Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas as a toolbox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This is a header"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### run the following command once on a terminal to install pandasql\n",
    "### pip install pandasql"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    import pandasql as ps\n",
    "except:\n",
    "    os.system(\"pip install pandasql\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "data": {
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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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa\n",
       "5           5.4          3.9           1.7          0.4  setosa\n",
       "6           4.6          3.4           1.4          0.3  setosa\n",
       "7           5.0          3.4           1.5          0.2  setosa\n",
       "8           4.4          2.9           1.4          0.2  setosa\n",
       "9           4.9          3.1           1.5          0.1  setosa"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/iris.csv')\n",
    "print(type(df))\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = sns.load_dataset('iris')\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic = sns.load_dataset('titanic')\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>29.03</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>27.18</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>22.67</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>17.82</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>18.78</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill   tip     sex smoker   day    time  size\n",
       "0         16.99  1.01  Female     No   Sun  Dinner     2\n",
       "1         10.34  1.66    Male     No   Sun  Dinner     3\n",
       "2         21.01  3.50    Male     No   Sun  Dinner     3\n",
       "3         23.68  3.31    Male     No   Sun  Dinner     2\n",
       "4         24.59  3.61  Female     No   Sun  Dinner     4\n",
       "..          ...   ...     ...    ...   ...     ...   ...\n",
       "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
       "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
       "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
       "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
       "243       18.78  3.00  Female     No  Thur  Dinner     2\n",
       "\n",
       "[244 rows x 7 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips = sns.load_dataset('tips')\n",
    "tips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['total_bill', 'tip', 'sex', 'smoker', 'day', 'time', 'size'], dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
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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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>29.03</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>27.18</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>22.67</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>17.82</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>18.78</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill   tip     sex smoker   day    time  size\n",
       "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
       "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
       "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
       "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
       "243       18.78  3.00  Female     No  Thur  Dinner     2"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
       "    }\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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>244.000000</td>\n",
       "      <td>244.000000</td>\n",
       "      <td>244.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19.785943</td>\n",
       "      <td>2.998279</td>\n",
       "      <td>2.569672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.902412</td>\n",
       "      <td>1.383638</td>\n",
       "      <td>0.951100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.070000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13.347500</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.795000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24.127500</td>\n",
       "      <td>3.562500</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>50.810000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       total_bill         tip        size\n",
       "count  244.000000  244.000000  244.000000\n",
       "mean    19.785943    2.998279    2.569672\n",
       "std      8.902412    1.383638    0.951100\n",
       "min      3.070000    1.000000    1.000000\n",
       "25%     13.347500    2.000000    2.000000\n",
       "50%     17.795000    2.900000    2.000000\n",
       "75%     24.127500    3.562500    3.000000\n",
       "max     50.810000   10.000000    6.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Female', 'Male']\n",
       "Categories (2, object): ['Male', 'Female']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.sex.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Who pay more tips, Male or Female?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using SQL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>sex</th>\n",
       "      <th>avg(tip)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>2.833448</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      sex  avg(tip)\n",
       "0  Female  2.833448"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql = \"select sex, avg(tip) from tips where sex = 'Female'\"\n",
    "df = ps.sqldf(sql)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      sex  count(*)  avg(tip)\n",
      "0  Female        87  2.833448\n",
      "1    Male       157  3.089618\n"
     ]
    }
   ],
   "source": [
    "sql = \"select sex, count(*), avg(tip) from tips group by sex\"\n",
    "print(ps.sqldf(sql))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer same questions using Pandas syntax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.99827868852459\n"
     ]
    }
   ],
   "source": [
    "print(tips['tip'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(244, 7)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       True\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4       True\n",
       "       ...  \n",
       "239    False\n",
       "240     True\n",
       "241    False\n",
       "242    False\n",
       "243     True\n",
       "Name: sex, Length: 244, dtype: bool"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips['sex'] == 'Female'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.8334482758620685\n"
     ]
    }
   ],
   "source": [
    "print(tips[tips['sex'] == 'Female'] ['tip'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = tips[tips['sex'] == 'Female']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     total_bill   tip     sex smoker   day    time  size\n",
      "0         16.99  1.01  Female     No   Sun  Dinner     2\n",
      "4         24.59  3.61  Female     No   Sun  Dinner     4\n",
      "11        35.26  5.00  Female     No   Sun  Dinner     4\n",
      "14        14.83  3.02  Female     No   Sun  Dinner     2\n",
      "16        10.33  1.67  Female     No   Sun  Dinner     3\n",
      "..          ...   ...     ...    ...   ...     ...   ...\n",
      "226       10.09  2.00  Female    Yes   Fri   Lunch     2\n",
      "229       22.12  2.88  Female    Yes   Sat  Dinner     2\n",
      "238       35.83  4.67  Female     No   Sat  Dinner     3\n",
      "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
      "243       18.78  3.00  Female     No  Thur  Dinner     2\n",
      "\n",
      "[87 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.0896178343949043\n"
     ]
    }
   ],
   "source": [
    "print(tips[tips['sex'] == 'Male'] ['tip'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Who pay more tips, Smoker or Non-Smoker?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  smoker  count(*)  avg(tip)\n",
