SMDM PROJECT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "318010c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns \n",
    "from warnings import filterwarnings\n",
    "filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "80b4b5b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "mydata = pd.read_csv('Wholesale+Customers+Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "95ff7434",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Buyer/Spender</th>\n",
       "      <th>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Other</td>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Other</td>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Other</td>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Other</td>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Other</td>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Buyer/Spender Channel Region  Fresh  Milk  Grocery  Frozen  \\\n",
       "0              1  Retail  Other  12669  9656     7561     214   \n",
       "1              2  Retail  Other   7057  9810     9568    1762   \n",
       "2              3  Retail  Other   6353  8808     7684    2405   \n",
       "3              4   Hotel  Other  13265  1196     4221    6404   \n",
       "4              5  Retail  Other  22615  5410     7198    3915   \n",
       "\n",
       "   Detergents_Paper  Delicatessen  \n",
       "0              2674          1338  \n",
       "1              3293          1776  \n",
       "2              3516          7844  \n",
       "3               507          1788  \n",
       "4              1777          5185  "
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "0728a908",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(440, 9)"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "3234c54c",
   "metadata": {},
   "outputs": [],
   "source": [
    "mydata[\"Spending\"]=mydata[\"Fresh\"]+mydata[\"Milk\"]+mydata[\"Grocery\"]+mydata[\"Frozen\"]+mydata[\"Detergents_Paper\"]+mydata[\"Delicatessen\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "5da063e5",
   "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>Buyer/Spender</th>\n",
       "      <th>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "      <th>Spending</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Other</td>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "      <td>34112</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Other</td>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "      <td>33266</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Other</td>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "      <td>36610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Other</td>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "      <td>27381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Other</td>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "      <td>46100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Buyer/Spender Channel Region  Fresh  Milk  Grocery  Frozen  \\\n",
       "0              1  Retail  Other  12669  9656     7561     214   \n",
       "1              2  Retail  Other   7057  9810     9568    1762   \n",
       "2              3  Retail  Other   6353  8808     7684    2405   \n",
       "3              4   Hotel  Other  13265  1196     4221    6404   \n",
       "4              5  Retail  Other  22615  5410     7198    3915   \n",
       "\n",
       "   Detergents_Paper  Delicatessen  Spending  \n",
       "0              2674          1338     34112  \n",
       "1              3293          1776     33266  \n",
       "2              3516          7844     36610  \n",
       "3               507          1788     27381  \n",
       "4              1777          5185     46100  "
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "22ba2008",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(440, 10)"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "971459df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hotel     0.677273\n",
       "Retail    0.322727\n",
       "Name: Channel, dtype: float64"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata[\"Channel\"].value_counts(\"df_variety\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "dda96e0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hotel     298\n",
       "Retail    142\n",
       "Name: Channel, dtype: int64"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata[\"Channel\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "751063e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Other     0.718182\n",
       "Lisbon    0.175000\n",
       "Oporto    0.106818\n",
       "Name: Region, dtype: float64"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata[\"Region\"].value_counts(\"df_variety\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "7b01b322",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(440, 10)"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "4428086c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hotel     298\n",
       "Retail    142\n",
       "Name: Channel, dtype: int64"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata[\"Channel\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "6740eddb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Other     316\n",
       "Lisbon     77\n",
       "Oporto     47\n",
       "Name: Region, dtype: int64"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata[\"Region\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "99047a8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 440 entries, 0 to 439\n",
      "Data columns (total 10 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   Buyer/Spender     440 non-null    int64 \n",
      " 1   Channel           440 non-null    object\n",
      " 2   Region            440 non-null    object\n",
      " 3   Fresh             440 non-null    int64 \n",
      " 4   Milk              440 non-null    int64 \n",
      " 5   Grocery           440 non-null    int64 \n",
      " 6   Frozen            440 non-null    int64 \n",
      " 7   Detergents_Paper  440 non-null    int64 \n",
      " 8   Delicatessen      440 non-null    int64 \n",
      " 9   Spending          440 non-null    int64 \n",
      "dtypes: int64(8), object(2)\n",
      "memory usage: 34.5+ KB\n"
     ]
    }
   ],
   "source": [
    "mydata.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "186a59c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use methods of descriptive statistics to summarize data. Which Region and which Channel spent the most? Which Region and which Channel spent the least?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "7c8aa75e",
   "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>Buyer/Spender</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "      <th>Spending</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>220.500000</td>\n",
       "      <td>12000.297727</td>\n",
       "      <td>5796.265909</td>\n",
       "      <td>7951.277273</td>\n",
       "      <td>3071.931818</td>\n",
       "      <td>2881.493182</td>\n",
       "      <td>1524.870455</td>\n",
       "      <td>33226.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>127.161315</td>\n",
       "      <td>12647.328865</td>\n",
       "      <td>7380.377175</td>\n",
       "      <td>9503.162829</td>\n",
       "      <td>4854.673333</td>\n",
       "      <td>4767.854448</td>\n",
       "      <td>2820.105937</td>\n",
       "      <td>26356.301730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>904.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>110.750000</td>\n",
       "      <td>3127.750000</td>\n",
       "      <td>1533.000000</td>\n",
       "      <td>2153.000000</td>\n",
       "      <td>742.250000</td>\n",
       "      <td>256.750000</td>\n",
       "      <td>408.250000</td>\n",
       "      <td>17448.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>220.500000</td>\n",
       "      <td>8504.000000</td>\n",
       "      <td>3627.000000</td>\n",
       "      <td>4755.500000</td>\n",
       "      <td>1526.000000</td>\n",
       "      <td>816.500000</td>\n",
       "      <td>965.500000</td>\n",
       "      <td>27492.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>330.250000</td>\n",
       "      <td>16933.750000</td>\n",
       "      <td>7190.250000</td>\n",
       "      <td>10655.750000</td>\n",
       "      <td>3554.250000</td>\n",
       "      <td>3922.000000</td>\n",
       "      <td>1820.250000</td>\n",
       "      <td>41307.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>112151.000000</td>\n",
       "      <td>73498.000000</td>\n",
       "      <td>92780.000000</td>\n",
       "      <td>60869.000000</td>\n",
       "      <td>40827.000000</td>\n",
       "      <td>47943.000000</td>\n",
       "      <td>199891.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Buyer/Spender          Fresh          Milk       Grocery        Frozen  \\\n",
       "count     440.000000     440.000000    440.000000    440.000000    440.000000   \n",
       "mean      220.500000   12000.297727   5796.265909   7951.277273   3071.931818   \n",
       "std       127.161315   12647.328865   7380.377175   9503.162829   4854.673333   \n",
       "min         1.000000       3.000000     55.000000      3.000000     25.000000   \n",
       "25%       110.750000    3127.750000   1533.000000   2153.000000    742.250000   \n",
       "50%       220.500000    8504.000000   3627.000000   4755.500000   1526.000000   \n",
       "75%       330.250000   16933.750000   7190.250000  10655.750000   3554.250000   \n",
       "max       440.000000  112151.000000  73498.000000  92780.000000  60869.000000   \n",
       "\n",
       "       Detergents_Paper  Delicatessen       Spending  \n",
       "count        440.000000    440.000000     440.000000  \n",
       "mean        2881.493182   1524.870455   33226.136364  \n",
       "std         4767.854448   2820.105937   26356.301730  \n",
       "min            3.000000      3.000000     904.000000  \n",
       "25%          256.750000    408.250000   17448.750000  \n",
       "50%          816.500000    965.500000   27492.000000  \n",
