Marketing Analytics For E-Commerce Market Place Company(Lovee).ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0dec9513",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy  as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fd1a37ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "cust = pd.read_csv(\"F:\\Python\\CASE\\Case-final\\CUSTOMERS.csv\")\n",
    "order = pd.read_csv(\"F:\\Python\\CASE\\Case-final\\ORDERS.csv\")\n",
    "products = pd.read_csv(\"F:\\Python\\CASE\\Case-final\\PRODUCTS.csv\")\n",
    "sellers = pd.read_csv(\"F:\\Python\\CASE\\Case-final\\SELLERS.csv\")\n",
    "order_items = pd.read_csv(\"F:\\Python\\CASE\\Case-final\\ORDER_ITEMS.csv\")\n",
    "order_payments = pd.read_csv(\"F:\\Python\\CASE\\Case-final\\ORDER_PAYMENTS.csv\")\n",
    "order_review = pd.read_csv(\"F:\\Python\\CASE\\Case-final\\ORDER_REVIEW_RATINGS.csv\")\n",
    "geo_location = pd.read_csv(\"F:\\Python\\CASE\\Case-final\\GEO_LOCATION.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b055a419",
   "metadata": {},
   "outputs": [
    {
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       "      <th>customer_unique_id</th>\n",
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      "text/plain": [
       "                        customer_id                customer_unique_id  \\\n",
       "0  06b8999e2fba1a1fbc88172c00ba8bc7  861eff4711a542e4b93843c6dd7febb0   \n",
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       "\n",
       "   customer_zip_code_prefix customer_city  customer_state  \n",
       "0                     14409      Adilabad  Andhra Pradesh  \n",
       "1                      9790         Adoni  Andhra Pradesh  \n",
       "2                      1151  Akkarampalle  Andhra Pradesh  \n",
       "3                      8775   Akkayapalle  Andhra Pradesh  \n",
       "4                     13056         Alwal  Andhra Pradesh  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "551e9134",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99441 entries, 0 to 99440\n",
      "Data columns (total 5 columns):\n",
      " #   Column                    Non-Null Count  Dtype \n",
      "---  ------                    --------------  ----- \n",
      " 0   customer_id               99441 non-null  object\n",
      " 1   customer_unique_id        99441 non-null  object\n",
      " 2   customer_zip_code_prefix  99441 non-null  int64 \n",
      " 3   customer_city             99441 non-null  object\n",
      " 4   customer_state            99441 non-null  object\n",
      "dtypes: int64(1), object(4)\n",
      "memory usage: 3.8+ MB\n"
     ]
    }
   ],
   "source": [
    "cust.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d81afdab",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>customer_id</th>\n",
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       "      <th>order_approved_at</th>\n",
       "      <th>order_delivered_carrier_date</th>\n",
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       "      <td>10/2/2017 11:07</td>\n",
       "      <td>10/4/2017 19:55</td>\n",
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      "text/plain": [
       "                           order_id                       customer_id  \\\n",
       "0  e481f51cbdc54678b7cc49136f2d6af7  9ef432eb6251297304e76186b10a928d   \n",
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       "4  ad21c59c0840e6cb83a9ceb5573f8159  8ab97904e6daea8866dbdbc4fb7aad2c   \n",
       "\n",
       "  order_status order_purchase_timestamp order_approved_at  \\\n",
       "0    delivered          10/2/2017 10:56   10/2/2017 11:07   \n",
       "1    delivered          7/24/2018 20:41    7/26/2018 3:24   \n",
       "2    delivered            8/8/2018 8:38     8/8/2018 8:55   \n",
       "3    delivered         11/18/2017 19:28  11/18/2017 19:45   \n",
       "4    delivered          2/13/2018 21:18   2/13/2018 22:20   \n",
       "\n",
       "  order_delivered_carrier_date order_delivered_customer_date  \\\n",
       "0              10/4/2017 19:55              10/10/2017 21:25   \n",
       "1              7/26/2018 14:31                8/7/2018 15:27   \n",
       "2               8/8/2018 13:50               8/17/2018 18:06   \n",
       "3             11/22/2017 13:39                12/2/2017 0:28   \n",
       "4              2/14/2018 19:46               2/16/2018 18:17   \n",
       "\n",
       "  order_estimated_delivery_date  \n",
       "0               10/18/2017 0:00  \n",
       "1                8/13/2018 0:00  \n",
       "2                 9/4/2018 0:00  \n",
       "3               12/15/2017 0:00  \n",
       "4                2/26/2018 0:00  "
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     },
     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "order.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "644b76bc",
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99441 entries, 0 to 99440\n",
      "Data columns (total 8 columns):\n",
      " #   Column                         Non-Null Count  Dtype \n",
      "---  ------                         --------------  ----- \n",
      " 0   order_id                       99441 non-null  object\n",
      " 1   customer_id                    99441 non-null  object\n",
      " 2   order_status                   99441 non-null  object\n",
      " 3   order_purchase_timestamp       99441 non-null  object\n",
      " 4   order_approved_at              99281 non-null  object\n",
      " 5   order_delivered_carrier_date   97658 non-null  object\n",
      " 6   order_delivered_customer_date  96476 non-null  object\n",
      " 7   order_estimated_delivery_date  99441 non-null  object\n",
      "dtypes: object(8)\n",
      "memory usage: 6.1+ MB\n"
     ]
    }
   ],
   "source": [
    "order.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "205625b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "order.order_purchase_timestamp = pd.to_datetime(order.order_purchase_timestamp, format= \"%m/%d/%Y %H:%M\")\n",
    "order.order_approved_at = pd.to_datetime(order.order_approved_at, format= \"%m/%d/%Y %H:%M\")\n",
    "order.order_delivered_carrier_date = pd.to_datetime(order.order_delivered_carrier_date, format= \"%m/%d/%Y %H:%M\")\n",
    "order.order_delivered_customer_date = pd.to_datetime(order.order_delivered_customer_date, format= \"%m/%d/%Y %H:%M\")\n",
    "order.order_estimated_delivery_date = pd.to_datetime(order.order_estimated_delivery_date, format= \"%m/%d/%Y %H:%M\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4deb883c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99441 entries, 0 to 99440\n",
      "Data columns (total 8 columns):\n",
      " #   Column                         Non-Null Count  Dtype         \n",
      "---  ------                         --------------  -----         \n",
      " 0   order_id                       99441 non-null  object        \n",
      " 1   customer_id                    99441 non-null  object        \n",
      " 2   order_status                   99441 non-null  object        \n",
      " 3   order_purchase_timestamp       99441 non-null  datetime64[ns]\n",
      " 4   order_approved_at              99281 non-null  datetime64[ns]\n",
      " 5   order_delivered_carrier_date   97658 non-null  datetime64[ns]\n",
      " 6   order_delivered_customer_date  96476 non-null  datetime64[ns]\n",
      " 7   order_estimated_delivery_date  99441 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](5), object(3)\n",
      "memory usage: 6.1+ MB\n"
     ]
    }
   ],
   "source": [
    "order.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2ce83be1",
   "metadata": {},
   "outputs": [
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       "      <td>9/19/2017 9:45</td>\n",
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       "      <td>9d7a1d34a5052409006425275ba1c2b4</td>\n",
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       "      <td>2/13/2017 13:57</td>\n",
       "      <td>199.90</td>\n",
       "      <td>18.14</td>\n",
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      "text/plain": [
       "                           order_id  order_item_id  \\\n",
       "0  00010242fe8c5a6d1ba2dd792cb16214              1   \n",
       "1  00018f77f2f0320c557190d7a144bdd3              1   \n",
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       "3  00024acbcdf0a6daa1e931b038114c75              1   \n",
       "4  00042b26cf59d7ce69dfabb4e55b4fd9              1   \n",
       "\n",
       "                         product_id                         seller_id  \\\n",
       "0  4244733e06e7ecb4970a6e2683c13e61  48436dade18ac8b2bce089ec2a041202   \n",
       "1  e5f2d52b802189ee658865ca93d83a8f  dd7ddc04e1b6c2c614352b383efe2d36   \n",
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       "4  ac6c3623068f30de03045865e4e10089  df560393f3a51e74553ab94004ba5c87   \n",
       "\n",
       "  shipping_limit_date   price  freight_value  \n",
       "0      9/19/2017 9:45   58.90          13.29  \n",
       "1      5/3/2017 11:05  239.90          19.93  \n",
       "2     1/18/2018 14:48  199.00          17.87  \n",
       "3     8/15/2018 10:10   12.99          12.79  \n",
       "4     2/13/2017 13:57  199.90          18.14  "
      ]
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     "execution_count": 9,
     "metadata": {},
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   ],
   "source": [
    "order_items.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fe594905",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 112650 entries, 0 to 112649\n",
      "Data columns (total 7 columns):\n",
      " #   Column               Non-Null Count   Dtype  \n",
      "---  ------               --------------   -----  \n",
      " 0   order_id             112650 non-null  object \n",
      " 1   order_item_id        112650 non-null  int64  \n",
      " 2   product_id           112650 non-null  object \n",
      " 3   seller_id            112650 non-null  object \n",
      " 4   shipping_limit_date  112650 non-null  object \n",
      " 5   price                112650 non-null  float64\n",
      " 6   freight_value        112650 non-null  float64\n",
      "dtypes: float64(2), int64(1), object(4)\n",
      "memory usage: 6.0+ MB\n"
     ]
    }
   ],
   "source": [
    "order_items.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "16a46a77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2017-09-19 09:45:00\n",
       "1        2017-05-03 11:05:00\n",
       "2        2018-01-18 14:48:00\n",
       "3        2018-08-15 10:10:00\n",
       "4        2017-02-13 13:57:00\n",