      "0     No       151  2.991854\n",
      "1    Yes        93  3.008710\n"
     ]
    }
   ],
   "source": [
    "sql = \"select smoker, count(*), avg(tip) from tips group by smoker\"\n",
    "print(ps.sqldf(sql))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        total_bill  tip  sex  day  time  size\n",
      "smoker                                       \n",
      "Yes             93   93   93   93    93    93\n",
      "No             151  151  151  151   151   151\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/54/wkbn7sg16737fs9875v7bgl40000gn/T/ipykernel_74252/322787854.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  print(tips.groupby(['smoker']).count())\n",
      "/var/folders/54/wkbn7sg16737fs9875v7bgl40000gn/T/ipykernel_74252/322787854.py:2: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  tips.groupby(['smoker']).mean()\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "category dtype does not support aggregation 'mean'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[28], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28mprint\u001b[39m(tips\u001b[38;5;241m.\u001b[39mgroupby([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msmoker\u001b[39m\u001b[38;5;124m'\u001b[39m])\u001b[38;5;241m.\u001b[39mcount())\n\u001b[0;32m----> 2\u001b[0m tips\u001b[38;5;241m.\u001b[39mgroupby([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msmoker\u001b[39m\u001b[38;5;124m'\u001b[39m])\u001b[38;5;241m.\u001b[39mmean()\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pandas/core/groupby/groupby.py:2452\u001b[0m, in \u001b[0;36mGroupBy.mean\u001b[0;34m(self, numeric_only, engine, engine_kwargs)\u001b[0m\n\u001b[1;32m   2445\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_numba_agg_general(\n\u001b[1;32m   2446\u001b[0m         grouped_mean,\n\u001b[1;32m   2447\u001b[0m         executor\u001b[38;5;241m.\u001b[39mfloat_dtype_mapping,\n\u001b[1;32m   2448\u001b[0m         engine_kwargs,\n\u001b[1;32m   2449\u001b[0m         min_periods\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m   2450\u001b[0m     )\n\u001b[1;32m   2451\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2452\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cython_agg_general(\n\u001b[1;32m   2453\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmean\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   2454\u001b[0m         alt\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mlambda\u001b[39;00m x: Series(x, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\u001b[38;5;241m.\u001b[39mmean(numeric_only\u001b[38;5;241m=\u001b[39mnumeric_only),\n\u001b[1;32m   2455\u001b[0m         numeric_only\u001b[38;5;241m=\u001b[39mnumeric_only,\n\u001b[1;32m   2456\u001b[0m     )\n\u001b[1;32m   2457\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgroupby\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pandas/core/groupby/groupby.py:1998\u001b[0m, in \u001b[0;36mGroupBy._cython_agg_general\u001b[0;34m(self, how, alt, numeric_only, min_count, **kwargs)\u001b[0m\n\u001b[1;32m   1995\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_agg_py_fallback(how, values, ndim\u001b[38;5;241m=\u001b[39mdata\u001b[38;5;241m.\u001b[39mndim, alt\u001b[38;5;241m=\u001b[39malt)\n\u001b[1;32m   1996\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[0;32m-> 1998\u001b[0m new_mgr \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mgrouped_reduce(array_func)\n\u001b[1;32m   1999\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wrap_agged_manager(new_mgr)\n\u001b[1;32m   2000\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m how \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124midxmin\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124midxmax\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pandas/core/internals/managers.py:1472\u001b[0m, in \u001b[0;36mBlockManager.grouped_reduce\u001b[0;34m(self, func)\u001b[0m\n\u001b[1;32m   1470\u001b[0m             result_blocks \u001b[38;5;241m=\u001b[39m extend_blocks(applied, result_blocks)\n\u001b[1;32m   1471\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1472\u001b[0m         applied \u001b[38;5;241m=\u001b[39m blk\u001b[38;5;241m.\u001b[39mapply(func)\n\u001b[1;32m   1473\u001b[0m         result_blocks \u001b[38;5;241m=\u001b[39m extend_blocks(applied, result_blocks)\n\u001b[1;32m   1475\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(result_blocks) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pandas/core/internals/blocks.py:393\u001b[0m, in \u001b[0;36mBlock.apply\u001b[0;34m(self, func, **kwargs)\u001b[0m\n\u001b[1;32m    387\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[1;32m    388\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply\u001b[39m(\u001b[38;5;28mself\u001b[39m, func, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Block]:\n\u001b[1;32m    389\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    390\u001b[0m \u001b[38;5;124;03m    apply the function to my values; return a block if we are not\u001b[39;00m\n\u001b[1;32m    391\u001b[0m \u001b[38;5;124;03m    one\u001b[39;00m\n\u001b[1;32m    392\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 393\u001b[0m     result \u001b[38;5;241m=\u001b[39m func(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    395\u001b[0m     result \u001b[38;5;241m=\u001b[39m maybe_coerce_values(result)\n\u001b[1;32m    396\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_split_op_result(result)\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pandas/core/groupby/groupby.py:1973\u001b[0m, in \u001b[0;36mGroupBy._cython_agg_general.<locals>.array_func\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m   1971\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21marray_func\u001b[39m(values: ArrayLike) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ArrayLike:\n\u001b[1;32m   1972\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1973\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_grouper\u001b[38;5;241m.\u001b[39m_cython_operation(\n\u001b[1;32m   1974\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maggregate\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   1975\u001b[0m             values,\n\u001b[1;32m   1976\u001b[0m             how,\n\u001b[1;32m   1977\u001b[0m             axis\u001b[38;5;241m=\u001b[39mdata\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m   1978\u001b[0m             min_count\u001b[38;5;241m=\u001b[39mmin_count,\n\u001b[1;32m   1979\u001b[0m             \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m   1980\u001b[0m         )\n\u001b[1;32m   1981\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m:\n\u001b[1;32m   1982\u001b[0m         \u001b[38;5;66;03m# generally if we have numeric_only=False\u001b[39;00m\n\u001b[1;32m   1983\u001b[0m         \u001b[38;5;66;03m# and non-applicable functions\u001b[39;00m\n\u001b[1;32m   1984\u001b[0m         \u001b[38;5;66;03m# try to python agg\u001b[39;00m\n\u001b[1;32m   1985\u001b[0m         \u001b[38;5;66;03m# TODO: shouldn't min_count matter?\u001b[39;00m\n\u001b[1;32m   1986\u001b[0m         \u001b[38;5;66;03m# TODO: avoid special casing SparseArray here\u001b[39;00m\n\u001b[1;32m   1987\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m how \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124many\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mall\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(values, SparseArray):\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pandas/core/groupby/ops.py:831\u001b[0m, in \u001b[0;36mBaseGrouper._cython_operation\u001b[0;34m(self, kind, values, how, axis, min_count, **kwargs)\u001b[0m\n\u001b[1;32m    829\u001b[0m ids, _, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroup_info\n\u001b[1;32m    830\u001b[0m ngroups \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mngroups\n\u001b[0;32m--> 831\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cy_op\u001b[38;5;241m.