       "75%         3922.000000   1820.250000   41307.500000  \n",
       "max        40827.000000  47943.000000  199891.000000  "
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "76d4125f",
   "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>Buyer/Spender</th>\n",
       "      <th>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "      <th>Spending</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>440</td>\n",
       "      <td>440</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>298</td>\n",
       "      <td>316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>220.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12000.297727</td>\n",
       "      <td>5796.265909</td>\n",
       "      <td>7951.277273</td>\n",
       "      <td>3071.931818</td>\n",
       "      <td>2881.493182</td>\n",
       "      <td>1524.870455</td>\n",
       "      <td>33226.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>127.161315</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12647.328865</td>\n",
       "      <td>7380.377175</td>\n",
       "      <td>9503.162829</td>\n",
       "      <td>4854.673333</td>\n",
       "      <td>4767.854448</td>\n",
       "      <td>2820.105937</td>\n",
       "      <td>26356.301730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>904.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>110.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3127.750000</td>\n",
       "      <td>1533.000000</td>\n",
       "      <td>2153.000000</td>\n",
       "      <td>742.250000</td>\n",
       "      <td>256.750000</td>\n",
       "      <td>408.250000</td>\n",
       "      <td>17448.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>220.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8504.000000</td>\n",
       "      <td>3627.000000</td>\n",
       "      <td>4755.500000</td>\n",
       "      <td>1526.000000</td>\n",
       "      <td>816.500000</td>\n",
       "      <td>965.500000</td>\n",
       "      <td>27492.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>330.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16933.750000</td>\n",
       "      <td>7190.250000</td>\n",
       "      <td>10655.750000</td>\n",
       "      <td>3554.250000</td>\n",
       "      <td>3922.000000</td>\n",
       "      <td>1820.250000</td>\n",
       "      <td>41307.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112151.000000</td>\n",
       "      <td>73498.000000</td>\n",
       "      <td>92780.000000</td>\n",
       "      <td>60869.000000</td>\n",
       "      <td>40827.000000</td>\n",
       "      <td>47943.000000</td>\n",
       "      <td>199891.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Buyer/Spender Channel Region          Fresh          Milk  \\\n",
       "count      440.000000     440    440     440.000000    440.000000   \n",
       "unique            NaN       2      3            NaN           NaN   \n",
       "top               NaN   Hotel  Other            NaN           NaN   \n",
       "freq              NaN     298    316            NaN           NaN   \n",
       "mean       220.500000     NaN    NaN   12000.297727   5796.265909   \n",
       "std        127.161315     NaN    NaN   12647.328865   7380.377175   \n",
       "min          1.000000     NaN    NaN       3.000000     55.000000   \n",
       "25%        110.750000     NaN    NaN    3127.750000   1533.000000   \n",
       "50%        220.500000     NaN    NaN    8504.000000   3627.000000   \n",
       "75%        330.250000     NaN    NaN   16933.750000   7190.250000   \n",
       "max        440.000000     NaN    NaN  112151.000000  73498.000000   \n",
       "\n",
       "             Grocery        Frozen  Detergents_Paper  Delicatessen  \\\n",
       "count     440.000000    440.000000        440.000000    440.000000   \n",
       "unique           NaN           NaN               NaN           NaN   \n",
       "top              NaN           NaN               NaN           NaN   \n",
       "freq             NaN           NaN               NaN           NaN   \n",
       "mean     7951.277273   3071.931818       2881.493182   1524.870455   \n",
       "std      9503.162829   4854.673333       4767.854448   2820.105937   \n",
       "min         3.000000     25.000000          3.000000      3.000000   \n",
       "25%      2153.000000    742.250000        256.750000    408.250000   \n",
       "50%      4755.500000   1526.000000        816.500000    965.500000   \n",
       "75%     10655.750000   3554.250000       3922.000000   1820.250000   \n",
       "max     92780.000000  60869.000000      40827.000000  47943.000000   \n",
       "\n",
       "             Spending  \n",
       "count      440.000000  \n",
       "unique            NaN  \n",
       "top               NaN  \n",
       "freq              NaN  \n",
       "mean     33226.136364  \n",
       "std      26356.301730  \n",
       "min        904.000000  \n",
       "25%      17448.750000  \n",
       "50%      27492.000000  \n",
       "75%      41307.500000  \n",
       "max     199891.000000  "
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "992a4d9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Channel', ylabel='Spending'>"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"Channel\",y=\"Spending\",data=mydata)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "415144f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Region', ylabel='Spending'>"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"Region\",y=\"Spending\",data=mydata)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "b8169ce5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f99bc2256d0>"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Channel\",y=\"Fresh\",kind=\"bar\",ci=None,data=mydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "734adabe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f99bc207d30>"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Region\",y=\"Fresh\",kind=\"bar\",ci=None,data=mydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "622b15a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f99bc2717c0>"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Channel\",y=\"Milk\",kind=\"bar\",ci=None,data=mydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "1426ea28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f99c9753a90>"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Region\",y=\"Milk\",kind=\"bar\",ci=None,data=mydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "c05ecbb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f99bc9d63d0>"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Channel\",y=\"Grocery\",kind=\"bar\",ci=None,data=mydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "aed770e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f99bca6c130>"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAFgCAYAAACFYaNMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAVMElEQVR4nO3df/SedX3f8efLUOTXmLAGDiapoItawIpLSlFcjy3dIbM9C25yjCsFlZbVovijzkHXVdeVHY+udtoVZo4WQoulCLSwcmTNCVirB4nhh4YfUjJZIYNCsAMBIUp874/7k3kbvkm+Id/r++F7f5+Pc+5zX9f7/lxX3nfum1cuPvd9XXeqCknS7HtB7wYkab4ygCWpEwNYkjoxgCWpEwNYkjrZp3cDQ1mxYkVdd911vduQJIBMVZzYI+BHHnmkdwuStEsTG8CS9HxnAEtSJwawJHViAEtSJwawJHViAEtSJwawJHViAEtSJwawJHViAEtSJwawJHViAEtSJwawJHUysZejlDR9J/7+ib1bmJO+/O4v79X2HgFLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUieDBnCS9yW5I8ntSf4kyX5JDk2yNsk97f6QsfHnJdmU5O4kJ4/VlyXZ2B77ZJIM2bckzYbBAjjJIuAcYHlVHQssAFYB5wLrqmopsK6tk+To9vgxwArggiQL2u4uBM4ClrbbiqH6lqTZMvQUxD7A/kn2AQ4AHgBWAmva42uAU9rySuCyqtpaVfcCm4DjkxwBHFxVN1ZVAZeMbSNJc9ZgAVxV/wf4L8B9wIPAY1X1l8DhVfVgG/MgcFjbZBFw/9guNrfaora8Y/1ZkpyVZEOSDVu2bJnJpyNJM27IKYhDGB3VHgW8GDgwyWm72mSKWu2i/uxi1eqqWl5VyxcuXLinLUvSrBpyCuLngHuraktVfQ+4Cngd8FCbVqDdP9zGbwaWjG2/mNGUxea2vGNdkua0fQbc933ACUkOAJ4CTgI2AE8CZwAfafdXt/HXAJ9N8nFGR8xLgfVVtS3J40lOAG4CTgd+fyYaXPZvL5mJ3cw7N3/s9N4tSBNhsACuqpuSXAHcAjwD3AqsBg4CLk9yJqOQPrWNvyPJ5cCdbfzZVbWt7e6dwMXA/sDn202S5rQhj4Cpqg8BH9qhvJXR0fBU488Hzp+ivgE4dsYblKSOPBNOkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoZ9Gpo0nTc99uv6t3CnPRjv7WxdwvaSx4BS1InBrAkdWIAS1InBrAkdWIAS1InBrAkdWIAS1InBrAkdWIAS1InBrAkdWIAS1InBrAkdWIAS1InBrAkdWIAS1InBrAkdWIAS1InBrAkdWIAS1InBrAkdWIAS1InBrAkdWIAS1InBrAkdWIAS1InBrAkdWIAS1InBrAkdWIAS1InBrAkdWIAS1InBrAkdWIAS1InBrAkdWIAS1InBrAkdWIAS1InBrAkdWIAS1InBrAkdTJoACd5UZIrknwjyV1JXpvk0CRrk9zT7g8ZG39ekk1J7k5y8lh9WZKN7bFPJsmQfUvSbBj6CPgTwHVV9Urg1cBdwLnAuqpaCqxr6yQ5GlgFHAOsAC5IsqDt50LgLGBpu60YuG9JGtxgAZzkYOCngc8AVNV3q+pRYCWwpg1bA5zSllcCl1XV1qq6F9gEHJ/kCODgqrqxqgq4ZGwbSZqzhjwCfimwBbgoya1JPp3kQODwqnoQoN0f1sYvAu4f235zqy1qyzvWnyXJWUk2JNmwZcuWmX02kjTDhgzgfYB/AlxYVa8BnqRNN+zEVPO6tYv6s4tVq6tqeVUtX7hw4Z72K0mzasgA3gxsrqqb2voVjAL5oTatQLt/eGz8krHtFwMPtPriKeqSNKcNFsBV9XfA/Ule0UonAXcC1wBntNoZwNVt+RpgVZIXJjmK0Ydt69s0xeNJTmjffjh9bBtJmrP2GXj/7wYuTbIv8E3g7YxC//IkZwL3AacCVNUdSS5nFNLPAGdX1ba2n3cCFwP7A59vN0ma0wYN4Kq6DVg+xUMn7WT8+cD5U9Q3AMfOaHOS1JlnwklSJwawJHViAEtSJwawJHViAEtSJwawJHViAEtSJwawJHViAEtSJwawJHViAEtSJwawJHViAEtSJwawJHViAEtSJ9MK4PZDl2cnOWTohiRpvpjuEfAq4MXAV5NcluTk9vNAkqTnaFoBXFWbqurfAy8HPgv8IXBfkv+Y5NAhG5SkSTXtOeAkPwH8LvAx4ErgzcC3geuHaU2SJtu0fhMuyc3Ao8BngHOramt76KYkJw7UmyRNtN0GcJIXAFdW1X+e6vGq+pcz3pUkzQO7nYKoqu8DK2ahF0maV6Y7B7w2yQeSLEly6PbboJ1J0oSb1hww8I52f/ZYrYCXzmw7kjR/TCuAq+qooRuRpPlmumfCHZDkN5OsbutLk/zCsK1J0mSb7hzwRcB3gde19c3A7wzSkSTNE9MN4JdV1UeB7wFU1VOApyJL0l6YbgB/N8n+jD54I8nLgK273kSStCvT/RbEh4DrgCVJLgVOBN42VFOSNB9M91sQa5PcApzAaOrhPVX1yKCdSdKEm+63IN4EPFNV11bVXwDPJDll0M4kacJNdw74Q1X12PaVqnqU0bSEJOk5mm4ATzVuuvPHkqQpTDeANyT5eJKXJXlpkt8Dbh6yMUmadNMN4HczOhHjT4HPAU/zw9eFkCTtoel+C+JJ4NwkBwPfr6onhm1LkibfdL8F8aoktwIbgTuS3Jzk2GFbk6TJNt0piE8B76+ql1TVS4BfB1YP15YkTb7pBvCBVXXD9pWq+gJw4CAdSdI8Md2vkn0zyX8A/qitnwbcO0xLkjQ/TPcI+B3AQuCqdvtR4O1DNSVJ88F0fhV5AfC5qvq5WehHkuaN6fwq8jbgO0n+4Sz0I0nzxnTngJ8GNiZZCzy5vVhV5wzSlSTNA9MN4GvbDdpF2fEXMSRpr+wygJOsBBZX1R+09fWMPowr4N8N354kTa7dzQF/ELhmbH1fYBnwBuBXB+pJkuaF3U1B7FtV94+tf6mq/h74+ySeiCFJe2F3R8CHjK9U1bvGVhfOfDuSNH/sLoBvSvIrOxaT/Btg/TAtSdL8sLspiPcBf57kXwO3tNoy4IXAKQP2JUkTb5cBXFUPA69L8rPAMa18bVVdP3hnkjThpntB9usBQ1eSZtB0L8YjSZphgwdwkgVJbk3yF2390CRrk9zT7g8ZG3tekk1J7k5y8lh9WZKN7bFPJvEsPElz3mwcAb8HuGts/VxgXVUtBda1dZIcDaxiNNe8ArigXYkN4ELgLGBpu62Yhb4laVCDBnCSxcDPA58eK68E1rTlNfzg2xQrgcuqamtV3QtsAo5PcgRwcFXdWFUFXILfwJA0AYY+Av6vjE5n/v5Y7fCqehCg3R/W6ouA8bPuNrfaora8Y/1ZkpyVZEOSDVu2bJmRJyBJQxksgJP8AvBwVd083U2mqNUu6s8uVq2uquVVtXzhQk/Uk/T8Nt3LUT4XJwL/Iskbgf2Ag5P8MfBQkiOq6sE2vfBwG78ZWDK2/WLggVZfPEVdkua0wY6Aq+q8qlpcVUcy+nDt+qo6jdHV1c5ow84Arm7L1wCrkrwwyVGMPmxb36YpHk9yQvv2w+lj20jSnDXkEfDOfAS4PMmZwH3AqQBVdUeSy4E7gWeAs9vPIQG8E7gY2B/4fLtJ0pw2KwFcVV8AvtCWvwWctJNx5wPnT1HfABw7XIeSNPs8E06SOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJamTwQI4yZIkNyS5K8kdSd7T6ocmWZvknnZ/yNg25yXZlOTuJCeP1Zcl2dge+2SSDNW3JM2WIY+AnwF+vap+HDgBODvJ0cC5wLqqWgqsa+u0x1YBxwArgAuSLGj7uhA4C1jabisG7FuSZsVgAVxVD1bVLW35ceAuYBGwEljThq0BTmnLK4HLqmprVd0LbAKOT3IEcHBV3VhVBVwyto0kzVmzMgec5EjgNcBNwOFV9SCMQho4rA1bBNw/ttnmVlvUlnesT/XnnJVkQ5INW7ZsmdHnIEkzbfAATnIQcCXw3qr69q6GTlGrXdSfXaxaXVXLq2r5woUL97xZSZpFgwZwkh9hFL6XVtVVrfxQm1ag3T/c6puBJWObLwYeaPXFU9QlaU4b8lsQAT4D3FVVHx976BrgjLZ8BnD1WH1VkhcmOYrRh23r2zTF40lOaPs8fWwbSZqz9hlw3ycCvwRsTHJbq/0G8BHg8iRnAvcBpwJU1R1JLgfuZPQNirOralvb7p3AxcD+wOfbTZLmtMECuKq+xNTztwAn7WSb84Hzp6hvAI6due4kqT/PhJOkTgxgSerEAJakTgxgSerEAJakTgxgSerEAJakTgxgSerEAJakTgxgSerEAJakTgxgSerEAJakTgxgSerEAJakTgxgSerEAJakTgxgSerEAJakTgxgSerEAJakTgxgSerEAJakTgxgSerEAJakTgxgSerEAJakTgxgSerEAJakTgxgSerEAJakTgxgSerEAJakTgxgSerEAJakTgxgSerEAJakTgxgSerEAJakTgxgSerEAJakTgxgSerEAJakTgxgSerEAJakTgxgSerEAJakTgxgSerEAJakTgxgSerEAJakTgxgSerEAJakTgxgSerEAJakTuZMACdZkeTuJJuSnNu7H0naW3MigJMsAP4A+OfA0cBbkxzdtytJ2jtzIoCB44FNVfXNqvoucBmwsnNPkrRXUlW9e9itJG8GVlTVL7f1XwJ+qqretcO4s4Cz2uorgLtntdGZ9aPAI72bkK/D88AkvAaPVNWKHYv79OjkOcgUtWf9y1FVq4HVw7czvCQbqmp57z7mO1+H/ib5NZgrUxCbgSVj64uBBzr1IkkzYq4E8FeBpUmOSrIvsAq4pnNPkrRX5sQURFU9k+RdwP8EFgB/WFV3dG5raBMxlTIBfB36m9jXYE58CCdJk2iuTEFI0sQxgCWpEwN4liRZnOTqJPck+V9JPpFk3yTHJXnj2LgPJ/lAz14nTZInpqj9apLTd7GNr8MM2dl7f4b2/RszsZ9eDOBZkCTAVcCfV9VS4OXAQcD5wHHAG3e+9R7/WQtmal+TrKr+e1Vd0ruPSbeb9/5e7TfJCwADWLv1s8DTVXURQFVtA94H/DLwUeAtSW5L8pY2/ugkX0jyzSTnbN9JktOSrG9jP7U9bJM8keS3k9wEvHZWn9kcNX6Em+ScJHcm+XqSy8aGvTrJ9e3I7Vfa2CT5WJLbk2zc/poleUN7za5I8o0kl7bwme929t5/R5Jfa0fG17ULbX1o+0ZJ3t/+jm9P8t5WOzLJXUkuAG4BPgPs3/57uHRn2z2fzYmvoU2AY4CbxwtV9e0k/xu4CHj59tOqk3wYeCXwM8A/AO5OciHwj4G3ACdW1ffam/AXgUuAA4Hbq+q3ZufpTJxzgaOqamuSF43VfwI4gdHf761JrmX0D9xxwKsZnSL71SRfbONfw+i1fgD4MnAi8KXZeALPYzt779/HKH+OB44FvsPo7/JaRme5vh34KUZnwd6U5K+A/8voEgNvr6pfA0hyalUd15aXTbVdVd06+LN8jjwCnh1hilOnd1G/tqq2VtUjwMPA4cBJwDJGb9Lb2vpL2/htwJUz3fQ88nXg0iSnAc+M1a+uqqfa63ADo7B4PfAnVbWtqh4C/gr4yTZ+fVVtrqrvA7cBR87WE3ge2917f21VfauqnmI0VfH6dvuzqnqyqp5o9X/atvvbqvrKTv6sXW33vGQAz447gB86lz3JwYxOr942xfitY8vbGB0pBFhTVce12yuq6sNtzNPtf+303Pw8o8udLgNuTrL9/wx3DI5i6uuSbDfV6zbf7e69v6d/x0/u4rE5N+VjAM+OdcAB2z91b3O3vwtcDDzEaKphOvt4c5LD2j4OTfKSYdqdP9oHOUuq6gbgg8CLGH1IBLAyyX5J/hHwBkanxH+R0Zz9giQLgZ8G1s9643PHrt773wH+WXsv7w+cwmjq5ovAKUkOSHIg8Cbgr3ey/+8l+ZG2vCfbPS8YwLOgRqcbvgk4Nck9wN8ATzP6BPcGRh+6jX8IN9U+7gR+E/jLJF8H1gJHDN78ZDggyeax2/vHHlsA/HGSjcCtwO9V1aPtsfXAtcBXgP9UVQ8Af8ZoyuJrwPXAB6vq72bricw1u3nvw2iO/I8YTdlcWVUbquoWRgG9HrgJ+PQu5nFXA19Pcukebve84KnIkrpI8jZg+Y7X9Z5PPAKWpE48ApakTjwClqRODGBJ6sQAlqRODGBNtCTb2lf8bk/yP3Y41XhP9vPiJFfMcHua5/wQThMtyRNVdVBbXgP8TVXt1ZW4pJniEbDmkxuBRQBJXtauwnVzkr9O8sqx+leSfLVdYe6JVj8yye1teb8kF7Wrod2a5Gda/W1Jrmr7vSfJRzs9T80RBrDmhXYK7En84Ne0VwPvrqplwAeAC1r9E8AnquonGV3VbCpnA1TVq4C3AmuS7NceO47RVetexeiU5SUz/FQ0QQxgTbr929XjvgUcCqxNchDwOuBz7bFP8YPTul8LfK4tf3Yn+3w9o9NnqapvAH/L6ELjAOuq6rGqehq4E/B6HdopA1iT7ql2vdiXAPsyOnp9AfDo2JXljquqH9+DfXpFNM0IA1jzQlU9BpzDaLrhKeDeJKfC//+Vi1e3oV8B/lVbXrWT3X2R0cXwSfJy4MeAuwdqXRPMANa80a6M9TVGwfqLwJlJvsbomrUr27D3Au9Psp7RtMRjU+zqAmBBu4LanwJvq6qtU4yTdsmvoUljkhzAaNqikqwC3lpVK3e3nfRcOD8l/bBlwH9rP6j5KPCOvu1oknkELEmdOAcsSZ0YwJLUiQEsSZ0YwJLUiQEsSZ38PyRybS0cixByAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Region\",y=\"Grocery\",kind=\"bar\",ci=None,data=mydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "ba62f41d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f99ca611340>"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Channel\",y=\"Frozen\",kind=\"bar\",ci=None,data=mydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "39689a5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f99d82ec910>"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Region\",y=\"Frozen\",kind=\"bar\",ci=None,data=mydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "b6740bcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f99caa69b50>"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Channel\",y=\"Detergents_Paper\",kind=\"bar\",ci=None,data=mydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "cfa86ef7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f99bca5e490>"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Region\",y=\"Detergents_Paper\",kind=\"bar\",ci=None,data=mydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "9f37871b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f99780aecd0>"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Channel\",y=\"Delicatessen\",kind=\"bar\",ci=None,data=mydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "95b41276",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f99cab65e20>"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Region\",y=\"Delicatessen\",kind=\"bar\",ci=None,data=mydata)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "25ac0409",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0539179237473149"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cov_Fresh=mydata[\"Fresh\"].std()/mydata[\"Fresh\"].mean()\n",
    "cov_Fresh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "7d449232",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2732985840065414"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cov_Milk=mydata[\"Milk\"].std()/mydata[\"Milk\"].mean()\n",
    "cov_Milk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "90dc89d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1951743730016824"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cov_Grocery=mydata[\"Grocery\"].std()/mydata[\"Grocery\"].mean()\n",
    "cov_Grocery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "e5172870",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5803323836352914"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cov_Frozen=mydata[\"Frozen\"].std()/mydata[\"Frozen\"].mean()\n",
    "cov_Frozen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "63f8d4b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6546471385005155"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cov_Detergents_Paper=mydata[\"Detergents_Paper\"].std()/mydata[\"Detergents_Paper\"].mean()\n",
    "cov_Detergents_Paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "8a17318a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.8494068981158382"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cov_Delicatessen=mydata[\"Delicatessen\"].std()/mydata[\"Delicatessen\"].mean()\n",
    "cov_Delicatessen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "32799036",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.8494068981158382"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cov_Fresh\n",
    "cov_Milk\n",
    "cov_Grocery\n",
    "cov_Frozen\n",
    "cov_Detergents_Paper\n",
    "cov_Delicatessen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "901419eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.boxplot(x= \"Fresh\",orient=\"h\", data=mydata)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "437be889",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.boxplot(x= \"Milk\",orient=\"h\", data=mydata)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "8d3d006b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.boxplot(x= \"Grocery\",orient=\"h\", data=mydata)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "028bfbd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.boxplot(x= \"Frozen\",orient=\"h\", data=mydata)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "99f91b14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.boxplot(x= \"Detergents_Paper\",orient=\"h\", data=mydata)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "id": "e4722a33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.boxplot(x= \"Delicatessen\",orient=\"h\", data=mydata)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "c3cd980b",
   "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>Buyer/Spender</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "      <th>Spending</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Buyer/Spender</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.061151</td>\n",
       "      <td>-0.162290</td>\n",
       "      <td>-0.140509</td>\n",
       "      <td>0.053802</td>\n",
       "      <td>-0.134365</td>\n",
       "      <td>-0.101845</td>\n",
       "      <td>-0.150745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fresh</th>\n",
       "      <td>-0.061151</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100510</td>\n",
       "      <td>-0.011854</td>\n",
       "      <td>0.345881</td>\n",
       "      <td>-0.101953</td>\n",
       "      <td>0.244690</td>\n",
       "      <td>0.575178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Milk</th>\n",
       "      <td>-0.162290</td>\n",
       "      <td>0.100510</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.728335</td>\n",
       "      <td>0.123994</td>\n",
       "      <td>0.661816</td>\n",
       "      <td>0.406368</td>\n",
       "      <td>0.776909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grocery</th>\n",
       "      <td>-0.140509</td>\n",
       "      <td>-0.011854</td>\n",
       "      <td>0.728335</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.040193</td>\n",
       "      <td>0.924641</td>\n",
       "      <td>0.205497</td>\n",
       "      <td>0.740680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frozen</th>\n",
       "      <td>0.053802</td>\n",
       "      <td>0.345881</td>\n",
       "      <td>0.123994</td>\n",
       "      <td>-0.040193</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.131525</td>\n",
       "      <td>0.390947</td>\n",
       "      <td>0.388436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <td>-0.134365</td>\n",
       "      <td>-0.101953</td>\n",
       "      <td>0.661816</td>\n",
       "      <td>0.924641</td>\n",
