OSGeoLive-Notebooks/Pandas/solved - 01-pandas_introduction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--<img width=700px; src=\"../img/logoUPSayPlusCDS_990.png\"> -->\n",
    "\n",
    "<p style=\"margin-top: 3em; margin-bottom: 2em;\"><b><big><big><big><big>Introduction to Pandas</big></big></big></big></b></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "pd.options.display.max_rows = 8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Let's start with a showcase\n",
    "\n",
    "#### Case 1: titanic survival data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/titanic.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Starting from reading this dataset, to answering questions about this data in a few lines of code:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What is the age distribution of the passengers?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff346277b38>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff34625eb38>"
      ]
     },
     "metadata": {},
     "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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived\n",
       "Sex             \n",
       "female  0.742038\n",
       "male    0.188908"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Sex')[['Survived']].aggregate(lambda x: x.sum() / len(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Or how does it differ between the different classes?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff34614a438>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WPVlV1/TvSBJguR9XPwi8pqruS3IzcB1AVb01ybXAjcCR3p2tdya5t7fvUJK3VNVnRvx9\nSEPzzF2Xm08Dr0/yXIAkzxmw5pnAfyV5Kotn7vTWPr+q7q2qvSze/bgxyY8Dx6vqj4Fp4LJ9F0Jd\nXDxz12Wlqo4meQ/w90m+B3wFuHnJst8C7gW+CjzAYuwB3tv7hWlY/J/EfcAe4A1JvsviW87uW/Vv\nQurAX6hKUoO8LCNJDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDfo/vti8bDCq8cMAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff34618a898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('Pclass')['Survived'].aggregate(lambda x: x.sum() / len(x)).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the needed functionality for the above examples will be explained throughout this tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Case 2: air quality measurement timeseries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "AirBase (The European Air quality dataBase): hourly measurements of all air quality monitoring stations from Europe\n",
    "\n",
    "Starting from these hourly data for different stations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BASCH</th>\n",
       "      <th>BONAP</th>\n",
       "      <th>PA18</th>\n",
       "      <th>VERS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01 01:00:00</th>\n",
       "      <td>108.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.0</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01 02:00:00</th>\n",
       "      <td>104.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01 03:00:00</th>\n",
       "      <td>97.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01 04:00:00</th>\n",
       "      <td>77.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01 05:00:00</th>\n",
       "      <td>79.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     BASCH  BONAP  PA18  VERS\n",
       "timestamp                                    \n",
       "2000-01-01 01:00:00  108.0    NaN  65.0  47.0\n",
       "2000-01-01 02:00:00  104.0   60.0  77.0  42.0\n",
       "2000-01-01 03:00:00   97.0   58.0  73.0  34.0\n",
       "2000-01-01 04:00:00   77.0   52.0  57.0  29.0\n",
       "2000-01-01 05:00:00   79.0   52.0  64.0  28.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "to answering questions about this data in a few lines of code:\n",
    "\n",
    "**Does the air pollution show a decreasing trend over the years?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff3428f4320>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lOkH5PTbU+BwYiiQhK6q4p8S2GYY+nDRGUpW7bVh75zmG8XLoQy0HIwnMrPak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\nS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lbknDPFZhRMYk24KoVeryEACk9VDYvJgmc+9Ayumq0c6eY0E7lpET0jHj2i53keBYHo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\nTnsP0n19UBbMh9wcRGZ8HIktWyB3dhZk4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MHBXHensckyiSXrZmVNO9rgIKRp1mcOgHIw\nCCJJkJqaoA3kAj1AM2YgF9RmggKJJcvoffO477ygKDTQM+rGRoLLr9dGpYsWLb2R0bvimaKBXmps\ntO8R8fuBqRgcogP39d4HNaviiT7KNSuTMUhNuRMy4+jTe/daNOVA7iRRjIvW2ClOljHx67uR2rED\nyGQw8eu7jedlDfROY/ZCssfeCI/P1TVko3JrK7UAaTFen6EhE3XjtqiCzOCvXSpL5xUsXgxks2j4\n3OfQcMUVmPerX0FqaoKjq8tyimMc/am178Z1a68DIQTXrLkGH12eS1AyY2O8GdKOumGBnihKgaPu\nwpqFeGP8DYRSIaxtWQstNFGWLXE+3jGBvhg8xpBjeV5n0UxI6eigbo+H8u+ecAIEjweD//41Lp9j\nGmNmY1wORK+Xc56C2436T3yC2ypXCjs+NzMxAbG+jlbvBaEkTcCv09YGtX+Az99lp4T0/j4o7e0z\nqnMwiLW1lKIoMvwlE54EslnLkPN8qENDfHPhgd4oyHb4OnDLabfgnPnnIBOlunS7bmQzWMMUcTq5\n5NDM9bJAH0jR52nXPV0M+Z3Yp3Wehg8t/RAdcTgyyrNiweNBJsY4+tLUDWDldHlWGtfK2oQAQAj4\nkY1E0ORuwmRqEi7JBU3XQLI6pHAMonFqAHIcPbLZguwyZ1VsH+jZ5u479VQgk4H7uHdDCgaRfP11\nOqPB9HcAqhgSGxuKjqDURkcB475L78llu+y+UAcGuUWx4HZBamqi6rE8Pyn22jlSWQgeD7zrT4Hr\nXe9C3Uc/Qr8/vxvdf/4T2m/5mfV189JN84yGE3jBvnCNY1C6u0AUxXaADDPFcx51lG1Gz7A2uJa6\nVZbRCZuPd3yg966jgd73nveUbfI/GyjtbWj51jeReOUVjN5yC4CcV7dZdvZ2Qm5rQzYatdzcrBu2\n/rJPoumLX5yRfJNNmkq+/jqAXDaZPnAAchn8PADuoV1s3gDLuiL3P2D782w6TT3zmZrK8CEyF2RP\naj+JmnEZjV52jqFmsIYpORjk95AcDHJOd0ntEhAQNKp0Q5jJ5sigtLcjMzbGs8vjWo/DdWvp6VIb\nGbEG+tExQNNKXl9pawMkCQPXXov+L38ZuqryrFSJpSH4ywv0op+qUZhN9OVHXw6f4kMgDhrsG80Z\nfS6BybfbznewzAdzSq390IfgPflkBK+/np/A7WY0AIBz6bISGf2IRUHENkAz7cYMzQSXi/tC5VOZ\nHtkDp+iEksxA8HrR+NnPouu3v7EkWVJtbYGMVvC4AUHgjre2azRmO0gNDXxWb/5zAAD3ypV08p3J\nQZUpb7oD3WiSaykl/M+c0StdXWj/r1vQcPnlb/nf9p91FvWm/ivNPvl4tQoy+jcDZmMtpl7IjNMh\nHK6jj0b9ZZ+c2XUMb3V2ckkbHbfq/v0zllYy5CaI2dM3mfAkIIpIvvEGn45khsYliXnUjUliyVDK\nX8gMIkm0M9PEBcutLdAGB6Fns3DLbnQHulGrUs7Xrnu6GPLlqZbnYg70bjd/fUtl5YLHg+777kXg\nrDMRuf8BJLdtQ72zHkJWh5xQy9qEALpp6ek0WqR6CETAeQvOw6kdp6LWGLVgDvTE6eRZdH5HMFMK\n5deA+HM1MnrHwgXo+Pn/g6O72xToW21/x7l0CVK7d0O3Of1pIyNQ5s3jPQJs4yGSBKmZ0m7M+oS4\nXLaiAoDWsm44/gbUZByWAe/TgRBizHouVN7xNY6N8ZNJMY5e8Pv51DOzQm5BzQKIRMRxLcdBM+ob\n5bhV5uMdH+gBehws9wY/VGDHrmwiwS2KxbmS0RtZzL5LPohd7zkduqbRjL7MG8b8QVTmzYM2MABt\nZATZeLxsCSiTc2ZCIcQ2bsTwTf9haSbJTobhO+00QBAQ/uOfAACRR/6G8TvvBGBtlgIKOXoz+DD2\naQI9QJumzHpzqaUFuqpyldIZXWdgiUSvU4pDzwfTh+dzsHomw33nzc8DmP7E4Fy+HI2GHUJi6zbU\nu+ppoXgGv5sP1t364bZzccNxN6DR3YgLFl2AYIJuarI50BuDRszPiz/PliCI04n0rt2wA6NuzOtz\nr14FKRi0HVYDGJPJVLXAf0bXdTpApamJy5fNUk85SLX0Zo4+V9uwZvQAcM78cyDGU7zYPFMIRZom\nAfB7R2qgdtPFOHqpqdF2YJBbduO2M27DlcdcmXvt/pmpm7cbjoULAV1Has8ebko2VzJ655LFqLnk\nErjXrIY2OEj1z6pa9g3DAqWycAEcy5ch3d+fGzSSZzkxHZjvfSYUQviPf8LE7bcj9swzAKg1cyYS\ngWPhAnjXr8fk734HLRTC0A03YOT7NyL5xhvcFI2tyY66YVD7+0EcjmnllQDQ9KVrUPvRXCHNrKUH\ngM+u+CzOqqdNUuW0nrNMM/naaxi95RZk2ISmiQkgk7FQNwwzCdZSMAixoQHJ119Hm7cNK11U2VUu\nR8/qDYulVly46EIAdNj9jcuvpX8nX65srDOfoyeiCMfixUUtBDITIQh+v4USIZKE+ff/GY2ft5ce\ns+bH/GtmIxHoqRSkxkbuJ2Wx6jbqK5yj91DFDHE6bU9Wuq4jE4tZqKmZQPAXz+jZyYa5kBYL9HJT\nU9FkYE1wDWqcNSa/pn9i6ubthmMR1camd+3KZfRlFOveTBBFQcs3vo7Gq64CkLMeKPeGYRy3e+Uq\nKG1t0AYGueWtY0l5gZ5RN1ooxG/s0Z/8FLquU521TiWCdR//ODKTkzhwxRXITE6CuFwY/Nr1GPt/\nP4fv9Pdw6el01E0x/jcftRdfDNdRR/Kv8wM9YEgHvd5pzdvMkBobQRwOjP3Xf2Pspz/jvk1MB87n\nt5p7NGYQ6AkhcB1xBBLbtsIpOfEfK64HUH5Gz7n1PE8kVjwU8wI9C4ZsgpYZzmWUU7dr98+EQpbh\nNgyi319g98CgzJsH4nAUFGT5a9fUCOeyZdRqxCRFZrQbE0cILldRuTFArS+gqmUHetHnt7Uqp2tk\nQ15ooM+EQpyai7/yCtT+fqgjI5CamiE1NhinoV3212KbRhn+8/moONATQjoIIU8QQrYTQrYRQq4y\nvl9HCHmUELLT+H/lq3sHQOnsBGQZqV27udve20UjFQNriWfud+VSN4LHg5bvfw/1l3+K6rhVFdFn\nn6MyzTKlpFydEQpBPXiQ6pu3bkX0iSd4IU8IBOA+dg0cS5YgueU1uI89Fo1XfQHJbdtAnE4Er7+e\nB+8cdWOf0c+EtrGD3fCL7DSKGDsQQuBctsxUDKQZJZOPyrYZ/cyoIedRRyG9ew9VF+WZoc0UjHpL\n7bbWQ7TRUYiBQIGniuD10gZEm9fBuWwZspGILT2ihSbKGpQB0Izf7pSgmV67mgsvxPyHHrTQaUpX\nF3RVRWoH3SBYQ5zc2mob6PmGUMImww6Cz1t0nq9lrkBjA6Dr0CZC0LNZHPj0FThw5Wc4/UQEgdb6\nHnrYth7BZzu/TRy9BuCLuq4vA/BuAJ8lhCwH8FUAj+u6vgjA48bXhy2ILMPRNQ+pXbsQfeppOJcv\nnzMZPYPo80FqbERik5HRV8D11Zx/PpT2dh44E6+8AkeZtA1AXy8NUpOFAAAVM0lEQVTB74c2NAxt\nZAQ1H/4QxLo6TP3t0ZxFb00N9XD5xCcAAA1XfBq1H/4wfGduQOv3vmdVghgfYnuOvr9ooW86iDU1\nEHw+pEwzfzOmYRvloOMXt2H+ww9BamriXbIsWNlTNzP7G66jjqS04fY3LJtkOWADRZJ5g9