Project-2_Hypothesis Testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import math\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import ttest_1samp, ttest_ind, ttest_rel,variation\n",
    "from statsmodels.stats.power import ttest_power\n",
    "from scipy.stats import chi2_contingency\n",
    "from warnings import filterwarnings\n",
    "filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
       "    </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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>436</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Other</td>\n",
       "      <td>29703</td>\n",
       "      <td>12051</td>\n",
       "      <td>16027</td>\n",
       "      <td>13135</td>\n",
       "      <td>182</td>\n",
       "      <td>2204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>437</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Other</td>\n",
       "      <td>39228</td>\n",
       "      <td>1431</td>\n",
       "      <td>764</td>\n",
       "      <td>4510</td>\n",
       "      <td>93</td>\n",
       "      <td>2346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>438</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Other</td>\n",
       "      <td>14531</td>\n",
       "      <td>15488</td>\n",
       "      <td>30243</td>\n",
       "      <td>437</td>\n",
       "      <td>14841</td>\n",
       "      <td>1867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>439</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Other</td>\n",
       "      <td>10290</td>\n",
       "      <td>1981</td>\n",
       "      <td>2232</td>\n",
       "      <td>1038</td>\n",
       "      <td>168</td>\n",
       "      <td>2125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>440</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Other</td>\n",
       "      <td>2787</td>\n",
       "      <td>1698</td>\n",
       "      <td>2510</td>\n",
       "      <td>65</td>\n",
       "      <td>477</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>440 rows × 9 columns</p>\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",
       "435            436   Hotel  Other  29703  12051    16027   13135   \n",
       "436            437   Hotel  Other  39228   1431      764    4510   \n",
       "437            438  Retail  Other  14531  15488    30243     437   \n",
       "438            439   Hotel  Other  10290   1981     2232    1038   \n",
       "439            440   Hotel  Other   2787   1698     2510      65   \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  \n",
       "..                ...           ...  \n",
       "435               182          2204  \n",
       "436                93          2346  \n",
       "437             14841          1867  \n",
       "438               168          2125  \n",
       "439               477            52  \n",
       "\n",
       "[440 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Codes for Question 1\n",
    "\n",
    "df = pd.read_csv('Wholesale+Customers+Data.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Buyer/Spender</th>\n",
       "      <td>440.0</td>\n",
       "      <td>220.500000</td>\n",
       "      <td>127.161315</td>\n",
       "      <td>1.0</td>\n",
       "      <td>110.75</td>\n",
       "      <td>220.5</td>\n",
       "      <td>330.25</td>\n",
       "      <td>440.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fresh</th>\n",
       "      <td>440.0</td>\n",
       "      <td>12000.297727</td>\n",
       "      <td>12647.328865</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3127.75</td>\n",
       "      <td>8504.0</td>\n",
       "      <td>16933.75</td>\n",
       "      <td>112151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Milk</th>\n",
       "      <td>440.0</td>\n",
       "      <td>5796.265909</td>\n",
       "      <td>7380.377175</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1533.00</td>\n",
       "      <td>3627.0</td>\n",
       "      <td>7190.25</td>\n",
       "      <td>73498.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grocery</th>\n",
       "      <td>440.0</td>\n",
       "      <td>7951.277273</td>\n",
       "      <td>9503.162829</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2153.00</td>\n",
       "      <td>4755.5</td>\n",
       "      <td>10655.75</td>\n",
       "      <td>92780.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frozen</th>\n",
       "      <td>440.0</td>\n",
       "      <td>3071.931818</td>\n",
       "      <td>4854.673333</td>\n",
       "      <td>25.0</td>\n",
       "      <td>742.25</td>\n",
       "      <td>1526.0</td>\n",
       "      <td>3554.25</td>\n",
       "      <td>60869.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <td>440.0</td>\n",
       "      <td>2881.493182</td>\n",
       "      <td>4767.854448</td>\n",
       "      <td>3.0</td>\n",
       "      <td>256.75</td>\n",
       "      <td>816.5</td>\n",
       "      <td>3922.00</td>\n",
       "      <td>40827.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delicatessen</th>\n",
       "      <td>440.0</td>\n",
       "      <td>1524.870455</td>\n",
       "      <td>2820.105937</td>\n",
       "      <td>3.0</td>\n",
       "      <td>408.25</td>\n",
       "      <td>965.5</td>\n",
       "      <td>1820.25</td>\n",
       "      <td>47943.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count          mean           std   min      25%     50%  \\\n",
       "Buyer/Spender     440.0    220.500000    127.161315   1.0   110.75   220.5   \n",
       "Fresh             440.0  12000.297727  12647.328865   3.0  3127.75  8504.0   \n",
       "Milk              440.0   5796.265909   7380.377175  55.0  1533.00  3627.0   \n",
       "Grocery           440.0   7951.277273   9503.162829   3.0  2153.00  4755.5   \n",
       "Frozen            440.0   3071.931818   4854.673333  25.0   742.25  1526.0   \n",
       "Detergents_Paper  440.0   2881.493182   4767.854448   3.0   256.75   816.5   \n",
       "Delicatessen      440.0   1524.870455   2820.105937   3.0   408.25   965.5   \n",
       "\n",
       "                       75%       max  \n",
       "Buyer/Spender       330.25     440.0  \n",
       "Fresh             16933.75  112151.0  \n",
       "Milk               7190.25   73498.0  \n",
       "Grocery           10655.75   92780.0  \n",
       "Frozen             3554.25   60869.0  \n",
       "Detergents_Paper   3922.00   40827.0  \n",
       "Delicatessen       1820.25   47943.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using describe,info,tail,head,size,shape,columns we get to know about EDA of dataset\n",
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 440 entries, 0 to 439\n",
      "Data columns (total 9 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",
      "dtypes: int64(7), object(2)\n",
      "memory usage: 31.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <td>Retail</td>\n",
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       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
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       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Retail</td>\n",
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       "      <td>9810</td>\n",
       "      <td>9568</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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>435</th>\n",
       "      <td>436</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Other</td>\n",
       "      <td>29703</td>\n",
       "      <td>12051</td>\n",
       "      <td>16027</td>\n",
       "      <td>13135</td>\n",
       "      <td>182</td>\n",
       "      <td>2204</td>\n",
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       "      <th>436</th>\n",
       "      <td>437</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Other</td>\n",
       "      <td>39228</td>\n",
       "      <td>1431</td>\n",
       "      <td>764</td>\n",
       "      <td>4510</td>\n",
       "      <td>93</td>\n",
       "      <td>2346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>438</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Other</td>\n",
       "      <td>14531</td>\n",
       "      <td>15488</td>\n",
       "      <td>30243</td>\n",
       "      <td>437</td>\n",
       "      <td>14841</td>\n",
       "      <td>1867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>439</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Other</td>\n",
       "      <td>10290</td>\n",
       "      <td>1981</td>\n",
       "      <td>2232</td>\n",
       "      <td>1038</td>\n",
       "      <td>168</td>\n",
       "      <td>2125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>440</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Other</td>\n",
       "      <td>2787</td>\n",
       "      <td>1698</td>\n",
       "      <td>2510</td>\n",
       "      <td>65</td>\n",
       "      <td>477</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Buyer/Spender Channel Region  Fresh   Milk  Grocery  Frozen  \\\n",
