Pizza Sales.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e21d72be",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "37de66b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "od=pd.read_csv('order_details.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6cbb9765",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_details_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>pizza_id</th>\n",
       "      <th>quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>hawaiian_m</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>classic_dlx_m</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>five_cheese_l</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>ital_supr_l</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>mexicana_m</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_details_id  order_id       pizza_id  quantity\n",
       "0                 1         1     hawaiian_m         1\n",
       "1                 2         2  classic_dlx_m         1\n",
       "2                 3         2  five_cheese_l         1\n",
       "3                 4         2    ital_supr_l         1\n",
       "4                 5         2     mexicana_m         1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "od.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "47d6423b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48620, 4)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "od.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "42f908cb",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>11:38:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>11:57:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>12:12:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>12:16:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>12:21:30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   order_id        date      time\n",
       "0         1  2015-01-01  11:38:36\n",
       "1         2  2015-01-01  11:57:40\n",
       "2         3  2015-01-01  12:12:28\n",
       "3         4  2015-01-01  12:16:31\n",
       "4         5  2015-01-01  12:21:30"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order=pd.read_csv('orders.csv')\n",
    "order.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "23148de3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21350, 3)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5510b62c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pt=pd.read_csv('pizza_types.csv', encoding = 'unicode_escape')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2ee9e002",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pizza_type_id</th>\n",
       "      <th>name</th>\n",
       "      <th>category</th>\n",
       "      <th>ingredients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bbq_ckn</td>\n",
       "      <td>The Barbecue Chicken Pizza</td>\n",
       "      <td>Chicken</td>\n",
       "      <td>Barbecued Chicken, Red Peppers, Green Peppers,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cali_ckn</td>\n",
       "      <td>The California Chicken Pizza</td>\n",
       "      <td>Chicken</td>\n",
       "      <td>Chicken, Artichoke, Spinach, Garlic, Jalapeno ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ckn_alfredo</td>\n",
       "      <td>The Chicken Alfredo Pizza</td>\n",
       "      <td>Chicken</td>\n",
       "      <td>Chicken, Red Onions, Red Peppers, Mushrooms, A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ckn_pesto</td>\n",
       "      <td>The Chicken Pesto Pizza</td>\n",
       "      <td>Chicken</td>\n",
       "      <td>Chicken, Tomatoes, Red Peppers, Spinach, Garli...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>southw_ckn</td>\n",
       "      <td>The Southwest Chicken Pizza</td>\n",
       "      <td>Chicken</td>\n",
       "      <td>Chicken, Tomatoes, Red Peppers, Red Onions, Ja...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  pizza_type_id                          name category  \\\n",
       "0       bbq_ckn    The Barbecue Chicken Pizza  Chicken   \n",
       "1      cali_ckn  The California Chicken Pizza  Chicken   \n",
       "2   ckn_alfredo     The Chicken Alfredo Pizza  Chicken   \n",
       "3     ckn_pesto       The Chicken Pesto Pizza  Chicken   \n",
       "4    southw_ckn   The Southwest Chicken Pizza  Chicken   \n",
       "\n",
       "                                         ingredients  \n",
       "0  Barbecued Chicken, Red Peppers, Green Peppers,...  \n",
       "1  Chicken, Artichoke, Spinach, Garlic, Jalapeno ...  \n",
       "2  Chicken, Red Onions, Red Peppers, Mushrooms, A...  \n",
       "3  Chicken, Tomatoes, Red Peppers, Spinach, Garli...  \n",
       "4  Chicken, Tomatoes, Red Peppers, Red Onions, Ja...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "765d18f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 4)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pt.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "80cdeb08",
   "metadata": {},
   "outputs": [],
   "source": [
    "pi=pd.read_csv('pizzas.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "77501bfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pizza_id</th>\n",
       "      <th>pizza_type_id</th>\n",
       "      <th>size</th>\n",
       "      <th>price</th>\n",
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       "      <td>bbq_ckn_s</td>\n",
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       "      <td>12.75</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bbq_ckn_m</td>\n",
       "      <td>bbq_ckn</td>\n",
       "      <td>M</td>\n",
       "      <td>16.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bbq_ckn_l</td>\n",
       "      <td>bbq_ckn</td>\n",
       "      <td>L</td>\n",
       "      <td>20.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cali_ckn_s</td>\n",
       "      <td>cali_ckn</td>\n",
       "      <td>S</td>\n",
       "      <td>12.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cali_ckn_m</td>\n",
       "      <td>cali_ckn</td>\n",
       "      <td>M</td>\n",
       "      <td>16.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     pizza_id pizza_type_id size  price\n",
       "0   bbq_ckn_s       bbq_ckn    S  12.75\n",
       "1   bbq_ckn_m       bbq_ckn    M  16.75\n",
       "2   bbq_ckn_l       bbq_ckn    L  20.75\n",
       "3  cali_ckn_s      cali_ckn    S  12.75\n",
       "4  cali_ckn_m      cali_ckn    M  16.75"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4d99337a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96, 4)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pi.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "32d7a499",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_od = pd.merge(od,order, on='order_id', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9ad56d6e",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>order_details_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>pizza_id</th>\n",
