class 03 Data manipulation with Pandas/class 03 Data Manipulation with Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e47e70e4",
   "metadata": {},
   "source": [
    "# Outline\n",
    "- DataFrames\n",
    "    - sorting & subsetting\n",
    "    - creating new columns\n",
    "- Aggregating Data\n",
    "    - summary stattistics\n",
    "    - counting\n",
    "    - grouped summary statistics\n",
    "- Slicing & indexing data\n",
    "    - subsetting using slicing\n",
    "    - indexes & subsetting using indexes\n",
    "- Creating & Visualizing Data\n",
    "    - plotting\n",
    "    - handling missing values\n",
    "    - reading data into DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab7bd9a1",
   "metadata": {},
   "source": [
    "- Pandas is built on NumPy and Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd4faa3f",
   "metadata": {},
   "source": [
    "## Tabular/ Rectangular Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6855cfd5",
   "metadata": {},
   "source": [
    "<img src = \"./media/tabular_dataset.png\" width = 500 height= 500>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a88345b1",
   "metadata": {},
   "source": [
    "## Pandas DataFrames"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea5b6a70",
   "metadata": {},
   "source": [
    "#### Loading the DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d117c682",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Breed</th>\n",
       "      <th>Color</th>\n",
       "      <th>Height(cm)</th>\n",
       "      <th>Weight(kg)</th>\n",
       "      <th>Date of Birth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bella</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Brown</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>Poodle</td>\n",
       "      <td>Black</td>\n",
       "      <td>43</td>\n",
       "      <td>23</td>\n",
       "      <td>2016-09-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lucy</td>\n",
       "      <td>Chow</td>\n",
       "      <td>Brown</td>\n",
       "      <td>46</td>\n",
       "      <td>22</td>\n",
       "      <td>2014-08-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cooper</td>\n",
       "      <td>Schnauzer</td>\n",
       "      <td>Gray</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>2011-12-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Max</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Black</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>2017-01-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Stella</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>Tan</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2015-04-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bernie</td>\n",
       "      <td>St. Bernard</td>\n",
       "      <td>White</td>\n",
       "      <td>77</td>\n",
       "      <td>74</td>\n",
       "      <td>2018-02-2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name         Breed   Color   Height(cm)   Weight(kg)  Date of Birth\n",
       "0    Bella      Labrador   Brown           56           25     2013-07-01\n",
       "1  Charlie        Poodle   Black           43           23     2016-09-16\n",
       "2     Lucy          Chow   Brown           46           22     2014-08-25\n",
       "3   Cooper     Schnauzer    Gray           49           17     2011-12-11\n",
       "4      Max      Labrador   Black           59           29     2017-01-20\n",
       "5   Stella     Chihuahua     Tan           18            2     2015-04-20\n",
       "6   Bernie   St. Bernard   White           77           74      2018-02-2"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "dogs = pd.read_csv('./dataset/dogs.csv')\n",
    "dogs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd7380e0",
   "metadata": {},
   "source": [
    "#### Exploring a DataFrame: .head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "857064c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Breed</th>\n",
       "      <th>Color</th>\n",
       "      <th>Height(cm)</th>\n",
       "      <th>Weight(kg)</th>\n",
       "      <th>Date of Birth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bella</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Brown</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>Poodle</td>\n",
       "      <td>Black</td>\n",
       "      <td>43</td>\n",
       "      <td>23</td>\n",
       "      <td>2016-09-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lucy</td>\n",
       "      <td>Chow</td>\n",
       "      <td>Brown</td>\n",
       "      <td>46</td>\n",
       "      <td>22</td>\n",
       "      <td>2014-08-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cooper</td>\n",
       "      <td>Schnauzer</td>\n",
       "      <td>Gray</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>2011-12-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Max</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Black</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>2017-01-20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name       Breed   Color   Height(cm)   Weight(kg)  Date of Birth\n",
       "0    Bella    Labrador   Brown           56           25     2013-07-01\n",
       "1  Charlie      Poodle   Black           43           23     2016-09-16\n",
       "2     Lucy        Chow   Brown           46           22     2014-08-25\n",
       "3   Cooper   Schnauzer    Gray           49           17     2011-12-11\n",
       "4      Max    Labrador   Black           59           29     2017-01-20"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f95106b",
   "metadata": {},
   "source": [
    "#### Customizing a DataFrame: .columns , .rename()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9ccb2d9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Name', 'Breed', ' Color', ' Height(cm)', ' Weight(kg)',\n",
       "       ' Date of Birth'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7505cac8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'breed', 'color', 'height_cm', 'weight_kg', 'date_of_birth'], dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.rename(columns = {'Name':'name',\n",
    "                       'Breed':'breed',\n",
    "                       ' Color' : 'color', \n",
    "                       ' Height(cm)': 'height_cm',\n",
    "                       ' Weight(kg)' : 'weight_kg',\n",
    "                       ' Date of Birth': 'date_of_birth'},\n",
    "            inplace = True)\n",
    "dogs.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "227df626",
   "metadata": {},
   "source": [
    "#### Exploring a DataFrame: .info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f064970f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7 entries, 0 to 6\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   name           7 non-null      object\n",
      " 1   breed          7 non-null      object\n",
      " 2   color          7 non-null      object\n",
      " 3   height_cm      7 non-null      int64 \n",
      " 4   weight_kg      7 non-null      int64 \n",
      " 5   date_of_birth  7 non-null      object\n",
      "dtypes: int64(2), object(4)\n",
      "memory usage: 464.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "dogs.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3a819e0",
   "metadata": {},
   "source": [
    "- Since the data type od the column `name` , `breed`, `color` is ***object*** which is not valid we have to convert it into specific one that is ***string***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8421d2b7",
   "metadata": {},
   "source": [
    "### Converting the datatypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3e906673",
   "metadata": {},
   "outputs": [],
   "source": [
    "dogs['name'] = dogs['name'].astype('string')\n",
    "dogs['breed'] = dogs['breed'].astype('string')\n",
    "dogs['color'] = dogs['color'].astype('string')\n",
    "dogs['date_of_birth'] = pd.to_datetime(dogs['date_of_birth'])\n",
    "dogs['date_of_birth'] = dogs['date_of_birth'].dt.strftime('%Y/%m/%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e32699d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7 entries, 0 to 6\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   name           7 non-null      string\n",
      " 1   breed          7 non-null      string\n",
      " 2   color          7 non-null      string\n",
      " 3   height_cm      7 non-null      int64 \n",
      " 4   weight_kg      7 non-null      int64 \n",
      " 5   date_of_birth  7 non-null      object\n",
      "dtypes: int64(2), object(1), string(3)\n",
      "memory usage: 464.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "dogs.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "298b95a1",
   "metadata": {},
   "source": [
    "#### Exploring a DataFrame: .shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c8069493",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7, 6)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64956088",
   "metadata": {},
   "source": [
    "#### Exploring a DataFrame: .describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6570061d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>49.714286</td>\n",
       "      <td>27.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17.960274</td>\n",
       "      <td>22.292429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>44.500000</td>\n",
       "      <td>19.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>49.000000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>57.500000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>74.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       height_cm  weight_kg\n",
       "count   7.000000   7.000000\n",
       "mean   49.714286  27.428571\n",
       "std    17.960274  22.292429\n",
       "min    18.000000   2.000000\n",
       "25%    44.500000  19.500000\n",
       "50%    49.000000  23.000000\n",
       "75%    57.500000  27.000000\n",
       "max    77.000000  74.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b5235d4",
   "metadata": {},
   "source": [
    "#### Components of a DataFrame: .values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b82c9a83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Bella', ' Labrador', ' Brown', 56, 25, '2013/07/01'],\n",
       "       ['Charlie', ' Poodle', ' Black', 43, 23, '2016/09/16'],\n",
       "       ['Lucy', ' Chow', ' Brown', 46, 22, '2014/08/25'],\n",
       "       ['Cooper', ' Schnauzer', ' Gray', 49, 17, '2011/12/11'],\n",
       "       ['Max', ' Labrador', ' Black', 59, 29, '2017/01/20'],\n",
       "       ['Stella', ' Chihuahua', ' Tan', 18, 2, '2015/04/20'],\n",
       "       ['Bernie', ' St. Bernard', ' White', 77, 74, '2018/02/02']],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d538fa9a",
   "metadata": {},
   "source": [
    "#### Components of a DataFrame: .columns and .index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "588978d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'breed', 'color', 'height_cm', 'weight_kg', 'date_of_birth'], dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "75bbc4f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=7, step=1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc33b343",
   "metadata": {},
   "source": [
    "## pandas Philosophy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c98e141d",
   "metadata": {},
   "source": [
    "- ***There should be one -- and preferably only one -- obvious way to do it.***\n",
    "                                 ~ The Zen of Python by Tim Peters, Item 13\n",
    "- [link of Zen of python](https://www.python.org/dev/peps/pep-0020/) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21debcc4",
   "metadata": {},
   "source": [
    "<img src = \"./media/knife.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59bd0fdf",
   "metadata": {},
   "source": [
    "## Sorting and subsetting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45bde4fe",
   "metadata": {},
   "source": [
    "### Sorting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d9f31620",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Stella</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>Tan</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2015/04/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cooper</td>\n",
       "      <td>Schnauzer</td>\n",
       "      <td>Gray</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>2011/12/11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lucy</td>\n",
       "      <td>Chow</td>\n",
       "      <td>Brown</td>\n",
       "      <td>46</td>\n",
       "      <td>22</td>\n",
       "      <td>2014/08/25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>Poodle</td>\n",
       "      <td>Black</td>\n",
       "      <td>43</td>\n",
       "      <td>23</td>\n",
       "      <td>2016/09/16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bella</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Brown</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013/07/01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Max</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Black</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>2017/01/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bernie</td>\n",
       "      <td>St. Bernard</td>\n",
       "      <td>White</td>\n",
       "      <td>77</td>\n",
       "      <td>74</td>\n",
       "      <td>2018/02/02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name         breed   color  height_cm  weight_kg date_of_birth\n",
       "5   Stella     Chihuahua     Tan         18          2    2015/04/20\n",
       "3   Cooper     Schnauzer    Gray         49         17    2011/12/11\n",
       "2     Lucy          Chow   Brown         46         22    2014/08/25\n",
       "1  Charlie        Poodle   Black         43         23    2016/09/16\n",
       "0    Bella      Labrador   Brown         56         25    2013/07/01\n",
       "4      Max      Labrador   Black         59         29    2017/01/20\n",
       "6   Bernie   St. Bernard   White         77         74    2018/02/02"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.sort_values(\"weight_kg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a5cbdd4",
   "metadata": {},
   "source": [
    "#### Sorting in descending order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "58bfaa02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bernie</td>\n",
       "      <td>St. Bernard</td>\n",
       "      <td>White</td>\n",
       "      <td>77</td>\n",
       "      <td>74</td>\n",
       "      <td>2018/02/02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Max</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Black</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>2017/01/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bella</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Brown</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013/07/01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>Poodle</td>\n",
       "      <td>Black</td>\n",
       "      <td>43</td>\n",
       "      <td>23</td>\n",
       "      <td>2016/09/16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lucy</td>\n",
       "      <td>Chow</td>\n",
       "      <td>Brown</td>\n",
       "      <td>46</td>\n",
       "      <td>22</td>\n",
       "      <td>2014/08/25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cooper</td>\n",
       "      <td>Schnauzer</td>\n",
       "      <td>Gray</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>2011/12/11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Stella</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>Tan</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2015/04/20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name         breed   color  height_cm  weight_kg date_of_birth\n",
       "6   Bernie   St. Bernard   White         77         74    2018/02/02\n",
       "4      Max      Labrador   Black         59         29    2017/01/20\n",
       "0    Bella      Labrador   Brown         56         25    2013/07/01\n",
       "1  Charlie        Poodle   Black         43         23    2016/09/16\n",
       "2     Lucy          Chow   Brown         46         22    2014/08/25\n",
       "3   Cooper     Schnauzer    Gray         49         17    2011/12/11\n",
       "5   Stella     Chihuahua     Tan         18          2    2015/04/20"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.sort_values(\"weight_kg\", ascending= False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "647884d4",
   "metadata": {},
   "source": [
    "#### Sorting by multiple variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "12105451",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
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       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "      <th>height_cm</th>\n",
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       "      <td>2</td>\n",
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       "      <th>3</th>\n",
       "      <td>Cooper</td>\n",
       "      <td>Schnauzer</td>\n",
       "      <td>Gray</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>2011/12/11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lucy</td>\n",
       "      <td>Chow</td>\n",
       "      <td>Brown</td>\n",
       "      <td>46</td>\n",
       "      <td>22</td>\n",
       "      <td>2014/08/25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>Poodle</td>\n",
       "      <td>Black</td>\n",
       "      <td>43</td>\n",
       "      <td>23</td>\n",
       "      <td>2016/09/16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bella</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Brown</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013/07/01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Max</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Black</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>2017/01/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bernie</td>\n",
       "      <td>St. Bernard</td>\n",
       "      <td>White</td>\n",
       "      <td>77</td>\n",
       "      <td>74</td>\n",
       "      <td>2018/02/02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name         breed   color  height_cm  weight_kg date_of_birth\n",
       "5   Stella     Chihuahua     Tan         18          2    2015/04/20\n",
       "3   Cooper     Schnauzer    Gray         49         17    2011/12/11\n",
       "2     Lucy          Chow   Brown         46         22    2014/08/25\n",
       "1  Charlie        Poodle   Black         43         23    2016/09/16\n",
       "0    Bella      Labrador   Brown         56         25    2013/07/01\n",
       "4      Max      Labrador   Black         59         29    2017/01/20\n",
       "6   Bernie   St. Bernard   White         77         74    2018/02/02"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.sort_values([\"weight_kg\",\"height_cm\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5a1013f",
   "metadata": {},
   "source": [
    "#### Sorting by multiple variables in specific order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "2f82b286",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Stella</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>Tan</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2015/04/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cooper</td>\n",
       "      <td>Schnauzer</td>\n",
       "      <td>Gray</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>2011/12/11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lucy</td>\n",
       "      <td>Chow</td>\n",
       "      <td>Brown</td>\n",
       "      <td>46</td>\n",
       "      <td>22</td>\n",
       "      <td>2014/08/25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>Poodle</td>\n",
       "      <td>Black</td>\n",
       "      <td>43</td>\n",
       "      <td>23</td>\n",
       "      <td>2016/09/16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bella</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Brown</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013/07/01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Max</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Black</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>2017/01/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bernie</td>\n",
       "      <td>St. Bernard</td>\n",
       "      <td>White</td>\n",
       "      <td>77</td>\n",
       "      <td>74</td>\n",