       "      <td>-0.131525</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.069291</td>\n",
       "      <td>0.633882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delicatessen</th>\n",
       "      <td>-0.101845</td>\n",
       "      <td>0.244690</td>\n",
       "      <td>0.406368</td>\n",
       "      <td>0.205497</td>\n",
       "      <td>0.390947</td>\n",
       "      <td>0.069291</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.496849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spending</th>\n",
       "      <td>-0.150745</td>\n",
       "      <td>0.575178</td>\n",
       "      <td>0.776909</td>\n",
       "      <td>0.740680</td>\n",
       "      <td>0.388436</td>\n",
       "      <td>0.633882</td>\n",
       "      <td>0.496849</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Buyer/Spender     Fresh      Milk   Grocery    Frozen  \\\n",
       "Buyer/Spender          1.000000 -0.061151 -0.162290 -0.140509  0.053802   \n",
       "Fresh                 -0.061151  1.000000  0.100510 -0.011854  0.345881   \n",
       "Milk                  -0.162290  0.100510  1.000000  0.728335  0.123994   \n",
       "Grocery               -0.140509 -0.011854  0.728335  1.000000 -0.040193   \n",
       "Frozen                 0.053802  0.345881  0.123994 -0.040193  1.000000   \n",
       "Detergents_Paper      -0.134365 -0.101953  0.661816  0.924641 -0.131525   \n",
       "Delicatessen          -0.101845  0.244690  0.406368  0.205497  0.390947   \n",
       "Spending              -0.150745  0.575178  0.776909  0.740680  0.388436   \n",
       "\n",
       "                  Detergents_Paper  Delicatessen  Spending  \n",
       "Buyer/Spender            -0.134365     -0.101845 -0.150745  \n",
       "Fresh                    -0.101953      0.244690  0.575178  \n",
       "Milk                      0.661816      0.406368  0.776909  \n",
       "Grocery                   0.924641      0.205497  0.740680  \n",
       "Frozen                   -0.131525      0.390947  0.388436  \n",
       "Detergents_Paper          1.000000      0.069291  0.633882  \n",
       "Delicatessen              0.069291      1.000000  0.496849  \n",
       "Spending                  0.633882      0.496849  1.000000  "
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f996343",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c6bfe8e8",
   "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>Buyer/Spender</th>\n",
       "      <th>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Other</td>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Other</td>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Other</td>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Other</td>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Other</td>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Buyer/Spender Channel Region  Fresh  Milk  Grocery  Frozen  \\\n",
       "0              1  Retail  Other  12669  9656     7561     214   \n",
       "1              2  Retail  Other   7057  9810     9568    1762   \n",
       "2              3  Retail  Other   6353  8808     7684    2405   \n",
       "3              4   Hotel  Other  13265  1196     4221    6404   \n",
       "4              5  Retail  Other  22615  5410     7198    3915   \n",
       "\n",
       "   Detergents_Paper  Delicatessen  \n",
       "0              2674          1338  \n",
       "1              3293          1776  \n",
       "2              3516          7844  \n",
       "3               507          1788  \n",
       "4              1777          5185  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4b80116e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mydata=sns.heatmap(mydata.corr(),annot=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "5843b032",
   "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>Buyer/Spender</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>220.500000</td>\n",
       "      <td>12000.297727</td>\n",
       "      <td>5796.265909</td>\n",
       "      <td>7951.277273</td>\n",
       "      <td>3071.931818</td>\n",
       "      <td>2881.493182</td>\n",
       "      <td>1524.870455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>127.161315</td>\n",
       "      <td>12647.328865</td>\n",
       "      <td>7380.377175</td>\n",
       "      <td>9503.162829</td>\n",
       "      <td>4854.673333</td>\n",
       "      <td>4767.854448</td>\n",
       "      <td>2820.105937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>110.750000</td>\n",
       "      <td>3127.750000</td>\n",
       "      <td>1533.000000</td>\n",
       "      <td>2153.000000</td>\n",
       "      <td>742.250000</td>\n",
       "      <td>256.750000</td>\n",
       "      <td>408.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>220.500000</td>\n",
       "      <td>8504.000000</td>\n",
       "      <td>3627.000000</td>\n",
       "      <td>4755.500000</td>\n",
       "      <td>1526.000000</td>\n",
       "      <td>816.500000</td>\n",
       "      <td>965.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>330.250000</td>\n",
       "      <td>16933.750000</td>\n",
       "      <td>7190.250000</td>\n",
       "      <td>10655.750000</td>\n",
       "      <td>3554.250000</td>\n",
       "      <td>3922.000000</td>\n",
       "      <td>1820.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>112151.000000</td>\n",
       "      <td>73498.000000</td>\n",
       "      <td>92780.000000</td>\n",
       "      <td>60869.000000</td>\n",
       "      <td>40827.000000</td>\n",
       "      <td>47943.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Buyer/Spender          Fresh          Milk       Grocery        Frozen  \\\n",
       "count     440.000000     440.000000    440.000000    440.000000    440.000000   \n",
       "mean      220.500000   12000.297727   5796.265909   7951.277273   3071.931818   \n",
       "std       127.161315   12647.328865   7380.377175   9503.162829   4854.673333   \n",
       "min         1.000000       3.000000     55.000000      3.000000     25.000000   \n",
       "25%       110.750000    3127.750000   1533.000000   2153.000000    742.250000   \n",
       "50%       220.500000    8504.000000   3627.000000   4755.500000   1526.000000   \n",
       "75%       330.250000   16933.750000   7190.250000  10655.750000   3554.250000   \n",
       "max       440.000000  112151.000000  73498.000000  92780.000000  60869.000000   \n",
       "\n",
       "       Detergents_Paper  Delicatessen  \n",
       "count        440.000000    440.000000  \n",
       "mean        2881.493182   1524.870455  \n",
       "std         4767.854448   2820.105937  \n",
       "min            3.000000      3.000000  \n",
       "25%          256.750000    408.250000  \n",
       "50%          816.500000    965.500000  \n",
       "75%         3922.000000   1820.250000  \n",
       "max        40827.000000  47943.000000  "
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d80cd6d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use methods of descriptive statistics to summarize data. Which Region and which Channel spent the most? Which Region and which Channel spent the least?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8c4fd3ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Buyer/Spender</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>220.500000</td>\n",
       "      <td>12000.297727</td>\n",
       "      <td>5796.265909</td>\n",
       "      <td>7951.277273</td>\n",
       "      <td>3071.931818</td>\n",
       "      <td>2881.493182</td>\n",
       "      <td>1524.870455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>127.161315</td>\n",
       "      <td>12647.328865</td>\n",
       "      <td>7380.377175</td>\n",
       "      <td>9503.162829</td>\n",
       "      <td>4854.673333</td>\n",
       "      <td>4767.854448</td>\n",
       "      <td>2820.105937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>110.750000</td>\n",
       "      <td>3127.750000</td>\n",
       "      <td>1533.000000</td>\n",
       "      <td>2153.000000</td>\n",
       "      <td>742.250000</td>\n",
       "      <td>256.750000</td>\n",
       "      <td>408.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>220.500000</td>\n",
       "      <td>8504.000000</td>\n",
       "      <td>3627.000000</td>\n",
       "      <td>4755.500000</td>\n",
       "      <td>1526.000000</td>\n",
       "      <td>816.500000</td>\n",
       "      <td>965.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>330.250000</td>\n",
       "      <td>16933.750000</td>\n",
       "      <td>7190.250000</td>\n",
       "      <td>10655.750000</td>\n",
       "      <td>3554.250000</td>\n",
       "      <td>3922.000000</td>\n",
       "      <td>1820.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>112151.000000</td>\n",
       "      <td>73498.000000</td>\n",
       "      <td>92780.000000</td>\n",
       "      <td>60869.000000</td>\n",
       "      <td>40827.000000</td>\n",
       "      <td>47943.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Buyer/Spender          Fresh          Milk       Grocery        Frozen  \\\n",
       "count     440.000000     440.000000    440.000000    440.000000    440.000000   \n",
       "mean      220.500000   12000.297727   5796.265909   7951.277273   3071.931818   \n",
       "std       127.161315   12647.328865   7380.377175   9503.162829   4854.673333   \n",
       "min         1.000000       3.000000     55.000000      3.000000     25.000000   \n",
       "25%       110.750000    3127.750000   1533.000000   2153.000000    742.250000   \n",
       "50%       220.500000    8504.000000   3627.000000   4755.500000   1526.000000   \n",
       "75%       330.250000   16933.750000   7190.250000  10655.750000   3554.250000   \n",
       "max       440.000000  112151.000000  73498.000000  92780.000000  60869.000000   \n",
       "\n",
       "       Detergents_Paper  Delicatessen  \n",
       "count        440.000000    440.000000  \n",
       "mean        2881.493182   1524.870455  \n",
       "std         4767.854448   2820.105937  \n",