       "435            436   Hotel  Other  29703  12051    16027   13135   \n",
       "436            437   Hotel  Other  39228   1431      764    4510   \n",
       "437            438  Retail  Other  14531  15488    30243     437   \n",
       "438            439   Hotel  Other  10290   1981     2232    1038   \n",
       "439            440   Hotel  Other   2787   1698     2510      65   \n",
       "\n",
       "     Detergents_Paper  Delicatessen  \n",
       "435               182          2204  \n",
       "436                93          2346  \n",
       "437             14841          1867  \n",
       "438               168          2125  \n",
       "439               477            52  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of rows are - 440 \n",
      "The number of columns are - 9\n"
     ]
    }
   ],
   "source": [
    "df.shape\n",
    "print(\"The number of rows are -\",df.shape[0],\"\\nThe number of columns are -\",df.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3960"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Buyer/Spender', 'Channel', 'Region', 'Fresh', 'Milk', 'Grocery',\n",
       "       'Frozen', 'Detergents_Paper', 'Delicatessen'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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",
       "    <tr>\n",
       "      <th>Region</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>Lisbon</th>\n",
       "      <td>18095</td>\n",
       "      <td>854833</td>\n",
       "      <td>422454</td>\n",
       "      <td>570037</td>\n",
       "      <td>231026</td>\n",
       "      <td>204136</td>\n",
       "      <td>104327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oporto</th>\n",
       "      <td>14899</td>\n",
       "      <td>464721</td>\n",
       "      <td>239144</td>\n",
       "      <td>433274</td>\n",
       "      <td>190132</td>\n",
       "      <td>173311</td>\n",
       "      <td>54506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>64026</td>\n",
       "      <td>3960577</td>\n",
       "      <td>1888759</td>\n",
       "      <td>2495251</td>\n",
       "      <td>930492</td>\n",
       "      <td>890410</td>\n",
       "      <td>512110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Buyer/Spender    Fresh     Milk  Grocery  Frozen  Detergents_Paper  \\\n",
       "Region                                                                       \n",
       "Lisbon          18095   854833   422454   570037  231026            204136   \n",
       "Oporto          14899   464721   239144   433274  190132            173311   \n",
       "Other           64026  3960577  1888759  2495251  930492            890410   \n",
       "\n",
       "        Delicatessen  \n",
       "Region                \n",
       "Lisbon        104327  \n",
       "Oporto         54506  \n",
       "Other         512110  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.groupby(df[\"Region\"]).sum()\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>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",
       "    <tr>\n",
       "      <th>Region</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>Lisbon</th>\n",
       "      <td>854833</td>\n",
       "      <td>422454</td>\n",
       "      <td>570037</td>\n",
       "      <td>231026</td>\n",
       "      <td>204136</td>\n",
       "      <td>104327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oporto</th>\n",
       "      <td>464721</td>\n",
       "      <td>239144</td>\n",
       "      <td>433274</td>\n",
       "      <td>190132</td>\n",
       "      <td>173311</td>\n",
       "      <td>54506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>3960577</td>\n",
       "      <td>1888759</td>\n",
       "      <td>2495251</td>\n",
       "      <td>930492</td>\n",
       "      <td>890410</td>\n",
       "      <td>512110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Fresh     Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "Region                                                                   \n",
       "Lisbon   854833   422454   570037  231026            204136        104327\n",
       "Oporto   464721   239144   433274  190132            173311         54506\n",
       "Other   3960577  1888759  2495251  930492            890410        512110"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.drop(\"Buyer/Spender\",axis=1,inplace=True)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Region\n",
       "Lisbon     2386813\n",
       "Oporto     1555088\n",
       "Other     10677599\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.sum(axis=1)\n",
    "# Oporto spent least and other spent max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
       "    <tr>\n",
       "      <th>Channel</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>Hotel</th>\n",
       "      <td>71034</td>\n",
       "      <td>4015717</td>\n",
       "      <td>1028614</td>\n",
       "      <td>1180717</td>\n",
       "      <td>1116979</td>\n",
       "      <td>235587</td>\n",
       "      <td>421955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retail</th>\n",
       "      <td>25986</td>\n",
       "      <td>1264414</td>\n",
       "      <td>1521743</td>\n",
       "      <td>2317845</td>\n",
       "      <td>234671</td>\n",
       "      <td>1032270</td>\n",
       "      <td>248988</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Buyer/Spender    Fresh     Milk  Grocery   Frozen  Detergents_Paper  \\\n",
       "Channel                                                                        \n",
       "Hotel            71034  4015717  1028614  1180717  1116979            235587   \n",
       "Retail           25986  1264414  1521743  2317845   234671           1032270   \n",
       "\n",
       "         Delicatessen  \n",
       "Channel                \n",
       "Hotel          421955  \n",
       "Retail         248988  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.groupby(df[\"Channel\"]).sum()\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.drop(\"Buyer/Spender\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    <tr>\n",
       "      <th>Channel</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>Hotel</th>\n",
       "      <td>4015717</td>\n",
       "      <td>1028614</td>\n",
       "      <td>1180717</td>\n",
       "      <td>1116979</td>\n",
       "      <td>235587</td>\n",
       "      <td>421955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retail</th>\n",
       "      <td>1264414</td>\n",
       "      <td>1521743</td>\n",
       "      <td>2317845</td>\n",
       "      <td>234671</td>\n",
       "      <td>1032270</td>\n",
       "      <td>248988</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Fresh     Milk  Grocery   Frozen  Detergents_Paper  Delicatessen\n",
       "Channel                                                                    \n",
       "Hotel    4015717  1028614  1180717  1116979            235587        421955\n",
       "Retail   1264414  1521743  2317845   234671           1032270        248988"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Channel\n",
       "Hotel     7999569\n",
       "Retail    6619931\n",
       "dtype: int64"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.sum(axis=1)\n",
    "# Retail spent least and Hotel spent max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Delicatessen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Milk</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <th>Channel</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Lisbon</th>\n",
       "      <th>Hotel</th>\n",
       "      <td>1197.152542</td>\n",
       "      <td>950.525424</td>\n",
       "      <td>12902.254237</td>\n",
       "      <td>3127.322034</td>\n",
       "      <td>4026.135593</td>\n",
       "      <td>3870.203390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retail</th>\n",
       "      <td>1871.944444</td>\n",
       "      <td>8225.277778</td>\n",
       "      <td>5200.000000</td>\n",
       "      <td>2584.111111</td>\n",
       "      <td>18471.944444</td>\n",
       "      <td>10784.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Oporto</th>\n",
       "      <th>Hotel</th>\n",
       "      <td>1105.892857</td>\n",
       "      <td>482.714286</td>\n",
       "      <td>11650.535714</td>\n",
       "      <td>5745.035714</td>\n",
       "      <td>4395.500000</td>\n",
       "      <td>2304.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retail</th>\n",
       "      <td>1239.000000</td>\n",
       "      <td>8410.263158</td>\n",
       "      <td>7289.789474</td>\n",
       "      <td>1540.578947</td>\n",
       "      <td>16326.315789</td>\n",
       "      <td>9190.789474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Other</th>\n",
       "      <th>Hotel</th>\n",
       "      <td>1518.284360</td>\n",
       "      <td>786.682464</td>\n",