       "      <td>2018/02/02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name         breed   color  height_cm  weight_kg date_of_birth\n",
       "5   Stella     Chihuahua     Tan         18          2    2015/04/20\n",
       "3   Cooper     Schnauzer    Gray         49         17    2011/12/11\n",
       "2     Lucy          Chow   Brown         46         22    2014/08/25\n",
       "1  Charlie        Poodle   Black         43         23    2016/09/16\n",
       "0    Bella      Labrador   Brown         56         25    2013/07/01\n",
       "4      Max      Labrador   Black         59         29    2017/01/20\n",
       "6   Bernie   St. Bernard   White         77         74    2018/02/02"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.sort_values([\"weight_kg\",\"height_cm\"], ascending=[True, False])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e26a45bb",
   "metadata": {},
   "source": [
    "### Subsetting columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "232b9779",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Bella\n",
       "1    Charlie\n",
       "2       Lucy\n",
       "3     Cooper\n",
       "4        Max\n",
       "5     Stella\n",
       "6     Bernie\n",
       "Name: name, dtype: string"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs[\"name\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e63c0ebe",
   "metadata": {},
   "source": [
    "#### Subsetting multiple columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "10dff895",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Labrador</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Poodle</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chow</td>\n",
       "      <td>46</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Schnauzer</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Labrador</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>St. Bernard</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          breed  height_cm\n",
       "0      Labrador         56\n",
       "1        Poodle         43\n",
       "2          Chow         46\n",
       "3     Schnauzer         49\n",
       "4      Labrador         59\n",
       "5     Chihuahua         18\n",
       "6   St. Bernard         77"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs[[\"breed\",\"height_cm\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "9a453c4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Poodle</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chow</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Schnauzer</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Labrador</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>St. Bernard</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          breed  height_cm\n",
       "0      Labrador         56\n",
       "1        Poodle         43\n",
       "2          Chow         46\n",
       "3     Schnauzer         49\n",
       "4      Labrador         59\n",
       "5     Chihuahua         18\n",
       "6   St. Bernard         77"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_to_subset = [\"breed\",\"height_cm\"]\n",
    "dogs[cols_to_subset]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cf62320",
   "metadata": {},
   "source": [
    "## Subsetting rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "ee8ddfe6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4     True\n",
       "5    False\n",
       "6     True\n",
       "Name: height_cm, dtype: bool"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs[\"height_cm\"] > 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "6fcf2ce0",
   "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>name</th>\n",
       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bella</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Brown</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013/07/01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Max</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Black</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>2017/01/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bernie</td>\n",
       "      <td>St. Bernard</td>\n",
       "      <td>White</td>\n",
       "      <td>77</td>\n",
       "      <td>74</td>\n",
       "      <td>2018/02/02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     name         breed   color  height_cm  weight_kg date_of_birth\n",
       "0   Bella      Labrador   Brown         56         25    2013/07/01\n",
       "4     Max      Labrador   Black         59         29    2017/01/20\n",
       "6  Bernie   St. Bernard   White         77         74    2018/02/02"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs[dogs[\"height_cm\"] > 50]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "474b4260",
   "metadata": {},
   "source": [
    "#### Subsetting based on text data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "61442ba6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'breed', 'color', 'height_cm', 'weight_kg', 'date_of_birth'], dtype='object')"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "6da3445b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Labrador\n",
       "1          Poodle\n",
       "2            Chow\n",
       "3       Schnauzer\n",
       "4        Labrador\n",
       "5       Chihuahua\n",
       "6     St. Bernard\n",
       "Name: breed, dtype: string"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.breed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "0e3ad868",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4     True\n",
       "5    False\n",
       "6    False\n",
       "Name: breed, dtype: boolean"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.breed == ' Labrador'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "70a90440",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bella</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Brown</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013/07/01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Max</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Black</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>2017/01/20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name      breed   color  height_cm  weight_kg date_of_birth\n",
       "0  Bella   Labrador   Brown         56         25    2013/07/01\n",
       "4    Max   Labrador   Black         59         29    2017/01/20"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs[dogs.breed == ' Labrador']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "b272a22c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bella</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Brown</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013/07/01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Max</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Black</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>2017/01/20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name      breed   color  height_cm  weight_kg date_of_birth\n",
       "0  Bella   Labrador   Brown         56         25    2013/07/01\n",
       "4    Max   Labrador   Black         59         29    2017/01/20"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs[dogs['breed'].isin([' Labrador'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "0754e42d",
   "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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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bella</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Brown</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013/07/01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Max</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Black</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>2017/01/20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name      breed   color  height_cm  weight_kg date_of_birth\n",
       "0  Bella   Labrador   Brown         56         25    2013/07/01\n",
       "4    Max   Labrador   Black         59         29    2017/01/20"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.loc[dogs['breed'] == ' Labrador']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "8987c022",
   "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",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bella</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Brown</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013/07/01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Max</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Black</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>2017/01/20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name      breed   color  height_cm  weight_kg date_of_birth\n",
       "0  Bella   Labrador   Brown         56         25    2013/07/01\n",
       "4    Max   Labrador   Black         59         29    2017/01/20"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.loc[dogs['breed'].str.contains(\"Labrador\", case=False)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "597e3e35",
   "metadata": {},
   "source": [
    "#### Subsetting based on dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "3d834502",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>Poodle</td>\n",
       "      <td>Black</td>\n",
       "      <td>43</td>\n",
       "      <td>23</td>\n",
       "      <td>2016/09/16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Max</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Black</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>2017/01/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Stella</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>Tan</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2015/04/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bernie</td>\n",
       "      <td>St. Bernard</td>\n",
       "      <td>White</td>\n",
       "      <td>77</td>\n",
       "      <td>74</td>\n",
       "      <td>2018/02/02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name         breed   color  height_cm  weight_kg date_of_birth\n",
       "1  Charlie        Poodle   Black         43         23    2016/09/16\n",
       "4      Max      Labrador   Black         59         29    2017/01/20\n",
       "5   Stella     Chihuahua     Tan         18          2    2015/04/20\n",
       "6   Bernie   St. Bernard   White         77         74    2018/02/02"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs[dogs[\"date_of_birth\"] > \"2015-01-01\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fa1fdbb",
   "metadata": {},
   "source": [
    "#### Subsetting based on multiple conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "22bb57d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bella</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Brown</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013/07/01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name      breed   color  height_cm  weight_kg date_of_birth\n",
       "0  Bella   Labrador   Brown         56         25    2013/07/01"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_lab = dogs[\"breed\"] == \" Labrador\"\n",
    "is_brown = dogs[\"color\"] == \" Brown\"\n",
    "\n",
    "dogs[is_lab & is_brown]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "8708556c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bella</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Brown</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013/07/01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name      breed   color  height_cm  weight_kg date_of_birth\n",
       "0  Bella   Labrador   Brown         56         25    2013/07/01"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs[ (dogs[\"breed\"] == \" Labrador\") & (dogs[\"color\"] == \" Brown\") ]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff874981",
   "metadata": {},
   "source": [
    "#### Subsetting using `.isin()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "9ecbcf29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\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>name</th>\n",
       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bella</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Brown</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013/07/01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>Poodle</td>\n",
       "      <td>Black</td>\n",
       "      <td>43</td>\n",
       "      <td>23</td>\n",
       "      <td>2016/09/16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lucy</td>\n",
       "      <td>Chow</td>\n",
       "      <td>Brown</td>\n",
       "      <td>46</td>\n",
       "      <td>22</td>\n",
       "      <td>2014/08/25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Max</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Black</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>2017/01/20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name      breed   color  height_cm  weight_kg date_of_birth\n",
       "0    Bella   Labrador   Brown         56         25    2013/07/01\n",
       "1  Charlie     Poodle   Black         43         23    2016/09/16\n",
       "2     Lucy       Chow   Brown         46         22    2014/08/25\n",
       "4      Max   Labrador   Black         59         29    2017/01/20"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_black_or_brown = dogs[\"color\"].isin([\" Black\",\" Brown\"])\n",
    "dogs[is_black_or_brown]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd329008",
   "metadata": {},
   "source": [
    "## New columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6af91ec6",
   "metadata": {},
   "source": [
    "#### Adding a new column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "1c3c5726",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>height_m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bella</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Brown</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013/07/01</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>Poodle</td>\n",
       "      <td>Black</td>\n",
       "      <td>43</td>\n",
       "      <td>23</td>\n",
       "      <td>2016/09/16</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lucy</td>\n",
       "      <td>Chow</td>\n",
       "      <td>Brown</td>\n",
       "      <td>46</td>\n",
       "      <td>22</td>\n",
       "      <td>2014/08/25</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cooper</td>\n",
       "      <td>Schnauzer</td>\n",
       "      <td>Gray</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>2011/12/11</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Max</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Black</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>2017/01/20</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Stella</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>Tan</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2015/04/20</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bernie</td>\n",
       "      <td>St. Bernard</td>\n",
       "      <td>White</td>\n",
       "      <td>77</td>\n",
       "      <td>74</td>\n",
       "      <td>2018/02/02</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name         breed   color  height_cm  weight_kg date_of_birth  height_m\n",
       "0    Bella      Labrador   Brown         56         25    2013/07/01      0.56\n",
       "1  Charlie        Poodle   Black         43         23    2016/09/16      0.43\n",
       "2     Lucy          Chow   Brown         46         22    2014/08/25      0.46\n",
       "3   Cooper     Schnauzer    Gray         49         17    2011/12/11      0.49\n",
       "4      Max      Labrador   Black         59         29    2017/01/20      0.59\n",
       "5   Stella     Chihuahua     Tan         18          2    2015/04/20      0.18\n",
       "6   Bernie   St. Bernard   White         77         74    2018/02/02      0.77"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs[\"height_m\"] = dogs[\"height_cm\"] / 100\n",
    "dogs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1229b64",
   "metadata": {},
   "source": [
    "#### Doggy mass index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f737fbd",
   "metadata": {},
   "source": [
    "<img src = './media/bmi.png' width = 400>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "b39d941b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>name</th>\n",
       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>height_m</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bella</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Brown</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013/07/01</td>\n",
       "      <td>0.56</td>\n",
       "      <td>79.719388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>Poodle</td>\n",
       "      <td>Black</td>\n",
       "      <td>43</td>\n",
       "      <td>23</td>\n",
       "      <td>2016/09/16</td>\n",
       "      <td>0.43</td>\n",
       "      <td>124.391563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lucy</td>\n",
       "      <td>Chow</td>\n",
       "      <td>Brown</td>\n",
       "      <td>46</td>\n",
       "      <td>22</td>\n",
       "      <td>2014/08/25</td>\n",
       "      <td>0.46</td>\n",
       "      <td>103.969754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cooper</td>\n",
       "      <td>Schnauzer</td>\n",
       "      <td>Gray</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>2011/12/11</td>\n",
       "      <td>0.49</td>\n",
       "      <td>70.803832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Max</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Black</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>2017/01/20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>83.309394</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name       breed   color  height_cm  weight_kg date_of_birth  height_m  \\\n",
       "0    Bella    Labrador   Brown         56         25    2013/07/01      0.56   \n",
       "1  Charlie      Poodle   Black         43         23    2016/09/16      0.43   \n",
       "2     Lucy        Chow   Brown         46         22    2014/08/25      0.46   \n",
       "3   Cooper   Schnauzer    Gray         49         17    2011/12/11      0.49   \n",
       "4      Max    Labrador   Black         59         29    2017/01/20      0.59   \n",
       "\n",
       "          bmi  \n",
       "0   79.719388  \n",
       "1  124.391563  \n",
       "2  103.969754  \n",
       "3   70.803832  \n",
       "4   83.309394  "
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs[\"bmi\"] = dogs[\"weight_kg\"] / dogs[\"height_m\"] ** 2\n",
    "dogs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc8a9d9c",
   "metadata": {},
   "source": [
    "#### Multiple manipulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "52ae1821",
   "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 tbody tr th {\n",
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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>name</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Max</td>\n",
       "      <td>59</td>\n",
       "      <td>83.309394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bella</td>\n",
       "      <td>56</td>\n",
       "      <td>79.719388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cooper</td>\n",
       "      <td>49</td>\n",
       "      <td>70.803832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Stella</td>\n",
       "      <td>18</td>\n",
       "      <td>61.728395</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     name  height_cm        bmi\n",
       "4     Max         59  83.309394\n",
       "0   Bella         56  79.719388\n",
       "3  Cooper         49  70.803832\n",
       "5  Stella         18  61.728395"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bmi_lt_100 = dogs[dogs[\"bmi\"] < 100]\n",
    "bmi_lt_100_height = bmi_lt_100.sort_values(\"height_cm\", ascending=False)\n",
    "bmi_lt_100_height[[\"name\",\"height_cm\",\"bmi\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33971697",
   "metadata": {},
   "source": [
    "# Aggregating Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "821e1f6c",
   "metadata": {},
   "source": [
    "## Summary Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "215d068d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49.714285714285715"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs[\"height_cm\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2940aca5",
