nb02c-data-wrangling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4e11600e-694a-4118-ae5c-bf669c0b7844",
   "metadata": {},
   "source": [
    "# `nb02c`: Data wrangling "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef463ca8-5378-4abe-b3bd-092fe003d5a0",
   "metadata": {
    "tags": []
   },
   "source": [
    "![](./figures/nb03/pandas.png)\n",
    "\n",
    "Pandas is a Python library for data manipulation and analysis. It offers data structures and operations for manipulating hetereogeneous and labeled data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fa992639-d7f1-4311-9cf5-b49e2f1c0b5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd  # Load pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aeae6a3-8111-4d8a-9ea0-854313048cda",
   "metadata": {},
   "source": [
    "# Let's start with a showcase"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4572757a-db42-46de-95bd-805b1c3deadb",
   "metadata": {},
   "source": [
    "From loading a dataset to answering questions in a few lines of code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b21008e2-fb5c-4fec-ad2b-7ed521da6f38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked\n",
      "1,0,3,\"Braund, Mr. Owen Harris\",male,22,1,0,A/5 21171,7.25,,S\n",
      "2,1,1,\"Cumings, Mrs. John Bradley (Florence Briggs Thayer)\",female,38,1,0,PC 17599,71.2833,C85,C\n",
      "3,1,3,\"Heikkinen, Miss. Laina\",female,26,0,0,STON/O2. 3101282,7.925,,S\n",
      "4,1,1,\"Futrelle, Mrs. Jacques Heath (Lily May Peel)\",female,35,1,0,113803,53.1,C123,S\n",
      "5,0,3,\"Allen, Mr. William Henry\",male,35,0,0,373450,8.05,,S\n",
      "6,0,3,\"Moran, Mr. James\",male,,0,0,330877,8.4583,,Q\n",
      "7,0,1,\"McCarthy, Mr. Timothy J\",male,54,0,0,17463,51.8625,E46,S\n",
      "8,0,3,\"Palsson, Master. Gosta Leonard\",male,2,3,1,349909,21.075,,S\n",
      "9,1,3,\"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)\",female,27,0,2,347742,11.1333,,S\n"
     ]
    }
   ],
   "source": [
    "!head data/titanic.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eb171d73-3541-4261-983b-099b205dc3d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/titanic.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aac84045-f71f-426f-8ce4-49cbcff1e228",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the age distribution of the passengers?\n",
    "df[\"Age\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2fa3ef3c-7b24-41d9-9e79-ac6bcf904319",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    0.742038\n",
       "male      0.188908\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How does the survival rate of the passengers differ between males and females?\n",
    "df.groupby(\"Sex\")[\"Survived\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f102d866-d687-46aa-8026-de4f928854f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Pclass'>"
      ]
     },
     "execution_count": 6,
     "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": [
    "# How does it differ between passenger classes?\n",
    "df.groupby(\"Pclass\")[\"Survived\"].mean().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e05a816d-e165-4df0-a45e-e357afcfb7f0",
   "metadata": {},
   "source": [
    "# Pandas: data analysis in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5734d034-c4e7-46af-a9b7-eab04f1d5694",
   "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",
    "Its documentation is available at http://pandas.pydata.org/pandas-docs/stable/.\n",
    "\n",
    "\n",
    "_When do you need Pandas?_\n",
    "\n",
    "When working with **tabular or structured data** (like R dataframe, SQL table, Excel spreadsheet, ...):\n",
    "\n",
    "- Import data\n",
    "- Clean up messy data\n",
    "- Explore data, gain insight into data\n",
    "- Process and prepare your data for analysis\n",
    "- Analyse your data (together with scikit-learn, statsmodels, ...)\n",
    "\n",
    "<div class=\"alert alert-danger\">\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), 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",
   "id": "f43ec44e-ccdc-4aae-b37e-a236752ffd9a",
   "metadata": {},
   "source": [
    "# DataFrames"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ee45e16-c288-43d1-b29c-33ada04f67ce",
   "metadata": {},
   "source": [
    "## `Series`\n",
    "\n",
    "A `Series` is a basic holder for **one-dimensional labeled data**. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dc8a8902-1b65-4e8e-96d8-f952ea7b6260",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "2    c\n",
       "3    d\n",
       "4    e\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([\"a\", \"b\", \"c\", \"d\", \"e\"])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "20024609-d208-48f6-b61a-60ed1ca8f680",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=5, step=1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "571d69eb-0a28-4353-af23-c25334aee452",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['a', 'b', 'c', 'd', 'e'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2321d0eb-e1e3-49cd-9760-e0e92cfc1daa",
   "metadata": {},
   "source": [
    "Series also represent the individual columns of a `DataFrame`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d3c688d0-625f-44db-bb03-fa0ee39556f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      22.0\n",