\u001b[39mcython_operation(\n\u001b[1;32m    832\u001b[0m     values\u001b[38;5;241m=\u001b[39mvalues,\n\u001b[1;32m    833\u001b[0m     axis\u001b[38;5;241m=\u001b[39maxis,\n\u001b[1;32m    834\u001b[0m     min_count\u001b[38;5;241m=\u001b[39mmin_count,\n\u001b[1;32m    835\u001b[0m     comp_ids\u001b[38;5;241m=\u001b[39mids,\n\u001b[1;32m    836\u001b[0m     ngroups\u001b[38;5;241m=\u001b[39mngroups,\n\u001b[1;32m    837\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    838\u001b[0m )\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pandas/core/groupby/ops.py:541\u001b[0m, in \u001b[0;36mWrappedCythonOp.cython_operation\u001b[0;34m(self, values, axis, min_count, comp_ids, ngroups, **kwargs)\u001b[0m\n\u001b[1;32m    537\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_axis(axis, values)\n\u001b[1;32m    539\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(values, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[1;32m    540\u001b[0m     \u001b[38;5;66;03m# i.e. ExtensionArray\u001b[39;00m\n\u001b[0;32m--> 541\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m values\u001b[38;5;241m.\u001b[39m_groupby_op(\n\u001b[1;32m    542\u001b[0m         how\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhow,\n\u001b[1;32m    543\u001b[0m         has_dropped_na\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhas_dropped_na,\n\u001b[1;32m    544\u001b[0m         min_count\u001b[38;5;241m=\u001b[39mmin_count,\n\u001b[1;32m    545\u001b[0m         ngroups\u001b[38;5;241m=\u001b[39mngroups,\n\u001b[1;32m    546\u001b[0m         ids\u001b[38;5;241m=\u001b[39mcomp_ids,\n\u001b[1;32m    547\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    548\u001b[0m     )\n\u001b[1;32m    550\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cython_op_ndim_compat(\n\u001b[1;32m    551\u001b[0m     values,\n\u001b[1;32m    552\u001b[0m     min_count\u001b[38;5;241m=\u001b[39mmin_count,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    556\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    557\u001b[0m )\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pandas/core/arrays/categorical.py:2740\u001b[0m, in \u001b[0;36mCategorical._groupby_op\u001b[0;34m(self, how, has_dropped_na, min_count, ngroups, ids, **kwargs)\u001b[0m\n\u001b[1;32m   2738\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m kind \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtransform\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m   2739\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m type does not support \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhow\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m operations\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 2740\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m dtype does not support aggregation \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhow\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   2742\u001b[0m result_mask \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   2743\u001b[0m mask \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39misna()\n",
      "\u001b[0;31mTypeError\u001b[0m: category dtype does not support aggregation 'mean'"
     ]
    }
   ],
   "source": [
    "print(tips.groupby(['smoker']).count())\n",
    "tips.groupby(['smoker']).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## When will people tend to pay more Tips, Dinner or Lunch?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     time  count(*)  avg(tip)\n",
      "0  Dinner       176  3.102670\n",
      "1   Lunch        68  2.728088\n"
     ]
    }
   ],
   "source": [
    "sql = \"select time, count(*), avg(tip) from tips group by time\"\n",
    "print(ps.sqldf(sql))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/54/wkbn7sg16737fs9875v7bgl40000gn/T/ipykernel_74252/793139560.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  tips.groupby(['time']).mean()\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "category dtype does not support aggregation 'mean'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[39], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m tips\u001b[38;5;241m.\u001b[39mgroupby([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m'\u001b[39m])\u001b[38;5;241m.\u001b[39mmean()\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pandas/core/groupby/groupby.py:2452\u001b[0m, in \u001b[0;36mGroupBy.mean\u001b[0;34m(self, numeric_only, engine, engine_kwargs)\u001b[0m\n\u001b[1;32m   2445\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_numba_agg_general(\n\u001b[1;32m   2446\u001b[0m         grouped_mean,\n\u001b[1;32m   2447\u001b[0m         executor\u001b[38;5;241m.\u001b[39mfloat_dtype_mapping,\n\u001b[1;32m   2448\u001b[0m         engine_kwargs,\n\u001b[1;32m   2449\u001b[0m         min_periods\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m   2450\u001b[0m     )\n\u001b[1;32m   2451\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2452\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cython_agg_general(\n\u001b[1;32m   2453\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmean\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   2454\u001b[0m         alt\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mlambda\u001b[39;00m x: Series(x, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\u001b[38;5;241m.\u001b[39mmean(numeric_only\u001b[38;5;241m=\u001b[39mnumeric_only),\n\u001b[1;32m   2455\u001b[0m         numeric_only\u001b[38;5;241m=\u001b[39mnumeric_only,\n\u001b[1;32m   2456\u001b[0m     )\n\u001b[1;32m   2457\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgroupby\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pandas/core/groupby/groupby.py:1998\u001b[0m, in \u001b[0;36mGroupBy._cython_agg_general\u001b[0;34m(self, how, alt, numeric_only, min_count, **kwargs)\u001b[0m\n\u001b[1;32m   1995\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_agg_py_fallback(how, values, ndim\u001b[38;5;241m=\u001b[39mdata\u001b[38;5;241m.\u001b[39mndim, alt\u001b[38;5;241m=\u001b[39malt)\n\u001b[1;32m   1996\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[0;32m-> 1998\u001b[0m new_mgr \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mgrouped_reduce(array_func)\n\u001b[1;32m   1999\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wrap_agged_manager(new_mgr)\n\u001b[1;32m   2000\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m how \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124midxmin\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124midxmax\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pandas/core/internals/managers.py:1472\u001b[0m, in \u001b[0;36mBlockManager.grouped_reduce\u001b[0;34m(self, func)\u001b[0m\n\u001b[1;32m   1470\u001b[0m             result_blocks \u001b[38;5;241m=\u001b[39m extend_blocks(applied, result_blocks)\n\u001b[1;32m   1471\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1472\u001b[0m         applied \u001b[38;5;241m=\u001b[39m blk\u001b[38;5;241m.\u001b[39mapply(func)\n\u001b[1;32m   1473\u001b[0m         result_blocks \u001b[38;5;241m=\u001b[39m extend_blocks(applied, result_blocks)\n\u001b[1;32m   1475\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(result_blocks) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pandas/core/internals/blocks.py:393\u001b[0m, in \u001b[0;36mBlock.apply\u001b[0;34m(self, func, **kwargs)\u001b[0m\n\u001b[1;32m    387\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[1;32m    388\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply\u001b[39m(\u001b[38;5;28mself\u001b[39m, func, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Block]:\n\u001b[1;32m    389\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    390\u001b[0m \u001b[38;5;124;03m    apply the function to my values; return a block if we are not\u001b[39;00m\n\u001b[1;32m    391\u001b[0m \u001b[38;5;124;03m    one\u001b[39;00m\n\u001b[1;32m    392\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 393\u001b[0m     result \u001b[38;5;241m=\u001b[39m func(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    395\u001b[0m     result \u001b[38;5;241m=\u001b[39m maybe_coerce_values(result)\n\u001b[1;32m    396\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_split_op_result(result)\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pandas/core/groupby/groupby.py:1973\u001b[0m, in \u001b[0;36mGroupBy._cython_agg_general.