   "metadata": {},
   "source": [
    "Similarly we can find:\n",
    "   - `.medain()`     \n",
    "   - `.mode()`\n",
    "   - `.min()`\n",
    "   - `.max()`\n",
    "   - `.var()`\n",
    "   - `.std()`\n",
    "   - `.sum()`\n",
    "   - `.quantile()`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd3a3965",
   "metadata": {},
   "source": [
    "<img src = \"./media/quantile.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b92ae31",
   "metadata": {},
   "source": [
    "<img src = \"./media/quantile_2.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0e1e7ac",
   "metadata": {},
   "source": [
    "#### Oldest dog:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "28fef420",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2011/12/11'"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs[\"date_of_birth\"].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b97deb1b",
   "metadata": {},
   "source": [
    "#### Youngest dog:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "96ec5dd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2018/02/02'"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs[\"date_of_birth\"].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ada4e63e",
   "metadata": {},
   "source": [
    "#### The `.agg()` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "878701bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pct30(column):\n",
    "    return column.quantile(0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "ac69b626",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.0"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs[\"weight_kg\"].agg(pct30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44a81d93",
   "metadata": {},
   "source": [
    "#### Summaries on multiple columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "cc83cb60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weight_kg    21.0\n",
       "height_cm    45.4\n",
       "dtype: float64"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs[[\"weight_kg\",\"height_cm\"]].agg(pct30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88469506",
   "metadata": {},
   "source": [
    "#### Multiple summaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "21fd1ff9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pct40(column):\n",
    "    return column.quantile(0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "13ae5a7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pct30    21.0\n",
       "pct40    22.4\n",
       "Name: weight_kg, dtype: float64"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs[\"weight_kg\"].agg([pct30, pct40])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff23a656",
   "metadata": {},
   "source": [
    "#### Cumulative sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "45df709a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    25\n",
       "1    23\n",
       "2    22\n",
       "3    17\n",
       "4    29\n",
       "5     2\n",
       "6    74\n",
       "Name: weight_kg, dtype: int64"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs[\"weight_kg\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "f21db392",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     25\n",
       "1     48\n",
       "2     70\n",
       "3     87\n",
       "4    116\n",
       "5    118\n",
       "6    192\n",
       "Name: weight_kg, dtype: int64"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs[\"weight_kg\"].cumsum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca40aadf",
   "metadata": {},
   "source": [
    "Similarly we can find\n",
    "- `cummax()`\n",
    "- `cummin()`\n",
    "- `cumprod()`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22a7b0ef",
   "metadata": {},
   "source": [
    "# Counting\n",
    "- we'll use dog_visits dataset for now let's create the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d398f16",
   "metadata": {},
   "source": [
    "### Avoiding double counting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e9e5759d",
   "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>name</th>\n",
       "      <th>breed</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ginger</td>\n",
       "      <td>Dachshund</td>\n",
       "      <td>22.00</td>\n",
       "      <td>10</td>\n",
       "      <td>2019-03-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Scout</td>\n",
       "      <td>Dalmatian</td>\n",
       "      <td>59.00</td>\n",
       "      <td>25</td>\n",
       "      <td>2019-05-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bella</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>24.87</td>\n",
       "      <td>44</td>\n",
       "      <td>2018-09-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Max</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>28.35</td>\n",
       "      <td>46</td>\n",
       "      <td>2019-06-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Stella</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>1.51</td>\n",
       "      <td>36</td>\n",
       "      <td>2018-01-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lucy</td>\n",
       "      <td>Chow Chow</td>\n",
       "      <td>24.07</td>\n",
       "      <td>26</td>\n",
       "      <td>2019-10-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Stella</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>2.83</td>\n",
       "      <td>34</td>\n",
       "      <td>2018-01-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Max</td>\n",
       "      <td>Chow Chow</td>\n",
       "      <td>24.01</td>\n",
       "      <td>38</td>\n",
       "      <td>2019-06-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Lucy</td>\n",
       "      <td>Chow Chow</td>\n",
       "      <td>24.40</td>\n",
       "      <td>41</td>\n",
       "      <td>2018-08-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Max</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>28.54</td>\n",
       "      <td>50</td>\n",
       "      <td>2019-04-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>Poodle</td>\n",
       "      <td>24.95</td>\n",
       "      <td>33</td>\n",
       "      <td>2019-03-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Cooper</td>\n",
       "      <td>Schnauzer</td>\n",
       "      <td>16.91</td>\n",
       "      <td>26</td>\n",
       "      <td>2019-03-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Bernie</td>\n",
       "      <td>St. Bernard</td>\n",
       "      <td>74.98</td>\n",
       "      <td>29</td>\n",
       "      <td>2019-01-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       name        breed  height_cm  weight_kg date_of_birth\n",
       "0    Ginger    Dachshund      22.00         10    2019-03-14\n",
       "1     Scout    Dalmatian      59.00         25    2019-05-09\n",
       "2     Bella     Labrador      24.87         44    2018-09-02\n",
       "3       Max     Labrador      28.35         46    2019-06-07\n",
       "4    Stella    Chihuahua       1.51         36    2018-01-17\n",
       "5      Lucy    Chow Chow      24.07         26    2019-10-19\n",
       "6    Stella    Chihuahua       2.83         34    2018-01-20\n",
       "7       Max    Chow Chow      24.01         38    2019-06-07\n",
       "8      Lucy    Chow Chow      24.40         41    2018-08-20\n",
       "9       Max     Labrador      28.54         50    2019-04-22\n",
       "10  Charlie       Poodle      24.95         33    2019-03-19\n",
       "11   Cooper    Schnauzer      16.91         26    2019-03-30\n",
       "12   Bernie  St. Bernard      74.98         29    2019-01-04"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_of_lists = {\"name\": [\"Ginger\", \"Scout\", \"Bella\", \"Max\", \"Stella\", \"Lucy\", \"Stella\", \"Max\", \"Lucy\", \"Max\", \"Charlie\", \"Cooper\", \"Bernie\"],\n",
    "                \"breed\": [\"Dachshund\",\"Dalmatian\", \"Labrador\", \"Labrador\", \"Chihuahua\", \"Chow Chow\", \"Chihuahua\", \"Chow Chow\", \"Chow Chow\", \"Labrador\", \"Poodle\", \"Schnauzer\", \"St. Bernard\"],\n",
    "                 \"height_cm\": [22, 59, 24.87, 28.35, 1.51, 24.07, 2.83, 24.01, 24.40, 28.54, 24.95, 16.91, 74.98],\n",
    "                 \"weight_kg\": [10, 25, 44, 46, 36, 26, 34, 38, 41, 50, 33, 26, 29],\n",
    "                 \"date_of_birth\": [\"2019-03-14\",\"2019-05-09\", \"2018-09-02\", \"2019-06-07\", \"2018-01-17\", \"2019-10-19\", \"2018-01-20\", \" 2019-06-07\", \"2018-08-20\", \"2019-04-22\", \"2019-03-19\", \"2019-03-30\", \"2019-01-04\"]}\n",
    "\n",
    "dogs_visits = pd.DataFrame(dict_of_lists)\n",
    "\n",
    "dogs_visits"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8cb89fe",
   "metadata": {},
   "source": [
    "#### Dropping duplicate names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "947181fd",
   "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>name</th>\n",
       "      <th>breed</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ginger</td>\n",
       "      <td>Dachshund</td>\n",
       "      <td>22.00</td>\n",
       "      <td>10</td>\n",
       "      <td>2019-03-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Scout</td>\n",
       "      <td>Dalmatian</td>\n",
       "      <td>59.00</td>\n",
       "      <td>25</td>\n",
       "      <td>2019-05-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bella</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>24.87</td>\n",
       "      <td>44</td>\n",
       "      <td>2018-09-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Max</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>28.35</td>\n",
       "      <td>46</td>\n",
       "      <td>2019-06-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Stella</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>1.51</td>\n",
       "      <td>36</td>\n",
       "      <td>2018-01-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lucy</td>\n",
       "      <td>Chow Chow</td>\n",
       "      <td>24.07</td>\n",
       "      <td>26</td>\n",
       "      <td>2019-10-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>Poodle</td>\n",
       "      <td>24.95</td>\n",
       "      <td>33</td>\n",
       "      <td>2019-03-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Cooper</td>\n",
       "      <td>Schnauzer</td>\n",
       "      <td>16.91</td>\n",
       "      <td>26</td>\n",
       "      <td>2019-03-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Bernie</td>\n",
       "      <td>St. Bernard</td>\n",
       "      <td>74.98</td>\n",
       "      <td>29</td>\n",
       "      <td>2019-01-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       name        breed  height_cm  weight_kg date_of_birth\n",
       "0    Ginger    Dachshund      22.00         10    2019-03-14\n",
       "1     Scout    Dalmatian      59.00         25    2019-05-09\n",
       "2     Bella     Labrador      24.87         44    2018-09-02\n",
       "3       Max     Labrador      28.35         46    2019-06-07\n",
       "4    Stella    Chihuahua       1.51         36    2018-01-17\n",
       "5      Lucy    Chow Chow      24.07         26    2019-10-19\n",
       "10  Charlie       Poodle      24.95         33    2019-03-19\n",
       "11   Cooper    Schnauzer      16.91         26    2019-03-30\n",
       "12   Bernie  St. Bernard      74.98         29    2019-01-04"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs_visits.drop_duplicates(subset=\"name\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2612bd94",
   "metadata": {},
   "source": [
    "#### Dropping duplicate pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "f0463e2f",
   "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>name</th>\n",
       "      <th>breed</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ginger</td>\n",
       "      <td>Dachshund</td>\n",
       "      <td>22.00</td>\n",
       "      <td>10</td>\n",
       "      <td>2019-03-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Scout</td>\n",
       "      <td>Dalmatian</td>\n",
       "      <td>59.00</td>\n",
       "      <td>25</td>\n",
       "      <td>2019-05-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bella</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>24.87</td>\n",
       "      <td>44</td>\n",
       "      <td>2018-09-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Max</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>28.35</td>\n",
       "      <td>46</td>\n",
       "      <td>2019-06-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Stella</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>1.51</td>\n",
       "      <td>36</td>\n",
       "      <td>2018-01-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lucy</td>\n",
       "      <td>Chow Chow</td>\n",
       "      <td>24.07</td>\n",
       "      <td>26</td>\n",
       "      <td>2019-10-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Max</td>\n",
       "      <td>Chow Chow</td>\n",
       "      <td>24.01</td>\n",
       "      <td>38</td>\n",
       "      <td>2019-06-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>Poodle</td>\n",
       "      <td>24.95</td>\n",
       "      <td>33</td>\n",
       "      <td>2019-03-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Cooper</td>\n",
       "      <td>Schnauzer</td>\n",
       "      <td>16.91</td>\n",
       "      <td>26</td>\n",
       "      <td>2019-03-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Bernie</td>\n",
       "      <td>St. Bernard</td>\n",
       "      <td>74.98</td>\n",
       "      <td>29</td>\n",
       "      <td>2019-01-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       name        breed  height_cm  weight_kg date_of_birth\n",
       "0    Ginger    Dachshund      22.00         10    2019-03-14\n",
       "1     Scout    Dalmatian      59.00         25    2019-05-09\n",
       "2     Bella     Labrador      24.87         44    2018-09-02\n",
       "3       Max     Labrador      28.35         46    2019-06-07\n",
       "4    Stella    Chihuahua       1.51         36    2018-01-17\n",
       "5      Lucy    Chow Chow      24.07         26    2019-10-19\n",
       "7       Max    Chow Chow      24.01         38    2019-06-07\n",
       "10  Charlie       Poodle      24.95         33    2019-03-19\n",
       "11   Cooper    Schnauzer      16.91         26    2019-03-30\n",
       "12   Bernie  St. Bernard      74.98         29    2019-01-04"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_dogs = dogs_visits.drop_duplicates(subset=[\"name\",\"breed\"])\n",
    "unique_dogs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "623399b5",
   "metadata": {},
   "source": [
    "#### Counting categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "6bd3054b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Labrador       2\n",
       "Chow Chow      2\n",
       "Poodle         1\n",
       "Schnauzer      1\n",
       "St. Bernard    1\n",
       "Chihuahua      1\n",
       "Dachshund      1\n",
       "Dalmatian      1\n",
       "Name: breed, dtype: int64"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_dogs[\"breed\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "e604e615",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Poodle         1\n",
       "Schnauzer      1\n",
       "Labrador       2\n",
       "St. Bernard    1\n",
       "Chow Chow      2\n",
       "Chihuahua      1\n",
       "Dachshund      1\n",
       "Dalmatian      1\n",
       "Name: breed, dtype: int64"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_dogs[\"breed\"].value_counts(sort= False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beec5ed8",
   "metadata": {},
   "source": [
    "#### Proportions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "c88a1b66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Labrador       0.2\n",
       "Chow Chow      0.2\n",
       "Poodle         0.1\n",
       "Schnauzer      0.1\n",
       "St. Bernard    0.1\n",
       "Chihuahua      0.1\n",
       "Dachshund      0.1\n",
       "Dalmatian      0.1\n",
       "Name: breed, dtype: float64"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_dogs[\"breed\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b473068",
   "metadata": {},
   "source": [
    "# Grouped summary statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e06c0b83",
   "metadata": {},
   "source": [
    "#### Summaries by group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "7df9b82a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26.0\n",
      "23.5\n",
      "74.0\n",
      "17.0\n",
      "2.0\n"
     ]
    }
   ],
   "source": [
    "print(dogs[dogs[\"color\"] == \" Black\"][\"weight_kg\"].mean())\n",
    "print(dogs[dogs[\"color\"] == \" Brown\"][\"weight_kg\"].mean())\n",
    "print(dogs[dogs[\"color\"] == \" White\"][\"weight_kg\"].mean())\n",
    "print(dogs[dogs[\"color\"] == \" Gray\"][\"weight_kg\"].mean())\n",
    "print(dogs[dogs[\"color\"] == \" Tan\"][\"weight_kg\"].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6faa4a82",
   "metadata": {},
   "source": [
    "#### Grouped summaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "a1f0e10a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color\n",
       " Black    26.0\n",
       " Brown    23.5\n",
       " Gray     17.0\n",
       " Tan       2.0\n",
       " White    74.0\n",
       "Name: weight_kg, dtype: float64"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.groupby(\"color\")[\"weight_kg\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e874d87",
   "metadata": {},
   "source": [
    "#### Multiple grouped summaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "862d2daf",
   "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>min</th>\n",
       "      <th>max</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>color</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>23</td>\n",
       "      <td>29</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brown</th>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gray</th>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tan</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>74</td>\n",
       "      <td>74</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        min  max  sum\n",
       "color                \n",
       " Black   23   29   52\n",
       " Brown   22   25   47\n",
       " Gray    17   17   17\n",
       " Tan      2    2    2\n",
       " White   74   74   74"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.groupby(\"color\")[\"weight_kg\"].agg([min, max, sum])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a365f0d",
   "metadata": {},
   "source": [
    "#### Grouping by multiple variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "0e0fa4bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color   breed       \n",
       " Black   Labrador       29\n",
       "         Poodle         23\n",
       " Brown   Chow           22\n",
       "         Labrador       25\n",
       " Gray    Schnauzer      17\n",
       " Tan     Chihuahua       2\n",
       " White   St. Bernard    74\n",
       "Name: weight_kg, dtype: int64"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.groupby([\"color\",\"breed\"])[\"weight_kg\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cff3c16",
   "metadata": {},
   "source": [
    "#### Many groups, many summaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "a4dd34f5",
   "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></th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>height_cm</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>color</th>\n",
       "      <th>breed</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Black</th>\n",
       "      <th>Labrador</th>\n",
       "      <td>29</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poodle</th>\n",
       "      <td>23</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Brown</th>\n",
       "      <th>Chow</th>\n",
       "      <td>22</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Labrador</th>\n",
       "      <td>25</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gray</th>\n",
       "      <th>Schnauzer</th>\n",
       "      <td>17</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tan</th>\n",
       "      <th>Chihuahua</th>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <th>St. Bernard</th>\n",
       "      <td>74</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     weight_kg  height_cm\n",
       "color  breed                             \n",
       " Black  Labrador            29         59\n",
       "        Poodle              23         43\n",