       "                 ...        \n",
       "112645   2018-05-02 04:11:00\n",
       "112646   2018-07-20 04:31:00\n",
       "112647   2017-10-30 17:14:00\n",
       "112648   2017-08-21 00:04:00\n",
       "112649   2018-06-12 17:10:00\n",
       "Name: shipping_limit_date, Length: 112650, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting Shipping_date variable to datetime format from object type\n",
    "order_items.shipping_limit_date = pd.to_datetime(order_items.shipping_limit_date, format = \"%m/%d/%Y %H:%M\")\n",
    "order_items.shipping_limit_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6a188b98",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Renaming Order_item_id column to Quantity\n",
    "order_items.rename(columns ={'order_item_id':'Quantity'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "02856add",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>order_id</th>\n",
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       "      <td>2017-09-19 09:45:00</td>\n",
       "      <td>58.90</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00018f77f2f0320c557190d7a144bdd3</td>\n",
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       "      <td>dd7ddc04e1b6c2c614352b383efe2d36</td>\n",
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       "      <td>239.90</td>\n",
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       "      <td>199.00</td>\n",
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       "      <td>b8bc237ba3788b23da09c0f1f3a3288c</td>\n",
       "      <td>2018-05-02 04:11:00</td>\n",
       "      <td>299.99</td>\n",
       "      <td>43.41</td>\n",
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       "      <th>102421</th>\n",
       "      <td>fffcd46ef2263f404302a634eb57f7eb</td>\n",
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       "      <td>f3c38ab652836d21de61fb8314b69182</td>\n",
       "      <td>2018-07-20 04:31:00</td>\n",
       "      <td>350.00</td>\n",
       "      <td>36.53</td>\n",
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       "    <tr>\n",
       "      <th>102422</th>\n",
       "      <td>fffce4705a9662cd70adb13d4a31832d</td>\n",
       "      <td>1</td>\n",
       "      <td>72a30483855e2eafc67aee5dc2560482</td>\n",
       "      <td>c3cfdc648177fdbbbb35635a37472c53</td>\n",
       "      <td>2017-10-30 17:14:00</td>\n",
       "      <td>99.90</td>\n",
       "      <td>16.95</td>\n",
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       "      <th>102423</th>\n",
       "      <td>fffe18544ffabc95dfada21779c9644f</td>\n",
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       "      <td>2b3e4a2a3ea8e01938cabda2a3e5cc79</td>\n",
       "      <td>2017-08-21 00:04:00</td>\n",
       "      <td>55.99</td>\n",
       "      <td>8.72</td>\n",
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       "      <th>102424</th>\n",
       "      <td>fffe41c64501cc87c801fd61db3f6244</td>\n",
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       "      <td>f7ccf836d21b2fb1de37564105216cc1</td>\n",
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      "text/plain": [
       "                                order_id  Quantity  \\\n",
       "0       00010242fe8c5a6d1ba2dd792cb16214         1   \n",
       "1       00018f77f2f0320c557190d7a144bdd3         1   \n",
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       "...                                  ...       ...   \n",
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       "102421  fffcd46ef2263f404302a634eb57f7eb         1   \n",
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       "102423  fffe18544ffabc95dfada21779c9644f         1   \n",
       "102424  fffe41c64501cc87c801fd61db3f6244         1   \n",
       "\n",
       "                              product_id                         seller_id  \\\n",
       "0       4244733e06e7ecb4970a6e2683c13e61  48436dade18ac8b2bce089ec2a041202   \n",
       "1       e5f2d52b802189ee658865ca93d83a8f  dd7ddc04e1b6c2c614352b383efe2d36   \n",
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       "...                                  ...                               ...   \n",
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       "102424  350688d9dc1e75ff97be326363655e01  f7ccf836d21b2fb1de37564105216cc1   \n",
       "\n",
       "       shipping_limit_date   price  freight_value  \n",
       "0      2017-09-19 09:45:00   58.90          13.29  \n",
       "1      2017-05-03 11:05:00  239.90          19.93  \n",
       "2      2018-01-18 14:48:00  199.00          17.87  \n",
       "3      2018-08-15 10:10:00   12.99          12.79  \n",
       "4      2017-02-13 13:57:00  199.90          18.14  \n",
       "...                    ...     ...            ...  \n",
       "102420 2018-05-02 04:11:00  299.99          43.41  \n",
       "102421 2018-07-20 04:31:00  350.00          36.53  \n",
       "102422 2017-10-30 17:14:00   99.90          16.95  \n",
       "102423 2017-08-21 00:04:00   55.99           8.72  \n",
       "102424 2018-06-12 17:10:00   43.00          12.79  \n",
       "\n",
       "[102425 rows x 7 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing Duplicate records from Order_Items table\n",
    "order_items = order_items.drop_duplicates(subset=['order_id','product_id', 'seller_id', 'shipping_limit_date'], keep='last',ignore_index = True)\n",
    "order_items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "43e8d1f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>fffc94f6ce00a00581880bf54a75a037</td>\n",
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       "      <td>343.40</td>\n",
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       "      <td>fffcd46ef2263f404302a634eb57f7eb</td>\n",
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       "      <td>386.53</td>\n",
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       "      <td>fffce4705a9662cd70adb13d4a31832d</td>\n",
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       "      <td>72a30483855e2eafc67aee5dc2560482</td>\n",
       "      <td>c3cfdc648177fdbbbb35635a37472c53</td>\n",
       "      <td>2017-10-30 17:14:00</td>\n",
       "      <td>99.90</td>\n",
       "      <td>16.95</td>\n",
       "      <td>116.85</td>\n",
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       "      <td>2017-08-21 00:04:00</td>\n",
       "      <td>55.99</td>\n",
       "      <td>8.72</td>\n",
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       "      <td>f7ccf836d21b2fb1de37564105216cc1</td>\n",
       "      <td>2018-06-12 17:10:00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>12.79</td>\n",
       "      <td>55.79</td>\n",
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      "text/plain": [
       "                                order_id  Quantity  \\\n",
       "0       00010242fe8c5a6d1ba2dd792cb16214         1   \n",
       "1       00018f77f2f0320c557190d7a144bdd3         1   \n",
       "2       000229ec398224ef6ca0657da4fc703e         1   \n",
       "3       00024acbcdf0a6daa1e931b038114c75         1   \n",
       "4       00042b26cf59d7ce69dfabb4e55b4fd9         1   \n",
       "...                                  ...       ...   \n",
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       "102421  fffcd46ef2263f404302a634eb57f7eb         1   \n",
       "102422  fffce4705a9662cd70adb13d4a31832d         1   \n",
       "102423  fffe18544ffabc95dfada21779c9644f         1   \n",
       "102424  fffe41c64501cc87c801fd61db3f6244         1   \n",
       "\n",
       "                              product_id                         seller_id  \\\n",
       "0       4244733e06e7ecb4970a6e2683c13e61  48436dade18ac8b2bce089ec2a041202   \n",
       "1       e5f2d52b802189ee658865ca93d83a8f  dd7ddc04e1b6c2c614352b383efe2d36   \n",
       "2       c777355d18b72b67abbeef9df44fd0fd  5b51032eddd242adc84c38acab88f23d   \n",
       "3       7634da152a4610f1595efa32f14722fc  9d7a1d34a5052409006425275ba1c2b4   \n",
       "4       ac6c3623068f30de03045865e4e10089  df560393f3a51e74553ab94004ba5c87   \n",
       "...                                  ...                               ...   \n",
       "102420  4aa6014eceb682077f9dc4bffebc05b0  b8bc237ba3788b23da09c0f1f3a3288c   \n",
       "102421  32e07fd915822b0765e448c4dd74c828  f3c38ab652836d21de61fb8314b69182   \n",
       "102422  72a30483855e2eafc67aee5dc2560482  c3cfdc648177fdbbbb35635a37472c53   \n",
       "102423  9c422a519119dcad7575db5af1ba540e  2b3e4a2a3ea8e01938cabda2a3e5cc79   \n",
       "102424  350688d9dc1e75ff97be326363655e01  f7ccf836d21b2fb1de37564105216cc1   \n",
       "\n",
       "       shipping_limit_date   price  freight_value  tot_cost  \n",
       "0      2017-09-19 09:45:00   58.90          13.29     72.19  \n",
       "1      2017-05-03 11:05:00  239.90          19.93    259.83  \n",
       "2      2018-01-18 14:48:00  199.00          17.87    216.87  \n",
       "3      2018-08-15 10:10:00   12.99          12.79     25.78  \n",
       "4      2017-02-13 13:57:00  199.90          18.14    218.04  \n",
       "...                    ...     ...            ...       ...  \n",
       "102420 2018-05-02 04:11:00  299.99          43.41    343.40  \n",
       "102421 2018-07-20 04:31:00  350.00          36.53    386.53  \n",
       "102422 2017-10-30 17:14:00   99.90          16.95    116.85  \n",
       "102423 2017-08-21 00:04:00   55.99           8.72     64.71  \n",
       "102424 2018-06-12 17:10:00   43.00          12.79     55.79  \n",
       "\n",
       "[102425 rows x 8 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding Total Cost variable in Order_Items table\n",
    "order_items.loc[:,'tot_cost'] = ((order_items.Quantity * order_items.price) + order_items.freight_value)\n",
    "order_items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fb801b6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 102425 entries, 0 to 102424\n",
      "Data columns (total 8 columns):\n",
      " #   Column               Non-Null Count   Dtype         \n",
      "---  ------               --------------   -----         \n",
      " 0   order_id             102425 non-null  object        \n",
      " 1   Quantity             102425 non-null  int64         \n",
      " 2   product_id           102425 non-null  object        \n",
      " 3   seller_id            102425 non-null  object        \n",
      " 4   shipping_limit_date  102425 non-null  datetime64[ns]\n",
      " 5   price                102425 non-null  float64       \n",
      " 6   freight_value        102425 non-null  float64       \n",