       "min            3.000000      3.000000  \n",
       "25%          256.750000    408.250000  \n",
       "50%          816.500000    965.500000  \n",
       "75%         3922.000000   1820.250000  \n",
       "max        40827.000000  47943.000000  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e76bfb85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "220.5"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata[\"Buyer/Spender\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "03ff7fe4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hotel     298\n",
       "Retail    142\n",
       "Name: Channel, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata[\"Channel\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "89edc1f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Other     316\n",
       "Lisbon     77\n",
       "Oporto     47\n",
       "Name: Region, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata[\"Region\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1653347d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#There are 6 different varieties of items that are considered. Describe and comment/explain all the varieties across Region and Channel? Provide a detailed justification for your answer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "917abcdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "mydata3 = pd.read_csv('A+&+B+shingles.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "cf25f401",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.44</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.61</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.47</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.30</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A     B\n",
       "0  0.44  0.14\n",
       "1  0.61  0.15\n",
       "2  0.47  0.31\n",
       "3  0.30  0.16\n",
       "4  0.15  0.37"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "a59aeeec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.316667\n",
       "B    0.273548\n",
       "dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata3.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "864c1377",
   "metadata": {},
   "outputs": [],
   "source": [
    "mydata2= pd.read_csv('Survey-1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f7f74a0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Class</th>\n",
       "      <th>Major</th>\n",
       "      <th>Grad Intention</th>\n",
       "      <th>GPA</th>\n",
       "      <th>Employment</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Social Networking</th>\n",
       "      <th>Satisfaction</th>\n",
       "      <th>Spending</th>\n",
       "      <th>Computer</th>\n",
       "      <th>Text Messages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Other</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>350</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>23</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.6</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>360</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Other</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>600</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>Junior</td>\n",
       "      <td>CIS</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>40.0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>600</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Male</td>\n",
       "      <td>23</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Other</td>\n",
       "      <td>Undecided</td>\n",
       "      <td>2.8</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>500</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  Gender  Age   Class       Major Grad Intention  GPA  Employment  \\\n",
       "0   1  Female   20  Junior       Other            Yes  2.9   Full-Time   \n",
       "1   2    Male   23  Senior  Management            Yes  3.6   Part-Time   \n",
       "2   3    Male   21  Junior       Other            Yes  2.5   Part-Time   \n",
       "3   4    Male   21  Junior         CIS            Yes  2.5   Full-Time   \n",
       "4   5    Male   23  Senior       Other      Undecided  2.8  Unemployed   \n",
       "\n",
       "   Salary  Social Networking  Satisfaction  Spending Computer  Text Messages  \n",
       "0    50.0                  1             3       350   Laptop            200  \n",
       "1    25.0                  1             4       360   Laptop             50  \n",
       "2    45.0                  2             4       600   Laptop            200  \n",
       "3    40.0                  4             6       600   Laptop            250  \n",
       "4    40.0                  2             4       500   Laptop            100  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c1ceb1e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(62, 14)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "fbb729a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Major</th>\n",
       "      <th>Accounting</th>\n",
       "      <th>CIS</th>\n",
       "      <th>Economics/Finance</th>\n",
       "      <th>International Business</th>\n",
       "      <th>Management</th>\n",
       "      <th>Other</th>\n",
       "      <th>Retailing/Marketing</th>\n",
       "      <th>Undecided</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Major   Accounting  CIS  Economics/Finance  International Business  \\\n",
       "Gender                                                               \n",
       "Female           3    3                  7                       4   \n",
       "Male             4    1                  4                       2   \n",
       "\n",
       "Major   Management  Other  Retailing/Marketing  Undecided  \n",
       "Gender                                                     \n",
       "Female           4      3                    9          0  \n",
       "Male             6      4                    5          3  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(mydata2[\"Gender\"],mydata2[\"Major\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "8a584d6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Grad Intention</th>\n",
       "      <th>No</th>\n",
       "      <th>Undecided</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Grad Intention  No  Undecided  Yes\n",
       "Gender                            \n",
       "Female           9         13   11\n",
       "Male             3          9   17"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(mydata2[\"Gender\"],mydata2[\"Grad Intention\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a67ead07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Employment</th>\n",
       "      <th>Full-Time</th>\n",
       "      <th>Part-Time</th>\n",
       "      <th>Unemployed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Employment  Full-Time  Part-Time  Unemployed\n",
       "Gender                                      \n",
       "Female              3         24           6\n",
       "Male                7         19           3"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(mydata2[\"Gender\"],mydata2[\"Employment\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "eb56c471",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Computer</th>\n",
       "      <th>Desktop</th>\n",
       "      <th>Laptop</th>\n",
       "      <th>Tablet</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Computer  Desktop  Laptop  Tablet\n",
       "Gender                           \n",
       "Female          2      29       2\n",
       "Male            3      26       0"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(mydata2[\"Gender\"],mydata2[\"Computer\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "026964bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    33\n",
       "Male      29\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata2[\"Gender\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "5cc393c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Male=29\n",
    "Female=33 \n",
    "T=Male+Female\n",
    "T\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "ef481b4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.46774193548387094"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#What is the probability that a randomly selected CMSU student will be male?\n",
    "\n",
    "29/62\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "0e4f1616",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.532258064516129"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "33/62"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "6feeb249",
   "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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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Grad Intention</th>\n",
       "      <th>No</th>\n",
       "      <th>Undecided</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Grad Intention  No  Undecided  Yes\n",
       "Gender                            \n",
       "Female           9         13   11\n",
       "Male             3          9   17"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(mydata2[\"Gender\"],mydata2[\"Grad Intention\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "99489df4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5862068965517241"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "17/29\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "f0aa0e3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Computer</th>\n",
       "      <th>Desktop</th>\n",
       "      <th>Laptop</th>\n",
       "      <th>Tablet</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "Computer  Desktop  Laptop  Tablet\n",
       "Gender                           \n",
       "Female          2      29       2\n",
       "Male            3      26       0"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(mydata2[\"Gender\"],mydata2[\"Computer\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "91371abe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12121212121212122"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4/33"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "e7fef617",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.24242424242424243"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "8/33\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "1b41481a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2=pd.crosstab(mydata2[\"Gender\"],mydata2[\"Grad Intention\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "50cacf6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Grad Intention</th>\n",
       "      <th>No</th>\n",
       "      <th>Undecided</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Grad Intention  No  Undecided  Yes\n",
       "Gender                            \n",
       "Female           9         13   11\n",
       "Male             3          9   17"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "cfc86d59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th>Grad Intention</th>\n",
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       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Grad Intention  No  Yes\n",
       "Gender                 \n",
       "Female           9   11\n",
       "Male             3   17"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2[['No','Yes']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "1a25eb7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <th></th>\n",
       "      <th>ID</th>\n",
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       "      <td>Yes</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>50.0</td>\n",
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       "      <th>2</th>\n",
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       "      <td>Yes</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>600</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>200</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>Junior</td>\n",
       "      <td>CIS</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>40.0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>600</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Male</td>\n",
       "      <td>23</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Other</td>\n",
       "      <td>Undecided</td>\n",
       "      <td>2.8</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>500</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Female</td>\n",
       "      <td>22</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Economics/Finance</td>\n",
       "      <td>Undecided</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>78.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>700</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Economics/Finance</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.8</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>400</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Undecided</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.6</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>400</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>Junior</td>\n",
       "      <td>International Business</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>900</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>Male</td>\n",
       "      <td>20</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Other</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>47.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>300</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Retailing/Marketing</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.6</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1400</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>500</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>Male</td>\n",
       "      <td>24</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Economics/Finance</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.8</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>600</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Retailing/Marketing</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>300</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Undecided</td>\n",
       "      <td>Undecided</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>500</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>58</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>Senior</td>\n",
       "      <td>International Business</td>\n",
       "      <td>No</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>59</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>Junior</td>\n",
       "      <td>CIS</td>\n",
       "      <td>No</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>350</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>CIS</td>\n",
       "      <td>No</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>500</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID  Gender  Age      Class                   Major Grad Intention  GPA  \\\n",
       "0    1  Female   20     Junior                   Other            Yes  2.9   \n",
       "2    3    Male   21     Junior                   Other            Yes  2.5   \n",
       "3    4    Male   21     Junior                     CIS            Yes  2.5   \n",
       "4    5    Male   23     Senior                   Other      Undecided  2.8   \n",
       "5    6  Female   22     Senior       Economics/Finance      Undecided  2.3   \n",
       "10  11  Female   23     Senior       Economics/Finance            Yes  2.8   \n",
       "23  24    Male   22     Senior               Undecided            Yes  2.6   \n",
       "27  28  Female   20     Junior  International Business            Yes  2.9   \n",
       "31  32    Male   20     Junior                   Other            Yes  2.9   \n",
       "33  34    Male   22     Senior     Retailing/Marketing            Yes  2.6   \n",
       "37  38  Female   21  Sophomore              Accounting            Yes  2.5   \n",
       "38  39    Male   24     Junior       Economics/Finance            Yes  2.8   \n",
       "39  40    Male   19  Sophomore     Retailing/Marketing            Yes  2.5   \n",
       "47  48    Male   19  Sophomore               Undecided      Undecided  2.5   \n",
       "57  58  Female   21     Senior  International Business             No  2.4   \n",
       "58  59  Female   20     Junior                     CIS             No  2.9   \n",
       "59  60  Female   20  Sophomore                     CIS             No  2.5   \n",
       "\n",
       "    Employment  Salary  Social Networking  Satisfaction  Spending Computer  \\\n",
       "0    Full-Time    50.0                  1             3       350   Laptop   \n",
       "2    Part-Time    45.0                  2             4       600   Laptop   \n",
       "3    Full-Time    40.0                  4             6       600   Laptop   \n",
       "4   Unemployed    40.0                  2             4       500   Laptop   \n",
       "5   Unemployed    78.0                  3             2       700   Laptop   \n",
       "10   Full-Time    50.0                  2             5       400   Laptop   \n",
       "23   Full-Time    45.0                  1             5       400   Laptop   \n",
       "27   Part-Time    50.0                  3             1       900   Laptop   \n",
       "31   Part-Time    47.0                  3             1       300   Laptop   \n",
       "33   Full-Time    40.0                  1             4      1400   Laptop   \n",
       "37   Part-Time    60.0                  2             3       500   Laptop   \n",
       "38   Part-Time    50.0                  1             6       600   Laptop   \n",
       "39  Unemployed    50.0                  2             5       300   Laptop   \n",
       "47   Part-Time    80.0                  2             4       500   Laptop   \n",
       "57   Part-Time    40.0                  1             3      1000   Laptop   \n",
       "58   Part-Time    40.0                  2             4       350   Laptop   \n",
       "59   Part-Time    55.0                  1             4       500   Laptop   \n",
       "\n",
       "    Text Messages  \n",
       "0             200  \n",
       "2             200  \n",
       "3             250  \n",
       "4             100  \n",
       "5              30  \n",
       "10            200  \n",
       "23            600  \n",
       "27            100  \n",
       "31            300  \n",
       "33            800  \n",
       "37            600  \n",
       "38             50  \n",
       "39            100  \n",
       "47            150  \n",
       "57             10  \n",
       "58            250  \n",
       "59            500  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata2[mydata2[\"GPA\"]<3]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "1b9948c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata2[mydata2[\"GPA\"]<3].shape[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "92d0d167",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.27419354838709675"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "17/62"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "ac0c32fe",
   "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>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Class</th>\n",
       "      <th>Major</th>\n",
       "      <th>Grad Intention</th>\n",
       "      <th>GPA</th>\n",
       "      <th>Employment</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Social Networking</th>\n",
       "      <th>Satisfaction</th>\n",
       "      <th>Spending</th>\n",
       "      <th>Computer</th>\n",
       "      <th>Text Messages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Female</td>\n",
       "      <td>22</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Economics/Finance</td>\n",
       "      <td>Undecided</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>78.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>700</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Female</td>\n",
       "      <td>22</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Other</td>\n",
       "      <td>Undecided</td>\n",
