pandas_titanic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--<img width=700px; src=\"../img/logoUPSayPlusCDS_990.png\"> -->\n",
    "\n",
    "<p style=\"margin-top: 3em; margin-bottom: 2em;\"><b><big><big><big><big>Introduction to Pandas</big></big></big></big></b></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "pd.options.display.max_rows = 8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**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-info\">\n",
    "<b>ATTENTION!</b>: <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 <a href=\"http://xarray.pydata.org/en/stable/\">xarray</a></li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we will use the well-known Titanic dataset of information about passengers on the Titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/titanic.csv\")"
   ]
  },
  {
   "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/dataframe.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "..           ...       ...     ...   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500   NaN        S  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 3,
     "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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=891, step=1)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "                ...   \n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "Length: 12, dtype: object"
      ]
     },
     "execution_count": 6,
     "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": 7,
   "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",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ 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": 8,
   "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": 8,
     "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>population</th>\n",
       "      <th>area</th>\n",
       "      <th>capital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>11.3</td>\n",
       "      <td>30510</td>\n",
       "      <td>Brussels</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>France</td>\n",
       "      <td>64.3</td>\n",
       "      <td>671308</td>\n",
       "      <td>Paris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Germany</td>\n",
       "      <td>81.3</td>\n",
       "      <td>357050</td>\n",
       "      <td>Berlin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>16.9</td>\n",
       "      <td>41526</td>\n",
       "      <td>Amsterdam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>64.9</td>\n",
       "      <td>244820</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  population    area    capital\n",
       "0         Belgium        11.3   30510   Brussels\n",
       "1          France        64.3  671308      Paris\n",
       "2         Germany        81.3  357050     Berlin\n",
       "3     Netherlands        16.9   41526  Amsterdam\n",
       "4  United Kingdom        64.9  244820     London"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      22.0\n",
       "1      38.0\n",
       "2      26.0\n",
       "3      35.0\n",
       "       ... \n",
       "887    19.0\n",
       "888     NaN\n",
       "889    26.0\n",
       "890    32.0\n",
       "Name: Age, Length: 891, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "age = df['Age']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Attributes of a Series: `index` and `values`\n",
    "\n",
    "The Series has also an `index` and `values` attribute, but no `columns`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=891, step=1)"
      ]
     },
     "execution_count": 12,
     "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": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([22., 38., 26., 35., 35., nan, 54.,  2., 27., 14.])"
      ]
     },
     "execution_count": 13,
     "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.0"
      ]
     },
     "execution_count": 14,
     "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Braund, Mr. Owen Harris</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heikkinen, Miss. Laina</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Futrelle, Mrs. Jacques Heath (Lily May Peel)</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Graham, Miss. Margaret Edith</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Johnston, Miss. Catherine Helen \"Carrie\"</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Behr, Mr. Karl Howell</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dooley, Mr. Patrick</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    PassengerId  Survived  \\\n",
       "Name                                                                        \n",
       "Braund, Mr. Owen Harris                                       1         0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...            2         1   \n",
       "Heikkinen, Miss. Laina                                        3         1   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)                  4         1   \n",
       "...                                                         ...       ...   \n",
       "Graham, Miss. Margaret Edith                                888         1   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                    889         0   \n",
       "Behr, Mr. Karl Howell                                       890         1   \n",
       "Dooley, Mr. Patrick                                         891         0   \n",
       "\n",
       "                                                    Pclass     Sex   Age  \\\n",
       "Name                                                                       \n",
       "Braund, Mr. Owen Harris                                  3    male  22.0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...       1  female  38.0   \n",
       "Heikkinen, Miss. Laina                                   3  female  26.0   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)             1  female  35.0   \n",
       "...                                                    ...     ...   ...   \n",
       "Graham, Miss. Margaret Edith                             1  female  19.0   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                 3  female   NaN   \n",