      " 7   tot_cost             102425 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(3), int64(1), object(3)\n",
      "memory usage: 6.3+ MB\n"
     ]
    }
   ],
   "source": [
    "order_items.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2ce4e69c",
   "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>order_id</th>\n",
       "      <th>payment_sequential</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>payment_installments</th>\n",
       "      <th>payment_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b81ef226f3fe1789b1e8b2acac839d17</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>8</td>\n",
       "      <td>99.33</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a9810da82917af2d9aefd1278f1dcfa0</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>1</td>\n",
       "      <td>24.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25e8ea4e93396b6fa0d3dd708e76c1bd</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>1</td>\n",
       "      <td>65.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ba78997921bbcdc1373bb41e913ab953</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>8</td>\n",
       "      <td>107.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42fdf880ba16b47b59251dd489d4441a</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2</td>\n",
       "      <td>128.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           order_id  payment_sequential payment_type  \\\n",
       "0  b81ef226f3fe1789b1e8b2acac839d17                   1  credit_card   \n",
       "1  a9810da82917af2d9aefd1278f1dcfa0                   1  credit_card   \n",
       "2  25e8ea4e93396b6fa0d3dd708e76c1bd                   1  credit_card   \n",
       "3  ba78997921bbcdc1373bb41e913ab953                   1  credit_card   \n",
       "4  42fdf880ba16b47b59251dd489d4441a                   1  credit_card   \n",
       "\n",
       "   payment_installments  payment_value  \n",
       "0                     8          99.33  \n",
       "1                     1          24.39  \n",
       "2                     1          65.71  \n",
       "3                     8         107.78  \n",
       "4                     2         128.45  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_payments.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a25478ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 103886 entries, 0 to 103885\n",
      "Data columns (total 5 columns):\n",
      " #   Column                Non-Null Count   Dtype  \n",
      "---  ------                --------------   -----  \n",
      " 0   order_id              103886 non-null  object \n",
      " 1   payment_sequential    103886 non-null  int64  \n",
      " 2   payment_type          103886 non-null  object \n",
      " 3   payment_installments  103886 non-null  int64  \n",
      " 4   payment_value         103886 non-null  float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 4.0+ MB\n"
     ]
    }
   ],
   "source": [
    "order_payments.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "41f9816c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>review_score</th>\n",
       "      <th>review_creation_date</th>\n",
       "      <th>review_answer_timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7bc2406110b926393aa56f80a40eba40</td>\n",
       "      <td>73fc7af87114b39712e6da79b0a377eb</td>\n",
       "      <td>4</td>\n",
       "      <td>1/18/2018 0:00</td>\n",
       "      <td>1/18/2018 21:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80e641a11e56f04c1ad469d5645fdfde</td>\n",
       "      <td>a548910a1c6147796b98fdf73dbeba33</td>\n",
       "      <td>5</td>\n",
       "      <td>3/10/2018 0:00</td>\n",
       "      <td>3/11/2018 3:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>228ce5500dc1d8e020d8d1322874b6f0</td>\n",
       "      <td>f9e4b658b201a9f2ecdecbb34bed034b</td>\n",
       "      <td>5</td>\n",
       "      <td>2/17/2018 0:00</td>\n",
       "      <td>2/18/2018 14:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>e64fb393e7b32834bb789ff8bb30750e</td>\n",
       "      <td>658677c97b385a9be170737859d3511b</td>\n",
       "      <td>5</td>\n",
       "      <td>4/21/2017 0:00</td>\n",
       "      <td>4/21/2017 22:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>f7c4243c7fe1938f181bec41a392bdeb</td>\n",
       "      <td>8e6bfb81e283fa7e4f11123a3fb894f1</td>\n",
       "      <td>5</td>\n",
       "      <td>3/1/2018 0:00</td>\n",
       "      <td>3/2/2018 10:26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          review_id                          order_id  \\\n",
       "0  7bc2406110b926393aa56f80a40eba40  73fc7af87114b39712e6da79b0a377eb   \n",
       "1  80e641a11e56f04c1ad469d5645fdfde  a548910a1c6147796b98fdf73dbeba33   \n",
       "2  228ce5500dc1d8e020d8d1322874b6f0  f9e4b658b201a9f2ecdecbb34bed034b   \n",
       "3  e64fb393e7b32834bb789ff8bb30750e  658677c97b385a9be170737859d3511b   \n",
       "4  f7c4243c7fe1938f181bec41a392bdeb  8e6bfb81e283fa7e4f11123a3fb894f1   \n",
       "\n",
       "   review_score review_creation_date review_answer_timestamp  \n",
       "0             4       1/18/2018 0:00         1/18/2018 21:46  \n",
       "1             5       3/10/2018 0:00          3/11/2018 3:05  \n",
       "2             5       2/17/2018 0:00         2/18/2018 14:36  \n",
       "3             5       4/21/2017 0:00         4/21/2017 22:02  \n",
       "4             5        3/1/2018 0:00          3/2/2018 10:26  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_review.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0f9b469e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 5 columns):\n",
      " #   Column                   Non-Null Count   Dtype \n",
      "---  ------                   --------------   ----- \n",
      " 0   review_id                100000 non-null  object\n",
      " 1   order_id                 100000 non-null  object\n",
      " 2   review_score             100000 non-null  int64 \n",
      " 3   review_creation_date     100000 non-null  object\n",
      " 4   review_answer_timestamp  100000 non-null  object\n",
      "dtypes: int64(1), object(4)\n",
      "memory usage: 3.8+ MB\n"
     ]
    }
   ],
   "source": [
    "order_review.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3a92a29a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting Review_creation_date and review_answer_timestamp  variable to datetime format from object type\n",
    "order_review.review_creation_date = pd.to_datetime(order_review.review_creation_date , format = \"%m/%d/%Y %H:%M\")\n",
    "order_review.review_answer_timestamp = pd.to_datetime(order_review.review_answer_timestamp, format = \"%m/%d/%Y %H:%M\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "28f4307a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 5 columns):\n",
      " #   Column                   Non-Null Count   Dtype         \n",
      "---  ------                   --------------   -----         \n",
      " 0   review_id                100000 non-null  object        \n",
      " 1   order_id                 100000 non-null  object        \n",
      " 2   review_score             100000 non-null  int64         \n",
      " 3   review_creation_date     100000 non-null  datetime64[ns]\n",
      " 4   review_answer_timestamp  100000 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](2), int64(1), object(2)\n",
      "memory usage: 3.8+ MB\n"
     ]
    }
   ],
   "source": [
    "order_review.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a88ef73d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_id</th>\n",
       "      <th>product_category_name</th>\n",
       "      <th>product_name_lenght</th>\n",
       "      <th>product_description_lenght</th>\n",
       "      <th>product_photos_qty</th>\n",
       "      <th>product_weight_g</th>\n",
       "      <th>product_length_cm</th>\n",
       "      <th>product_height_cm</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1e9e8ef04dbcff4541ed26657ea517e5</td>\n",
       "      <td>Perfumery</td>\n",
       "      <td>40.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3aa071139cb16b67ca9e5dea641aaa2f</td>\n",
       "      <td>Art</td>\n",
       "      <td>44.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>96bd76ec8810374ed1b65e291975717f</td>\n",
       "      <td>Sports_Leisure</td>\n",
       "      <td>46.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cef67bcfe19066a932b7673e239eb23d</td>\n",
       "      <td>Baby</td>\n",
       "      <td>27.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9dc1a7de274444849c219cff195d0b71</td>\n",
       "      <td>Housewares</td>\n",
       "      <td>37.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>625.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         product_id product_category_name  \\\n",
       "0  1e9e8ef04dbcff4541ed26657ea517e5             Perfumery   \n",
       "1  3aa071139cb16b67ca9e5dea641aaa2f                   Art   \n",
       "2  96bd76ec8810374ed1b65e291975717f        Sports_Leisure   \n",
       "3  cef67bcfe19066a932b7673e239eb23d                  Baby   \n",
       "4  9dc1a7de274444849c219cff195d0b71            Housewares   \n",
       "\n",
       "   product_name_lenght  product_description_lenght  product_photos_qty  \\\n",
       "0                 40.0                       287.0                 1.0   \n",
       "1                 44.0                       276.0                 1.0   \n",
       "2                 46.0                       250.0                 1.0   \n",
       "3                 27.0                       261.0                 1.0   \n",
       "4                 37.0                       402.0                 4.0   \n",
       "\n",
       "   product_weight_g  product_length_cm  product_height_cm  product_width_cm  \n",
       "0             225.0               16.0               10.0              14.0  \n",
       "1            1000.0               30.0               18.0              20.0  \n",
       "2             154.0               18.0                9.0              15.0  \n",
       "3             371.0               26.0                4.0              26.0  \n",
       "4             625.0               20.0               17.0              13.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "85fd435e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32951 entries, 0 to 32950\n",
      "Data columns (total 9 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   product_id                  32951 non-null  object \n",