       "1      38.0\n",
       "2      26.0\n",
       "3      35.0\n",
       "4      35.0\n",
       "       ... \n",
       "886    27.0\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 = pd.read_csv(\"data/titanic.csv\")\n",
    "age = df[\"Age\"]\n",
    "age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "aa14ad82-559f-4e77-963b-439bc0ed1555",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(age)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc2e97aa-738f-4f87-b30e-edd37f9bcb2b",
   "metadata": {},
   "source": [
    "We can access series values via an index, just like for NumPy arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "14255c78-178f-49fc-b382-42f8d5b6b9be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25f95406-dc94-45b0-bfc0-42316e8d2e88",
   "metadata": {},
   "source": [
    "Unlike the Numpy array, though, this index can be something other than integers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "94eb4c98-2a33-435c-a53c-486ff2b1006c",
   "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",
       "Allen, Mr. William Henry                               35.0\n",
       "                                                       ... \n",
       "Montvila, Rev. Juozas                                  27.0\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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.set_index(\"Name\")\n",
    "age = df2[\"Age\"]\n",
    "age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "800abd99-c071-469b-8321-459e4839a1cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age[\"Braund, Mr. Owen Harris\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bc8f765-3342-4d86-83b1-4e1370d1efcb",
   "metadata": {},
   "source": [
    "## `DataFrame`\n",
    "\n",
    "<img src=\"./figures/nb03/dataframe.svg\" width=\"50%\" />\n",
    "\n",
    "A `DataFrame` is a **tabular data structure** (multi-dimensional object to hold labeled data) comprised of rows and columns, akin to a spreadsheet or a database table. It combines multiple `Series` objects sharing the same index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3842537a-ff64-4ee6-8521-a54d1d784132",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <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>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \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",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \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",
       "4        0            373450   8.0500   NaN        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "900bca6b-9a33-43c4-9b82-3e5ec25bca59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=891, step=1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9d4efb54-0215-41c4-9f11-ae8623ec1af9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "799430ff-e951-4463-b06e-55b686588f47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "37eda53c-5cf9-4c85-b1b9-9496f4846b61",
   "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": "code",
   "execution_count": 20,
   "id": "5a38808a-72cf-4634-b59d-643ac1356743",
   "metadata": {},
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb6f8688-dd75-49cd-8f0a-c757ba7f4e92",
   "metadata": {},
   "source": [
    "# Loading data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e40b2f52-b9c8-413d-ba8c-4d2f510c7fe2",
   "metadata": {},
   "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",
    "* Parquet\n",
    "* ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "aa8b632a-fd0a-4af6-9346-afe1fcba4b33",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pd.read"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5f65bdb8-d062-464e-8b27-d909c73b6c0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.to"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddd94b02-639e-486b-9e58-bb3b28b20786",
   "metadata": {},
   "source": [
    "## Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "45854461-14ed-482f-bb6a-88956218da17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from CSV files\n",
    "df = pd.read_csv(\"data/titanic.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5c027693-7ecb-445c-aae3-a680259f7a91",
   "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from dict\n",
    "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",
   "id": "2e75f4f9-3497-4ec5-ae16-729fa672fa1a",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    \n",
    "**Exercise**. Read the `data/no2.csv` file into a DataFrame `no2`.\n",
    "\n",
    "Some aspects about the file:\n",
    "- Which separator is used in the file?\n",
    "- The second row includes unit information and should be skipped (check `skiprows` keyword).\n",
    "- For missing values, it uses the `'n/d'` notation (check `na_values` keyword).\n",
    "- We want to parse the 'timestamp' column as datetimes (check the `parse_dates` keyword).\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1120e3af-741c-48bb-8ee6-a530c2f7bd5c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2f69d148-2228-4ab5-afe8-1acb3dc6108f",
   "metadata": {},
   "source": [