       " Brown  Chow                22         46\n",
       "        Labrador            25         56\n",
       " Gray   Schnauzer           17         49\n",
       " Tan    Chihuahua            2         18\n",
       " White  St. Bernard         74         77"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.groupby([\"color\",\"breed\"])[[\"weight_kg\",\"height_cm\"]].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d3083e1",
   "metadata": {},
   "source": [
    "# Pivot Table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d2963a3",
   "metadata": {},
   "source": [
    "#### Group by to pivot table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "38eeaa3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color\n",
       " Black    26.0\n",
       " Brown    23.5\n",
       " Gray     17.0\n",
       " Tan       2.0\n",
       " White    74.0\n",
       "Name: weight_kg, dtype: float64"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.groupby(\"color\")[\"weight_kg\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "9ff4b522",
   "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>weight_kg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>color</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brown</th>\n",
       "      <td>23.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gray</th>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tan</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        weight_kg\n",
       "color            \n",
       " Black       26.0\n",
       " Brown       23.5\n",
       " Gray        17.0\n",
       " Tan          2.0\n",
       " White       74.0"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.pivot_table(values=\"weight_kg\",index=\"color\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b882ee8e",
   "metadata": {},
   "source": [
    "#### Different statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "1869d8e6",
   "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>weight_kg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>color</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brown</th>\n",
       "      <td>23.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gray</th>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tan</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        weight_kg\n",
       "color            \n",
       " Black       26.0\n",
       " Brown       23.5\n",
       " Gray        17.0\n",
       " Tan          2.0\n",
       " White       74.0"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "dogs.pivot_table(values=\"weight_kg\", index=\"color\", aggfunc=np.median)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5d9a887",
   "metadata": {},
   "source": [
    "#### Multiple statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "b912911c",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>weight_kg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>color</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>26.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brown</th>\n",
       "      <td>23.5</td>\n",
       "      <td>23.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gray</th>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tan</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>74.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            mean    median\n",
       "       weight_kg weight_kg\n",
       "color                     \n",
       " Black      26.0      26.0\n",
       " Brown      23.5      23.5\n",
       " Gray       17.0      17.0\n",
       " Tan         2.0       2.0\n",
       " White      74.0      74.0"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.pivot_table(values=\"weight_kg\", index=\"color\", aggfunc=[np.mean, np.median])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e99df359",
   "metadata": {},
   "source": [
    "#### Pivot on two variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "id": "bb319a91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color   breed       \n",
       " Black   Labrador       29\n",
       "         Poodle         23\n",
       " Brown   Chow           22\n",
       "         Labrador       25\n",
       " Gray    Schnauzer      17\n",
       " Tan     Chihuahua       2\n",
       " White   St. Bernard    74\n",
       "Name: weight_kg, dtype: int64"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.groupby([\"color\",\"breed\"])[\"weight_kg\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "id": "23c9eaf5",
   "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>breed</th>\n",
       "      <th>Chihuahua</th>\n",
       "      <th>Chow</th>\n",
       "      <th>Labrador</th>\n",
       "      <th>Poodle</th>\n",
       "      <th>Schnauzer</th>\n",
       "      <th>St. Bernard</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>color</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>Black</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brown</th>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gray</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tan</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "breed    Chihuahua   Chow   Labrador   Poodle   Schnauzer   St. Bernard\n",
       "color                                                                  \n",
       " Black         NaN    NaN       29.0     23.0         NaN           NaN\n",
       " Brown         NaN   22.0       25.0      NaN         NaN           NaN\n",
       " Gray          NaN    NaN        NaN      NaN        17.0           NaN\n",
       " Tan           2.0    NaN        NaN      NaN         NaN           NaN\n",
       " White         NaN    NaN        NaN      NaN         NaN          74.0"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.pivot_table(values=\"weight_kg\", index=\"color\", columns=\"breed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c17cb07",
   "metadata": {},
   "source": [
    "#### Fill in missing values and sum values with pivot tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "id": "9fa8e60f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>breed</th>\n",
       "      <th>Chihuahua</th>\n",
       "      <th>Chow</th>\n",
       "      <th>Labrador</th>\n",
       "      <th>Poodle</th>\n",
       "      <th>Schnauzer</th>\n",
       "      <th>St. Bernard</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>color</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>Black</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brown</th>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gray</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tan</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "breed    Chihuahua   Chow   Labrador   Poodle   Schnauzer   St. Bernard\n",
       "color                                                                  \n",
       " Black           0      0         29       23           0             0\n",
       " Brown           0     22         25        0           0             0\n",
       " Gray            0      0          0        0          17             0\n",
       " Tan             2      0          0        0           0             0\n",
       " White           0      0          0        0           0            74"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.pivot_table(values=\"weight_kg\", index=\"color\", columns=\"breed\", fill_value=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a05105da",
   "metadata": {},
   "source": [
    "#### Summing with pivot tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "id": "07e9a3e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>breed</th>\n",
       "      <th>Chihuahua</th>\n",
       "      <th>Chow</th>\n",
       "      <th>Labrador</th>\n",
       "      <th>Poodle</th>\n",
       "      <th>Schnauzer</th>\n",
       "      <th>St. Bernard</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>color</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>Black</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brown</th>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gray</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tan</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>27</td>\n",
       "      <td>23</td>\n",
       "      <td>17</td>\n",
       "      <td>74</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "breed    Chihuahua   Chow   Labrador   Poodle   Schnauzer   St. Bernard   All\n",
       "color                                                                        \n",
       " Black           0      0         29       23           0             0  26.0\n",
       " Brown           0     22         25        0           0             0  23.5\n",
       " Gray            0      0          0        0          17             0  17.0\n",
       " Tan             2      0          0        0           0             0   2.0\n",
       " White           0      0          0        0           0            74  74.0\n",
       "All              2     22         27       23          17            74  27.0"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.pivot_table(values=\"weight_kg\", index=\"color\", columns=\"breed\", fill_value=0, margins= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94b83e20",
   "metadata": {},
   "source": [
    "# Explicit indexes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98366975",
   "metadata": {},
   "source": [
    "#### The dog dataset, revisited"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "id": "92c169e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>height_m</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bella</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Brown</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013/07/01</td>\n",
       "      <td>0.56</td>\n",
       "      <td>79.719388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>Poodle</td>\n",
       "      <td>Black</td>\n",
       "      <td>43</td>\n",
       "      <td>23</td>\n",
       "      <td>2016/09/16</td>\n",
       "      <td>0.43</td>\n",
       "      <td>124.391563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lucy</td>\n",
       "      <td>Chow</td>\n",
       "      <td>Brown</td>\n",
       "      <td>46</td>\n",
       "      <td>22</td>\n",
       "      <td>2014/08/25</td>\n",
       "      <td>0.46</td>\n",
       "      <td>103.969754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cooper</td>\n",
       "      <td>Schnauzer</td>\n",
       "      <td>Gray</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>2011/12/11</td>\n",
       "      <td>0.49</td>\n",
       "      <td>70.803832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Max</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Black</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>2017/01/20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>83.309394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Stella</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>Tan</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2015/04/20</td>\n",
       "      <td>0.18</td>\n",
       "      <td>61.728395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bernie</td>\n",
       "      <td>St. Bernard</td>\n",
       "      <td>White</td>\n",
       "      <td>77</td>\n",
       "      <td>74</td>\n",
       "      <td>2018/02/02</td>\n",
       "      <td>0.77</td>\n",
       "      <td>124.810255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name         breed   color  height_cm  weight_kg date_of_birth  \\\n",
       "0    Bella      Labrador   Brown         56         25    2013/07/01   \n",
       "1  Charlie        Poodle   Black         43         23    2016/09/16   \n",
       "2     Lucy          Chow   Brown         46         22    2014/08/25   \n",
       "3   Cooper     Schnauzer    Gray         49         17    2011/12/11   \n",
       "4      Max      Labrador   Black         59         29    2017/01/20   \n",
       "5   Stella     Chihuahua     Tan         18          2    2015/04/20   \n",
       "6   Bernie   St. Bernard   White         77         74    2018/02/02   \n",
       "\n",
       "   height_m         bmi  \n",
       "0      0.56   79.719388  \n",
       "1      0.43  124.391563  \n",
       "2      0.46  103.969754  \n",
       "3      0.49   70.803832  \n",
       "4      0.59   83.309394  \n",
       "5      0.18   61.728395  \n",
       "6      0.77  124.810255  "
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8ecebc0",
   "metadata": {},
   "source": [
    "#### .columns and .index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "id": "01225b31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'breed', 'color', 'height_cm', 'weight_kg', 'date_of_birth',\n",
       "       'height_m', 'bmi'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "id": "500842ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=7, step=1)"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72baaa98",
   "metadata": {},
   "source": [
    "#### Setting a column as the index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "id": "a11962da",
   "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>breed</th>\n",
       "      <th>color</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>height_m</th>\n",
       "      <th>bmi</th>\n",
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       "    <tr>\n",
       "      <th>name</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bella</th>\n",
       "      <td>Labrador</td>\n",
       "      <td>Brown</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013/07/01</td>\n",
       "      <td>0.56</td>\n",
       "      <td>79.719388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Charlie</th>\n",
       "      <td>Poodle</td>\n",
       "      <td>Black</td>\n",
       "      <td>43</td>\n",
       "      <td>23</td>\n",
       "      <td>2016/09/16</td>\n",
       "      <td>0.43</td>\n",
       "      <td>124.391563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lucy</th>\n",
       "      <td>Chow</td>\n",
       "      <td>Brown</td>\n",
       "      <td>46</td>\n",
       "      <td>22</td>\n",
       "      <td>2014/08/25</td>\n",
       "      <td>0.46</td>\n",
       "      <td>103.969754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cooper</th>\n",
       "      <td>Schnauzer</td>\n",
       "      <td>Gray</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>2011/12/11</td>\n",
       "      <td>0.49</td>\n",
       "      <td>70.803832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max</th>\n",
       "      <td>Labrador</td>\n",
       "      <td>Black</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>2017/01/20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>83.309394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stella</th>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>Tan</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2015/04/20</td>\n",
       "      <td>0.18</td>\n",
       "      <td>61.728395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bernie</th>\n",
       "      <td>St. Bernard</td>\n",
       "      <td>White</td>\n",
       "      <td>77</td>\n",
       "      <td>74</td>\n",
       "      <td>2018/02/02</td>\n",
       "      <td>0.77</td>\n",
       "      <td>124.810255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                breed   color  height_cm  weight_kg date_of_birth  height_m  \\\n",
       "name                                                                          \n",
       "Bella        Labrador   Brown         56         25    2013/07/01      0.56   \n",
       "Charlie        Poodle   Black         43         23    2016/09/16      0.43   \n",
       "Lucy             Chow   Brown         46         22    2014/08/25      0.46   \n",
       "Cooper      Schnauzer    Gray         49         17    2011/12/11      0.49   \n",
       "Max          Labrador   Black         59         29    2017/01/20      0.59   \n",
       "Stella      Chihuahua     Tan         18          2    2015/04/20      0.18   \n",
       "Bernie    St. Bernard   White         77         74    2018/02/02      0.77   \n",
       "\n",
       "                bmi  \n",
       "name                 \n",
       "Bella     79.719388  \n",
       "Charlie  124.391563  \n",
       "Lucy     103.969754  \n",
       "Cooper    70.803832  \n",
       "Max       83.309394  \n",
       "Stella    61.728395  \n",
       "Bernie   124.810255  "
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs_ind = dogs.set_index(\"name\")\n",
    "dogs_ind"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "601348a7",
   "metadata": {},
   "source": [
    "#### Removing an index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "id": "ad3feb22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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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>name</th>\n",
       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>height_m</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bella</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Brown</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013/07/01</td>\n",
       "      <td>0.56</td>\n",
       "      <td>79.719388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>Poodle</td>\n",
       "      <td>Black</td>\n",
       "      <td>43</td>\n",
       "      <td>23</td>\n",
       "      <td>2016/09/16</td>\n",
       "      <td>0.43</td>\n",
       "      <td>124.391563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lucy</td>\n",
       "      <td>Chow</td>\n",
       "      <td>Brown</td>\n",
       "      <td>46</td>\n",
       "      <td>22</td>\n",
       "      <td>2014/08/25</td>\n",
       "      <td>0.46</td>\n",
       "      <td>103.969754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cooper</td>\n",
       "      <td>Schnauzer</td>\n",
       "      <td>Gray</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>2011/12/11</td>\n",
       "      <td>0.49</td>\n",
       "      <td>70.803832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Max</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Black</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>2017/01/20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>83.309394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Stella</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>Tan</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2015/04/20</td>\n",
       "      <td>0.18</td>\n",
       "      <td>61.728395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bernie</td>\n",
       "      <td>St. Bernard</td>\n",
       "      <td>White</td>\n",
       "      <td>77</td>\n",
       "      <td>74</td>\n",
       "      <td>2018/02/02</td>\n",
       "      <td>0.77</td>\n",
       "      <td>124.810255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name         breed   color  height_cm  weight_kg date_of_birth  \\\n",
       "0    Bella      Labrador   Brown         56         25    2013/07/01   \n",
       "1  Charlie        Poodle   Black         43         23    2016/09/16   \n",
       "2     Lucy          Chow   Brown         46         22    2014/08/25   \n",
       "3   Cooper     Schnauzer    Gray         49         17    2011/12/11   \n",
       "4      Max      Labrador   Black         59         29    2017/01/20   \n",
       "5   Stella     Chihuahua     Tan         18          2    2015/04/20   \n",
       "6   Bernie   St. Bernard   White         77         74    2018/02/02   \n",
       "\n",
       "   height_m         bmi  \n",
       "0      0.56   79.719388  \n",
       "1      0.43  124.391563  \n",
       "2      0.46  103.969754  \n",
       "3      0.49   70.803832  \n",
       "4      0.59   83.309394  \n",
       "5      0.18   61.728395  \n",
       "6      0.77  124.810255  "
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs_ind.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "089ec0a8",
   "metadata": {},
   "source": [
    "#### Dropping an index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "id": "25778324",
   "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",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>height_m</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Labrador</td>\n",
       "      <td>Brown</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013/07/01</td>\n",
       "      <td>0.56</td>\n",
       "      <td>79.719388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Poodle</td>\n",