       "      <td>13878.052133</td>\n",
       "      <td>3656.900474</td>\n",
       "      <td>3886.734597</td>\n",
       "      <td>3486.981043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retail</th>\n",
       "      <td>1826.209524</td>\n",
       "      <td>6899.238095</td>\n",
       "      <td>9831.504762</td>\n",
       "      <td>1513.200000</td>\n",
       "      <td>15953.809524</td>\n",
       "      <td>10981.009524</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Delicatessen  Detergents_Paper         Fresh       Frozen  \\\n",
       "Region Channel                                                              \n",
       "Lisbon Hotel     1197.152542        950.525424  12902.254237  3127.322034   \n",
       "       Retail    1871.944444       8225.277778   5200.000000  2584.111111   \n",
       "Oporto Hotel     1105.892857        482.714286  11650.535714  5745.035714   \n",
       "       Retail    1239.000000       8410.263158   7289.789474  1540.578947   \n",
       "Other  Hotel     1518.284360        786.682464  13878.052133  3656.900474   \n",
       "       Retail    1826.209524       6899.238095   9831.504762  1513.200000   \n",
       "\n",
       "                     Grocery          Milk  \n",
       "Region Channel                              \n",
       "Lisbon Hotel     4026.135593   3870.203390  \n",
       "       Retail   18471.944444  10784.000000  \n",
       "Oporto Hotel     4395.500000   2304.250000  \n",
       "       Retail   16326.315789   9190.789474  \n",
       "Other  Hotel     3886.734597   3486.981043  \n",
       "       Retail   15953.809524  10981.009524  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pvt = pd.pivot_table(df,['Fresh','Milk', 'Grocery','Frozen', 'Detergents_Paper', 'Delicatessen'],index = [\"Region\",\"Channel\"])\n",
    "pvt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.boxplot(data=pvt)\n",
    "plt.title(\"Box Plot of Pivot Table\",color= 'g',fontweight=\"bold\",fontsize=12)\n",
    "plt.xlabel(\"Types of food products\",color= 'g',fontweight=\"bold\",fontsize=12)\n",
    "plt.ylabel(\"Amount spent\",color= 'g',fontweight=\"bold\",fontsize=12)\n",
    "plt.savefig(\"pvt_boxplot.png\",bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new  = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new.drop([\"Buyer/Spender\",\"Channel\",\"Region\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>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>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>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>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>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>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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>29703</td>\n",
       "      <td>12051</td>\n",
       "      <td>16027</td>\n",
       "      <td>13135</td>\n",
       "      <td>182</td>\n",
       "      <td>2204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>39228</td>\n",
       "      <td>1431</td>\n",
       "      <td>764</td>\n",
       "      <td>4510</td>\n",
       "      <td>93</td>\n",
       "      <td>2346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>14531</td>\n",
       "      <td>15488</td>\n",
       "      <td>30243</td>\n",
       "      <td>437</td>\n",
       "      <td>14841</td>\n",
       "      <td>1867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>10290</td>\n",
       "      <td>1981</td>\n",
       "      <td>2232</td>\n",
       "      <td>1038</td>\n",
       "      <td>168</td>\n",
       "      <td>2125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>2787</td>\n",
       "      <td>1698</td>\n",
       "      <td>2510</td>\n",
       "      <td>65</td>\n",
       "      <td>477</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>440 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Fresh   Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "0    12669   9656     7561     214              2674          1338\n",
       "1     7057   9810     9568    1762              3293          1776\n",
       "2     6353   8808     7684    2405              3516          7844\n",
       "3    13265   1196     4221    6404               507          1788\n",
       "4    22615   5410     7198    3915              1777          5185\n",
       "..     ...    ...      ...     ...               ...           ...\n",
       "435  29703  12051    16027   13135               182          2204\n",
       "436  39228   1431      764    4510                93          2346\n",
       "437  14531  15488    30243     437             14841          1867\n",
       "438  10290   1981     2232    1038               168          2125\n",
       "439   2787   1698     2510      65               477            52\n",
       "\n",
       "[440 rows x 6 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To find cofficient of variance of all food category\n",
    "cv = lambda x: np.std(x, ddof=1) / np.mean(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fresh               1.053918\n",
       "Milk                1.273299\n",
       "Grocery             1.195174\n",
       "Frozen              1.580332\n",
       "Detergents_Paper    1.654647\n",
       "Delicatessen        1.849407\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.apply(cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1296 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Making a box plot by dropping Buyer/Spender column.\n",
    "df3 = df.drop(\"Buyer/Spender\",axis=1)\n",
    "plt.figure(figsize=(14,18))\n",
    "sns.boxplot(data=df3)\n",
    "plt.title(\"Box Plot of Dataset\",color= 'g',fontweight=\"bold\",fontsize=12)\n",
    "plt.xlabel(\"Types of food products\",color= 'g',fontweight=\"bold\",fontsize=12)\n",
    "plt.ylabel(\"Amount spent\",color= 'g',fontweight=\"bold\",fontsize=12)\n",
    "plt.savefig(\"Outliers_boxplot.png\",bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "## End of Question 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</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",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>61</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>490</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>50</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",
       "<p>62 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID  Gender  Age      Class                   Major Grad Intention  GPA  \\\n",
       "0    1  Female   20     Junior                   Other            Yes  2.9   \n",
       "1    2    Male   23     Senior              Management            Yes  3.6   \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",
       "..  ..     ...  ...        ...                     ...            ...  ...   \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",
       "60  61  Female   23     Senior              Accounting            Yes  3.5   \n",
       "61  62  Female   23     Senior       Economics/Finance             No  3.2   \n",
       "\n",
       "    Employment  Salary  Social Networking  Satisfaction  Spending Computer  \\\n",
       "0    Full-Time    50.0                  1             3       350   Laptop   \n",
       "1    Part-Time    25.0                  1             4       360   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",
       "..         ...     ...                ...           ...       ...      ...   \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",
       "60   Part-Time    30.0                  2             3       490   Laptop   \n",
       "61   Part-Time    70.0                  2             3       250   Laptop   \n",
       "\n",
       "    Text Messages  \n",
       "0             200  \n",
       "1              50  \n",
       "2             200  \n",
       "3             250  \n",
       "4             100  \n",
       "..            ...  \n",
       "57             10  \n",
       "58            250  \n",
       "59            500  \n",
       "60             50  \n",
       "61              0  \n",
       "\n",
       "[62 rows x 14 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Codes for Question 2\n",
    "q2_df = pd.read_csv('Survey-1.csv')\n",
    "q2_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 62 entries, 0 to 61\n",
      "Data columns (total 14 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   ID                 62 non-null     int64  \n",
      " 1   Gender             62 non-null     object \n",
      " 2   Age                62 non-null     int64  \n",
      " 3   Class              62 non-null     object \n",
      " 4   Major              62 non-null     object \n",
      " 5   Grad Intention     62 non-null     object \n",
      " 6   GPA                62 non-null     float64\n",
      " 7   Employment         62 non-null     object \n",
      " 8   Salary             62 non-null     float64\n",
      " 9   Social Networking  62 non-null     int64  \n",
