  0.0  1.0  0.0    0.0   \n",
       "\n",
       "   Second  Third  \n",
       "1     0.0    0.0  \n",
       "2     0.0    1.0  \n",
       "3     0.0    0.0  \n",
       "4     0.0    1.0  \n",
       "5     0.0    1.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = (df - df.min())/(df.max() - df.min())\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df[['Survived']]\n",
    "df = df.drop('Survived',axis = 1)\n",
    "y_train = y[:int(0.7*len(y))]\n",
    "y_test = y[int(0.7*len(y)):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = df[:int(0.7*len(df))]\n",
    "x_test = df[int(0.7*len(df)):]\n",
    "theta = np.ones([len(x_train.columns),1])\n",
    "y_train.columns = [0]\n",
    "y_test.columns = [0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(623, 11)\n",
      "(623, 1)\n",
      "(11, 1)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape)\n",
    "print(y_train.shape)\n",
    "print(theta.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Male</th>\n",
       "      <th>Female</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.472229</td>\n",
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       "      <td>0.139136</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.321438</td>\n",
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       "      <td>0.015469</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.434531</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.103644</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.434531</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.015713</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.346569</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.016510</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age  SibSp      Fare  Male  Female    C    Q    S  First  Second  \\\n",
       "1  0.472229  0.125  0.139136   0.0     1.0  1.0  0.0  0.0    1.0     0.0   \n",
       "2  0.321438  0.000  0.015469   0.0     1.0  0.0  0.0  1.0    0.0     0.0   \n",
       "3  0.434531  0.125  0.103644   0.0     1.0  0.0  0.0  1.0    1.0     0.0   \n",
       "4  0.434531  0.000  0.015713   1.0     0.0  0.0  0.0  1.0    0.0     0.0   \n",
       "5  0.346569  0.000  0.016510   1.0     0.0  0.0  1.0  0.0    0.0     0.0   \n",
       "\n",
       "   Third  \n",
       "1    0.0  \n",
       "2    1.0  \n",
       "3    0.0  \n",
       "4    1.0  \n",
       "5    1.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(x):\n",
    "    return 1/(1+np.exp(-x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import random\n",
    "theta = np.random.randint(2,10,(len(x_train.columns),1))\n",
    "bias = np.random.randint(10)\n",
    "iters = 2000\n",
    "alpha = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "costs = []\n",
    "for i in range(iters):\n",
    "    m = len(x_train)\n",
    "    h = sigmoid(x_train@theta + bias)\n",
    "    #print(h)\n",
    "    cost = (-1/m)*((y_train.T)@np.log(h) + ((1-y_train).T)@np.log(1-h))\n",