       "      <td>3.1</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>200</td>\n",
       "      <td>Tablet</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.2</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>54.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>600</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>Female</td>\n",
       "      <td>19</td>\n",
       "      <td>Junior</td>\n",
       "      <td>CIS</td>\n",
       "      <td>Undecided</td>\n",
       "      <td>3.7</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>450</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Economics/Finance</td>\n",
       "      <td>Undecided</td>\n",
       "      <td>3.1</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>600</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Economics/Finance</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>500</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Management</td>\n",
       "      <td>Undecided</td>\n",
       "      <td>3.2</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>300</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>Female</td>\n",
       "      <td>22</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Retailing/Marketing</td>\n",
       "      <td>Undecided</td>\n",
       "      <td>3.2</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>690</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>Undecided</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>600</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>Female</td>\n",
       "      <td>22</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Retailing/Marketing</td>\n",
       "      <td>Undecided</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>300</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Economics/Finance</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>600</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>Male</td>\n",
       "      <td>24</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.3</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>300</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>Male</td>\n",
       "      <td>20</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Economics/Finance</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.1</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>375</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Retailing/Marketing</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.3</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1100</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>20</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>Undecided</td>\n",
       "      <td>3.4</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>500</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.3</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>450</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>500</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.2</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>680</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Management</td>\n",
       "      <td>Undecided</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>650</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Retailing/Marketing</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>350</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Undecided</td>\n",
       "      <td>Undecided</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>500</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>CIS</td>\n",
       "      <td>No</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>500</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>62</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Economics/Finance</td>\n",
       "      <td>No</td>\n",
       "      <td>3.2</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>250</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID  Gender  Age      Class                Major Grad Intention  GPA  \\\n",
       "5    6  Female   22     Senior    Economics/Finance      Undecided  2.3   \n",
       "7    8  Female   22     Senior                Other      Undecided  3.1   \n",
       "14  15    Male   21     Senior           Management            Yes  3.2   \n",
       "16  17  Female   19     Junior                  CIS      Undecided  3.7   \n",
       "17  18    Male   21     Junior    Economics/Finance      Undecided  3.1   \n",
       "18  19    Male   19     Junior    Economics/Finance            Yes  3.5   \n",
       "19  20  Female   20     Junior           Management      Undecided  3.2   \n",
       "20  21  Female   22     Junior  Retailing/Marketing      Undecided  3.2   \n",
       "21  22    Male   18  Sophomore           Accounting      Undecided  3.0   \n",
       "22  23  Female   22     Senior  Retailing/Marketing      Undecided  3.0   \n",
       "24  25  Female   20     Junior    Economics/Finance            Yes  3.0   \n",
       "25  26    Male   24     Senior           Management            Yes  3.3   \n",
       "26  27    Male   20     Junior    Economics/Finance            Yes  3.1   \n",
       "28  29    Male   22     Senior  Retailing/Marketing            Yes  3.3   \n",
       "30  31    Male   20     Junior           Accounting      Undecided  3.4   \n",
       "35  36  Female   26     Junior           Accounting            Yes  3.3   \n",
       "37  38  Female   21  Sophomore           Accounting            Yes  2.5   \n",
       "40  41    Male   22     Junior           Accounting            Yes  3.2   \n",
       "45  46  Female   21     Senior           Management      Undecided  3.8   \n",
       "46  47  Female   20     Junior  Retailing/Marketing            Yes  3.5   \n",
       "47  48    Male   19  Sophomore            Undecided      Undecided  2.5   \n",
       "59  60  Female   20  Sophomore                  CIS             No  2.5   \n",
       "61  62  Female   23     Senior    Economics/Finance             No  3.2   \n",
       "\n",
       "    Employment  Salary  Social Networking  Satisfaction  Spending Computer  \\\n",
       "5   Unemployed    78.0                  3             2       700   Laptop   \n",
       "7    Full-Time    80.0                  1             2       200   Tablet   \n",
       "14   Part-Time    54.0                  3             4       600   Laptop   \n",
       "16   Part-Time    55.0                  1             4       450   Laptop   \n",
       "17   Part-Time    55.0                  2             3       600   Laptop   \n",
       "18   Part-Time    52.0                  2             5       500   Laptop   \n",
       "19  Unemployed    60.0                  2             6       300   Laptop   \n",
       "20   Part-Time    55.0                  1             3       690   Laptop   \n",
       "21  Unemployed    60.0                  1             4       600   Laptop   \n",
       "22   Part-Time    55.0                  0             4       300   Laptop   \n",
       "24   Part-Time    55.0                  1             3       600   Laptop   \n",
       "25   Full-Time    60.0                  0             1       300   Laptop   \n",
       "26   Full-Time    65.0                  1             5       375   Laptop   \n",
       "28   Part-Time    55.0                  1             6      1100   Laptop   \n",
       "30   Part-Time    55.0                  2             3       500   Laptop   \n",
       "35   Part-Time    60.0                  1             4       450  Desktop   \n",
       "37   Part-Time    60.0                  2             3       500   Laptop   \n",
       "40   Full-Time    60.0                  1             4       680  Desktop   \n",
       "45   Part-Time    60.0                  1             4       650   Laptop   \n",
       "46  Unemployed    60.0                  1             3       350   Laptop   \n",
       "47   Part-Time    80.0                  2             4       500   Laptop   \n",
       "59   Part-Time    55.0                  1             4       500   Laptop   \n",
       "61   Part-Time    70.0                  2             3       250   Laptop   \n",
       "\n",
       "    Text Messages  \n",
       "5              30  \n",
       "7             300  \n",
       "14            400  \n",
       "16            150  \n",
       "17            300  \n",
       "18            300  \n",
       "19            350  \n",
       "20             50  \n",
       "21            500  \n",
       "22             35  \n",
       "24            300  \n",
       "25             40  \n",
       "26            300  \n",
       "28             60  \n",
       "30            750  \n",
       "35            300  \n",
       "37            600  \n",
       "40            200  \n",
       "45            150  \n",
       "46            200  \n",
       "47            150  \n",
       "59            500  \n",
       "61              0  "
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_28=mydata2[mydata2[\"Salary\"]>50]\n",
    "df_28\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "8fe7d0a7",
   "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>Gender</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Female</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Female</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Male</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Female</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Male</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Male</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Female</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Female</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Male</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Female</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Female</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Male</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Male</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Male</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Male</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Female</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Female</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Male</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Female</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Female</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Male</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Female</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Female</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Gender  Salary\n",