    "# Data manipulation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20455615-5e40-44ea-97b9-1ff752dd5b71",
   "metadata": {},
   "source": [
    "## Selecting and filtering\n",
    "\n",
    "One of Pandas' basic features is the labeling of rows and columns, which makes indexing slightly more complex compared to Numpy. We now have to distinguish between:\n",
    "- selection by **label**\n",
    "- selection by **position**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3509376c-10fb-4768-802c-afb84f4f50ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/titanic.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5f9a21d9-f227-4d73-8c96-80bc90343fbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      22.0\n",
       "1      38.0\n",
       "2      26.0\n",
       "3      35.0\n",
       "4      35.0\n",
       "       ... \n",
       "886    27.0\n",
       "887    19.0\n",
       "888     NaN\n",
       "889    26.0\n",
       "890    32.0\n",
       "Name: Age, Length: 891, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting a single column\n",
    "df[\"Age\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0d9194e0-5666-47c6-9401-d70c59d97654",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age\n",
       "0    22.0\n",
       "1    38.0\n",
       "2    26.0\n",
       "3    35.0\n",
       "4    35.0\n",
       "..    ...\n",
       "886  27.0\n",
       "887  19.0\n",
       "888   NaN\n",
       "889  26.0\n",
       "890  32.0\n",
       "\n",
       "[891 rows x 1 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"Age\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "55fbb680-090e-40c9-a6f6-32c5dd2c257a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<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>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0000</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",
       "4    35.0   8.0500\n",
       "..    ...      ...\n",
       "886  27.0  13.0000\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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ... or multiple columns\n",
    "df[[\"Age\", \"Fare\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "059ff12f-911b-4447-8003-e30a3abf0079",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass                                  Name  \\\n",
       "10           11         1       3       Sandstrom, Miss. Marguerite Rut   \n",
       "11           12         1       1              Bonnell, Miss. Elizabeth   \n",
       "12           13         0       3        Saundercock, Mr. William Henry   \n",
       "13           14         0       3           Andersson, Mr. Anders Johan   \n",
       "14           15         0       3  Vestrom, Miss. Hulda Amanda Adolfina   \n",
       "\n",
       "       Sex   Age  SibSp  Parch     Ticket     Fare Cabin Embarked  \n",
       "10  female   4.0      1      1    PP 9549  16.7000    G6        S  \n",
       "11  female  58.0      0      0     113783  26.5500  C103        S  \n",
       "12    male  20.0      0      0  A/5. 2151   8.0500   NaN        S  \n",
       "13    male  39.0      1      5     347082  31.2750   NaN        S  \n",
       "14  female  14.0      0      0     350406   7.8542   NaN        S  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Slicing accesses the rows\n",
    "df[10:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c24dd953-eee2-4aa2-9974-c0fa0ddc28fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>96</th>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Goldschmidt, Mr. George B</td>\n",
       "      <td>male</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17754</td>\n",
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       "      <td>Connors, Mr. Patrick</td>\n",
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       "      <td>70.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370369</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "      <th>493</th>\n",
       "      <td>494</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Artagaveytia, Mr. Ramon</td>\n",
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       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17609</td>\n",
       "      <td>49.5042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>631</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Barkworth, Mr. Algernon Henry Wilson</td>\n",
       "      <td>male</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27042</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>A23</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>851</th>\n",
       "      <td>852</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Svensson, Mr. Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347060</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                  Name  \\\n",
       "96            97         0       1             Goldschmidt, Mr. George B   \n",
       "116          117         0       3                  Connors, Mr. Patrick   \n",
       "493          494         0       1               Artagaveytia, Mr. Ramon   \n",
       "630          631         1       1  Barkworth, Mr. Algernon Henry Wilson   \n",