       "Behr, Mr. Karl Howell                                    1    male  26.0   \n",
       "Dooley, Mr. Patrick                                      3    male  32.0   \n",
       "\n",
       "                                                    SibSp  Parch  \\\n",
       "Name                                                               \n",
       "Braund, Mr. Owen Harris                                 1      0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...      1      0   \n",
       "Heikkinen, Miss. Laina                                  0      0   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)            1      0   \n",
       "...                                                   ...    ...   \n",
       "Graham, Miss. Margaret Edith                            0      0   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                1      2   \n",
       "Behr, Mr. Karl Howell                                   0      0   \n",
       "Dooley, Mr. Patrick                                     0      0   \n",
       "\n",
       "                                                              Ticket     Fare  \\\n",
       "Name                                                                            \n",
       "Braund, Mr. Owen Harris                                    A/5 21171   7.2500   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...          PC 17599  71.2833   \n",
       "Heikkinen, Miss. Laina                              STON/O2. 3101282   7.9250   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)                  113803  53.1000   \n",
       "...                                                              ...      ...   \n",
       "Graham, Miss. Margaret Edith                                  112053  30.0000   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                  W./C. 6607  23.4500   \n",
       "Behr, Mr. Karl Howell                                         111369  30.0000   \n",
       "Dooley, Mr. Patrick                                           370376   7.7500   \n",
       "\n",
       "                                                   Cabin Embarked  \n",
       "Name                                                               \n",
       "Braund, Mr. Owen Harris                              NaN        S  \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...   C85        C  \n",
       "Heikkinen, Miss. Laina                               NaN        S  \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)        C123        S  \n",
       "...                                                  ...      ...  \n",
       "Graham, Miss. Margaret Edith                         B42        S  \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"             NaN        S  \n",
       "Behr, Mr. Karl Howell                               C148        C  \n",
       "Dooley, Mr. Patrick                                  NaN        Q  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index('Name')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "Braund, Mr. Owen Harris                                22.0\n",
       "Cumings, Mrs. John Bradley (Florence Briggs Thayer)    38.0\n",
       "Heikkinen, Miss. Laina                                 26.0\n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)           35.0\n",
       "                                                       ... \n",
       "Graham, Miss. Margaret Edith                           19.0\n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                NaN\n",
       "Behr, Mr. Karl Howell                                  26.0\n",
       "Dooley, Mr. Patrick                                    32.0\n",
       "Name: Age, Length: 891, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age = df['Age']\n",
    "age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.0"
      ]
     },
     "execution_count": 17,
     "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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "Braund, Mr. Owen Harris                                22000.0\n",
       "Cumings, Mrs. John Bradley (Florence Briggs Thayer)    38000.0\n",
       "Heikkinen, Miss. Laina                                 26000.0\n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)           35000.0\n",
       "                                                        ...   \n",
       "Graham, Miss. Margaret Edith                           19000.0\n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                   NaN\n",
       "Behr, Mr. Karl Howell                                  26000.0\n",
       "Dooley, Mr. Patrick                                    32000.0\n",
       "Name: Age, Length: 891, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age * 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A range of methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.69911764705882"
      ]
     },
     "execution_count": 19,
     "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": 20,
   "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": 20,
     "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Embarked\n",
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Embarked'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "Question 181: What is the maximum Fare that was paid?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "Question 182: What is the median Fare that was paid?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "Question 183: Calculate the average survival ratio for all passengers (note: the 'Survived' column indicates whether someone survived (1) or not (0)). Present it as a percentage, rounded as an integer.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Data import and export"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "A wide range of input/output formats are natively supported by pandas:\n",
    "\n",
    "* CSV, text\n",
    "* SQL database\n",
    "* Excel\n",
    "* HDF5\n",
    "* json\n",
    "* html\n",
    "* pickle\n",
    "* sas, stata\n",
    "* (parquet)\n",