       "      <th>quantity</th>\n",
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       "      <td>hawaiian_m</td>\n",
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       "      <td>2015-01-01</td>\n",
       "      <td>11:38:36</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>classic_dlx_m</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>11:57:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>five_cheese_l</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>11:57:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>ital_supr_l</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>11:57:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>mexicana_m</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>11:57:40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   order_details_id  order_id       pizza_id  quantity        date      time\n",
       "0                 1         1     hawaiian_m         1  2015-01-01  11:38:36\n",
       "1                 2         2  classic_dlx_m         1  2015-01-01  11:57:40\n",
       "2                 3         2  five_cheese_l         1  2015-01-01  11:57:40\n",
       "3                 4         2    ital_supr_l         1  2015-01-01  11:57:40\n",
       "4                 5         2     mexicana_m         1  2015-01-01  11:57:40"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_od.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "506fb8d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_p = pd.merge(pi, pt, on='pizza_type_id', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b76ec26c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pizza_id</th>\n",
       "      <th>pizza_type_id</th>\n",
       "      <th>size</th>\n",
       "      <th>price</th>\n",
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       "      <td>bbq_ckn_s</td>\n",
       "      <td>bbq_ckn</td>\n",
       "      <td>S</td>\n",
       "      <td>12.75</td>\n",
       "      <td>The Barbecue Chicken Pizza</td>\n",
       "      <td>Chicken</td>\n",
       "      <td>Barbecued Chicken, Red Peppers, Green Peppers,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bbq_ckn_m</td>\n",
       "      <td>bbq_ckn</td>\n",
       "      <td>M</td>\n",
       "      <td>16.75</td>\n",
       "      <td>The Barbecue Chicken Pizza</td>\n",
       "      <td>Chicken</td>\n",
       "      <td>Barbecued Chicken, Red Peppers, Green Peppers,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bbq_ckn_l</td>\n",
       "      <td>bbq_ckn</td>\n",
       "      <td>L</td>\n",
       "      <td>20.75</td>\n",
       "      <td>The Barbecue Chicken Pizza</td>\n",
       "      <td>Chicken</td>\n",
       "      <td>Barbecued Chicken, Red Peppers, Green Peppers,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cali_ckn_s</td>\n",
       "      <td>cali_ckn</td>\n",
       "      <td>S</td>\n",
       "      <td>12.75</td>\n",
       "      <td>The California Chicken Pizza</td>\n",
       "      <td>Chicken</td>\n",
       "      <td>Chicken, Artichoke, Spinach, Garlic, Jalapeno ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cali_ckn_m</td>\n",
       "      <td>cali_ckn</td>\n",
       "      <td>M</td>\n",
       "      <td>16.75</td>\n",
       "      <td>The California Chicken Pizza</td>\n",
       "      <td>Chicken</td>\n",
       "      <td>Chicken, Artichoke, Spinach, Garlic, Jalapeno ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     pizza_id pizza_type_id size  price                          name  \\\n",
       "0   bbq_ckn_s       bbq_ckn    S  12.75    The Barbecue Chicken Pizza   \n",
       "1   bbq_ckn_m       bbq_ckn    M  16.75    The Barbecue Chicken Pizza   \n",
       "2   bbq_ckn_l       bbq_ckn    L  20.75    The Barbecue Chicken Pizza   \n",
       "3  cali_ckn_s      cali_ckn    S  12.75  The California Chicken Pizza   \n",
       "4  cali_ckn_m      cali_ckn    M  16.75  The California Chicken Pizza   \n",
       "\n",
       "  category                                        ingredients  \n",
       "0  Chicken  Barbecued Chicken, Red Peppers, Green Peppers,...  \n",
       "1  Chicken  Barbecued Chicken, Red Peppers, Green Peppers,...  \n",
       "2  Chicken  Barbecued Chicken, Red Peppers, Green Peppers,...  \n",
       "3  Chicken  Chicken, Artichoke, Spinach, Garlic, Jalapeno ...  \n",
       "4  Chicken  Chicken, Artichoke, Spinach, Garlic, Jalapeno ...  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_p.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1358147b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pizza_id         0\n",
       "pizza_type_id    0\n",
       "size             0\n",
       "price            0\n",
       "name             0\n",
       "category         0\n",
       "ingredients      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_p.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8225d56b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.merge(final_od, final_p, on='pizza_id', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fd8571a3",
   "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>order_details_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>pizza_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>pizza_type_id</th>\n",
       "      <th>size</th>\n",
       "      <th>price</th>\n",
       "      <th>name</th>\n",
       "      <th>category</th>\n",
       "      <th>ingredients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>hawaiian_m</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>11:38:36</td>\n",
       "      <td>hawaiian</td>\n",
       "      <td>M</td>\n",
       "      <td>13.25</td>\n",
       "      <td>The Hawaiian Pizza</td>\n",
       "      <td>Classic</td>\n",
       "      <td>Sliced Ham, Pineapple, Mozzarella Cheese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>classic_dlx_m</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>11:57:40</td>\n",
       "      <td>classic_dlx</td>\n",
       "      <td>M</td>\n",
       "      <td>16.00</td>\n",
       "      <td>The Classic Deluxe Pizza</td>\n",
       "      <td>Classic</td>\n",
       "      <td>Pepperoni, Mushrooms, Red Onions, Red Peppers,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>five_cheese_l</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>11:57:40</td>\n",
       "      <td>five_cheese</td>\n",
       "      <td>L</td>\n",
       "      <td>18.50</td>\n",