      " 10  Satisfaction       62 non-null     int64  \n",
      " 11  Spending           62 non-null     int64  \n",
      " 12  Computer           62 non-null     object \n",
      " 13  Text Messages      62 non-null     int64  \n",
      "dtypes: float64(2), int64(6), object(6)\n",
      "memory usage: 6.9+ KB\n"
     ]
    }
   ],
   "source": [
    "q2_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Gender', 'Age', 'Class', 'Major', 'Grad Intention', 'GPA',\n",
       "       'Employment', 'Salary', 'Social Networking', 'Satisfaction', 'Spending',\n",
       "       'Computer', 'Text Messages'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q2_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>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",
       "      <th>All</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",
       "      <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",
       "      <td>33</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",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>62</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",
       "All              7    4                 11                       6   \n",
       "\n",
       "Major   Management  Other  Retailing/Marketing  Undecided  All  \n",
       "Gender                                                          \n",
       "Female           4      3                    9          0   33  \n",
       "Male             6      4                    5          3   29  \n",
       "All             10      7                   14          3   62  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct1 = pd.crosstab(q2_df[\"Gender\"],q2_df[\"Major\"],margins=True)\n",
    "ct1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>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": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct2 = pd.crosstab(q2_df[\"Gender\"],q2_df[\"Grad Intention\"])\n",
    "ct2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>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": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct3 = pd.crosstab(q2_df[\"Gender\"],q2_df[\"Employment\"])\n",
    "ct3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct4 = pd.crosstab(q2_df[\"Gender\"],q2_df[\"Computer\"])\n",
    "ct4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    33\n",
       "Male      29\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q2_df[\"Gender\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4677"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Prob_male = round(29/(33+29),4)\n",
    "Prob_male"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5323"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Prob_female = round(33/(33+29),4)\n",
    "Prob_female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Gender</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Major</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accounting</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CIS</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Economics/Finance</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>International Business</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Management</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retailing/Marketing</th>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Undecided</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Gender                  Female  Male\n",
       "Major                               \n",
       "Accounting                   3     4\n",
       "CIS                          3     1\n",
       "Economics/Finance            7     4\n",
       "International Business       4     2\n",
       "Management                   4     6\n",
       "Other                        3     4\n",
       "Retailing/Marketing          9     5\n",
       "Undecided                    0     3"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(q2_df[\"Major\"],q2_df[\"Gender\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Retailing/Marketing       14\n",
       "Economics/Finance         11\n",
       "Management                10\n",
       "Accounting                 7\n",
       "Other                      7\n",
       "International Business     6\n",
       "CIS                        4\n",
       "Undecided                  3\n",
       "Name: Major, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q2_df[\"Major\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(Retailing/Marketing|Male)    -  0.1724\n",
      "P(Economics/Finance|Male)      -  0.1379\n",
      "P(Management|Male)             -  0.2069\n",
      "P(Accounting|Male)             -  0.1379\n",
      "P(Other|Male)                  -  0.1379\n",
      "P(International Business|Male) -  0.069\n",
      "P(CIS|Male)                    -  0.0345\n",
      "P(Undecided|Male)              -  0.1034\n"
     ]
    }
   ],
   "source": [
    "# To find Conditional probability of different majors among male students(i.e 29 Males out of 62 students)\n",
    "\n",
    "##P(Retailing/Marketing|Male)\n",
    "cpm1 = round(5/29,4)\n",
    "##P(Economics/Finance|Male)\n",
    "cpm2 = round(4/29,4)\n",
    "##P(Management|Male) \n",
    "cpm3 = round(6/29,4)\n",
    "##P(Accounting|Male)  \n",
    "cpm4 = round(4/29,4)\n",
    "##P(Other|Male)    \n",
    "cpm5 = round(4/29,4)\n",
    "##P(International Business|Male) \n",
    "cpm6 = round(2/29,4)\n",
    "##P(CIS|Male)    \n",
    "cpm7 = round(1/29,4)\n",
    "##P(Undecided|Male)     \n",
    "cpm8 = round(3/29,4)\n",
    "print(\"P(Retailing/Marketing|Male)    - \",cpm1)\n",
    "print(\"P(Economics/Finance|Male)      - \",cpm2)\n",
    "print(\"P(Management|Male)             - \",cpm3)\n",
    "print(\"P(Accounting|Male)             - \",cpm4)\n",
    "print(\"P(Other|Male)                  - \",cpm5)\n",
    "print(\"P(International Business|Male) - \",cpm6)\n",
    "print(\"P(CIS|Male)                    - \",cpm7)\n",
    "print(\"P(Undecided|Male)              - \",cpm8)\n",
    "\n",
    "# Conclusion - Management is most preferred subject among male students and \n",
    "# CIS is the least preferred subject among male students"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(Retailing/Marketing|Female)    -  0.2727\n",
      "P(Economics/Finance|Female)      -  0.2121\n",
      "P(Management|Female)             -  0.1212\n",
      "P(Accounting|Female)             -  0.0909\n",
      "P(Other|Female)                  -  0.0909\n",
      "P(International Business|Female) -  0.1212\n",
      "P(CIS|Female)                    -  0.0909\n",
      "P(Undecided|Female)              -  0.0\n"
     ]
    }
   ],
   "source": [
    "# To find Conditional probability of different majors among female students(i.e 33 females out of 62 students)\n",
    "\n",
    "##P(Retailing/Marketing|Female)\n",
    "cpf1 = round(9/33,4)\n",
    "##P(Economics/Finance|Female)\n",
    "cpf2 = round(7/33,4)\n",
    "##P(Management|Female) \n",
    "cpf3 = round(4/33,4)\n",
    "##P(Accounting|Female)  \n",
    "cpf4 = round(3/33,4)\n",
    "##P(Other|Male)    \n",
    "cpf5 = round(3/33,4)\n",
    "##P(International Business|Female) \n",
    "cpf6 = round(4/33,4)\n",
    "##P(CIS|Female)    \n",
    "cpf7 = round(3/33,4)\n",
    "##P(Undecided|Female)     \n",
    "cpf8 = round(0/33,4)\n",
    "print(\"P(Retailing/Marketing|Female)    - \",cpf1)\n",
    "print(\"P(Economics/Finance|Female)      - \",cpf2)\n",
    "print(\"P(Management|Female)             - \",cpf3)\n",
    "print(\"P(Accounting|Female)             - \",cpf4)\n",
    "print(\"P(Other|Female)                  - \",cpf5)\n",
    "print(\"P(International Business|Female) - \",cpf6)\n",
    "print(\"P(CIS|Female)                    - \",cpf7)\n",
    "print(\"P(Undecided|Female)              - \",cpf8)\n",
    "\n",
    "# Conclusion - Retailing/Marketing is most preferred subject among female students"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes          28\n",
       "Undecided    22\n",
       "No           12\n",
       "Name: Grad Intention, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Probablility of random is male and graduate.\n",
    "\n",
    "q2_df[\"Grad Intention\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4516"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Prob_Grduate = round(28/62,4)\n",
    "Prob_Grduate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2115"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result1 = Prob_male*Prob_Grduate\n",
    "result1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Laptop     55\n",
       "Desktop     5\n",
       "Tablet      2\n",
       "Name: Computer, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Probablility of random chosen student is female and does not have laptop.\n",
    "\n",
    "q2_df[\"Computer\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1129"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Prob_Notlaptop = round(7/62,4)\n",