    "    d_theta = (1/m)*((x_train.T)@(h-y_train))\n",
    "    d_bias = (1/m)*np.sum(h-y_train)\n",
    "    theta = theta - alpha*d_theta\n",
    "    bias = bias - alpha*d_bias\n",
    "    costs.append(cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>-2.816483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-2.250297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>-0.831032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2.099483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>4.792342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.945706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>1.312507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>0.578178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>First</th>\n",
       "      <td>2.287145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Second</th>\n",
       "      <td>1.485489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Third</th>\n",
       "      <td>0.119191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0\n",
       "Age    -2.816483\n",
       "SibSp  -2.250297\n",
       "Fare   -0.831032\n",
       "Male    2.099483\n",
       "Female  4.792342\n",
       "C       0.945706\n",
       "Q       1.312507\n",
       "S       0.578178\n",
       "First   2.287145\n",
       "Second  1.485489\n",
       "Third   0.119191"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a16227d68>]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAD8CAYAAACRkhiPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAGA9JREFUeJzt3X+QHOWd3/H3Z3f1w/wwEtaeDksikl2ClKAOgTdYCWeXHc4giMvC+cORKmdkH3Wy78TFTly5ArsquHxFirrzj4Q6n1yy0QEJB6cYMKqUfFin+Ez5ymBWWBYSv7TCYHajH2t0EWCM0Erf/DHPQms1Mz27M7u96v68qram55mnu7+7O9Jnu5/ueRQRmJlZNXUVXYCZmRXHIWBmVmEOATOzCnMImJlVmEPAzKzCHAJmZhXmEDAzqzCHgJlZhTkEzMwqrKfoAvLMmzcvFi9eXHQZZmanjR07dvwqInpb6TvtQ2Dx4sX09/cXXYaZ2WlD0out9vXpIDOzCnMImJlVmEPAzKzCHAJmZhWWGwKSFkn6oaSnJO2R9LnUfq6kbZL2pse5qV2Sbpc0IGmXpMsy21qb+u+VtHbyvi0zM2tFK0cCI8AXImIZsAJYL2kZcBOwPSKWAtvTc4BrgKXpax2wAWqhAdwCvB+4HLhlNDjMzKwYuSEQEfsj4om0/CrwNLAAWAXclbrdBVyXllcBd0fNo8AcSecBVwPbIuJwRPwTsA1Y2dHvxszMxmVcYwKSFgOXAo8B8yNif3rpADA/LS8AXsqsNpjaGrVPitu37+VHzw1P1ubNzEqh5RCQdBZwP/D5iHgl+1rUJiru2GTFktZJ6pfUPzw8sf/IN/zDPv5x4FedKsnMrJRaCgFJM6gFwD0R8UBqPphO85AeD6X2IWBRZvWFqa1R+ykiYmNE9EVEX29vS3c+m5nZBLRydZCAO4CnI+LrmZe2AKNX+KwFHsq0X5+uEloBHEmnjR4GrpI0Nw0IX5XazMysIK18dtAVwCeBJyXtTG1fBG4DNku6AXgR+ER6bStwLTAAvA58GiAiDkv6M+Dx1O8rEXG4I9+FmZlNSG4IRMSPATV4+co6/QNY32Bbm4BN4ynQzMwmj+8YNjOrMIeAmVmFOQTMzCrMIWBmVmEOATOzCnMImJlVmEPAzKzCHAJmZhXmEDAzqzCHgJlZhTkEzMwqzCFgZlZhDgEzswpzCJiZVVipQ6D2qdZmZtZIaUNAjWZAMDOzt7QyveQmSYck7c60/a2knenrhdEZxyQtlvSbzGvfyqzzPklPShqQdHuattLMzArUyvSSdwJ/Cdw92hAR/250WdLXgCOZ/vsiYnmd7WwA/hB4jNoUlCuB74+/ZDMz65TcI4GIeASoOxdw+mv+E8C9zbYh6TzgnRHxaJp+8m7guvGXa2ZmndTumMAHgIMRsTfTtkTSzyT9SNIHUtsCYDDTZzC1mZlZgVo5HdTMGk4+CtgPnB8RL0t6H/A9SReNd6OS1gHrAM4///w2SzQzs0YmfCQgqQf4t8DfjrZFxNGIeDkt7wD2ARcAQ8DCzOoLU1tdEbExIvoioq+3t3eiJZqZWY52Tgf9HvBMRLx1mkdSr6TutPweYCnwfETsB16RtCKNI1wPPNTGvs3MrANauUT0XuAnwIWSBiXdkF5azakDwh8EdqVLRr8LfDYiRgeV/xj4DjBA7QjBVwaZmRUsd0wgItY0aP9Unbb7gfsb9O8HLh5nfWZmNolKe8ewmZnlcwiYmVWYQ8DMrMIcAmZmFeYQMDOrMIeAmVmFOQTMzCrMIWBmVmGlDgHPLmlm1lxpQ8DTlpmZ5SttCJiZWT6HgJlZhTkEzMwqzCFgZlZhDgEzswpzCJiZVVgrM4ttknRI0u5M25clDUnamb6uzbx2s6QBSc9KujrTvjK1DUi6qfPfipmZjVcrRwJ3AivrtH8jIpanr60AkpZRm3byorTOX0nqTvMOfxO4BlgGrEl9zcysQK1ML/mIpMUtbm8VcF9EHAV+IWkAuDy9NhARzwNIui/1fWrcFZuZWce0MyZwo6Rd6XTR3NS2AHgp02cwtTVqNzOzAk00BDYA7wWWA/uBr3WsIkDSOkn9kvqHh4c7uWkzM8uYUAhExMGIOB4RJ4Bv8/YpnyFgUabrwtTWqL3R9jdGRF9E9PX29k6kRDMza8GEQkDSeZmnHwdGrxzaAqyWNEvSEmAp8FPgcWCppCWSZlIbPN4y8bLNzKwTcgeGJd0LfAiYJ2kQuAX4kKTlQAAvAJ8BiIg9kjZTG/AdAdZHxPG0nRuBh4FuYFNE7On4d2NmZuPSytVBa+o039Gk/63ArXXatwJbx1WdmZlNKt8xbGZWYQ4BM7MKcwiYmVVYqUPAUwybmTVX2hCQPMuwmVme0oaAmZnlcwiYmVWYQ8DMrMIcAmZmFeYQMDOrMIeAmVmFOQTMzCrMIWBmVmEOATOzCnMImJlVmEPAzKzCckNA0iZJhyTtzrT9haRnJO2S9KCkOal9saTfSNqZvr6VWed9kp6UNCDpdvnDfczMCtfKkcCdwMoxbduAiyPid4DngJszr+2LiOXp67OZ9g3AH1Kbd3hpnW2amdkUyw2BiHgEODym7QcRMZKePgosbLaNNDH9OyPi0YgI4G7guomVbGZmndKJMYE/AL6feb5E0s8k/UjSB1LbAmAw02cwtdUlaZ2kfkn9w8PDHSjRzMzqaSsEJH0JGAHuSU37gfMj4lLgPwF/I+md491uRGyMiL6I6Ovt7W2nRDMza6JnoitK+hTwUeDKdIqHiDgKHE3LOyTtAy4Ahjj5lNHC1GZmZgWa0JGApJXAnwIfi4jXM+29krrT8nuoDQA/HxH7gVckrUhXBV0PPNR29WZm1pbcIwFJ9wIfAuZJGgRuoXY10CxgW7rS89F0JdAHga9IOgacAD4bEaODyn9M7Uqjd1AbQ8iOI0yK8CTDZmZN5YZARKyp03xHg773A/c3eK0fuHhc1bXBNyGYmeXzHcNmZhXmEDAzqzCHgJlZhTkEzMwqzCFgZlZhDgEzswpzCJiZVZhDwMyswhwCZmYV5hAwM6swh4CZWYU5BMzMKswhYGZWYQ4BM7MKcwiYmVVYSyEgaZOkQ5J2Z9rOlbRN0t70ODe1S9LtkgYk7ZJ0WWadtan/XklrO//tmJnZeLR6JHAnsHJM203A9ohYCmxPzwGuoTat5FJgHbABaqFBbVay9wOXA7eMBoeZmRWjpRCIiEeAw2OaVwF3peW7gOsy7XdHzaPAHEnnAVcD2yLicET8E7CNU4PFzMymUDtjAvPTBPIAB4D5aXkB8FKm32Bqa9Q+aQJPMmxm1kxHBoYjIqBz/+NKWiepX1L/8PDwBDfSqWrMzMqrnRA4mE7zkB4PpfYhYFGm38LU1qj9FBGxMSL6IqKvt7d3wgWGDwTMzJpqJwS2AKNX+KwFHsq0X5+uEloBHEmnjR4GrpI0Nw0IX5XaJoUPBMzM8vW00knSvcCHgHmSBqld5XMbsFnSDcCLwCdS963AtcAA8DrwaYCIOCzpz4DHU7+vRMTYwWYzM5tCLYVARKxp8NKVdfoGsL7BdjYBm1quzszMJlVp7xiWfELIzCxPaUMAIDwybGbWVGlDwAcCZmb5ShsCZmaWr9Qh4JNBZmbNlTYEfDbIzCxfaUMAfMewmVme0oaALxE1M8tX2hAAf4qomVmeUoeAmZk1V9oQ8MkgM7N8pQ0B8MCwmVme0oaAx4XNzPKVNgTAN4uZmeUpcQj4UMDMLE+JQ8DMzPJMOAQkXShpZ+brFUmfl/RlSUOZ9msz69wsaUDSs5Ku7sy30JgHhs3MmmtpZrF6IuJZYDmApG5qk8Y/SG06yW9ExFez/SUtA1YDFwHvBv5e0gURcXyiNTTjgWEzs3ydOh10JbAvIl5s0mcVcF9EHI2IX1Cbg/jyDu2/AR8KmJk106kQWA3cm3l+o6RdkjZJmpvaFgAvZfoMprZJ4QMBM7N8bYeApJnAx4D/lZo2AO+ldqpoP/C1CWxznaR+Sf3Dw8PtlmhmZg104kjgGuCJiDgIEBEHI+J4RJwAvs3bp3yGgEWZ9RamtlNExMaI6IuIvt7e3gkX5oFhM7PmOhECa8icCpJ0Xua1jwO70/IWYLWkWZKWAEuBn3Zg/3V5YNjMLN+Erw4CkHQm8BHgM5nmP5e0nNqo7Aujr0XEHkmbgaeAEWD9ZF0ZNMpHAmZmzbUVAhHxa+BdY9o+2aT/rcCt7eyzVfLQsJlZLt8xbGZWYaUOAc8sZmbWXGlDwAPDZmb5ShsC4IFhM7M8pQ0BHwiYmeUrbQiYmVm+UoeAzwaZmTVX2hCQR4bNzHKVNgTAA8NmZnlKHQJmZtacQ8DMrMJKHQK+Y9jMrLnShoDHhc3M8pU2BABfI2pmlqO0IeAjATOzfKUNATMzy9eJieZfkPSkpJ2S+lPbuZK2SdqbHuemdkm6XdKApF2SLmt3/834bJCZWXOdOhL4cEQsj4i+9PwmYHtELAW2p+dQm5R+afpaB2zo0P5P4ZnFzMzyTdbpoFXAXWn5LuC6TPvdUfMoMGfMxPQdFb5l2MysqU6EQAA/kLRD0rrUNj8i9qflA8D8tLwAeCmz7mBqO4mkdZL6JfUPDw9PqCgPDJuZ5WtrovnkdyNiSNJvAdskPZN9MSJC0rj+JI+IjcBGgL6+Pv85b2Y2Sdo+EoiIofR4CHgQuBw4OHqaJz0eSt2HgEWZ1Remtknh9DAza66tEJB0pqSzR5eBq4DdwBZgbeq2FngoLW8Brk9XCa0AjmROG3WUzwaZmeVr93TQfODB9Nn9PcDfRMTfSXoc2CzpBuBF4BOp/1bgWmAAeB34dJv7b8rjwmZmzbUVAhHxPHBJnfaXgSvrtAewvp19tqpL8ukgM7Mcpb1jWIITPhQwM2uqxCEg3ydgZpajtCHQJY8JmJnlKXEIyKeDzMxylDYEAE44A8zMmiptCHRJPh1kZpajvCHQ5Q+QMzPLU9oQEB4TMDPLU9oQ6JI/O8jMLE9pQ0CSB4bNzHKUNgRq9wk4BczMmiltCMj3CZiZ5SptCPiOYTOzfKUNAR8JmJnlK28I4DuGzczylDYEuuRrRM3M8kw4BCQtkvRDSU9J2iPpc6n9y5KGJO1MX9dm1rlZ0oCkZyVd3YlvoJGuLs8nYGaWp52ZxUaAL0TEE2me4R2StqXXvhERX812lrQMWA1cBLwb+HtJF0TE8TZqaMh3DJuZ5ZvwkUBE7I+IJ9Lyq8DTwIImq6wC7ouIoxHxC2rzDF8+0f3nqc0sNllbNzMrh46MCUhaDFwKPJaabpS0S9ImSXNT2wLgpcxqgzQIDUnrJPVL6h8eHp5QTTO6uxg5cWJC65qZVUXbISDpLOB+4PMR8QqwAXgvsBzYD3xtvNuMiI0R0RcRfb29vROqq6dLjBz3oYCZWTNthYCkGdQC4J6IeAAgIg5GxPGIOAF8m7dP+QwBizKrL0xtk2JGdxfHjvtIwMysmXauDhJwB/B0RHw9035eptvHgd1peQuwWtIsSUuApcBPJ7r/PD3dYsSDAmZmTbVzddAVwCeBJyXtTG1fBNZIWk7tKv0XgM8ARMQeSZuBp6hdWbR+sq4MAujp6vLpIDOzHBMOgYj4MbUbc8fa2mSdW4FbJ7rP8ZjRLZ8OMjPLUdo7hn06yMwsX3lDoMsDw2ZmeUobAj4dZGaWr7QhMKunmzdHTnh2MTOzJkobAu+Y2c2JgKMjPhowM2uktCFw5sxuAF5/c9KuQjUzO+2VNgTOmFW7+vXXR0cKrsTMbPoqbQicObMWAr855iMBM7NGShsCZ8yqnQ56zUcCZmYNlTYE3jl7BgCv/OZYwZWYmU1fpQ2BeWfNBODl194suBIzs+mrtCHwrrNmAfCr144WXImZ2fRV2hA4c2Y3s2d0cehVh4CZWSOlDQFJLJl3FvuGXyu6FDOzaau0IQCwfNEcHnv+MAeOvFF0KWZm09KUh4CklZKelTQg6abJ3NcNv7sEgFXf/DH/4ycv8MobvlLIzCxLU/kBa5K6geeAjwCDwOPAmoh4qtE6fX190d/fP+F97h46wpcefJKfDx6hp0tcsmgOK95zLsvOO4cLf/tsFr/rDHq6S31AZGYVI2lHRPS10red6SUn4nJgICKeB5B0H7CK2pSTk+LiBefwvfVXsGvwCA/vOcA/7nuZDf+wj9H5Zmb2dPHuc2bTe/Yses+exdmzZjB7RhezZnQzu6f2OKuni9mZx+6u2oRq2WnV9NYTnfT85D4nr/dWn0wncfKKb/dtvE812Sct9Hlr26fsc8xG6vZpvS5a6DO2fSrrOun30Oh31WSfjX7Wzepq+rsfz3toAj/rluuqt1ErjakOgQXAS5nng8D7J3unUu0I4JJFcwB449hxBg69xjMHXuW5g6+y/8gbDL/6Bs8ceJVfHx3hjWMneOPYcX8CqVkdjQL35LbR56cmWSsB1jDMx/VHTOO6xv6x1kpd9fbJ2HXqhHorf6DU+6Pv3DNmsvmz/5LJNtUh0BJJ64B1AOeff37Htz97RjcXLziHixec07RfRHB05ETtK4XCG8eOcyIgiEy/MY/ptXpn2pr1ibf6xJjnY3tktzO2hkyfBvuk7j7r15X9FsbWxdjtUu9nkV8XTffZel156zSr66QttLTPnD7Nfg919tns59XqPk9+L9V/D570e2j0HmryPmNM7c322azP2LfieOqq/++qvbpOeQ+29O+0/vdU7/s6dZ3GtY8unD17av57nuoQGAIWZZ4vTG0niYiNwEaojQlMTWmnksTsGd3MntEN75hRVBlmZpNmqkdEHweWSloiaSawGtgyxTWYmVkypUcCETEi6UbgYaAb2BQRe6ayBjMze9uUjwlExFZg61Tv18zMTuUL5M3MKswhYGZWYQ4BM7MKcwiYmVWYQ8DMrMKm9APkJkLSMPBinZfmAb+a4nJa4brGx3W1bjrWBK5rvKairn8WEb2tdJz2IdCIpP5WPyVvKrmu8XFdrZuONYHrGq/pVpdPB5mZVZhDwMyswk7nENhYdAENuK7xcV2tm441gesar2lV12k7JmBmZu07nY8EzMysTadlCEzlZPWtkrRI0g8lPSVpj6TPFV3TKEndkn4m6X8XXcsoSXMkfVfSM5KeljT5Uyi1QNJ/TL+/3ZLulTS7oDo2STokaXem7VxJ2yTtTY9zp0ldf5F+j7skPShpznSoK/PaFySFpHnToSZJf5J+Xnsk/flU1lTPaRcCabL6bwLXAMuANZKWFVsVACPAFyJiGbACWD9N6gL4HPB00UWM8d+Bv4uIfw5cwjSoT9IC4D8AfRFxMbWPO19dUDl3AivHtN0EbI+IpcD29Hyq3cmpdW0DLo6I3wGeA26e6qKoXxeSFgFXAb+c6oKoU5OkD1ObV/2SiLgI+GoBdZ3ktAsBMpPVR8SbwOhk9YWKiP0R8URafpXaf2oLiq0KJC0E/g3wnaJrGSXpHOCDwB0AEfFmRPy/Yqt6Sw/wDkk9wBnA/y2iiIh4BDg8pnkVcFdavgu4bkqLon5dEfGDiBhJTx+lNmNg4XUl3wD+lDEziE6FBjX9EXBbRBxNfQ5NdV1jnY4hUG+y+sL/s82StBi4FHis2EoA+G/U/hGcKLqQjCXAMPDX6TTVdySdWXRRETFE7S+zXwL7gSMR8YNiqzrJ/IjYn5YPAPOLLKaBPwC+X3QRAJJWAUMR8fOia8m4APiApMck/UjSvyi6oNMxBKY1SWcB9wOfj4