       "851          852         0       3                   Svensson, Mr. Johan   \n",
       "\n",
       "      Sex   Age  SibSp  Parch    Ticket     Fare Cabin Embarked  \n",
       "96   male  71.0      0      0  PC 17754  34.6542    A5        C  \n",
       "116  male  70.5      0      0    370369   7.7500   NaN        Q  \n",
       "493  male  71.0      0      0  PC 17609  49.5042   NaN        C  \n",
       "630  male  80.0      0      0     27042  30.0000   A23        S  \n",
       "851  male  74.0      0      0    347060   7.7750   NaN        S  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtering rows\n",
    "df[df.Age > 70]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "58026091-9ff2-4bf1-9509-f00f380ab2f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Andrews, Miss. Kornelia Theodosia</td>\n",
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       "      <td>63.0</td>\n",
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       "      <td>Turkula, Mrs. (Hedwig)</td>\n",
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       "      <td>63.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4134</td>\n",
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       "      <th>829</th>\n",
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       "      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
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       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80.0000</td>\n",
       "      <td>B28</td>\n",
       "      <td>NaN</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                       Name  \\\n",
       "275          276         1       1          Andrews, Miss. Kornelia Theodosia   \n",
       "483          484         1       3                     Turkula, Mrs. (Hedwig)   \n",
       "829          830         1       1  Stone, Mrs. George Nelson (Martha Evelyn)   \n",
       "\n",
       "        Sex   Age  SibSp  Parch  Ticket     Fare Cabin Embarked  \n",
       "275  female  63.0      1      0   13502  77.9583    D7        S  \n",
       "483  female  63.0      0      0    4134   9.5875   NaN        S  \n",
       "829  female  62.0      0      0  113572  80.0000   B28      NaN  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query(\"Age > 60 and Sex == 'female'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "016df53e-074a-46ff-b55a-a4d38cbe7831",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Louch, Mrs. Charles Alexander (Alice Adelaide ...</td>\n",
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       "      <td>42.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>SC/AH 3085</td>\n",
       "      <td>26.0000</td>\n",
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       "      <td>S</td>\n",
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       "      <th>617</th>\n",
       "      <td>618</td>\n",
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       "      <td>Lobb, Mrs. William Arthur (Cordelia K Stanlick)</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
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       "      <td>Longley, Miss. Gretchen Fiske</td>\n",
       "      <td>female</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13502</td>\n",
       "      <td>77.9583</td>\n",
       "      <td>D9</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>782</th>\n",
       "      <td>783</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Long, Mr. Milton Clyde</td>\n",
       "      <td>male</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113501</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>D6</td>\n",
       "      <td>S</td>\n",
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      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "227          228         0       3   \n",
       "253          254         0       3   \n",
       "432          433         1       2   \n",
       "617          618         0       3   \n",
       "627          628         1       1   \n",
       "782          783         0       1   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "227                    Lovell, Mr. John Hall (\"Henry\")    male  20.5      0   \n",
       "253                           Lobb, Mr. William Arthur    male  30.0      1   \n",
       "432  Louch, Mrs. Charles Alexander (Alice Adelaide ...  female  42.0      1   \n",
       "617    Lobb, Mrs. William Arthur (Cordelia K Stanlick)  female  26.0      1   \n",
       "627                      Longley, Miss. Gretchen Fiske  female  21.0      0   \n",
       "782                             Long, Mr. Milton Clyde    male  29.0      0   \n",
       "\n",
       "     Parch      Ticket     Fare Cabin Embarked  \n",
       "227      0   A/5 21173   7.2500   NaN        S  \n",
       "253      0   A/5. 3336  16.1000   NaN        S  \n",
       "432      0  SC/AH 3085  26.0000   NaN        S  \n",
       "617      0   A/5. 3336  16.1000   NaN        S  \n",
       "627      0       13502  77.9583    D9        S  \n",
       "782      0      113501  30.0000    D6        S  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query(\"Name.str.startswith('Lo')\", engine=\"python\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b4724ea-2de7-4912-92f7-8abdc3344da6",