      " 1   product_category_name       32328 non-null  object \n",
      " 2   product_name_lenght         32341 non-null  float64\n",
      " 3   product_description_lenght  32341 non-null  float64\n",
      " 4   product_photos_qty          32341 non-null  float64\n",
      " 5   product_weight_g            32949 non-null  float64\n",
      " 6   product_length_cm           32949 non-null  float64\n",
      " 7   product_height_cm           32949 non-null  float64\n",
      " 8   product_width_cm            32949 non-null  float64\n",
      "dtypes: float64(7), object(2)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "products.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f9ffa52d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seller_id</th>\n",
       "      <th>seller_zip_code_prefix</th>\n",
       "      <th>seller_city</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>3442f8959a84dea7ee197c632cb2df15</td>\n",
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       "      <td>ce3ad9de960102d0677a81f5d0bb7b2d</td>\n",
       "      <td>20031</td>\n",
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       "      <td>12914</td>\n",
       "      <td>Koratla</td>\n",
       "      <td>Andhra Pradesh</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          seller_id  seller_zip_code_prefix   seller_city  \\\n",
       "0  3442f8959a84dea7ee197c632cb2df15                   13023         Alwal   \n",
       "1  d1b65fc7debc3361ea86b5f14c68d2e2                   13023         Alwal   \n",
       "2  ce3ad9de960102d0677a81f5d0bb7b2d                   20031     Badepalle   \n",
       "3  c0f3eea2e14555b6faeea3dd58c1b1c3                    4195  Akkarampalle   \n",
       "4  51a04a8a6bdcb23deccc82b0b80742cf                   12914       Koratla   \n",
       "\n",
       "     seller_state  \n",
       "0  Andhra Pradesh  \n",
       "1  Andhra Pradesh  \n",
       "2  Andhra Pradesh  \n",
       "3  Andhra Pradesh  \n",
       "4  Andhra Pradesh  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sellers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b1aeacda",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3095 entries, 0 to 3094\n",
      "Data columns (total 4 columns):\n",
      " #   Column                  Non-Null Count  Dtype \n",
      "---  ------                  --------------  ----- \n",
      " 0   seller_id               3095 non-null   object\n",
      " 1   seller_zip_code_prefix  3095 non-null   int64 \n",
      " 2   seller_city             3038 non-null   object\n",
      " 3   seller_state            3038 non-null   object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 96.8+ KB\n"
     ]
    }
   ],
   "source": [
    "sellers.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5662ba42",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geolocation_zip_code_prefix</th>\n",
       "      <th>geolocation_lat</th>\n",
       "      <th>geolocation_lng</th>\n",
       "      <th>geolocation_city</th>\n",
       "      <th>geolocation_state</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1037</td>\n",
       "      <td>-23.545621</td>\n",
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       "      <td>Akkarampalle</td>\n",
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       "      <th>1</th>\n",
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       "      <td>-23.546081</td>\n",
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       "      <td>Akkarampalle</td>\n",
       "      <td>Andhra Pradesh</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>-23.544392</td>\n",
       "      <td>-46.639499</td>\n",
       "      <td>Akkarampalle</td>\n",
       "      <td>Andhra Pradesh</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1035</td>\n",
       "      <td>-23.541578</td>\n",
       "      <td>-46.641607</td>\n",
       "      <td>Akkarampalle</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
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       "      <td>-23.547762</td>\n",
       "      <td>-46.635361</td>\n",
       "      <td>Akkarampalle</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   geolocation_zip_code_prefix  geolocation_lat  geolocation_lng  \\\n",
       "0                         1037       -23.545621       -46.639292   \n",
       "1                         1046       -23.546081       -46.644820   \n",
       "2                         1041       -23.544392       -46.639499   \n",
       "3                         1035       -23.541578       -46.641607   \n",
       "4                         1012       -23.547762       -46.635361   \n",
       "\n",
       "  geolocation_city geolocation_state  \n",
       "0     Akkarampalle    Andhra Pradesh  \n",
       "1     Akkarampalle    Andhra Pradesh  \n",
       "2     Akkarampalle    Andhra Pradesh  \n",
       "3     Akkarampalle    Andhra Pradesh  \n",
       "4     Akkarampalle    Andhra Pradesh  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_location.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "de77a06f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19015 entries, 0 to 19014\n",
      "Data columns (total 5 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   geolocation_zip_code_prefix  19015 non-null  int64  \n",
      " 1   geolocation_lat              19015 non-null  float64\n",
      " 2   geolocation_lng              19015 non-null  float64\n",
      " 3   geolocation_city             19015 non-null  object \n",
      " 4   geolocation_state            19015 non-null  object \n",
      "dtypes: float64(2), int64(1), object(2)\n",
      "memory usage: 742.9+ KB\n"
     ]
    }
   ],
   "source": [
    "geo_location.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "883ccb24",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>product_id</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>shipping_limit_date</th>\n",
       "      <th>price</th>\n",
       "      <th>freight_value</th>\n",
       "      <th>tot_cost</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>order_purchase_timestamp</th>\n",
       "      <th>order_approved_at</th>\n",
       "      <th>order_delivered_carrier_date</th>\n",
       "      <th>order_delivered_customer_date</th>\n",
       "      <th>order_estimated_delivery_date</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>00010242fe8c5a6d1ba2dd792cb16214</td>\n",
       "      <td>1</td>\n",
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       "      <td>48436dade18ac8b2bce089ec2a041202</td>\n",
       "      <td>2017-09-19 09:45:00</td>\n",
       "      <td>58.90</td>\n",
       "      <td>13.29</td>\n",
       "      <td>72.19</td>\n",
       "      <td>3ce436f183e68e07877b285a838db11a</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-09-13 08:59:00</td>\n",
       "      <td>2017-09-13 09:45:00</td>\n",
       "      <td>2017-09-19 18:34:00</td>\n",
       "      <td>2017-09-20 23:43:00</td>\n",
       "      <td>2017-09-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00018f77f2f0320c557190d7a144bdd3</td>\n",
       "      <td>1</td>\n",
       "      <td>e5f2d52b802189ee658865ca93d83a8f</td>\n",
       "      <td>dd7ddc04e1b6c2c614352b383efe2d36</td>\n",
       "      <td>2017-05-03 11:05:00</td>\n",
       "      <td>239.90</td>\n",
       "      <td>19.93</td>\n",
       "      <td>259.83</td>\n",
       "      <td>f6dd3ec061db4e3987629fe6b26e5cce</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-04-26 10:53:00</td>\n",
       "      <td>2017-04-26 11:05:00</td>\n",
       "      <td>2017-05-04 14:35:00</td>\n",
       "      <td>2017-05-12 16:04:00</td>\n",
       "      <td>2017-05-15</td>\n",
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       "      <th>2</th>\n",
       "      <td>000229ec398224ef6ca0657da4fc703e</td>\n",
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       "      <td>c777355d18b72b67abbeef9df44fd0fd</td>\n",
       "      <td>5b51032eddd242adc84c38acab88f23d</td>\n",
       "      <td>2018-01-18 14:48:00</td>\n",
       "      <td>199.00</td>\n",
       "      <td>17.87</td>\n",
       "      <td>216.87</td>\n",
       "      <td>6489ae5e4333f3693df5ad4372dab6d3</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-01-14 14:33:00</td>\n",
       "      <td>2018-01-14 14:48:00</td>\n",
       "      <td>2018-01-16 12:36:00</td>\n",
       "      <td>2018-01-22 13:19:00</td>\n",
       "      <td>2018-02-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00024acbcdf0a6daa1e931b038114c75</td>\n",
       "      <td>1</td>\n",
       "      <td>7634da152a4610f1595efa32f14722fc</td>\n",
       "      <td>9d7a1d34a5052409006425275ba1c2b4</td>\n",
       "      <td>2018-08-15 10:10:00</td>\n",
       "      <td>12.99</td>\n",
       "      <td>12.79</td>\n",
       "      <td>25.78</td>\n",
       "      <td>d4eb9395c8c0431ee92fce09860c5a06</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-08-08 10:00:00</td>\n",
       "      <td>2018-08-08 10:10:00</td>\n",
       "      <td>2018-08-10 13:28:00</td>\n",
       "      <td>2018-08-14 13:32:00</td>\n",
       "      <td>2018-08-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00042b26cf59d7ce69dfabb4e55b4fd9</td>\n",
       "      <td>1</td>\n",
       "      <td>ac6c3623068f30de03045865e4e10089</td>\n",
       "      <td>df560393f3a51e74553ab94004ba5c87</td>\n",
       "      <td>2017-02-13 13:57:00</td>\n",
       "      <td>199.90</td>\n",
       "      <td>18.14</td>\n",
       "      <td>218.04</td>\n",
       "      <td>58dbd0b2d70206bf40e62cd34e84d795</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-02-04 13:57:00</td>\n",
       "      <td>2017-02-04 14:10:00</td>\n",
       "      <td>2017-02-16 09:46:00</td>\n",
       "      <td>2017-03-01 16:42:00</td>\n",
       "      <td>2017-03-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>102420</th>\n",
       "      <td>fffc94f6ce00a00581880bf54a75a037</td>\n",
       "      <td>1</td>\n",
       "      <td>4aa6014eceb682077f9dc4bffebc05b0</td>\n",
       "      <td>b8bc237ba3788b23da09c0f1f3a3288c</td>\n",
       "      <td>2018-05-02 04:11:00</td>\n",
       "      <td>299.99</td>\n",
       "      <td>43.41</td>\n",
       "      <td>343.40</td>\n",
       "      <td>b51593916b4b8e0d6f66f2ae24f2673d</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-04-23 13:57:00</td>\n",
       "      <td>2018-04-25 04:11:00</td>\n",
       "      <td>2018-04-25 12:09:00</td>\n",
       "      <td>2018-05-10 22:56:00</td>\n",
       "      <td>2018-05-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102421</th>\n",