hXCq7lo8ChiNhRZB119ACXARsi4lLg1xRzauMk6Rz7Kmoh9W7gTEm/X2xV9UXtsr5pdWmfpC9ROy16zzSo5Qzgi8B/KbqWMXqAc6mdMv7PwGZJKrKg0zEEWpqsvgiSZlALgHsi4oGi6wGuAD4m6QVqp83+taT/WWxJQO3obTAiRo+UvkstFIr2e8AvImI4Io4BDwD/quCasg5KOg8gPRZ+KmGUpE8BHwX+fUyP687fSy3Mf57e/wuBJyT9dqFV1d77D0TNT6kdoU/pgPVYp2MITMvJ6lOa3wE8HRFfL7oegIi4OSIWRsRiaj+n/xMRhf9lGxEHgJckXZiargSeKrCkUb8EVkg6I/0+r2QaDFhnbAHWpuW1wEMF1vIWSSupnXL8WES8XnQ9ABHxZET8VkQsTu//QeCy9N4r0veADwNIugCYScEfcnfahUAagBqdrP5pYPM0maz+CuCT1P7a3pm+ri26qGnsT4B7JO0ClgP/teB6SEcm3wWeAJ6k9u+jkLs7Jd0L/AS4UNKgpBuA24CPSNpL7ajltmlS118CZwPb0vv+W9OkrkI1qGkT8J502eh9wNqij5x8x7CZWYWddkcCZmbWOQ4BM7MKcwiYmVWYQ8DMrMIcAmZmFeYQMDOrMIeAmVmFOQTMzCrs/wNkubWmVSKmqgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "costs = pd.DataFrame(costs)\n",
    "costs.dropna()\n",
    "plt.plot(costs,range(iters))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81.27340823970037"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "predictions = sigmoid(x_test@theta + bias)\n",
    "predictions[predictions > 0.5] = 1\n",
    "predictions[predictions <= 0.5] = 0\n",
    "100*accuracy_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a16eb8a20>]"
      ]
     },
     "execution_count": 42,
     "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": [
    "fpr, tpr, thresholds = roc_curve(y_test, predictions)\n",
    "plt.plot(fpr,tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7852704678362573\n"
     ]
    }
   ],
   "source": [
    "auc = roc_auc_score(y_test, predictions)\n",
    "print(auc)"
   ]
  },
  {
   "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": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "#theta = (np.linalg.inv(x_train.T@x_train))@(x_train.T)@y_train "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "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": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Code here, Refer to google for any doubts firsthand there is precedence for this task\n",
    "df = pd.read_csv(\"titanic.csv\") # Add 'data/' to the start of the filepath for Windows/Colab\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['PassengerId','Name','Ticket','Cabin','SibSp','Parch','Embarked'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n",
      "/Applications/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of      Survived  Pclass   Age     Fare  Male  Female\n",
       "0         0.0     3.0  22.0   7.2500   1.0     0.0\n",
       "1         1.0     1.0  38.0  71.2833   0.0     1.0\n",
       "2         1.0     3.0  26.0   7.9250   0.0     1.0\n",
       "3         1.0     1.0  35.0  53.1000   0.0     1.0\n",
       "..        ...     ...   ...      ...   ...     ...\n",
       "887       1.0     1.0  19.0  30.0000   0.0     1.0\n",
       "888       0.0     3.0  19.0  23.4500   0.0     1.0\n",
       "889       1.0     1.0  26.0  30.0000   1.0     0.0\n",
       "890       0.0     3.0  32.0   7.7500   1.0     0.0\n",
       "\n",
       "[891 rows x 6 columns]>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.fillna(method = 'ffill')\n",