<locals>.array_func\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m   1971\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21marray_func\u001b[39m(values: ArrayLike) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ArrayLike:\n\u001b[1;32m   1972\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1973\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_grouper\u001b[38;5;241m.\u001b[39m_cython_operation(\n\u001b[1;32m   1974\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maggregate\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   1975\u001b[0m             values,\n\u001b[1;32m   1976\u001b[0m             how,\n\u001b[1;32m   1977\u001b[0m             axis\u001b[38;5;241m=\u001b[39mdata\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m   1978\u001b[0m             min_count\u001b[38;5;241m=\u001b[39mmin_count,\n\u001b[1;32m   1979\u001b[0m             \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m   1980\u001b[0m         )\n\u001b[1;32m   1981\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m:\n\u001b[1;32m   1982\u001b[0m         \u001b[38;5;66;03m# generally if we have numeric_only=False\u001b[39;00m\n\u001b[1;32m   1983\u001b[0m         \u001b[38;5;66;03m# and non-applicable functions\u001b[39;00m\n\u001b[1;32m   1984\u001b[0m         \u001b[38;5;66;03m# try to python agg\u001b[39;00m\n\u001b[1;32m   1985\u001b[0m         \u001b[38;5;66;03m# TODO: shouldn't min_count matter?\u001b[39;00m\n\u001b[1;32m   1986\u001b[0m         \u001b[38;5;66;03m# TODO: avoid special casing SparseArray here\u001b[39;00m\n\u001b[1;32m   1987\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m how \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124many\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mall\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(values, SparseArray):\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pandas/core/groupby/ops.py:831\u001b[0m, in \u001b[0;36mBaseGrouper._cython_operation\u001b[0;34m(self, kind, values, how, axis, min_count, **kwargs)\u001b[0m\n\u001b[1;32m    829\u001b[0m ids, _, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroup_info\n\u001b[1;32m    830\u001b[0m ngroups \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mngroups\n\u001b[0;32m--> 831\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cy_op\u001b[38;5;241m.\u001b[39mcython_operation(\n\u001b[1;32m    832\u001b[0m     values\u001b[38;5;241m=\u001b[39mvalues,\n\u001b[1;32m    833\u001b[0m     axis\u001b[38;5;241m=\u001b[39maxis,\n\u001b[1;32m    834\u001b[0m     min_count\u001b[38;5;241m=\u001b[39mmin_count,\n\u001b[1;32m    835\u001b[0m     comp_ids\u001b[38;5;241m=\u001b[39mids,\n\u001b[1;32m    836\u001b[0m     ngroups\u001b[38;5;241m=\u001b[39mngroups,\n\u001b[1;32m    837\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    838\u001b[0m )\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pandas/core/groupby/ops.py:541\u001b[0m, in \u001b[0;36mWrappedCythonOp.cython_operation\u001b[0;34m(self, values, axis, min_count, comp_ids, ngroups, **kwargs)\u001b[0m\n\u001b[1;32m    537\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_axis(axis, values)\n\u001b[1;32m    539\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(values, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[1;32m    540\u001b[0m     \u001b[38;5;66;03m# i.e. ExtensionArray\u001b[39;00m\n\u001b[0;32m--> 541\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m values\u001b[38;5;241m.\u001b[39m_groupby_op(\n\u001b[1;32m    542\u001b[0m         how\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhow,\n\u001b[1;32m    543\u001b[0m         has_dropped_na\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhas_dropped_na,\n\u001b[1;32m    544\u001b[0m         min_count\u001b[38;5;241m=\u001b[39mmin_count,\n\u001b[1;32m    545\u001b[0m         ngroups\u001b[38;5;241m=\u001b[39mngroups,\n\u001b[1;32m    546\u001b[0m         ids\u001b[38;5;241m=\u001b[39mcomp_ids,\n\u001b[1;32m    547\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    548\u001b[0m     )\n\u001b[1;32m    550\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cython_op_ndim_compat(\n\u001b[1;32m    551\u001b[0m     values,\n\u001b[1;32m    552\u001b[0m     min_count\u001b[38;5;241m=\u001b[39mmin_count,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    556\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    557\u001b[0m )\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pandas/core/arrays/categorical.py:2740\u001b[0m, in \u001b[0;36mCategorical._groupby_op\u001b[0;34m(self, how, has_dropped_na, min_count, ngroups, ids, **kwargs)\u001b[0m\n\u001b[1;32m   2738\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m kind \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtransform\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m   2739\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m type does not support \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhow\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m operations\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 2740\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m dtype does not support aggregation \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhow\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   2742\u001b[0m result_mask \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   2743\u001b[0m mask \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39misna()\n",
      "\u001b[0;31mTypeError\u001b[0m: category dtype does not support aggregation 'mean'"
     ]
    }
   ],
   "source": [
    "tips.groupby(['time']).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Group By using Pandas with more than one attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/54/wkbn7sg16737fs9875v7bgl40000gn/T/ipykernel_74252/715150746.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  tips.groupby(['sex','smoker','time']).mean()\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "category dtype does not support aggregation 'mean'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[42], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m tips\u001b[38;5;241m.\u001b[39mgroupby([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msex\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msmoker\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m'\u001b[39m])\u001b[38;5;241m.\u001b[39mmean()\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pandas/core/groupby/groupby.py:2452\u001b[0m, in \u001b[0;36mGroupBy.mean\u001b[0;34m(self, numeric_only, engine, engine_kwargs)\u001b[0m\n\u001b[1;32m   2445\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_numba_agg_general(\n\u001b[1;32m   2446\u001b[0m         grouped_mean,\n\u001b[1;32m   2447\u001b[0m         executor\u001b[38;5;241m.\u001b[39mfloat_dtype_mapping,\n\u001b[1;32m   2448\u001b[0m         engine_kwargs,\n\u001b[1;32m   2449\u001b[0m         min_periods\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m   2450\u001b[0m     )\n\u001b[1;32m   2451\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2452\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cython_agg_general(\n\u001b[1;32m   2453\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmean\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   2454\u001b[0m         alt\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mlambda\u001b[39;00m x: Series(x, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\u001b[38;5;241m.