       "      <td>fffcd46ef2263f404302a634eb57f7eb</td>\n",
       "      <td>1</td>\n",
       "      <td>32e07fd915822b0765e448c4dd74c828</td>\n",
       "      <td>f3c38ab652836d21de61fb8314b69182</td>\n",
       "      <td>2018-07-20 04:31:00</td>\n",
       "      <td>350.00</td>\n",
       "      <td>36.53</td>\n",
       "      <td>386.53</td>\n",
       "      <td>84c5d4fbaf120aae381fad077416eaa0</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-07-14 10:26:00</td>\n",
       "      <td>2018-07-17 04:31:00</td>\n",
       "      <td>2018-07-17 08:05:00</td>\n",
       "      <td>2018-07-23 20:31:00</td>\n",
       "      <td>2018-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102422</th>\n",
       "      <td>fffce4705a9662cd70adb13d4a31832d</td>\n",
       "      <td>1</td>\n",
       "      <td>72a30483855e2eafc67aee5dc2560482</td>\n",
       "      <td>c3cfdc648177fdbbbb35635a37472c53</td>\n",
       "      <td>2017-10-30 17:14:00</td>\n",
       "      <td>99.90</td>\n",
       "      <td>16.95</td>\n",
       "      <td>116.85</td>\n",
       "      <td>29309aa813182aaddc9b259e31b870e6</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-10-23 17:07:00</td>\n",
       "      <td>2017-10-24 17:14:00</td>\n",
       "      <td>2017-10-26 15:13:00</td>\n",
       "      <td>2017-10-28 12:22:00</td>\n",
       "      <td>2017-11-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102423</th>\n",
       "      <td>fffe18544ffabc95dfada21779c9644f</td>\n",
       "      <td>1</td>\n",
       "      <td>9c422a519119dcad7575db5af1ba540e</td>\n",
       "      <td>2b3e4a2a3ea8e01938cabda2a3e5cc79</td>\n",
       "      <td>2017-08-21 00:04:00</td>\n",
       "      <td>55.99</td>\n",
       "      <td>8.72</td>\n",
       "      <td>64.71</td>\n",
       "      <td>b5e6afd5a41800fdf401e0272ca74655</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-08-14 23:02:00</td>\n",
       "      <td>2017-08-15 00:04:00</td>\n",
       "      <td>2017-08-15 19:02:00</td>\n",
       "      <td>2017-08-16 21:59:00</td>\n",
       "      <td>2017-08-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102424</th>\n",
       "      <td>fffe41c64501cc87c801fd61db3f6244</td>\n",
       "      <td>1</td>\n",
       "      <td>350688d9dc1e75ff97be326363655e01</td>\n",
       "      <td>f7ccf836d21b2fb1de37564105216cc1</td>\n",
       "      <td>2018-06-12 17:10:00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>12.79</td>\n",
       "      <td>55.79</td>\n",
       "      <td>96d649da0cc4ff33bb408b199d4c7dcf</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-06-09 17:00:00</td>\n",
       "      <td>2018-06-09 17:10:00</td>\n",
       "      <td>2018-06-11 14:11:00</td>\n",
       "      <td>2018-06-14 17:56:00</td>\n",
       "      <td>2018-06-28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102425 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                order_id  Quantity  \\\n",
       "0       00010242fe8c5a6d1ba2dd792cb16214         1   \n",
       "1       00018f77f2f0320c557190d7a144bdd3         1   \n",
       "2       000229ec398224ef6ca0657da4fc703e         1   \n",
       "3       00024acbcdf0a6daa1e931b038114c75         1   \n",
       "4       00042b26cf59d7ce69dfabb4e55b4fd9         1   \n",
       "...                                  ...       ...   \n",
       "102420  fffc94f6ce00a00581880bf54a75a037         1   \n",
       "102421  fffcd46ef2263f404302a634eb57f7eb         1   \n",
       "102422  fffce4705a9662cd70adb13d4a31832d         1   \n",
       "102423  fffe18544ffabc95dfada21779c9644f         1   \n",
       "102424  fffe41c64501cc87c801fd61db3f6244         1   \n",
       "\n",
       "                              product_id                         seller_id  \\\n",
       "0       4244733e06e7ecb4970a6e2683c13e61  48436dade18ac8b2bce089ec2a041202   \n",
       "1       e5f2d52b802189ee658865ca93d83a8f  dd7ddc04e1b6c2c614352b383efe2d36   \n",
       "2       c777355d18b72b67abbeef9df44fd0fd  5b51032eddd242adc84c38acab88f23d   \n",
       "3       7634da152a4610f1595efa32f14722fc  9d7a1d34a5052409006425275ba1c2b4   \n",
       "4       ac6c3623068f30de03045865e4e10089  df560393f3a51e74553ab94004ba5c87   \n",
       "...                                  ...                               ...   \n",
       "102420  4aa6014eceb682077f9dc4bffebc05b0  b8bc237ba3788b23da09c0f1f3a3288c   \n",
       "102421  32e07fd915822b0765e448c4dd74c828  f3c38ab652836d21de61fb8314b69182   \n",
       "102422  72a30483855e2eafc67aee5dc2560482  c3cfdc648177fdbbbb35635a37472c53   \n",
       "102423  9c422a519119dcad7575db5af1ba540e  2b3e4a2a3ea8e01938cabda2a3e5cc79   \n",
       "102424  350688d9dc1e75ff97be326363655e01  f7ccf836d21b2fb1de37564105216cc1   \n",
       "\n",
       "       shipping_limit_date   price  freight_value  tot_cost  \\\n",
       "0      2017-09-19 09:45:00   58.90          13.29     72.19   \n",
       "1      2017-05-03 11:05:00  239.90          19.93    259.83   \n",
       "2      2018-01-18 14:48:00  199.00          17.87    216.87   \n",
       "3      2018-08-15 10:10:00   12.99          12.79     25.78   \n",
       "4      2017-02-13 13:57:00  199.90          18.14    218.04   \n",
       "...                    ...     ...            ...       ...   \n",
       "102420 2018-05-02 04:11:00  299.99          43.41    343.40   \n",
       "102421 2018-07-20 04:31:00  350.00          36.53    386.53   \n",
       "102422 2017-10-30 17:14:00   99.90          16.95    116.85   \n",
       "102423 2017-08-21 00:04:00   55.99           8.72     64.71   \n",
       "102424 2018-06-12 17:10:00   43.00          12.79     55.79   \n",
       "\n",
       "                             customer_id order_status  \\\n",
       "0       3ce436f183e68e07877b285a838db11a    delivered   \n",
       "1       f6dd3ec061db4e3987629fe6b26e5cce    delivered   \n",
       "2       6489ae5e4333f3693df5ad4372dab6d3    delivered   \n",
       "3       d4eb9395c8c0431ee92fce09860c5a06    delivered   \n",
       "4       58dbd0b2d70206bf40e62cd34e84d795    delivered   \n",
       "...                                  ...          ...   \n",
       "102420  b51593916b4b8e0d6f66f2ae24f2673d    delivered   \n",
       "102421  84c5d4fbaf120aae381fad077416eaa0    delivered   \n",
       "102422  29309aa813182aaddc9b259e31b870e6    delivered   \n",
       "102423  b5e6afd5a41800fdf401e0272ca74655    delivered   \n",
       "102424  96d649da0cc4ff33bb408b199d4c7dcf    delivered   \n",
       "\n",
       "       order_purchase_timestamp   order_approved_at  \\\n",
       "0           2017-09-13 08:59:00 2017-09-13 09:45:00   \n",
       "1           2017-04-26 10:53:00 2017-04-26 11:05:00   \n",
       "2           2018-01-14 14:33:00 2018-01-14 14:48:00   \n",
       "3           2018-08-08 10:00:00 2018-08-08 10:10:00   \n",
       "4           2017-02-04 13:57:00 2017-02-04 14:10:00   \n",
       "...                         ...                 ...   \n",
       "102420      2018-04-23 13:57:00 2018-04-25 04:11:00   \n",
       "102421      2018-07-14 10:26:00 2018-07-17 04:31:00   \n",
       "102422      2017-10-23 17:07:00 2017-10-24 17:14:00   \n",
       "102423      2017-08-14 23:02:00 2017-08-15 00:04:00   \n",
       "102424      2018-06-09 17:00:00 2018-06-09 17:10:00   \n",
       "\n",
       "       order_delivered_carrier_date order_delivered_customer_date  \\\n",
       "0               2017-09-19 18:34:00           2017-09-20 23:43:00   \n",
       "1               2017-05-04 14:35:00           2017-05-12 16:04:00   \n",
       "2               2018-01-16 12:36:00           2018-01-22 13:19:00   \n",
       "3               2018-08-10 13:28:00           2018-08-14 13:32:00   \n",
       "4               2017-02-16 09:46:00           2017-03-01 16:42:00   \n",
       "...                             ...                           ...   \n",
       "102420          2018-04-25 12:09:00           2018-05-10 22:56:00   \n",
       "102421          2018-07-17 08:05:00           2018-07-23 20:31:00   \n",
       "102422          2017-10-26 15:13:00           2017-10-28 12:22:00   \n",
       "102423          2017-08-15 19:02:00           2017-08-16 21:59:00   \n",
       "102424          2018-06-11 14:11:00           2018-06-14 17:56:00   \n",
       "\n",
       "       order_estimated_delivery_date  \n",
       "0                         2017-09-29  \n",
       "1                         2017-05-15  \n",
       "2                         2018-02-05  \n",
       "3                         2018-08-20  \n",
       "4                         2017-03-17  \n",
       "...                              ...  \n",
       "102420                    2018-05-18  \n",
       "102421                    2018-08-01  \n",
       "102422                    2017-11-10  \n",
       "102423                    2017-08-25  \n",
       "102424                    2018-06-28  \n",
       "\n",
       "[102425 rows x 15 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combining Table Orders and Order_Items\n",
    "ord_item_orders = pd.merge(left = order_items, right = order, how = 'left', on = 'order_id')\n",
    "ord_item_orders"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c3d31ee",
   "metadata": {},
   "source": [
    "##### Q1. a) Define & calculate high level metrics like (Total Revenue, Total quantity, Total products, Total categories, Total sellers, Total locations, Total channels, Total payment methods etc…)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c3413721",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Revenue:  12743924.0\n",
      "Total Quantity:  117844\n",
      "Total Products: 32951\n",
      "Total customers:  96096\n",
      "Total sellers : 3095\n",
      "Total location:  19015\n",
      "Total product categores : 71\n",
      "Total payments:  5\n"
     ]
    }
   ],
   "source": [
    "print('Total Revenue: ', round(order_items.price.sum(),2))\n",
    "print('Total Quantity: ',order_items.Quantity.sum())\n",
    "print('Total Products:',products.product_id.nunique())\n",
    "print('Total customers: ', cust.customer_unique_id.nunique())\n",
    "print('Total sellers :',sellers.seller_id.nunique())\n",
    "print('Total location: ',geo_location.geolocation_zip_code_prefix.nunique())\n",
    "print('Total product categores :',products.product_category_name.nunique())\n",
    "print('Total payments: ',order_payments.payment_type.nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82950b6b",
   "metadata": {},
   "source": [
    "##### Q1.b) Understanding how many new customers acquired every month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c418c252",
