Tutorial_1.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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas import Series,DataFrame\n",
    "from sklearn.metrics import (confusion_matrix, precision_recall_curve, auc,\n",
    "                             roc_curve, recall_score, classification_report, f1_score,\n",
    "                             precision_recall_fscore_support)\n",
    "\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \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",
       "\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  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f2018006a0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Age'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**How does the survival rate of the passengers differ between sexes?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('data/20000101_20161231-NO2.csv', sep=';', skiprows=[1], na_values=['n/d'], index_col=0, parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['1999':].resample('M').mean().plot(ylim=[0,120])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": 7,
   "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": 7,
     "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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=891, step=1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "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": 10,
     "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": 11,
   "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": 12,
   "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": 12,
     "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": 13,
   "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": 13,
     "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=891, step=1)"
      ]
     },
     "execution_count": 15,
     "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": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22.0\n",
       "1    38.0\n",
       "2    26.0\n",
       "3    35.0\n",
       "     ... \n",
       "6    54.0\n",
       "7     2.0\n",
       "8    27.0\n",
       "9    14.0\n",
       "Length: 10, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k=Series(age.values[:10])\n",
    "k"
   ]
  },
  {
   "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": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.0"
      ]
     },
     "execution_count": 27,
     "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": 28,
   "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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index('Name')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age = df['Age']\n",
    "age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.0"
      ]
     },
     "execution_count": 30,
     "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": 31,
   "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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age * 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A range of methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.69911764705882"
      ]
     },
     "execution_count": 32,
     "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": 33,
   "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": 33,
     "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": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 34,
     "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": 35,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "512.3292"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# code here (for maximum)\n",
    "df['Fare'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.4542"
      ]
     },
     "execution_count": 36,
     "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": 41,
   "metadata": {
    "clear_cell": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3838383838383838"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# code here \n",
    "df['Survived'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Data import and export"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "A wide range of input/output formats are natively supported by pandas:\n",
    "\n",
    "* CSV, text\n",
    "* SQL database\n",
    "* Excel\n",
    "* HDF5\n",
    "* json\n",
    "* html\n",
    "* pickle\n",
    "* sas, stata\n",
    "* (parquet)\n",
    "* ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Very powerful csv reader:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Luckily, if we have a well formed csv file, we don't need many of those arguments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/titanic.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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": 45,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [],
   "source": [
    "# code here \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": 46,
   "metadata": {
    "clear_cell": 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>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": 46,
     "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": 47,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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",
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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>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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "`info()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BASCH</th>\n",
       "      <th>BONAP</th>\n",
       "      <th>PA18</th>\n",
       "      <th>VERS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>139949.000000</td>\n",
       "      <td>136493.000000</td>\n",
       "      <td>142259.000000</td>\n",
       "      <td>143813.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>89.270098</td>\n",
       "      <td>64.001714</td>\n",
       "      <td>45.104211</td>\n",
       "      <td>27.613227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>36.772855</td>\n",
       "      <td>27.866767</td>\n",
       "      <td>23.212719</td>\n",
       "      <td>19.604953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>63.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>87.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>112.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>38.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>358.000000</td>\n",
       "      <td>345.000000</td>\n",
       "      <td>306.000000</td>\n",
       "      <td>197.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               BASCH          BONAP           PA18           VERS\n",
       "count  139949.000000  136493.000000  142259.000000  143813.000000\n",
       "mean       89.270098      64.001714      45.104211      27.613227\n",
       "std        36.772855      27.866767      23.212719      19.604953\n",
       "min         4.000000       0.000000       2.000000       0.000000\n",
       "25%        63.000000      44.000000      28.000000      13.000000\n",
       "50%        87.000000      62.000000      42.000000      22.000000\n",