    "* ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take a look at the documentation of the `read_csv` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "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": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/titanic.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "Question 184: What character is used as a field delimiter by default in pandas?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "Question 185: What keyword argument is used to specify the file row number to use as the column names?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "Question 186: What keyword argument is used to specify the format of strings to recognize as NaN values?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Some useful methods:\n",
    "\n",
    "`head` and `tail`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "  </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",
       "\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",
       "\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  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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>13.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>30.00</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>23.45</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>30.00</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>7.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
       "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
       "887  female  19.0      0      0      112053  30.00   B42        S  \n",
       "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
       "889    male  26.0      0      0      111369  30.00  C148        C  \n",
       "890    male  32.0      0      0      370376   7.75   NaN        Q  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "`info()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default plot (when not specifying `kind`) is a line plot of all columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(figsize=(12,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Other types of plots can be specified using `kind`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HAYWIiIgUhwGFiIiIFIcBhYiIiBSHAYWIiIgUhwGFiIiIFIcBhYiIiBSHAYWIiIgUhwGFiIiIFIcBhYiIiBSHAYWIiIgUhwGFiIiIFIcBhYiIiBSHAYWIiIgUhwGFiIiIFIcBhYiIiBSHAYWIiIgUhwGFiIiIFIcBhYiIiBSHAYWIiIgUhwGFiIiIFIcBhYiIiBRHbeoOEBER0f1RVFSEyMhIJCUlwcfHB5MnT4ZGozF1t6qEAYWIiMgMzZw5E0uXLoXBYJDmhYeHIywsDBERESbsWdVwFw8REZGZmTlzJhYuXAhnZ2esXbsWaWlpWLt2LZydnbFw4ULMnDnT1F28J5UQQpi6E9WVk5MDBwcH6PV62Nvbm7o7OHlFj8AVhxA9pRfaN3EwdXeIiOgBVlRUBFtbWzg7OyMlJQVq9T87SwwGA7y8vJCdnY28vLx6391Tnd/fHEEhIiIyI5GRkTAYDJg/f74snACAWq3Gu+++C4PBgMjISBP1sGoYUIiIiMxIUlISACAwMLDc9tL5pXVKxYBCRERkRnx8fAAA0dHR5baXzi+tUyoeg1IHeAwKEREpBY9BISIiIsXRaDQICwtDRkYGvLy8sGbNGqSmpmLNmjXw8vJCRkYGwsLCFH89FF4HhYiIyMyUXudk6dKlmDhxojRfrVYjPDy8QVwHhQGFiIjIDEVERGD+/Pm8kiwREREpi0ajwdSpU03djRrhMShERESkOAwoREREpDgMKERERKQ4DChERESkOAwoREREZio/Px+hoaEICAhAaGgo8vPzTd2lKmNAISIiMkNBQUGwsbHBypUrsWvXLqxcuRI2NjYICgoyddeqhAGFiIjIzAQFBWH79u3QaDSYPXs2EhMTMXv2bGg0Gmzfvr1BhBQGFCIiIjOSn58vhZO0tDScPXsWQ4cOxdmzZ5GWliaFFKXv7mFAISIiMiPh4eEAAGdnZzg7OyMqKgonTpxAVFQUnJ2d8dBDD8nqlIoBhYiIyIycP38eAJCWlgaVSoXg4GAcO3YMwcHBUKlUyMjIkNUpFS91T0REZEaaNm0q/Xzr1i1YWVkBANavX481a9bA2tq6TJ0SVXsE5eDBgxg8eDA8PT2hUqkQFRUlax8/fjxUKpXsMWDAAFnNtWvXMHr0aNjb28PR0RETJkxAbm5urVaEiIiIgPT0dACAhYUFLCzkv+bvnFdap1TVDih5eXl49NFHsXLlygprBgwYgLS0NOnx1VdfydpHjx6NU6dOYffu3YiOjsbBgwfx6quvVr/3REREJHPp0iUAgNFohJ2dHWbNmoW//voLs2bNgp2dHYxGo6xOqaq9i2fgwIEYOHBgpTVarRbu7u7ltp05cwY7duxAfHw8Hn/8cQDAihUr8Oyzz2LRokXw9PSsbpeIiIjof3x8fHDixAl4eXkhJSUFERERiIiIkNqbNGmCK1euwMfHx4S9vLf7cpDs/v374erqilatWmHSpEnIzs6W2mJjY+Ho6CiFEwDo168fLCws8Pvvv5e7vMLCQuTk5MgeREREVNaXX34JALhy5QquXbuGkJAQ9O/fHyEhIbh27RpSU1NldUpV5wFlwIABWL9+Pfbs2YOPPvoIBw4cwMCBA1FSUgLg9j4vV1dX2XPUajWcnJwq3B+2YMECODg4SA+lH9hDRERkKjqdDn5+fhBCwNnZGYcPH4ajoyMOHz4MZ2dnCCHg5+cHnU5n6q5Wqs7P4hk1apT0c4cOHdCxY0f4+Phg//796Nu3b42WOWfOHEybNk2azsnJYUghIiKqQFxcHNzd3ZGRkYEjR47gyJEjUpubmxvi4uJM2Luque/XQWnZsiVcXFyQmJgIAHB3d0dmZqasxmAw4Nq1axUet6LVamFvby97EBERUflmzpyJjIwMODs749FHH0WbNm3w6KOPwtnZGRkZGZg5c6apu3hP9z2gpKSkIDs7Gx4eHgAAf39/3LhxQ5bm9u7dC6PRiG7dut3v7hAREZm1oqIiLF26FG5ubkhPT0dCQgJOnz6NhIQEpKenw83NDUuXLkVRUZGpu1qpageU3NxcJCQkICEhAQCQnJyMhIQEXLp0Cbm5uQgPD8fhw4dx8eJF7NmzB0OGDIGvry8CAgIAAG3atMGAAQPwyiuvIC4uDr/++itCQ0MxatQonsFDRERUS5GRkTAYDJg/fz7UavmRHGq1Gu+++y4MBgMiIyNN1MOqqXZA+eOPP/DYY4/hscceAwBMmzYNjz32GObNmwdLS0scP34czz33HB555BFMmDABXbp0QUxMDLRarbSMjRs3onXr1ujbty+effZZ9OrVC2vWrKm7tSIiInpAJSUlAQACAwPLbS+dX1qnVNU+SLZPnz4QQlTYvnPnznsuw8nJCZs2baruSxMREdE9lF7fJDo6Gi+//HKZ9ujoaFmdUqlEZWlDoXJycuDg4AC9Xq+IA2ZPXtEjcMUhRE/phfZNHEzdHSIieoAVFRXB1tYWzs7OSElJke3mMRgM8PLyQnZ2NvLy8qDRaOq1b9X5/c27GRMREZkRjUaDsLAwZGRkwMvLC2vWrEFqairWrFkDLy8vZGRkICwsrN7DSXXxbsZERERmpvTS9kuXLsXEiROl+Wq1GuHh4bJL3ysVAwoREZEZioiIwPz58xEZGYmkpCT4+Phg8uTJih85KcWAQkREZKY0Gg2mTp1q6m7UCI9BISIiIsVhQCEiIiLFYUAhIiIixWFAISIiIsVhQCEiIjJT+fn5CA0NRUBAAEJDQ5Gfn2/qLlUZAwoREZEZCgoKgo2NDVauXIldu3Zh5cqVsLGxQVBQkKm7ViUMKERERGYmKCgI27dvL7dt+/btDSKk8DooREREZiQ/P18KJy4uLnj66adha2uLvLw87N27F1evXsX27duRn58Pa2trE/e2YgwoREREZiQsLAzA7cvaX79+HVu2bJHaLC0toVarYTAYEBYWhtWrV5uqm/fEXTxERERmZN++fQBu37nYxcUFa9euRVpaGtauXQsXFxcYDAZZnVJxBIWIiMiMqNW3f7VbW1sjJSVFmn755Zcxfvx42NvbIz8/X5qvVBxBISIiMiMtWrQAABQWFsJoNMrajEYjCgsLZXVKxYBCRERkRpo1awbgdhixs7PDrFmz8Ndff2HWrFmws7OTQktpnVIpe3yHiIiIquWRRx6Rfi4qKkJERAQiIiIqrVMijqAQERGZkcmTJ0OtVsPGxgYqlUrWZmFhARsbG6jVakyePNlEPawajqAQERGZEY1Gg7CwMCxcuBCurq548sknpeugHDhwAJmZmQgPD4dGozF1VyvFgEJERGRmSnfpLF26FN988400X61WIzw8vNxdPkrDgEJERGSGIiIiMH/+fERGRiIpKQk+Pj6YPHmy4kdOSjGgEBERmSmNRoOpU6eauhs1woNkiYiISHEYUIiIiEhxGFCIiIhIcRhQiIiISHEYUIiIiEhxGFCIiIhIcRhQiIiISHEYUIiIiEhxGFCIiIjMVG5uLoYOHYqOHTti6NChyM3NNXWXqoxXkiUiIjJDXbt2RXx8vDR94sQJ2NnZwc/PD3FxcSbsWdVwBIWIiMjMlIYTlUqF4OBgHDt2DMHBwVCpVIiPj0fXrl1N3cV74ggKERGRGcnNzZXCya1bt2BlZQUAWL9+PdasWQMbGxvEx8cjNzcXOp3OxL2tGEdQiIiIzEhwcDAAYMyYMVI4KWVlZYUXX3xRVqdUDChERERmJCkpCQAwY8aMctunTZsmq1MqBhQiIiIz4uPjAwBYtGhRue1LliyR1SkVAwoREZEZ+fLLLwEAGzZsQEFBgaytoKAAmzZtktUpFQMKERGRGdHpdPDz84MQAjY2NhgzZgyOHj2KMWPGwMbGBkII+Pn5KfoAWYABhYiIyOzExcVJIWXjxo3o0qULNm7cKIUTXgeFiIiITCIuLg6ZmZnw9vaGra0tvL29kZmZ2SDCCcCAQkREZJaCgoLg6uqKixcvIi8vDxcvXoSrqyuCgoJM3bUqYUAhIiIyM0FBQdi+fTs0Gg1mz56NxMREzJ49GxqNBtu3b28QIYUBhYiIyIzk5+dL4SQtLQ1nz57F0KFDcfbsWaSlpUkhJT8/39RdrRQDChERkRkJDw8HADg7O8PZ2RlRUVE4ceIEoqKi4OzsDCcnJ1mdUjGgEBERmZHz588DANLS0sq9WWB6erqsTqkYUIiIiMxI06ZNpZ9v3LiBzp07Y+3atejcuTNu3LhRbp0S8W7GREREZqR0hAQAHnroIRiNRml6+vTp5dYpEUdQiIiIzMilS5ekn41GIwICAnDo0CEEBATIwsqddUrEERQiIiIz4u3tjRMnTkClUkEIgZ07d2Lnzp1Se+l8b29v03WyCjiCQkREZEb8/f0BAEIIpKeno2fPnmjatCl69uyJ9PR0CCFkdUrFERQiIiIzkpqaKv3s7u4u/Xz58mXZ9J11SsQRFCIiIjPi4+NTp3WmwoBCRERkRl5++WUAt481ycrKQlBQEDp06ICgoCBkZWVBpVLJ6pSKAYWIiMiMfPLJJwBuH4PSvn17DBw4EDt27MDAgQPRvn176RiU0jqlYkAhIiIyI0lJSQCASZMmITs7GxMnTkSTJk0wceJEZGdnY9KkSbI6peJBskRERGak9NiSzp07Iy8vD5GRkUhKSoKPjw8mT56MdevWyeqUSiVKx3oakJycHDg4OECv18Pe3t7U3cHJK3oErjiE6Cm90L6Jg6m7Q0RED7CioiLY2trC2dkZKSkpUKv/GYswGAzw8vJCdnY28vLyoNFo6rVv1fn9zV08REREZkSj0SAsLAwZGRnw8vLCmjVrkJqaijVr1sDLywsZGRkICwur93BSXdzFQ0REZGYiIiIAAEuXLsXEiROl+Wq1GuHh4VK7kjGgEBERmaGIiAjMnz+/zDEoSh85KVXtXTwHDx7E4MGD4enpCZVKhaioKFm7EALz5s2Dh4cHrK2t0a9fP5w/f15Wc+3aNYwePRr29vZwdHTEhAkTkJubW6sVISIiIjmNRoOpU6dixYoVmDp1aoMJJ0ANAkpeXh4effRRrFy5stz2iIgILF++HKtXr8bvv/8OW1tbBAQEoKCgQKoZPXo0Tp06hd27dyM6OhoHDx7Eq6++WvO1ICIiIrNS7V08AwcOxMCBA8ttE0Jg2bJlmDt3LoYMGQIAWL9+Pdzc3BAVFYVRo0bhzJkz2LFjB+Lj4/H4448DAFasWIFnn30WixYtgqenZ5nlFhYWorCwUJrOycmpbreJiIioAanTs3iSk5ORnp6Ofv36SfMcHBzQrVs3xMbGAgBiY2Ph6OgohRMA6NevHywsLPD777+Xu9wFCxbAwcFBejRt2rQuu01EREQKU6cBJT09HQDg5uYmm+/m5ia1paenw9XVVdauVqvh5OQk1dxtzpw50Ov10uPy5ct12W0iIiJSmAZxFo9Wq4VWqzV1N4iIiKie1OkIiru7OwAgIyNDNj8jI0Nqc3d3R2ZmpqzdYDDg2rVrUg0RERE92Oo0oLRo0QLu7u7Ys2ePNC8nJwe///47/P39AQD+/v64ceMGjhw5ItXs3bsXRqMR3bp1q8vuEBERUQNV7V08ubm5SExMlKaTk5ORkJAAJycnNGvWDFOnTsX8+fPx8MMPo0WLFnjrrbfg6emJoKAgAECbNm0wYMAAvPLKK1i9ejWKi4sRGhqKUaNGlXsGDxEREdVMUVFRg71QG0Q17du3TwAo8xg3bpwQQgij0Sjeeust4ebmJrRarejbt684d+6cbBnZ2dnihRdeEDqdTtjb24t///vf4ubNm1Xug16vFwCEXq+vbvfvixMpN0TzWdHiRMoNU3eFiIhICCFEeHi4UKvVst/VarVahIeHm6xP1fn9zbsZ1wHezZiIiJRk5syZWLhwIdzc3DB//nwEBgYiOjoac+fORUZGhsnux1Od398MKHWAAYWIiJSiqKgItra2cHZ2RkpKCtTqf47mMBgM8PLyQnZ2NvLy8up9d091fn/X6UGyREREZFqRkZEwGAyYP38+jEYjli1bhilTpmDZsmUwGo149913YTAYEBkZaequVqpBXAeFiIiIqiYpKQkAcPToUUycOBFGo1Fqmz59OiZOnCirUyoGFCIiIjPi4+MDAFi1alWZNqPRKM0vrVMq7uIhIiIyIy+//LJsukuXLhg1ahS6dOlSaZ3SMKAQERGZkf/7v/+TTR85cgRff/217AKp5dUpDQMKERGRGfn444/rtM5UGFCIiIjMSH5+PgDA1tYWV69eRc+ePdG0aVP07NkTV69ehY2NjaxOqXiQLBERkRlxdnZGRkYG8vLy4OLiIs2/fPmybNrZ2dkU3asyjqAQERGZkV69esmmAwIC8OuvvyIgIKDSOqXhCAoREZEZadmypWx6586d2Llz5z3rlIYjKERERGbkwoULAAALi/J/xatUKlmdUjGgEBERmZGLFy8CuH1RNhcXF1hbW8PS0hLW1tZwcXFB6S34SuuUigGFiIjIjDz88MMAAI1Gg6tXryI/Px8lJSXIz8/H1atXpRsEltYpFQMKERGRGVm4cCGA23c1BgAHBwc0btwYDg4OsvmldUrFg2SJiIjMSGkAKaXX6yuss7a2ro8u1QhHUIiIiMzIoEGD6rTOVBhQiEgxSkpKsH//fnz11VfYv38/SkpKTN0logbn77//BgBYWloiMzNTdiXZzMxMWFpayuqUirt4iEgRtm7diunTp8vOLPD29sbixYsxbNgw03WMqIEpLi4GcPtS9x4eHlLQv3z5Mjw8PGBra4ucnBypTqk4gkJEJrd161aMGDECHTp0QGxsLG7evInY2Fh06NABI0aMwNatW03dRaIGo23btgCAnJycMqOQJSUlyMnJkdUpFQMKEZlUSUkJpk+fjsDAQERFRaF79+7Q6XTo3r07oqKiEBgYiBkzZnB3D1EVVfX0YZ5mTERUiZiYGFy8eBFvvPFGmStfWlhYYM6cOUhOTkZMTIyJekjUsNx5Q8C6qDMVBhQiMqm0tDQAQPv27cttL51fWkdElVu+fHmd1pkKAwoRmZSHhwcA4OTJk+W2l84vrSOiyuXl5dVpnakwoBCRSfXu3Rve3t744IMPYDQaZW1GoxELFixAixYt0Lt3bxP1kKhhKb3XTikvLy88/PDD8PLyqrROaRhQiMikLC0tsXjxYkRHRyMoKEh2Fk9QUBCio6OxaNEi6doNRFQ5tVp+BZGUlBScP38eKSkpldYpjbJ7R0QPhGHDhuHbb7/FtGnT0KNHD2m+t7c3vv32W14HhagaVCpVndaZCkdQiEgxlP6FSdQQODo61mmdqTCgEJHJ8UJtRHXH19e3TutMhQGFiEyKF2ojqluvvPJKndaZCgMKEZnUnRdqMxgMWLZsGaZMmYJly5bBYDDwQm1E1eTs7FyndabCg2SJyKRKL8D29ddfo1evXrKRkhkzZiA0NFRWR0SV++ijj6pc99xzz93n3tQcR1CIyKRKL8D23//+t9wbm/33v/+V1RFR5S5fviybLr2FxN23kri7TmkYUIjIpLp161andUQPOk9PT9l06QUQ774Q4t11SsOAQkQmFRkZKf2s0Wgwe/ZsnD9/HrNnz4ZGoym3jogq1q5duzqtMxUGFCIyqYMHDwIAbGxs4OnpiQ8//BAPP/wwPvzwQzRp0gQ2NjayOiKqXFJSkmy6bdu2iIqKQtu2bSutUxoGFCIyqdLLbz/33HNITEzEvn37sGnTJuzbtw/nz59HYGCgrI6IKnfkyBHZ9OnTpxEUFITTp09XWqc0PIuHiEzKy8sLR48exY4dOyCEQJ8+faQ2g8GAnTt3SnVEdG+FhYXSzykpKejVqxeysrLQuHFjHDp0SPos3VmnRAwoRGRSTzzxBL7//nvcuHEDTZo0wZgxY9CyZUtcuHABGzZsgF6vl+qI6N60Wi2Ki4sByIN9Xl6ebFqr1dZ736qDu3iIyKSmTJkinf6YmZmJJUuWIDQ0FEuWLEFmZiaA26dHTpkyxZTdJGowZs6cWad1psKAQkQmpdFoMHjw4EprBg8eLDujh4gqFhYWJpt2dHREx44dy9wc8O46pWFAISKTKikpwa5duyqt2bVrF+/FQ1RFn3zyiWz6xo0bOH78OG7cuFFpndIwoBCRSe3atQv5+flo1KgRbt68iaVLlyI0NBRLly7FzZs30ahRI+Tn598zxBDRbaWnD0+aNKlMm0qlkuYr/TRjHiRLRCa1ZMkSALePRdHpdJg6daqsPSQkBMuWLcOSJUswcOBAE/SQSLnyi0qQlJUrm2fjfPsKsW4tWuPIhUxs+HwNzv2ViFaP+GLMv1/F9m82SXUnr+hlz/VprIO1xrJ+On8PDChEZFKlw87+/v7ltpde4v7u4WkiApKychG44pBsntHQGlBZ4N133sanWc1hYdEJ8O6EU0XAt6t+w5WV7wAqS3yV2xqb73pu9JReaN/Eof5WoBIMKERUb8r7a69lm474448/MC18Jh7p1hdFJQIp1/Ph9ZA1NJYqzJw9R6pT8l97RKbg01iH6Cm9ysxfkheCz1evwK3PX8HzE2fgh+vuGPxQOr75eBGMt27g3/+ZgmlhT5e7PKVgQCGielPeX3slLgMBfIbLF5Ph9+QzcOo5Co1cmqP46t+49uvXKPr7IgDgN5eBZZ6rpL/2iEzBWmNZ7mfgs1XL4WJnhaVLl2L1+7MAAKsBqNVqhIeHIyIiop57Wn0qIYQwdSeqKycnBw4ODtDr9bC3tzd1d3Dyih6BKw7xy5LoHsobQQGA1156Aft2/Vzh857qPxDLP/uqzHyOoBBVrqioCG99sBirfvgNkwb3wHtvTDfpKfvV+f3NERQiqjcV/bW3d+dPCAoKwvbt28u0DRkyBFFRUfXQOyLzo9FoEPzKZGwu6IjgV3o1qOsJ8TRjIlKEqKgo3Lp1C6PGvQwr78cwatzLuHXrFsMJ0QOKIyhEpBjW1tZ48/1FiF1xCG9O6QVra2tTd4mITIQjKERERKQ4DChERESkOAwoREREpDgMKERERKQ4DChERESkOAwoREREpDgMKERERKQ4DChERESkOAwoREREpDgMKERERKQ4DChERESkOAwoREREpDh1HlDeeecdqFQq2aN169ZSe0FBAUJCQuDs7AydTofhw4cjIyOjrrtBREREDdh9GUFp164d0tLSpMehQ4ektrCwMPzwww/45ptvcODAAaSmpmLYsGH3oxtERETUQKnvy0LVari7u5eZr9fr8emnn2LTpk14+umnAQCff/452rRpg8OHD6N79+7lLq+wsBCFhYXSdE5Ozv3oNhERESnEfRlBOX/+PDw9PdGyZUuMHj0aly5dAgAcOXIExcXF6Nevn1TbunVrNGvWDLGxsRUub8GCBXBwcJAeTZs2vR/dJiIiIoWo84DSrVs3rFu3Djt27MCqVauQnJyM3r174+bNm0hPT4dGo4Gjo6PsOW5ubkhPT69wmXPmzIFer5cely9frutuExERkYLU+S6egQMHSj937NgR3bp1Q/PmzbFlyxZYW1vXaJlarRZarbauukhEREQKd99PM3Z0dMQjjzyCxMREuLu7o6ioCDdu3JDVZGRklHvMChERET2Y7ntAyc3NRVJSEjw8PNClSxc0atQIe/bskdrPnTuHS5cuwd/f/353hYiIiBqIOt/FM2PGDAwePBjNmzdHamoq3n77bVhaWuKFF16Ag4MDJkyYgGnTpsHJyQn29vaYMmUK/P39KzyDh4iIiB48dR5QUlJS8MILLyA7OxuNGzdGr169cPjwYTRu3BgAsHTpUlhYWGD48OEoLCxEQEAAIiMj67obRERE1IDVeUD5+uuvK223srLCypUrsXLlyrp+aSIiIjITvBcPERERKQ4DChERESkOAwoREREpDgMKERERKQ4DChERESkOAwoREREpDgMKERERKQ4DChERESkOAwoREREpTp1fSZaIHlzJV/OQV2io1TISM3Nl/9aGrVaNFi62tV4OEdU/BhQiqhPJV/Pw1KL9dba8qZsT6mQ5+2b0YUghaoAYUIioTpSOnCwb2Qm+rroaL6eguAQp1/Ph9ZA1rBpZ1ng5iZm5mLo5odYjOkRkGgwoRFSnfF11aN/EoVbLeNy7bvpCRA0XD5IlIiIixWFAISIiIsVhQCEiIiLFYUAhIiIixWFAISIiIsVhQCEiIiLF4WnGRERECqWkqzPX95WZGVCIiIgUSIlXZ67PKzMzoBARESmQkq7ObIorMzOgEBERKdiDenVmHiRLREREisMRFCKqE4UlBbCwuoLknHOwsKr5cHRdSc7JhYXVFRSWFACo3V+fRFT/GFCIqE6k5v0N2xYr8EacqXvyD9sWQGpeJ3SBm6m7QkTVxIBCRHXC07Y58pKn4L8jO8GnFgf01ZWkzFy8vjkBnk81N3VXiKgGGFCIqE5oLa1gLGiCFvat0NbZ9LtUjAV6GAuyoLW0MnVXiKgGeJAsERERKQ4DChERESkOAwoREREpDgMKERERKQ4DChERESkOAwoREREpDgMKERERKQ4DChERESkOL9RGRHUiv7gEAHDyir5Wy6mLW8MDt28PT0QNFwMKEdWJpP8FgtlbT5i4J3K2Wn7NETVE/OQSUZ3o384dAODjqoN1LUc+pm5OwLKRneBby3v62GrVaOFiW6tlEJFpMKAQUZ1wstVgVNdmdbY8X1cd2jcx/T19iMg0eJAsERERKQ4DChERESkOAwoREREpDgMKERERKQ4PkiUiIlKgwpICWFhdQXLOOVhY1e6MttpKzsmFhdUVFJYUAKifg9cZUIiIiBQoNe9v2LZYgTfiTN2T22xbAKl5ndAFbvXyegwoRERECuRp2xx5yVPw35Gd4FPLawLVVlJmLl7fnADPp5rX22syoBARESmQ1tIKxoImaGHfCm2dTXtNIGOBHsaCLGgtrertNXmQLBERESkOAwoRUQOQlZWFFi1aQKfToUWLFsjKyjJ1l4juK+7iISJSOEdHR+j1/9wlOi8vD66urnBwcMCNGzdM1zGi+4gjKERECnZnOGnXrh2io6PRrl07AIBer4ejo6MJe0d0/3AEhYhIobKysqRwotfrYW9vDwAYNGgQcnJy4ODgAL1ej6ysLDRu3NiUXTWpkpISxMTEIC0tDR4eHujduzcsLWt+R21SBo6gEBEpVNeuXQHcHjkpDSel7O3t0aZNG1ndg2jr1q3w9fXFU089hRdffBFPPfUUfH19sXXrVlN3jWqJIyhERApVeiDsRx99BL1ej0GDBuHSpUto1qwZfvzxR7z//vsYNmzYA3vA7NatWzFixAgEBATA3t4e2dnZcHZ2hqenJ0aMGIFvv/0Ww4YNM3U3ayy/uAQAcPKK/h6VlSsoLkHK9Xx4PWQNq0Y1G1lKzMytVR9qggGFiEihGjdujLy8PAwdOhTFxcXS/MuXL8PR0RFqtVqqe9CUlJRg+vTpsLa2xo4dO6T5V65cwfHjx2FjY4MZM2ZgyJAhDXZ3T9L/QsHsrSdM3JN/2GrrLzYwoBARKVRcXBxcXV1l4eROBoNBqnvQxMTE4OLFixW237p1C8nJyYiJiUGfPn3qrV91qX87dwCAj6sO1jUc+QBuj35M3ZyAZSM7wbcWV6S11arRwsW2xs+vLgYUIiKF0mg0dVpnTpKSkmTTLi4usLS0RElJCa5evSqra6gBxclWg1Fdm9XZ8nxddWjfxLRXpK0OHiRLRKRQPXv2rNM6c7J48WLZ9NWrV5GRkSELJ+XVUcPBgEJEpFCnTp0qM6+84ynKqzN3Fy5cqNM6Uh4GFCKiBiA5ORlCCBgMBgghkJycbOoumZQQok7rSHl4DApRNeUb8vHr36eRX1RSbnuhwYjMnII6ez1Xeyto1RX/LWGtsUTP5m1hrbaus9ck5XF1da10+kFTVFRUp3WkPAwoRNW0J/Ek5vz+kqm7IbMU69DPp4upu0G1kF9UgqSsiq81YWtri25PPI3B40Lwwxcr8fvBvbL2u6+V4dNYB2tNwzy9lggwcUBZuXIlFi5ciPT0dDz66KNYsWLFA31FRGoYrt9wQF7yFFN3Q8ZrgLepu0C1lJSVi8AVhyqt+f3g3jLBpNTdz42e0qtBnbFBdDeTBZTNmzdj2rRpWL16Nbp164Zly5YhICAA586dU9TQ5b3+qgH+ucLeva6019D+okm+moe8QkOF7aVXJ6wL97rCYX2ff1+ZQR280chiQIXXJqjKdrl87RYW7/4L0595BE2dbCqtbUjbhipWlc/TspGdZPMutIvG9LGB91z24vXRaNle/tyC4pJKr0DK9w0pnckCypIlS/DKK6/g3//+NwBg9erV+PHHH/HZZ59h9uzZstrCwkIUFhZK06U3z8rJybnv/Tydqse/Pj5cpdrX1v9WafuWid3R1rNh/EVzLCULwV9Gm7obMt9OGIJHXJ1M3Q2oATzb2rHC9tOpeXhtfdXeMwujE+5Zs2VidzRzqux9U1Ivn4W6kF9UguSrlQf5C1l5MBbewvELaci9Wfl6tXBpGKG/Np8nbVPtPWuWn04ETidWe9lK+Eyl6fXYeiKh0pr8ohKkXL8lm3f3dtFa20Gt0cJQVIjC/Juytpc+3ySb9nrIptL3jau9FQLbdmwQx3U1tM9U6XdVlQ5eFiZQWFgoLC0txbZt22Tzx44dK5577rky9W+//bYAwAcffPDBBx98mMHj8uXL98wKJhlBuXr1KkpKSuDm5iab7+bmhrNnz5apnzNnDqZNmyZNG41GXLt2Dc7OzlCpVPe9v/eSk5ODpk2b4vLly2XuOPog43apGLdNxbhtKsZtUzFum4opadsIIXDz5k14enres7ZBnMWj1Wqh1cqH8xwdHU3TmUrY29ub/D9fibhdKsZtUzFum4px21SM26ZiStk2Dg4OVaozyYXaSu+ZkJGRIZufkZEBd3d3U3SJiIiIFMQkAUWj0aBLly7Ys2ePNM9oNGLPnj3w9/c3RZeIiIhIQUy2i2fatGkYN24cHn/8cXTt2hXLli1DXl6edFZPQ6LVavH222+X2Q31oON2qRi3TcW4bSrGbVMxbpuKNdRtoxLCdDcq+L//+z/pQm2dOnXC8uXL0a1bN1N1h4iIiBTCpAGFiIiIqDy8mzEREREpDgMKERERKQ4DChERESkOA4qZ69OnD6ZOnWrqbmD//v1QqVS4cePGfX2d8ePHIygo6L6+Rk0o5f+BlEulUiEqKgoAcPHiRahUKiQkJJi0T+aqvr6PqHYaREAZP348VCoVVCoVNBoNfH198e6778JgqPjOoA+CmvwyzsrKwqRJk9CsWTNotVq4u7sjICAAv/766/3p5P/06NEDaWlpVb6CoBLxfVh7sbGxsLS0xKBBg0zdlXp3r89eWloaBg4cWK1lbtu2Dd27d4eDgwPs7OzQrl27BhmE+dmquju31Z2PxMTq3yxS6RrEpe4BYMCAAfj8889RWFiIn376CSEhIWjUqBHmzJlj6q7Vu6KiImg0mho9d/jw4SgqKsIXX3yBli1bIiMjA3v27EF2dnaNlieEQElJCdTqyt9KGo3GLK4SzPdh7Xz66aeYMmUKPv30U6Smplbpfhzm4l6fvep+Pvbs2YORI0fi/fffx3PPPQeVSoXTp09j9+7d96P7911dfbZKSkqgUqlgYdEg/v6ukdJtdafGjRtXaxkNYjvV/t7E99+4cePEkCFDZPOeeeYZ0b17d7F48WLRvn17YWNjI7y8vMSkSZPEzZs3pbqLFy+KwMBA4ejoKGxsbETbtm3Fjz/+KIQQ4tq1a+LFF18ULi4uwsrKSvj6+orPPvtMeu6lS5fE888/LxwcHMRDDz0knnvuOZGcnFymXwsXLhTu7u7CyclJTJ48WRQVFUk1qamp4tlnnxVWVlbC29tbbNy4UTRv3lwsXbpUqrl+/bqYMGGCcHFxEXZ2duKpp54SCQkJUvvbb78tHn30UbF27Vrh7e0tVCpVudslNzdXBAcHC1tbW+Hu7i4WLVoknnzySfH6669LrwNA7N+/v9ztnJycLACIP//8U9Y3AGLfvn1CCCH27dsnAIiffvpJdO7cWTRq1Eh8/PHHAoA4c+aMbHlLliwRLVu2lD3v+vXrQq/XCysrK/HTTz/J6rdu3Sp0Op3Iy8ur0vY3GAwiLCxMODg4CCcnJxEeHi7Gjh1b5r1SVyp7HwohxKFDh8STTz4prK2thaOjo+jfv7+4du2aEELI/h+EEGL9+vWiS5cuQqfTCTc3N/HCCy+IjIwMqb2y92ZhYaEICQkR7u7uQqvVimbNmokPPvjgvqxzXbp586bQ6XTi7NmzYuTIkeL999+XtW/fvl34+voKrVYr+vTpI9atWye9Z0rFxMSIXr16CSsrK+Hl5SWmTJkicnNz63lNqu9enz0hhAAg3eG99LP41VdfCX9/f6HVakW7du1kz3/99ddFnz59Kn3d0u+O1atXCy8vL2FtbS2ef/55cePGjTpZr7pSm+/4zz//XDg4OIjt27eLNm3aCEtLS5GcnCwKCgrEzJkzhZeXl9BoNMLHx0d88sknQoh/vo9++eUX0aVLF2FtbS38/f3F2bNn63O1a6S8bSWEqNV2mj59uvD09BQ2Njaia9eu0ve9qSk4OlXO2toaRUVFsLCwwPLly3Hq1Cl88cUX2Lt3L2bOnCnVhYSEoLCwEAcPHsSJEyfw0UcfQafTAQDeeustnD59Gj///DPOnDmDVatWwcXFBQBQXFyMgIAA2NnZISYmBr/++it0Oh0GDBiAoqIiafn79u1DUlIS9u3bhy+++ALr1q3DunXrpPaxY8ciNTUV+/fvx3fffYc1a9YgMzNTti7PP/88MjMz8fPPP+PIkSPo3Lkz+vbti2vXrkk1iYmJ+O6777B169YK90uHh4fjwIED2L59O3bt2oX9+/fj6NGjUrtOp4NOp0NUVBQKCwtrvO0BYPbs2fjwww9x5swZjBgxAo8//jg2btwoq9m4cSNefPHFMs+1t7dHYGAgNm3aVKY+KCgINjY2Vdr+ixcvxrp16/DZZ5/h0KFDuHbtGrZt21ar9aqu0vdhQkIC+vbti7Zt2yI2NhaHDh3C4MGDUVJSUu7ziouL8d577+HYsWOIiorCxYsXMX78eKm9svfm8uXL8f3332PLli04d+4cNm7cCG9v73pY29rZsmULWrdujVatWmHMmDH47LPPIP53Gabk5GSMGDECQUFBOHbsGCZOnIg333xT9vykpCQMGDAAw4cPx/Hjx7F582YcOnQIoaGhplidaqnpZy88PBzTp0/Hn3/+CX9/fwwePFg24nLq1CmcPHmy0mUkJiZiy5Yt+OGHH7Bjxw78+eefmDx5cq3Wpz5U9TseAG7duoWPPvoIn3zyCU6dOgVXV1eMHTsWX331FZYvX44zZ87g448/lr77S7355ptYvHgx/vjjD6jVarz00kv1uYp1qqbbKTQ0FLGxsfj6669x/PhxPP/88xgwYADOnz9vojW5g6kTUlXcmRiNRqPYvXu30Gq1YsaMGWVqv/nmG+Hs7CxNd+jQQbzzzjvlLnfw4MHi3//+d7ltX375pWjVqpUwGo3SvMLCQmFtbS127twp9at58+bCYDBINc8//7wYOXKkEEKIM2fOCAAiPj5eaj9//rwAII2gxMTECHt7e1FQUCB7fR8fH/Hxxx8LIW7/FdSoUSORmZlZ4Xa5efOm0Gg0YsuWLVJ7dna2sLa2lv3l/u2334qHHnpIWFlZiR49eog5c+aIY8eOCSGqN4ISFRUl68vSpUuFj4+PNH3u3DnZqMqdIyhCCLFt2zbZaEnpqMrPP/9c5e3v4eEhIiIipPbi4mLh5eVVLyMod78PX3jhBdGzZ88Kn3v3CMrd4uPjBQDpL57K3ptTpkwRTz/9tGzbNAQ9evQQy5YtE0Lc/r9ycXGR3lezZs0S7du3l9W/+eabsvfMhAkTxKuvviqriYmJERYWFiI/P/++97+2KvvsCVH+CMqHH34otZe+vz/66CMhxO0R02effVYAEM2bNxcjR44Un376qey75O233xaWlpYiJSVFmvfzzz8LCwsLkZaWdp/XuOpq8x3/+eefCwCyUefS75/du3eX+3p3jqCU+vHHHwUAxb+Xxo0bJywtLYWtra30GDFiRJm6qmynv//+W1haWoorV67Intu3b18xZ86c+7cSVdRgRlCio6Oh0+lgZWWFgQMHYuTIkXjnnXfwyy+/oG/fvmjSpAns7OwQHByM7Oxs3Lp1CwDw2muvYf78+ejZsyfefvttHD9+XFrmpEmT8PXXX6NTp06YOXMmfvvtN6nt2LFjSExMhJ2dnfTXj5OTEwoKCpCUlCTVtWvXDpaWltK0h4eHNEJy7tw5qNVqdO7cWWr39fXFQw89JHud3NxcODs7S6+j0+mQnJwse53mzZtXuo8xKSkJRUVFslsFODk5oVWrVrK64cOHIzU1Fd9//z0GDBiA/fv3o3PnzrJRn6p4/PHHZdOjRo3CxYsXcfjwYQC3R0M6d+6M1q1bl/v8Z599Fo0aNcL3338PAPjuu+9gb2+Pfv36Abj39tfr9UhLS5Otr1qtLtOvulbR+7B0BKWqjhw5gsGDB6NZs2aws7PDk08+CQC4dOkSgMrfm+PHj0dCQgJatWqF1157Dbt27arblbwPzp07h7i4OLzwwgsAbv9fjRw5Ep9++qnU7ufnJ3tO165dZdPHjh3DunXrZJ+TgIAAGI1GJCcn18+K1EJNPnt33jy19P195swZAICtrS1+/PFHJCYmYu7cudDpdJg+fTq6du0qff8BQLNmzdCkSRPZMo1GI86dO1f3K1kLNf2OB24f49axY0dpOiEhAZaWltLnqiJ3PsfDwwMAyoxwK9FTTz2FhIQE6bF8+fIabacTJ06gpKQEjzzyiOxzdeDAAdnvH1NpMAGl9D/k/PnzyM/PxxdffIGsrCwEBgaiY8eO+O6773DkyBGsXLkSAKTdAC+//DIuXLiA4OBgnDhxAo8//jhWrFgBABg4cCD+/vtvhIWFITU1FX379sWMGTMAALm5uejSpYvsTZCQkIC//vpLttuiUaNGsn6qVCoYjcYqr1dubi48PDzKvM65c+cQHh4u1dna2tZsw5XDysoKzzzzDN566y389ttvGD9+PN5++23pYClxx90PiouLy13G3f1xd3fH008/Le222bRpE0aPHl1hHzQaDUaMGCGrHzlypHSwbVW3f30r731oa2sLa2vrKi8jLy8PAQEBsLe3x8aNGxEfHy/tmip931b23uzcuTOSk5Px3nvvIT8/H//6178wYsSIul/ZOvTpp5/CYDDA09MTarUaarUaq1atwnfffQe9Xl+lZeTm5mLixImy98OxY8dw/vx5+Pj43Oc1qBsVffZqw8fHBy+//DI++eQTHD16FKdPn8bmzZvrqMf1p6bf8cDt3UEqlUo2XRV3fn+XPr8639+mYmtrC19fX+lRWFhYo+2Um5sLS0tLHDlyRPa5OnPmDP773//W+3rdrcEElNL/kGbNmkm/xI4cOQKj0YjFixeje/fueOSRR5CamlrmuU2bNsV//vMfbN26FdOnT8fatWultsaNG2PcuHHYsGEDli1bhjVr1gC4/Uvg/PnzcHV1lb0RfH19q3yqbKtWrWAwGPDnn39K8xITE3H9+nVpunPnzkhPT4darS7zOqXHHFSFj48PGjVqhN9//12ad/36dfz111/3fG7btm2Rl5cnjdCkpaVJbdW5DsPo0aOxefNmxMbG4sKFCxg1atQ963fs2IFTp05h7969skBzr+3v4OAADw8P2foaDAYcOXKkyv2tifLeh8Dtv8T27NlTpWWcPXsW2dnZ+PDDD9G7d2+0bt263L/aKnpvAreP4xk5ciTWrl2LzZs347vvvpMds6QkBoMB69evx+LFi8uEC09PT3z11Vdo1aoV/vjjD9nz4uPjZdOdO3fG6dOny7wffH19a3xWm6mVfvYqUjoiCfzz/m7Tpk2F9d7e3rCxsZEt89KlS7LvxcOHD8PCwqLM6Kqp1eY7/m4dOnSA0WjEgQMH7ne3FaGm2+mxxx5DSUkJMjMzy3ymlHDWZYMJKOXx9fVFcXExVqxYgQsXLuDLL7/E6tWrZTVTp07Fzp07kZycjKNHj2Lfvn3SB3zevHnYvn07EhMTcerUKURHR0tto0ePhouLC4YMGYKYmBgkJydj//79eO2115CSklKl/rVu3Rr9+vXDq6++iri4OPz555949dVXZSm2X79+8Pf3R1BQEHbt2oWLFy/it99+w5tvvlnmC7syOp0OEyZMQHh4OPbu3YuTJ09i/PjxslPIsrOz8fTTT2PDhg04fvw4kpOT8c033yAiIgJDhgyBtbU1unfvLh38euDAAcydO7fKfRg2bBhu3ryJSZMm4amnnrrnKaRPPPEE3N3dMXr0aLRo0UK2u6Yq2//111/Hhx9+iKioKJw9exaTJ0822YWX5syZg/j4eEyePBnHjx/H2bNnsWrVKly9erVMbbNmzaDRaKT37ffff4/33ntPVlPZe3PJkiX46quvcPbsWfz111/45ptv4O7uDkdHx/pY1WqLjo7G9evXMWHCBLRv3172GD58OD799FNMnDgRZ8+exaxZs/DXX39hy5Yt0q6P0s/KrFmz8NtvvyE0NFT6S3v79u0N4iDZe332KrJy5Ups27YNZ8+eRUhICK5fvy4dyPnOO+9g5syZ2L9/P5KTk/Hnn3/ipZdeQnFxMZ555hlpGVZWVhg3bhyOHTuGmJgYvPbaa/jXv/6liF9A91KV7/jyeHt7Y9y4cXjppZcQFRUlfX9s2bKlHnpd/2q6nR555BGMHj0aY8eOxdatW5GcnIy4uDgsWLAAP/74Yz30/B5MfRBMVVR0WpUQt09l9fDwENbW1iIgIECsX79edmBdaGio8PHxEVqtVjRu3FgEBweLq1evCiGEeO+990SbNm2EtbW1cHJyEkOGDBEXLlyQlp2WlibGjh0rXFxchFarFS1bthSvvPKK0Ov1Ffbr9ddfF08++aQ0nZqaKgYOHCi0Wq1o3ry52LRpk3B1dRWrV6+WanJycsSUKVOEp6enaNSokWjatKkYPXq0uHTpkhDin1MF77Vdbt68KcaMGSNsbGyEm5ubiIiIkB2cWVBQIGbPni06d+4sHBwchI2NjWjVqpWYO3euuHXrlhBCiNOnTwt/f39hbW0tOnXqJHbt2lXuQbJ3nvp5p3/9618CgOx07cqeN3PmTAFAzJs3r8yy7rX9i4uLxeuvvy7s7e2Fo6OjmDZtWr2fZnyn/fv3ix49egitViscHR1FQECAtL53HyS7adMm4e3tLbRarfD39xfff/+97ADlyt6ba9asEZ06dRK2trbC3t5e9O3bVxw9evS+rHNdCAwMFM8++2y5bb///rsAII4dO1bmNONVq1aVOWgxLi5OPPPMM0Kn0wlbW1vRsWPHMqcrK1FVPnso5yDZTZs2ia5duwqNRiPatm0r9u7dKy1z7969Yvjw4aJp06ZCo9EINzc3MWDAABETEyPVlH53REZGCk9PT2FlZSVGjBghnf6uFLX5ji89ffZu+fn5IiwsTHh4eAiNRiM7Vb+876M///xTAJBdykCJKtpWNd1ORUVFYt68ecLb21s0atRIeHh4iKFDh4rjx4/f3xWpApUQdxxwQPddSkoKmjZtKh3QRETle//997F69WpcvnzZ1F1psN555x1ERUXxkvnUIDWYK8k2VHv37kVubi46dOiAtLQ0zJw5E97e3njiiSdM3TUiRYmMjISfnx+cnZ3x66+/YuHChQ1i9w0R3R8MKPdZcXEx3njjDVy4cAF2dnbo0aMHNm7cWObsH6IH3fnz5zF//nxcu3YNzZo1w/Tp03kLAaIHGHfxEBERkeI06LN4iIiIyDwxoBAREZHiMKAQERGR4jCgEBERkeIwoBAREZHiMKAQERGR4jCgEBERkeIwoBAREZHi/H/WioIsZu3o2AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='box', ylim=[0,250])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = 'Age'\n",
    "df[c].plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[c].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "Question 187: What is the pandas code to plot a histogram of just the column c (df[c])? \n",
    "    \n",
    "Plot the age distribution of passengers using this code and c='Age'\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Selecting and filtering data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\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": "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": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      22.0\n",
       "1      38.0\n",
       "2      26.0\n",
       "3      35.0\n",
       "       ... \n",
       "887    19.0\n",
       "888     NaN\n",
       "889    26.0\n",
       "890    32.0\n",
       "Name: Age, Length: 891, dtype: float64"
      ]
     },
     "execution_count": 31,
     "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": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19.0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>NaN</td>\n",
       "      <td>23.4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32.0</td>\n",
       "      <td>7.7500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age     Fare\n",
       "0    22.0   7.2500\n",
       "1    38.0  71.2833\n",
       "2    26.0   7.9250\n",
       "3    35.0  53.1000\n",
       "..    ...      ...\n",
       "887  19.0  30.0000\n",
       "888   NaN  23.4500\n",
       "889  26.0  30.0000\n",
       "890  32.0   7.7500\n",
       "\n",
       "[891 rows x 2 columns]"
      ]
     },
     "execution_count": 32,
     "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": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
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       "      <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",
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       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <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",
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       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass                                  Name  \\\n",
       "10           11         1       3       Sandstrom, Miss. Marguerite Rut   \n",
       "11           12         1       1              Bonnell, Miss. Elizabeth   \n",
       "12           13         0       3        Saundercock, Mr. William Henry   \n",
       "13           14         0       3           Andersson, Mr. Anders Johan   \n",
       "14           15         0       3  Vestrom, Miss. Hulda Amanda Adolfina   \n",
       "\n",
       "       Sex   Age  SibSp  Parch     Ticket     Fare Cabin Embarked  \n",
       "10  female   4.0      1      1    PP 9549  16.7000    G6        S  \n",
       "11  female  58.0      0      0     113783  26.5500  C103        S  \n",
       "12    male  20.0      0      0  A/5. 2151   8.0500   NaN        S  \n",
       "13    male  39.0      1      5     347082  31.2750   NaN        S  \n",
       "14  female  14.0      0      0     350406   7.8542   NaN        S  "
      ]
     },
     "execution_count": 33,