    "df['Male'] = np.zeros([len(df),1])\n",
    "df['Female'] = np.zeros([len(df),1])\n",
    "df['Male'][df['Sex'] == 'male'] = 1\n",
    "df['Female'][df['Sex'] == 'female'] = 1\n",
    "df = df.drop('Sex', axis = 1)\n",
    "df = df.astype('float')\n",
    "#df['Bias'] = np.ones([len(df),1])\n",
    "#df = (df - f.mean())/(df.std()) \n",
    "df.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>30.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>3.0</td>\n",
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       "      <td>7.7500</td>\n",
       "      <td>1.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass   Age     Fare  Male  Female  Survive  Die\n",
       "0       3.0  22.0   7.2500   1.0     0.0      0.0  1.0\n",
       "1       1.0  38.0  71.2833   0.0     1.0      1.0  0.0\n",
       "2       3.0  26.0   7.9250   0.0     1.0      1.0  0.0\n",
       "3       1.0  35.0  53.1000   0.0     1.0      1.0  0.0\n",
       "..      ...   ...      ...   ...     ...      ...  ...\n",
       "887     1.0  19.0  30.0000   0.0     1.0      1.0  0.0\n",
       "888     3.0  19.0  23.4500   0.0     1.0      0.0  1.0\n",
       "889     1.0  26.0  30.0000   1.0     0.0      1.0  0.0\n",
       "890     3.0  32.0   7.7500   1.0     0.0      0.0  1.0\n",
       "\n",
       "[891 rows x 7 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Survive'] = df['Die'] = np.zeros([len(df),1])\n",
    "df['Survive'][df['Survived'] == 1] = 1\n",
    "df['Die'][df['Survived'] == 0] = 1\n",
    "df = df.drop('Survived', axis = 1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "      Age     Fare  Male  Female  Survive  Die  First  Second  Third\n",
       "0    22.0   7.2500   1.0     0.0      0.0  1.0    0.0     0.0    1.0\n",
       "1    38.0  71.2833   0.0     1.0      1.0  0.0    1.0     0.0    0.0\n",
       "2    26.0   7.9250   0.0     1.0      1.0  0.0    0.0     0.0    1.0\n",
       "3    35.0  53.1000   0.0     1.0      1.0  0.0    1.0     0.0    0.0\n",
       "..    ...      ...   ...     ...      ...  ...    ...     ...    ...\n",
       "887  19.0  30.0000   0.0     1.0      1.0  0.0    1.0     0.0    0.0\n",
       "888  19.0  23.4500   0.0     1.0      0.0  1.0    0.0     0.0    1.0\n",
       "889  26.0  30.0000   1.0     0.0      1.0  0.0    1.0     0.0    0.0\n",
       "890  32.0   7.7500   1.0     0.0      0.0  1.0    0.0     0.0    1.0\n",
       "\n",
       "[891 rows x 9 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['First'] = df['Second'] = df['Third'] = np.zeros([len(df),1])\n",
    "df['First'][df['Pclass'] == 1] = 1\n",
    "df['Second'][df['Pclass'] == 2] = 1\n",
    "df['Third'][df['Pclass'] == 3] = 1\n",
    "df = df.drop('Pclass', axis = 1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "\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>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Male</th>\n",
       "      <th>Female</th>\n",
       "      <th>Survive</th>\n",