       "      <td>Black</td>\n",
       "      <td>43</td>\n",
       "      <td>23</td>\n",
       "      <td>2016/09/16</td>\n",
       "      <td>0.43</td>\n",
       "      <td>124.391563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chow</td>\n",
       "      <td>Brown</td>\n",
       "      <td>46</td>\n",
       "      <td>22</td>\n",
       "      <td>2014/08/25</td>\n",
       "      <td>0.46</td>\n",
       "      <td>103.969754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Schnauzer</td>\n",
       "      <td>Gray</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>2011/12/11</td>\n",
       "      <td>0.49</td>\n",
       "      <td>70.803832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Labrador</td>\n",
       "      <td>Black</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>2017/01/20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>83.309394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>Tan</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2015/04/20</td>\n",
       "      <td>0.18</td>\n",
       "      <td>61.728395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>St. Bernard</td>\n",
       "      <td>White</td>\n",
       "      <td>77</td>\n",
       "      <td>74</td>\n",
       "      <td>2018/02/02</td>\n",
       "      <td>0.77</td>\n",
       "      <td>124.810255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          breed   color  height_cm  weight_kg date_of_birth  height_m  \\\n",
       "0      Labrador   Brown         56         25    2013/07/01      0.56   \n",
       "1        Poodle   Black         43         23    2016/09/16      0.43   \n",
       "2          Chow   Brown         46         22    2014/08/25      0.46   \n",
       "3     Schnauzer    Gray         49         17    2011/12/11      0.49   \n",
       "4      Labrador   Black         59         29    2017/01/20      0.59   \n",
       "5     Chihuahua     Tan         18          2    2015/04/20      0.18   \n",
       "6   St. Bernard   White         77         74    2018/02/02      0.77   \n",
       "\n",
       "          bmi  \n",
       "0   79.719388  \n",
       "1  124.391563  \n",
       "2  103.969754  \n",
       "3   70.803832  \n",
       "4   83.309394  \n",
       "5   61.728395  \n",
       "6  124.810255  "
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs_ind.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cf991b8",
   "metadata": {},
   "source": [
    "#### Indexes make subsetting simpler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "id": "1723b6cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>height_m</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bella</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Brown</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013/07/01</td>\n",
       "      <td>0.56</td>\n",
       "      <td>79.719388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Stella</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>Tan</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2015/04/20</td>\n",
       "      <td>0.18</td>\n",
       "      <td>61.728395</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     name       breed   color  height_cm  weight_kg date_of_birth  height_m  \\\n",
       "0   Bella    Labrador   Brown         56         25    2013/07/01      0.56   \n",
       "5  Stella   Chihuahua     Tan         18          2    2015/04/20      0.18   \n",
       "\n",
       "         bmi  \n",
       "0  79.719388  \n",
       "5  61.728395  "
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs[dogs[\"name\"].isin([\"Bella\",\"Stella\"])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "id": "ec58a6eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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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>breed</th>\n",
       "      <th>color</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>height_m</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bella</th>\n",
       "      <td>Labrador</td>\n",
       "      <td>Brown</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013/07/01</td>\n",
       "      <td>0.56</td>\n",
       "      <td>79.719388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stella</th>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>Tan</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2015/04/20</td>\n",
       "      <td>0.18</td>\n",
       "      <td>61.728395</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             breed   color  height_cm  weight_kg date_of_birth  height_m  \\\n",
       "name                                                                       \n",
       "Bella     Labrador   Brown         56         25    2013/07/01      0.56   \n",
       "Stella   Chihuahua     Tan         18          2    2015/04/20      0.18   \n",
       "\n",
       "              bmi  \n",
       "name               \n",
       "Bella   79.719388  \n",
       "Stella  61.728395  "
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs_ind.loc[[\"Bella\",\"Stella\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3b553e1",
   "metadata": {},
   "source": [
    "#### Index values don't need to be unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "id": "1bf3542a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "    <tr>\n",
       "      <th>breed</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Labrador</th>\n",
       "      <td>Bella</td>\n",
       "      <td>Brown</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013/07/01</td>\n",
       "      <td>0.56</td>\n",
       "      <td>79.719388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poodle</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>Black</td>\n",
       "      <td>43</td>\n",
       "      <td>23</td>\n",
       "      <td>2016/09/16</td>\n",
       "      <td>0.43</td>\n",
       "      <td>124.391563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chow</th>\n",
       "      <td>Lucy</td>\n",
       "      <td>Brown</td>\n",
       "      <td>46</td>\n",
       "      <td>22</td>\n",
       "      <td>2014/08/25</td>\n",
       "      <td>0.46</td>\n",
       "      <td>103.969754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schnauzer</th>\n",
       "      <td>Cooper</td>\n",
       "      <td>Gray</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>2011/12/11</td>\n",
       "      <td>0.49</td>\n",
       "      <td>70.803832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Labrador</th>\n",
       "      <td>Max</td>\n",
       "      <td>Black</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>2017/01/20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>83.309394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chihuahua</th>\n",
       "      <td>Stella</td>\n",
       "      <td>Tan</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2015/04/20</td>\n",
       "      <td>0.18</td>\n",
       "      <td>61.728395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>St. Bernard</th>\n",
       "      <td>Bernie</td>\n",
       "      <td>White</td>\n",
       "      <td>77</td>\n",
       "      <td>74</td>\n",
       "      <td>2018/02/02</td>\n",
       "      <td>0.77</td>\n",
       "      <td>124.810255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 name   color  height_cm  weight_kg date_of_birth  height_m  \\\n",
       "breed                                                                         \n",
       " Labrador       Bella   Brown         56         25    2013/07/01      0.56   \n",
       " Poodle       Charlie   Black         43         23    2016/09/16      0.43   \n",
       " Chow            Lucy   Brown         46         22    2014/08/25      0.46   \n",
       " Schnauzer     Cooper    Gray         49         17    2011/12/11      0.49   \n",
       " Labrador         Max   Black         59         29    2017/01/20      0.59   \n",
       " Chihuahua     Stella     Tan         18          2    2015/04/20      0.18   \n",
       " St. Bernard   Bernie   White         77         74    2018/02/02      0.77   \n",
       "\n",
       "                     bmi  \n",
       "breed                     \n",
       " Labrador      79.719388  \n",
       " Poodle       124.391563  \n",
       " Chow         103.969754  \n",
       " Schnauzer     70.803832  \n",
       " Labrador      83.309394  \n",
       " Chihuahua     61.728395  \n",
       " St. Bernard  124.810255  "
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs_ind2 = dogs.set_index(\"breed\")\n",
    "dogs_ind2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "defe9a7c",
   "metadata": {},
   "source": [
    "#### Subsetting on duplicated index values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "id": "6d5dc518",
   "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 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>name</th>\n",
       "      <th>color</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>height_m</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>breed</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>Labrador</th>\n",
       "      <td>Bella</td>\n",
       "      <td>Brown</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013/07/01</td>\n",
       "      <td>0.56</td>\n",
       "      <td>79.719388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Labrador</th>\n",
       "      <td>Max</td>\n",
       "      <td>Black</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>2017/01/20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>83.309394</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            name   color  height_cm  weight_kg date_of_birth  height_m  \\\n",
       "breed                                                                    \n",
       " Labrador  Bella   Brown         56         25    2013/07/01      0.56   \n",
       " Labrador    Max   Black         59         29    2017/01/20      0.59   \n",
       "\n",
       "                 bmi  \n",
       "breed                 \n",
       " Labrador  79.719388  \n",
       " Labrador  83.309394  "
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs_ind2.loc[\" Labrador\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb922dbc",
   "metadata": {},
   "source": [
    "#### Multi-level indexes a.k.a. hierarchical indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "id": "522d6793",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>height_m</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>breed</th>\n",
       "      <th>color</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>Labrador</th>\n",
       "      <th>Brown</th>\n",
       "      <td>Bella</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013/07/01</td>\n",
       "      <td>0.56</td>\n",
       "      <td>79.719388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poodle</th>\n",
       "      <th>Black</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>43</td>\n",
       "      <td>23</td>\n",
       "      <td>2016/09/16</td>\n",
       "      <td>0.43</td>\n",
       "      <td>124.391563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chow</th>\n",
       "      <th>Brown</th>\n",
       "      <td>Lucy</td>\n",
       "      <td>46</td>\n",
       "      <td>22</td>\n",
       "      <td>2014/08/25</td>\n",
       "      <td>0.46</td>\n",
       "      <td>103.969754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schnauzer</th>\n",
       "      <th>Gray</th>\n",
       "      <td>Cooper</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>2011/12/11</td>\n",
       "      <td>0.49</td>\n",
       "      <td>70.803832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Labrador</th>\n",
       "      <th>Black</th>\n",
       "      <td>Max</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>2017/01/20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>83.309394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chihuahua</th>\n",
       "      <th>Tan</th>\n",
       "      <td>Stella</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2015/04/20</td>\n",
       "      <td>0.18</td>\n",
       "      <td>61.728395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>St. Bernard</th>\n",
       "      <th>White</th>\n",
       "      <td>Bernie</td>\n",
       "      <td>77</td>\n",
       "      <td>74</td>\n",
       "      <td>2018/02/02</td>\n",
       "      <td>0.77</td>\n",
       "      <td>124.810255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        name  height_cm  weight_kg date_of_birth  height_m  \\\n",
       "breed        color                                                           \n",
       " Labrador     Brown    Bella         56         25    2013/07/01      0.56   \n",
       " Poodle       Black  Charlie         43         23    2016/09/16      0.43   \n",
       " Chow         Brown     Lucy         46         22    2014/08/25      0.46   \n",
       " Schnauzer    Gray    Cooper         49         17    2011/12/11      0.49   \n",
       " Labrador     Black      Max         59         29    2017/01/20      0.59   \n",
       " Chihuahua    Tan     Stella         18          2    2015/04/20      0.18   \n",
       " St. Bernard  White   Bernie         77         74    2018/02/02      0.77   \n",
       "\n",
       "                            bmi  \n",
       "breed        color               \n",
       " Labrador     Brown   79.719388  \n",
       " Poodle       Black  124.391563  \n",
       " Chow         Brown  103.969754  \n",
       " Schnauzer    Gray    70.803832  \n",
       " Labrador     Black   83.309394  \n",
       " Chihuahua    Tan     61.728395  \n",
       " St. Bernard  White  124.810255  "
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs_ind3 = dogs.set_index([\"breed\",\"color\"])\n",
    "dogs_ind3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9743f34",
   "metadata": {},
   "source": [
    "#### Subset the outer level with a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "id": "d93aa0f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>height_m</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Labrador</th>\n",
       "      <th>Brown</th>\n",
       "      <td>Bella</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013/07/01</td>\n",
       "      <td>0.56</td>\n",
       "      <td>79.719388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>Max</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>2017/01/20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>83.309394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chihuahua</th>\n",
       "      <th>Tan</th>\n",
       "      <td>Stella</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2015/04/20</td>\n",
       "      <td>0.18</td>\n",
       "      <td>61.728395</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     name  height_cm  weight_kg date_of_birth  height_m  \\\n",
       "breed      color                                                          \n",
       " Labrador   Brown   Bella         56         25    2013/07/01      0.56   \n",
       "            Black     Max         59         29    2017/01/20      0.59   \n",
       " Chihuahua  Tan    Stella         18          2    2015/04/20      0.18   \n",
       "\n",
       "                         bmi  \n",
       "breed      color              \n",
       " Labrador   Brown  79.719388  \n",
       "            Black  83.309394  \n",
       " Chihuahua  Tan    61.728395  "
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs_ind3.loc[[\" Labrador\",\" Chihuahua\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03ed87d6",
   "metadata": {},
   "source": [
    "#### Subset inner levels with a list of tuples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "id": "03d89990",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>name</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>height_m</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>breed</th>\n",
       "      <th>color</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>Labrador</th>\n",
       "      <th>Brown</th>\n",
       "      <td>Bella</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013/07/01</td>\n",
       "      <td>0.56</td>\n",
       "      <td>79.719388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chihuahua</th>\n",
       "      <th>Tan</th>\n",
       "      <td>Stella</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2015/04/20</td>\n",
       "      <td>0.18</td>\n",
       "      <td>61.728395</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     name  height_cm  weight_kg date_of_birth  height_m  \\\n",
       "breed      color                                                          \n",
       " Labrador   Brown   Bella         56         25    2013/07/01      0.56   \n",
       " Chihuahua  Tan    Stella         18          2    2015/04/20      0.18   \n",
       "\n",
       "                         bmi  \n",
       "breed      color              \n",
       " Labrador   Brown  79.719388  \n",
       " Chihuahua  Tan    61.728395  "
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs_ind3.loc[[(\" Labrador\",\" Brown\"), (\" Chihuahua\",\" Tan\")]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c857d37",
   "metadata": {},
   "source": [
    "#### Sorting by index values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "id": "fd9c40e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>breed</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chihuahua</th>\n",
       "      <th>Tan</th>\n",
       "      <td>Stella</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
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       "      <td>0.18</td>\n",
       "      <td>61.728395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chow</th>\n",
       "      <th>Brown</th>\n",
       "      <td>Lucy</td>\n",
       "      <td>46</td>\n",
       "      <td>22</td>\n",
       "      <td>2014/08/25</td>\n",
       "      <td>0.46</td>\n",
       "      <td>103.969754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Labrador</th>\n",
       "      <th>Black</th>\n",
       "      <td>Max</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>2017/01/20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>83.309394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brown</th>\n",
       "      <td>Bella</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013/07/01</td>\n",
       "      <td>0.56</td>\n",
       "      <td>79.719388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poodle</th>\n",
       "      <th>Black</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>43</td>\n",
       "      <td>23</td>\n",
       "      <td>2016/09/16</td>\n",
       "      <td>0.43</td>\n",
       "      <td>124.391563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schnauzer</th>\n",
       "      <th>Gray</th>\n",
       "      <td>Cooper</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>2011/12/11</td>\n",
       "      <td>0.49</td>\n",
       "      <td>70.803832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>St. Bernard</th>\n",
       "      <th>White</th>\n",
       "      <td>Bernie</td>\n",
       "      <td>77</td>\n",
       "      <td>74</td>\n",
       "      <td>2018/02/02</td>\n",
       "      <td>0.77</td>\n",
       "      <td>124.810255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        name  height_cm  weight_kg date_of_birth  height_m  \\\n",
       "breed        color                                                           \n",
       " Chihuahua    Tan     Stella         18          2    2015/04/20      0.18   \n",
       " Chow         Brown     Lucy         46         22    2014/08/25      0.46   \n",
       " Labrador     Black      Max         59         29    2017/01/20      0.59   \n",
       "              Brown    Bella         56         25    2013/07/01      0.56   \n",
       " Poodle       Black  Charlie         43         23    2016/09/16      0.43   \n",
       " Schnauzer    Gray    Cooper         49         17    2011/12/11      0.49   \n",
       " St. Bernard  White   Bernie         77         74    2018/02/02      0.77   \n",
       "\n",
       "                            bmi  \n",
       "breed        color               \n",
       " Chihuahua    Tan     61.728395  \n",
       " Chow         Brown  103.969754  \n",
       " Labrador     Black   83.309394  \n",
       "              Brown   79.719388  \n",
       " Poodle       Black  124.391563  \n",
       " Schnauzer    Gray    70.803832  \n",
       " St. Bernard  White  124.810255  "