   "metadata": {},
   "source": [
    "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": 33,
   "id": "b46cba5b-155e-450b-93cf-3c0feb207e58",
   "metadata": {},
   "outputs": [
    {
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       "      <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>Allen, Mr. William Henry</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</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>Montvila, Rev. Juozas</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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",
       "Allen, Mr. William Henry                                      5         0   \n",
       "...                                                         ...       ...   \n",
       "Montvila, Rev. Juozas                                       887         0   \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",
       "Allen, Mr. William Henry                                 3    male  35.0   \n",
       "...                                                    ...     ...   ...   \n",
       "Montvila, Rev. Juozas                                    2    male  27.0   \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",
       "Allen, Mr. William Henry                                0      0   \n",
       "...                                                   ...    ...   \n",
       "Montvila, Rev. Juozas                                   0      0   \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",
       "Allen, Mr. William Henry                                      373450   8.0500   \n",
       "...                                                              ...      ...   \n",
       "Montvila, Rev. Juozas                                         211536  13.0000   \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",
       "Allen, Mr. William Henry                             NaN        S  \n",
       "...                                                  ...      ...  \n",
       "Montvila, Rev. Juozas                                NaN        S  \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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index(\"Name\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9a403693-b924-40ea-845b-902c8bb1e77f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26.55"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"Bonnell, Miss. Elizabeth\", \"Fare\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "63a214d1-e066-4897-934e-4dfa13d9e5a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <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",
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       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bonnell, Miss. Elizabeth</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.550</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saundercock, Mr. William Henry</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>8.050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andersson, Mr. Anders Johan</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.275</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                PassengerId  Survived  Pclass     Sex   Age  \\\n",
       "Name                                                                          \n",
       "Bonnell, Miss. Elizabeth                 12         1       1  female  58.0   \n",
       "Saundercock, Mr. William Henry           13         0       3    male  20.0   \n",
       "Andersson, Mr. Anders Johan              14         0       3    male  39.0   \n",
       "\n",
       "                                SibSp  Parch     Ticket    Fare Cabin Embarked  \n",
       "Name                                                                            \n",
       "Bonnell, Miss. Elizabeth            0      0     113783  26.550  C103        S  \n",
       "Saundercock, Mr. William Henry      0      0  A/5. 2151   8.050   NaN        S  \n",
       "Andersson, Mr. Anders Johan         1      5     347082  31.275   NaN        S  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"Bonnell, Miss. Elizabeth\":\"Andersson, Mr. Anders Johan\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "30b0108a-021c-4928-8ee7-e809bacaf372",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>Braund, Mr. Owen Harris</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Survived  Pclass\n",
       "Name                                                                \n",
       "Braund, Mr. Owen Harris                                    0       3\n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...         1       1"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:2, 1:3] # similar to Numpy arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac0acb3a-c722-42d4-872f-4d69244f88dc",
   "metadata": {},
   "source": [
    "## Summaries\n",
    "\n",
    "An essential piece of analysis of large data is efficient summarization: computing aggregations like `sum()`, `mean()`, `median()`, `min()`, and `max()`, in which a single number gives insights into the nature of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "42f2da1c-3c1b-4486-9fb0-94e7b2f166ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/titanic.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "640deb84-b145-40ad-b94a-57d9c8625ffd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9f92299-4898-4e50-990e-adb18b33dcb7",