\u001b[39mmean(numeric_only\u001b[38;5;241m=\u001b[39mnumeric_only),\n\u001b[1;32m   2455\u001b[0m         numeric_only\u001b[38;5;241m=\u001b[39mnumeric_only,\n\u001b[1;32m   2456\u001b[0m     )\n\u001b[1;32m   2457\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgroupby\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pandas/core/groupby/groupby.py:1998\u001b[0m, in \u001b[0;36mGroupBy._cython_agg_general\u001b[0;34m(self, how, alt, numeric_only, min_count, **kwargs)\u001b[0m\n\u001b[1;32m   1995\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_agg_py_fallback(how, values, ndim\u001b[38;5;241m=\u001b[39mdata\u001b[38;5;241m.\u001b[39mndim, alt\u001b[38;5;241m=\u001b[39malt)\n\u001b[1;32m   1996\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[0;32m-> 1998\u001b[0m new_mgr \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mgrouped_reduce(array_func)\n\u001b[1;32m   1999\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wrap_agged_manager(new_mgr)\n\u001b[1;32m   2000\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m how \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124midxmin\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124midxmax\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pandas/core/internals/managers.py:1472\u001b[0m, in \u001b[0;36mBlockManager.grouped_reduce\u001b[0;34m(self, func)\u001b[0m\n\u001b[1;32m   1470\u001b[0m             result_blocks \u001b[38;5;241m=\u001b[39m extend_blocks(applied, result_blocks)\n\u001b[1;32m   1471\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1472\u001b[0m         applied \u001b[38;5;241m=\u001b[39m blk\u001b[38;5;241m.\u001b[39mapply(func)\n\u001b[1;32m   1473\u001b[0m         result_blocks \u001b[38;5;241m=\u001b[39m extend_blocks(applied, result_blocks)\n\u001b[1;32m   1475\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(result_blocks) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pandas/core/internals/blocks.py:393\u001b[0m, in \u001b[0;36mBlock.apply\u001b[0;34m(self, func, **kwargs)\u001b[0m\n\u001b[1;32m    387\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[1;32m    388\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply\u001b[39m(\u001b[38;5;28mself\u001b[39m, func, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Block]:\n\u001b[1;32m    389\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    390\u001b[0m \u001b[38;5;124;03m    apply the function to my values; return a block if we are not\u001b[39;00m\n\u001b[1;32m    391\u001b[0m \u001b[38;5;124;03m    one\u001b[39;00m\n\u001b[1;32m    392\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 393\u001b[0m     result \u001b[38;5;241m=\u001b[39m func(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    395\u001b[0m     result \u001b[38;5;241m=\u001b[39m maybe_coerce_values(result)\n\u001b[1;32m    396\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_split_op_result(result)\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pandas/core/groupby/groupby.py:1973\u001b[0m, in \u001b[0;36mGroupBy._cython_agg_general.<locals>.array_func\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m   1971\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21marray_func\u001b[39m(values: ArrayLike) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ArrayLike:\n\u001b[1;32m   1972\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1973\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_grouper\u001b[38;5;241m.\u001b[39m_cython_operation(\n\u001b[1;32m   1974\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maggregate\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   1975\u001b[0m             values,\n\u001b[1;32m   1976\u001b[0m             how,\n\u001b[1;32m   1977\u001b[0m             axis\u001b[38;5;241m=\u001b[39mdata\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m   1978\u001b[0m             min_count\u001b[38;5;241m=\u001b[39mmin_count,\n\u001b[1;32m   1979\u001b[0m             \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m   1980\u001b[0m         )\n\u001b[1;32m   1981\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m:\n\u001b[1;32m   1982\u001b[0m         \u001b[38;5;66;03m# generally if we have numeric_only=False\u001b[39;00m\n\u001b[1;32m   1983\u001b[0m         \u001b[38;5;66;03m# and non-applicable functions\u001b[39;00m\n\u001b[1;32m   1984\u001b[0m         \u001b[38;5;66;03m# try to python agg\u001b[39;00m\n\u001b[1;32m   1985\u001b[0m         \u001b[38;5;66;03m# TODO: shouldn't min_count matter?\u001b[39;00m\n\u001b[1;32m   1986\u001b[0m         \u001b[38;5;66;03m# TODO: avoid special casing SparseArray here\u001b[39;00m\n\u001b[1;32m   1987\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m how \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124many\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mall\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(values, SparseArray):\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pandas/core/groupby/ops.py:831\u001b[0m, in \u001b[0;36mBaseGrouper._cython_operation\u001b[0;34m(self, kind, values, how, axis, min_count, **kwargs)\u001b[0m\n\u001b[1;32m    829\u001b[0m ids, _, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroup_info\n\u001b[1;32m    830\u001b[0m ngroups \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mngroups\n\u001b[0;32m--> 831\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cy_op\u001b[38;5;241m.\u001b[39mcython_operation(\n\u001b[1;32m    832\u001b[0m     values\u001b[38;5;241m=\u001b[39mvalues,\n\u001b[1;32m    833\u001b[0m     axis\u001b[38;5;241m=\u001b[39maxis,\n\u001b[1;32m    834\u001b[0m     min_count\u001b[38;5;241m=\u001b[39mmin_count,\n\u001b[1;32m    835\u001b[0m     comp_ids\u001b[38;5;241m=\u001b[39mids,\n\u001b[1;32m    836\u001b[0m     ngroups\u001b[38;5;241m=\u001b[39mngroups,\n\u001b[1;32m    837\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    838\u001b[0m )\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pandas/core/groupby/ops.py:541\u001b[0m, in \u001b[0;36mWrappedCythonOp.cython_operation\u001b[0;34m(self, values, axis, min_count, comp_ids, ngroups, **kwargs)\u001b[0m\n\u001b[1;32m    537\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_axis(axis, values)\n\u001b[1;32m    539\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(values, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[1;32m    540\u001b[0m     \u001b[38;5;66;03m# i.e. ExtensionArray\u001b[39;00m\n\u001b[0;32m--> 541\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m values\u001b[38;5;241m.\u001b[39m_groupby_op(\n\u001b[1;32m    542\u001b[0m         how\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhow,\n\u001b[1;32m    543\u001b[0m         has_dropped_na\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhas_dropped_na,\n\u001b[1;32m    544\u001b[0m         min_count\u001b[38;5;241m=\u001b[39mmin_count,\n\u001b[1;32m    545\u001b[0m         ngroups\u001b[38;5;241m=\u001b[39mngroups,\n\u001b[1;32m    546\u001b[0m         ids\u001b[38;5;241m=\u001b[39mcomp_ids,\n\u001b[1;32m    547\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    548\u001b[0m     )\n\u001b[1;32m    550\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cython_op_ndim_compat(\n\u001b[1;32m    551\u001b[0m     values,\n\u001b[1;32m    552\u001b[0m     min_count\u001b[38;5;241m=\u001b[39mmin_count,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    556\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    557\u001b[0m )\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pandas/core/arrays/categorical.py:2740\u001b[0m, in \u001b[0;36mCategorical._groupby_op\u001b[0;34m(self, how, has_dropped_na, min_count, ngroups, ids, **kwargs)\u001b[0m\n\u001b[1;32m   2738\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m kind \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtransform\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m   2739\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m type does not support \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhow\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m operations\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 2740\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m dtype does not support aggregation \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhow\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   2742\u001b[0m result_mask \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   2743\u001b[0m mask \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39misna()\n",