    "Prob_Notlaptop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.058300000000000005"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result2 = Prob_female*Prob_Notlaptop\n",
    "result2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Part-Time     43\n",
       "Full-Time     10\n",
       "Unemployed     9\n",
       "Name: Employment, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Probablility of randomly chosen student is a male or has full-time employment\n",
    "\n",
    "q2_df[\"Employment\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1613"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Prob_fulltime = round(10/62,4)\n",
    "Prob_fulltime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5548"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result3 = Prob_male + Prob_fulltime - (Prob_male*Prob_fulltime)\n",
    "result3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.6</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>500</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>400</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>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>50</th>\n",
       "      <td>51</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Management</td>\n",
       "      <td>No</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>600</td>\n",
       "      <td>Tablet</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",
       "8    9  Female   20  Junior  Management            Yes  3.6  Unemployed   \n",
       "19  20  Female   20  Junior  Management      Undecided  3.2  Unemployed   \n",
       "45  46  Female   21  Senior  Management      Undecided  3.8   Part-Time   \n",
       "50  51  Female   21  Junior  Management             No  3.5  Unemployed   \n",
       "\n",
       "    Salary  Social Networking  Satisfaction  Spending Computer  Text Messages  \n",
       "8     30.0                  0             4       500   Laptop            400  \n",
       "19    60.0                  2             6       300   Laptop            350  \n",
       "45    60.0                  1             4       650   Laptop            150  \n",
       "50    35.0                  2             4       600   Tablet            100  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Conditional probability that given a female student is randomly chosen, she is majoring in international business or management.\n",
    "\n",
    "q2_df[(q2_df[\"Gender\"] == \"Female\") & (q2_df[\"Major\"] == \"Management\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>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>44</th>\n",
       "      <td>45</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>Senior</td>\n",
       "      <td>International Business</td>\n",
       "      <td>No</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>650</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>57</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>Senior</td>\n",
       "      <td>International Business</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.4</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>200</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>100</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID  Gender  Age   Class                   Major Grad Intention  GPA  \\\n",
       "27  28  Female   20  Junior  International Business            Yes  2.9   \n",
       "44  45  Female   21  Senior  International Business             No  3.0   \n",
       "56  57  Female   21  Senior  International Business            Yes  3.4   \n",
       "57  58  Female   21  Senior  International Business             No  2.4   \n",
       "\n",
       "   Employment  Salary  Social Networking  Satisfaction  Spending Computer  \\\n",
       "27  Part-Time    50.0                  3             1       900   Laptop   \n",
       "44  Part-Time    30.0                  2             5       650  Desktop   \n",
       "56  Part-Time    42.0                  1             1       200   Laptop   \n",
       "57  Part-Time    40.0                  1             3      1000   Laptop   \n",
       "\n",
       "    Text Messages  \n",
       "27            100  \n",
       "44            500  \n",
       "56            100  \n",
       "57             10  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q2_df[(q2_df[\"Gender\"] == \"Female\") & (q2_df[\"Major\"] == \"International Business\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2424"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# There are 8 total female doing Management and International Business, so the result is as follows\n",
    "\n",
    "# Result = Probability of Female doing both subjects / Total Number of Females\n",
    "\n",
    "result4 = round(8/33,4)\n",
    "result4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>Grad Intention</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <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",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <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": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct5 = pd.crosstab(q2_df[\"Gender\"],q2_df[\"Grad Intention\"])\n",
    "ct5.drop(\"Undecided\",axis=1,inplace=True)\n",
    "ct5\n",
    "\n",
    "# Yes being female and graduate intention are both independent events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 17 students have GPA less than 3\n",
    "\n",
    "#q2_df[q2_df[\"GPA\"] < 3].groupby([\"Gender\"]).count()[\"GPA\"].sum()\n",
    "q2_df.Gender.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2742"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# probability that his/her GPA is less than 3\n",
    "\n",
    "result5 = round(17/62,4)\n",
    "result5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 14)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 32 students have Salary of 50 or more \n",
    "\n",
    "q2_df[q2_df[\"Salary\"] >= 50].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    18\n",
       "Male      14\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = q2_df[q2_df[\"Salary\"] >= 50]\n",
    "new_df[\"Gender\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4828"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Conditional probability that a randomly selected male earns 50 or more\n",
    "\n",
    "result6 = round(14/29,4)\n",
    "result6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5455"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Conditional probability that a randomly selected female earns 50 or more\n",
    "\n",
    "result6 = round(18/33,4)\n",
    "result6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OAP61nbUr8JkOY5KkkTGHSZokw3SufwGwF83dnqmqHwA7dRmUJI2QOUzSxBim8Lq+qm6YmkiyBc09cCRpKTCHSZoYwxReX03ySmCrJI8FPgH8e7dhSdLImMMkTYxhCq8jgfXAOTSDzn4BeHWXQUnSCJnDNBa3B5J08li13FvTLVXDXNV4C/De9iFJS4o5TONyPd2d0866dR1tWV2btfBKcg5zfGaq6n6dRCRJI2AOkzSJ5mrx2r/9+YL250fan08HfrmYnSZ5CfAXNEnxHODZVfXrxWxTkqbpLIdJ0kLNWnhV1UUASfaqqr0GFh2Z5H+Av13IDpPsCvw1zaC1v0pyPPA04IML2Z4kzaSrHCZJizFM5/o7JnnE1ESShwN3XOR+t6C5wmgLYGvg8kVuT5Jm00UOk6QFGWasxkOBY5LcqZ2+BnjOQndYVZcleQtwMfAr4MSqOnH6ekkOAw4DWLly5UJ3N3IrVy7nkku66dS42247c/HFV3aybWkTNtIcJkmLMcxVjWcA90+yHZCq+tlidphkB+BA4G40CfATSQ6uqo9O2+8aYA3A6tWrJ+Zmh5dcso6TT+5m2/vs41Uq0qiNOodJ0mLMW3glec20aQCqaqH9Ix4DXFBV69vtfQp4OPDROV8lSQvQQQ6TpAUbpo/XLwYeNwOPB1YtYp8XAw9NsnWaDLgvcP4itidJcxl1DpOkBRvmVONbB6fb/lmfW+gOq+r0JCcAZwI3Ad+iPaUoSaM26hwmSYsxTOf66bYG7r6YnVbVa4HXLmYbkrRAi85hkrRQw/TxGrz78+bAMuD1XQYlSaNiDpM0SYZp8dp/4PlNwLqquqmjeCRp1MxhkibGMJ3r/66qLmofl1XVTUk+Mv/LJGkijDyHJXlJkvOSnJvkuCR3GFWwkjZuwxRe9xmcaO82/6BuwpGkkRtpDhsY9mx1Vd2X5vTl0xYVoaRNxqyFV5JXJLkOuF+Sa5Nc106vAz7bW4SStAAd5zCHPZO0ILMWXlX1hqraFnhzVW1XVdu2j7tU1St6jFGSNlhXOayqLgOmhj27AvjZ9GHPkhyWZG2StevXr1/U+5BmcnuamwF38Vi1fPm4395Gba4Wr92T3GkqQSXZJ8nRbd+G2/UXoiRtuK5y2LRhz+5KMwj3wYPrVNWaqlpdVauXLVu2mLchzeh6mkt1u3hctM7h67o0Vx+v44E7AiTZE/gEzTe8PYF/7jowSVqkrnLYb4Y9q6obgalhzyRpXnPdTmKrqprqt3AwcExVvTXJZsBZnUcmSYvTVQ77zbBnwK9ohj1bu6hIJW0y5mrxysDzRwNfBqiqWzqNSJJGo5McVlWnA1PDnp1Dk0cd9kzSUOZq8fpKkuNpOo/uAHwFIMkuwA09xCZJi9FZDnPYM0kLNVfhdTjw58AuwCPavgwAy4FXdRyXJC3W4ZjDJE2YWQuvqirgYzPM/1anEUnSCJjDJE2iYcZq1EZg5crlXHJJN5cI3+EOm/HrX9v1T5Kk+Vh4bSIuuWQdJ5/czbb32eeWDrfdzXYlSRqHuW6g+uX255v6C0eSRsMcJmkSzdXitUuSRwEHJPkYt700m6o6s9PIJGlxzGGSJs5chddrgCOBFcDbpi0rmvviSNKkModJmjhzXdV4AnBCkr+pqtf3GJMkLZo5TNIkmrdzfVW9PskBwCPbWadU1ee7DUuSRsMcJmmSzDVkEABJ3gC8GPhO+3hxO0+SJp45TNIkGeZ2Ek8A9pwa3yzJh4BvAa/oMjBJGhFzmKSJMW+LV2v7ged36iAOSerS9gPPzWGSxmaYFq83AN9KcjLN5diPxG+KkpYOc5ikiTFM5/rjkpwCPJgmaR1RVVd2HZgkjYI5TNIkGWrIoKq6Avhcx7FIUifMYZImxbB9vCRJkrRIFl6SJEk9mbPwSrJZknNHvdMk2yc5Icl3k5yf5GGj3ockdZXDJGmh5iy82vvenJ1k5Yj3ezTwn1X1u8D9gfNHvH1J6jKHSdKCDNO5fhfgvCTfAH4xNbOqDljIDpNsR3M597Pa7dwA3LCQbUnSEEaawyRpMYYpvF434n3eHVgPfCDJ/YEzgBdX1S8GV0pyGHAYwMqVflnV0rLllpCkk23vttvOXHyxd0PYAKPOYZK0YMPcx+urSXYH7lVV/5Vka2DzRe7zgcCLqur0JEcDRwJ/M22/a4A1AKtXr65F7E/q3Y03wsknd7PtffZZ182GN1Id5DBJWrBhBsl+LnAC8K/trF2Bzyxin5cCl1bV6e30CTSFmCSNXAc5TJIWbJjbSbwA2Au4FqCqfgDstNAdtneMviTJHu2sfYHvLHR7kjSPkeYwSVqMYfp4XV9VN0z1V0myBbDYU38vAo5Ncjvgx8CzF7k9SZrNyHNYku2B9wH3bbf1nKo6bZFxStoEDFN4fTXJK4GtkjwW+Cvg3xez06o6C1i9mG1I0pBGnsO49ZY4T22/QG692CAlbRqGOdV4JM1ViOcAzwO+ALy6y6AkaYRGmsMGbonzfmhuiVNV1yw+TEmbgmGuarwlyYeA02ma1L9XVV5lKGlJ6CCHzXtLnMXcDmfV8uVctK6bK1d333lnLrxy6d2K5PZ0d3sWqW/DXNX4BOBHwD8B7wJ+mOTxXQcmSaPQQQ6buiXOv1TVA2huynrk4ApVtaaqVlfV6mXLlm3Qxi9at46CTh5dFXRdu55ujoctCBqHYfp4vRXYp6p+CJDkHsB/AF/sMjBJGpFR57CZbolz5BzrS9JvDNPH66qphNX6MXBVR/FI0qiNNId5SxxJizFri1eSp7RPz0vyBeB4mpbZPwW+2UNskrRgHecwb4kjaUHmOtX4xIHn64BHtc/XAzt0FpEkjUZnOcxb4khaqFkLr6ryG5ykJcscJmkSzdu5PsndaJrVVw2uX1UHdBeWJI2GOUzSJBnmqsbP0Nwo8N+BWzqNRpJG7zOYwyRNiGEKr19X1T91HokkdcMcJmliDFN4HZ3ktcCJNPexA6CqzuwsKkkaHXOYpIkxTOH1+8AzgEdzazN9tdOSerbllt0Nn7Lbbjtz8cVLb0iZeZjDJE2MYQqvJwN3r6obug5G0vxuvBFOPrmbbe+zz9IcUmYe5jBJE2OYO9efDWzfcRyS1BVzmKSJMUyL187Ad5N8k9v2j/BSbElLgTlM0sQYpvB6bedRSFJ3zGGSJsa8hVdVfbWPQCSpC+YwSZNkmDvXX0dzBRDA7YAtgV9U1XZdBiZJo2AOkzRJhmnx2nZwOsmTgId0FZAkjZI5TNIkGeaqxtuoqs/g/W8kLVHmMEnjNMypxqcMTG4GrObWZntJmmjmMEmTZJirGp848Pwm4ELgwE6ikaTRM4dJmhjD9PF6dh+BSFIXzGGSJsmshVeS18zxuqqq13cQjySNhDlM0iSaq8XrFzPMuyNwKHAXwKQlaZKZwyRNnFkLr6p669TzJNsCLwaeDXwMeOtsr5OkSWAOkzSJ5rydRJI7J/k74Ns0RdoDq+qIqrpqsTtOsnmSbyX5/GK3JUkz6TKHSdJCzNXH683AU4A1wO9X1c9HvO8XA+cD3j1a0sj1kMMkaYPN1eL1UuCuwKuBy5Nc2z6uS3LtYnaaZAXwBOB9i9mOJM2hsxwmSQs1Vx+vDb6r/QZ4B/ByYNvZVkhyGHAYwMqVKzsMRdLGqOMcJkkL0ntiSrI/cFVVnTHXelW1pqpWV9XqZcuW9RSdJElSd8bxjXAv4IAkF9JcXfToJB8dQxyStCBeHCRpoXovvKrqFVW1oqpWAU8DvlJVB/cdhyQtwtTFQZK0QewDIUkbwIuDJC3GWAuvqjqlqvYfZwyStIHeQXNx0C2zrZDksCRrk6xdv359b4FJmny2eEnSkLw4SNJiWXhJ0vC8OEjSolh4SdKQvDhI0mJZeEmSJPVk1jvXS5JmV1WnAKeMOQxJS4wtXpIkST2x8JIkSeqJhZckSVJPLLwkSZJ6YuElSZLUEwsvSZKknlh4SZIk9cTCS5IkqScWXpIkST2x8JIkSeqJhZckSVJPLLwkSdKSt2r5cpJ08li1fPnI4nSQbEmStORdtG4d1dG2s27dyLZli5ckSVJPLLwkSZJ6YuElSZLUEwsvSZKknlh4SZIk9cTCS5IkqScWXpIkST2x8JIkSeqJhZckSVJPei+8kuyW5OQk5yc5L8mL+45BkiRpHMYxZNBNwEur6swk2wJnJDmpqr4zhlgkSZJ603uLV1VdUVVnts+vA84Hdu07DkmSpL6NtY9XklXAA4DTZ1h2WJK1SdauX79+6G2uXNnd6ORJRvfmpQm05ZZ09rezcuXycb+9kbC7hKTFGMepRgCSbAN8Eji8qq6dvryq1gBrAFavXj30gOOXXLKOk08eWZi/ZZ99utu2NG433khnfz/77LOumw33z+4SkhZsLC1eSbakKbqOrapPjSMGSVoIu0tIWozeW7zSnK97P3B+Vb2t7/1L0qjM1l0iyWHAYQArV67sP7BZ3B7sMiGN2ThavPYCngE8OslZ7eOPxxCHJC3YXN0lqmpNVa2uqtXLli0bT4AzuB6ojh6ShtN7i1dVfQ3wK5ekJcvuEpIWyjvXS9IGsLuEpMWw8JKkDWN3CUkLNrbbSUjSUmR3CUmLYYuXJElSTyy8JEmSemLhJUmS1BMLL0mSpJ5YeEmSJPXEwkuSJKknFl6SJEk9sfCSJEnqiYWXJElSTyy8JEmSeuKQQZIk6TduDzRjwY/e1pttxi9vuaWTbS8VFl6SJOk3rgeqo23nllu623ZH2x01TzVKkiT1xMJLkiSpJxZekiRJPbHwkiRJ6omFlyRJUk8svCRJknpi4SVJktQTCy9JkqSeWHhJkiT1xMJLkiSpJxZekiRJPbHwkiRJ6slYCq8k+yX5XpIfJjlyHDFI0kKZwyQtVO+FV5LNgXcDjwfuDRyU5N59xyFJC2EOk7QY42jxegjww6r6cVXdAHwMOHAMcUjSQpjDJC1YqqrfHSZPBfarqr9op58B/EFVvXDaeocBh7WTewDfG1i8I3B1D+EOy3jmZjxzM56Z7V5Vy8YdxHTD5LB58tc4TMrvdD7GOVpLIc6lECNseJyz5q8tRhPPBskM836r+quqNcCaGTeQrK2q1aMObKGMZ27GMzfjWXLmzWFz5a9xWCq/U+McraUQ51KIEUYb5zhONV4K7DYwvQK4fAxxSNJCmMMkLdg4Cq9vAvdKcrcktwOeBnxuDHFI0kKYwyQtWO+nGqvqpiQvBL4EbA4cU1XnbeBmJqYJv2U8czOeuRnPEjKiHNa3pfI7Nc7RWgpxLoUYYYRx9t65XpIkaVPlneslSZJ6YuElSZLUk4kqvJLsluTkJOcnOS/Ji2dY5+lJvt0+/l+S+w8suzDJOUnOSrK2p3j2TvKzdp9nJXnNwLKRDisyZDz/dyCWc5PcnOTO7bJRH587JPlGkrPbeF43wzpJ8k/tMfh2kgcOLBv18Rkmnj4/P8PE0+fnZ5h4evv8aPRm+x0nuXOSk5L8oP25w7hjhWYUgCTfSvL5dnri4pzpcz+hcW6f5IQk323/Rzxs0uJMssdAfjkrybVJDp+0ONtYX9L+DZ2b5Lj2b2s0cVbVxDyAXYAHts+3Bb4P3HvaOg8HdmifPx44fWDZhcCOPcezN/D5GV67OfAj4O7A7YCzp7+2i3imrf9E4CsdHp8A27TPtwROBx46bZ0/Br7YrvvQqd9XR8dnmHj6/PwME0+fn5954+nz8+Nj9I/ZfsfAPwJHtvOPBN407ljbWP4/4N+m/gYmMc6ZPvcTGueHgL9on98O2H4S4xyId3PgSmD3SYsT2BW4ANiqnT4eeNao4pyoFq+quqKqzmyfXwecT3MABtf5f1X1v+3k12nuoTO2eOYw8mFFFhDPQcBxi9nnPPFUVf28ndyyfUy/WuNA4MPtul8Htk+yC90cn3nj6fnzM8zxmc1Yjs80nX5+NHpz/I4PpPnHTPvzSf1Hd1tJVgBPAN43MHvi4pzFRMWZZDvgkcD7Aarqhqq6hgmLc5p9gR9V1UVMZpxbAFsl2QLYmuZefSOJc6IKr0FJVgEPoPnGNptDaVpTphRwYpIz0gzZ0Vc8D2ub9r+Y5D7tvF2BSwbWuZThi7bFxkOSrYH9gE8OzB758WlPFZwFXAWcVFXT45ntOHRyfIaIZ1Dnn58h4+nt8zPs8enr86PRm+V3vHNVXQHNFzhgpzGGOOUdwMuBWwbmTWKcM33uJy3OuwPrgQ+0p27fl+SOTF6cg57GrV/sJirOqroMeAtwMXAF8LOqOpERxTmOIYPmlWQbmoR/eFVdO8s6+9D843zEwOy9quryJDsBJyX5blWd2nE8Z9KMyfTzJH8MfAa4F0MOjdRBPFOeCPxPVf10YN7Ij09V3QzsmWR74NNJ7ltV5w6GO9PL5pi/KEPE0wTV0+dniHh6/fwMe3zo6fOj0ZvpdzzmkH5Lkv2Bq6rqjCR7jzmc+fzW537cAc1gC+CBwIuq6vQkR9OcCptIaW48fADwinHHMpO279aBwN2Aa4BPJDl4VNufuBavJFvSFBXHVtWnZlnnfjTN0wdW1U+m5lfV5e3Pq4BP05yu6TSeqrp2qmm/qr4AbJlkRzoaVmSY49Ma/DYxFevIj8/Atq8BTqFpJRk023HodNiVOeLp9fMzXzx9f37mi2dAr58fjd603/G69hQ/7c+rxhcZAHsBByS5kOY0+qOTfJTJi3O2z/2kxXkpcOlAC/YJNIXYpMU55fHAmVW1rp2etDgfA1xQVeur6kbgUzT9g0cS50QVXklCc476/Kp62yzrrKQ5CM+oqu8PzL9jkm2nngOPA2b6Jj/qeJa365HkITTH9Cd0MKzIMPG0690JeBTw2YF5XRyfZe23apJsRfNhnf5t8HPAM9N4KE2T7RV0c3zmjafnz88w8fT5+Rnm99Xb50ejN8fv+HPAIe1qhzDwux2HqnpFVa2oqlU0n+2vVNXBTFicc3zuJyrOqroSuCTJHu2sfYHvMGFxDpjef3TS4rwYeGiSrdv8vC9Nn+qRxDlppxr3Ap4BnNP2UQB4JbASoKreA7wGuAvwz+3/q5uqGTF8Z5pmdWje179V1X/2EM9TgecnuQn4FfC0qiqgi2FFhokH4MnAiVX1i4HXdnF8dgE+lGRzmoLh+Kr6fJK/HIjnCzRXNv4Q+CXw7HZZF8dnmHj6/PwME0+fn59h4oH+Pj8avdl+x6cBxyc5lOafyp+OM8g5vJHJinPGz32SbzJZcQK8CDi2/aL2Y5pcuxkTFmea/qOPBZ43MHuifu/t6doTaLqC3AR8i2bIoG0YQZwOGSRJktSTiTrVKEmStDGz8JIkSeqJhZckSVJPLLwkSZJ6YuElSZLUEwsvLViSnZP8W5IfpxlO47QkT26X7Z3kZ2mGrzg/yWsHXveAJJXkj+bY9nOSnJPk22lGh1/UOIWStFBJXpXkvDYfnZXkD+ZY94NJntpnfFpaJu0+Xloi2pvKfQb4UFX9n3be7jTDQEz576rav73x4FlJPl9VZ9DcPO9r7c8vzbDtFcCrgAdW1c/SDJG0bJHxbt4OpSJJQ0vyMGB/mnx0fZqRJW43wu1vUVU3jWp7mny2eGmhHg3cMHDTTarqoqp65/QV2xtxngHcoy3Yngo8C3hckjvMsO2dgOuAqaF0fl5VFwAkuWeS/0ozqPSZSe6RxpvblrFzkvx5u+7eSU5O8m80N53dvF3vm+031+fNsG9JGrQLcHVVXQ9QVVe3Yze+ps0l5yZZ0+a225htnSSnJPmHJF8FXpXkgjTDwZFkuyQXTk1r42PhpYW6D81dfeeV5C7AQ4HzaO6+f0FV/YhmHLk/nuElZwPrgAuSfCDJEweWHQu8u6ruTzN21hXAU4A9gfvTDJHy5rTjadGMq/aqqro3zaDYP6uqBwMPBp6b5G5Dv2NJm6ITgd2SfD/JPyd5VDv/XVX14Kq6L7AVTavYdHOts31VPaqqXkeTC5/Qzn8a8Ml2jEBthCy8NBJJ3t22Qn1zYPYfJvkWTeJ6YzvkzUE0g+LS/jxo+rbaU4L70bSMfR94e5Kj0oyZtmtVfbpd79dV9UvgEcBxVXVzO+jqV2kKK4BvTLWW0Yyz9sw0wy2dTjN00L1GdAgkbYTaQewfBBwGrAc+nuRZwD5JTk9yDs0ZgPvM8PK51vn4wPP30Q6n1v78wGjfhSaJfby0UOcBfzI1UVUvaPs+rB1Y57+r6jff8Nrx4/4EOCDJq4AAd0mybVVdN7jxdrzCbwDfSHISTSKabWDw32riHzA43mCAF1XVb/Urk6TZtF8GTwFOaYuo5wH3A1ZX1SVJjgJu022i7Ubxz3Os85vcVFX/k2RV25q2eVU5AP1GzBYvLdRXgDskef7AvK3nec1jgLOrareqWlVVuwOfBJ40uFKSuyZ54MCsPYGLqupa4NIkT2rXu32aAVdPBf687cO1DHgkTdE23ZdoBqSe6kvxO23Hf0maUZI9kgy2jO8JfK99fnV78c9MVzHeYYh1Bn0YOA5buzZ6tnhpQaqq2gLo7UleTtME/wvgiDledhDw6WnzPgk8H/jIwLwtgbckuSvw63bbf9kuewbwr0n+FriRZnT4TwMPo+kbVsDLq+rKJL87bV/vA1YBZ7adXNczreiTpGm2Ad6ZZHvgJuCHNKcdrwHOAS4Evjn9RVV1TZL3zrXONMcCf0dTfGkjluaMjiRJGpf23l8HVtUzxh2LumWLlyRJY5TkncDjmfkqb21kbPGSJEnqiZ3rJUmSemLhJUmS1BMLryWoHax173HHMU5JnpzkkiQ/T/KAccczm3bA3L9rn/9hku/N9xppkpl/lk7+0WSy8Jow7Rhdj5k271lJvjY1XVX3qapT5tnOqiSVZGO9gOItwAurapuq+tb0hUkOTHJWkmuTXJ3ky0lW9R/mrarqv6tqj3HGIM3F/DO0GfNPkpVtMTb1qCS/GJj+ww3d0fTjP8s6p7T7uv+0+Z9p5++9oftVdyy8tCATkFB3p7l7/m9Jck+amxG+FLgTcDeaO0jf0lt0kjozqfmnqi5ui7Ftqmqbdvb9B+b9d4cxfR945tTEwBi56zvcpxbAwmsJGvxWmuQhSda2LTvrkkwNq3Nq+/Oa9pvWw5JsluTVSS5KclWSDye508B2n9ku+0mSv5m2n6OSnJDko0muBZ7V7vu0JNckuSLJu5LcbmB7leSvkvwgyXVJXp/kHu1rrk1y/OD6097jjLGmuVv9z4HNgbOT/GiGl+9JMxD3l6txXVV9sqounvZePt7GdebgN8U0d87/ZJL1SS5I8tcDy45q4/5w+9rzkqweWP6AdnvXJfk4A0OEJNk7yaXTfo8vS/LtJD9r4xlc/+Xtcb08yV+0x/Oes34wpB6Yf+bNP7Mdt9sneUuSi9tj9Z4kW7XLvpDkrQPrfjzJMUl+D3gP8LD2OF4zxy6OpR3Bo52eumH1DdPe15FJftQe5+OT3Llddof2+P6kPabfTLJzu+xZSX7cHscLkjy9nX+PJF9pX3N1kmPT3Gh2an8PTPKt9nWfaN/X3w0s3z/NmYlrkvy/JPcbWHZEksva134vyb7DHuuJV1U+JuhBc4fjx0yb9yzgazOtA5wGPKN9vg3w0Pb5Kpq7uG8x8Lrn0Nx1+e7tup8CPtIuuzfwc5oBp29H05R+48B+jmqnn0RTsG9FM3DsQ2nuB7cKOB84fGB/BXwO2I5mcNjrgS+3+78T8B3gkFmOw6yxDmz7nrO89u40d7x/O7APsM205VPv5ak0d8l/GXBB+3wz4AzgNe1xuDvwY+CPBl77a5r77WwOvAH4ervsdsBFwEvabT213c/ftcv3Bi6d9nv8BnBX4M7t8fvLdtl+wJXtcdua5s7+s75nHz5G8cD8M2+sA9ue929xcD3gHW08dwa2Bf4deEO7bDlwFc1A2k9vc862Mx3/WfZzCvAXwInA49t536AZ0eNSYO923uHA14EVwO2BfwWOa5c9r41pa5rc9qD22N0RuBbYo11vF+A+7fN7Ao9tt7WMpuB+R7tsKh++mCYfPoWmCJzKhw9s3/MftPs7hOazdXtgD+AS4K4Dn6d7jPvvY2R/Z+MOwMe0X0jzwfs5zXAUU49fMnviOxV4HbDjtO2s4rcT35eBvxqY3oMmmW1BU2gcN7Bs6/aPZDDxnTpP7IcDnx6YLmCvgekzgCMGpt869Uc6w7ZmjXVg27MmPpqEfDxNM/uvgQ/SFmDte/n6wLqbAVcAf9gmgYunbesVwAcGXvtfA8vuDfyqff5I4HLa++O18/4fcxdeBw9M/yPwnvb5MbRJuZ2+53zv2YePxT7MP/PHOrDtoQsvIDRDqt1jYNnDaFrmp6afQlNsXA08YmD+sxi+8DqYZsihPYDvt8sGC6/zgX0HXrfLwO/gOW2+ut+0bd+x/Rz8CbDVPHE8CfhW+/yRwGXcNh9+jVvz4b8Ar5/2+u8Bj2qP2VU04/tuOe6/i1E/PNU4mZ5UVdtPPYC/mmPdQ4HfAb7bNg3vP8e6d6X5BjLlIpo/uJ3bZZdMLaiqXwI/mfb6SwYn0gwy/fkkV7bN//8A7DjtNesGnv9qhultmNlcsc6rqr5eVX9WVctoCqpHAq+a6b1U1S00yemuNH037to2fV/TNu2/ctp+rxx4/kuawcK3aF9/WbUZZCDuuUzf1tTxuM3vY9pzqUvmn0XmnxksoykmzxjIK//Zzp/yeZqWn+9V1Zyd6efwKZpWsxdx2/Fvp+wOfHoghvOBm2ne10eALwEfS9O94R+TbFlVvwD+nGa83CuS/EfacXCT7JTkY+0pwWuBj3Lr72CmfDj4O9wdeOm0XLsbTSvXD2kK6aOAq9p93HWBx2TiWHgtcVX1g6o6CNgJeBNwQpI70nzTmu5ymg/7lJU0g76uo2nxWTG1oO17cJfpu5s2/S/Ad4F7VdV2NAVKFv5uho51g1TVN2kS0n0HZu829STJZjTv/XKaxHDB4D+eqtq2qoYZyuMKYNckg8dg5YbGO7CtFQPTu822ojQu5p+hXU1T6N1nIK/cqW7tgA/w9zSF0C5JDhqYP9OxnFFbsH4ReD4zF16X0JyKHMxvd6iqy6rqxqp6XVXdG3g4sD9tZ/2q+lJVPZamhey7wHvb7b2hje9+7e/gYG79HcyUDwfz2CXA30+LZeuqOq7d579V1SNofg9F8/naKFh4LXFJDk6yrG21uaadfTPNKbZbaPooTDkOeEmSuyXZhuYb4ser6ibgBOCJSR7edjh9HfMnsW1pzv3/vP0G9PxRva95Yp1TkkckeW6Sndrp3wUOoOnbMOVBSZ7StlQdTtP/4+s0/SKubTt2bpVk8yT3TfLgIWI+jSY5/3WSLZI8BXjI8G/5No4Hnp3k95JsTXMqRpoo5p/htMfnvcDbB/LSrkn+qH3+