       "      <td>The Five Cheese Pizza</td>\n",
       "      <td>Veggie</td>\n",
       "      <td>Mozzarella Cheese, Provolone Cheese, Smoked Go...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>ital_supr_l</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>11:57:40</td>\n",
       "      <td>ital_supr</td>\n",
       "      <td>L</td>\n",
       "      <td>20.75</td>\n",
       "      <td>The Italian Supreme Pizza</td>\n",
       "      <td>Supreme</td>\n",
       "      <td>Calabrese Salami, Capocollo, Tomatoes, Red Oni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>mexicana_m</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>11:57:40</td>\n",
       "      <td>mexicana</td>\n",
       "      <td>M</td>\n",
       "      <td>16.00</td>\n",
       "      <td>The Mexicana Pizza</td>\n",
       "      <td>Veggie</td>\n",
       "      <td>Tomatoes, Red Peppers, Jalapeno Peppers, Red O...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_details_id  order_id       pizza_id  quantity        date      time  \\\n",
       "0                 1         1     hawaiian_m         1  2015-01-01  11:38:36   \n",
       "1                 2         2  classic_dlx_m         1  2015-01-01  11:57:40   \n",
       "2                 3         2  five_cheese_l         1  2015-01-01  11:57:40   \n",
       "3                 4         2    ital_supr_l         1  2015-01-01  11:57:40   \n",
       "4                 5         2     mexicana_m         1  2015-01-01  11:57:40   \n",
       "\n",
       "  pizza_type_id size  price                       name category  \\\n",
       "0      hawaiian    M  13.25         The Hawaiian Pizza  Classic   \n",
       "1   classic_dlx    M  16.00   The Classic Deluxe Pizza  Classic   \n",
       "2   five_cheese    L  18.50      The Five Cheese Pizza   Veggie   \n",
       "3     ital_supr    L  20.75  The Italian Supreme Pizza  Supreme   \n",
       "4      mexicana    M  16.00         The Mexicana Pizza   Veggie   \n",
       "\n",
       "                                         ingredients  \n",
       "0           Sliced Ham, Pineapple, Mozzarella Cheese  \n",
       "1  Pepperoni, Mushrooms, Red Onions, Red Peppers,...  \n",
       "2  Mozzarella Cheese, Provolone Cheese, Smoked Go...  \n",
       "3  Calabrese Salami, Capocollo, Tomatoes, Red Oni...  \n",
       "4  Tomatoes, Red Peppers, Jalapeno Peppers, Red O...  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "da32089f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 48620 entries, 0 to 48619\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   order_details_id  48620 non-null  int64  \n",
      " 1   order_id          48620 non-null  int64  \n",
      " 2   pizza_id          48620 non-null  object \n",
      " 3   quantity          48620 non-null  int64  \n",
      " 4   date              48620 non-null  object \n",
      " 5   time              48620 non-null  object \n",
      " 6   pizza_type_id     48620 non-null  object \n",
      " 7   size              48620 non-null  object \n",
      " 8   price             48620 non-null  float64\n",
      " 9   name              48620 non-null  object \n",
      " 10  category          48620 non-null  object \n",
      " 11  ingredients       48620 non-null  object \n",
      "dtypes: float64(1), int64(3), object(8)\n",
      "memory usage: 4.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d8bdd885",
   "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>Table</th>\n",
       "      <th>Field</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>orders</td>\n",
       "      <td>order_id</td>\n",
       "      <td>Unique identifier for each order placed by a table</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>orders</td>\n",
       "      <td>date</td>\n",
       "      <td>Date the order was placed (entered into the system prior to cooking &amp; serving)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>orders</td>\n",
       "      <td>time</td>\n",
       "      <td>Time the order was placed (entered into the system prior to cooking &amp; serving)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>order_details</td>\n",
       "      <td>order_details_id</td>\n",
       "      <td>Unique identifier for each pizza placed within each order (pizzas of the same type and size are kept in the same row, and the quantity increases)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>order_details</td>\n",
       "      <td>order_id</td>\n",
       "      <td>Foreign key that ties the details in each order to the order itself</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>order_details</td>\n",
       "      <td>pizza_id</td>\n",
       "      <td>Foreign key that ties the pizza ordered to its details, like size and price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>order_details</td>\n",
       "      <td>quantity</td>\n",
       "      <td>Quantity ordered for each pizza of the same type and size</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>pizzas</td>\n",
       "      <td>pizza_id</td>\n",
       "      <td>Unique identifier for each pizza (constituted by its type and size)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>pizzas</td>\n",
       "      <td>pizza_type_id</td>\n",
       "      <td>Foreign key that ties each pizza to its broader pizza type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>pizzas</td>\n",
       "      <td>size</td>\n",
       "      <td>Size of the pizza (Small, Medium, Large, X Large, or XX Large)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>pizzas</td>\n",
       "      <td>price</td>\n",
       "      <td>Price of the pizza in USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>pizza_types</td>\n",
       "      <td>pizza_type_id</td>\n",
       "      <td>Unique identifier for each pizza type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>pizza_types</td>\n",
       "      <td>name</td>\n",
       "      <td>Name of the pizza as shown in the menu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>pizza_types</td>\n",
       "      <td>category</td>\n",
       "      <td>Category that the pizza fall under in the menu (Classic, Chicken, Supreme, or Veggie)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>pizza_types</td>\n",
       "      <td>ingredients</td>\n",
       "      <td>Comma-delimited ingredients used in the pizza as shown in the menu (they all include Mozzarella Cheese, even if not specified; and they all include Tomato Sauce, unless another sauce is specified)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Table             Field  \\\n",
       "0          orders          order_id   \n",