tpEGos\neLzU1ATHAns5IRtoktz+RsHPMqHJigKV610rkHjlFa5oA3KbpNTYCCJJFn6eroOqceKbX6azIQy6\nSG4rlBsDJll0JRm9aV1mZExjG1ljmzY2ivRuaqaY2rGDDpY33v+a91+ITCiEqSeeLLxWaAIwTUSr\nBBUHel3XB3Vdf9n49xSA7QDaAJwH4C7jYXcBOL/i1b1DoCxciMSrryKxZQu8p576di/HFsqCBTwY\nzKbxgmWmyGaLFtGmg1RbS4ei6DqUefOgdHRAHR7KZaVGsAqcfx7mP/QgPMcfD0FR0P7DH8J36nrL\ntYgk0eHecWtGP1MNfTEQQuA84ggkX9+au2YFGT1AA4igKJYGNj4y0AgCrNZAnE5LY14pOI+kVFPi\n9a3cmbHcYEAHty+xpUfy+XkACF7/NbT9+Me213IsWgQIAlLbCyWRmVCorEEZDP4NZ0JPpxH9+98t\nawMK6wd8HQsWALIM9eBB62yK1jZkJicLkgIuiy430Pt9RTN6dYiqw8T6+tyQ98FBxF+l5ngOo1ue\nbaaeE06A1NyMyT/8vuBa2sQE9TAqo18lH4eEoyeEdAF4F4AXATTruj4I0M0AwKHtVJqDcCxcSCvj\nug7v+lPe7uXYwmFyNJzNEZAHehQ6hc4UYm0tkjt2AKBqCSkYhDY0zDX0jLoghMBhqCpKwTx8hIFR\nJOb1lgvXUUch2dvL9c3TGY5NB2ugH4FYX8+zTZbRl7ORSPX1kFpbkNy6NbdJVtCV7VyyFMneXosn\nerFAL9XVFW0+FFwuKPO7C6gWXdepAqUCjtm14hhILS2IPPhQbm0jo5RC8tjLIYmiwGk0T1oDvb2W\nno1HLFt14/UhG4vZGq8lt22DsmABBIcDjiVLQNxuRJ95FoktWyAEAmj/2U/hWbcOrhV0dCYRRQQu\nOB+xZ57ljVQMmYnQrD6zwCEI9IQQL4DfA7ha1/Xi3QOFv3c5IWQTIWTT6CzdDN9uOBbSm0pqbrY0\nccwlKAtooCdut+XmLxeC281vOsfSygM9DAdDuaMDcrAZ6vCwibops6BoeLmbwa1lK8zoAcB59FFU\n3mcojCrN6BnkNmMQiaZZNPRAZYEeAFxHHImEEegFn6+sQjGDY+kS6PE41ANU3scmIxXLmEvBuWx5\noUomFodegdoLoENI/Bs2IPrcc3wzU/v6pl0bm6RlCfRtRQK9ce9UktEDOeqHQdd1JLZs4WZogtMJ\n78knYeqxx5B4+RW4jjkaSmcnOm+71eLD5N+wAchmEXvhecv16CS42ZU6ZxXoCSEyaJC/W9f1Pxjf\nHiaEtBg/bwFga1qi6/qtuq6v1nV9dWMFN9RcAmt48K4/5S3tzi0HjFedTeWeQW5theD1VkyLsKIc\nNaJqhNQcpIHGCM7l1jioT4yVuilHQ18M7IOaeO11GvzyxueVC7m9jToyDg8XcOCsEFhuoHcedRTU\nvj6ofX0Vb0LM2jtp0DeZyUnoqlpZoD9iObShIYsHFOuKLbcYy+A/60xAVTH07e9g5Ac/QPSpp7j1\nSdF1GAkXcVupG4AGei0U4ieYbKzSYiy9F/KVN+rBg8hMTNBxjuw5nHEGMuPjSO/Zw7P4fDgWL4YY\nCCD+D+s4Qy00YZntXAlmo7ohAH4JYLuu6/9p+tH9AC41/n0pgD9Xvrx3BpTubtR/6l9Qd+ml0z/4\nbYLSTTP62R4BAbqh+c85u+JNjXG13IjKmAKW7O0pW74I2FM36f37qeNgnodKOZCbmyE1NSHx+mvQ\n43Hag1DmacMMhfvUHKD2ESZHVaZPL/f6riPpxKjYSy9VvAkxbp25O7JCYiWvnfeEEwAA0aef5t/j\nDT8VZqXOI49E7Uc+gshDD2H8F79E4IIL0PzVr5T+HcP8UHDlgrfU2AAiyxj5wc3Yedzx2HHc8Rj6\n9ncqLsayfpl8nj7xKp314FqRC/SedSdxGal5AzCDCAJca1YXzK09FNTNbGyKTwDwMQCvE0JeNb53\nHYDvA7iPEHIZqF35RbNa4TsARBDK8rd/OyA1NULwemd9BASAxs9+dnZrMTI71nTDpIyp3h0VKQsE\nt7vAETO9Zw8c3d2zPmE5jz4KyddeLygUVwJ2uog89DCykQg8717Lf0YUpWAw+4zWZ4wG1OPxijch\nwemE0t2NxEubELr3PkSfovbJlWT0ysKFkFpbEHvmGdRefDGAXKCv9DRJCEHwa/+O+ss/hcRrr8F3\n2mnTejQ5liwBBMFC3RBBgPv446ANj8D/3jMQfe45hH7zGzRccQWACqgbltHnNU0lXnsNxO22+M+L\nXg88J56I6BNP8JOiHTxr1iD62OPcoVXPZpGZnKyokG1GxYFe1/VnART7FJ1W6XWreHNACEH9v1wG\nub08E7I3AyIP9DTwMeuBzMREWZPtGewGrKT27oF7ZWXTeMxwHXU0oo89jnQf5a/LDcRmSC0tACEI\nP/AAIAgFE8cC558Hz7rpRxSaIQYCkOd1Qt3fN6u1OZctQ+Qvf0F80yaIdXXwbdhgmS87UxBC4D3p\nJETufwB6Ok2dG5kOfJa0odzcDPn002f0WMHlguvoowuK8Z0//zn/tzJ/Afo3babT1kTR1q+/FHIc\nfV6g37IFriOOKDiZNl59FXynnVqyYO5eQ0cFxl96CYFzz6UJRjY7K58b4B0yeKSKQwOWubzdYB94\npn+WGhsBQnhXbLnIL8Zm43FoA4NQPjC9Ymc6sGM2y3LFMt0hzRAUBVJzM7ShIbhWrCg4vbR861uV\nrfHIo6Dur5yjB4Cmf70a3lNOgeuoIyF3ds7qJOQ96SRM3nMv3TRqapB4mdpWHArasBx0/vIXQAmr\nb+cSKnFMvPoqpQzLfM5s7oQ2Ns6/l02lkNy+HfWfKKRxZzKEyLFkCQS/Pxfomf3BLDn6aqCv4i2H\nZHjPMBsDoigQG+qRGR2riH7I5+jT++l8TUfekOxK4FpxDIgsc+uC2XD0AKVvtKEhOhv4EMF55JGI\n/PWvs5Z+BmZRuDbDs3YtiCzjwKevyDlyNjYUlUO+WZju78kdHSAuF7KxWEUyXKm5GVJrC4Zvugli\nfR38p5+O6BNPAKoKd4VDg4gowr16NWIvbOSyVGB2PjdA1eumircBzqOPRusPfgDf+lzzk9xsuFFW\nktF73MjG49xjhdkbswL0bCA4nXCtWMEHQ8zW3oJJ/LxlUjSlwDx65or1huDxoO4Tl8Kzbh1ab/w+\nuv/4Byx8+OE5p0gjosiHFVWyCQmKgq7f3gPHggXov+pqJLZswcT//BpyRwc8xx9f8bq8606EevAg\n0nv2cDO02VI31UBfxVsOQggC55xtHe5gKG8qo248QDbLBy2n9+wFCIHSVZ6FcjG41+aKprMNpp5j\nj4XziCN4EfVQwHnkkfCsW8f53bmApi9+ER3/dQsC550H57Jlb3k2P1Mw+qbcQiyD3NyEzjtuh9TU\nhINXXY3E5s2o/ciHZ9XF6j3lFABA9MknMfXY4xD8fu5XVSmqgb6KOQG5yQj0FahumKVDynD/S+/d\nQz1cTEO/ZwP3sTSAElkuu2CXj5oPfADdv//fGU32mikEpxOdt91qcd+sYmZwLKZ+TZUGeoDKMoNf\nvwHa0BCI242aCy+c1ZrklhY4li5F+IG/YOrRRxF43/tmfS9XA30VcwJMYlkJz+w74wwIPh/GDEVF\nau8+KPNnX4hlcK1YAeJwQKgJzDn6oYrZwcEz+tmdOHynnILGq76A5i9/6ZCYGnrXn4JUTw/0dBo1\nF31g1terBvoq5gTkWVA3ot+PuksvRfSxxxF/+WWk9+6F4xDw8wyCosC9ahWk+sqbr6qYm2AqmHKb\npezQcOWVqP3gB2d9HYBuHACl5ZwVWo2YUVXdVDEn4Fi6FJBlPjGoXNR9/GOYuOsu7P/wR+j1Fi86\ndIsD0PLtbxXYLFTxzodYUwPfGWfMqfoGQK0tfKe/B4ELLjgk1yN202DeaqxevVrfZHilV/HPC13T\nKjLlYog+8yySb7wBZX43fCefXHRyURVVHC4ghGzWdX31dI+rZvRVzBnMJsgDVJbmXVfa7KqKKv4Z\nUeXoq6iiiioOc1QDfRVVVFHFYY5qoK+iiiqqOMxRDfRVVFFFFYc5qoG+iiqqqOIwRzXQV1FFFVUc\n5qgG+iqqqKKKwxzVQF9FFVVUcZijGuirqKKKKg5zVAN9FVVUUcVhjmqgr6KKKqo4zFEN9FVUUUUV\nhzmqgb6KKqqo4jBHNdBXUUUVVRzmqAb6KqqooorDHNVAX0UVVVRxmKMa6KuooooqDnNUA30VVVRR\nxWGONy3QE0I2EEJ6CSG7CCFffbP+ThVVVFFFFaXxpgR6QogI4BYAZwJYDuBDhJDlb8bfqqKKKqqo\nojTerIz+WAC7dF3fo+t6GsA9AM57k/5WFVVUUUUVJSC9SddtA3DA9PVBAGvNDyCEXA7gcuPLJCFk\n2yH8+50A+g7h9QIAwofweodyfXN5bcA/1/rm8tqAub2+ubw2YO6ub96MHqXr+iH/D8BFAH5h+vpj\nAH5a4vG3HuK/P3qIrzdn1zeX1/bPtr65vLa5vr65vLZ3wvqm++/Nom4OAugwfd0OYKDE4x84xH9/\n8hBfby6vby6vDfjnWt9cXhswt9c3l9cGzP31lcSbFehfArCIENJNCFEAfBDA/cUerOv6oX7Sh/LI\nNqfXN5fXBvzTrW8urw2Y2+uby2sD5v76SuJN4eh1XdcIIZ8D8AgAEcDtuq4fSg5+Otz6Fv6tSjCX\n1zeX1wbM7fXN5bUBc3t9c3ltwNxfX0kQgy+qoooqqqjiMEW1M7aKKqqo4jBHNdBXUUUVVRzmeEcE\nekJIByHkCULIdkLINkLIVcb36wghjxJCdhr/rzW+TwghPzHsF14jhKw0XethQsgkIeQvc219hJB5\nhJDNhJBXjetcMVfWZvwsY6ztVUJI0eL627E+Qsh609peJYQkCSHnz4W1GT+7kRCy1fjvktmsaxbr\nW0oIeYEQkiKEXJN3rdsJISOEkK2HYm2Hcn2EECch5B+EkC3Gdb4xV9Zm/GwfIeR1477bNNu1vSl4\nK7Wcs9CctgBYafzbB2AHqLXCTQC+anz/qwBuNP59FoCHABAA7wbwoulapwF4H4C/zLX1AVAAOIx/\newHsA9A6F9Zm/Cw6l99b0zXrAEwAcM+FtQE4G8CjoOIHD4BNAPxvw2vXBGANgO8AuCbvWicBWAlg\n69v43tquz3g9vca/ZQAvAnj3XFib8bN9ABoO9WfjUP73ti+gwjfpzwBOB9ALoMX0xvUa//45gA+Z\nHs8fZ3x9Cg5hoD/U6zO+Vw/aiTerQH8o14Y3IdC/Sa/d5QDunitrA/AlAP9u+v4vAVz8Vq/P9Liv\n5wcr4/tdOISB/lCvz/iZG8DLANbOlbXhHRDo3xHUjRmEkC4A7wLd1Zt1XR8EAOP/TcbD7CwY2t4J\n6zOOlK8ZP79R1/VSjWZv6doAOAkhmwghG2dLi7xJ62P4IIDfzqG1bQFwJiHETQhpALAe1obCt2p9\nbxtmuz5CiEgIeRXACPD/2zuXUKuqMI7//paY5ONaGWURoTgozO6gMtIa2QMnFlQSUVqSiKMGzQIb\nRDiQHoMGQQQ1iKJ8RM0U9WJoGUHqNTPMHPRCe3kVEjH9N/jWicPN8t579vHsc/p+sDjrrLPPWv+z\n91nfXns9vsVm27vqog0wsEnR7brivEd3gHb5umkLkiYB64GnbR+X9K+HniOt7fNIq9Bn+ztgrqQZ\nwAeS1tk+UgdtwHW2f5Q0E9gqadD2oVa1VagPSVcDNxFrOCqhVW22N0m6FdgJ/Ax8AvzZAX0doQp9\nts8A/ZL6gI2S5thueTyhonM3v9SLK4HNkg7Y3t6qtirpmha9pPHEBXnb9oaSfKRU7EYFP1rSR+uC\noXb6Skv+S+DOumhrPF3Y/hYYIFpBLVPxuXsY2Gj7dJ202X7Bdr/tu4kbwsEO6LvgVK3P9jHiv3df\nXbQ11YujwEbCe2+t6ApDr7jNvgF8Zfulpo8+BJaW+FKin62R/riC24GhxuNYnfVJulbSxJLnNGA+\n0WdYB23TJE0oeV5RtO1vRVuV+pq+9wgVddtUeO4uknR5yXMuMBfY1AF9F5Sq9EmaXlrylPqxEDhQ\nE22XSprciAP3AJXNXKqMTg8SjCQAC4jH873A7hIWEQOWW4jW0RbgsnK8iI1PDgGDwC1NeX1MPD6f\nJFpg99ZFHzEYtJfo090LrKiRtjvK+z3ldXkNr+31wA/AuDppAy4hbor7gU+B/g7pu6r8548TTrq+\np8z+IW6OPwGnS3rL17cqfcSN8YuSzz5gdY20zSx1Yg/xBP5sFde26pAuEJIkSXqcrui6SZIkScZO\nGvokSZIeJw19kiRJj5OGPkmSpMdJQ58kSdLjpKFPuhJJfZJWlfgMSevaWFa/pEXtyj9J2k0a+qRb\n6QNWQaxMtP1gG8vqJ+ZYJ0lXkvPok65E0rvAYmLl8EHgBttzJC0D7if2Kp4DvEi4f34MOAUssv2b\npFnE4qbpwB/AU7YPSHoIeA44Q2wIvRD4BphILMZaAxwGXilpJ4EnbH89irIHiAU6txGLbp60/Vl7\nzlSS0B0rYzNkGB5ocqk7LL6MMMyTCSM+BKwsn71MOK+CWPU4u8TnAVtLfBC4psT7mvJ8tansKcDF\nJb4QWD/KsgeA10v8LtroGjhDBtvd5b0ySUbINtsngBOShoCPSvog4Rl0EuHS4f0mb4UTyusO4E1J\n7wEbODdTgbckzSaW0Y8fadlNx70DYHu7pCmS+hwOu5KkctLQJ73Iqab42ab3Z4n//DjgmO3+4V+0\nvVLSPGJXqN2S/nEM8Dxh0B9Q+DIfGEXZfxc1vOj/+D1J0hI5GJt0KyeILpJRY/s4cLj0xzf2er25\nxGfZ3mV7NfAL4XZ4eFlTif56iO6asbCklLeA8HI5NMZ8kuS8pKFPuhLbvwI7FJtZrx1DFo8CyyU1\nvA4uLulrFRs97wO2E14JtwE3KjZ/XkLsK7pG0g5i4HUs/C5pJ/AasHyMeSTJiMhZN0lygSmzbp6x\n/XmntST/D7JFnyRJ0uNkiz5JkqTHyRZ9kiRJj5OGPkmSpMdJQ58kSdLjpKFPkiTpcdLQJ0mS9Dh/\nAcJIipH1wZxBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff34617efd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['1999':].resample('M').mean().plot(ylim=[0,120])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff3427e3860>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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10qEfwVyh3vSpx2Uw6A51oF9X7dLsRB0mDBRF8Qc+Avqh9r27EzgIfAnEAkeB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\nK2So96u6RP8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff34288e358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['1999':].resample('A').mean().plot(ylim=[0,100])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**What is the difference in diurnal profile between weekdays and weekend?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff34271d320>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PYup3sdT0cuOzh1vafZIH7aL/uPbBHDp/hdjTFXuVN/v/11ZKbFVcEqHVKtG0ZhWLxSCE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\n0bg0+oiIxqXRR0Q07m/2GVQsI+JQAgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3428a5908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['weekday'] = data.index.weekday\n",