       "5   Female    78.0\n",
       "7   Female    80.0\n",
       "14    Male    54.0\n",
       "16  Female    55.0\n",
       "17    Male    55.0\n",
       "18    Male    52.0\n",
       "19  Female    60.0\n",
       "20  Female    55.0\n",
       "21    Male    60.0\n",
       "22  Female    55.0\n",
       "24  Female    55.0\n",
       "25    Male    60.0\n",
       "26    Male    65.0\n",
       "28    Male    55.0\n",
       "30    Male    55.0\n",
       "35  Female    60.0\n",
       "37  Female    60.0\n",
       "40    Male    60.0\n",
       "45  Female    60.0\n",
       "46  Female    60.0\n",
       "47    Male    80.0\n",
       "59  Female    55.0\n",
       "61  Female    70.0"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_28[['Gender','Salary']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "1dac0d62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "Female    13\n",
       "Male      10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_28[['Gender','Salary']].value_counts('Gender')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "180a3f67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3448275862068966"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10/29\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "6959ff4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3939393939393939"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "13/33"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "cbd37bda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.129032258064516"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata2[\"GPA\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "4b9ed8bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3.0\n",
       "1    3.1\n",
       "2    3.4\n",
       "dtype: float64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata2[\"GPA\"].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "2af333bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.1500000000000004"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata2[\"GPA\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "0b912494",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48.54838709677419"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata2[\"Salary\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "c87c0b58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    40.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata2[\"Salary\"].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "1eec917e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50.0"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata2[\"Salary\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "af3436f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "482.01612903225805"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata2[\"Spending\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "eeab27af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    500\n",
       "dtype: int64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata2[\"Spending\"].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "63211d2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500.0"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata2[\"Spending\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "d5d017b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "246.20967741935485"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata2[\"Text Messages\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "9cf1f0de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200.0"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata2[\"Text Messages\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "fea2470e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    300\n",
       "dtype: int64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata2[\"Text Messages\"].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "f9f30d60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'GPA'}>]], dtype=object)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mydata2.hist(\"GPA\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "13723528",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'Salary'}>]], dtype=object)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mydata2.hist(\"Salary\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "bbb0fea9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'Spending'}>]], dtype=object)"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4kap646L5tgHburvPA+5de9yR2gg8POkQa9BqbjD7pJh9MgbJ/v1V9b2LBwe5kCdLjH3b/wZVdT1w/QDbGYsku6pqdtI5VqvV3GD2STH7ZIwi+yCHUB4Ezl9w/zzgocHiSJL6NUiB/z/ggiTPTXIq8CrgluHEkiStZM2HUKrqWJI3Av8TeAbw3qq6Z2jJxm/dH+ZZRqu5weyTYvbJGHr2Nb+JKUmaLK/ElKRGWeCS1KiTosCTnJ/k40n2JbknyZu68XOS3JHkvu732QuW+fXuIwLuTfLPJ5e+d9Vrkv+f5Nbufiu5z0ryoSSf6/72P9pQ9n/dPVf2Jrkpyd9br9mTvDfJ4SR7F4ytOmuSi5Ps6ab9fpKlThUeR/Z/2z1nPpvkI0nOaiX7gmm/mqSSbBxp9qr6jv8BNgE/1N1+FvB5epf//x6woxvfAfxud/tC4DPAdwPPBf4SeMYE8/8K8AHg1u5+K7lvBN7Q3T4VOKuF7MBm4H7gtO7+B4Fr1mt24CeAHwL2LhhbdVbg/wI/Su8aj/8OXD6h7C8BNnS3f7el7N34+fRO7ngA2DjK7CfFHnhVHayqT3e3jwD76P0jvZJeydD9fkV3+0pgvqq+UVX3A1+g99EBY5fkPOAK4A8XDLeQ+wx6T/AbAKrqm1X1GA1k72wATkuyAXgmvWsc1mX2qroLeGTR8KqyJtkEnFFVf1a9VvnjBcuMNXtV3V5Vx7q7f07vGpMmsnfeBfwbTrywcSTZT4oCXyjJDPAC4FPAdFUdhF7JA+d2s20GvrxgsQe7sUl4N70nw18vGGsh998Hvgr8UXf45w+TnE4D2avqAPAO4EvAQeDxqrqdBrIvsNqsm7vbi8cn7XX09kqhgexJXg4cqKrPLJo0kuwnVYEnmQI+DPxyVX3t6WZdYmzs51sm+WngcFXt7neRJcYmdZ7oBnovL/9TVb0AeILeS/nlrJvs3fHiK+m91H02cHqSn3+6RZYYW6/n5y6Xdd09hiRvBo4B7z8+tMRs6yZ7kmcCbwZ+a6nJS4wNnP2kKfAkp9Ar7/dX1c3d8KHuJQzd78Pd+Hr5mIAXAy9Psp/epz3+syR/wvrPfTzLg1X1qe7+h+gVegvZfxK4v6q+WlXfAm4Gfow2sh+32qwP8neHKhaOT0SSq4GfBl7dHVqA9Z/9B+j9p/+Z7t/secCnk3wfI8p+UhR4967uDcC+qnrngkm3AFd3t68GPrpg/FVJvjvJc4EL6L3RMFZV9etVdV5VzdD7qIL/VVU/zzrPDVBVXwG+nOR53dClwF/QQHZ6h05elOSZ3XPnUnrvm7SQ/bhVZe0OsxxJ8qLuMb92wTJjleQy4NeAl1fV1xdMWtfZq2pPVZ1bVTPdv9kH6Z088ZWRZR/1O7Xr4Qe4hN7Lks8Cd3c/LwW+B7gTuK/7fc6CZd5M753iexnDO9p9PIY5/u4slCZyAxcBu7q/+38Dzm4o+1uBzwF7gf9C7+yBdZkduInesfpvdaXx+rVkBWa7x/uXwL+nu1J7Atm/QO948fF/q/+5leyLpu+nOwtlVNm9lF6SGnVSHEKRpO9EFrgkNcoCl6RGWeCS1CgLXJIaZYFLUqMscElq1N8ASdmZEY3ZAL0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mydata2.hist(\"Spending\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "d12881c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'Text Messages'}>]], dtype=object)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xjbIfsw1DP5LMG+AreRFzN7C7qm7olq8EnrKCx5MkHYS+A7yqvg7cnuTUbuhMetMpkqRDYKV/g78a2Nq9A+U24FdWXpIkaTlWFOBVdRPwY/MykqTV55mYktQoA1ySGmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhplgEtSowxwSWqUAS5JjTLAJalRBrgkNcoAl6RGGeCS1KgVB3iSw5J8Osk1gyhIkrQ8gzgCfw2wcwCPI0k6CCsK8CSnAM8H3jGYciRJy5Wq6n/j5Ergj4BjgNdW1Qvmuc9mYDPA6Ojo6ZOTk33ta+++/ew50HepQ2f0SIayH5s2HHfQ20xPTzMyMrIK1bTJfsw2DP2YmJjYXlU/dgH5vq9Kn+QFwN6q2p5kfKH7VdUWYAvA2NhYjY8veNdFXbL1ai7e0Xe5Q+eCTfcNZT92nTN+0NtMTU3R7/fVMLIfsw1zP1YyhXIG8KIku4BJ4NlJLhtIVZKkJfUd4FX1+qo6pao2Ai8HPlpV5w6sMknSonwfuCQ1aiCTqFU1BUwN4rEkScvjEbgkNcoAl6RGGeCS1CgDXJIaZYBLUqMMcElqlAEuSY0avg/TkPqw8cJr12zfuy56/prtW23zCFySGmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhrVd4AneXSSjyXZmeSWJK8ZZGGSpMWt5FT6+4ALqurGJMcA25NcV1WfG1BtkqRFrOSq9HdV1Y3d7e8CO4ENgypMkrS4VNXKHyTZCFwPnFZV35mzbjOwGWB0dPT0ycnJvvaxd99+9hxYYaFDZPRI7EfHXsxmP2ab2Y9NG45b22L6NDExsb2qxuaOrzjAk4wA/wC8saquWuy+Y2NjtW3btr72c8nWq7l4hx+eeL8LNt1nPzr2Yjb7MdvMfrT6yY9J5g3wFb0LJclDgfcBW5cKb0nSYK3kXSgB3gnsrKo3D64kSdJyrOQI/AzglcCzk9zUfT1vQHVJkpbQ90RZVf0jkAHWIkk6CJ6JKUmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhplgEtSo/zABEnrxsYLr12zfa/G57B4BC5JjTLAJalRBrgkNcoAl6RGGeCS1CgDXJIaZYBLUqMMcElqlAEuSY1a6UWNz0ry+SRfSnLhoIqSJC1tJRc1Pgx4O/Bc4AnAK5I8YVCFSZIWt5Ij8J8HvlRVt1XVvcAkcPZgypIkLSVV1d+GyUuAs6rqV7vlVwJPrarfmnO/zcDmbvFU4PN91noi8M0+tx1G9uMB9mI2+zHbMPTjMVV10tzBlXwa4XxXpP+x3wZVtQXYsoL99HaWbKuqsZU+zrCwHw+wF7PZj9mGuR8rmULZDTx6xvIpwJ0rK0eStFwrCfBPAY9P8tgkRwAvBz44mLIkSUvpewqlqu5L8lvAh4DDgEur6paBVfbjVjwNM2TsxwPsxWz2Y7ah7UffL2JKktaWZ2JKUqMMcElqVBMBvt5O2U/y6CQfS7IzyS1JXtONPyLJdUm+2P17woxtXt/15/NJfmntql8dSQ5L8ukk13TL67kXxye5Msmt3ffI09d5P36n+zm5OcnlSR6+bvpRVQ/qL3ovkH4ZeBxwBPAZ4AlrXdcqP+eTgad0t48BvkDv4wr+GLiwG78QeFN3+wldXx4GPLbr12Fr/TwG3JP/DPwNcE23vJ578W7gV7vbRwDHr9d+ABuArwBHdstXAOevl360cAS+7k7Zr6q7qurG7vZ3gZ30vlHPpvfDS/fvi7vbZwOTVfX9qvoK8CV6fRsKSU4Bng+8Y8bweu3FscAzgXcCVNW9VXU367QfncOBI5McDhxF73yUddGPFgJ8A3D7jOXd3di6kGQj8LPADcBoVd0FvZAHHtndbdh79BbgdcCPZoyt1148DvgG8FfdlNI7khzNOu1HVd0B/CnwNeAuYH9VfZh10o8WAnxZp+wPoyQjwPuA/1RV31nsrvOMDUWPkrwA2FtV25e7yTxjQ9GLzuHAU4C/qKqfBe6hN0WwkKHuRze3fTa96ZBHAUcnOXexTeYZa7YfLQT4ujxlP8lD6YX31qq6qhvek+Tkbv3JwN5ufJh7dAbwoiS76E2fPTvJZazPXkDv+e2uqhu65SvpBfp67ccvAF+pqm9U1Q+Aq4BnsE760UKAr7tT9pOE3hznzqp684xVHwTO626fB1w9Y/zlSR6W5LHA44FPHqp6V1NVvb6qTqmqjfT+7z9aVeeyDnsBUFVfB25Pcmo3dCbwOdZpP+hNnTwtyVHdz82Z9F4zWhf9WMmnER4SdehP2X8wOAN4JbAjyU3d2O8BFwFXJHkVvW/clwJU1S1JrqD3g3wf8JtV9cNDXvWhtZ578Wpga3dAcxvwK/QOxtZdP6rqhiRXAjfSe36fpnfq/AjroB+eSi9JjWphCkWSNA8DXJIaZYBLUqMMcElqlAEuSY0ywCWpUQa4JDXq/wOITaUBFzkhhQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mydata2.hist(\"Text Messages\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "id": "dbf52bdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "mydata3 = pd.read_csv('A+&+B+shingles (1).csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "48c73196",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.316667\n",
       "B    0.273548\n",
       "dtype: float64"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata3.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "ef5394b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.135731\n",
       "B    0.137296\n",
       "dtype: float64"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata3.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce05ed31",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}