       "75%       112.000000      81.000000      59.000000      38.000000\n",
       "max       358.000000     345.000000     306.000000     197.000000"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2.describe()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Quickly visualizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f203b3fac8>"
      ]
     },
     "execution_count": 62,
     "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": [
    "\n",
    "no2.plot(kind='box', ylim=[100,400])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f203e09978>"
      ]
     },
     "execution_count": 58,
     "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": 70,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f205add6d8>"
      ]
     },
     "execution_count": 70,
     "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')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default plot (when not specifying `kind`) is a line plot of all columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f2059a8be0>"
      ]
     },
     "execution_count": 71,
     "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": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f206dd1518>"
      ]
     },
     "execution_count": 73,
     "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": 75,
   "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": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['Age']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "or multiple columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "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": 194,
     "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": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass                                  Name  \\\n",
       "10           11         1       3       Sandstrom, Miss. Marguerite Rut   \n",
       "11           12         1       1              Bonnell, Miss. Elizabeth   \n",
       "12           13         0       3        Saundercock, Mr. William Henry   \n",
       "13           14         0       3           Andersson, Mr. Anders Johan   \n",
       "14           15         0       3  Vestrom, Miss. Hulda Amanda Adolfina   \n",
       "\n",
       "       Sex   Age  SibSp  Parch     Ticket     Fare Cabin Embarked  \n",
       "10  female   4.0      1      1    PP 9549  16.7000    G6        S  \n",
       "11  female  58.0      0      0     113783  26.5500  C103        S  \n",
       "12    male  20.0      0      0  A/5. 2151   8.0500   NaN        S  \n",
       "13    male  39.0      1      5     347082  31.2750   NaN        S  \n",
       "14  female  14.0      0      0     350406   7.8542   NaN        S  "
      ]
     },
     "execution_count": 76,
     "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": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.set_index('Name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26.55"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Bonnell, Miss. Elizabeth', 'Fare']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bonnell, Miss. Elizabeth</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
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       "      <th>Saundercock, Mr. William Henry</th>\n",
       "      <td>13</td>\n",
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       "      <td>3</td>\n",
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       "      <td>20.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>8.050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andersson, Mr. Anders Johan</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.275</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                PassengerId  Survived  Pclass     Sex   Age  \\\n",
       "Name                                                                          \n",
       "Bonnell, Miss. Elizabeth                 12         1       1  female  58.0   \n",
       "Saundercock, Mr. William Henry           13         0       3    male  20.0   \n",
       "Andersson, Mr. Anders Johan              14         0       3    male  39.0   \n",
       "\n",
       "                                SibSp  Parch     Ticket    Fare Cabin Embarked  \n",
       "Name                                                                            \n",
       "Bonnell, Miss. Elizabeth            0      0     113783  26.550  C103        S  \n",
       "Saundercock, Mr. William Henry      0      0  A/5. 2151   8.050   NaN        S  \n",
       "Andersson, Mr. Anders Johan         1      5     347082  31.275   NaN        S  "
      ]
     },
     "execution_count": 79,
     "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": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></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",
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       "    <tr>\n",
       "      <th>Name</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Braund, Mr. Owen Harris</th>\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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Survived  Pclass     Sex  \\\n",
       "Name                                                                           \n",
       "Braund, Mr. Owen Harris                                    0       3    male   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...         1       1  female   \n",
       "\n",
       "                                                     Age  SibSp  Parch  \\\n",
       "Name                                                                     \n",
       "Braund, Mr. Owen Harris                             22.0      1      0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...  38.0      1      0   \n",
       "\n",
       "                                                       Ticket     Fare Cabin  \\\n",
       "Name                                                                           \n",
       "Braund, Mr. Owen Harris                             A/5 21171   7.2500   NaN   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...   PC 17599  71.2833   C85   \n",
       "\n",
       "                                                   Embarked  \n",
       "Name                                                         \n",
       "Braund, Mr. Owen Harris                                   S  \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...        C  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:2,1:]"
   ]
  },
  {
   "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": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.loc['Braund, Mr. Owen Harris', 'Survived'] = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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": 84,
   "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": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Fare'] > 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</th>\n",
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       "      <th>McCarthy, Mr. Timothy J</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <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",