       "1          orders              date   \n",
       "2          orders              time   \n",
       "3   order_details  order_details_id   \n",
       "4   order_details          order_id   \n",
       "5   order_details          pizza_id   \n",
       "6   order_details          quantity   \n",
       "7          pizzas          pizza_id   \n",
       "8          pizzas     pizza_type_id   \n",
       "9          pizzas              size   \n",
       "10         pizzas             price   \n",
       "11    pizza_types     pizza_type_id   \n",
       "12    pizza_types              name   \n",
       "13    pizza_types          category   \n",
       "14    pizza_types       ingredients   \n",
       "\n",
       "                                                                                                                                                                                             Description  \n",
       "0                                                                                                                                                     Unique identifier for each order placed by a table  \n",
       "1                                                                                                                         Date the order was placed (entered into the system prior to cooking & serving)  \n",
       "2                                                                                                                         Time the order was placed (entered into the system prior to cooking & serving)  \n",
       "3                                                      Unique identifier for each pizza placed within each order (pizzas of the same type and size are kept in the same row, and the quantity increases)  \n",
       "4                                                                                                                                    Foreign key that ties the details in each order to the order itself  \n",
       "5                                                                                                                            Foreign key that ties the pizza ordered to its details, like size and price  \n",
       "6                                                                                                                                              Quantity ordered for each pizza of the same type and size  \n",
       "7                                                                                                                                    Unique identifier for each pizza (constituted by its type and size)  \n",
       "8                                                                                                                                             Foreign key that ties each pizza to its broader pizza type  \n",
       "9                                                                                                                                         Size of the pizza (Small, Medium, Large, X Large, or XX Large)  \n",
       "10                                                                                                                                                                             Price of the pizza in USD  \n",
       "11                                                                                                                                                                 Unique identifier for each pizza type  \n",
       "12                                                                                                                                                                Name of the pizza as shown in the menu  \n",
       "13                                                                                                                 Category that the pizza fall under in the menu (Classic, Chicken, Supreme, or Veggie)  \n",
       "14  Comma-delimited ingredients used in the pizza as shown in the menu (they all include Mozzarella Cheese, even if not specified; and they all include Tomato Sauce, unless another sauce is specified)  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dict = pd.read_csv('data_dictionary.csv')\n",
    "pd.set_option('display.max_colwidth',None) # to show full discription \n",
    "data_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dc6749b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('order_details_id',axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "02b3429d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('pizza_id', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5a5dc19f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 48620 entries, 0 to 48619\n",
      "Data columns (total 10 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   order_id       48620 non-null  int64  \n",
      " 1   quantity       48620 non-null  int64  \n",
      " 2   date           48620 non-null  object \n",
      " 3   time           48620 non-null  object \n",
      " 4   pizza_type_id  48620 non-null  object \n",
      " 5   size           48620 non-null  object \n",
      " 6   price          48620 non-null  float64\n",
      " 7   name           48620 non-null  object \n",
      " 8   category       48620 non-null  object \n",
      " 9   ingredients    48620 non-null  object \n",
      "dtypes: float64(1), int64(2), object(7)\n",
      "memory usage: 4.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3ab0a7bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename({'pizza_type_id':'pizza_id'}, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "17f93f82",
   "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>order_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>pizza_id</th>\n",
       "      <th>size</th>\n",
       "      <th>price</th>\n",
       "      <th>name</th>\n",
       "      <th>category</th>\n",
       "      <th>ingredients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>11:38:36</td>\n",
       "      <td>hawaiian</td>\n",
       "      <td>M</td>\n",
       "      <td>13.25</td>\n",
       "      <td>The Hawaiian Pizza</td>\n",
       "      <td>Classic</td>\n",
       "      <td>Sliced Ham, Pineapple, Mozzarella Cheese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>11:57:40</td>\n",
       "      <td>classic_dlx</td>\n",
       "      <td>M</td>\n",
       "      <td>16.00</td>\n",
       "      <td>The Classic Deluxe Pizza</td>\n",
       "      <td>Classic</td>\n",
       "      <td>Pepperoni, Mushrooms, Red Onions, Red Peppers, Bacon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>11:57:40</td>\n",
       "      <td>five_cheese</td>\n",
       "      <td>L</td>\n",
       "      <td>18.50</td>\n",
       "      <td>The Five Cheese Pizza</td>\n",
       "      <td>Veggie</td>\n",
       "      <td>Mozzarella Cheese, Provolone Cheese, Smoked Gouda Cheese, Romano Cheese, Blue Cheese, Garlic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>11:57:40</td>\n",
       "      <td>ital_supr</td>\n",
       "      <td>L</td>\n",
       "      <td>20.75</td>\n",
       "      <td>The Italian Supreme Pizza</td>\n",
       "      <td>Supreme</td>\n",
       "      <td>Calabrese Salami, Capocollo, Tomatoes, Red Onions, Green Olives, Garlic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>11:57:40</td>\n",
       "      <td>mexicana</td>\n",
       "      <td>M</td>\n",
       "      <td>16.00</td>\n",
       "      <td>The Mexicana Pizza</td>\n",
       "      <td>Veggie</td>\n",
       "      <td>Tomatoes, Red Peppers, Jalapeno Peppers, Red Onions, Cilantro, Corn, Chipotle Sauce, Garlic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  quantity        date      time     pizza_id size  price  \\\n",
       "0         1         1  2015-01-01  11:38:36     hawaiian    M  13.25   \n",
       "1         2         1  2015-01-01  11:57:40  classic_dlx    M  16.00   \n",
       "2         2         1  2015-01-01  11:57:40  five_cheese    L  18.50   \n",
       "3         2         1  2015-01-01  11:57:40    ital_supr    L  20.75   \n",
       "4         2         1  2015-01-01  11:57:40     mexicana    M  16.00   \n",
       "\n",
       "                        name category  \\\n",
       "0         The Hawaiian Pizza  Classic   \n",
       "1   The Classic Deluxe Pizza  Classic   \n",
       "2      The Five Cheese Pizza   Veggie   \n",
       "3  The Italian Supreme Pizza  Supreme   \n",
       "4         The Mexicana Pizza   Veggie   \n",
       "\n",
       "                                                                                    ingredients  \n",
       "0                                                      Sliced Ham, Pineapple, Mozzarella Cheese  \n",
       "1                                          Pepperoni, Mushrooms, Red Onions, Red Peppers, Bacon  \n",
       "2  Mozzarella Cheese, Provolone Cheese, Smoked Gouda Cheese, Romano Cheese, Blue Cheese, Garlic  \n",
       "3                       Calabrese Salami, Capocollo, Tomatoes, Red Onions, Green Olives, Garlic  \n",
       "4   Tomatoes, Red Peppers, Jalapeno Peppers, Red Onions, Cilantro, Corn, Chipotle Sauce, Garlic  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b21e65df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 48620 entries, 0 to 48619\n",
      "Data columns (total 10 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   order_id     48620 non-null  int64  \n",
      " 1   quantity     48620 non-null  int64  \n",
      " 2   date         48620 non-null  object \n",
      " 3   time         48620 non-null  object \n",
      " 4   pizza_id     48620 non-null  object \n",
      " 5   size         48620 non-null  object \n",
      " 6   price        48620 non-null  float64\n",
      " 7   name         48620 non-null  object \n",
      " 8   category     48620 non-null  object \n",
      " 9   ingredients  48620 non-null  object \n",
      "dtypes: float64(1), int64(2), object(7)\n",
      "memory usage: 4.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "51b1cafe",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename({'quantity':'pizza_quantity',\n",
    "           'date':'order_date',\n",
    "           'time':'order_time',\n",
    "           'size':'pizza_size',\n",
    "           'price':'pizza_price',\n",
    "           'name':'pizza_name',\n",
    "           'category':'pizza_category',\n",
    "           'ingredients':'pizza_ingredients'}, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d571453c",
   "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>order_id</th>\n",
       "      <th>pizza_quantity</th>\n",
       "      <th>order_date</th>\n",
       "      <th>order_time</th>\n",
       "      <th>pizza_id</th>\n",
       "      <th>pizza_size</th>\n",
       "      <th>pizza_price</th>\n",
       "      <th>pizza_name</th>\n",
       "      <th>pizza_category</th>\n",
       "      <th>pizza_ingredients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>11:38:36</td>\n",
       "      <td>hawaiian</td>\n",
       "      <td>M</td>\n",
       "      <td>13.25</td>\n",
       "      <td>The Hawaiian Pizza</td>\n",
       "      <td>Classic</td>\n",
       "      <td>Sliced Ham, Pineapple, Mozzarella Cheese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>11:57:40</td>\n",
       "      <td>classic_dlx</td>\n",
       "      <td>M</td>\n",
       "      <td>16.00</td>\n",
       "      <td>The Classic Deluxe Pizza</td>\n",
       "      <td>Classic</td>\n",
       "      <td>Pepperoni, Mushrooms, Red Onions, Red Peppers, Bacon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>11:57:40</td>\n",
       "      <td>five_cheese</td>\n",
       "      <td>L</td>\n",
       "      <td>18.50</td>\n",
       "      <td>The Five Cheese Pizza</td>\n",
       "      <td>Veggie</td>\n",
       "      <td>Mozzarella Cheese, Provolone Cheese, Smoked Gouda Cheese, Romano Cheese, Blue Cheese, Garlic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>11:57:40</td>\n",
       "      <td>ital_supr</td>\n",
       "      <td>L</td>\n",
       "      <td>20.75</td>\n",
       "      <td>The Italian Supreme Pizza</td>\n",
       "      <td>Supreme</td>\n",
       "      <td>Calabrese Salami, Capocollo, Tomatoes, Red Onions, Green Olives, Garlic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>11:57:40</td>\n",
       "      <td>mexicana</td>\n",
       "      <td>M</td>\n",
       "      <td>16.00</td>\n",
       "      <td>The Mexicana Pizza</td>\n",
       "      <td>Veggie</td>\n",
       "      <td>Tomatoes, Red Peppers, Jalapeno Peppers, Red Onions, Cilantro, Corn, Chipotle Sauce, Garlic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  pizza_quantity  order_date order_time     pizza_id pizza_size  \\\n",
       "0         1               1  2015-01-01   11:38:36     hawaiian          M   \n",
       "1         2               1  2015-01-01   11:57:40  classic_dlx          M   \n",
       "2         2               1  2015-01-01   11:57:40  five_cheese          L   \n",
       "3         2               1  2015-01-01   11:57:40    ital_supr          L   \n",
       "4         2               1  2015-01-01   11:57:40     mexicana          M   \n",
       "\n",
       "   pizza_price                 pizza_name pizza_category  \\\n",
       "0        13.25         The Hawaiian Pizza        Classic   \n",
       "1        16.00   The Classic Deluxe Pizza        Classic   \n",