SODZNIXOM4F3Jtm1ffk6YEVmuRBgBq8EHlVVF86w7D3A3yfZvd3vsiQHts/3SfL7aTrnX0tzCvLmJDsnOaAtqK+nORV9c7u9bdvpa9p4/+/Avk5r13thmw8P5Lb58L3AXyb5gzTumOQJSbZNskeSRye5PU1XkV8N7HPJs/Ba+vYDzktzpc3RwNOq6tftN5+/B/6nbcZ9KE2/oY/Q9Mu4gOYD/SKAqjqvff4xmm8q19GcY79+jn2/DPg/7brvBT4+wvc1a6xDuIam0DqnPS7/SXN1zz8OrPNZmubz/wWeATyl/cZ3M/BE2isjab6pvo+mM+6cquoGmn4az2q3++c0LW0brKq+CPwTcDJNJ9/T2kVz/T6kvpl/hncEzd/y19vTcv8F7JFkO5rb37ywbXn6GvB+4ANta9FXaG5dcWWSq+fbSVVdPsepyqNpOvifmOQ6mi+bf9AuW05TAF9L0/L2VZpTh5vR3JrncuCnNH2wpk4/v46mk/zPgP9gIN8N5MNDaXLywTSnU69vl68Fngu8iyZf/pAmd0LTwf6NNPn3SpoW1VfO996XCgfJ1ozab3nX0DTjXzDmcEYqyVE0HWMPHncsw0pzWfm5wO0X+q1bWio25vyzKUtyOs0FRB8YdyzjZIuXfiPJE5Ns3TYpvwU4h+YKJo1BmmFJbpdkB5r+Df9u0aWNlfln45PkUUmWt6caDwHuR3MGYpNm4aVBB9I0J18O3IvmtIFNouPzPJq+Mj+i6d8wyj4s0qQx/2x89gDOpjkV+VLgqVV1xXhDGj9PNUqSJPXEFi9JkqSejHug0aHsuOOOtWrVqnGHIalHZ5xxxtXtDXCXNPOXtOmZK38ticJr1apVrF27dtxhSOpRkvnu+r8kmL+kTc9c+ctTjZIkST2x8JIkSeqJhZckSVJPLLwkSZJ6YuElSZLUEwsvSZKknnRWeCXZLcnJSc5Pcl6SF7fzj0pyWZKz2scfdxWDJC1UkmOSXJXk3GnzX5Tke21e+8dxxSdpaeryPl43AS+tqjOTbAuckeSkdtnbq+otHe5bkhbrg8C7gA9PzUiyD82YgverquuT7DSm2CQtUZ0VXu1AmFe0z69Lcj6wa1f7k6RRqqpTk6yaNvv5wBur6vp2nat6D0zSktZLH682eT0AOL2d9cIk326b8neY5TWHJVmbZO369ev7CHOjtnzFcpJ08li+Yvm4357Ul98B/jDJ6Um+muTBM600qflrlxUrO8sDu6xYOe63Jy0JnQ8ZlGQb4JPA4VV1bZJ/AV4PVPvzrcBzpr+uqtYAawBWr15dXce5sVt32To4qqNtH7Wumw1Lk2cLYAfgocCDgeOT3L2qbpOjJjV/XXnZJex+xOc72fZFb9q/k+1KG5tOW7ySbElTdB1bVZ8CqKp1VXVzVd0CvBd4SJcxSNIIXQp8qhrfAG4BdhxzTJKWkC6vagzwfuD8qnrbwPxdBlZ7MnDu9NdK0oT6DPBogCS/A9wOuHqcAUlaWro81bgX8AzgnCRntfNeCRyUZE+aU40XAs/rMAZJWpAkxwF7AzsmuRR4LXAMcEx7i4kbgEOmn2aUpLl0eVXj14DMsOgLXe1Tkkalqg6aZdHBvQYiaaPineslSZJ6YuElSZLUEwsvSZKknlh4SZIk9cTCS5IkqScWXpIkST2x8JIkSeqJhZckSVJPLLwkSZJ6YuElSZLUEwsvSZKknlh4SZIk9cTCS5IkqScWXpIkST2x8JIkSeqJhZckSVJPLLwkSZJ6YuElSZLUEwsvSZKknlh4SZIk9cTCS5IkqScWXpI0gyTHJLkqybkzLHtZkkqy4zhik7R0WXhJ0sw+COw3fWaS3YDHAhf3HZCkpc/CS5JmUFWnAj+dYdHbgZcD1W9EkjYGFl6SNKQkBwCXVdXZ86x3WJK1SdauX79+g/axy4qVJOnkIWn8thh3AJK0FCTZGngV8Lj51q2qNcAagNWrV29Qy9iVl13C7kd8fkExzueiN+3fyXYlDc8WL0kazj2AuwFnJ7kQWAGcmWT5WKOStKTY4iVJQ6iqc4Cdpqbb4mt1VV09tqAkLTm2eEnSDJIcB5wG7JHk0iSHjjsmSUufLV6SNIOqOmie5at6CkXSRsQWL0mSpJ5YeEmSJPXEwkuSJKknFl6SJEk9sfCSJEnqiYWXJElSTyy8JEmSemLhJUmS1BMLL0mSpJ50Vngl2S3JyUnOT3Jekhe38++c5KQkP2h/7tBVDJIkSZOkyxavm4CXVtXvAQ8FXpDk3sCRwJer6l7Al9tpSZKkjV5nhVdVXVFVZ7bPrwPOB3YFDgQ+1K72IeBJXcUgSZI0SXrp45VkFfAA4HRg56q6ApriDNhpltcclmRtkrXr16/vI0xJkqROdV54JdkG+CRweFVdO+zrqmpNVa2uqtXLli3rLkBJkqSedFp4JdmSpug6tqo+1c5el2SXdvkuwFVdxiBJkjQpuryqMcD7gfOr6m0Diz4HHNI+PwT4bFcxSJIkTZItOtz2XsAzgHOSnNXOeyXwRuD4JIcCFwN/2mEMkiRJE6OzwquqvgZklsX7drVfSZKkSeWd6yVJknpi4SVJktQTCy9JmkGSY5JcleTcgXlvTvLdJN9O8ukk248xRElLkIWXJM3sg8B+0+adBNy3qu4HfB94Rd9BSVraLLwkaQZVdSrw02nzTqyqm9rJrwMreg9M0pJm4SVJC/Mc4IszLXDIs9HaZcVKknTy2GXFynG/PW1iuryPlyRtlJK8CrgJOHam5VW1BlgDsHr16uoxtI3SlZddwu5HfL6TbV/0pv072a40GwsvSdoASQ4B9gf2rSqLKkkbxMJLkoaUZD/gCOBRVfXLcccjaemxj5ckzSDJccBpwB5JLm2HOXsXsC1wUpKzkrxnrEFKWnLmLbySvDjJdmm8P8mZSR7XR3CStFgLzWFVdVBV7VJVW1bViqp6f1Xds6p2q6o928df9vEeJG08hmnxek5VXQs8DlgGPJtmoGtJWgrMYZImxjCF19RA138MfKCqzmb2wa8ladKYwyRNjGEKrzOSnEiTtL6UZFvglm7DkqSRMYdJmhjDXNV4KLAn8OOq+mWSu9A01UvSUmAOkzQxhmnxOqmqzqyqawCq6ifA2zuNSpJGxxwmaWLM2uKV5A7A1sCOSXbg1j4R2wF37SE2SVowc5ikSTTXqcbnAYfTJKgzuDVpXQu8u9uwJGnRzGGSJs6shVdVHQ0cneRFVfXOHmOSpEUzh0maRPN2rq+qdyZ5OLBqcP2q+nCHcUnSSJjDJE2SeQuvJB8B7gGcBdzczi7ApCVp4pnDJE2SYW4nsRq4d1VV18FIUgfMYZImxjC3kzgXWN51IJLUEXOYpIkxTIvXjsB3knwDuH5qZlUd0FlUkjQ65jBJE2OYwuuoroOQpA4dNe4AJGnKMFc1fjXJ7sC9quq/kmwNbN59aJK0eOYwSZNk3j5eSZ4LnAD8aztrV+AzHcYkSSNjDpM0SYbpXP8CYC+auz1TVT8AduoyKEkaIXOYpIkxTOF1fVXdMDWRZAuae+BI0lJgDpM0MYYpvL6a5JXAVkkeC3wC+Pduw5KkkTGHSZoYwxReRwLrgXNoBp39AvDqLoOSpBEyh0maGMNc1XgL8N72IUlLijlM0iSZtfBKcg5z9IOoqvt1EpEkjcBic1iSY4D9gauq6r7tvDsDH6cZcPtC4M+q6n9HFLKkTcBcLV77tz9f0P78SPvz6cAvO4tIkkZjsTnsg8C7uO1g2kcCX66qNyY5sp0+YvGhStpUzFp4VdVFAEn2qqq9BhYdmeR/gL/tOjhJWqjF5rCqOjXJqmmzDwT2bp9/CDgFCy9JG2CYzvV3TPKIqYkkDwfu2F1IkjRSo8xhO1fVFQDtzxnvB5bksCRrk6xdv379AnclaWM0zFiNhwLHJLlTO30N8JzOIpKk0eo9h1XVGmANwOrVq71nmKTfGOaqxjOA+yfZDkhV/az7sCRpNEacw9Yl2aWqrkiyC3DVaKKUtKmYt/BK8ppp0wBU1Zz9I2a5Iugo4Lk099QBeGVVfWGDo5akIS00h83ic8AhwBvbn59dbHySNi3D9PH6xcDjZuDxNJdSz+eDwH4zzH97Ve3ZPiy6JHVtQTksyXHAacAeSS5NcihNwfXYJD8AHttOS9LQhjnV+NbB6SRvofnWN9/rZroiSJJ6tYgcdtAsi/YdRVySNk3DtHhNtzVw90Xs84VJvp3kmCQ7zLaSVwVJ6shic5gkLdi8hVeSc9pC6dtJzgO+B/zTAvf3L8A9gD2BK4C3zrZiVa2pqtVVtXrZsmUL3J2kTd2Ic5gkLcowt5PYf+D5TcC6qrppITurqnVTz5O8F/j8QrYjSRtgZDlMkhZrmFONf1dVF7WPy6rqpiQfmf9lv629/HrKk4FzF7IdSdoAI8thkrRYw7R43WdwIskWwIPme1F7RdDewI5JLgVeC+ydZE+agWsvBJ63YeFK0gZbUA6TpC7MWngleQXwSmCrJNcCaRfdQHtH5rnMckXQ+xcSpCRtqMXmMEnqwqynGqvqDVW1LfDmqtquqrZtH3epqlf0GKMkbTBzmKRJNFeL1+7ANVMJKsk+wJNoThG+u6pu6CNASVoIc5ikSTRX5/rjgTsCtP2yPgFcTHMriH/uOjBJWiRzmKSJM1fn+q2q6vL2+cHAMVX11iSbAWd1HpkkLY45TNLEmavFKwPPHw18GaCqbuk0IkkaDXOYpIkzV4vXV5IcT3OH+R2Ar8Bv7sVl3whJk84cJmnizFV4HQ78ObAL8IiqurGdvxx4VcdxSdJiHY45TNKEmbXwqqoCPjbD/G91GpEkjYA5TNIkGmbIIEmSJI2AhZckSVJPZi28kny5/fmm/sKRpNEwh0maRHN1rt8lyaOAA5J8jNtemk1VndlpZJK0OOYwSRNnrsLrNcCRwArgbdOWFc19cTY5y1csZ91l6zrZ9s677syVl17ZybalTZA5TNLEmeuqxhOAE5L8TVW9vseYJtq6y9bBUR1t+6huCjppU2QOkzSJ5mrxAqCqXp/kAOCR7axTqurz3YYlSaPRRQ5L8hLgL2hazs4Bnl1Vv15cpJI2BfNe1ZjkDcCLge+0jxe38yRp4o06hyXZFfhrYHVV3RfYHHjaKGKVtPGbt8ULeAKw59T4Zkk+BHwLeEWXgUnSiHSRw7YAtkpyI7A1cPk860sSMPx9vLYfeH6nDuKQpC5tP/B8UTmsqi4D3gJcTDMO5M+q6sTBdZIclmRtkrXr169fzO6Wjs23JEknD2ljMkyL1xuAbyU5meZy7Edia5ekpWOkOSzJDsCBwN2Aa4BPJDm4qj46tU5VrQHWAKxevboWHvoScvON7H5EN91/L3rT/p1sVxqHYTrXH5fkFODBNEnriKryngeSloQOcthjgAuqaj1Akk8BDwc+OuerJInhWryoqiuAz3UciyR1YsQ57GLgoUm2Bn4F7AusHdG2JW3kHKtRkjZAVZ0OnACcSXMric1oTytK0nyGavGSJN2qql4LvHbccUhaeuZs8UqyWZJz+wpGkkbJHCZp0sxZeLX3vTk7ycqe4pGkkTGHSZo0w5xq3AU4L8k3gF9MzayqAzqLSpJGxxwmaWIMU3i9rvMoJKk75jBJE2OY+3h9NcnuwL2q6r/aS6g37z40SVo8c5ikSTLMINnPpbl0+l/bWbsCn+kwJkkaGXOYpEkyzH28XgDsBVwLUFU/AHbqMihJGiFzmKSJMUzhdX1V3TA1kWQLYNMYe0zSxsAcJmliDFN4fTXJK4GtkjwW+ATw792GJUkjYw6TNDGGKbyOBNbTDI3xPOALwKu7DEqSRsgcJmliDHNV4y1JPgScTtM8/72qsple0pJgDpM0SeYtvJI8AXgP8CMgwN2SPK+qvth1cJK0WOYwSZNkmBuovhXYp6p+CJDkHsB/ACYtSUuBOUzSxBimj9dVUwmr9WPgqo7ikaRRM4dJmhiztngleUr79LwkXwCOp+kf8afAN3uITZIWzBwmaRLNdarxiQPP1wGPap+vB3aYb8NJjgH2p/m2ed923p2BjwOrgAuBP6uq/93gqCVpfovKYZLUhVkLr6p69iK3/UHgXcCHB+YdCXy5qt6Y5Mh2+ohF7keSfssIcpgkjdwwVzXeDXgRTSvVb9avqgPmel1VnZpk1bTZBwJ7t88/BJyChZekDi00h0lSF4a5qvEzwPtp7vR8yyL3t3NVXQFQVVckmXW8tCSHAYcBrFy5cpG7lbQJ+wyjy2GStCjDFF6/rqp/6jySaapqDbAGYPXq1d7sUNJCjSWHSdJMhim8jk7yWuBE4PqpmVV15gL2ty7JLm1r1y54Sbek7o0yh0nSogxTeP0+8Azg0dzaTF/t9Ib6HHAI8Mb252cXsA1J2hCjzGEAJNkeeB9w33Zbz6mq0xYXpqRNwTCF15OBu1fVDRuy4STH0XSk3zHJpcBraQqu45McClxMcz8dSerSgnLYPI4G/rOqnprkdsDWI9y2pI3YMIXX2cD2bOBpwao6aJZF+27IdiRpkRaUw2aTZDvgkcCzANqCbpRFnaSN2DCF187Ad5N8k9v2j/BSbElLwahz2N1pbsL6gST3B84AXlxVv5hawauyJc1mmMLrtZ1HIUndGXUO2wJ4IPCiqjo9ydE0N4P+m6kVvCpb0mzmLbyq6qt9BCJJXeggh10KXFpVp7fTJ9AUXpI0r2HuXH8dzVU7ALcDtgR+UVXbdRnYJmlzSDLuKKSNyqhzWFVdmeSSJHtU1fdo+q1+ZzTRStrYDdPite3gdJInAQ/pKqBN2s3AUR1tu6vtShOuoxz2IuDY9orGHwOOCylpKMP08bqNqvpMO8C1JC05o8hhVXUWsHo0EUnalAxzqvEpA5Ob0SQbO4tKWhLMYZImyTAtXk8ceH4TcCFwYCfRSNLomcMkTYxh+njZd0HSkmUOkzRJZi28krxmjtdVVb2+g3gkaSTMYZIm0VwtXr+YYd4dgUOBuwAmLUmTzBwmaeLMWnhV1VunnifZFngxzSXTHwPeOtvrJGkSmMMkTaI5+3gluTPw/wFPBz4EPLCq/rePwCRpscxhkibNXH283gw8hWa8sd+vqp/3FpUkLZI5TNIk2myOZS8F7gq8Grg8ybXt47ok1/YTniQtmDlM0sSZq4/XXEWZJE00c5ikSWRikiRJ6omFlyRJUk8svCRJknpi4SVJktQTCy9JkqSeWHhJkiT1xMJLkiSpJxZekiRJPbHwkiRJ6omFlyRJUk8svCRpAyXZPMm3knx+3LFIWlosvCRpw70YOH/cQUhaeiy8JGkDJFkBPAF437hjkbT0WHhJ0oZ5B/By4JbZVkhyWJK1SdauX7++t8AkTT4LL0kaUpL9gauq6oy51quqNVW1uqpWL1u2rKfoJC0FG13htXzFcpJ09pC0SdsLOCDJhcDHgEcn+eh4Q5K0lGwx7gBGbd1l6+CoDnfQ5bYlTbSqegXwCoAkewMvq6qDxxmTpKVlo2vxkiRJmlQbXYuXJPWhqk4BThlzGJKWGFu8JEmSemLhJUmS1BMLL0mSpJ6MpY9Xeyn2dcDNwE1VtXoccUiSJPVpnJ3r96mqq8e4f0mSpF55qlGSJKkn4yq8CjgxyRlJDptpBcc6W0I2p7ORApavWD7udydJ0siM61TjXlV1eZKdgJOSfLeqTh1coarWAGsAVq9eXeMIUkO6mc7u6L/uqHXdbFiSpDEYS4tXVV3e/rwK+DTwkHHEIUmS1KfeC68kd0yy7dRz4HHAuX3HIUlSl3ZZsbKzbhi7rFg57renBRrHqcadgU8nmdr/v1XVf44hDkmSOnPlZZew+xGf72TbF71p/062q+71XnhV1Y+B+/e9X0mSpHHzdhKSJEk9sfCSJEnqiYWXJElSTyy8JEmSemLhJUmS1BMLL0mSpJ5YeEmSJPXEwkuSJKknFl6StAGS7Jbk5CTnJzkvyYvHHZOkpWMcQwZJ0lJ2E/DSqjqzHXf2jCQnVdV3xh2YpMlni5ckbYCquqKqzmyfXwecD+w63qgkLRW2eEnSAiVZBTwAOH3a/MOAwwBWrlzZf2Aa3uZbkmTcUWy4DuPe/HZ34OYbfr3ktr1819244tKLO9n2KFl4SdICJNkG+CRweFVdO7isqtYAawBWr15dYwhPw7r5RnY/4vOdbPqiN+3fyXaBzuNeqtteCjzVKEkbKMmWNEXXsVX1qXHHI2npsPCSpA2Q5vzO+4Hzq+pt445H0tJi4SVJG2Yv4BnAo5Oc1T7+eNxBSVoa7OMlSRugqr4GLMHe2JImgS1ekiRJPbHwkiRJ6omFlyRJUk8svCRJknpi4SVJktQTCy9tspavWE6STh7LVywf99uTJE0gbyehTda6y9bBUR1t+6h13WxYkrSk2eIlSZLUEwsvSZKknlh4SZKkpW/zLTvrt7vLipUjC9M+XpIkaem7+UZ2P+LznWz6ojftP7Jt2eIlSZLUEwsvSZKknlh4SZIk9cTCS+rC5izJm7N6U1lJ6pad66Uu3MySvDmrN5WVpG7Z4iVJktQTCy9JkqSeWHhJkiT1xMJLkiSpJxZekiRJPRlL4ZVkvyTfS/LDJEeOIwZJWihzmKSF6r3wSrI58G7g8cC9gYOS3LvvOCRpIcxhkhZjHC1eDwF+WFU/rqobgI8BB44hDklaCHOYpAVLVfW7w+SpwH5V9Rft9DOAP6iqF05b7zDgsHZyD+B7vQYKOwJX97zPuUxSPJMUC0xWPJMUC0xWPBsay+5VtayrYBZqmBy2yPw1Sb+zxfK9TK6N6f1M4nuZNX+N4871mWHeb1V/VbUGWNN9ODNLsraqVo9r/9NNUjyTFAtMVjyTFAtMVjyTFMsizZvDFpO/NqLj5HuZYBvT+1lq72UcpxovBXYbmF4BXD6GOCRpIcxhkhZsHIXXN4F7JblbktsBTwM+N4Y4JGkhzGGSFqz3U41VdVOSFwJfAjYHjqmq8/qOYwhjO805i0mKZ5JigcmKZ5JigcmKZ5JiWbAecthGcZxavpfJtTG9nyX1XnrvXC9JkrSp8s71kiRJPbHwkiRJ6skmWXgl2S3JyUnOT3Jekhe38++c5