   "metadata": {},
   "source": [
    "In Pandas, summary functions include:\n",
    "- `sum()`\n",
    "- `count()`\n",
    "- `median()`\n",
    "- `quantile()`\n",
    "- `min()`\n",
    "- `max()`\n",
    "- `mean()`\n",
    "- `var()`\n",
    "- `std()`\n",
    "- `apply()`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e53d3b2a-8a2c-4217-9473-645eb1fa1175",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28693.9493"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aggregate by sum\n",
    "df[\"Fare\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "508f0d23-d2eb-4094-b380-a2ae6fadd27e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32.204207968574636, 2469.436845743117)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ... by mean and variance\n",
    "df[\"Fare\"].mean(), df[\"Fare\"].var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4b525bdd-d655-4e50-9e0b-5a2582c07299",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fare    32.204208\n",
       "Age     29.699118\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ... by mean, over two columns\n",
    "df[[\"Fare\", \"Age\"]].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2492435-7502-441b-a19c-4a2044f04470",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    \n",
    "**Exercise.** What is the maximum Fare that was paid? and the median?\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2e37f57",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b79838a1-603d-4b13-b93d-0affab845e03",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    \n",
    "**Exercise**. Compute the average survival ratio for all passengers.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0844ade1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7d4083d8-bb46-44f3-b479-7b042425352e",
   "metadata": {
    "tags": []
   },
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    \n",
    "**Exercise.** How many passengers older than 70 were on the Titanic?\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72e3e6b2-3846-4da9-896a-8095fc77d7ec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "09d8effe-adff-4a59-a012-1322b5da25af",
   "metadata": {},
   "source": [
    "## Groups\n",
    "\n",
    "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` using one of the many available methods. 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9837f593-fe58-4b29-b198-f00d2f121c67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>key</th>\n",
       "      <th>data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
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       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>10</td>\n",
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       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data\n",
       "0   A     0\n",
       "1   B     5\n",
       "2   C    10\n",
       "3   A     5\n",
       "4   B    10\n",
       "5   C    15\n",
       "6   A    10\n",
       "7   B    15\n",
       "8   C    20"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'key':['A','B','C','A','B','C','A','B','C'],\n",
    "                   'data': [0, 5, 10, 5, 10, 15, 10, 15, 20]})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13e4c2f1-ca70-4ea5-8488-8ea8720ba73d",
   "metadata": {},
   "source": [
    "For example, in the DataFrame above, 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": 43,
   "id": "751912ea-e263-4f68-99f2-3b9927a5365d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A 15\n",
      "B 30\n",
      "C 45\n"
     ]
    }
   ],
   "source": [
    "for key in ['A', 'B', 'C']:\n",
    "    print(key, df[df['key'] == key]['data'].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67a317b7-cbb5-4c56-9c99-86f3ef8d24e4",
   "metadata": {},
   "source": [
    "What we did above, applying a function on different groups, is a \"groupby operation\", and Pandas provides some convenient functionality for this.\n",
    "\n",
    "Formally, 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=\"./figures/nb03/split-apply-combine.png\" width=\"50%\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "848a68a5-4810-48e4-b229-7203af05a011",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     data\n",
       "key      \n",
       "A      15\n",
       "B      30\n",
       "C      45"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# groupby and aggregate with sum\n",
    "df.groupby(\"key\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e9246f73-2483-484b-85b5-7a00564d3afe",
   "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>data</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     data\n",
       "key      \n",
       "A      16\n",
       "B      31\n",
       "C      46"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# groupby and aggregate with your own function\n",
    "df.groupby(\"key\").aggregate(lambda x: np.sum(x)+1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5ecacdd-491a-4ad1-8414-a99955cb1ec8",