    "data['weekend'] = data['weekday'].isin([5, 6])\n",
    "data_weekend = data.groupby(['weekend', data.index.hour])['BASCH'].mean().unstack(level=0)\n",
    "data_weekend.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will come back to these example, and build them up step by step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Pandas: data analysis in python\n",
    "\n",
    "For data-intensive work in Python the [Pandas](http://pandas.pydata.org) library has become essential.\n",
    "\n",
    "What is `pandas`?\n",
    "\n",
    "* Pandas can be thought of as *NumPy arrays with labels* for rows and columns, and better support for heterogeneous data types, but it's also much, much more than that.\n",
    "* Pandas can also be thought of as `R`'s `data.frame` in Python.\n",
    "* Powerful for working with missing data, working with time series data, for reading and writing your data, for reshaping, grouping, merging your data, ...\n",
    "\n",
    "It's documentation: http://pandas.pydata.org/pandas-docs/stable/\n",
    "\n",
    "\n",
    "** When do you need pandas? **\n",
    "\n",
    "When working with **tabular or structured data** (like R dataframe, SQL table, Excel spreadsheet, ...):\n",
    "\n",
    "- Import data\n",
    "- Clean up messy data\n",
    "- Explore data, gain insight into data\n",
    "- Process and prepare your data for analysis\n",
    "- Analyse your data (together with scikit-learn, statsmodels, ...)\n",
    "\n",
    "<div class=\"alert alert-warning\">\n",
    "<b>ATTENTION!</b>: <br><br>\n",
    "\n",
    "Pandas is great for working with heterogeneous and tabular 1D/2D data, but not all types of data fit in such structures!\n",
    "<ul>\n",
    "<li>When working with array data (e.g. images, numerical algorithms): just stick with numpy</li>\n",
    "<li>When working with multidimensional labeled data (e.g. climate data): have a look at [xarray](http://xarray.pydata.org/en/stable/)</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. The pandas data structures: `DataFrame` and `Series`\n",
    "\n",
    "A `DataFrame` is a **tablular data structure** (multi-dimensional object to hold labeled data) comprised of rows and columns, akin to a spreadsheet, database table, or R's data.frame object. You can think of it as multiple Series object which share the same index.\n",
    "\n",
    "\n",
    "<img align=\"left\" width=50% src=\"img/schema-dataframe.svg\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "..           ...       ...     ...   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500   NaN        S  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Attributes of the DataFrame\n",
    "\n",
    "A DataFrame has besides a `index` attribute, also a `columns` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=891, step=1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "To check the data types of the different columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "                ...   \n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "An overview of that information can be given with the `info()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Also a DataFrame has a `values` attribute, but attention: when you have heterogeneous data, all values will be upcasted:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 3, ..., 7.25, nan, 'S'],\n",
       "       [2, 1, 1, ..., 71.2833, 'C85', 'C'],\n",
       "       [3, 1, 3, ..., 7.925, nan, 'S'],\n",
       "       ..., \n",
       "       [889, 0, 3, ..., 23.45, nan, 'S'],\n",
       "       [890, 1, 1, ..., 30.0, 'C148', 'C'],\n",
       "       [891, 0, 3, ..., 7.75, nan, 'Q']], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Apart from importing your data from an external source (text file, excel, database, ..), one of the most common ways of creating a dataframe is from a dictionary of arrays or lists.\n",
    "\n",
    "Note that in the IPython notebook, the dataframe will display in a rich HTML view:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>capital</th>\n",
       "      <th>country</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30510</td>\n",
       "      <td>Brussels</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>671308</td>\n",
       "      <td>Paris</td>\n",
       "      <td>France</td>\n",
       "      <td>64.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>357050</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Germany</td>\n",
       "      <td>81.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41526</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>16.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>244820</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>64.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     area    capital         country  population\n",
       "0   30510   Brussels         Belgium        11.3\n",
       "1  671308      Paris          France        64.3\n",
       "2  357050     Berlin         Germany        81.3\n",
       "3   41526  Amsterdam     Netherlands        16.9\n",
       "4  244820     London  United Kingdom        64.9"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'country': ['Belgium', 'France', 'Germany', 'Netherlands', 'United Kingdom'],\n",
    "        'population': [11.3, 64.3, 81.3, 16.9, 64.9],\n",
    "        'area': [30510, 671308, 357050, 41526, 244820],\n",
    "        'capital': ['Brussels', 'Paris', 'Berlin', 'Amsterdam', 'London']}\n",
    "df_countries = pd.DataFrame(data)\n",
    "df_countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### One-dimensional data: `Series` (a column of a DataFrame)\n",
    "\n",
    "A Series is a basic holder for **one-dimensional labeled data**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      22.0\n",
       "1      38.0\n",
       "2      26.0\n",
       "3      35.0\n",
       "       ... \n",
       "887    19.0\n",
       "888     NaN\n",
       "889    26.0\n",
       "890    32.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "age = df['Age']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Attributes of a Series: `index` and `values`\n",
    "\n",
    "The Series has also an `index` and `values` attribute, but no `columns`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=891, step=1)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can access the underlying numpy array representation with the `.values` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 22.,  38.,  26.,  35.,  35.,  nan,  54.,   2.,  27.,  14.])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age.values[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We can access series values via the index, just like for NumPy arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Unlike the NumPy array, though, this index can be something other than integers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Braund, Mr. Owen Harris</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heikkinen, Miss. Laina</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Futrelle, Mrs. Jacques Heath (Lily May Peel)</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Graham, Miss. Margaret Edith</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Johnston, Miss. Catherine Helen \"Carrie\"</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Behr, Mr. Karl Howell</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dooley, Mr. Patrick</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    PassengerId  Survived  \\\n",
       "Name                                                                        \n",
       "Braund, Mr. Owen Harris                                       1         0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...            2         1   \n",
       "Heikkinen, Miss. Laina                                        3         1   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)                  4         1   \n",