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs_ind3.sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27e17d6a",
   "metadata": {},
   "source": [
    "#### Controlling sort_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "id": "e39b0aa8",
   "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></th>\n",
       "      <th>name</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>height_m</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>breed</th>\n",
       "      <th>color</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>Poodle</th>\n",
       "      <th>Black</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>43</td>\n",
       "      <td>23</td>\n",
       "      <td>2016/09/16</td>\n",
       "      <td>0.43</td>\n",
       "      <td>124.391563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Labrador</th>\n",
       "      <th>Black</th>\n",
       "      <td>Max</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>2017/01/20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>83.309394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brown</th>\n",
       "      <td>Bella</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013/07/01</td>\n",
       "      <td>0.56</td>\n",
       "      <td>79.719388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chow</th>\n",
       "      <th>Brown</th>\n",
       "      <td>Lucy</td>\n",
       "      <td>46</td>\n",
       "      <td>22</td>\n",
       "      <td>2014/08/25</td>\n",
       "      <td>0.46</td>\n",
       "      <td>103.969754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schnauzer</th>\n",
       "      <th>Gray</th>\n",
       "      <td>Cooper</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>2011/12/11</td>\n",
       "      <td>0.49</td>\n",
       "      <td>70.803832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chihuahua</th>\n",
       "      <th>Tan</th>\n",
       "      <td>Stella</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2015/04/20</td>\n",
       "      <td>0.18</td>\n",
       "      <td>61.728395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>St. Bernard</th>\n",
       "      <th>White</th>\n",
       "      <td>Bernie</td>\n",
       "      <td>77</td>\n",
       "      <td>74</td>\n",
       "      <td>2018/02/02</td>\n",
       "      <td>0.77</td>\n",
       "      <td>124.810255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        name  height_cm  weight_kg date_of_birth  height_m  \\\n",
       "breed        color                                                           \n",
       " Poodle       Black  Charlie         43         23    2016/09/16      0.43   \n",
       " Labrador     Black      Max         59         29    2017/01/20      0.59   \n",
       "              Brown    Bella         56         25    2013/07/01      0.56   \n",
       " Chow         Brown     Lucy         46         22    2014/08/25      0.46   \n",
       " Schnauzer    Gray    Cooper         49         17    2011/12/11      0.49   \n",
       " Chihuahua    Tan     Stella         18          2    2015/04/20      0.18   \n",
       " St. Bernard  White   Bernie         77         74    2018/02/02      0.77   \n",
       "\n",
       "                            bmi  \n",
       "breed        color               \n",
       " Poodle       Black  124.391563  \n",
       " Labrador     Black   83.309394  \n",
       "              Brown   79.719388  \n",
       " Chow         Brown  103.969754  \n",
       " Schnauzer    Gray    70.803832  \n",
       " Chihuahua    Tan     61.728395  \n",
       " St. Bernard  White  124.810255  "
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs_ind3.sort_index(level=[\"color\",\"breed\"], ascending=[True, False])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fd848fd",
   "metadata": {},
   "source": [
    "#### Now you have two problems\n",
    "\n",
    "- Index values are just data\n",
    "- Indexes violate \"tidy data\" principles\n",
    "- You need to learn two syntaxes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a06a087",
   "metadata": {},
   "source": [
    "# Slicing and subsetting with .loc and .iloc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5ea42c7",
   "metadata": {},
   "source": [
    "### Slicing lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "86ad8953",
   "metadata": {},
   "outputs": [],
   "source": [
    "breeds = [\"Labrador\",\"Poodle\",\"Chow Chow\",\"Schnauzer\",\"Labrador\",\"Chihuahua\",\"St. Bernard\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "id": "297c05c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Chow Chow', 'Schnauzer', 'Labrador']"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breeds[2:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "id": "76890d46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Labrador', 'Poodle', 'Chow Chow']"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breeds[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "id": "bb59a01f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Labrador',\n",
       " 'Poodle',\n",
       " 'Chow Chow',\n",
       " 'Schnauzer',\n",
       " 'Labrador',\n",
       " 'Chihuahua',\n",
       " 'St. Bernard']"
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breeds[:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d7bea5c",
   "metadata": {},
   "source": [
    "#### Sort the index before you slice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "id": "b1e62a78",
   "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></th>\n",
       "      <th>name</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>height_m</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>breed</th>\n",
       "      <th>color</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>Chihuahua</th>\n",
       "      <th>Tan</th>\n",
       "      <td>Stella</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2015/04/20</td>\n",
       "      <td>0.18</td>\n",
       "      <td>61.728395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chow</th>\n",
       "      <th>Brown</th>\n",
       "      <td>Lucy</td>\n",
       "      <td>46</td>\n",
       "      <td>22</td>\n",
       "      <td>2014/08/25</td>\n",
       "      <td>0.46</td>\n",
       "      <td>103.969754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Labrador</th>\n",
       "      <th>Black</th>\n",
       "      <td>Max</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>2017/01/20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>83.309394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brown</th>\n",
       "      <td>Bella</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013/07/01</td>\n",
       "      <td>0.56</td>\n",
       "      <td>79.719388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poodle</th>\n",
       "      <th>Black</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>43</td>\n",
       "      <td>23</td>\n",
       "      <td>2016/09/16</td>\n",
       "      <td>0.43</td>\n",
       "      <td>124.391563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schnauzer</th>\n",
       "      <th>Gray</th>\n",
       "      <td>Cooper</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>2011/12/11</td>\n",
       "      <td>0.49</td>\n",
       "      <td>70.803832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>St. Bernard</th>\n",
       "      <th>White</th>\n",
       "      <td>Bernie</td>\n",
       "      <td>77</td>\n",
       "      <td>74</td>\n",
       "      <td>2018/02/02</td>\n",
       "      <td>0.77</td>\n",
       "      <td>124.810255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        name  height_cm  weight_kg date_of_birth  height_m  \\\n",
       "breed        color                                                           \n",
       " Chihuahua    Tan     Stella         18          2    2015/04/20      0.18   \n",
       " Chow         Brown     Lucy         46         22    2014/08/25      0.46   \n",
       " Labrador     Black      Max         59         29    2017/01/20      0.59   \n",
       "              Brown    Bella         56         25    2013/07/01      0.56   \n",
       " Poodle       Black  Charlie         43         23    2016/09/16      0.43   \n",
       " Schnauzer    Gray    Cooper         49         17    2011/12/11      0.49   \n",
       " St. Bernard  White   Bernie         77         74    2018/02/02      0.77   \n",
       "\n",
       "                            bmi  \n",
       "breed        color               \n",
       " Chihuahua    Tan     61.728395  \n",
       " Chow         Brown  103.969754  \n",
       " Labrador     Black   83.309394  \n",
       "              Brown   79.719388  \n",
       " Poodle       Black  124.391563  \n",
       " Schnauzer    Gray    70.803832  \n",
       " St. Bernard  White  124.810255  "
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs_srt = dogs.set_index([\"breed\",\"color\"]).sort_index()\n",
    "dogs_srt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "id": "36ce65d7",
   "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></th>\n",
       "      <th>name</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>height_m</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Labrador</th>\n",
       "      <th>Black</th>\n",
       "      <td>Max</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>2017/01/20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>83.309394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brown</th>\n",
       "      <td>Bella</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013/07/01</td>\n",
       "      <td>0.56</td>\n",
       "      <td>79.719388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poodle</th>\n",
       "      <th>Black</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>43</td>\n",
       "      <td>23</td>\n",
       "      <td>2016/09/16</td>\n",
       "      <td>0.43</td>\n",
       "      <td>124.391563</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     name  height_cm  weight_kg date_of_birth  height_m  \\\n",
       "breed     color                                                           \n",
       " Labrador  Black      Max         59         29    2017/01/20      0.59   \n",
       "           Brown    Bella         56         25    2013/07/01      0.56   \n",
       " Poodle    Black  Charlie         43         23    2016/09/16      0.43   \n",
       "\n",
       "                         bmi  \n",
       "breed     color               \n",
       " Labrador  Black   83.309394  \n",
       "           Brown   79.719388  \n",
       " Poodle    Black  124.391563  "
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs_srt.loc[\" Chow Chow\":\" Poodle\"] # The final value \"Poodle\" is added"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbab483d",
   "metadata": {},
   "source": [
    "#### Slicing the inner index levels badly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "id": "234821bb",
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>height_m</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>breed</th>\n",
       "      <th>color</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [name, height_cm, weight_kg, date_of_birth, height_m, bmi]\n",
       "Index: []"
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs_srt.loc[\" Tan\":\" Grey\"]  # Ans is Empty DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cffb3a96",
   "metadata": {},
   "source": [
    "#### Slicing the inner index levels correctly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "id": "73c92830",
   "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></th>\n",
       "      <th>name</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>height_m</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>breed</th>\n",
       "      <th>color</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>Labrador</th>\n",
       "      <th>Brown</th>\n",
       "      <td>Bella</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013/07/01</td>\n",
       "      <td>0.56</td>\n",
       "      <td>79.719388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poodle</th>\n",
       "      <th>Black</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>43</td>\n",
       "      <td>23</td>\n",
       "      <td>2016/09/16</td>\n",
       "      <td>0.43</td>\n",
       "      <td>124.391563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schnauzer</th>\n",
       "      <th>Gray</th>\n",
       "      <td>Cooper</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>2011/12/11</td>\n",
       "      <td>0.49</td>\n",
       "      <td>70.803832</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      name  height_cm  weight_kg date_of_birth  height_m  \\\n",
       "breed      color                                                           \n",
       " Labrador   Brown    Bella         56         25    2013/07/01      0.56   \n",
       " Poodle     Black  Charlie         43         23    2016/09/16      0.43   \n",
       " Schnauzer  Gray    Cooper         49         17    2011/12/11      0.49   \n",
       "\n",
       "                          bmi  \n",
       "breed      color               \n",
       " Labrador   Brown   79.719388  \n",
       " Poodle     Black  124.391563  \n",
       " Schnauzer  Gray    70.803832  "
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs_srt.loc[(\" Labrador\",\" Brown\"):(\" Schnauzer\",\" Grey\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d117b228",
   "metadata": {},
   "source": [
    "#### Slicing columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "id": "c60fb9c2",
   "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></th>\n",
       "      <th>name</th>\n",
       "      <th>height_cm</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chihuahua</th>\n",
       "      <th>Tan</th>\n",
       "      <td>Stella</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chow</th>\n",
       "      <th>Brown</th>\n",
       "      <td>Lucy</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Labrador</th>\n",
       "      <th>Black</th>\n",
       "      <td>Max</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brown</th>\n",
       "      <td>Bella</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poodle</th>\n",
       "      <th>Black</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schnauzer</th>\n",
       "      <th>Gray</th>\n",
       "      <td>Cooper</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>St. Bernard</th>\n",
       "      <th>White</th>\n",
       "      <td>Bernie</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        name  height_cm\n",
       "breed        color                     \n",
       " Chihuahua    Tan     Stella         18\n",
       " Chow         Brown     Lucy         46\n",
       " Labrador     Black      Max         59\n",
       "              Brown    Bella         56\n",
       " Poodle       Black  Charlie         43\n",
       " Schnauzer    Gray    Cooper         49\n",
       " St. Bernard  White   Bernie         77"
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs_srt.loc[:,\"name\":\"height_cm\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b803fa15",
   "metadata": {},
   "source": [
    "#### Slice twice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "id": "eadd89ff",
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>height_cm</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Labrador</th>\n",
       "      <th>Brown</th>\n",
       "      <td>Bella</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poodle</th>\n",
       "      <th>Black</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schnauzer</th>\n",
       "      <th>Gray</th>\n",
       "      <td>Cooper</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      name  height_cm\n",
       "breed      color                     \n",
       " Labrador   Brown    Bella         56\n",
       " Poodle     Black  Charlie         43\n",
       " Schnauzer  Gray    Cooper         49"
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs_srt.loc[(\" Labrador\",\" Brown\"):(\" Schnauzer\",\" Grey\"),\"name\":\"height_cm\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22551ca2",
   "metadata": {},
   "source": [
    "#### Dog days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "id": "e6a75935",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>height_m</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_of_birth</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>2011/12/11</th>\n",
       "      <td>Cooper</td>\n",
       "      <td>Schnauzer</td>\n",
       "      <td>Gray</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>0.49</td>\n",
       "      <td>70.803832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013/07/01</th>\n",
       "      <td>Bella</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Brown</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>0.56</td>\n",
       "      <td>79.719388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014/08/25</th>\n",
       "      <td>Lucy</td>\n",
       "      <td>Chow</td>\n",
       "      <td>Brown</td>\n",
       "      <td>46</td>\n",
       "      <td>22</td>\n",
       "      <td>0.46</td>\n",
       "      <td>103.969754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015/04/20</th>\n",
       "      <td>Stella</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>Tan</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>0.18</td>\n",
       "      <td>61.728395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016/09/16</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>Poodle</td>\n",
       "      <td>Black</td>\n",
       "      <td>43</td>\n",
       "      <td>23</td>\n",
       "      <td>0.43</td>\n",
       "      <td>124.391563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017/01/20</th>\n",
       "      <td>Max</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Black</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>0.59</td>\n",
       "      <td>83.309394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/02/02</th>\n",
       "      <td>Bernie</td>\n",
       "      <td>St. Bernard</td>\n",
       "      <td>White</td>\n",
       "      <td>77</td>\n",
       "      <td>74</td>\n",
       "      <td>0.77</td>\n",
       "      <td>124.810255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  name         breed   color  height_cm  weight_kg  height_m  \\\n",
       "date_of_birth                                                                  \n",
       "2011/12/11      Cooper     Schnauzer    Gray         49         17      0.49   \n",
       "2013/07/01       Bella      Labrador   Brown         56         25      0.56   \n",
       "2014/08/25        Lucy          Chow   Brown         46         22      0.46   \n",
       "2015/04/20      Stella     Chihuahua     Tan         18          2      0.18   \n",
       "2016/09/16     Charlie        Poodle   Black         43         23      0.43   \n",
       "2017/01/20         Max      Labrador   Black         59         29      0.59   \n",
       "2018/02/02      Bernie   St. Bernard   White         77         74      0.77   \n",
       "\n",
       "                      bmi  \n",
       "date_of_birth              \n",
       "2011/12/11      70.803832  \n",
       "2013/07/01      79.719388  \n",
       "2014/08/25     103.969754  \n",
       "2015/04/20      61.728395  \n",
       "2016/09/16     124.391563  \n",
       "2017/01/20      83.309394  \n",
       "2018/02/02     124.810255  "
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs_1 = dogs.set_index(\"date_of_birth\").sort_index()\n",
    "dogs_1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d699130",
   "metadata": {},
   "source": [
    "#### Slicing by dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "id": "0f37d5c6",
   "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>name</th>\n",
       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>height_m</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_of_birth</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>2014/08/25</th>\n",
       "      <td>Lucy</td>\n",
       "      <td>Chow</td>\n",
       "      <td>Brown</td>\n",
       "      <td>46</td>\n",
       "      <td>22</td>\n",
       "      <td>0.46</td>\n",
       "      <td>103.969754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015/04/20</th>\n",
       "      <td>Stella</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>Tan</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>0.18</td>\n",