   "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>order_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>order_purchase_timestamp</th>\n",
       "      <th>order_approved_at</th>\n",
       "      <th>order_delivered_carrier_date</th>\n",
       "      <th>order_delivered_customer_date</th>\n",
       "      <th>order_estimated_delivery_date</th>\n",
       "      <th>year_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>e481f51cbdc54678b7cc49136f2d6af7</td>\n",
       "      <td>9ef432eb6251297304e76186b10a928d</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-10-02 10:56:00</td>\n",
       "      <td>2017-10-02 11:07:00</td>\n",
       "      <td>2017-10-04 19:55:00</td>\n",
       "      <td>2017-10-10 21:25:00</td>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>2017-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53cdb2fc8bc7dce0b6741e2150273451</td>\n",
       "      <td>b0830fb4747a6c6d20dea0b8c802d7ef</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-07-24 20:41:00</td>\n",
       "      <td>2018-07-26 03:24:00</td>\n",
       "      <td>2018-07-26 14:31:00</td>\n",
       "      <td>2018-08-07 15:27:00</td>\n",
       "      <td>2018-08-13</td>\n",
       "      <td>2018-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47770eb9100c2d0c44946d9cf07ec65d</td>\n",
       "      <td>41ce2a54c0b03bf3443c3d931a367089</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-08-08 08:38:00</td>\n",
       "      <td>2018-08-08 08:55:00</td>\n",
       "      <td>2018-08-08 13:50:00</td>\n",
       "      <td>2018-08-17 18:06:00</td>\n",
       "      <td>2018-09-04</td>\n",
       "      <td>2018-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>949d5b44dbf5de918fe9c16f97b45f8a</td>\n",
       "      <td>f88197465ea7920adcdbec7375364d82</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-11-18 19:28:00</td>\n",
       "      <td>2017-11-18 19:45:00</td>\n",
       "      <td>2017-11-22 13:39:00</td>\n",
       "      <td>2017-12-02 00:28:00</td>\n",
       "      <td>2017-12-15</td>\n",
       "      <td>2017-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ad21c59c0840e6cb83a9ceb5573f8159</td>\n",
       "      <td>8ab97904e6daea8866dbdbc4fb7aad2c</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-02-13 21:18:00</td>\n",
       "      <td>2018-02-13 22:20:00</td>\n",
       "      <td>2018-02-14 19:46:00</td>\n",
       "      <td>2018-02-16 18:17:00</td>\n",
       "      <td>2018-02-26</td>\n",
       "      <td>2018-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           order_id                       customer_id  \\\n",
       "0  e481f51cbdc54678b7cc49136f2d6af7  9ef432eb6251297304e76186b10a928d   \n",
       "1  53cdb2fc8bc7dce0b6741e2150273451  b0830fb4747a6c6d20dea0b8c802d7ef   \n",
       "2  47770eb9100c2d0c44946d9cf07ec65d  41ce2a54c0b03bf3443c3d931a367089   \n",
       "3  949d5b44dbf5de918fe9c16f97b45f8a  f88197465ea7920adcdbec7375364d82   \n",
       "4  ad21c59c0840e6cb83a9ceb5573f8159  8ab97904e6daea8866dbdbc4fb7aad2c   \n",
       "\n",
       "  order_status order_purchase_timestamp   order_approved_at  \\\n",
       "0    delivered      2017-10-02 10:56:00 2017-10-02 11:07:00   \n",
       "1    delivered      2018-07-24 20:41:00 2018-07-26 03:24:00   \n",
       "2    delivered      2018-08-08 08:38:00 2018-08-08 08:55:00   \n",
       "3    delivered      2017-11-18 19:28:00 2017-11-18 19:45:00   \n",
       "4    delivered      2018-02-13 21:18:00 2018-02-13 22:20:00   \n",
       "\n",
       "  order_delivered_carrier_date order_delivered_customer_date  \\\n",
       "0          2017-10-04 19:55:00           2017-10-10 21:25:00   \n",
       "1          2018-07-26 14:31:00           2018-08-07 15:27:00   \n",
       "2          2018-08-08 13:50:00           2018-08-17 18:06:00   \n",
       "3          2017-11-22 13:39:00           2017-12-02 00:28:00   \n",
       "4          2018-02-14 19:46:00           2018-02-16 18:17:00   \n",
       "\n",
       "  order_estimated_delivery_date year_month  \n",
       "0                    2017-10-18    2017-10  \n",
       "1                    2018-08-13    2018-07  \n",
       "2                    2018-09-04    2018-08  \n",
       "3                    2017-12-15    2017-11  \n",
       "4                    2018-02-26    2018-02  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order['year_month'] = order.order_purchase_timestamp.dt.strftime(\"%Y-%m\")\n",
    "order.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cb2fc308",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>order_purchase_timestamp</th>\n",
       "      <th>order_approved_at</th>\n",
       "      <th>order_delivered_carrier_date</th>\n",
       "      <th>order_delivered_customer_date</th>\n",
       "      <th>order_estimated_delivery_date</th>\n",
       "      <th>year_month</th>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th>customer_zip_code_prefix</th>\n",
       "      <th>customer_city</th>\n",
       "      <th>customer_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>e481f51cbdc54678b7cc49136f2d6af7</td>\n",
       "      <td>9ef432eb6251297304e76186b10a928d</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-10-02 10:56:00</td>\n",
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       "      <td>2017-10-04 19:55:00</td>\n",
       "      <td>2017-10-10 21:25:00</td>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>2017-10</td>\n",
       "      <td>7c396fd4830fd04220f754e42b4e5bff</td>\n",
       "      <td>3149</td>\n",
       "      <td>Akkarampalle</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53cdb2fc8bc7dce0b6741e2150273451</td>\n",
       "      <td>b0830fb4747a6c6d20dea0b8c802d7ef</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-07-24 20:41:00</td>\n",
       "      <td>2018-07-26 03:24:00</td>\n",
       "      <td>2018-07-26 14:31:00</td>\n",
       "      <td>2018-08-07 15:27:00</td>\n",
       "      <td>2018-08-13</td>\n",
       "      <td>2018-07</td>\n",
       "      <td>af07308b275d755c9edb36a90c618231</td>\n",
       "      <td>47813</td>\n",
       "      <td>Pandariya</td>\n",
       "      <td>Chhattisgarh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47770eb9100c2d0c44946d9cf07ec65d</td>\n",
       "      <td>41ce2a54c0b03bf3443c3d931a367089</td>\n",
       "      <td>delivered</td>\n",
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       "      <td>2018-08-08 08:55:00</td>\n",
       "      <td>2018-08-08 13:50:00</td>\n",
       "      <td>2018-08-17 18:06:00</td>\n",
       "      <td>2018-09-04</td>\n",
       "      <td>2018-08</td>\n",
       "      <td>3a653a41f6f9fc3d2a113cf8398680e8</td>\n",
       "      <td>75265</td>\n",
       "      <td>Dhamdha</td>\n",
       "      <td>Chhattisgarh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>949d5b44dbf5de918fe9c16f97b45f8a</td>\n",
       "      <td>f88197465ea7920adcdbec7375364d82</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-11-18 19:28:00</td>\n",
       "      <td>2017-11-18 19:45:00</td>\n",
       "      <td>2017-11-22 13:39:00</td>\n",
       "      <td>2017-12-02 00:28:00</td>\n",
       "      <td>2017-12-15</td>\n",
       "      <td>2017-11</td>\n",
       "      <td>7c142cf63193a1473d2e66489a9ae977</td>\n",
       "      <td>59296</td>\n",
       "      <td>Kartarpur</td>\n",
       "      <td>Punjab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ad21c59c0840e6cb83a9ceb5573f8159</td>\n",
       "      <td>8ab97904e6daea8866dbdbc4fb7aad2c</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-02-13 21:18:00</td>\n",
       "      <td>2018-02-13 22:20:00</td>\n",
       "      <td>2018-02-14 19:46:00</td>\n",
       "      <td>2018-02-16 18:17:00</td>\n",
       "      <td>2018-02-26</td>\n",
       "      <td>2018-02</td>\n",
       "      <td>72632f0f9dd73dfee390c9b22eb56dd6</td>\n",
       "      <td>9195</td>\n",
       "      <td>Bheemavaram</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           order_id                       customer_id  \\\n",
       "0  e481f51cbdc54678b7cc49136f2d6af7  9ef432eb6251297304e76186b10a928d   \n",
       "1  53cdb2fc8bc7dce0b6741e2150273451  b0830fb4747a6c6d20dea0b8c802d7ef   \n",
       "2  47770eb9100c2d0c44946d9cf07ec65d  41ce2a54c0b03bf3443c3d931a367089   \n",
       "3  949d5b44dbf5de918fe9c16f97b45f8a  f88197465ea7920adcdbec7375364d82   \n",
       "4  ad21c59c0840e6cb83a9ceb5573f8159  8ab97904e6daea8866dbdbc4fb7aad2c   \n",
       "\n",
       "  order_status order_purchase_timestamp   order_approved_at  \\\n",
       "0    delivered      2017-10-02 10:56:00 2017-10-02 11:07:00   \n",
       "1    delivered      2018-07-24 20:41:00 2018-07-26 03:24:00   \n",
       "2    delivered      2018-08-08 08:38:00 2018-08-08 08:55:00   \n",
       "3    delivered      2017-11-18 19:28:00 2017-11-18 19:45:00   \n",
       "4    delivered      2018-02-13 21:18:00 2018-02-13 22:20:00   \n",
       "\n",
       "  order_delivered_carrier_date order_delivered_customer_date  \\\n",
       "0          2017-10-04 19:55:00           2017-10-10 21:25:00   \n",
       "1          2018-07-26 14:31:00           2018-08-07 15:27:00   \n",
       "2          2018-08-08 13:50:00           2018-08-17 18:06:00   \n",
       "3          2017-11-22 13:39:00           2017-12-02 00:28:00   \n",
       "4          2018-02-14 19:46:00           2018-02-16 18:17:00   \n",
       "\n",
       "  order_estimated_delivery_date year_month                customer_unique_id  \\\n",
       "0                    2017-10-18    2017-10  7c396fd4830fd04220f754e42b4e5bff   \n",
       "1                    2018-08-13    2018-07  af07308b275d755c9edb36a90c618231   \n",
       "2                    2018-09-04    2018-08  3a653a41f6f9fc3d2a113cf8398680e8   \n",
       "3                    2017-12-15    2017-11  7c142cf63193a1473d2e66489a9ae977   \n",
       "4                    2018-02-26    2018-02  72632f0f9dd73dfee390c9b22eb56dd6   \n",
       "\n",
       "   customer_zip_code_prefix customer_city  customer_state  \n",
       "0                      3149  Akkarampalle  Andhra Pradesh  \n",
       "1                     47813     Pandariya    Chhattisgarh  \n",
       "2                     75265       Dhamdha    Chhattisgarh  \n",
       "3                     59296     Kartarpur          Punjab  \n",
       "4                      9195   Bheemavaram  Andhra Pradesh  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#combing customer table and order table\n",
    "order_cust = pd.merge(left = order, right = cust,how = \"left\", on = 'customer_id')\n",