      "\u001b[0;31mTypeError\u001b[0m: category dtype does not support aggregation 'mean'"
     ]
    }
   ],
   "source": [
    "tips.groupby(['sex','smoker','time']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/54/wkbn7sg16737fs9875v7bgl40000gn/T/ipykernel_74252/2844327552.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  tips.groupby(['sex'])['tip'].mean()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "sex\n",
       "Male      3.089618\n",
       "Female    2.833448\n",
       "Name: tip, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby(['sex'])['tip'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Simple plots using Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='total_bill', ylabel='tip'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tips.plot.scatter(x='total_bill', y='tip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/54/wkbn7sg16737fs9875v7bgl40000gn/T/ipykernel_74252/2946917802.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  tips['tip'].hist(by=tips['sex'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([<Axes: title={'center': 'Male'}>,\n",
       "       <Axes: title={'center': 'Female'}>], dtype=object)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AABiF8AIAAIxCeAEAAEYhvADwm3Xr1qlfv36KioqSZVlavny5R/uwYcNkWZbH0qVLl7Med8mSJWrdurUcDodat26tZcuWVdAZAPAHwgsAvzl8+LDatWunGTNmlNnnj3/8o3bv3u1e3nvvvXKPuWHDBg0cOFB33323vvjiC919992644479Nlnn/m6fAB+EuTvAgBUXYmJiUpMTCy3j8PhkNPpPOdjpqenq1evXkpOTpYkJScnKzMzU+np6Vq0aNEF1QsgMDDzAiCgrV27VuHh4WrZsqWGDx+uvXv3ltt/w4YNSkhI8NjWu3dvrV+/vsx9iouLVVRU5LEACFyEFwABKzExUW+99ZY+/vhjPffcc8rKytKNN96o4uLiMvdxuVyKiIjw2BYRESGXy1XmPmlpaQoNDXUv0dHRPjsHAL7HZSMAAWvgwIHu/27Tpo06duyopk2batWqVRowYECZ+1mW5bFu23aJbWdKTk5WUlKSe72oqIgAAwQwwgsAY0RGRqpp06bKzc0ts4/T6Swxy7J3794SszFncjgccjgcPqsTQMXishEAY+zfv18FBQWKjIwss0/Xrl2VkZHhsW316tXq1q1bRZcHoJIw8wLAbw4dOqQdO3a41/Py8pSTk6OwsDCFhYUpJSVFt912myIjI7Vz50498cQTuvTSS3Xrrbe69xkyZIgaNWqktLQ0SdIjjzyiG264QVOmTFH//v21YsUKffjhh/r0008r/fwAVAzCCwC/yc7OVo8ePdzrp+87GTp0qGbOnKmvvvpKb7zxhn755RdFRkaqR48eevvttxUcHOzeJz8/X9Wq/WcSuVu3blq8eLH+9re/6cknn1Tz5s319ttvq3PnzpV3YgAqFOEFgN/Ex8fLtu0y2z/44IOzHmPt2rUltt1+++26/fbbL6Q0AAGMe14AAIBRCC8AAMAohBcAAGAU7nmpYM0eX+XT4+2c3MenxwMAwDTMvAAAAKMQXgAAgFEuKLykpaXJsiyNHj3avc22baWkpCgqKkq1a9dWfHy8tm7deqF1AgAASLqA8JKVlaVXX31VV111lcf2qVOnavr06ZoxY4aysrLkdDrVq1cvHTx48IKLBQAAOK/wcujQId1111167bXXdMkll7i327at9PR0jRs3TgMGDFCbNm00f/58/frrr1q4cKHPigYAAFXXeYWXBx54QH369NEf/vAHj+15eXlyuVxKSEhwb3M4HIqLi9P69etLPVZxcbGKioo8FgAAgLJ4/VXpxYsXa/PmzcrKyirRdvo19L9/9XxERIR27dpV6vHS0tI0YcIEb8sAAABVlFczLwUFBXrkkUe0YMEC1apVq8x+lmV5rNu2XWLbacnJySosLHQvBQUF3pQEAACqGK9mXjZt2qS9e/eqQ4cO7m0nT57UunXrNGPGDG3fvl3SbzMwkZGR7j579+4tMRtzmsPhkMPhOJ/aAQBn4esHZQKBwKuZl549e+qrr75STk6Oe+nYsaPuuusu5eTk6LLLLpPT6VRGRoZ7n2PHjikzM1PdunXzefEAAKDq8WrmJTg4WG3atPHYVrduXTVo0MC9ffTo0UpNTVVsbKxiY2OVmpqqOnXqaPDgwb6rGgAAVFk+f7fR2LFjdeTIEY0aNUoHDhxQ586dtXr1agUHB/v6owAAQBV0weFl7dq1HuuWZSklJUUpKSkXemgAAIASeLcRAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8APCbdevWqV+/foqKipJlWVq+fLm77fjx43rsscfUtm1b1a1bV1FRURoyZIh++umnco85b948WZZVYjl69GgFnw2AykJ4AeA3hw8fVrt27TRjxowSbb/++qs2b96sJ598Ups3b9bSpUv13Xff6eabbz7rcUNCQrR7926PpVatWhVxCgD8IMjfBQCouhITE5WYmFhqW2hoqDIyMjy2vfTSS7r22muVn5+vJk2alHlcy7LkdDrPuY7i4mIVFxe714uKis55XwCVj5kXAMYoLCyUZVmqX79+uf0OHTqkpk2bqnHjxurbt6+2bNlSbv+0tDSFhoa6l+joaB9WDcDXCC8AjHD06FE9/vjjGjx4sEJCQsrsd/nll2vevHlauXKlFi1apFq1aum6665Tbm5umfskJyersLDQvRQUFFTEKQDwES4bAQh4x48f15133qlTp07plVdeKbdvly5d1KVLF/f6ddddp2uuuUYvvfSSXnzxxVL3cTgccjgcPq0ZQMUhvAAIaMePH9cdd9yhvLw8ffzxx+XOupSmWrVq6tSpU7kzLwDMwmUjAAHrdHDJzc3Vhx9+qAYNGnh9DNu2lZOTo8jIyAqoEIA/MPMCwG8OHTqkHTt2uNfz8vKUk5OjsLAwRUVF6fbbb9fmzZv17rvv6uTJk3K5XJKksLAw1axZU5I0ZMgQNWrUSGlpaZKkCRMmqEuXLoqNjVVRUZFefPFF5eTk6OWXX678EwRQIQgvAPwmOztbPXr0cK8nJSVJkoYOHaqUlBStXLlSknT11Vd77LdmzRrFx8dLkvLz81Wt2n8mkX/55Rfdd999crlcCg0NVfv27bVu3Tpde+21FXsyACoN4QWA38THx8u27TLby2s7be3atR7rzz//vJ5//vkLLQ1AAOOeFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYJ8ncBAGCyZo+v8ncJxvP1z3Dn5D4+PR4CDzMvAADAKIQXAABgFMILAAAwilfhZebMmbrqqqsUEhKikJAQde3aVf/4xz/c7bZtKyUlRVFRUapdu7bi4+O1detWnxcNAACqLq/CS+PGjTV58mRlZ2crOztbN954o/r37+8OKFOnTtX06dM1Y8YMZWVlyel0qlevXjp48GCFFA8AAKoer8JLv379dNNNN6lly5Zq2bKlnnnmGdWrV08bN26UbdtKT0/XuHHjNGDAALVp00bz58/Xr7/+qoULF1ZU/QAAoIo573teTp48qcWLF+vw4cPq2rWr8vLy5HK5lJCQ4O7jcDgUFxen9evXl3mc4uJiFRUVeSwAAABl8Tq8fPXVV6pXr54cDodGjhypZcuWqXXr1nK5XJKkiIgIj/4RERHuttKkpaUpNDTUvURHR3tbEgAAqEK8Di+tWrVSTk6ONm7cqPvvv19Dhw7VN9984263LMujv23bJbadKTk5WYWFhe6loKDA25IAAEAV4vUTdmvWrKkWLVpIkjp27KisrCy98MILeuyxxyRJLpdLkZGR7v579+4tMRtzJofDIYfD4W0ZAACgirrg57zYtq3i4mLFxMTI6XQqIyPD3Xbs2DFlZmaqW7duF/oxAAAAkryceXniiSeUmJio6OhoHTx4UIsXL9batWv1/vvvy7IsjR49WqmpqYqNjVVsbKxSU1NVp04dDR48uKLqBwAAVYxX4WXPnj26++67tXv3boWGhuqqq67S+++/r169ekmSxo4dqyNHjmjUqFE6cOCAOnfurNWrVys4OLhCigcAAFWPV+Fl9uzZ5bZblqWUlBSlpKRcSE0AAABl4t1GAPxm3bp16tevn6KiomRZlpYvX+7Rfr6vHFmyZIlat24th8Oh1q1ba9myZRV0BgD8gfACwG8OHz6sdu3aacaMGaW2n88rRzZs2KCBAwfq7rvv1hdffKG7775bd9xxhz777LOKOg0Alczrr0oDgK8kJiYqMTGx1Lbfv3JEkubPn6+IiAgtXLhQI0aMKHW/9PR09erVS8nJyZJ+e5ZUZmam0tPTtWjRooo5EQCVipkXAAHpfF85smHDBo99JKl37968pgS4iBBeAASk833liMvl4jUlwEWO8AIgoHn7ypHz2YfXlABm4Z4XAAHJ6XRK8v6VI06ns8QsC68pAS4uzLwACEjn+8qRrl27euwjSatXr+Y1JcBFhJkXAH5z6NAh7dixw72el5ennJwchYWFqUmTJuf0ypEhQ4aoUaNGSktLkyQ98sgjuuGGGzRlyhT1799fK1as0IcffqhPP/200s8PQMUgvADwm+zsbPXo0cO9npSUJEkaOnSo5s2bd06vHMnPz1e1av+ZRO7WrZsWL16sv/3tb3ryySfVvHlzvf322+rcuXPlnRiACmXZtm37u4gzFRUVKTQ0VIWFhQoJCan0z2/2+KpK/0xv7Jzcx98l4CLl79+9QOLNzyLQx4yqiHHSTN783nHPCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFGC/F3AhWr2+Cp/lwAAACoRMy8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFGMf0gdAABn8vXDS3dO7uPT4+HCMfMCAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAatZs2ayLKvE8sADD5Taf+3ataX2//bbbyu5cgAVia9KAwhYWVlZOnnypHv966+/Vq9evfRf//Vf5e63fft2hYSEuNcbNmxYYTUCqHyEFwAB6/ehY/LkyWrevLni4uLK3S88PFz169evwMoA+BOXjQAY4dixY1qwYIHuueceWZZVbt/27dsrMjJSPXv21Jo1a8567OLiYhUVFXksAAIX4QWAEZYvX65ffvlFw4YNK7NPZGSkXn31VS1ZskRLly5Vq1at1LNnT61bt67cY6elpSk0NNS9REdH+7h6AL7EZSMARpg9e7YSExMVFRVVZp9WrVqpVatW7vWuXbuqoKBAzz77rG644YYy90tOTlZSUpJ7vaioiAADBDDCC4CAt2vXLn344YdaunSp1/t26dJFCxYsKLePw+GQw+E43/IAVDIuGwEIeHPnzlV4eLj69PH+BXlbtmxRZGRkBVQFwF+8Ci9paWnq1KmTgoODFR4erltuuUXbt2/36GPbtlJSUhQVFaXatWsrPj5eW7du9WnRAKqOU6dOae7cuRo6dKiCgjwni5OTkzVkyBD3enp6upYvX67c3Fxt3bpVycnJWrJkiR588MHKLhtABfIqvGRmZuqBBx7Qxo0blZGRoRMnTighIUGHDx9295k6daqmT5+uGTNmKCsrS06nU7169dLBgwd9XjyAi9+HH36o/Px83XPPPSXadu/erfz8fPf6sWPHNGbMGF111VXq3r27Pv30U61atUoDBgyozJIBVDDLtm37fHf++eefFR4erszMTN1www2ybVtRUVEaPXq0HnvsMUm/fQUxIiJCU6ZM0YgRI856zKKiIoWGhqqwsNDjIVNlafb4qvMt30g7J3s/bQ6cC29/9y5m3vwsqtoYVBUx7lYOb37vLuiel8LCQklSWFiYJCkvL08ul0sJCQnuPg6HQ3FxcVq/fn2px+D5CgAAwBvnHV5s21ZSUpKuv/56tWnTRpLkcrkkSRERER59IyIi3G2/x/MVAACAN877q9IPPvigvvzyS3366acl2n7/9Evbtst8IibPVwAAVDW+vtxY1S5tnVd4eeihh7Ry5UqtW7dOjRs3dm93Op2SfpuBOfOriXv37i0xG3Maz1cAAADe8OqykW3bevDBB7V06VJ9/PHHiomJ8WiPiYmR0+lURkaGe9uxY8eUmZmpbt26+aZiAABQpXk18/LAAw9o4cKFWrFihYKDg933sYSGhqp27dqyLEujR49WamqqYmNjFRsbq9TUVNWpU0eDBw+ukBMAAABVi1fhZebMmZKk+Ph4j+1z5851vyxt7NixOnLkiEaNGqUDBw6oc+fOWr16tYKDg31SMAAAqNq8Ci/n8kgYy7KUkpKilJSU860JAACgTLyY0TAV8UCsqnaXOgDAbLyYEQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAASslJQUWZblsTidznL3yczMVIcOHVSrVi1ddtllmjVrViVVC6CyBPm7AAAoz5VXXqkPP/zQvV69evUy++bl5emmm27S8OHDtWDBAv3zn//UqFGj1LBhQ912222VUS6ASkB4ARDQgoKCzjrbctqsWbPUpEkTpaenS5KuuOIKZWdn69lnny03vBQXF6u4uNi9XlRUdEE1A6hYXDYCENByc3MVFRWlmJgY3Xnnnfrhhx/K7LthwwYlJCR4bOvdu7eys7N1/PjxMvdLS0tTaGioe4mOjvZZ/QB8j/ACIGB17txZb7zxhj744AO99tprcrlc6tatm/bv319qf5fLpYiICI9tEREROnHihPbt21fm5yQnJ6uwsNC9FBQU+PQ8APgWl40ABKzExET3f7dt21Zdu3ZV8+bNNX/+fCUlJZW6j2VZHuu2bZe6/UwOh0MOh8MHFQOoDMy8ADBG3bp11bZtW+Xm5pba7nQ65XK5PLbt3btXQUFBatCgQWWUCKASEF4AGKO4uFjbtm1TZGRkqe1du3ZVRkaGx7bVq1erY8eOqlGjRmWUCKASEF4ABKwxY8YoMzNTeXl5+uyzz3T77berqKhIQ4cOlfTbvSpDhgxx9x85cqR27dqlpKQkbdu2TXPmzNHs2bM