       "      <td>61.728395</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 name       breed   color  height_cm  weight_kg  height_m  \\\n",
       "date_of_birth                                                               \n",
       "2014/08/25       Lucy        Chow   Brown         46         22      0.46   \n",
       "2015/04/20     Stella   Chihuahua     Tan         18          2      0.18   \n",
       "\n",
       "                      bmi  \n",
       "date_of_birth              \n",
       "2014/08/25     103.969754  \n",
       "2015/04/20      61.728395  "
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get dogs with date_of_birth between 2014-08-25 and 2016-09-16\n",
    "dogs_1.loc[\"2014-08-25\":\"2016-09-16\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "446ce945",
   "metadata": {},
   "source": [
    "#### Slicing by partial dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "id": "7d6837a1",
   "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>name</th>\n",
       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>height_m</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_of_birth</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>2014/08/25</th>\n",
       "      <td>Lucy</td>\n",
       "      <td>Chow</td>\n",
       "      <td>Brown</td>\n",
       "      <td>46</td>\n",
       "      <td>22</td>\n",
       "      <td>0.46</td>\n",
       "      <td>103.969754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015/04/20</th>\n",
       "      <td>Stella</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>Tan</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>0.18</td>\n",
       "      <td>61.728395</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 name       breed   color  height_cm  weight_kg  height_m  \\\n",
       "date_of_birth                                                               \n",
       "2014/08/25       Lucy        Chow   Brown         46         22      0.46   \n",
       "2015/04/20     Stella   Chihuahua     Tan         18          2      0.18   \n",
       "\n",
       "                      bmi  \n",
       "date_of_birth              \n",
       "2014/08/25     103.969754  \n",
       "2015/04/20      61.728395  "
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get dogs with date_of_birth between 2014-01-01 and 2016-12-31\n",
    "dogs_1.loc[\"2014\":\"2016\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c489474f",
   "metadata": {},
   "source": [
    "### Subsetting by row/column number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "956fcdab",
   "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>breed</th>\n",
       "      <th>color</th>\n",
       "      <th>height_cm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chow</td>\n",
       "      <td>Brown</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Schnauzer</td>\n",
       "      <td>Gray</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Labrador</td>\n",
       "      <td>Black</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        breed   color  height_cm\n",
       "2        Chow   Brown         46\n",
       "3   Schnauzer    Gray         49\n",
       "4    Labrador   Black         59"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.iloc[2:5, 1:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "113c9bb7",
   "metadata": {},
   "source": [
    "## Working with pivot tables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f178663",
   "metadata": {},
   "source": [
    "#### datset review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a7b92bca",
   "metadata": {},
   "outputs": [],
   "source": [
    "dog_pack = dogs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "523951c1",
   "metadata": {},
   "source": [
    "#### Pivoting the dog pack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1414386a",
   "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>color</th>\n",
       "      <th>Black</th>\n",
       "      <th>Brown</th>\n",
       "      <th>Gray</th>\n",
       "      <th>Tan</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>breed</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>Chihuahua</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chow</th>\n",
       "      <td>NaN</td>\n",
       "      <td>46.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Labrador</th>\n",
       "      <td>59.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poodle</th>\n",
       "      <td>43.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schnauzer</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>St. Bernard</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "color          Black   Brown   Gray   Tan   White\n",
       "breed                                            \n",
       " Chihuahua       NaN     NaN    NaN  18.0     NaN\n",
       " Chow            NaN    46.0    NaN   NaN     NaN\n",
       " Labrador       59.0    56.0    NaN   NaN     NaN\n",
       " Poodle         43.0     NaN    NaN   NaN     NaN\n",
       " Schnauzer       NaN     NaN   49.0   NaN     NaN\n",
       " St. Bernard     NaN     NaN    NaN   NaN    77.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs_height_by_breed_vs_color = dog_pack.pivot_table(\"height_cm\", index=\"breed\", columns=\"color\")\n",
    "dogs_height_by_breed_vs_color"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e7af9fc",
   "metadata": {},
   "source": [
    "#### .loc[] + slicing is a power combo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6a58783e",
   "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>color</th>\n",
       "      <th>Black</th>\n",
       "      <th>Brown</th>\n",
       "      <th>Gray</th>\n",
       "      <th>Tan</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>breed</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>Chow</th>\n",
       "      <td>NaN</td>\n",
       "      <td>46.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Labrador</th>\n",
       "      <td>59.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poodle</th>\n",
       "      <td>43.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "color       Black   Brown   Gray   Tan   White\n",
       "breed                                         \n",
       " Chow         NaN    46.0    NaN   NaN     NaN\n",
       " Labrador    59.0    56.0    NaN   NaN     NaN\n",
       " Poodle      43.0     NaN    NaN   NaN     NaN"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs_height_by_breed_vs_color.loc[\" Chow\":\" Poodle\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23f92146",
   "metadata": {},
   "source": [
    "#### The axis argument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8dee9860",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color\n",
       " Black    51.0\n",
       " Brown    51.0\n",
       " Gray     49.0\n",
       " Tan      18.0\n",
       " White    77.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs_height_by_breed_vs_color.mean(axis = 'index')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99206908",
   "metadata": {},
   "source": [
    "#### Calculating summary stats across columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7e9d5ee3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "breed\n",
       " Chihuahua      18.0\n",
       " Chow           46.0\n",
       " Labrador       57.5\n",
       " Poodle         43.0\n",
       " Schnauzer      49.0\n",
       " St. Bernard    77.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs_height_by_breed_vs_color.mean(axis = 'columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bb68a3a",
   "metadata": {},
   "source": [
    "# Visualizing your data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "49c05d3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "40af160d",
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic = pd.read_csv('./dataset/titanic.csv')\n",
    "titanic.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c1a11a7",
   "metadata": {},
   "source": [
    "### Histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "77cb8ac2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic['Age'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "511c3ca1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XdB0T3YZsnwO+lplvBt5K53s31FyZeaj6Pl0DrAP+F/jHYeeKiDXAJ4CJzLyazsEmGxvLlZlL6gt4J/D1ruk7gTuHnGkMONA1fQhYXd1fDRxqwfftATrX+mlNNuBi4DHgHW3IRed8jD3Ae4AH2/SzBI4Al542b6jZgDcAz1AdmNGWXKdl+R3g39uQC1gDfB9YRedIxQerfI3kWnIjd175Bp1ytJrXJqOZeQygun3TMMNExBjwNuBRWpCt2vXxBHAC2J2ZrcgF/BXwJ8D/dc1rQy7onOH9cETsqy7d0YZsvwH8EPi7alfWFyJieQtyddsI3FvdH2quzHwO+EvgWeAY8GJmPtxUrqVY7ue9tIFeEREjwD8An8zMnww7D0Bm/iI7fzJfBlwbEVcPORIR8UHgRGbuG3aWeVyfmW+nszvy9oh497AD0Rl9vh3428x8G/ASw91t9UuqEyg/BHx12FkAqn3pG4ArgF8DlkfELU1tbymW+1K4tMHxiFgNUN2eGEaIiHgNnWL/Smbe36ZsAJl5Epih857FsHNdD3woIo7QuYrpeyLiyy3IBUBmPl/dnqCz//jaFmQ7Chyt/vIC2Emn7Ied65T3A49l5vFqeti53gs8k5k/zMyfA/cD72oq11Is96VwaYNdwKbq/iY6+7sXVUQEcA9wMDM/25ZsEfHGiFhR3V9G5wX/3WHnysw7M/OyzByj85r6RmbeMuxcABGxPCJef+o+nf20B4adLTN/AHw/Iq6sZq0Hnhp2ri4f5pVdMjD8XM8C10XExdX/z/V03oBuJtew3uio+cbEB4D/BP4L+LMhZ7mXzv6zn9MZydwG/CqdN+YOV7erhpDrt+nsrvoP4Inq6wPDzgb8FvB4lesA8OfV/KF/z7oyTvLKG6pDz0Vn3/Z3qq8nT73mW5LtGmBv9fP8J2BlS3JdDPwYuKRrXhtyfYbOYOYA8PfA65rK5eUHJKlAS3G3jCTpPCx3SSqQ5S5JBbLcJalAlrskFchyl6QCWe6SVKD/B6CguLGiv3utAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic['Age'].hist(bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "4503b431",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAVAUlEQVR4nO3df6yV92Hf8fcn2HMc38zGs3tFAA1PpVlts5ByRd15mu6105o6U0m0ZcJyIlA9kT+olmxIK3TS6qhC8qQ42SQn0cjwgurUd8xJZmTXbV3mqyjTPGpcJ4AJMyvIBTxoU2znZhbLJZ/9cR7qE7iX8+s+nIcvn5d0dM7zfZ7vOZ977+Fzn/uc5xxkm4iIKMt7hh0gIiLmX8o9IqJAKfeIiAKl3CMiCpRyj4go0DXDDgBwyy23eNmyZT3P+9GPfsQNN9ww/4EGlFy9a2q25OpNU3NBc7MNkmvfvn1/afvWWVfaHvpl1apV7scLL7zQ17y6JVfvmpotuXrT1Fx2c7MNkgt4yXP0ag7LREQUKOUeEVGglHtERIE6lruk90raK+m7kg5K+lw1/rCkE5JeqS73t83ZKumIpMOS7qvzC4iIiIt1c7bMWeAe29OSrgW+I+m5at0XbX++fWNJtwPrgDuADwB/LOnnbJ+bz+ARETG3jnvu1Yuy09XitdXlUp82thaYtH3W9lHgCLB64KQREdE1uYtPhZS0ANgH/CzwJdu/KelhYAPwNvASsNn2GUmPAS/afqKauwN4zvZTF9znRmAjwOjo6KrJycmew09PTzMyMtLzvLolV++ami25etPUXNDcbIPkmpiY2Gd7bNaVc50jOdsFuAl4AbgTGAUW0Nr73wY8Xm3zJeCTbXN2AP/4Uveb89wvj6bmspubLbl609RcdnOzNeI8d9tvAlPAGtunbJ+z/RPgq7x76OU4sLRt2hLgZC+PExERg+n4gqqkW4Ef235T0vXAR4B/K2mR7TeqzT4OHKhu7wZ+T9IXaL2guhzYO//Rr17Ltjzb17zNK2bY0OfcunXKduyRj17GNBFXvm7OllkE7KyOu78H2GX7GUm/K2klrRdXjwGfBrB9UNIu4FVgBtjknCkTEXFZdSx3298DPjzL+KcuMWcbrePwERExBHmHakREgVLuEREFSrlHRBQo5R4RUaCUe0REgVLuEREFSrlHRBQo5R4RUaCUe0REgVLuEREFSrlHRBQo5R4RUaCUe0REgVLuEREFSrlHRBQo5R4RUaCUe0REgVLuEREFSrlHRBQo5R4RUaCUe0REgTqWu6T3Stor6buSDkr6XDV+s6TnJb1WXS9sm7NV0hFJhyXdV+cXEBERF+tmz/0scI/tDwErgTWS7gK2AHtsLwf2VMtIuh1YB9wBrAG+LGlBDdkjImIOHcvdLdPV4rXVxcBaYGc1vhP4WHV7LTBp+6zto8ARYPV8ho6IiEuT7c4btfa89wE/C3zJ9m9KetP2TW3bnLG9UNJjwIu2n6jGdwDP2X7qgvvcCGwEGB0dXTU5Odlz+OnpaUZGRnqeV7e6c+0/8VZf80avh1PvzHOYedIp24rFN16+MG2u1udYv5qaC5qbbZBcExMT+2yPzbbumm7uwPY5YKWkm4BvSbrzEptrtruY5T63A9sBxsbGPD4+3k2UnzI1NUU/8+pWd64NW57ta97mFTM8ur+rH/ll1ynbsQfHL1+YNlfrc6xfTc0Fzc1WV66ezpax/SYwRetY+ilJiwCq69PVZseBpW3TlgAnBw0aERHd6+ZsmVurPXYkXQ98BPg+sBtYX222Hni6ur0bWCfpOkm3AcuBvfOcOyIiLqGbv9EXATur4+7vAXbZfkbS/wB2SXoIeB34BIDtg5J2Aa8CM8Cm6rBORERcJh3L3fb3gA/PMv4D4N455mwDtg2cLiIi+pJ3qEZEFCjlHhFRoJR7RESBUu4REQVKuUdEFCjlHhFRoJR7RESBUu4REQVKuUdEFCjlHhFRoJR7RESBUu4REQVKuUdEFCjlHhFRoJR7RESBUu4REQVKuUdEFCjlHhFRoJR7RESBUu4REQVKuUdEFKhjuUtaKukFSYckHZT0mWr8YUknJL1SXe5vm7NV0hFJhyXdV+cXEBERF7umi21mgM22X5b0fmCfpOerdV+0/fn2jSXdDqwD7gA+APyxpJ+zfW4+g0dExNw67rnbfsP2y9XtHwKHgMWXmLIWmLR91vZR4Aiwej7CRkREd2S7+42lZcC3gTuBfwlsAN4GXqK1d39G0mPAi7afqObsAJ6z/dQF97UR2AgwOjq6anJysufw09PTjIyM9DyvbnXn2n/irb7mjV4Pp96Z5zDzpFO2FYtvvHxh2lytz7F+NTUXNDfbILkmJib22R6bbV03h2UAkDQCfAP4rO23JX0F+B3A1fWjwK8DmmX6Rb9BbG8HtgOMjY15fHy82yh/bWpqin7m1a3uXBu2PNvXvM0rZnh0f9c/8suqU7ZjD45fvjBtrtbnWL+amguam62uXF2dLSPpWlrF/nXb3wSwfcr2Ods/Ab7Ku4dejgNL26YvAU7OX+SIiOikm7NlBOwADtn+Qtv4orbNPg4cqG7vBtZJuk7SbcByYO/8RY6IiE66+Rv9buBTwH5Jr1RjvwU8IGklrUMux4BPA9g+KGkX8CqtM2025UyZiIjLq2O52/4Osx9H//1LzNkGbBsgV0REDCDvUI2IKFDKPSKiQCn3iIgCpdwjIgqUco+IKFDKPSKiQCn3iIgCpdwjIgqUco+IKFDKPSKiQCn3iIgCpdwjIgqUco+IKFDKPSKiQCn3iIgCpdwjIgqUco+IKFDKPSKiQCn3iIgCpdwjIgqUco+IKFDHcpe0VNILkg5JOijpM9X4zZKel/Radb2wbc5WSUckHZZ0X51fQEREXKybPfcZYLPtnwfuAjZJuh3YAuyxvRzYUy1TrVsH3AGsAb4saUEd4SMiYnYdy932G7Zfrm7/EDgELAbWAjurzXYCH6turwUmbZ+1fRQ4Aqye59wREXEJst39xtIy4NvAncDrtm9qW3fG9kJJjwEv2n6iGt8BPGf7qQvuayOwEWB0dHTV5ORkz+Gnp6cZGRnpeV7d6s61/8Rbfc0bvR5OvTPPYeZJp2wrFt94+cK0uVqfY/1qai5obrZBck1MTOyzPTbbumu6vRNJI8A3gM/aflvSnJvOMnbRbxDb24HtAGNjYx4fH+82yl+bmpqin3l1qzvXhi3P9jVv84oZHt3f9Y/8suqU7diD45cvTJur9TnWr6bmguZmqytXV2fLSLqWVrF/3fY3q+FTkhZV6xcBp6vx48DStulLgJPzEzciIrrRzdkyAnYAh2x/oW3VbmB9dXs98HTb+DpJ10m6DVgO7J2/yBER0Uk3f6PfDXwK2C/plWrst4BHgF2SHgJeBz4BYPugpF3Aq7TOtNlk+9x8B4+IiLl1LHfb32H24+gA984xZxuwbYBcERExgLxDNSKiQM08dSLiAsv6PENoUJtXzPR9dtKgjj3y0aE8bpQhe+4REQVKuUdEFCjlHhFRoJR7RESBUu4REQVKuUdEFCjlHhFRoJR7RESBUu4REQVKuUdEFCjlHhFRoJR7RESBUu4REQVKuUdEFCjlHhFRoJR7RESBUu4REQVKuUdEFCjlHhFRoI7lLulxSaclHWgbe1jSCUmvVJf729ZtlXRE0mFJ99UVPCIi5tbNnvvXgDWzjH/R9srq8vsAkm4H1gF3VHO+LGnBfIWNiIjudCx3298G/qrL+1sLTNo+a/socARYPUC+iIjog2x33khaBjxj+85q+WFgA/A28BKw2fYZSY8BL9p+otpuB/Cc7admuc+NwEaA0dHRVZOTkz2Hn56eZmRkpOd5das71/4Tb/U1b/R6OPXOPIeZJ03NNsxcKxbfOOe6q/W5P4imZhsk18TExD7bY7Otu6bPPF8Bfgdwdf0o8OuAZtl21t8etrcD2wHGxsY8Pj7ec4ipqSn6mVe3unNt2PJsX/M2r5jh0f39/sjr1dRsw8x17MHxOdddrc/9QTQ1W125+jpbxvYp2+ds/wT4Ku8eejkOLG3bdAlwcrCIERHRq77KXdKitsWPA+fPpNkNrJN0naTbgOXA3sEiRkRErzr+vSnpSWAcuEXSceC3gXFJK2kdcjkGfBrA9kFJu4BXgRlgk+1ztSSPiIg5dSx32w/MMrzjEttvA7YNEioiIgaTd6hGRBQo5R4RUaCUe0REgVLuEREFSrlHRBQo5R4RUaCUe0REgVLuEREFSrlHRBQo5R4RUaCUe0REgVLuEREFSrlHRBQo5R4RUaCUe0REgVLuEREFSrlHRBQo5R4RUaCUe0REgVLuEREFSrlHRBSoY7lLelzSaUkH2sZulvS8pNeq64Vt67ZKOiLpsKT76goeERFz62bP/WvAmgvGtgB7bC8H9lTLSLodWAfcUc35sqQF85Y2IiK60rHcbX8b+KsLhtcCO6vbO4GPtY1P2j5r+yhwBFg9P1EjIqJbst15I2kZ8IztO6vlN23f1Lb+jO2Fkh4DXrT9RDW+A3jO9lOz3OdGYCPA6OjoqsnJyZ7DT09PMzIywv4Tb/U8t06j18Opd4ad4mJNzQXNzTbMXCsW3zjnuvPP/aZpai5obrZBck1MTOyzPTbbumsGSnUxzTI2628P29uB7QBjY2MeHx/v+cGmpqYYHx9nw5Zne55bp80rZnh0/3x/awfX1FzQ3GzDzHXswfE5151/7jdNU3NBc7PVlavfs2VOSVoEUF2frsaPA0vbtlsCnOw/XkRE9KPfct8NrK9urweebhtfJ+k6SbcBy4G9g0WMiIhedfx7U9KTwDhwi6TjwG8DjwC7JD0EvA58AsD2QUm7gFeBGWCT7XM1ZY+IiDl0LHfbD8yx6t45tt8GbBskVEREDCbvUI2IKFDKPSKiQCn3iIgCpdwjIgqUco+IKFDKPSKiQCn3iIgCpdwjIgqUco+IKFDKPSKiQCn3iIgCpdwjIgqUco+IKFDz/uubiABg2SX+h7HNK2Ya9z+QweC5jj3y0XlMc3XLnntERIFS7hERBUq5R0QUKOUeEVGglHtERIFS7hERBUq5R0QUaKDz3CUdA34InANmbI9Juhn4z8Ay4BjwT22fGSxmRET0Yj723Cdsr7Q9Vi1vAfbYXg7sqZYjIuIyquOwzFpgZ3V7J/CxGh4jIiIuQbb7nywdBc4ABv6D7e2S3rR9U9s2Z2wvnGXuRmAjwOjo6KrJycmeH396epqRkRH2n3ir3y+hFqPXw6l3hp3iYk3NBc3Nlly9GTTXisU3zl+YC5zvi6YZJNfExMS+tqMmP2XQz5a52/ZJST8DPC/p+91OtL0d2A4wNjbm8fHxnh98amqK8fHxxn3GxuYVMzy6v3kf29PUXNDcbMnVm0FzHXtwfP7CXOB8XzRNXbkGOixj+2R1fRr4FrAaOCVpEUB1fXrQkBER0Zu+y13SDZLef/428CvAAWA3sL7abD3w9KAhIyKiN4P8XTcKfEvS+fv5Pdt/IOlPgF2SHgJeBz4xeMyIiOhF3+Vu+8+AD80y/gPg3kFCRUTEYPIO1YiIAqXcIyIKlHKPiChQyj0iokAp94iIAqXcIyIKlHKPiChQyj0iokAp94iIAqXcIyIKlHKPiChQyj0iokAp94iIAqXcIyIKlHKPiChQyj0iokAp94iIAjXvv0+PiKvWsi3P1nbfm1fMsKHG++/X19bcUMv9Zs89IqJAKfeIiAKl3CMiClRbuUtaI+mwpCOSttT1OBERcbFayl3SAuBLwK8CtwMPSLq9jseKiIiL1bXnvho4YvvPbP8/YBJYW9NjRUTEBWR7/u9U+ifAGtv/rFr+FPCLtn+jbZuNwMZq8YPA4T4e6hbgLweMW4fk6l1TsyVXb5qaC5qbbZBcf9v2rbOtqOs8d80y9lO/RWxvB7YP9CDSS7bHBrmPOiRX75qaLbl609Rc0NxsdeWq67DMcWBp2/IS4GRNjxUREReoq9z/BFgu6TZJfwNYB+yu6bEiIuICtRyWsT0j6TeAPwQWAI/bPljDQw10WKdGydW7pmZLrt40NRc0N1stuWp5QTUiIoYr71CNiChQyj0iokBXZLk36aMNJD0u6bSkA21jN0t6XtJr1fXCIeRaKukFSYckHZT0mSZkk/ReSXslfbfK9bkm5GrLt0DSn0p6pmG5jknaL+kVSS81JZukmyQ9Jen71XPtl4adS9IHq+/T+cvbkj477FxVtn9RPe8PSHqy+vdQS64rrtwb+NEGXwPWXDC2Bdhjezmwp1q+3GaAzbZ/HrgL2FR9n4ad7Sxwj+0PASuBNZLuakCu8z4DHGpbbkougAnbK9vOiW5Ctn8P/IHtvwt8iNb3bqi5bB+uvk8rgVXA/wW+NexckhYD/xwYs30nrZNN1tWWy/YVdQF+CfjDtuWtwNYhZ1oGHGhbPgwsqm4vAg434Pv2NPDLTcoGvA94GfjFJuSi9X6MPcA9wDNN+lkCx4BbLhgbajbgbwJHqU7MaEquC7L8CvDfm5ALWAz8OXAzrTMVn6ny1ZLrittz591v0HnHq7EmGbX9BkB1/TPDDCNpGfBh4H/SgGzVoY9XgNPA87YbkQv4d8C/An7SNtaEXNB6h/cfSdpXfXRHE7L9HeAvgP9UHcr6j5JuaECuduuAJ6vbQ81l+wTweeB14A3gLdt/VFeuK7HcO360QbxL0gjwDeCztt8edh4A2+fc+pN5CbBa0p1DjoSkfwSctr1v2FnmcLftX6B1OHKTpH847EC09j5/AfiK7Q8DP2K4h61+SvUGyl8D/suwswBUx9LXArcBHwBukPTJuh7vSiz3K+GjDU5JWgRQXZ8eRghJ19Iq9q/b/maTsgHYfhOYovWaxbBz3Q38mqRjtD7F9B5JTzQgFwC2T1bXp2kdP17dgGzHgePVX14AT9Eq+2HnOu9XgZdtn6qWh53rI8BR239h+8fAN4G/X1euK7Hcr4SPNtgNrK9ur6d1vPuykiRgB3DI9heakk3SrZJuqm5fT+sJ//1h57K91fYS28toPaf+m+1PDjsXgKQbJL3//G1ax2kPDDub7f8D/LmkD1ZD9wKvDjtXmwd495AMDD/X68Bdkt5X/fu8l9YL0PXkGtYLHQO+MHE/8L+A/w386yFneZLW8bMf09qTeQj4W7RemHutur55CLn+Aa3DVd8DXqku9w87G/D3gD+tch0A/k01PvTvWVvGcd59QXXouWgd2/5udTl4/jnfkGwrgZeqn+d/BRY2JNf7gB8AN7aNNSHX52jtzBwAfhe4rq5c+fiBiIgCXYmHZSIiooOUe0REgVLuEREFSrlHRBQo5R4RUaCUe0REgVLuEREF+v/+WoP0PpNSbwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic['Age'].hist(bins=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c107d292",
   "metadata": {},
   "source": [
    "### Bar plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d19e2f70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    261\n",
       "male      453\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_count = titanic.groupby('Sex')['Age'].count()\n",
    "gender_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "ae239fce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0pCV5BvAp4FzgH4APMfPRDZ8E1lfVdJKfBK6qqjcn+XHg14HfB36mqvxFFDoleVhGS1pVPZbkCuDlzFzl+yHgN4HnAbcnAVgGHBi2vz3JNcAfAS+YyNDSCNxzl2ZJ8gbgeuCcqnrpHM+fwcxe/aXAq6vqrpM8ojQSj7lrSUvy3OGTNI+4HLgHmBp+2EqSs5JcNjz/9uH5a4H3JznrZM4rjco9dy1pwyGZPwSeBRwG9gGbgBXAHwDPZObw5XuZ2WO/FVhbVY8meQ/waFW96+RPLh2fcZekhjwsI0kNGXdJasi4S1JDxl2SGjLuktSQcZekhoy7JDVk3CWpof8D58F+uuWwxe8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender_count.plot(kind= 'bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "4ac56406",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender_count.plot(kind= 'bar', title= 'Male & female Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81a4202a",
   "metadata": {},
   "source": [
    "### Line plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "bfe6fd99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7      2.0\n",
       "8     27.0\n",
       "9     14.0\n",
       "10     4.0\n",
       "11    58.0\n",
       "12    20.0\n",
       "13    39.0\n",
       "14    14.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ten_ages = titanic.Age.iloc[7:15]\n",
    "ten_ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "8b09077c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ten_ages.plot(x = ten_ages.index, y= ten_ages.values , kind= 'line') # < -- rotating x_labe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "087cf818",
   "metadata": {},
   "source": [
    "### Rotating axis labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "10a14945",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ten_ages.plot(x = ten_ages.index, y= ten_ages.values , kind= 'line', rot= 60)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a10b67ee",
   "metadata": {},
   "source": [
    "- Best example can be "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "90a4e69a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date_of_birth'>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dog_pack.plot(x='date_of_birth', y = \"height_cm\", kind = 'line' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f07ebd5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date_of_birth'>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dog_pack.plot(x='date_of_birth', y = \"height_cm\", kind = 'line' , rot = 45)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b18a3e51",
   "metadata": {},
   "source": [
    "### Scatter plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "07953748",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic.plot(x = 'Age', y= 'PassengerId' , kind= 'scatter')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa423cd2",
   "metadata": {},
   "source": [
    "#### Layering plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "b009bb6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic[ titanic['Sex'] ==  \"female\"]['Fare'].hist() # < -- Blue Female\n",
    "titanic[ titanic['Sex'] ==  \"male\"]['Fare'].hist() # <-- orange male\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9365342b",
   "metadata": {},
   "source": [