   "metadata": {},
   "source": [
    "## Aggregate, filter, transform, apply "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55092278-6689-49f3-9596-5ec998e7a376",
   "metadata": {},
   "source": [
    "In addition to `aggregate()`, the `GroupBy` objects have `filter()`, `transform()` and `apply()` methods that efficiently implement a variety of useful operations before combining the grouped data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5062ec25-6c27-4ec6-8d00-39f39fe1356c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
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       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data1  data2\n",
       "0   A      0      2\n",
       "1   B      1      8\n",
       "2   C      2      5\n",
       "3   A      3      6\n",
       "4   B      4      3\n",
       "5   C      5      8"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'key': ['A', 'B', 'C', 'A', 'B', 'C'],\n",
    "                   'data1': range(6),\n",
    "                   'data2': np.random.randint(0, 10, 6)},\n",
    "                   columns = ['key', 'data1', 'data2'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "92551f6a-7855-4906-8875-69897740928c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th colspan=\"3\" halign=\"left\">data1</th>\n",
       "      <th colspan=\"3\" halign=\"left\">data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>median</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>median</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</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>A</th>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5.5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    data1            data2           \n",
       "      min median max   min median max\n",
       "key                                  \n",
       "A       0    1.5   3     2    4.0   6\n",
       "B       1    2.5   4     3    5.5   8\n",
       "C       2    3.5   5     5    6.5   8"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aggregate along multiple functions\n",
    "df.groupby('key').aggregate(['min', np.median, max])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c8d41173-22d4-4e54-87af-3c6d00e89e96",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "     data1  data2\n",
       "key              \n",
       "A        0      6\n",
       "B        1      8\n",
       "C        2      8"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aggregate columns with specific functions\n",
    "df.groupby('key').aggregate({'data1': 'min',\n",
    "                             'data2': 'max'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "89363c45-788d-4bfd-aae8-36e3c99035b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter out data based on its group\n",
    "def filter_func(g):\n",
    "    return g['data2'].std() > 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "60a0a3db-2b42-4ecf-a927-a04445ecb941",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>key</th>\n",
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       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
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       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data1  data2\n",
       "0   A      0      2\n",
       "1   B      1      8\n",
       "2   C      2      5\n",
       "3   A      3      6\n",
       "4   B      4      3\n",
       "5   C      5      8"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d4af0ab9-8a50-48b5-8a85-bd21fde536d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>2.12132</td>\n",
       "      <td>2.828427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>2.12132</td>\n",
       "      <td>3.535534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>2.12132</td>\n",
       "      <td>2.121320</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       data1     data2\n",
       "key                   \n",
       "A    2.12132  2.828427\n",
       "B    2.12132  3.535534\n",
       "C    2.12132  2.121320"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key').std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2d916235-d0b5-4110-ba59-bd0e8a0fd764",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>1</th>\n",
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      "text/plain": [
       "  key  data1  data2\n",
       "1   B      1      8\n",
       "4   B      4      3"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key').filter(filter_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e50b37d9-cdc9-40a0-a33e-92362ea4a647",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.5</td>\n",
       "      <td>-2.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.5</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.5</td>\n",
       "      <td>-1.5</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.5</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.5</td>\n",
       "      <td>-2.5</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   data1  data2\n",
       "0   -1.5   -2.0\n",
       "1   -1.5    2.5\n",
       "2   -1.5   -1.5\n",
       "3    1.5    2.0\n",
       "4    1.5   -2.5\n",