     "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": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.set_index('Name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26.55"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Bonnell, Miss. Elizabeth', 'Fare']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>Bonnell, Miss. Elizabeth</th>\n",
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       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>Andersson, Mr. Anders Johan</th>\n",
       "      <td>14</td>\n",
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       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.275</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "</table>\n",
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      ],
      "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": 36,
     "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": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "                                                    Survived  Pclass\n",
       "Name                                                                \n",
       "Braund, Mr. Owen Harris                                    0       3\n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...         1       1"
      ]
     },
     "execution_count": 37,
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   "source": [
    "df.iloc[0:2,1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The different indexing methods can also be used to assign data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc['Braund, Mr. Owen Harris', 'Survived'] = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>71.2833</td>\n",
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       "      <td>C</td>\n",
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       "      <th>Heikkinen, Miss. Laina</th>\n",
       "      <td>3</td>\n",
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       "      <th>Futrelle, Mrs. Jacques Heath (Lily May Peel)</th>\n",
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       "      <td>53.1000</td>\n",
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       "    <tr>\n",
       "      <th>Johnston, Miss. Catherine Helen \"Carrie\"</th>\n",
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       "      <th>Behr, Mr. Karl Howell</th>\n",
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       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>Dooley, Mr. Patrick</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    PassengerId  Survived  \\\n",
       "Name                                                                        \n",
       "Braund, Mr. Owen Harris                                       1       100   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...            2         1   \n",
       "Heikkinen, Miss. Laina                                        3         1   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)                  4         1   \n",
       "...                                                         ...       ...   \n",
       "Graham, Miss. Margaret Edith                                888         1   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                    889         0   \n",
       "Behr, Mr. Karl Howell                                       890         1   \n",
       "Dooley, Mr. Patrick                                         891         0   \n",
       "\n",
       "                                                    Pclass     Sex   Age  \\\n",
       "Name                                                                       \n",
       "Braund, Mr. Owen Harris                                  3    male  22.0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...       1  female  38.0   \n",
       "Heikkinen, Miss. Laina                                   3  female  26.0   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)             1  female  35.0   \n",
       "...                                                    ...     ...   ...   \n",
       "Graham, Miss. Margaret Edith                             1  female  19.0   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                 3  female   NaN   \n",
       "Behr, Mr. Karl Howell                                    1    male  26.0   \n",
       "Dooley, Mr. Patrick                                      3    male  32.0   \n",
       "\n",
       "                                                    SibSp  Parch  \\\n",
       "Name                                                               \n",
       "Braund, Mr. Owen Harris                                 1      0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...      1      0   \n",
       "Heikkinen, Miss. Laina                                  0      0   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)            1      0   \n",
       "...                                                   ...    ...   \n",
       "Graham, Miss. Margaret Edith                            0      0   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                1      2   \n",
       "Behr, Mr. Karl Howell                                   0      0   \n",
       "Dooley, Mr. Patrick                                     0      0   \n",
       "\n",
       "                                                              Ticket     Fare  \\\n",
       "Name                                                                            \n",
       "Braund, Mr. Owen Harris                                    A/5 21171   7.2500   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...          PC 17599  71.2833   \n",
       "Heikkinen, Miss. Laina                              STON/O2. 3101282   7.9250   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)                  113803  53.1000   \n",
       "...                                                              ...      ...   \n",
       "Graham, Miss. Margaret Edith                                  112053  30.0000   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                  W./C. 6607  23.4500   \n",
       "Behr, Mr. Karl Howell                                         111369  30.0000   \n",
       "Dooley, Mr. Patrick                                           370376   7.7500   \n",
       "\n",