KQkP2h/7jDwmle0w4N8L8kfdRTX5km+leTz44wnyfZJTkjy3fYYPWycxybJS9rf07lJjktyh77iSXJMkquSnDswb4P3neRBSc5pl/1TkpluSbDQeN7c/q6+neTTSbbvI56ZYhlY9rIklWTHPmLZGGSJDUM0qXl0MSYlB4/CpOXxxRjn/4BOVNUm9wB2AR7YPt8W+D7N0B//CBzZzj8SeFP7/N7A2cDtgbsBPwI27yCu/w/4N+Dz7fRY4gE+BPxF+/x2wPZjjGVX4AJgq3b6eOBZfcUDPBJ4IHDuwLwN3jfwDeBhNPeA+iLw+BHG8zhgi/b5m/qKZ6ZY2vm70XQ8vwjYsa9js5QfNJ30fwTcvf2bOxu497jjmifmicyji3xPE5GDR/ReJiaPL/J9jPV/QBePTbLFq6quqKoz2+fXAefT/HIPpPmw0v58Uvv8QOBjVXV9VV0A/JBm2JCRSbICeALwvoHZvceTZDuaf6jvB6iqG6rqmnHEMmALYKskWwBb09wzqZd4qupU4KfTZm/QvpPsAmxXVadVkxk+PPCaRcdTVSdW1U3t5Ndp7ivVeTyzHBuAtwMv57Y3Fe382CxxS24YoknMo4sxKTl4FCY0jy/G2P4HdGGTLLwGJVkFPAA4Hdi5qq6AJqkAO7Wr7QpcMvCyS9t5o/QOmn9WtwzMG0c8dwfWAx9om9zfl+SOY4qFqroMeAtwMXAF8LOqOnFc8bQ2dN+7ts+7jGnKc2hajcYST5IDgMuq6uxpiybh2EyyPj63nZmgPLoY72AycvAoTFQeX4wJ/R+wKJt04ZVkG+CTwOFVde1cq84wb2T34UiyP3BVVZ0x7Es6jGcLmtNH/1JVDwB+QdOMO45YaM/bH0jTZHxX4I5JDh5XPPOYbd+9xJTkVcBNwLHjiCfJ1sCrgNfMtLjPWJagJXscJiWPLsaE5eBRmKg8vhhL7H/AUDbZwivJljTJ4tiq+lQ7e1176oP251Xt/K6HCNkLOCDJhTSnGB6d5KNjiudS4NKqOr2dPoHmD3hcx+YxwAVVtb6qbgQ+BTx8jPGwgH1fyq2n/zqJKckhwP7A09tTduOI5x40yfHs9rO8AjgzyfIxxLLULMlhiCYsjy7GJOXgUZi0PL4Yk/g/YFE2ycKrvWrq/cD5VfW2gUWfAw5pnx8CfHZg/tOS3D7J3YB70XQIHomqekVVraiqVTTDj3ylqg4eRzxVdSVwSZI92ln7At8ZRyyti4GHJtm6/b3tS9OXZFzxTO1j6H23zeDXJXlo+x6eOfCaRUuyH3AEcEBV/XJanL3FU1XnVNVOVbWq/SxfStP5+sq+Y1mCltwwRJOWRxdjknLwKExgHl+MSfwfsDij6qW/lB7AI2iaHr8NnNU+/hi4C/Bl4AftzzsPvOZVNFdHfI8Or7oC9ubWK2rGEg+wJ7C2PT6fAXYY57EBXgd8FzgX+AjN1Sq9xAMcR9Ov4EaaQuLQhewbWN3G/yPgXbSjRowonh/S9GmY+iy/p494Zopl2vILaa9q7OPYLPVHm4O+3x6HV407niHindg8usj3tTdjzsEjeh97MkF5fJHvZWz/A7p4OGSQJElSTzbJU42SJEnjYOElSZLUEwsvSZKknlh4SZIk9cTCS5IkqScWXhqJJK9qR4//dpKzkvxBh/s6KsnL2ud/m+QxXe1L0tKR5C5t/jkryZVJLhuYvt2Q23jlHMsuTPLf0+adleTcxcauTccW4w5AS1+Sh9HcNf2BVXV9kh2BoZLcYlXVTMPTSNoEVdVPaO5fRZKjgJ9X1Vs2cDOvBP5hjuXbJtmtqi5J8nsLClSbNFu8NAq7AFdX1fUAVXV1VV3efjt8U5JvtI97AiRZluSTSb7ZPvZq5x+V5JgkpyT5cZK/ntpB26L2vST/BewxMP+DSZ7aPr8wyeuSnJnknCS/O7C/k9r5/5rkorY4lLSRS/KgJF9NckaSLyXZJcmd2nyyR7vOcUmem+SNwFZtK9axs2zyeODP2+cH0dxIeGpfmyd5c5vXvp3kee38XZKcOtU6luQP23U/2E6fk+Ql7brPbV9/dpsnt27n3yPJ19tlf5vk5wP7/b8D+3xdO++OSf6j3c65SaZi1phZeGkUTgR2S/L9JP+c5FEDy66tqofQ3JH8He28o4G3V9WDgT8B3jew/u8CfwQ8BHhtki2TPIhmGI8HAE8BHjxHLFdX1QOBfwFe1s57Lc0QIA8EPg2sXPhblbSEBHgn8NSqehBwDPD3VfUz4IXAB5M8Ddihqt5bVUcCv6qqPavq6bNs8wSaPATwRODfB5YdCvyszW0PBp7bDlvzf4AvVdWewP1p7vK/J7BrVd23qn4f+EC7jU9V1YOr6v40Q+Mc2s4/Gji63fZvxh5M8jiaYXEe0m7zQUkeCewHXF5V96+q+wL/uSEHTt3xVKMWrap+3hZHfwjsA3w8yZHt4uMGfr69ff4Y4N7NsFsAbJdk2/b5f7QtZ9cnuQrYud3up6sdhzDJXGPYTQ3Uewa3JsdHAE9uY/3PJP+7sHcqaYm5PXBf4KQ232xOM8wVVXVSkj8F3k1TDA3rp8D/tgXb+cDg+KiPA+431QoP3ImmKPomcEyaQcU/U1VnJfkxcPck7wT+g+YLLMB9k/wdsD2wDfCldv7DgCe1z/8NmDqF+rj28a12ept2n/8NvCXJm2iGQLpN3zSNj4WXRqKqbgZOAU5Jcg63Dl46OCbV1PPNgIdV1a8Gt9EmxusHZt3MrZ/RYce2mnr94Gszy7qSNm4Bzquqh/3WgmQz4PeAXwF3phlvdFgfpynYnjXD/l5UVV+a/oK2FeoJwEeSvLmqPpzk/jQt/C8A/gx4DvBB4ElVdXaSZ9GMHTmXAG+oqn+dYZ8Pohk/8w1JTqyqvx36HaoznmrUoiXZI8m9BmbtCVzUPv/zgZ+ntc9PpGnmn3r9nvPs4lTgyUm2alvGnriBIX6NJqlNNcvvsIGvl7Q0XQ8sS3MBEG3Xhfu0y15C02J1ELe2RgHcOPB8Np8G/pFbW6OmfAl4/tTrk/xO29dqd+Cqqnov8H7ggW0/082q6pPA3wAPbLexLXBFu43B051fp+maAU3Xi8F9PifJNu0+d02yU5K7Ar+sqo/StI49EE0EW7w0CtsA70yyPXAT8EPgMJorHW+f5HSaIv+gdv2/Bt6d5Ns0n8FTgb+cbeNVdWaSj9P0i7iIpgl9Q7wOOK7tXPpVmlMN123gNiQtPbcATwX+KcmdaPLNO5LcCPwF8JCqui7JqcCrafqDrgG+neTM2fp5VdV1wJvgNy31U94HrALOTLNgPc3pwb2B/9vu9+fAM4FdgQ+0LW8Ar2h//g1wOk2uO4emEAM4HPhokpfSnJr8WRvLiWmurjytjeXnwMHAPYE3J7kFuBF4/gYcN3UoVcOewZE2TJILgdVVdfWY47g9cHNV3dR+8/2XtpOrJC0J7dWNv6qqavuXHVRVB447Lm04W7y0KVgJHN9+s7wBeO6Y45GkDfUg4F1tS9o1NP3BtATZ4iVJktQTO9dLkiT1xMJLkiSpJxZekiRJPbHwkiRJ6omFlyRJUk/+f+T47LtoT1ncAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.subplot(2,2,1)\n",
    "plt.hist(q2_df[\"GPA\"],edgecolor=\"k\",color=\"y\")\n",
    "plt.xlabel('GPA Score')\n",
    "plt.ylabel('Number of Students')\n",
    "plt.title('Histogram of GPA')\n",
    "plt.subplot(2,2,2)\n",
    "plt.hist(q2_df[\"Salary\"],edgecolor=\"k\",color=\"r\")\n",
    "plt.xlabel('Salary')\n",
    "plt.ylabel('Number of Students')\n",
    "plt.title('Histogram of Salary')\n",
    "plt.subplot(2,2,3)\n",
    "plt.hist(q2_df[\"Spending\"],edgecolor=\"k\",color=\"g\")\n",
    "plt.xlabel('Spending')\n",
    "plt.ylabel('Number of Students')\n",
    "plt.title('Histogram of Spending')\n",
    "plt.subplot(2,2,4)\n",
    "plt.hist(q2_df[\"Text Messages\"],edgecolor=\"k\")\n",
    "plt.xlabel('Text Messages')\n",
    "plt.ylabel('Number of Students')\n",
    "plt.title('Histogram of Text Messages')\n",
    "plt.savefig(\"Histogram_DistCheck.png\",bbox_inches=\"tight\")\n",
    "\n",
    "# Judging from the graphs plotted for the 4 continuous variable we can tell the graph of GPA Score and Salary\n",
    "# follow a normal distribution whereas\n",
    "# graph of spending and text Messages is both right skewed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "## End of Question 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code for Question 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.16</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.20</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.20</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.20</td>\n",
       "      <td>0.41</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\n",
       "5  0.24  0.18\n",
       "6  0.16  0.42\n",
       "7  0.20  0.58\n",
       "8  0.20  0.25\n",
       "9  0.20  0.41"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q3_df = pd.read_csv('A+&+B+shingles.csv')\n",
    "q3_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.49</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.34</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.29</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.27</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.29</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.43</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.34</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.37</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       A     B\n",
       "26  0.49  0.16\n",
       "27  0.34  0.52\n",
       "28  0.36  0.36\n",
       "29  0.29  0.22\n",
       "30  0.27  0.39\n",
       "31  0.40   NaN\n",
       "32  0.29   NaN\n",
       "33  0.43   NaN\n",
       "34  0.34   NaN\n",
       "35  0.37   NaN"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q3_df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 36 entries, 0 to 35\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   A       36 non-null     float64\n",
      " 1   B       31 non-null     float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 704.0 bytes\n"
     ]
    }
   ],
   "source": [
    "q3_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0\n",
       "B    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# There are 5 null values in Column B\n",
    "q3_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# aplha = 0.05 ,mean = 0.35\n",
    "\n",
    "# For Column A\n",
    "t_statistics,p_value = ttest_1samp(q3_df[\"A\"],0.35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.4735046253382782, 0.0748)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_statistics,round(p_value/2,4)\n",
    "\n",
    "# since ttest_1samp give result for a 2 tailed p-value, we need to divide the p-value by 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Conclusion - Since p-value more than alpha we fail the reject H0 or accept H0 hypothesis.\n",
    "### Therefore no evidence to conclude that mean moisture content for Shingle A is less than 0.35 pounds per 100 square feet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# aplha = 0.05 ,mean = 0.35\n",
    "\n",
    "# For Column B\n",
    "t_statistics,p_value = ttest_1samp(q3_df[\"B\"],0.35,nan_policy='omit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-3.1003313069986995, 0.0021)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_statistics,round(p_value/2,4)\n",
    "\n",
    "# since ttest_1samp give result for a 2 tailed p-value, we need to divide the p-value by 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Conclusion - Since p-value less than alpha we the reject H0 hypothesis.\n",
    "### Therefore there is enough evidence that we can to conclude that mean moisture content for \n",
    "### Shingle B is less than 0.35 pounds per 100 square feet. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assumption - Data of both shingles follow normal distribution and the variance of both data is same\n",
    "\n",
    "t_statistics,p_value = ttest_ind(q3_df[\"A\"],q3_df[\"B\"],equal_var=True,nan_policy='omit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.2896282719661123, 0.2017)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_statistics,round(p_value,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Conclusion - Since p-value is greater than alpha we fail to reject H0 hypothesis.\n",
    "### Therefore there is enough evidence that we can conclude that population mean of both shingles A and B is equal "
   ]
  }
 ],
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