1ZswYf50CgArAPS8AAtaPP/6oQYMGad++fWrYsKG6dOmijRs3qmnTppKk3bt3Kz8/390/JiZG7733nh599FG9/PLLioqK0osvvsgzXoCLDOEFQMBavHhxue3z5s0rsS0uLk6bN2+uoIoABAIuGwEAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYJcjfBcD/mj2+yqfH2zm5j0+PBwDAmZh5AQAARiG8AAAAo3gdXtatW6d+/fopKipKlmVp+fLlHu22bSslJUVRUVGqXbu24uPjtXXrVl/VCwAAqjivw8vhw4fVrl07zZgxo9T2qVOnavr06ZoxY4aysrLkdDrVq1cvHTx48IKLBQAA8PqG3cTERCUmJpbaZtu20tPTNW7cOA0YMECSNH/+fEVERGjhwoUaMWJEiX2Ki4tVXFzsXi8qKvK2JAAAUIX49J6XvLw8uVwuJSQkuLc5HA7FxcVp/fr1pe6Tlpam0NBQ9xIdHe3LkgAAwEXGp+HF5XJJkiIiIjy2R0REuNt+Lzk5WYWFhe6loKDAlyUBAICLTIU858WyLI9127ZLbDvN4XDI4XBURBkAAOAi5NOZF6fTKUklZln27t1bYjYGAADgfPg0vMTExMjpdCojI8O97dixY8rMzFS3bt18+VEAAKCK8vqy0aFDh7Rjxw73el5ennJychQWFqYmTZpo9OjRSk1NVWxsrGJjY5Wamqo6depo8ODBPi0cAABUTV6Hl+zsbPXo0cO9npSUJEkaOnSo5s2bp7Fjx+rIkSMaNWqUDhw4oM6dO2v16tUKDg72XdUAAKDK8jq8xMfHy7btMtsty1JKSopSUlIupC4AAIBS8W4jAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAASstLU2dOnVScHCwwsPDdcstt2j79u3l7rN27VpZllVi+fbbbyupagAVjfACIGBlZmbqgQce0MaNG5WRkaETJ04oISFBhw8fPuu+27dv1+7du91LbGxsJVQMoDJ4/XoAAKgs77//vsf63LlzFR4erk2bNumGG24od9/w8HDVr1+/AqsD4C/MvAAwRmFhoSQpLCzsrH3bt2+vyMhI9ezZU2vWrCm3b3FxsYqKijwWAIGL8ALACLZtKykpSddff73atGlTZr/IyEi9+uqrWrJkiZYuXapWrVqpZ8+eWrduXZn7pKWlKTQ01L1ER0dXxCkA8BEuGwEwwoMPPqgvv/xSn376abn9WrVqpVatWrnXu3btqoKCAj377LNlXmpKTk5WUlKSe72oqIgAAwQwZl4ABLyHHnpIK1eu1Jo1a9S4cWOv9+/SpYtyc3PLbHc4HAoJCfFYAAQuZl4ABCzbtvXQQw9p2bJlWrt2rWJiYs7rOFu2bFFkZKSPqwPgL4QXAAHrgQce0MKFC7VixQoFBwfL5XJJkkJDQ1W7dm1Jv13y+de//qU33nhDkpSenq5mzZrpyiuv1LFjx7RgwQItWbJES5Ys8dt5APAtwguAgDVz5kxJUnx8vMf2uXPnatiwYZKk3bt3Kz8/39127NgxjRkzRv/6179Uu3ZtXXnllVq1apVuuummyiobQAUjvAAIWLZtn7XPvHnzPNbHjh2rsWPHVlBFAAIBN+wCAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIzCc17gc80eX+XT4+2c3MenxwMAmI2ZFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMwosZEfB40SMA4EyEFwAAyuHrP6BMEOh/NHLZCAAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACj8GJGVDlV8SVrvEkbwMWEmRcAAGAUwgsAADBKhYWXV155RTExMapVq5Y6dOigTz75pKI+CsBFztvxJDMzUx06dFCtWrV02WWXadasWZVUKYDKUCHh5e2339bo0aM1btw4bdmyRd27d1diYqLy8/Mr4uMAXMS8HU/y8vJ00003qXv37tqyZYueeOIJPfzww1qyZEklVw6golRIeJk+fbruvfde/fnPf9YVV1yh9PR0RUdHa+bMmRXxcQAuYt6OJ7NmzVKTJk2Unp6uK664Qn/+8591zz336Nlnn63kygFUFJ9/2+jYsWPatGmTHn/8cY/tCQkJWr9+fYn+xcXFKi4udq8XFhZKkoqKis7p804V/3oB1QJVw7n8Pp3uY9t2RZdzzrwdTyRpw4YNSkhI8NjWu3dvzZ49W8ePH1eNGjVK7HMh4xBjEALBuf5/5rny9b9rX49BPg8v+/bt08mTJxUREeGxPSIiQi6Xq0T/tLQ0TZgwocT26OhoX5cGVFmh6efe9+DBgwoNDa2wWrzh7XgiSS6Xq9T+J06c0L59+xQZGVliH8YhmM6b33F/8PUYVGHPebEsy2Pdtu0S2yQpOTlZSUlJ7vVTp07p3//+txo0aFBq/8pQVFSk6OhoFRQUKCQkxC81eIuaK8fFXLNt2zp48KCioqIqsbpzc67jSXn9S9t+WiCOQ+fLxH+jlY2f0bmp7J+TN2OQz8PLpZdequrVq5f4q2jv3r0l/hqSJIfDIYfD4bGtfv36vi7rvISEhBj3D5uaK8fFWnOgzLic5u14IklOp7PU/kFBQWrQoEGp+wTyOHS+TPw3Wtn4GZ2byvw5nesY5PMbdmvWrKkOHTooIyPDY3tGRoa6devm648DcBE7n/Gka9euJfqvXr1aHTt2LPV+FwDmqZBvGyUlJen111/XnDlztG3bNj366KPKz8/XyJEjK+LjAFzEzjaeJCcna8iQIe7+I0eO1K5du5SUlKRt27Zpzpw5mj17tsaMGeOvUwDgYxVyz8vAgQO1f/9+TZw4Ubt371abNm303nvvqWnTphXxcT7ncDg0fvz4EtPIgYyaKwc1V76zjSe7d+/2eOZLTEyM3nvvPT366KN6+eWXFRUVpRdffFG33Xabv06hUpn+v3dl4Gd0bgL552TZgfS9SAAAgLPg3UYAAMAohBcAAGAUwgsAADAK4QUAABiF8HIR4d5rADg3jJdmI7xcRBwOh7Zt2+bvMgAg4DFemq3C3m1kkiNHjmjTpk0KCwtT69atPdqOHj2qd955x+MhWP525jtYznTy5ElNnjzZ/Qj06dOnV2ZZ5dqyZYvq16+vmJgYSdKCBQs0c+ZM5efnq2nTpnrwwQd15513+rnKkl566SVlZ2erT58+uuOOO/Tmm28qLS1Np06d0oABAzRx4kQFBfFrBP8xbfyqbCaOl/5g2hhd5Z/z8t133ykhIUH5+fmyLEvdu3fXokWL3G+e3bNnj6KionTy5Ek/V/of1apVU7t27Uq8eyUzM1MdO3ZU3bp1ZVmWPv74Y/8UWIprrrlGzz33nHr06KHXX39dDz/8sIYPH64rrrhC27dv1+uvv64XXnhB99xzj79LdZs0aZKmTZumhIQE/fOf/9To0aM1bdo0Pfroo6pWrZqef/553X///aW+jdjfDh8+rIULF2r9+vVyuVyyLEsRERG67rrrNGjQINWtW9ffJcIHTBy/KpuJ46U/GDdG21XcLbfcYvft29f++eef7dzcXLtfv352TEyMvWvXLtu2bdvlctnVqlXzc5WeUlNT7ZiYGPujjz7y2B4UFGRv3brVT1WVr06dOu6fafv27e3/+Z//8Wh/66237NatW/ujtDJddtll9pIlS2zbtu2cnBy7evXq9oIFC9ztS5cutVu0aOGv8sq0detWOyoqyq5fv77dv39/+7777rOHDx9u9+/f365fv77dqFGjgP13Au+YOH5VNhPHS38wbYyu8uElPDzc/vLLLz22jRo1ym7SpIn9/fffB+wv/+eff263bNnS/stf/mIfO3bMtu3A/mVs0KCBnZ2dbdv2bz/znJwcj/YdO3bYtWvX9kdpZapdu7b7l9m2bbtGjRr2119/7V7fuXOnXadOHX+UVq74+Hj7zjvvtIuLi0u0FRcX24MGDbLj4+P9UBl8zdTxq7KZNl76g2ljdJW/YffIkSMl7ll4+eWXdfPNNysuLk7fffednyorX6dOnbRp0yb9/PPP6tixo7766itZluXvssqUmJiomTNnSpLi4uL097//3aP9nXfeUYsWLfxRWpmcTqe++eYbSVJubq5OnjzpXpekrVu3Kjw83F/llemzzz7Tk08+qZo1a5Zoq1mzpp544gl99tlnfqgMvmbq+FXZTBsv/cG0MbrK32l4+eWXKzs7W1dccYXH9pdeekm2bevmm2/2U2VnV69ePc2fP1+LFy9Wr169Avq69pQpU3TdddcpLi5OHTt21HPPPae1a9e6r6du3LhRy5Yt83eZHgYPHqwhQ4aof//++uijj/TYY49pzJgx2r9/vyzL0jPPPKPbb7/d32WWcMkllyg3N7fEzZun7dixQ5dcckklV4WKYPL4VdlMGi/9wbgx2t9TP/6WmppqJyYmltl+//3325ZlVWJF56egoMBevny5fejQIX+XUqYDBw7Yjz32mN26dWu7Vq1ads2aNe2mTZvagwcPtrOysvxdXgknTpywn376abtv37725MmTbdu27UWLFtnR0dF2gwYN7GHDhgXkz3v8+PF2aGioPW3aNDsnJ8fevXu37XK57JycHHvatGn2JZdcYk+YMMHfZcIHLpbxq7KZMF76g0ljdJX/thFwMZoyZYpeeOEF9zeNpN8eyuV0OjV69GiNHTvWzxUCwPkjvAAXsby8PLlcLkm/3cNz+hkOAGAywgtQxRQUFGj8+PGaM2eOv0sBgPNCeAGqmC+++ELXXHMNNywCMFaV/7YRcLFZuXJlue0//PBDJVUCABWDmRfgIlOtWjVZllXuW3Mty2LmBYCxqvxD6oCLTWRkpJYsWaJTp06VumzevNnfJQLABSG8ABeZDh06lBtQzjYrAwCBjntegIvMX//6Vx0+fLjM9hYtWmjNmjWVWBEA+Bb3vAAAAKNw2QgAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYJT/B2+JZdUFcB6JAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tips['tip'].hist(by=tips['sex'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'No'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[52], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m tips\u001b[38;5;241m.\u001b[39mcorr()\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pandas/core/frame.py:11049\u001b[0m, in \u001b[0;36mDataFrame.corr\u001b[0;34m(self, method, min_periods, numeric_only)\u001b[0m\n\u001b[1;32m  11047\u001b[0m cols \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mcolumns\n\u001b[1;32m  11048\u001b[0m idx \u001b[38;5;241m=\u001b[39m cols\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[0;32m> 11049\u001b[0m mat \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mto_numpy(dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mfloat\u001b[39m, na_value\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mnan, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m  11051\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpearson\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m  11052\u001b[0m     correl \u001b[38;5;241m=\u001b[39m libalgos\u001b[38;5;241m.\u001b[39mnancorr(mat, minp\u001b[38;5;241m=\u001b[39mmin_periods)\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pandas/core/frame.py:1993\u001b[0m, in \u001b[0;36mDataFrame.to_numpy\u001b[0;34m(self, dtype, copy, na_value)\u001b[0m\n\u001b[1;32m   1991\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1992\u001b[0m     dtype \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mdtype(dtype)\n\u001b[0;32m-> 1993\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mgr\u001b[38;5;241m.\u001b[39mas_array(dtype\u001b[38;5;241m=\u001b[39mdtype, copy\u001b[38;5;241m=\u001b[39mcopy, na_value\u001b[38;5;241m=\u001b[39mna_value)\n\u001b[1;32m   1994\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m dtype:\n\u001b[1;32m   1995\u001b[0m     result \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(result, dtype\u001b[38;5;241m=\u001b[39mdtype)\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pandas/core/internals/managers.py:1694\u001b[0m, in \u001b[0;36mBlockManager.as_array\u001b[0;34m(self, dtype, copy, na_value)\u001b[0m\n\u001b[1;32m   1692\u001b[0m         arr\u001b[38;5;241m.\u001b[39mflags\u001b[38;5;241m.\u001b[39mwriteable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m   1693\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1694\u001b[0m     arr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_interleave(dtype\u001b[38;5;241m=\u001b[39mdtype, na_value\u001b[38;5;241m=\u001b[39mna_value)\n\u001b[1;32m   1695\u001b[0m     \u001b[38;5;66;03m# The underlying data was copied within _interleave, so no need\u001b[39;00m\n\u001b[1;32m   1696\u001b[0m     \u001b[38;5;66;03m# to further copy if copy=True or setting na_value\u001b[39;00m\n\u001b[1;32m   1698\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m na_value \u001b[38;5;129;01mis\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mno_default:\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pandas/core/internals/managers.py:1747\u001b[0m, in \u001b[0;36mBlockManager._interleave\u001b[0;34m(self, dtype, na_value)\u001b[0m\n\u001b[1;32m   1741\u001b[0m rl \u001b[38;5;241m=\u001b[39m blk\u001b[38;5;241m.