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       "    <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",
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       "      <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",
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       "    <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": 85,
     "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": 86,
   "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",
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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>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": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"titanic.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.72664459161148"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Code here\n",
    "a=df[df['Sex']=='male']\n",
    "a['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.915708812260537"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Code here\n",
    "b=df[df['Sex']=='female']\n",
    "b\n",
    "b['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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",
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       "      <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",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
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       "      <th>882</th>\n",
       "      <td>883</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dahlberg, Miss. Gerda Ulrika</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7552</td>\n",
       "      <td>10.5167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>886</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</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",
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       "    <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",
       "  </tbody>\n",
       "</table>\n",
       "<p>314 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "8              9         1       3   \n",
       "..           ...       ...     ...   \n",
       "882          883         0       3   \n",
       "885          886         0       3   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\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",
       "8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "882                       Dahlberg, Miss. Gerda Ulrika  female  22.0      0   \n",
       "885               Rice, Mrs. William (Margaret Norton)  female  39.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \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",
       "8        2            347742  11.1333   NaN        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "882      0              7552  10.5167   NaN        S  \n",
       "885      5            382652  29.1250   NaN        Q  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "\n",
       "[314 rows x 12 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Code here\n",
    "b"
   ]
  },
  {
   "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": 96,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    5\n",
       "Survived       5\n",
       "Pclass         5\n",
       "Name           5\n",
       "              ..\n",
       "Ticket         5\n",
       "Fare           5\n",
       "Cabin          2\n",
       "Embarked       5\n",
       "Length: 12, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Code here \n",
    "df[df['Age']>70].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
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       "   key  data\n",
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   "source": [
    "# Code here\n",
    "df"
   ]
  },
  {
   "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": 97,
   "metadata": {
    "run_control": {
     "frozen": false,
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   "outputs": [
    {
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       "   key  data\n",
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       "7    B    15\n",
       "8    C    20\n",
       "\n",
       "[9 rows x 2 columns]"
      ]
     },
     "execution_count": 97,
     "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": 98,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90"
      ]
     },
     "execution_count": 98,
     "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": 100,
   "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": null,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "     data\n",
       "key      \n",
       "A      15\n",
       "B      30\n",
       "C      45"
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    "df.groupby('key').aggregate(np.sum)  # 'sum'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And many more methods are available. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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": 105,
     "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": 106,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"titanic.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
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       "      <td>male</td>\n",
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       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
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       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "      <th>2</th>\n",
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       "      <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",
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       "      <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",
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       "      <td>53.1000</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
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       "      <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": 107,
     "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": 109,
   "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": 109,
     "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": 110,
   "metadata": {
    "clear_cell": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3838383838383838"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Code here\n",
    "df['Survived'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>:\n",
    "\n",
    " <ul>\n",
    "  <li>Calculate this survival ratio for all passengers younger that 25 (remember: filtering/boolean indexing).</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "clear_cell": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4019370460048426"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Code here\n",
    "df[df['Age']>25]['Survived'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>:\n",
    "\n",
    " <ul>\n",
    "  <li>What is the difference in the survival ratio between the sexes?</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "clear_cell": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</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",