    "#### Add a legend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "1e8ff59c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAWJElEQVR4nO3df4xV5Z3H8fcHRAadWeSHnUwZ02G7LFUUpYw/qpv2ol1BbYQmusHVZhpt+KM21W5bi91atQ1Zd2ONpi1pSW0ktTJLtBSicStSb8gmuiDFWhAptM6yU6hjUSlDGJah3/1jDuwFZpjLnblzZ577eSWTe85zznPP873Rz30499xzFRGYmVlaRlV6AGZmNvgc7mZmCXK4m5klyOFuZpYgh7uZWYLOqPQAACZPnhxNTU0l9z9w4ABnn3324A1oGHOt6aqmequpVihfvZs2bfpTRJzb27ZhEe5NTU28+uqrJffP5/PkcrnBG9Aw5lrTVU31VlOtUL56Jf13X9t8WsbMLEEOdzOzBDnczcwSNCzOuZuZ9eXw4cO0t7fT1dVV6aGUbPz48Wzbtq3k/jU1NTQ2NjJmzJii+zjczWxYa29vp66ujqamJiRVejgl2b9/P3V1dSX1jQj27t1Le3s7U6dOLbqfT8uY2bDW1dXFpEmTRmywD5QkJk2adNr/cnG4m9mwV63BflQp9TvczcwSVNQ5d0ltwH7gCNAdEc2SJgL/DjQBbcA/RMR72f73Andk+38xIn4x6CM3s6rUtPi5QX2+todu6Hef0aNHc9FFFx1b//nPf85AvlV/Kke/1Dl58uQBPc/pfKA6JyL+VLC+GFgXEQ9JWpytf03SBcBCYAbwQeBFSX8bEUcGNNJT2fMaPDC/bE/fpwf2Df0xzWzIjRs3jtdee63SwzgtAzktMx9Yni0vBxYUtLdGxKGIeAvYCVw2gOOYmQ07mzZt4hOf+ASzZ89m7ty57NmzB4BcLseXvvQlPv7xj3P++eezceNGbr31VqZNm8Y3vvGNY/0XLFjA7NmzmTFjBsuWLRv08RU7cw/gBUkB/DAilgH1EbEHICL2SPpAtu8U4JWCvu1Z23EkLQIWAdTX15PP50urAOgc+0Hy0x8suX/JBjDmUnV2dg7otRpJqqlWqK56T6fW8ePHs3///rKNpZjnPnjwIDNnzgTgQx/6EMuXL+fzn/88ra2tTJ48mWeeeYZ77rmHpUuXcuRIz0mK5557jqVLl3LjjTfy0ksvMXnyZC6++GI+97nPMWnSJB577DEmTpzIwYMHyeVyXHvttUyaNImIoLOzk7Fjxx43hq6urtP676PYcL8qInZnAb5W0pun2Le3j3VP+qHW7A1iGUBzc3MM5KY6+RWPktt+f8n9S3bL0J+WqaYbLlVTrVBd9Z5Ordu2bSv5GvFiFPPc48aN4/XXXz+2vmXLFrZt28anP/1pAI4cOUJDQwN1dXWMHj2am266ibq6Oi699FIuvPBCpkyZQl1dHR/+8Id5//33aWpq4jvf+Q6rVq0C4A9/+AN//OMfj13LX1tbe9K4ampqmDVrVtF1FRXuEbE7e+yQtIqe0yxvS2rIZu0NQEe2eztwXkH3RmB30SMyMxvmIoIZM2bw8ssv97r96Kx71KhRx83AR40aRXd3N/l8nhdffJGXX36Zs846i1wuN+jfwO33nLuksyXVHV0GrgW2AGuAlmy3FmB1trwGWChprKSpwDRgw6CO2sysgqZPn84777xzLNwPHz7M1q1bi+6/b98+JkyYwFlnncWbb77JK6+80n+n01TMzL0eWJVdRH8G8FRE/IekjcBKSXcAu4CbASJiq6SVwBtAN3BnWa+UMbOqUsyli+V25pln8vTTT/PFL36Rffv20d3dzd13382MGTOK6j9v3jx+8IMfMHPmTKZPn84VV1wx6GPsN9wj4vfAxb207wWu6aPPEmDJgEdnZjYMdHZ2ntR2ySWXsH79+pPaCz/0zOVy5HK5Yx/aFm57/vnnez1WW1vbgMZ6lL+hamaWIIe7mVmCHO5mZglyuJuZJcjhbmaWIIe7mVmC/DN7ZjayPDB+kJ+v/9uISOK2227jJz/5CQDd3d00NDRw+eWX8+yzz/bZL5/P8/DDD7NixYpBG26xPHM3M+vH2WefzZYtWzh48CAAa9euZcqUk+6HOKw43M3MinDdddfx3HM9PxSyYsUKbrnllmPbNmzYwJVXXsmsWbO48sor2b59+0n9Dxw4wO23386ll17KrFmzWL169Un7DCaHu5lZERYuXEhraytdXV28/vrrXH755ce2feQjH2H9+vVs3ryZb33rW3z9618/qf+SJUu4+uqr2bhxIy+99BJf/epXOXDgQNnG63PuZmZFmDlzJm1tbaxYsYLrr7/+uG379u2jpaWFHTt2IInDhw+f1P+FF15gzZo1PPzww0DP/dl37drF+eefX5bxOtzNzIp044038pWvfIV8Ps/evXuPtd93333MmTOHVatW0dbW1uu96iOCZ555hunTpw/JWH1axsysSLfffjvf/OY3j/uxbOiZuR/9gPWJJ57ote/cuXP57ne/S0TPbxdt3ry5rGP1zN3MRpYK/jB9Y2Mjd91110nt99xzDy0tLTzyyCNcffXVvfa97777uPvuu5k5cyYRQVNT0ykvoxwoh7uZWT96u+Xv0dv5AnzsYx/jt7/97bFt3/72t4/bZ//+/YwbN44f/vCHQzJe8GkZM7MkOdzNzBLkcDezYe/oh5DVqpT6He5mNqzV1NSwd+/eqg34iGDv3r3U1NScVj9/oGpmw1pjYyPt7e288847lR5Kybq6uk47nAvV1NTQ2Nh4Wn0c7mY2rI0ZM4apU6dWehgDks/nmTVr1pAe06dlzMwS5HA3M0uQw93MLEEOdzOzBDnczcwS5HA3M0uQw93MLEEOdzOzBDnczcwS5HA3M0uQw93MLEFFh7uk0ZI2S3o2W58oaa2kHdnjhIJ975W0U9J2SXPLMXAzM+vb6czc7wK2FawvBtZFxDRgXbaOpAuAhcAMYB6wVNLowRmumZkVo6hwl9QI3AD8qKB5PrA8W14OLChob42IQxHxFrATuGxQRmtmZkVRMTfAl/Q08C9AHfCViPiUpPcj4pyCfd6LiAmSvge8EhFPZu2PA89HxNMnPOciYBFAfX397NbW1pKL6Hy3g9pDu0vuX7KGS4b8kJ2dndTW1g75cSuhmmqF6qq3mmqF8tU7Z86cTRHR3Nu2fu/nLulTQEdEbJKUK+J46qXtpHeQiFgGLANobm6Oo78iXor8ikfJbb+/5P4lu2XfkB8yn88zkNdqJKmmWqG66q2mWqEy9RbzYx1XATdKuh6oAf5K0pPA25IaImKPpAagI9u/HTivoH8jUIFptZlZ9er3nHtE3BsRjRHRRM8Hpb+MiNuANUBLtlsLsDpbXgMslDRW0lRgGrBh0EduZmZ9GsjP7D0ErJR0B7ALuBkgIrZKWgm8AXQDd0bEkQGP1MzMinZa4R4ReSCfLe8FruljvyXAkgGOzczMSuRvqJqZJcjhbmaWIIe7mVmCHO5mZglyuJuZJcjhbmaWIIe7mVmCHO5mZglyuJuZJcjhbmaWIIe7mVmCHO5mZglyuJuZJcjhbmaWIIe7mVmCHO5mZglyuJuZJcjhbmaWIIe7mVmCHO5mZglyuJuZJcjhbmaWIIe7mVmCHO5mZglyuJuZJcjhbmaWIIe7mVmCHO5mZglyuJuZJcjhbmaWIIe7mVmCHO5mZgnqN9wl1UjaIOnXkrZKejBrnyhpraQd2eOEgj73StopabukueUswMzMTlbMzP0QcHVEXAxcAsyTdAWwGFgXEdOAddk6ki4AFgIzgHnAUkmjyzB2MzPrQ7/hHj06s9Ux2V8A84HlWftyYEG2PB9ojYhDEfEWsBO4bDAHbWZmp6aI6H+nnpn3JuBvgO9HxNckvR8R5xTs815ETJD0PeCViHgya38ceD4inj7hORcBiwDq6+tnt7a2llxE57sd1B7aXXL/kjVcMuSH7OzspLa2dsiPWwnVVCtUV73VVCuUr945c+Zsiojm3radUcwTRMQR4BJJ5wCrJF14it3V21P08pzLgGUAzc3NkcvlihlKr/IrHiW3/f6S+5fsln1Dfsh8Ps9AXquRpJpqheqqt5pqhcrUe1pXy0TE+0CennPpb0tqAMgeO7Ld2oHzCro1AhWYVpuZVa9irpY5N5uxI2kc8EngTWAN0JLt1gKszpbXAAsljZU0FZgGbBjkcZuZ2SkUc1qmAVienXcfBayMiGclvQyslHQHsAu4GSAitkpaCbwBdAN3Zqd1zMxsiPQb7hHxOjCrl/a9wDV99FkCLBnw6MzMrCT+hqqZWYIc7mZmCXK4m5klyOFuZpYgh7uZWYIc7mZmCXK4m5klyOFuZpYgh7uZWYIc7mZmCXK4m5klyOFuZpYgh7uZWYIc7mZmCXK4m5klyOFuZpYgh7uZWYIc7mZmCXK4m5klqJgfyLY+NC1+7rj1toduqNBIzMyO55m7mVmCHO5mZglyuJuZJcjhbmaWIIe7mVmCHO5mZglyuJuZJcjhbmaWIIe7mVmCHO5mZglyuJuZJcjhbmaWoH7DXdJ5kl6StE3SVkl3Ze0TJa2VtCN7nFDQ515JOyVtlzS3nAWYmdnJipm5dwNfjojzgSuAOyVdACwG1kXENGBdtk62bSEwA5gHLJU0uhyDNzOz3vUb7hGxJyJ+lS3vB7YBU4D5wPJst+XAgmx5PtAaEYci4i1gJ3DZII/bzMxOQRFR/M5SE7AeuBDYFRHnFGx7LyImSPoe8EpEPJm1Pw48HxFPn/Bci4BFAPX19bNbW1tLLqLz3Q5qD+0uuX+pfvOXqcetXzRlfNmP2dnZSW1tbdmPMxxUU61QXfVWU61QvnrnzJmzKSKae9tW9I91SKoFngHujog/S+pz117aTnoHiYhlwDKA5ubmyOVyxQ7lJPkVj5Lbfn/J/Uv12a6njltvuzVX9mPm83kG8lqNJNVUK1RXvdVUK1Sm3qKulpE0hp5g/2lE/CxrfltSQ7a9AejI2tuB8wq6NwJDP602M6tixVwtI+BxYFtEPFKwaQ3Qki23AKsL2hdKGitpKjAN2DB4QzYzs/4Uc1rmKuAzwG8kvZa1fR14CFgp6Q5gF3AzQERslbQSeIOeK23ujIgjgz1wMzPrW7/hHhH/Se/n0QGu6aPPEmDJAMZlZmYD4G+ompklyOFuZpYgh7uZWYIc7mZmCXK4m5klyOFuZpYgh7uZWYIc7mZmCXK4m5klyOFuZpYgh7uZWYIc7mZmCXK4m5klyOFuZpYgh7uZWYIc7mZmCXK4m5klyOFuZpYgh7uZWYIc7mZmCXK4m5klyOFuZpYgh7uZWYIc7mZmCXK4m5klyOFuZpYgh7uZWYIc7mZmCXK4m5klyOFuZpYgh7uZWYIc7mZmCeo33CX9WFKHpC0FbRMlrZW0I3ucULDtXkk7JW2XNLdcAzczs74VM3N/Aph3QttiYF1ETAPWZetIugBYCMzI+iyVNHrQRmtmZkXpN9wjYj3w7gnN84Hl2fJyYEFBe2tEHIqIt4CdwGWDM1QzMyuWIqL/naQm4NmIuDBbfz8izinY/l5ETJD0PeCViHgya38ceD4inu7lORcBiwDq6+tnt7a2llxE57sd1B7aXXL/Uv3mL1OPW79oyviyH7Ozs5Pa2tqyH2c4qKZaobrqraZaoXz1zpkzZ1NENPe27YxBPpZ6aev13SMilgHLAJqbmyOXy5V80PyKR8ltv7/k/qX6bNdTx6233Zor+zHz+TwDea1GkmqqFaqr3mqqFSpTb6lXy7wtqQEge+zI2tuB8wr2awSGfkptZlblSg33NUBLttwCrC5oXyhprKSpwDRgw8CGaGZmp6vf0zKSVgA5YLKkduB+4CFgpaQ7gF3AzQARsVXSSuANoBu4MyKOlGnsZmbWh37DPSJu6WPTNX3svwRYMpBBmZnZwPgbqmZmCXK4m5klyOFuZpYgh7uZWYIc7mZmCXK4m5klyOFuZpYgh7uZWYIc7mZmCXK4m5klyOFuZpYgh7uZWYIc7mZmCRrsX2KqKm01/3h8wwNDcNDpD9JzB2Yzs7555m5mliCHu5lZghzuZmYJcribmSXIH6iOQE2Lnztuve2hGyo0EjMbrjxzNzNLkMPdzCxBDnczswQ53M3MEuRwNzNLkMPdzCxBDnczswQ53M3MEuQvMSXgxC81DRV/ecps+PLM3cwsQQ53M7ME+bTMCHTSj4QMoaaupyp2bDMrnmfuZmYJKtvMXdI84DFgNPCjiHioXMey6uIPkM36V5ZwlzQa+D7w90A7sFHSmoh4oxzHsyrwwPhji201Q3voo6eiyv2m8uWLuvlsL8fwm4qVolwz98uAnRHxewBJrcB8wOGekKEMu6EO9EJD9RlHftSDtNXcf2zdn28MoYLJQ1lMfxAemN/HsfeV5ZCKiMF/UukmYF5EfC5b/wxweUR8oWCfRcCibHU6sH0Ah5wM/GkA/UcS15quaqq3mmqF8tX7oYg4t7cN5Zq5q5e2495FImIZsGxQDia9GhHNg/Fcw51rTVc11VtNtUJl6i3X1TLtwHkF643A7jIdy8zMTlCucN8ITJM0VdKZwEJgTZmOZWZmJyjLaZmI6Jb0BeAX9FwK+eOI2FqOY2UG5fTOCOFa01VN9VZTrVCBesvygaqZmVWWv6FqZpYgh7uZWYJGdLhLmidpu6SdkhZXejyDQdKPJXVI2lLQNlHSWkk7sscJBdvuzerfLmluZUZdGknnSXpJ0jZJWyXdlbUnV6+kGkkbJP06q/XBrD25Wo+SNFrSZknPZusp19om6TeSXpP0atZW2XojYkT+0fNB7e+AvwbOBH4NXFDpcQ1CXR8HPgpsKWj7N2BxtrwY+Nds+YKs7rHA1Oz1GF3pGk6j1gbgo9lyHfDbrKbk6qXnux+12fIY4L+AK1KstaDmfwKeAp7N1lOutQ2YfEJbResdyTP3Y7c4iIj/BY7e4mBEi4j1wLsnNM8HlmfLy4EFBe2tEXEoIt4CdtLzuowIEbEnIn6VLe8HtgFTSLDe6NGZrY7J/oIEawWQ1AjcAPyooDnJWk+hovWO5HCfAvxPwXp71pai+ojYAz2BCHwga0/mNZDUBMyiZ0abZL3ZaYrXgA5gbUQkWyvwKHAP8JeCtlRrhZ436hckbcpurQIVrnck/1hHv7c4qAJJvAaSaoFngLsj4s9Sb2X17NpL24ipNyKOAJdIOgdYJenCU+w+YmuV9CmgIyI2ScoV06WXthFRa4GrImK3pA8AayW9eYp9h6TekTxzr6ZbHLwtqQEge+zI2kf8ayBpDD3B/tOI+FnWnGy9ABHxPpAH5pFmrVcBN0pqo+d06dWSniTNWgGIiN3ZYwewip7TLBWtdySHezXd4mAN0JIttwCrC9oXShoraSowDdhQgfGVRD1T9MeBbRHxSMGm5OqVdG42Y0fSOOCTwJskWGtE3BsRjRHRRM//l7+MiNtIsFYASWdLqju6DFwLbKHS9Vb6U+YBfkJ9PT1XWPwO+OdKj2eQaloB7AEO0/MOfwcwCVgH7MgeJxbs/89Z/duB6yo9/tOs9e/o+efo68Br2d/1KdYLzAQ2Z7VuAb6ZtSdX6wl15/j/q2WSrJWeK/Z+nf1tPZpFla7Xtx8wM0vQSD4tY2ZmfXC4m5klyOFuZpYgh7uZWYIc7mZmCXK4m5klyOFuZpag/wOJDjywXStrnQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic[ titanic['Sex'] ==  \"female\"]['Fare'].hist()\n",
    "titanic[ titanic['Sex'] ==  \"male\"]['Fare'].hist() \n",
    "plt.legend(['Femal', 'Male']) #adding legend to make it more clear\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da1a85b1",
   "metadata": {},
   "source": [
    "#### Transparency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "11f489c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic[titanic['Sex'] == 'female']['Fare'].hist(alpha= 0.5) #<-- Blue\n",
    "titanic[titanic['Sex'] == 'male']['Fare'].hist(alpha= 0.3)#<-- Orange"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "583975dc",
   "metadata": {},
   "source": [
    "## Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "44d0defc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "1c884e68",
   "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>882</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>704</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass   Name    Sex    Age  SibSp  Parch  Ticket  \\\n",
       "882        False     False   False  False  False  False  False  False   False   \n",
       "691        False     False   False  False  False  False  False  False   False   \n",
       "312        False     False   False  False  False  False  False  False   False   \n",
       "318        False     False   False  False  False  False  False  False   False   \n",
       "270        False     False   False  False  False   True  False  False   False   \n",
       "704        False     False   False  False  False  False  False  False   False   \n",
       "295        False     False   False  False  False   True  False  False   False   \n",
       "674        False     False   False  False  False   True  False  False   False   \n",
       "\n",
       "      Fare  Cabin  Embarked  \n",
       "882  False   True     False  \n",
       "691  False   True     False  \n",
       "312  False   True     False  \n",
       "318  False  False     False  \n",
       "270  False   True     False  \n",
       "704  False   True     False  \n",
       "295  False   True     False  \n",
       "674  False   True     False  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.isna().sample(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "29af0376",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    False\n",
       "Survived       False\n",
       "Pclass         False\n",
       "Name           False\n",
       "Sex            False\n",
       "Age             True\n",
       "SibSp          False\n",
       "Parch          False\n",
       "Ticket         False\n",
       "Fare           False\n",
       "Cabin           True\n",
       "Embarked        True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "caeb5915",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee6cdb54",
   "metadata": {},
   "source": [
    "#### ploting missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "d8f48626",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic.isna().sum().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27758bf6",
   "metadata": {},
   "source": [
    "#### Removing missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "80f30dc0",
   "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>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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "1            2         1       1   \n",
       "3            4         1       1   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "\n",
       "   Parch    Ticket     Fare Cabin Embarked  \n",
       "1      0  PC 17599  71.2833   C85        C  \n",
       "3      0    113803  53.1000  C123        S  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.dropna().head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "de40c8ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(183, 12)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.dropna().shape #<--method1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "adabaea7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.fillna(0).shape #<--method2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69aeff11",
   "metadata": {},
   "source": [
    "## Creating DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b255819f",
   "metadata": {},
   "source": [
    "<img src= './media/creating_dataframe.png'>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7526252",
   "metadata": {},
   "source": [
    "### list of Dictionaries - by row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "09fd5641",
   "metadata": {},
   "outputs": [],
   "source": [
    "list_of_dictionary = [\n",
    "    {'name':'Ginger',\n",
    "     'breed': 'lusi',\n",
    "     'height_cm': 22,\n",
    "     'weight_kgs':10 ,\n",
    "     'date_of_birth':\"2020-02-14\"},\n",
    "    \n",
    "    { 'name': 'Scout',\n",
    "     'breed': 'Dalmatian',\n",
    "     'height_cm': 59,\n",
    "     'weight_kgs':35 ,\n",
    "     'date_of_birth':\"2019-05-09\"}\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "fc714c32",
   "metadata": {},
   "outputs": [],
   "source": [
    "dogs = pd.DataFrame(list_of_dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "44aba061",
   "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>name</th>\n",
       "      <th>breed</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kgs</th>\n",
       "      <th>date_of_birth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ginger</td>\n",
       "      <td>lusi</td>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "      <td>2020-02-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Scout</td>\n",
       "      <td>Dalmatian</td>\n",
       "      <td>59</td>\n",
       "      <td>35</td>\n",
       "      <td>2019-05-09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     name      breed  height_cm  weight_kgs date_of_birth\n",
       "0  Ginger       lusi         22          10    2020-02-14\n",
       "1   Scout  Dalmatian         59          35    2019-05-09"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30812cb5",
   "metadata": {},
   "source": [
    "### Dixtionary pf list -by column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "69897e39",
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_of_list = {\n",
    "    'name': ['lusi', 'Scout'],\n",
    "    'breed': ['Desi', 'farmi'],\n",
    "    'weight_kg': [22,20]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "25beae53",
   "metadata": {},
   "outputs": [],
   "source": [
    "dogs1 = pd.DataFrame(dict_of_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "dcefe938",
   "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>name</th>\n",
       "      <th>breed</th>\n",
       "      <th>weight_kg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lusi</td>\n",
       "      <td>Desi</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Scout</td>\n",
       "      <td>farmi</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name  breed  weight_kg\n",
       "0   lusi   Desi         22\n",
       "1  Scout  farmi         20"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a41f95e",
   "metadata": {},
   "source": [
    "## Reading & writing CSVs\n",
    "\n",
    "<img src = './media/csv.png'>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b46ef76",
   "metadata": {},
   "source": [
    "#### CSV to DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "b961db5c",
   "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",
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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>Name</th>\n",
       "      <th>Breed</th>\n",
       "      <th>Color</th>\n",
       "      <th>Height(cm)</th>\n",
       "      <th>Weight(kg)</th>\n",
       "      <th>Date of Birth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bella</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Brown</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>Poodle</td>\n",
       "      <td>Black</td>\n",
       "      <td>43</td>\n",
       "      <td>23</td>\n",
       "      <td>2016-09-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lucy</td>\n",
       "      <td>Chow</td>\n",
       "      <td>Brown</td>\n",
       "      <td>46</td>\n",
       "      <td>22</td>\n",
       "      <td>2014-08-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cooper</td>\n",
       "      <td>Schnauzer</td>\n",
       "      <td>Gray</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>2011-12-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Max</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Black</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>2017-01-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Stella</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>Tan</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2015-04-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bernie</td>\n",
       "      <td>St. Bernard</td>\n",
       "      <td>White</td>\n",
       "      <td>77</td>\n",
       "      <td>74</td>\n",
       "      <td>2018-02-2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name         Breed   Color   Height(cm)   Weight(kg)  Date of Birth\n",
       "0    Bella      Labrador   Brown           56           25     2013-07-01\n",
       "1  Charlie        Poodle   Black           43           23     2016-09-16\n",
       "2     Lucy          Chow   Brown           46           22     2014-08-25\n",
       "3   Cooper     Schnauzer    Gray           49           17     2011-12-11\n",
       "4      Max      Labrador   Black           59           29     2017-01-20\n",
       "5   Stella     Chihuahua     Tan           18            2     2015-04-20\n",
       "6   Bernie   St. Bernard   White           77           74      2018-02-2"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs = pd.read_csv('./dataset/dogs.csv')\n",
    "dogs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "69f4255e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dogs['new_column'] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "09d6e0f5",
   "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>Name</th>\n",
       "      <th>Breed</th>\n",
       "      <th>Color</th>\n",
       "      <th>Height(cm)</th>\n",
       "      <th>Weight(kg)</th>\n",
       "      <th>Date of Birth</th>\n",
       "      <th>new_column</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bella</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Brown</td>\n",
       "      <td>56</td>\n",
       "      <td>25</td>\n",
       "      <td>2013-07-01</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>Poodle</td>\n",
       "      <td>Black</td>\n",
       "      <td>43</td>\n",
       "      <td>23</td>\n",
       "      <td>2016-09-16</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lucy</td>\n",
       "      <td>Chow</td>\n",
       "      <td>Brown</td>\n",
       "      <td>46</td>\n",
       "      <td>22</td>\n",
       "      <td>2014-08-25</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cooper</td>\n",
       "      <td>Schnauzer</td>\n",
       "      <td>Gray</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>2011-12-11</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Max</td>\n",
       "      <td>Labrador</td>\n",
       "      <td>Black</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>2017-01-20</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Stella</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>Tan</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2015-04-20</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bernie</td>\n",
       "      <td>St. Bernard</td>\n",
       "      <td>White</td>\n",
       "      <td>77</td>\n",
       "      <td>74</td>\n",
       "      <td>2018-02-2</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name         Breed   Color   Height(cm)   Weight(kg)  Date of Birth  \\\n",
       "0    Bella      Labrador   Brown           56           25     2013-07-01   \n",
       "1  Charlie        Poodle   Black           43           23     2016-09-16   \n",
       "2     Lucy          Chow   Brown           46           22     2014-08-25   \n",
       "3   Cooper     Schnauzer    Gray           49           17     2011-12-11   \n",
       "4      Max      Labrador   Black           59           29     2017-01-20   \n",
       "5   Stella     Chihuahua     Tan           18            2     2015-04-20   \n",
       "6   Bernie   St. Bernard   White           77           74      2018-02-2   \n",
       "\n",
       "   new_column  \n",
       "0        True  \n",
       "1        True  \n",
       "2        True  \n",
       "3        True  \n",
       "4        True  \n",
       "5        True  \n",
       "6        True  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4eb4a80",
   "metadata": {},
   "source": [
    "#### DataFrame to CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "0f9d13ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "dogs.to_csv('./dataset/new_dogs.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3649551d",
   "metadata": {},
   "source": [
    "<img src = './media/motivation.jpg' width = 500 height = 400>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f6cac71",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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