\u001b[39mmgr_locs\n\u001b[1;32m   1742\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m blk\u001b[38;5;241m.\u001b[39mis_extension:\n\u001b[1;32m   1743\u001b[0m     \u001b[38;5;66;03m# Avoid implicit conversion of extension blocks to object\u001b[39;00m\n\u001b[1;32m   1744\u001b[0m \n\u001b[1;32m   1745\u001b[0m     \u001b[38;5;66;03m# error: Item \"ndarray\" of \"Union[ndarray, ExtensionArray]\" has no\u001b[39;00m\n\u001b[1;32m   1746\u001b[0m     \u001b[38;5;66;03m# attribute \"to_numpy\"\u001b[39;00m\n\u001b[0;32m-> 1747\u001b[0m     arr \u001b[38;5;241m=\u001b[39m blk\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mto_numpy(  \u001b[38;5;66;03m# type: ignore[union-attr]\u001b[39;00m\n\u001b[1;32m   1748\u001b[0m         dtype\u001b[38;5;241m=\u001b[39mdtype,\n\u001b[1;32m   1749\u001b[0m         na_value\u001b[38;5;241m=\u001b[39mna_value,\n\u001b[1;32m   1750\u001b[0m     )\n\u001b[1;32m   1751\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1752\u001b[0m     arr \u001b[38;5;241m=\u001b[39m blk\u001b[38;5;241m.\u001b[39mget_values(dtype)\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pandas/core/arrays/base.py:568\u001b[0m, in \u001b[0;36mExtensionArray.to_numpy\u001b[0;34m(self, dtype, copy, na_value)\u001b[0m\n\u001b[1;32m    539\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mto_numpy\u001b[39m(\n\u001b[1;32m    540\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    541\u001b[0m     dtype: npt\u001b[38;5;241m.\u001b[39mDTypeLike \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    542\u001b[0m     copy: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    543\u001b[0m     na_value: \u001b[38;5;28mobject\u001b[39m \u001b[38;5;241m=\u001b[39m lib\u001b[38;5;241m.\u001b[39mno_default,\n\u001b[1;32m    544\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray:\n\u001b[1;32m    545\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    546\u001b[0m \u001b[38;5;124;03m    Convert to a NumPy ndarray.\u001b[39;00m\n\u001b[1;32m    547\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    566\u001b[0m \u001b[38;5;124;03m    numpy.ndarray\u001b[39;00m\n\u001b[1;32m    567\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 568\u001b[0m     result \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(\u001b[38;5;28mself\u001b[39m, dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[1;32m    569\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m copy \u001b[38;5;129;01mor\u001b[39;00m na_value \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mno_default:\n\u001b[1;32m    570\u001b[0m         result \u001b[38;5;241m=\u001b[39m result\u001b[38;5;241m.\u001b[39mcopy()\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pandas/core/arrays/_mixins.py:81\u001b[0m, in \u001b[0;36mravel_compat.<locals>.method\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     78\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(meth)\n\u001b[1;32m     79\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmethod\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m     80\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m---> 81\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m meth(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m     83\u001b[0m     flags \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ndarray\u001b[38;5;241m.\u001b[39mflags\n\u001b[1;32m     84\u001b[0m     flat \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mravel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mK\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pandas/core/arrays/categorical.py:1664\u001b[0m, in \u001b[0;36mCategorical.__array__\u001b[0;34m(self, dtype, copy)\u001b[0m\n\u001b[1;32m   1662\u001b[0m ret \u001b[38;5;241m=\u001b[39m take_nd(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcategories\u001b[38;5;241m.\u001b[39m_values, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_codes)\n\u001b[1;32m   1663\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype \u001b[38;5;129;01mand\u001b[39;00m np\u001b[38;5;241m.\u001b[39mdtype(dtype) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcategories\u001b[38;5;241m.\u001b[39mdtype:\n\u001b[0;32m-> 1664\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39masarray(ret, dtype)\n\u001b[1;32m   1665\u001b[0m \u001b[38;5;66;03m# When we're a Categorical[ExtensionArray], like Interval,\u001b[39;00m\n\u001b[1;32m   1666\u001b[0m \u001b[38;5;66;03m# we need to ensure __array__ gets all the way to an\u001b[39;00m\n\u001b[1;32m   1667\u001b[0m \u001b[38;5;66;03m# ndarray.\u001b[39;00m\n\u001b[1;32m   1668\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39masarray(ret)\n",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'No'"
     ]
    }
   ],
   "source": [
    "tips.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What is the average tips as percent of the total bill?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First we need to calculate an extra \"Percent\" column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tips['percent'] = tips['tip'] /tips['total_bill']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tips['tips_minus_3'] = tips['tip'] - 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tips.percent.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tips.groupby(['sex'])['percent'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Female tips more generously while they pay less in absolute dollars amount in tip. Why?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Would it be because Male spend more in dinner or lunch? Let's check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tips.groupby(['sex'])['total_bill'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## So one has to be careful what questions you are asking!!!  Answer can be different"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Need an Analytical Mind, not just calculating some numbers or running a library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing Data and Outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load a similar dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tips2 = pd.read_csv('data/tips2.csv')\n",
    "tips2['percent'] = tips2['tip'] /tips2['total_bill']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tips2.groupby(['sex'])['tip'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "tips2.groupby(['sex'])['percent'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This dataset has Female paying higher tip both in absolute dollar amount as well as in percentage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## But let's check for null and outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tips2.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tips2.plot.scatter(x='total_bill', y='tip')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other ways to check for outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tips2.tip.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.boxplot(x=tips2['tip'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove outliers and Null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(tips2.shape)\n",
    "tips2.dropna(inplace=True)\n",
    "print(tips2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tips2[ tips2.percent > 0.6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tips2 = tips2[tips2.percent < 0.8]\n",
    "tips2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now Female pay a lower amount of tip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tips2.groupby(['sex'])['tip'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tips2.groupby(['sex'])['percent'].mean()"
   ]
  }
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