       "  </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",
       "5            6         0       3   \n",
       "6            7         0       1   \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",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      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  \n",
       "5      0            330877   8.4583   NaN        Q  \n",
       "6      0             17463  51.8625   E46        S  "
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Code here\n",
    "df[df['Sex']=='female']['Survived'].mean()-df[df['Sex']=='male']['Survived'].mean()\n",
    "df[:7]"
   ]
  },
  {
   "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": 131,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f219f6a438>"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JbgGuB6iqtyS5Dng1cLR3Z+tdSe7r7Tuc5M1V9ekRfx/S0Dxz1+XmU8DrkzwPIMlzB6x5FvBfSZ7Kwpk7vbUvrKr7qmofC3c/bkzy48CJqvpjYAq4bN+FUBcXz9x1WamqY0neBfx9ku8BXwZuWbTst4D7gK8AD7IQe4D39H5gGhb+krgf2Au8Icl3WXjL2f0r/k1IHfgDVUlqkJdlJKlBxl2SGmTcJalBxl2SGmTcJalBxl2SGmTcJalB/weESrs8Sx/JzQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code here \n",
    "df.groupby('Pclass')['Survived'].mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>:\n",
    "\n",
    " <ul>\n",
    "  <li>Make a bar plot to visualize the average Fare payed by people depending on their age. The age column is devided is separate classes using the `pd.cut` function as provided below.</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "clear_cell": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.13533528],\n",
       "       [0.01831564]])"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['AgeClass'] = pd.cut(df['Age'], bins=np.arange(0,90,10))\n",
    "a=np.array([[2],[4]])\n",
    "b=np.exp(-a)\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "clear_cell": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f219f54630>"
      ]
     },
     "execution_count": 139,
     "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.groupby('AgeClass')['Fare'].mean().plot(kind='bar')\n"
   ]
  },
  {
   "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": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is: 79.28579503293565\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Code here, Refer to google for any doubts firsthand there is precedence for this task\n",
    "def sigmoid(z):\n",
    "    g = np.zeros(np.shape(z))\n",
    "    g=np.exp(-z)\n",
    "    g=1+g\n",
    "    g=1/g\n",
    "    return g\n",
    "\n",
    "\n",
    "def gradient(X,y):#X&Y are training set. K is set given as input\n",
    "    \n",
    "    m =np.shape(y)[0]\n",
    "    theta=np.zeros(6).reshape(6,1)\n",
    "    for i in range(0,2000):\n",
    "        g=sigmoid(np.dot(X,theta))\n",
    "        theta=theta*(1-(0.05/m))-(0.09*(np.dot(X.T,(g-y)))/m)\n",
    "       \n",
    "    m = np.shape(X)[0]\n",
    "    p = np.zeros(m).reshape(m,1)\n",
    "    g=np.dot(X,theta)\n",
    "  #  print(g)\n",
    "    for i in range(0,m):        \n",
    "        if g[i]>=0:\n",
    "            p[i]=1\n",
    "        else:\n",
    "            p[i]=0\n",
    "    return p    \n",
    "    \n",
    "\n",
    "           \n",
    "df=pd.read_csv(\"titanic.csv\")\n",
    "df['one']=1\n",
    "df['sex']=0\n",
    "df['embarked']=1\n",
    "# male->0\n",
    "# female->1\n",
    "# embarked S->1\n",
    "# embarked C->2\n",
    "# embarked Q->3\n",
    "\n",
    "for i in range(0,891):\n",
    "    if df.iloc[i,4] is \"female\":\n",
    "        df.iloc[i,13]=5\n",
    "    if df.iloc[i,11] is 'C':\n",
    "        df.iloc[i,14]=2\n",
    "    elif df.iloc[i,11] is 'Q':\n",
    "        df.iloc[i,14]=3   \n",
    "        \n",
    "X=np.array(df[df['Age']>=0][['one','Age','Pclass','Fare','embarked','sex']]).reshape(714,6)\n",
    "Y=np.array(df[df['Age']>=0][['Survived']]).reshape(714,1)\n",
    "p=gradient(X,Y)\n",
    "K=0\n",
    "for i in range(0,714):\n",
    "    if Y[i]==p[i]:\n",
    "        k+=1\n",
    "k=k*100/714\n",
    "print('Accuracy is:',k)\n",
    "fpr, tpr, thresholds = roc_curve(Y, p)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, label='AUC = %0.4f'% roc_auc)\n",
    "plt.legend(loc='lower right')\n",
    "plt.plot([0,1],[0,1],'r--')\n",
    "plt.xlim([-0.001, 1])\n",
    "plt.ylim([0, 1.001])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show();        \n"
   ]
  },
  {
   "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": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE: 92.08585783838426 %\n"
     ]
    }
   ],
   "source": [
    "##Code here, Refer to google for any doubts firsthand there is precedence for this task\n",
    "def gradient(X,y):\n",
    "    # X&Y are training set. \n",
    "    m=714\n",
    "    theta=np.ones(8).reshape(8,1)\n",
    "    for i in range(0,5000):\n",
    "        g=np.dot(X,theta)\n",
    "        theta=theta*(1-(0.001/m))-(0.0001*(np.dot(X.T,(g-y)))/m)\n",
    "   \n",
    "    p=np.dot(X,theta)\n",
    "    return p  \n",
    "    \n",
    "           \n",
    "df=pd.read_csv(\"titanic.csv\")\n",
    "df['one']=1\n",
    "df['sex']=2\n",
    "df['embarked']=1\n",
    "# male->2\n",
    "# female->1\n",
    "# embarked S->1\n",
    "# embarked C->2\n",
    "# embarked Q->3\n",
    "\n",
    "for i in range(0,891):\n",
    "    if df.iloc[i,4]==\"female\":\n",
    "        df.iloc[i,13]=1\n",
    "    if df.iloc[i,11] is 'C':\n",
    "        df.iloc[i,14]=2\n",
    "    elif df.iloc[i,11] is 'Q':\n",
    "        df.iloc[i,14]=3   \n",
    "        \n",
    "        \n",
    "X=np.array(df[df['Age']>=0][['one','sex','Pclass','Fare','Survived','embarked']]).reshape(714,6)\n",
    "Y=np.array(df[df['Age']>=0][['Age']]).reshape(714,1)\n",
    "K=np.array(df[df['Age']>=0][['Fare','embarked']]).reshape(714,2)\n",
    "K=K**2\n",
    "X=np.c_[X,K]\n",
    "X_norm = X.copy()\n",
    "\n",
    "m=np.shape(X)[0];\n",
    "A=X.sum(0)\n",
    "mu=A/714;\n",
    "Std=np.std(X,axis=0)\n",
    "X_norm[:,1]=(X[:,1]-mu[1])/Std[1]\n",
    "X_norm[:,2]=(X[:,2]-mu[2])/Std[2]\n",
    "X_norm[:,3]=(X[:,3]-mu[3])/Std[3]\n",
    "X_norm[:,4]=(X[:,4]-mu[4])/Std[4]\n",
    "X_norm[:,5]=(X[:,5]-mu[5])/Std[5]\n",
    "X_norm[:,6]=(X[:,6]-mu[6])/Std[6]\n",
    "X_norm[:,7]=(X[:,7]-mu[7])/Std[7]\n",
    "g=gradient(X_norm,Y)\n",
    "a=0\n",
    "a=(abs((Y-g)/Y)).sum()\n",
    "\n",
    "k=a/714*100\n",
    "print(\"MAPE:\",k,\"%\")\n",
    "\n",
    "    \n"
   ]
  },
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   "metadata": {},
   "source": [
    "# Submission rules\n",
    "* Report the AUC score of the logistic regression model and the MAPE score of the Linear Regression model on the test data\n",
    "* AUC score as .4 and MAPE as 5 percent then **name_4_95.ipynb** should be the name of your submission file \n",
    "* All submissions Are to be made as pull requests on the Business Club IIT Kharagpur's github account on the Summer assignment 1 repository (https://github.com/business-club-iit-kharagpur)"
   ]
  }
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