    "order_cust.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cadf96f2",
   "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>customer_unique_id</th>\n",
       "      <th>year_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7c396fd4830fd04220f754e42b4e5bff</td>\n",
       "      <td>2017-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>af07308b275d755c9edb36a90c618231</td>\n",
       "      <td>2018-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3a653a41f6f9fc3d2a113cf8398680e8</td>\n",
       "      <td>2018-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7c142cf63193a1473d2e66489a9ae977</td>\n",
       "      <td>2017-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72632f0f9dd73dfee390c9b22eb56dd6</td>\n",
       "      <td>2018-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 customer_unique_id year_month\n",
       "0  7c396fd4830fd04220f754e42b4e5bff    2017-10\n",
       "1  af07308b275d755c9edb36a90c618231    2018-07\n",
       "2  3a653a41f6f9fc3d2a113cf8398680e8    2018-08\n",
       "3  7c142cf63193a1473d2e66489a9ae977    2017-11\n",
       "4  72632f0f9dd73dfee390c9b22eb56dd6    2018-02"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_month = order_cust[['customer_unique_id','year_month']]\n",
    "cust_month.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "632719d3",
   "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>customer_unique_id</th>\n",
       "      <th>year_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7c396fd4830fd04220f754e42b4e5bff</td>\n",
       "      <td>2017-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>af07308b275d755c9edb36a90c618231</td>\n",
       "      <td>2018-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3a653a41f6f9fc3d2a113cf8398680e8</td>\n",
       "      <td>2018-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7c142cf63193a1473d2e66489a9ae977</td>\n",
       "      <td>2017-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72632f0f9dd73dfee390c9b22eb56dd6</td>\n",
       "      <td>2018-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 customer_unique_id year_month\n",
       "0  7c396fd4830fd04220f754e42b4e5bff    2017-10\n",
       "1  af07308b275d755c9edb36a90c618231    2018-07\n",
       "2  3a653a41f6f9fc3d2a113cf8398680e8    2018-08\n",
       "3  7c142cf63193a1473d2e66489a9ae977    2017-11\n",
       "4  72632f0f9dd73dfee390c9b22eb56dd6    2018-02"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_month_new = cust_month.drop_duplicates(subset = 'customer_unique_id', keep = 'first')\n",
    "cust_month_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1eb4a542",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Summary Table of New Customer\n",
    "cust_month_new_summ = cust_month_new.groupby('year_month')[['customer_unique_id']].count().reset_index()\n",
    "cust_month_new_summ.rename(columns = {'customer_unique_id': 'count'},inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a99ea5da",
   "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>year_month</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-09</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-02</td>\n",
       "      <td>1729</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  year_month  count\n",
       "0    2016-09      4\n",
       "1    2016-10    315\n",
       "2    2016-12      1\n",
       "3    2017-01    742\n",
       "4    2017-02   1729"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_month_new_summ.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b4b53f9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cust_month_new_summ.plot.bar(x = 'year_month',y = 'count',title = \"New customer over month\")\n",
    "plt.ylabel('customer count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1db4f63",
   "metadata": {},
   "source": [
    "##### c. Understand the retention of customers on month on month basis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1da815d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99441\n",
      "96096\n"
     ]
    }
   ],
   "source": [
    "print(cust_month.shape[0])\n",
    "print(cust_month_new.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "788672db",
   "metadata": {},
   "outputs": [],
   "source": [
    "cust_month_retention = cust_month[~cust_month.isin(cust_month_new).all(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "fb786d0d",
   "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>customer_unique_id</th>\n",
       "      <th>year_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2374</th>\n",
       "      <td>7e30d2336a1f1182766cf6fb863d5554</td>\n",
       "      <td>2018-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2829</th>\n",
       "      <td>c5400d2fca348bd1313426efe4b10a51</td>\n",
       "      <td>2018-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3660</th>\n",
       "      <td>8d50f5eadf50201ccdcedfb9e2ac8455</td>\n",
       "      <td>2018-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4058</th>\n",
       "      <td>a7f45c1961dbd98e7a14f3d38ea11c06</td>\n",
       "      <td>2018-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4070</th>\n",
       "      <td>7a380cb5434e6b6b5b37d45bb99dbe8a</td>\n",
       "      <td>2018-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99307</th>\n",
       "      <td>62b8761192c86f4ead06f4a433096a2b</td>\n",
       "      <td>2018-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99344</th>\n",
       "      <td>dca9a13536adcef18c6c5859487347b1</td>\n",
       "      <td>2018-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99347</th>\n",
       "      <td>d05c44a138277ad325d915c6b7ccbcdf</td>\n",
       "      <td>2018-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99419</th>\n",
       "      <td>39fff7cb5873a7116b02234bd1e6c6dc</td>\n",
       "      <td>2017-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99427</th>\n",
       "      <td>9e4accf95024aa6565ca1efcadb96752</td>\n",
       "      <td>2018-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3345 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     customer_unique_id year_month\n",
       "2374   7e30d2336a1f1182766cf6fb863d5554    2018-05\n",
       "2829   c5400d2fca348bd1313426efe4b10a51    2018-04\n",
       "3660   8d50f5eadf50201ccdcedfb9e2ac8455    2018-05\n",
       "4058   a7f45c1961dbd98e7a14f3d38ea11c06    2018-07\n",
       "4070   7a380cb5434e6b6b5b37d45bb99dbe8a    2018-05\n",
       "...                                 ...        ...\n",
       "99307  62b8761192c86f4ead06f4a433096a2b    2018-05\n",
       "99344  dca9a13536adcef18c6c5859487347b1    2018-03\n",
       "99347  d05c44a138277ad325d915c6b7ccbcdf    2018-09\n",
       "99419  39fff7cb5873a7116b02234bd1e6c6dc    2017-10\n",
       "99427  9e4accf95024aa6565ca1efcadb96752    2018-05\n",
       "\n",
       "[3345 rows x 2 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_month_retention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "da8fd4b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "cust_month_retention_summ = cust_month_retention.groupby('year_month')[['customer_unique_id']].count().reset_index()\n",
    "cust_month_retention_summ.rename(columns = {'customer_unique_id':'count'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "345f3f55",
   "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>year_month</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-02</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-03</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-04</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  year_month  count\n",
       "0    2016-10      9\n",
       "1    2017-01     58\n",
       "2    2017-02     51\n",
       "3    2017-03     83\n",
       "4    2017-04     90"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_month_retention_summ.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6fc952fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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l41IJAPyd5SM7xhi5XK4Syw8ePKjo6GivhAIAAPCWiz6yc+2118rlcsnlcqlr164KCvp/Dy0qKtKePXvUq1cvW0ICAACU10WXnX79+kmSNm/erJ49e6pq1arudcHBwWrQoIH69+/v9YAAAAAVcdFlZ/z48ZKkBg0a6K677lJoaKhtoQAAALzF8gnKAwcOlPTLp69ycnJUXFzssb5+/freSQYAAOAFlsvOrl27NHjwYK1bt85j+bkTl4uKirwWDgAAoKIsl51BgwYpKChIn3zyieLj4y/4ySwAAABfYbnsbN68WRs3btSVV15pRx4AAACvsvw9O82bN9dPP/1kRxYAAACvs1x2Jk+erLFjx2rVqlX6+eeflZeX53EDAADwJZbfxurWrZskqWvXrh7LOUEZAAD4IstlJyMjw44cAAAAtrBcdpKSkuzIAVxWuLgmAFw6lsvOmjVrylzfuXPncocBcPmg8AG4VCyXneTk5BLLfv1dO5yzAwAAfInlT2MdPXrU45aTk6PPPvtM7dq107Jly+zICAAAUG6Wj+xER0eXWNa9e3eFhITo0Ucf1caNG70SDAAAwBssl53S1KpVSzt37vTWOAAAfAbnmPk3y2Xnm2++8bhvjFFWVpYmTZqk1q1bey0YAACAN1guO9dcc41cLpeMMR7Lb7jhBs2ePdtrwQAAALzBctnZs2ePx/2AgADVqlVLoaGhXgsFAADgLZbLTmJioh05AAAAbGH5o+eStHr1avXp00dNmjRR06ZN1bdvX/373//2djYAAIAKs1x23n77bXXr1k3h4eH605/+pJEjRyosLExdu3bVggUL7MgIAABQbpbfxpowYYJefPFFPfroo+5lf/7znzV16lQ9//zzGjBggFcDAgAAVITlIzs//PCD+vTpU2J53759S5y8DAAAUNksl5169erp888/L7H8888/V7169bwSCgAAwFssv4312GOP6U9/+pM2b96sjh07yuVyKTMzU3PnztUrr7xiR0YAAIBys1x2HnnkEcXFxWnKlCn6xz/+IUm66qqrtGjRIv3ud7/zekAAAJyKy1BcGuW6Ntbtt9+u22+/3dtZAAAAvM5y2dmwYYOKi4vVvn17j+VffPGFAgMD1bZtW6+FA6zgX0gAgAuxfILyiBEjdODAgRLLDx06pBEjRnglFAAAgLdYLjvbt2/XddddV2L5tddeq+3bt3slFAAAgLdYLjshISE6fPhwieVZWVkKCirXKUAAAAC2sVx2unfvrnHjxik3N9e97NixY3rqqafUvXt3r4YDAACoKMtlZ8qUKTpw4IASExPVpUsXdenSRQ0bNlR2dramTJliadaaNWvUp08fJSQkyOVy6YMPPvBYP2jQILlcLo/bDTfc4LFNYWGhRo0apZo1ayoiIkJ9+/bVwYMHrT4tAADgUJbLTp06dfTNN9/oxRdfVPPmzdWmTRu98sor2rJli+VvUD5x4oRat26tGTNmlLpNr169lJWV5b59+umnHutTUlK0ePFiLVy4UJmZmTp+/Lhuu+02FRUVWX1qAADAgcp1kk1ERIQeeuihCv/w3r17q3fv3mVuExISori4uAuuy83N1axZszR//nx169ZN0i9XZa9Xr55WrFihnj17VjgjAADwb5aP7Fxqq1atUu3atdWsWTM9+OCDysnJca/buHGjzpw5ox49eriXJSQkqGXLllq3bl2pMwsLC5WXl+dxAwAAzuTTZad379565513tHLlSk2ZMkUbNmzQzTffrMLCQklSdna2goODVb16dY/HxcbGKjs7u9S5EydOVHR0tPvGBUwBAHAun/6s+F133eX+c8uWLdW2bVslJiZqyZIluuOOO0p9nDFGLper1PXjxo3T6NGj3ffz8vIoPAAAOJSlslNUVKTMzExdffXVJY6mXArx8fFKTEzUrl27JElxcXE6ffq0jh496pEnJydHHTt2LHVOSEiIQkJCbM8LZ7jYy1BwCQoA8E2W3sYKDAxUz549dezYMZvilO3nn3/WgQMHFB8fL0lq06aNqlSpouXLl7u3ycrK0tatW8ssOwAA4PJh+W2sVq1a6YcfflDDhg0r/MOPHz+u3bt3u+/v2bNHmzdvVo0aNVSjRg2lpqaqf//+io+P1969e/XUU0+pZs2a7iuuR0dHa8iQIXrssccUExOjGjVqaMyYMWrVqpX701kAAODyZrnsTJgwQWPGjNHzzz+vNm3aKCIiwmN9VFTURc/68ssv1aVLF/f9c+fRDBw4UDNnztSWLVuUnp6uY8eOKT4+Xl26dNGiRYsUGRnpfsy0adMUFBSkO++8UwUFBeratavmzp2rwMBAq08NAAA4kOWy06tXL0lS3759PU4CPndSsJUv80tOTpYxptT1S5cu/c0ZoaGhmj59uqZPn37RPxcAAFw+LJedjIwMO3IAAADYwnLZSUpKsiMHAACALcr1pYL//ve/de+996pjx446dOiQJGn+/PnKzMz0ajgAAICKsnxk57333tN9992ne+65R1999ZX724zz8/OVlpZW4kKdAADg0uL7wTxZPrLzwgsv6LXXXtObb76pKlWquJd37NhRX331