       "...                                                         ...       ...   \n",
       "Graham, Miss. Margaret Edith                                888         1   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                    889         0   \n",
       "Behr, Mr. Karl Howell                                       890         1   \n",
       "Dooley, Mr. Patrick                                         891         0   \n",
       "\n",
       "                                                    Pclass     Sex   Age  \\\n",
       "Name                                                                       \n",
       "Braund, Mr. Owen Harris                                  3    male  22.0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...       1  female  38.0   \n",
       "Heikkinen, Miss. Laina                                   3  female  26.0   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)             1  female  35.0   \n",
       "...                                                    ...     ...   ...   \n",
       "Graham, Miss. Margaret Edith                             1  female  19.0   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                 3  female   NaN   \n",
       "Behr, Mr. Karl Howell                                    1    male  26.0   \n",
       "Dooley, Mr. Patrick                                      3    male  32.0   \n",
       "\n",
       "                                                    SibSp  Parch  \\\n",
       "Name                                                               \n",
       "Braund, Mr. Owen Harris                                 1      0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...      1      0   \n",
       "Heikkinen, Miss. Laina                                  0      0   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)            1      0   \n",
       "...                                                   ...    ...   \n",
       "Graham, Miss. Margaret Edith                            0      0   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                1      2   \n",
       "Behr, Mr. Karl Howell                                   0      0   \n",
       "Dooley, Mr. Patrick                                     0      0   \n",
       "\n",
       "                                                              Ticket     Fare  \\\n",
       "Name                                                                            \n",
       "Braund, Mr. Owen Harris                                    A/5 21171   7.2500   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...          PC 17599  71.2833   \n",
       "Heikkinen, Miss. Laina                              STON/O2. 3101282   7.9250   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)                  113803  53.1000   \n",
       "...                                                              ...      ...   \n",
       "Graham, Miss. Margaret Edith                                  112053  30.0000   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                  W./C. 6607  23.4500   \n",
       "Behr, Mr. Karl Howell                                         111369  30.0000   \n",
       "Dooley, Mr. Patrick                                           370376   7.7500   \n",
       "\n",
       "                                                   Cabin Embarked  \n",
       "Name                                                               \n",
       "Braund, Mr. Owen Harris                              NaN        S  \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...   C85        C  \n",
       "Heikkinen, Miss. Laina                               NaN        S  \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)        C123        S  \n",
       "...                                                  ...      ...  \n",
       "Graham, Miss. Margaret Edith                         B42        S  \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"             NaN        S  \n",
       "Behr, Mr. Karl Howell                               C148        C  \n",
       "Dooley, Mr. Patrick                                  NaN        Q  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index('Name')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "Braund, Mr. Owen Harris                                22.0\n",
       "Cumings, Mrs. John Bradley (Florence Briggs Thayer)    38.0\n",
       "Heikkinen, Miss. Laina                                 26.0\n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)           35.0\n",
       "                                                       ... \n",
       "Graham, Miss. Margaret Edith                           19.0\n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                NaN\n",
       "Behr, Mr. Karl Howell                                  26.0\n",
       "Dooley, Mr. Patrick                                    32.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age = df['Age']\n",
    "age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age['Dooley, Mr. Patrick']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "but with the power of numpy arrays. Many things you can do with numpy arrays, can also be applied on DataFrames / Series.\n",
    "\n",
    "Eg element-wise operations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "Braund, Mr. Owen Harris                                22000.0\n",
       "Cumings, Mrs. John Bradley (Florence Briggs Thayer)    38000.0\n",
       "Heikkinen, Miss. Laina                                 26000.0\n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)           35000.0\n",
       "                                                        ...   \n",
       "Graham, Miss. Margaret Edith                           19000.0\n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                   NaN\n",
       "Behr, Mr. Karl Howell                                  26000.0\n",
       "Dooley, Mr. Patrick                                    32000.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age * 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A range of methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.69911764705882"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fancy indexing, like indexing with a list or boolean indexing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "Goldschmidt, Mr. George B               71.0\n",
       "Connors, Mr. Patrick                    70.5\n",
       "Artagaveytia, Mr. Ramon                 71.0\n",
       "Barkworth, Mr. Algernon Henry Wilson    80.0\n",
       "Svensson, Mr. Johan                     74.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age[age > 70]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But also a lot of pandas specific methods, e.g."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Embarked'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>:\n",
    "\n",
    " <ul>\n",
    "  <li>What is the maximum Fare that was paid? And the median?</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "512.32920000000001"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Fare'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.4542"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Fare'].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>:\n",
    "\n",
    " <ul>\n",
    "  <li>Calculate the average survival ratio for all passengers (note: the 'Survived' column indicates whether someone survived (1) or not (0)).</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "clear_cell": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3838383838383838"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df['Survived'].sum() / len(df['Survived'])\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": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#pd.read"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#df.to"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Very powerful csv reader:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.read_csv?"