       "2        18.50      The Five Cheese Pizza         Veggie   \n",
       "3        20.75  The Italian Supreme Pizza        Supreme   \n",
       "4        16.00         The Mexicana Pizza         Veggie   \n",
       "\n",
       "                                                                              pizza_ingredients  \n",
       "0                                                      Sliced Ham, Pineapple, Mozzarella Cheese  \n",
       "1                                          Pepperoni, Mushrooms, Red Onions, Red Peppers, Bacon  \n",
       "2  Mozzarella Cheese, Provolone Cheese, Smoked Gouda Cheese, Romano Cheese, Blue Cheese, Garlic  \n",
       "3                       Calabrese Salami, Capocollo, Tomatoes, Red Onions, Green Olives, Garlic  \n",
       "4   Tomatoes, Red Peppers, Jalapeno Peppers, Red Onions, Cilantro, Corn, Chipotle Sauce, Garlic  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "84375a51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 48620 entries, 0 to 48619\n",
      "Data columns (total 10 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   order_id           48620 non-null  int64  \n",
      " 1   pizza_quantity     48620 non-null  int64  \n",
      " 2   order_date         48620 non-null  object \n",
      " 3   order_time         48620 non-null  object \n",
      " 4   pizza_id           48620 non-null  object \n",
      " 5   pizza_size         48620 non-null  object \n",
      " 6   pizza_price        48620 non-null  float64\n",
      " 7   pizza_name         48620 non-null  object \n",
      " 8   pizza_category     48620 non-null  object \n",
      " 9   pizza_ingredients  48620 non-null  object \n",
      "dtypes: float64(1), int64(2), object(7)\n",
      "memory usage: 4.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6bb0cbc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Classic    29.985603\n",
       "Supreme    24.222542\n",
       "Veggie     23.547923\n",
       "Chicken    22.243933\n",
       "Name: pizza_category, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top selling category of pizza\n",
    "top_selling_category = df.pizza_category.value_counts(normalize=True)*100\n",
    "top_selling_category   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "7a78745d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize top_selling_category\n",
    "plt.figure(figsize=(6,15))\n",
    "top_selling_category.plot.pie(autopct='%1.2f%%')\n",
    "plt.title('Top selling Categories of Pizza')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbdcbcb4",
   "metadata": {},
   "source": [
    "### `OBSERVATION 1`\n",
    "- __classic__ is most selling category where as __chicken__ is less selling category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "40d245e2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "L      18526\n",
       "M      15385\n",
       "S      14137\n",
       "XL       544\n",
       "XXL       28\n",
       "Name: pizza_size, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# most prefer size of pizza\n",
    "most_preferred_size = df.pizza_size.value_counts()\n",
    "most_preferred_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "ce3c321a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x1152 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize top_selling_category\n",
    "plt.figure(figsize=(6,16))\n",
    "most_preferred_size.plot.pie(autopct='%1.2f%%')\n",
    "plt.legend(loc=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29f74982",
   "metadata": {},
   "source": [
    "### `OBSERVATION 2`\n",
    "- customer are prefer __L__ size of pizza most."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "fb3185ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "The Classic Deluxe Pizza      2416\n",
       "The Barbecue Chicken Pizza    2372\n",
       "The Hawaiian Pizza            2370\n",
       "The Pepperoni Pizza           2369\n",
       "The Thai Chicken Pizza        2315\n",
       "Name: pizza_name, dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top 5 most selling pizza  \n",
    "df.pizza_name.value_counts()[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fba33cbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "The Brie Carre Pizza    480\n",
       "Name: pizza_name, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# least selling pizza  \n",
    "df.pizza_name.value_counts()[-1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e48f218",
   "metadata": {},
   "source": [
    "### `OBSERVATION 3`\n",
    "- __The Classic Deluxe Pizza__ is the top selling pizza where as __The Brie Carre Pizza__ is least selling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3f34930",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>pizza_quantity</th>\n",
       "      <th>pizza_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>92.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>37.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>16.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>21345</th>\n",
       "      <td>21346</td>\n",
       "      <td>4</td>\n",
       "      <td>62.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21346</th>\n",
       "      <td>21347</td>\n",
       "      <td>4</td>\n",
       "      <td>66.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21347</th>\n",
       "      <td>21348</td>\n",
       "      <td>3</td>\n",
       "      <td>46.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21348</th>\n",
       "      <td>21349</td>\n",
       "      <td>1</td>\n",
       "      <td>20.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21349</th>\n",
       "      <td>21350</td>\n",
       "      <td>1</td>\n",
       "      <td>12.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21350 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       order_id  pizza_quantity  pizza_price\n",
       "0             1               1        13.25\n",
       "1             2               5        92.00\n",
       "2             3               2        37.25\n",
       "3             4               1        16.50\n",
       "4             5               1        16.50\n",
       "...         ...             ...          ...\n",
       "21345     21346               4        62.25\n",
       "21346     21347               4        66.50\n",
       "21347     21348               3        46.70\n",
       "21348     21349               1        20.25\n",
       "21349     21350               1        12.75\n",
       "\n",
       "[21350 rows x 3 columns]"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2 = df.groupby('order_id').sum().reset_index()\n",