       "      <th>Die</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>-0.502163</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>0.786404</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>-0.488580</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0.420494</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19.0</td>\n",
       "      <td>-0.044356</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>19.0</td>\n",
       "      <td>-0.176164</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>26.0</td>\n",
       "      <td>-0.044356</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32.0</td>\n",
       "      <td>-0.492101</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age      Fare  Male  Female  Survive  Die  First  Second  Third\n",
       "0    22.0 -0.502163   1.0     0.0      0.0  1.0    0.0     0.0    1.0\n",
       "1    38.0  0.786404   0.0     1.0      1.0  0.0    1.0     0.0    0.0\n",
       "2    26.0 -0.488580   0.0     1.0      1.0  0.0    0.0     0.0    1.0\n",
       "3    35.0  0.420494   0.0     1.0      1.0  0.0    1.0     0.0    0.0\n",
       "..    ...       ...   ...     ...      ...  ...    ...     ...    ...\n",
       "887  19.0 -0.044356   0.0     1.0      1.0  0.0    1.0     0.0    0.0\n",
       "888  19.0 -0.176164   0.0     1.0      0.0  1.0    0.0     0.0    1.0\n",
       "889  26.0 -0.044356   1.0     0.0      1.0  0.0    1.0     0.0    0.0\n",
       "890  32.0 -0.492101   1.0     0.0      0.0  1.0    0.0     0.0    1.0\n",
       "\n",
       "[891 rows x 9 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = df['Fare']\n",
    "df['Fare'] = (df['Fare']-df['Fare'].mean())/df['Fare'].std()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = df['Fare'].std()\n",
    "m = df['Fare'].mean()\n",
    "#df[df['Fare']> 3*z] = m # remove outliers with a std > 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['Male', 'Female'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11f794b70>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "corr = x_train.corr()\n",
    "sns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool), cmap=sns.diverging_palette(220, 10, as_cmap=True), square=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = 0.00001\n",
    "iters = 1500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df[['Age']]\n",
    "df = df.drop('Age',axis = 1)\n",
    "x_train = df[:int(0.7*len(df))]\n",
    "x_test = df[int(0.7*len(df)):]\n",
    "y_train = y[:int(0.7*len(y))]\n",
    "y_test = y[int(0.7*len(y)):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test.columns = y_train.columns = [0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.999998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survive</th>\n",
       "      <td>1.000104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Die</th>\n",
       "      <td>1.000174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>First</th>\n",
       "      <td>1.000081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Second</th>\n",