   ]
  },
  {
   "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": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/titanic.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>: Read the `data/20000101_20161231-NO2.csv` file into a DataFrame `no2`\n",
    "<br><br>\n",
    "Some aspects about the file:\n",
    " <ul>\n",
    "  <li>Which separator is used in the file?</li>\n",
    "  <li>The second row includes unit information and should be skipped (check `skiprows` keyword)</li>\n",
    "  <li>For missing values, it uses the `'n/d'` notation (check `na_values` keyword)</li>\n",
    "  <li>We want to parse the 'timestamp' column as datetimes (check the `parse_dates` keyword)</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "clear_cell": true,
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "no2 = 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": 40,
   "metadata": {
    "clear_cell": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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": 40,
     "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": 41,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "`info()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Quickly visualizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff34267beb8>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FqYg4nLsu6avA7uRmG3B+3qJLgENjrs7MbBKdPHmydzhlZ2cnPT09Ra6oMMa0Ry+pOu/m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\nB72ZWco56M3MUs5Bb2aWcv8faXoqDJZvwy8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3427d02b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "no2.plot(kind='box', ylim=[0,250])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff3428a26d8>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff342826f28>"
      ]
     },
     "metadata": {},
     "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": 47,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff33b4737b8>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3426154a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Age'].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": 48,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff33b9fa7b8>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CEARBEAQhCfkmB0OWZBH+/Oc/Y8WKFWhtbQUA5OXlobi4GEOGDJG1/7XXXouioiIAQElJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3408984a8>"
      ]
     },
     "metadata": {},
     "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": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff338480710>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hHpGQVzUMTFgiuaMkku2x1B62zTk3s8YRc0hKJByqGaPIFDUko5J9YRJkzIYg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\n8Bh+9ocDSEbDuGzFvEkfOy9mXeg2GLcYySoNjaQ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hufs+vd0q3XvVrGbZki3LBRuMMQkG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\nunn1pld7TEa1/GHY6GDaKZM38yu7W1TrAauZ5Phm8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yOv5dh6sSpZOuE96EuFJeKBOEE9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7u78SwqLZIVjwffWWfNuC3ryBNPYGWzVL/vj2Z0/MlAa7QrQOSOTJ28t7bBFr/f2P4Nanw1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\nyWZmZmZmdf4LqZMQ6I2bRrwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff340852080>"
      ]
     },
     "metadata": {},
     "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": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/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": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      22.0\n",
       "1      38.0\n",
       "2      26.0\n",
       "3      35.0\n",
       "       ... \n",
       "887    19.0\n",
       "888     NaN\n",
       "889    26.0\n",
       "890    32.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "or multiple columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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": 52,
     "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": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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": 53,
     "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": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.set_index('Name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26.550000000000001"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Bonnell, Miss. Elizabeth', 'Fare']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bonnell, Miss. Elizabeth</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.550</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saundercock, Mr. William Henry</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>8.050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andersson, Mr. Anders Johan</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.275</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                PassengerId  Survived  Pclass     Sex   Age  \\\n",
       "Name                                                                          \n",
       "Bonnell, Miss. Elizabeth                 12         1       1  female  58.0   \n",
       "Saundercock, Mr. William Henry           13         0       3    male  20.0   \n",
       "Andersson, Mr. Anders Johan              14         0       3    male  39.0   \n",
       "\n",
       "                                SibSp  Parch     Ticket    Fare Cabin Embarked  \n",
       "Name                                                                            \n",
       "Bonnell, Miss. Elizabeth            0      0     113783  26.550  C103        S  \n",
       "Saundercock, Mr. William Henry      0      0  A/5. 2151   8.050   NaN        S  \n",
       "Andersson, Mr. Anders Johan         1      5     347082  31.275   NaN        S  "
      ]
     },
     "execution_count": 56,
     "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": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Braund, Mr. Owen Harris</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Survived  Pclass\n",
       "Name                                                                \n",
       "Braund, Mr. Owen Harris                                    0       3\n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...         1       1"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:2,1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The different indexing methods can also be used to assign data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.loc['Braund, Mr. Owen Harris', 'Survived'] = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
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       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
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       "      <th>Braund, Mr. Owen Harris</th>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heikkinen, Miss. Laina</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <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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       "      <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",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <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",
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       "      <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",
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       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    PassengerId  Survived  \\\n",
       "Name                                                                        \n",
       "Braund, Mr. Owen Harris                                       1       100   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...            2         1   \n",
       "Heikkinen, Miss. Laina                                        3         1   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)                  4         1   \n",
       "...                                                         ...       ...   \n",
       "Graham, Miss. Margaret Edith                                888         1   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                    889         0   \n",
       "Behr, Mr. Karl Howell                                       890         1   \n",
       "Dooley, Mr. Patrick                                         891         0   \n",
       "\n",
       "                                                    Pclass     Sex   Age  \\\n",
       "Name                                                                       \n",
       "Braund, Mr. Owen Harris                                  3    male  22.0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...       1  female  38.0   \n",
       "Heikkinen, Miss. Laina                                   3  female  26.0   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)             1  female  35.0   \n",
       "...                                                    ...     ...   ...   \n",
       "Graham, Miss. Margaret Edith                             1  female  19.0   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                 3  female   NaN   \n",
       "Behr, Mr. Karl Howell                                    1    male  26.0   \n",
       "Dooley, Mr. Patrick                                      3    male  32.0   \n",
       "\n",
       "                                                    SibSp  Parch  \\\n",
       "Name                                                               \n",
       "Braund, Mr. Owen Harris                                 1      0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...      1      0   \n",
       "Heikkinen, Miss. Laina                                  0      0   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)            1      0   \n",
       "...                                                   ...    ...   \n",
       "Graham, Miss. Margaret Edith                            0      0   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                1      2   \n",
       "Behr, Mr. Karl Howell                                   0      0   \n",
       "Dooley, Mr. Patrick                                     0      0   \n",
       "\n",
       "                                                              Ticket     Fare  \\\n",
       "Name                                                                            \n",
       "Braund, Mr. Owen Harris                                    A/5 21171   7.2500   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...          PC 17599  71.2833   \n",
       "Heikkinen, Miss. Laina                              STON/O2. 3101282   7.9250   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)                  113803  53.1000   \n",
       "...                                                              ...      ...   \n",
       "Graham, Miss. Margaret Edith                                  112053  30.0000   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                  W./C. 6607  23.4500   \n",
       "Behr, Mr. Karl Howell                                         111369  30.0000   \n",
       "Dooley, Mr. Patrick                                           370376   7.7500   \n",
       "\n",
       "                                                   Cabin Embarked  \n",
       "Name                                                               \n",
       "Braund, Mr. Owen Harris                              NaN        S  \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...   C85        C  \n",
       "Heikkinen, Miss. Laina                               NaN        S  \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)        C123        S  \n",
       "...                                                  ...      ...  \n",
       "Graham, Miss. Margaret Edith                         B42        S  \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"             NaN        S  \n",
       "Behr, Mr. Karl Howell                               C148        C  \n",
       "Dooley, Mr. Patrick                                  NaN        Q  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boolean indexing (filtering)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Often, you want to select rows based on a certain condition. This can be done with 'boolean indexing' (like a where clause in SQL) and comparable to numpy. \n",
    "\n",