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d587c130",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id          10675.500000\n",
       "pizza_quantity        2.321967\n",
       "pizza_price          37.561813\n",
       "dtype: float64"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a2596d4",
   "metadata": {},
   "source": [
    "### `OBSERVATION 4`\n",
    "- the average selling price of pizza is 37$ with quantity of 2 pizzas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "8dd47d1d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df['order_date'] = pd.to_datetime(df['order_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "ac2e2438",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['order_month'] = df['order_date'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "857b3f23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>pizza_quantity</th>\n",
       "      <th>order_date</th>\n",
       "      <th>order_time</th>\n",
       "      <th>pizza_id</th>\n",
       "      <th>pizza_size</th>\n",
       "      <th>pizza_price</th>\n",
       "      <th>pizza_name</th>\n",
       "      <th>pizza_category</th>\n",
       "      <th>pizza_ingredients</th>\n",
       "      <th>order_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48615</th>\n",
       "      <td>21348</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>21:23:10</td>\n",
       "      <td>ckn_alfredo</td>\n",
       "      <td>M</td>\n",
       "      <td>16.75</td>\n",
       "      <td>The Chicken Alfredo Pizza</td>\n",
       "      <td>Chicken</td>\n",
       "      <td>Chicken, Red Onions, Red Peppers, Mushrooms, Asiago Cheese, Alfredo Sauce</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48616</th>\n",
       "      <td>21348</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>21:23:10</td>\n",
       "      <td>four_cheese</td>\n",
       "      <td>L</td>\n",
       "      <td>17.95</td>\n",
       "      <td>The Four Cheese Pizza</td>\n",
       "      <td>Veggie</td>\n",
       "      <td>Ricotta Cheese, Gorgonzola Piccante Cheese, Mozzarella Cheese, Parmigiano Reggiano Cheese, Garlic</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48617</th>\n",
       "      <td>21348</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>21:23:10</td>\n",
       "      <td>napolitana</td>\n",
       "      <td>S</td>\n",
       "      <td>12.00</td>\n",
       "      <td>The Napolitana Pizza</td>\n",
       "      <td>Classic</td>\n",
       "      <td>Tomatoes, Anchovies, Green Olives, Red Onions, Garlic</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48618</th>\n",
       "      <td>21349</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>22:09:54</td>\n",
       "      <td>mexicana</td>\n",
       "      <td>L</td>\n",
       "      <td>20.25</td>\n",
       "      <td>The Mexicana Pizza</td>\n",
       "      <td>Veggie</td>\n",
       "      <td>Tomatoes, Red Peppers, Jalapeno Peppers, Red Onions, Cilantro, Corn, Chipotle Sauce, Garlic</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48619</th>\n",
       "      <td>21350</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>23:02:05</td>\n",
       "      <td>bbq_ckn</td>\n",
       "      <td>S</td>\n",
       "      <td>12.75</td>\n",
       "      <td>The Barbecue Chicken Pizza</td>\n",
       "      <td>Chicken</td>\n",
       "      <td>Barbecued Chicken, Red Peppers, Green Peppers, Tomatoes, Red Onions, Barbecue Sauce</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       order_id  pizza_quantity order_date order_time     pizza_id pizza_size  \\\n",
       "48615     21348               1 2015-12-31   21:23:10  ckn_alfredo          M   \n",
       "48616     21348               1 2015-12-31   21:23:10  four_cheese          L   \n",
       "48617     21348               1 2015-12-31   21:23:10   napolitana          S   \n",
       "48618     21349               1 2015-12-31   22:09:54     mexicana          L   \n",
       "48619     21350               1 2015-12-31   23:02:05      bbq_ckn          S   \n",
       "\n",
       "       pizza_price                  pizza_name pizza_category  \\\n",
       "48615        16.75   The Chicken Alfredo Pizza        Chicken   \n",
       "48616        17.95       The Four Cheese Pizza         Veggie   \n",
       "48617        12.00        The Napolitana Pizza        Classic   \n",
       "48618        20.25          The Mexicana Pizza         Veggie   \n",
       "48619        12.75  The Barbecue Chicken Pizza        Chicken   \n",
       "\n",
       "                                                                                       pizza_ingredients  \\\n",
       "48615                          Chicken, Red Onions, Red Peppers, Mushrooms, Asiago Cheese, Alfredo Sauce   \n",
       "48616  Ricotta Cheese, Gorgonzola Piccante Cheese, Mozzarella Cheese, Parmigiano Reggiano Cheese, Garlic   \n",
       "48617                                              Tomatoes, Anchovies, Green Olives, Red Onions, Garlic   \n",
       "48618        Tomatoes, Red Peppers, Jalapeno Peppers, Red Onions, Cilantro, Corn, Chipotle Sauce, Garlic   \n",
       "48619                Barbecued Chicken, Red Peppers, Green Peppers, Tomatoes, Red Onions, Barbecue Sauce   \n",
       "\n",
       "       order_month  \n",
       "48615           12  \n",
       "48616           12  \n",
       "48617           12  \n",
       "48618           12  \n",
       "48619           12  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "203b4026",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['weekday']= df['order_date'].dt.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "c0db2630",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pizza_quantity</th>\n",
       "      <th>pizza_price</th>\n",
       "      <th>order_month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekday</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td>8242</td>\n",
       "      <td>133773.90</td>\n",
       "      <td>51694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>7478</td>\n",
       "      <td>120912.45</td>\n",
       "      <td>48435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>7493</td>\n",
       "      <td>120865.30</td>\n",
       "      <td>47465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>6946</td>\n",
       "      <td>112006.45</td>\n",
       "      <td>44439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>6895</td>\n",
       "      <td>111764.70</td>\n",
       "      <td>43526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>6485</td>\n",
       "      <td>105357.15</td>\n",
       "      <td>39770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>6035</td>\n",