       "5    1.5    1.5"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transform data based on its group\n",
    "df.groupby('key').transform(lambda g: g - g.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "9fbea799-ed41-4f87-bd3d-b1211fb7e0ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">A</th>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">B</th>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">C</th>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      key     data1  data2\n",
       "key                       \n",
       "A   0   A  0.000000      2\n",
       "    3   A  0.375000      6\n",
       "B   1   B  0.090909      8\n",
       "    4   B  0.363636      3\n",
       "C   2   C  0.153846      5\n",
       "    5   C  0.384615      8"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Apply an arbitrary function to the group results\n",
    "def norm_by_data2(g):\n",
    "    g['data1'] /= g['data2'].sum()\n",
    "    return g\n",
    "\n",
    "df.groupby('key').apply(norm_by_data2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4767db18-500b-4d33-8d60-bab827300714",
   "metadata": {},
   "source": [
    "## Combining DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "88fd2fb7-df4f-46eb-a062-4466623ab07d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(\n",
    "    {\"a\": [1, 2, 3],\n",
    "     \"b\": [4, 5, 6]})\n",
    "\n",
    "df2 = pd.DataFrame(\n",
    "    {\"a\": [10, 11],\n",
    "     \"b\": [14, 15]})\n",
    "\n",
    "df3 = pd.DataFrame(\n",
    "    {\"c\": [7, 8, 9]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d784c84f-df65-4cbc-96c5-64ee990d8890",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b\n",
       "0  1  4\n",
       "1  2  5\n",
       "2  3  6"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4bd82fae-9971-4153-89da-faf799f5f187",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
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       "</div>"
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      "text/plain": [
       "    a   b\n",
       "0  10  14\n",
       "1  11  15"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "36fdafb5-f5af-4548-b8dd-36db68cbfb0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
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      ],
      "text/plain": [
       "   c\n",
       "0  7\n",
       "1  8\n",
       "2  9"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "268d0634-fd22-4f48-b570-b1a9f392c68f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    a   b\n",
       "0   1   4\n",
       "1   2   5\n",
       "2   3   6\n",
       "0  10  14\n",
       "1  11  15"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concat by rows\n",
    "pd.concat([df1, df2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "026cc1e8-4a63-4e2c-b6a1-3a0f9e9effe8",
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b  c\n",
       "0  1  4  7\n",
       "1  2  5  8\n",
       "2  3  6  9"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concat by columns\n",
    "pd.concat([df1, df3], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d1ef87ca-2f0a-4bb2-a87a-6d721f9b24c8",
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b    c\n",
       "0  1  4  NaN\n",
       "1  2  5  7.0\n",
       "2  3  6  8.0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge / joins\n",
    "df1 = pd.DataFrame(\n",
    "    {\"a\": [1, 2, 3],\n",
    "     \"b\": [4, 5, 6]})\n",
    "\n",
    "df2 = pd.DataFrame(\n",
    "    {\"a\": [2, 3, 4],\n",
    "     \"c\": [7, 8, 9]})\n",
    "\n",
    "pd.merge(df1, df2, how=\"left\", on=\"a\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "313e51fb-b601-4919-b525-0f87b2e9600a",
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a    b    c\n",
       "0  1  4.0  NaN\n",
       "1  2  5.0  7.0\n",
       "2  3  6.0  8.0\n",
       "3  4  NaN  9.0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df2, how=\"outer\", on=\"a\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fba8ed4-f2a7-4b27-9c56-bea3faaf1ffb",
   "metadata": {},
   "source": [
    "## Plots\n",
    "\n",
    "Dataframes come equipped with plotting functions wrapped around `matplotlib`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a88b5fce-c14a-48fa-aafa-e55764a7a46c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age', ylabel='Fare'>"
      ]
     },
     "execution_count": 63,
     "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 = pd.read_csv(\"data/titanic.csv\")\n",
    "df.plot(x=\"Age\", y=\"Fare\", kind=\"scatter\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "9605a631-1534-4b8d-b04b-8205a4c9dad8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 64,
     "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[[\"Age\"]].plot(kind=\"hist\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d366819d-ac4a-427f-a6a0-542551a014d9",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    \n",
    "**Exercise**. Make a bar plot to visualize the average Fare payed by people depending on their age.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b602cc5-26b9-4dc6-aa48-430dcbf70223",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dats0001",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}