    "The indexer (or boolean mask) should be 1-dimensional and the same length as the thing being indexed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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, dtype: bool"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Fare'] > 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Futrelle, Mrs. Jacques Heath (Lily May Peel)</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>McCarthy, Mr. Timothy J</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fortune, Mr. Charles Alexander</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <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>Sage, Miss. Dorothy Edith \"Dolly\"</th>\n",
       "      <td>864</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Roebling, Mr. Washington Augustus II</th>\n",
       "      <td>868</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17590</td>\n",
       "      <td>50.4958</td>\n",
       "      <td>A24</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</th>\n",
       "      <td>872</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11751</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>D35</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</th>\n",
       "      <td>880</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11767</td>\n",
       "      <td>83.1583</td>\n",
       "      <td>C50</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>160 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    PassengerId  Survived  \\\n",
       "Name                                                                        \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...            2         1   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)                  4         1   \n",
       "McCarthy, Mr. Timothy J                                       7         0   \n",
       "Fortune, Mr. Charles Alexander                               28         0   \n",
       "...                                                         ...       ...   \n",
       "Sage, Miss. Dorothy Edith \"Dolly\"                           864         0   \n",
       "Roebling, Mr. Washington Augustus II                        868         0   \n",
       "Beckwith, Mrs. Richard Leonard (Sallie Monypeny)            872         1   \n",
       "Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)               880         1   \n",
       "\n",
       "                                                    Pclass     Sex   Age  \\\n",
       "Name                                                                       \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...       1  female  38.0   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)             1  female  35.0   \n",
       "McCarthy, Mr. Timothy J                                  1    male  54.0   \n",
       "Fortune, Mr. Charles Alexander                           1    male  19.0   \n",
       "...                                                    ...     ...   ...   \n",
       "Sage, Miss. Dorothy Edith \"Dolly\"                        3  female   NaN   \n",
       "Roebling, Mr. Washington Augustus II                     1    male  31.0   \n",
       "Beckwith, Mrs. Richard Leonard (Sallie Monypeny)         1  female  47.0   \n",
       "Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)            1  female  56.0   \n",
       "\n",
       "                                                    SibSp  Parch    Ticket  \\\n",
       "Name                                                                         \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...      1      0  PC 17599   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)            1      0    113803   \n",
       "McCarthy, Mr. Timothy J                                 0      0     17463   \n",
       "Fortune, Mr. Charles Alexander                          3      2     19950   \n",
       "...                                                   ...    ...       ...   \n",
       "Sage, Miss. Dorothy Edith \"Dolly\"                       8      2  CA. 2343   \n",
       "Roebling, Mr. Washington Augustus II                    0      0  PC 17590   \n",
       "Beckwith, Mrs. Richard Leonard (Sallie Monypeny)        1      1     11751   \n",
       "Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)           0      1     11767   \n",
       "\n",
       "                                                        Fare        Cabin  \\\n",
       "Name                                                                        \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...   71.2833          C85   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)         53.1000         C123   \n",
       "McCarthy, Mr. Timothy J                              51.8625          E46   \n",
       "Fortune, Mr. Charles Alexander                      263.0000  C23 C25 C27   \n",
       "...                                                      ...          ...   \n",
       "Sage, Miss. Dorothy Edith \"Dolly\"                    69.5500          NaN   \n",
       "Roebling, Mr. Washington Augustus II                 50.4958          A24   \n",
       "Beckwith, Mrs. Richard Leonard (Sallie Monypeny)     52.5542          D35   \n",
       "Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)        83.1583          C50   \n",
       "\n",
       "                                                   Embarked  \n",
       "Name                                                         \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...        C  \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)              S  \n",
       "McCarthy, Mr. Timothy J                                   S  \n",
       "Fortune, Mr. Charles Alexander                            S  \n",
       "...                                                     ...  \n",
       "Sage, Miss. Dorothy Edith \"Dolly\"                         S  \n",
       "Roebling, Mr. Washington Augustus II                      S  \n",
       "Beckwith, Mrs. Richard Leonard (Sallie Monypeny)          S  \n",
       "Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)             C  \n",
       "\n",
       "[160 rows x 11 columns]"
      ]
     },
     "execution_count": 61,
     "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": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/titanic.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>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",
       "    <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>884</th>\n",
       "      <td>885</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sutehall, Mr. Henry Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 392076</td>\n",
       "      <td>7.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</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>577 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                      Name   Sex   Age  \\\n",
       "0              1         0       3   Braund, Mr. Owen Harris  male  22.0   \n",
       "4              5         0       3  Allen, Mr. William Henry  male  35.0   \n",
       "5              6         0       3          Moran, Mr. James  male   NaN   \n",
       "6              7         0       1   McCarthy, Mr. Timothy J  male  54.0   \n",
       "..           ...       ...     ...                       ...   ...   ...   \n",
       "884          885         0       3    Sutehall, Mr. Henry Jr  male  25.0   \n",
       "886          887         0       2     Montvila, Rev. Juozas  male  27.0   \n",
       "889          890         1       1     Behr, Mr. Karl Howell  male  26.0   \n",
       "890          891         0       3       Dooley, Mr. Patrick  male  32.0   \n",
       "\n",
       "     SibSp  Parch           Ticket     Fare Cabin Embarked  \n",
       "0        1      0        A/5 21171   7.2500   NaN        S  \n",
       "4        0      0           373450   8.0500   NaN        S  \n",
       "5        0      0           330877   8.4583   NaN        Q  \n",
       "6        0      0            17463  51.8625   E46        S  \n",
       "..     ...    ...              ...      ...   ...      ...  \n",
       "884      0      0  SOTON/OQ 392076   7.0500   NaN        S  \n",
       "886      0      0           211536  13.0000   NaN        S  \n",
       "889      0      0           111369  30.0000  C148        C  \n",
       "890      0      0           370376   7.7500   NaN        Q  \n",
       "\n",
       "[577 rows x 12 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Sex'] == 'male']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.72664459161148"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Sex'] == 'male', 'Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.915708812260537"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Sex'] == 'female', 'Age'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>:\n",
    "\n",
    " <ul>\n",
    "  <li>Based on the titanic data set, how many passengers older than 70 were on the Titanic?</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[df['Age'] > 70])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df['Age'] > 70).sum()"
   ]
  },
  {
   "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": 68,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    data key\n",
       "0      0   A\n",
       "1      5   B\n",
       "2     10   C\n",
       "3      5   A\n",
       "..   ...  ..\n",
       "5     15   C\n",
       "6     10   A\n",
       "7     15   B\n",
       "8     20   C\n",
       "\n",
       "[9 rows x 2 columns]"
      ]
     },
     "execution_count": 68,
     "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": 69,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90"
      ]
     },
     "execution_count": 69,
     "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": 70,
   "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": 71,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     data\n",
       "key      \n",
       "A      15\n",
       "B      30\n",
       "C      45"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     data\n",
       "key      \n",
       "A      15\n",
       "B      30\n",
       "C      45"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key').aggregate(np.sum)  # 'sum'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And many more methods are available. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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": 73,
     "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": 74,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/titanic.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 75,
     "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": 76,
   "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": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Sex')['Age'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>:\n",
    "\n",
    " <ul>\n",
    "  <li>Calculate the average survival ratio for all passengers.</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "clear_cell": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3838383838383838"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df['Survived'].sum() / len(df['Survived'])\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": 78,
   "metadata": {
    "clear_cell": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4119601328903654"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df25 = df[df['Age'] <= 25]\n",
    "df25['Survived'].sum() / len(df25['Survived'])"
   ]
  },
  {
   "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": 79,
   "metadata": {
    "clear_cell": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived\n",
       "Sex             \n",
       "female  0.742038\n",
       "male    0.188908"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.groupby('Sex')[['Survived']].aggregate(lambda x: x.sum() / len(x))\n",
    "df.groupby('Sex')[['Survived']].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>:\n",
    "\n",
    " <ul>\n",
    "  <li>Or how does it differ between the different classes? Make a bar plot visualizing the survival ratio for the 3 classes.</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff33930eac8>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WPVlV1/TvSBJguR9XPwi8pqruS3IzcB1AVb01ybXAjcCR3p2tdya5t7fvUJK3VNVnRvx9\nSEPzzF2Xm08Dr0/yXIAkzxmw5pnAfyV5Kotn7vTWPr+q7q2qvSze/bgxyY8Dx6vqj4Fp4LJ9F0Jd\nXDxz12Wlqo4meQ/w90m+B3wFuHnJst8C7gW+CjzAYuwB3tv7hWlY/J/EfcAe4A1JvsviW87uW/Vv\nQurAX6hKUoO8LCNJDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDfo/vti8bDCq8cMAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff339312eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": 81,
   "metadata": {
    "clear_cell": false,