       "      <td>97264.75</td>\n",
       "      <td>38325</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           pizza_quantity  pizza_price  order_month\n",
       "weekday                                            \n",
       "Friday               8242    133773.90        51694\n",
       "Thursday             7478    120912.45        48435\n",
       "Saturday             7493    120865.30        47465\n",
       "Wednesday            6946    112006.45        44439\n",
       "Tuesday              6895    111764.70        43526\n",
       "Monday               6485    105357.15        39770\n",
       "Sunday               6035     97264.75        38325"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dws = df.groupby('weekday').sum().sort_values('pizza_price', ascending=False).rename({0:'Monday',1:'Tuesday',2:'Wednesday',3:'Thursday',4:'Friday',5:'Saturday',6:'Sunday'}, axis=0)\n",
    "day_wise_summary = dws.drop(\"order_id\", axis=1)\n",
    "day_wise_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66b82297",
   "metadata": {},
   "source": [
    "### `OBSERVATION 5`\n",
    "- Customers prefer to eat pizza on __Fridays__, but not on Sundays. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "9c97ee98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 48620 entries, 0 to 48619\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count  Dtype         \n",
      "---  ------             --------------  -----         \n",
      " 0   order_id           48620 non-null  int64         \n",
      " 1   pizza_quantity     48620 non-null  int64         \n",
      " 2   order_date         48620 non-null  datetime64[ns]\n",
      " 3   order_time         48620 non-null  object        \n",
      " 4   pizza_id           48620 non-null  object        \n",
      " 5   pizza_size         48620 non-null  object        \n",
      " 6   pizza_price        48620 non-null  float64       \n",
      " 7   pizza_name         48620 non-null  object        \n",
      " 8   pizza_category     48620 non-null  object        \n",
      " 9   pizza_ingredients  48620 non-null  object        \n",
      " 10  order_month        48620 non-null  int64         \n",
      " 11  weekday            48620 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(1), int64(4), object(6)\n",
      "memory usage: 4.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "1109931d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['order_time'] = pd.to_datetime(df['order_time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "5bcb8592",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['hour']= df['order_time'].dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "91636551",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 48620 entries, 0 to 48619\n",
      "Data columns (total 13 columns):\n",
      " #   Column             Non-Null Count  Dtype         \n",
      "---  ------             --------------  -----         \n",
      " 0   order_id           48620 non-null  int64         \n",
      " 1   pizza_quantity     48620 non-null  int64         \n",
      " 2   order_date         48620 non-null  datetime64[ns]\n",
      " 3   order_time         48620 non-null  datetime64[ns]\n",
      " 4   pizza_id           48620 non-null  object        \n",
      " 5   pizza_size         48620 non-null  object        \n",
      " 6   pizza_price        48620 non-null  float64       \n",
      " 7   pizza_name         48620 non-null  object        \n",
      " 8   pizza_category     48620 non-null  object        \n",
      " 9   pizza_ingredients  48620 non-null  object        \n",
      " 10  order_month        48620 non-null  int64         \n",
      " 11  weekday            48620 non-null  int64         \n",
      " 12  hour               48620 non-null  int64         \n",
      "dtypes: datetime64[ns](2), float64(1), int64(5), object(5)\n",
      "memory usage: 5.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "09f8fe85",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
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       "      <td>13.457425</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>12.758124</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>10.577951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>8.946935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16</td>\n",
       "      <td>8.607569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14</td>\n",
       "      <td>7.241876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20</td>\n",
       "      <td>7.171946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15</td>\n",
       "      <td>6.519951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11</td>\n",
       "      <td>5.495681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>21</td>\n",
       "      <td>5.199506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>22</td>\n",
       "      <td>2.817770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>23</td>\n",
       "      <td>0.139860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10</td>\n",
       "      <td>0.034965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>9</td>\n",
       "      <td>0.008227</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    hour  percentage\n",
       "0     12   13.457425\n",
       "1     13   12.758124\n",
       "2     18   11.022213\n",
       "3     17   10.577951\n",
       "4     19    8.946935\n",
       "5     16    8.607569\n",
       "6     14    7.241876\n",
       "7     20    7.171946\n",
       "8     15    6.519951\n",
       "9     11    5.495681\n",
       "10    21    5.199506\n",
       "11    22    2.817770\n",
       "12    23    0.139860\n",
       "13    10    0.034965\n",
       "14     9    0.008227"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# peak order time \n",
    "df3 = (df['hour'].value_counts(normalize=True)*100).reset_index().rename({'index':'hour', 'hour':'percentage'},axis=1)\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "5beaa4a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[16,8])\n",
    "sns.barplot(data=df3, x='hour', y='percentage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "7ff9a4fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# weekday wise peak hour  \n",
    "plt.figure(figsize=[25,10])\n",
    "sns.countplot(data=df, x='hour', hue='weekday')\n",
    "plt.legend(loc=1, title='Weekdays')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.7 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "vscode": {
   "interpreter": {
    "hash": "ad2bdc8ecc057115af97d19610ffacc2b4e99fae6737bb82f5d7fb13d2f2c186"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}