       "      <td>1.000057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Third</th>\n",
       "      <td>1.000140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                0\n",
       "Fare     0.999998\n",
       "Survive  1.000104\n",
       "Die      1.000174\n",
       "First    1.000081\n",
       "Second   1.000057\n",
       "Third    1.000140"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta = np.ones([len(x_train.columns),1])\n",
    "theta = theta + (alpha/len(x_train))*(((y_train - x_train@theta).T)@x_train).T\n",
    "theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "theta = np.ones([len(x_train.columns),1])\n",
    "for k in range(iters):\n",
    "    theta = theta + (alpha/len(x_train))*(((y_train - x_train@theta).T)@x_train).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error = 90.58%\n"
     ]
    }
   ],
   "source": [
    "error = float((abs(100*((x_test@theta)-y_test)/y_test)).mean())\n",
    "print('Error = {:.2f}%'.format(error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>1.979169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>2.144690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>2.382881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>1.946265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>2.231634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>2.292230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>2.231634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>1.977077</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>268 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0\n",
       "623  1.979169\n",
       "624  2.144690\n",
       "625  2.382881\n",
       "626  1.946265\n",
       "..        ...\n",
       "887  2.231634\n",
       "888  2.292230\n",
       "889  2.231634\n",
       "890  1.977077\n",
       "\n",
       "[268 rows x 1 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test@theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.76399303679251 %\n"
     ]
    }
   ],
   "source": [
    "# This is just for comparison. \n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(x_train,y_train)\n",
    "accuracy = regressor.score(x_test,y_test)\n",
    "predictions = regressor.predict(x_test)\n",
    "mape = mean_absolute_error(y_test,predictions)\n",
    "print(100*accuracy,'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LinReg using Normal equation. \n",
    "theta = np.ones([len(x_train.columns),1])\n",
    "df['Fare'] = arr\n",
    "x_train = df[:int(0.7*len(df))]\n",
    "x_test = df[int(0.7*len(df)):]\n",
    "theta = (np.linalg.inv(x_train.T@x_train))@(x_train.T)@y_train"
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  {
   "cell_type": "code",
   "execution_count": 116,
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   "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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   "cell_type": "code",
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