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "df['AgeClass'] = pd.cut(df['Age'], bins=np.arange(0,90,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "clear_cell": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff339341be0>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/M2VbY+3uQy7/j+Gt/IKJxoeZ0KxtLa5tOV8HDmhvqSZeT5LDgE8Ar62q29vkHW35BZO0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\nBrokdcJAl6ROGOiS1In/D6NNaFSndckzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3392b4668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('AgeClass')['Fare'].mean().plot(kind='bar', rot=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Working with time series data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "no2 = pd.read_csv('data/20000101_20161231-NO2.csv', sep=';', skiprows=[1], na_values=['n/d'], index_col=0, parse_dates=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "When we ensure the DataFrame has a `DatetimeIndex`, time-series related functionality becomes available:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2000-01-01 01:00:00', '2000-01-01 02:00:00',\n",
       "               '2000-01-01 03:00:00', '2000-01-01 04:00:00',\n",
       "               '2000-01-01 05:00:00', '2000-01-01 06:00:00',\n",
       "               '2000-01-01 07:00:00', '2000-01-01 08:00:00',\n",
       "               '2000-01-01 09:00:00', '2000-01-01 10:00:00',\n",
       "               ...\n",
       "               '2016-12-31 14:00:00', '2016-12-31 15:00:00',\n",
       "               '2016-12-31 16:00:00', '2016-12-31 17:00:00',\n",
       "               '2016-12-31 18:00:00', '2016-12-31 19:00:00',\n",
       "               '2016-12-31 20:00:00', '2016-12-31 21:00:00',\n",
       "               '2016-12-31 22:00:00', '2016-12-31 23:00:00'],\n",
       "              dtype='datetime64[ns]', name='timestamp', length=149039, freq=None)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Indexing a time series works with strings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>2010-01-01 09:00:00</th>\n",
       "      <td>31.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 10:00:00</th>\n",
       "      <td>41.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 11:00:00</th>\n",
       "      <td>48.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 12:00:00</th>\n",
       "      <td>63.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     BASCH  BONAP  PA18  VERS\n",
       "timestamp                                    \n",
       "2010-01-01 09:00:00   31.0   27.0  28.0  14.0\n",
       "2010-01-01 10:00:00   41.0   31.0  30.0  14.0\n",
       "2010-01-01 11:00:00   48.0   32.0  33.0  16.0\n",
       "2010-01-01 12:00:00   63.0   33.0  39.0  19.0"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2[\"2010-01-01 09:00\": \"2010-01-01 12:00\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "A nice feature is \"partial string\" indexing, so you don't need to provide the full datetime string."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "E.g. all data of January up to March 2012:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>2012-01-01 00:00:00</th>\n",
       "      <td>44.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 01:00:00</th>\n",
       "      <td>56.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 02:00:00</th>\n",
       "      <td>50.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 03:00:00</th>\n",
       "      <td>46.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8.0</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>2012-03-31 20:00:00</th>\n",
       "      <td>61.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-31 21:00:00</th>\n",
       "      <td>46.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-31 22:00:00</th>\n",
       "      <td>56.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-31 23:00:00</th>\n",
       "      <td>59.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2184 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     BASCH  BONAP  PA18  VERS\n",
       "timestamp                                    \n",
       "2012-01-01 00:00:00   44.0   34.0  32.0   7.0\n",
       "2012-01-01 01:00:00   56.0   35.0  29.0   9.0\n",
       "2012-01-01 02:00:00   50.0   36.0  29.0   7.0\n",
       "2012-01-01 03:00:00   46.0   34.0  22.0   8.0\n",
       "...                    ...    ...   ...   ...\n",
       "2012-03-31 20:00:00   61.0   55.0  29.0  17.0\n",
       "2012-03-31 21:00:00   46.0   49.0  32.0  14.0\n",
       "2012-03-31 22:00:00   56.0   41.0  27.0  14.0\n",
       "2012-03-31 23:00:00   59.0   51.0  29.0  13.0\n",
       "\n",
       "[2184 rows x 4 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2['2012-01':'2012-03']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Time and date components can be accessed from the index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3, ..., 21, 22, 23], dtype=int32)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2.index.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2000, 2000, 2000, ..., 2016, 2016, 2016], dtype=int32)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2.index.year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Converting your time series with `resample`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A very powerfull method is **`resample`: converting the frequency of the time series** (e.g. from hourly to daily data).\n",
    "\n",
    "Remember the air quality data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff339305e48>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MRZWdqetmeI/+Aq23MF5+dyXlVSgorURGfqnL8m7dumHNmjUuy8aNG1etVxMfH1/9M2fO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\nY7KcJXpjjMly/w+SwXXyEAXciwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3392d9cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "no2.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The time series has a frequency of 1 hour. I want to change this to daily:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BASCH</th>\n",
       "      <th>BONAP</th>\n",
       "      <th>PA18</th>\n",
       "      <th>VERS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01 01:00:00</th>\n",
       "      <td>108.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.0</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01 02:00:00</th>\n",
       "      <td>104.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01 03:00:00</th>\n",
       "      <td>97.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01 04:00:00</th>\n",
       "      <td>77.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01 05:00:00</th>\n",
       "      <td>79.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     BASCH  BONAP  PA18  VERS\n",
       "timestamp                                    \n",
       "2000-01-01 01:00:00  108.0    NaN  65.0  47.0\n",
       "2000-01-01 02:00:00  104.0   60.0  77.0  42.0\n",
       "2000-01-01 03:00:00   97.0   58.0  73.0  34.0\n",
       "2000-01-01 04:00:00   77.0   52.0  57.0  29.0\n",
       "2000-01-01 05:00:00   79.0   52.0  64.0  28.0"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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</th>\n",
       "      <td>83.173913</td>\n",
       "      <td>53.772727</td>\n",
       "      <td>64.695652</td>\n",
       "      <td>36.521739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>78.708333</td>\n",
       "      <td>59.250000</td>\n",
       "      <td>63.708333</td>\n",
       "      <td>17.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>82.333333</td>\n",
       "      <td>73.541667</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>23.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>78.500000</td>\n",
       "      <td>73.708333</td>\n",
       "      <td>48.863636</td>\n",
       "      <td>23.791667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>94.291667</td>\n",
       "      <td>90.458333</td>\n",
       "      <td>60.166667</td>\n",
       "      <td>28.214286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                BASCH      BONAP       PA18       VERS\n",
       "timestamp                                             \n",
       "2000-01-01  83.173913  53.772727  64.695652  36.521739\n",
       "2000-01-02  78.708333  59.250000  63.708333  17.166667\n",
       "2000-01-03  82.333333  73.541667  61.000000  23.083333\n",
       "2000-01-04  78.500000  73.708333  48.863636  23.791667\n",
       "2000-01-05  94.291667  90.458333  60.166667  28.214286"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2.resample('D').mean().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Above I take the mean, but as with `groupby` I can also specify other methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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</th>\n",
       "      <td>109.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>109.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>120.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>115.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>141.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            BASCH  BONAP  PA18  VERS\n",
       "timestamp                           \n",
       "2000-01-01  109.0   62.0  77.0  59.0\n",
       "2000-01-02  109.0   96.0  78.0  45.0\n",
       "2000-01-03  120.0  102.0  72.0  37.0\n",
       "2000-01-04  115.0  107.0  78.0  43.0\n",
       "2000-01-05  141.0  115.0  82.0  41.0"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2.resample('D').max().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "The string to specify the new time frequency: http://pandas.pydata.org/pandas-docs/dev/timeseries.html#offset-aliases  \n",
    "These strings can also be combined with numbers, eg `'10D'`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Further exploring the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff3313a6cf8>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xiPRA6bRbDWvM6ovtDg9EefVomdsOn9OGjmF6/Pp7KpKUMKIdf4XbjoAvt6KP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dAAAg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\nDb3jOE6V8yf1GrldB9Fs4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3391d8b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "no2.resample('M').mean().plot() # 'A'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# no2['2012'].resample('D').plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "clear_cell": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff331361b70>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TPH22MvAalO0MX89i2FYeen4GoiCZwDNTsAaeh75s+0ymmhR6F8slj4vQ/jmA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awT1s9a18s6W2HQDP0jA36/brFe1gnrZ63+OnuL9bJOWD9rXS/rbIqBMnQfPnz48NE/+GWLPnz4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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3304cc5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "no2.loc['2009':, 'VERS'].resample('M').agg(['mean', 'median']).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>: The evolution of the yearly averages with, and the overall mean of all stations\n",
    "\n",
    " <ul>\n",
    "  <li>Use `resample` and `plot` to plot the yearly averages for the different stations.</li>\n",
    "  <li>The overall mean of all stations can be calculated by taking the mean of the different columns (`.mean(axis=1)`).</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "clear_cell": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff331351278>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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L/ThIy8zmk+2XGbX4KAoKa57vxFv9m+ltbkxFURSFqV0bsH5GZ8xMNYxafIxF+66TrZXd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3312f6c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "no2.resample('A').mean().plot()\n",
    "no2.mean(axis=1).resample('A').mean().plot(color='k', linestyle='--', linewidth=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>: how does the *typical monthly profile* look like for the different stations?\n",
    "\n",
    " <ul>\n",
    "  <li>Add a 'month' column to the dataframe.</li>\n",
    "  <li>Group by the month to obtain the typical monthly averages over the different years.</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we add a column to the dataframe that indicates the month (integer value of 1 to 12):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "clear_cell": true,
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "no2['month'] = no2.index.month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Now, we can calculate the mean of each month over the different years:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>83.907542</td>\n",
       "      <td>65.387329</td>\n",