       "                                                   Cabin Embarked  \n",
       "Name                                                               \n",
       "Braund, Mr. Owen Harris                              NaN        S  \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...   C85        C  \n",
       "Heikkinen, Miss. Laina                               NaN        S  \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)        C123        S  \n",
       "...                                                  ...      ...  \n",
       "Graham, Miss. Margaret Edith                         B42        S  \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"             NaN        S  \n",
       "Behr, Mr. Karl Howell                               C148        C  \n",
       "Dooley, Mr. Patrick                                  NaN        Q  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 39,
     "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": 40,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "Braund, Mr. Owen Harris                                False\n",
       "Cumings, Mrs. John Bradley (Florence Briggs Thayer)     True\n",
       "Heikkinen, Miss. Laina                                 False\n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)            True\n",
       "                                                       ...  \n",
       "Graham, Miss. Margaret Edith                           False\n",
       "Johnston, Miss. Catherine Helen \"Carrie\"               False\n",
       "Behr, Mr. Karl Howell                                  False\n",
       "Dooley, Mr. Patrick                                    False\n",
       "Name: Fare, Length: 891, dtype: bool"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Fare'] > 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Fare'] > 50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.72664459161148"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Sex']=='male']['Age'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "Question 188: Select all rows for male passengers and calculate the mean age of those passengers. Round the value to the nearest integer.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "Question 189: how many passengers older than 70 were on the Titanic?\n",
    "</div>"
   ]
  },
  {
   "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": null,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "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": null,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "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": null,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "for key in ['A', 'B', 'C']:\n",
    "    print(key, df[df['key'] == key]['data'].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This becomes very verbose when having multiple groups. You could make the above a bit easier by looping over the different values, but still, it is not very convenient to work with.\n",
    "\n",
    "What we did above, applying a function on different groups, is a \"groupby operation\", and pandas provides some convenient functionality for this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Groupby: applying functions per group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The \"group by\" concept: we want to **apply the same function on subsets of your dataframe, based on some key to split the dataframe in subsets**\n",
    "\n",
    "This operation is also referred to as the \"split-apply-combine\" operation, involving the following steps:\n",
    "\n",
    "* **Splitting** the data into groups based on some criteria\n",
    "* **Applying** a function to each group independently\n",
    "* **Combining** the results into a data structure\n",
    "\n",
    "<img src=\"img/splitApplyCombine.png\">\n",
    "\n",
    "Similar to SQL `GROUP BY`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of doing the manual filtering as above\n",
    "\n",
    "\n",
    "    df[df['key'] == \"A\"].sum()\n",
    "    df[df['key'] == \"B\"].sum()\n",
    "    ...\n",
    "\n",
    "pandas provides the `groupby` method to do exactly this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "df.groupby('key').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "df.groupby('key').aggregate(np.sum)  # 'sum'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And many more methods are available. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "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": null,
   "metadata": {
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   "source": [
    "df = pd.read_csv(\"data/titanic.csv\")"
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  {
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   "execution_count": null,
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    "df.head()"
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   "source": [
    "<div class=\"alert alert-warning\">\n",
    "Question 190: Calculate the mean age of all female passengers using groupby. Round the value to the nearest integer.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "Question 191: Calculate the survival ratio for all passengers who are 25 or younger. Present it as a percentage rounded to the nearest integer.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "Question 192: What is the difference in the survival percentage between the sexes?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "Question 193: How does survival differ between the different classes? Present the response as a tuple of three integer percentages, for class 1, class 2, class 3, eg (0%, 0%, 100%).\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "Question 194: Using the definition of AgeClass below, which age class payed the highest fare on average? Present the response like `(0, 10]`. Consider plotting the result as a bar plot to visualize the difference in fares.\n",
    "    \n",
    "```\n",
    "df['AgeClass'] = pd.cut(df['Age'], bins=np.arange(0,90,10))\n",
    "```\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "Question 195: How many passengers have a valid cabin number in the dataset?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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