       "      <td>52.771067</td>\n",
       "      <td>30.995293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>88.347532</td>\n",
       "      <td>67.387637</td>\n",
       "      <td>53.922040</td>\n",
       "      <td>33.890926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>94.812926</td>\n",
       "      <td>73.588946</td>\n",
       "      <td>54.171491</td>\n",
       "      <td>35.508674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>95.841468</td>\n",
       "      <td>71.824767</td>\n",
       "      <td>47.619196</td>\n",
       "      <td>30.184283</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>9</th>\n",
       "      <td>95.748555</td>\n",
       "      <td>65.478451</td>\n",
       "      <td>46.283842</td>\n",
       "      <td>29.060800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>92.084073</td>\n",
       "      <td>65.832718</td>\n",
       "      <td>48.139287</td>\n",
       "      <td>29.232252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>86.824953</td>\n",
       "      <td>62.795296</td>\n",
       "      <td>49.082940</td>\n",
       "      <td>29.815414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>85.031296</td>\n",
       "      <td>63.964938</td>\n",
       "      <td>51.229126</td>\n",
       "      <td>31.136172</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           BASCH      BONAP       PA18       VERS\n",
       "month                                            \n",
       "1      83.907542  65.387329  52.771067  30.995293\n",
       "2      88.347532  67.387637  53.922040  33.890926\n",
       "3      94.812926  73.588946  54.171491  35.508674\n",
       "4      95.841468  71.824767  47.619196  30.184283\n",
       "...          ...        ...        ...        ...\n",
       "9      95.748555  65.478451  46.283842  29.060800\n",
       "10     92.084073  65.832718  48.139287  29.232252\n",
       "11     86.824953  62.795296  49.082940  29.815414\n",
       "12     85.031296  63.964938  51.229126  31.136172\n",
       "\n",
       "[12 rows x 4 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2.groupby('month').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "clear_cell": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff33124c320>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5Ol6eyCy0d/MazKIdJ2WrZ36V76481GDvuX///gZ5H7PFKjPyS+TB5Dy5+2S23H86V6bl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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3312347b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "no2.groupby('month').mean().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>: The typical diurnal profile for the different stations\n",
    "\n",
    " <ul>\n",
    "  <li>Similar as for the month, you can now group by the hour of the day.</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "clear_cell": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff3311e4668>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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10xBCX8k4dC2OmJRsJrRxVrcpAoGgCD1dHUa3dOSfGZ14t2s9jofE0+2nf5i9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3304d1d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "no2.groupby(no2.index.hour).mean().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>: What is the difference in the typical diurnal profile between week and weekend days for the 'BASCH' station.\n",
    "\n",
    " <ul>\n",
    "  <li>Add a column 'weekday' defining the different days in the week.</li>\n",
    "  <li>Add a column 'weekend' defining if a days is in the weekend (i.e. days 5 and 6) or not (True/False).</li>\n",
    "  <li>You can groupby on multiple items at the same time. In this case you would need to group by both weekend/weekday and hour of the day.</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add a column indicating the weekday:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "no2.index.weekday?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "clear_cell": true,
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "no2['weekday'] = no2.index.weekday"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Add a column indicating week/weekend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "clear_cell": true,
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "no2['weekend'] = no2['weekday'].isin([5, 6])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can groupby the hour of the day and the weekend (or use `pivot_table`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>BASCH</th>\n",
       "      <th>BONAP</th>\n",
       "      <th>PA18</th>\n",
       "      <th>VERS</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekend</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 rowspan=\"5\" valign=\"top\">False</th>\n",
       "      <th>0</th>\n",
       "      <td>62.683270</td>\n",
       "      <td>49.385498</td>\n",
       "      <td>41.966667</td>\n",
       "      <td>25.601584</td>\n",
       "      <td>6.522435</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51.150107</td>\n",
       "      <td>41.151063</td>\n",
       "      <td>37.160479</td>\n",
       "      <td>22.988806</td>\n",
       "      <td>6.522435</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44.088698</td>\n",
       "      <td>36.148094</td>\n",
       "      <td>33.933945</td>\n",
       "      <td>21.275548</td>\n",
       "      <td>6.522435</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>43.542551</td>\n",
       "      <td>33.898973</td>\n",
       "      <td>32.919567</td>\n",
       "      <td>20.782081</td>\n",
       "      <td>6.522435</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53.439094</td>\n",
       "      <td>35.102370</td>\n",
       "      <td>35.566087</td>\n",
       "      <td>22.241620</td>\n",
       "      <td>6.522435</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               BASCH      BONAP       PA18       VERS     month  weekday\n",
       "weekend                                                                 \n",
       "False   0  62.683270  49.385498  41.966667  25.601584  6.522435      2.0\n",
       "        1  51.150107  41.151063  37.160479  22.988806  6.522435      2.0\n",
       "        2  44.088698  36.148094  33.933945  21.275548  6.522435      2.0\n",
       "        3  43.542551  33.898973  32.919567  20.782081  6.522435      2.0\n",
       "        4  53.439094  35.102370  35.566087  22.241620  6.522435      2.0"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_weekend = no2.groupby(['weekend', no2.index.hour]).mean()\n",
    "data_weekend.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "clear_cell": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>weekend</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>62.683270</td>\n",
       "      <td>77.040828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51.150107</td>\n",
       "      <td>68.010059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44.088698</td>\n",
       "      <td>59.186060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>43.542551</td>\n",
       "      <td>53.515366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53.439094</td>\n",
       "      <td>53.383797</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "weekend      False      True \n",
       "0        62.683270  77.040828\n",
       "1        51.150107  68.010059\n",
       "2        44.088698  59.186060\n",
       "3        43.542551  53.515366\n",
       "4        53.439094  53.383797"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_weekend_BASCH = data_weekend['BASCH'].unstack(level=0)\n",
    "data_weekend_BASCH.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "clear_cell": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff331195b70>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PYup3sdT0cuOzh1vafZIH7aL/uPbBHDp/hdjTFXuVN/v/11ZKbFVcEqHVKtG0ZhWLxSCE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\n0bg0+oiIxqXRR0Q07m/2GVQsI+JQAgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3304b9da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_weekend_BASCH.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>weekend</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>62.683270</td>\n",
       "      <td>77.040828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51.150107</td>\n",
       "      <td>68.010059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44.088698</td>\n",
       "      <td>59.186060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>43.542551</td>\n",
       "      <td>53.515366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>103.314564</td>\n",
       "      <td>94.654967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>91.401094</td>\n",
       "      <td>85.394768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>86.642586</td>\n",
       "      <td>81.529132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>79.266397</td>\n",
       "      <td>76.563615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "weekend       False      True \n",
       "hour                          \n",
       "0         62.683270  77.040828\n",
       "1         51.150107  68.010059\n",
       "2         44.088698  59.186060\n",
       "3         43.542551  53.515366\n",
       "...             ...        ...\n",
       "20       103.314564  94.654967\n",
       "21        91.401094  85.394768\n",
       "22        86.642586  81.529132\n",
       "23        79.266397  76.563615\n",
       "\n",
       "[24 rows x 2 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2['hour'] = no2.index.hour\n",
    "no2.pivot_table(columns='weekend', index='hour', values='BASCH')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>: What are the number of exceedances of hourly values above the European limit 200 µg/m3 ?\n",
    "\n",
    "Count the number of exceedances of hourly values above the European limit 200 µg/m3 for each year and station after 2005. Make a barplot of the counts. Add an horizontal line indicating the maximum number of exceedances (which is 18) allowed per year?\n",
    "<br><br>\n",
    "\n",
    "Hints:\n",
    "\n",
    " <ul>\n",
    "  <li>Create a new DataFrame, called `exceedances`, (with boolean values) indicating if the threshold is exceeded or not</li>\n",
    "  <li>Remember that the sum of True values can be used to count elements. Do this using groupby for each year.</li>\n",
    "  <li>Adding a horizontal line can be done with the matplotlib function `ax.axhline`.</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# re-reading the data to have a clean version\n",
    "no2 = 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": 109,
   "metadata": {
    "clear_cell": true,
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "exceedances = no2 > 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "clear_cell": true,
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# group by year and count exceedances (sum of boolean)\n",
    "exceedances = exceedances.groupby(exceedances.index.year).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7ff3310a9ef0>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oioJeROqMhg0b0qFDh9puRnA0dCMiEjgFvYhI4BT0IiKBU9CLiAROQS8iEjgFvYhI4BT0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\nEhkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3310a9198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = exceedances.loc[2005:].plot(kind='bar')\n",
    "ax.axhline(18, color='k', linestyle='--')"
   ]
  },
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   "source": [
    "# 9. What I didn't talk about"
   ]
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    "- Concatenating data: `pd.concat`\n",
    "- Merging and joining data: `pd.merge`\n",
    "- Reshaping data: `pivot_table`, `melt`, `stack`, `unstack`\n",
    "- Working with missing data: `isnull`, `dropna`, `interpolate`, ...\n",
    "- ..."
   ]
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    "\n",
    "## Further reading\n",
    "\n",
    "* Pandas documentation: http://pandas.pydata.org/pandas-docs/stable/\n",
    "\n",
    "* Books\n",
    "\n",
    "    * \"Python for Data Analysis\" by Wes McKinney\n",
    "    * \"Python Data Science Handbook\" by Jake VanderPlas\n",
    "\n",
    "* Tutorials (many good online tutorials!)\n",
    "\n",
    "  * https://github.com/jorisvandenbossche/pandas-tutorial\n",
    "  * https://github.com/brandon-rhodes/pycon-pandas-tutorial\n",
    "\n",
    "* Tom Augspurger's blog\n",
    "\n",
    "  * https://tomaugspurger.github.io/modern-1.html"
   ]
  }
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