lVfDAQAAVJTlsrNz50517ty5xPKoqKhK+7JBAACA0lguO/Hx8R5fBHhOZmamGjVq5JVQAAAA3mK57Dz88MP685//rC+++EIul0s//vij3nnnHY0ZM0bDhw+3IyMAAEC5WT5BeezYscrNzVWXLl106tQpde7cWSEhIRozZoxGjhxpR0YAAIBys1x2pF8uGfH0009r+/btKi4uVvPmzVW1alVvZwMAAKiwcpUdSQoPD1fbtm29mQUAAMDrLJedU6dOafr06crIyFBOTo6Ki4s91vPxcwAA4Essl53Bgwdr+fLl+v3vf6/rr7/e42KgAAAAvsZy2VmyZIk+/fRTderUyY48AAAAXmX5o+d16tRRZGSkHVkAAAC8znLZmTJlip544gnt27fPjjwAAABeZfltrLZt2+rUqVNq1KiRwsPDPa6PJUlHjhzxWjgAAICKslx27r77bh06dEhpaWmKjY3lBGUAAODTLJeddevW6T//+Y9at25tRx4AAACvsnzOzpVXXqmCggI7sgAAAHid5bIzadIkPfbYY1q1apV+/vln5eXledwAAAB8ieW3sXr16iVJ6tq1q8dyY4xcLpeKioq8kwwAAMALLJedjIwMO3IAAADYwnLZSUpKsiMHAACALcp11fNjx45p1qxZ2rFjh1wul5o3b67BgwcrOjra2/kAAAAqxPIJyl9++aUaN26sadOm6ciRI/rpp580depUNW7cmCueAwAAn2P5yM6jjz6qvn376s0331RQ0C8PP3v2rIYOHaqUlBStWbPG6yEBAADKy3LZ+fLLLz2KjiQFBQVp7Nixatu2rVfDAQAAVJTlt7GioqK0f//+EssPHDjA1dABAIDPsVx27rrrLg0ZMkSLFi3SgQMHdPDgQS1cuFBDhw7V3XffbUdGAACAcrP8NtZf//pXuVwu3X///Tp79qwkqUqVKnrkkUc0adIkrwcEAACoCMtlJzg4WK+88oomTpyo77//XsYYNWnSROHh4XbkAwAAqBDLb2MNHjxY+fn5Cg8PV6tWrXT11VcrPDxcJ06c0ODBg+3ICAAAUG6Wy868efMueNXzgoICpaeneyUUAACAt1z021h5eXkyxsgYo/z8fIWGhrrXFRUV6dNPP1Xt2rVtCQkAAFBeF112qlWrJpfLJZfLpWbNmpVY73K59Oyzz3o1HAAAQEVddNnJyMiQMUY333yz3nvvPdWoUcO9Ljg4WImJiUpISLAlJAAAQHlddNk5d7XzPXv2qH79+nK5XLaFAgAA8BbLJyjv2LFDa9eudd9/9dVXdc0112jAgAE6evSoV8MBAABUlOWy8/jjjysvL0+StGXLFo0ePVq33HKLfvjhB40ePdrrAQEAACrC8pcK7tmzR82bN5ckvffee+rTp4/S0tL01Vdf6ZZbbvF6QAAAgIqwfGQnODhYJ0+elCStWLFCPXr0kCTVqFHDfcQHAADAV1g+snPjjTdq9OjR6tSpk/773/9q0aJFkqTvvvtOdevW9XpAAACAirB8ZGfGjBkKCgrSu+++q5kzZ6pOnTqSpH/961/q1auX1wMCAABUhOUjO/Xr19cnn3xSYvm0adO8EggAAMCbLJed/fv3l7m+fv365Q4DAADgbZbLToMGDcr8QsGioqIKBQIAAPAmy2Vn06ZNHvfPnDmjTZs2aerUqZowYYLXggEAAHiD5bLTunXrEsvatm2rhIQEvfTSS7rjjju8EgwAAMAbLH8aqzTNmjXThg0bvDUOAADAKywf2Tn/iwONMcrKylJqaqqaNm3qtWAAAADeYLnsVKtWrcQJysYY1atXTwsXLvRaMAAAAG+wXHZWrlzpUXYCAgJUq1YtNWnSREFBlscBAADYynI7SU5OtiEGAACAPSyfoDxx4kTNnj27xPLZs2dr8uTJXgkFAADgLZbLzuuvv64rr7yyxPIWLVrotddeszRrzZo16tOnjxISEuRyufTBBx94rDfGKDU1VQkJCQoLC1NycrK2bdvmsU1hYaFGjRqlmjVrKiIiQn379tXBgwetPi0AAOBQlstOdna24uPjSyyvVauWsrKyLM06ceKEWrdurRkzZlxw/YsvvqipU6dqxowZ2rBhg+Li4tS9e3fl5+e7t0lJSdHixYu1cOFCZWZm6vjx47rtttv4JmcAACCpHOfs1KtXT2vXrlXDhg09lq9du1YJCQmWZvXu3Vu9e/e+4DpjjF5++WU9/fTT7i8qnDdvnmJjY7VgwQI9/PDDys3N1axZszR//nx169ZNkvT222+rXr16WrFihXr27Gn16QEAAIexfGRn6NChSklJ0Zw5c7Rv3z7t27dPs2fP1qOPPqoHH3zQa8H27Nmj7Oxs9ejRw70sJCRESUlJWrdunSRp48aNOnPmjMc2CQkJatmypXubCyksLFReXp7HDQAAOJPlIztjx47VkSNHNHz4cJ0+fVqSFBoaqieeeELjxo3zWrDs7GxJUmxsrMfy2NhY7du3z71NcHCwqlevXmKbc4+/kIkTJ+rZZ5/1WlYAAOC7LB/Zcblcmjx5sv7v//5P69ev19dff60jR47omWeesSPfBb/AsKyrrl/MNuPGjVNubq77duDAAa9kBQAAvqfc3wJYtWpVtWvXzptZPMTFxUkqeUJ0Tk6O+2hPXFycTp8+raNHj3oc3cnJyVHHjh1LnR0SEqKQkBCbkgMAAF/itQuBelvDhg0VFxen5cuXu5edPn1aq1evdheZNm3aqEqVKh7bZGVlaevWrWWWHQAAcPmo1Os7HD9+XLt373bf37NnjzZv3qwaNWqofv36SklJUVpampo2baqmTZsqLS1N4eHhGjBggCQpOjpaQ4YM0WOPPaaYmBjVqFFDY8aMUatWrdyfzgIAAJe3Si07X375pbp06eK+P3r0aEnSwIEDNXfuXI0dO1YFBQUaPny4jh49qvbt22vZsmWKjIx0P2batGkKCgrSnXfeqYKCAnXt2lVz585VYGDgJX8+AADA91Rq2UlOTpYxptT1LpdLqampSk1NLXWb0NBQTZ8+XdOnT7chIQAA8Hc+e84OAACAN1B2AACAo1F2AACAo1F2AACAo1F2AACAo1F2AACAo1XqR89x+Wrw5JKL2m7vpFttTgIAcDqO7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEfjSwVRpov98j+JLwAEAPgmjuwAAABHo+wAAABHo+wAAABHo+wAAABHo+wAAABHo+wAAABHo+wAAABHo+wAAABHo+wAAABHo+wAAABHo+wAAABHo+wAAABHo+wAAABHo+wAAABHo+wAAABHo+wAAABHo+wAAABHo+wAAABHo+wAAABHo+wAAABHo+wAAABHo+wAAABHo+wAAABHo+wAAABHo+wAAABHo+wAAABHo+wAAABHo+wAAABHo+wAAABHo+wAAABHo+wAAABHo+wAAABHC6rsAJejBk8uueht90661cYkAAA4H0d2AACAo1F2AACAo1F2AACAo1F2AACAo1F2AACAo/l02UlNTZXL5fK4xcXFudcbY5SamqqEhASFhYUpOTlZ27Ztq8TEAADA1/h02ZGkFi1aKCsry33bsmWLe92LL76oqVOnasaMGdqwYYPi4uLUvXt35efnV2JiAADgS3y+7AQFBSkuLs59q1WrlqRfjuq8/PLLevrpp3XHHXeoZcuWmjdvnk6ePKkFCxZUcmoAAOArfP5LBXft2qWEhASFhISoffv2SktLU6NGjbRnzx5lZ2erR48e7m1DQkKUlJSkdevW6eGHH67E1JceX1QIAMCF+XTZad++vdLT09WsWTMdPnxYL7zwgjp27Kht27YpOztbkhQbG+vxmNjYWO3bt6/MuYWFhSosLHTfz8vL8354AADgE3y67PTu3dv951atWqlDhw5q3Lix5s2bpxtuuEGS5HK5PB5jjCmx7HwTJ07Us88+6/3AAADA5/j8OTu/FhERoVatWmnXrl3uT2WdO8JzTk5OTomjPecbN26ccnNz3bcDBw7YlhkAAFQuvyo7hYWF2rFjh+Lj49WwYUPFxcVp+fLl7vWnT5/W6tWr1bFjxzLnhISEKCoqyuMGAACcyaffxhozZoz69Omj+vXrKycnRy+88ILy8vI0cOBAuVwupaSkKC0tTU2bNlXTpk2Vlpam8PBwDRgwoLKjAwAAH+HTZefgwYO6++679dNPP6lWrVq64YYbtH79eiUmJkqSxo4dq4KCAg0fPlxHjx5V+/bttWzZMkVGRlZycgAA4Ct8uuwsXLiwzPUul0upqalKTU29NIEAAIDf8atzdgAAAKyi7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEej7AAAAEcLquwAvqzBk0suetu9k261MQkAACgvjuwAAABHo+wAAABHo+wAAABHo+wAAABH4wRlAADwm/z5Qzsc2QEAAI7mmLLz97//XQ0bNlRoaKjatGmjf//735UdCQAA+ABHlJ1FixYpJSVFTz/9tDZt2qSbbrpJvXv31v79+ys7GgAAqGSOOGdn6tSpGjJkiIYOHSpJevnll7V06VLNnDlTEydOrOR0AACgNJfiXCC/LzunT5/Wxo0b9eSTT3os79Gjh9atW3fBxxQWFqqwsNB9Pzc3V5KUl5fnsV1x4cmLznH+Y8tix1x/ymplLlkrf64/ZbUyl6yVP5eslT/Xn7JeaO65+8aYsh9o/NyhQ4eMJLN27VqP5RMmTDDNmjW74GPGjx9vJHHjxo0bN27cHHA7cOBAmV3B74/snONyuTzuG2NKLDtn3LhxGj16tPt+cXGxjhw5opiYmFIfc05eXp7q1aunAwcOKCoqquLBbZpp11x/ymrXXLKS1Z+y2jWXrGS1a66VmcYY5efnKyEhoczt/L7s1KxZU4GBgcrOzvZYnpOTo9jY2As+JiQkRCEhIR7LqlWrZunnRkVFefU/GLtm2jXXn7LaNZesZPWnrHbNJStZ7Zp7sTOjo6N/cxu//zRWcHCw2rRpo+XLl3ssX758uTp27FhJqQAAgK/w+yM7kjR69Gjdd999atu2rTp06KA33nhD+/fv17Bhwyo7GgAAqGSOKDt33XWXfv75Zz333HPKyspSy5Yt9emnnyoxMdHrPyskJETjx48v8TaYr820a64/ZbVrLlnJ6k9Z7ZpLVrLaNdeOmS5jfuvzWgAAAP7L78/ZAQAAKAtlBwAAOBplBwAAOBplBwAAOBplBwAAOBpl5yIVFRXp8OHDysnJUVFRUWXHcaRVq1apoKCgsmP8psLCQn3//fceF5P1ZYcPHy7xDePlde518NNPP3ll3q9n8trCOewL7HE57wsoO79h8eLF6tSpk8LDw5WQkKD4+HiFh4erU6dO+uCDD7z+83bs2KFGjRqV67Fff/21XnjhBf39738v8R9gXl6eBg8ebHnmW2+9pYEDB2rOnDmSpEWLFumqq65So0aNNH78+HLlLE2PHj20d+/ecj/+u+++87jybWZmpvr166cWLVqoW7du+vDDDy3PnDt3rtavXy9JOnXqlIYOHaqIiAg1a9ZMVatW1bBhw8q1o2vVqpWef/55HThwwPJjS3PkyBH1799fiYmJGjFihIqKijR06FDFx8erTp066tixo7Kysso1e8mSJercubMiIiKUkJCg2NhYVatWTffdd5/2799frpmX+rUllf/1Zcdr67ewL9hbrsfasR+Q7NkX2LEfkNgXXJAXLjzuWK+99poJDg42w4YNM4sXLzbr1q0za9euNYsXLzbDhg0zISEh5o033vDqz9y8ebMJCAiw/LilS5ea4OBg06JFC1O/fn1Ts2ZNs3LlSvf67Oxsy3OnTZtmIiIizB133GHi4+PNCy+8YGJiYswLL7xgnnvuORMdHW1ef/11y1mvvfbaC95cLpe56qqr3PetCggIMIcPHzbGGJORkWECAgJMnz59zIQJE0z//v1NQECA+eyzzyzNbNKkidmwYYMxxpgxY8aYBg0amPfff9/s2LHDfPDBB6ZZs2bm8ccft5zV5XKZmJgYExgYaHr27Gneffddc+bMGctzfu2BBx4wLVu2NNOnTzdJSUmmX79+5uqrrzaZmZlm3bp1pl27dub++++3PDc9Pd1ERkaalJQU8+STT5rY2Fjz5JNPmpkzZ5qkpCRTs2ZN891331maWRmvLWPK9/qy47V1MdgXlG9fYMd+wBh79gV27AeMYV9wIZSdMjRu3Ni89dZbpa6fNWuWadSokaWZjz76aJm3e++9t1w7uA4dOpinnnrKGGNMcXGxefHFF03VqlXNv/71L2NM+XZwV155pXnnnXeMMcZ89dVXJigoyOP3MXv2bNOmTRvLWYOCgkyvXr1Mamqq+zZ+/HgTEBBghg8f7l5mlcvlcu/kunbtaoYPH+6x/sknnzSdO3e2NDMkJMTs27fPGGNMs2bN3L/Pc1avXm3q169frqyHDh0yixcvNn369DFBQUGmVq1a5rHHHjPbt2+3PM8YY+Lj483atWuNMb/8fbtcLrNs2TL3+szMTFOnTh3Lc6+88kqzcOFC9/0NGzaYunXrmuLiYmOMMXfddZe5/fbbLc2047VljD2vLzteW3ZltSuvP+0L7NgPGGPPvsCO/YAx7AsuhLJThtDQUPPtt9+Wun7Hjh0mNDTU0syAgABz3XXXmeTk5Ave2rZtW64dXFRUlNm9e7fHsgULFpiIiAjz0UcflWsHFxYW5n5xG/PLi33r1q3u+7t27TLVqlWznDUzM9M0btzYPPPMM6aoqMi9PCgoyGzbts3yvHN+vZOLj48369ev91i/bds2ExMTY2lmYmKi+1/FderUcf/L7pzt27ebiIiICmU1xpisrCyTlpZmmjZtagICAkyHDh3MrFmzLM0MDw83e/fudd+vUqWK2bJli/v+Dz/8UK6sYWFhZs+ePR7LgoKCzKFDh4wxxnzxxReW/zuw47VljD2vLzteW3ZltSuvP+0L7NgPGGPPvsCO/YAx7AsuhLJThjZt2pjRo0eXun706NGW/zVzxRVXmPnz55e6ftOmTeXawdWqVct8+eWXJZYvXLjQhIeHm5kzZ1qeGxMT4/Gvi7p163q8gHbt2mWqVq1qOasxxuTm5po//vGP5vrrr3fvmL1Rdnbv3m1yc3NNo0aNzKZNmzzW79q1y4SHh1ua+dRTT5kOHTqYo0ePmieffNL06dPH5OfnG2OMOXHihLnzzjtNjx49LGf99aH282VkZJh7773X8s6odevWZsaMGcYYYz799FMTGRlppkyZ4l4/c+ZM07JlS8tZr7rqKvPPf/7TfX/jxo0mODjYnD171hjzy+/ValY7XlvG2PP6suO1ZVdWY9gX2LEfMMaefYEd+wFj2BdcCGWnDKtWrTIRERGmefPmJiUlxUycONFMmjTJpKSkmBYtWpiqVauaNWvWWJo5YMAAk5KSUur6zZs3G5fLZTlr9+7dzUsvvXTBdQsWLDBVqlSxvIPr1KmTxyHL83388cflesH82uzZs01cXJx5/fXXTZUqVSpcdgICAkxAQIBxuVwlDo1+8MEHpmnTppZmFhYWmr59+5rq1aub7t27m9DQUBMeHm6aNm1qIiIiTP369c3OnTvLlbW0ndw5ubm5lma+/fbbJjAw0DRp0sSEhoaad9991yQkJJg777zT/PGPfzTBwcHuHaAVM2bMMNHR0Wbs2LHmmWeeMQkJCWbIkCEeP9fqeRV2vLaMsef1Zcdryxj2Befz1r7Ajv2AMfbsC+zYDxjDvuBCHHHVc7skJSVp69atmjlzptavX+/+yF5cXJxuu+02DRs2TA0aNLA0c8qUKWWesd+6dWsVFxdbzvrII49ozZo1F1x39913S5LeeOMNSzMnT56siIiIUtfv379fDz/8sKWZ53vggQd044036p577tHZs2crNCsjI8Pjfnx8vMf9vXv36sEHH7Q0Mzg4WB9++KE+++wzffzxxwoMDFRxcbHi4+PVqVMnDRgwoMzfUWkGDhyosLCwMreJioqyNPOee+5RYmKivvjiC3Xs2FEdOnTQVVddpUmTJunkyZN64403NHDgQMtZR4wYoYCAAL399tsqLCzUoEGD9Je//MW9/vrrr9eCBQsszbTjtSXZ8/qy47UlsS84n7f2BXbsByR79gV27Ack9gUXwlXP4ROKi4uVn5+vqKgouVyuyo4DoJKwL4AdOLJTifbt26fs7Gy5XC7FxsYqMTHRZ+deqqzR0dG2zPWn38HllhX+9fflT/sCfq/+N9c2FX4j7DJW3u/BmDp1qqlbt677PeVz7zHXrVvXTJs2rdx57JjrT1ntmktW++aWpryvrcqYW5GZ/vT3RVay2jm3NN56zVJ2KqA8JxA+99xzJioqykyaNMls2rTJ/Pjjj+bQoUNm06ZNZtKkSSY6Oto8//zzlrPYMdefsto1l6z2zS1LeU/OrYy55Z3pT39fZCWrnXPL4q3XLGWnDLfffnuZt5tvvtly46xbt65ZvHhxqevff/99k5CQYDmrHXP9Katdc8lqz1w7Xlt2zbUrqz/9fZGVrHbNtev1dT7O2SnDxx9/rO7duys2NvaC68tzobKff/5ZV1xxRanrmzVrpqNHj/rEXH/Katdcstoz147Xll1z7crqT39fZCWrXXPten2VUOG65GCtWrUq82usy/OlX0lJSeaee+654PVPzpw5YwYMGGCSkpKsRrVlrj9ltWsuWe2Za8dry665dmX1p78vspLVrrl2vb7Ox0fPy/DAAw8oPDxcr7766gXX79ixQ7fccov27Nlz0TO3bNmiHj16qLCwUElJSYqNjZXL5VJ2drbWrFmjkJAQLV++XC1atLCU1Y65/pSV34F/ZbXjtWXXXLuy+tPfF1nJatdcu15f56PslKGwsFBFRUUKDw/36tz8/Hy9/fbbJb5AqUOHDhowYEC5vkTKrrn+lNWuuWT1/ly7Xlt2zLUrq+Q/f19kJatdc+18ff0aZQcAADhaQGUH8De33nqrsrKyfH6mXXP9Katdc8lKVn/KatdcspLVrrl2zKTsWLRmzRoVFBT4/Ey75vpTVrvmkpWs/pTVrrlkJatdc+2YSdkBAACORtmxKDExUVWqVPH5mXbN9aesds0lK1n9Katdc8lKVrvm2jGTE5QBAICjcWTHgmPHjunNN9/UX/7yF7311lvKzc21PGPjxo02JLNnrj9ltWsuWclqV1ZJysnJUUZGhvLy8iRJhw8f1osvvqhJkyZpy5YtPjWXrGS1a65dWT1U+GsJHax///7mvffeM8YYs23bNlOzZk1Tq1Yt0759exMbG2vi4uLM9u3bLc10uVymUaNGZsKECebgwYNey2rHXH/KatdcspLVrqwZGRkmIiLCuFwuEx8fb77++mtTt25d07RpU3PFFVeYkJAQs3TpUp+YS1ay2jXXrqzno+yUoWbNmua7774zxhjTu3dvM2DAAFNYWGiMMeb06dNmyJAhpkePHpZmulwu8+CDD5rY2FgTFBRkbr31VrN48WJz9uzZCmW1Y64/ZbVrLlnJalfWTp06mREjRpj8/Hzz0ksvmbp165oRI0a4148ZM8Z07NjRJ+aSlax2zbUr6/koO2UICwszu3fvNsYYEx8fb7766iuP9Tt37jTR0dGWZrpcLnP48GFz5swZ8+6775pbbrnFBAYGmtjYWDN27Fjz7bffliurHXP9Katdc8lKVruyRkVFufcvZ86cMUFBQWbTpk3u9d99953l/Ytdc8lKVrvm2pX1fJSdMrRv39688cYbxhhjrr322hKXtl+2bJmJi4uzNPPcjvPXDh48aJ577jnTqFEjExAQYG666SbLWe2Y609Z7ZpLVrLalbVmzZpm69atxhhjTpw4YQICAsx//vMf9/qvv/7a1KxZ0yfmkpWsds21K+v5KDtl+OSTT0yNGjXMnDlzzJw5c0yDBg3MW2+9ZdauXWtmz55t6tWrZx5//HFLMwMCAkrsOH9txYoVZsCAAZaz2jHXn7LaNZesZLUr6+9+9ztz2223mczMTPPQQw+Ztm3bmltvvdUcP37cnDhxwvz+9783vXr18om5ZCWrXXPtyno+ys5vePfdd03dunVNQECAcblc7ltoaKhJSUmx/L79hf6V6A12zPWnrHbNJStZ7cr63XffmSZNmhiXy2VatGhhDh06ZPr27WuCgoJMUFCQqVWrltm4caNPzCUrWe2aa1fW8/E9OxehqKhIX331lX744QcVFxcrPj5ebdq0UWRkpOVZq1evVqdOnRQUFOTVjHbM9aesds0lK1ntynrOzz//rJiYGPf9zz//XAUFBerQoYPHcl+YS1ay2jXXrqznUHYAAICj8aWCFXD06FGlp6f7/Ey75vpTVrvmkpWs/pTVrrlkJatdc702s8JvhF3GNm/ebAICAnx+pl1z/SmrXXPJSlZ/ymrXXLKS1a653pppz5vQDnHuq6tLk5+f7xMz7ZrrT1ntmktWsvpTVrvmkpWsds21K+v5OGenDAEBAXK5XKWuN8bI5XKpqKioUmeS1b65ZCWrP2W1ay5ZyWrXXLuyno8jO2WIjIzU008/rfbt219w/a5du/Twww9X+kyy2jeXrGT1p6x2zSUrWe2aa1fW81F2ynDddddJkpKSki64vlq1arJ6YMyOmXbN9aesds0lK1n9Katdc8lKVrvm2pX1fHwaqwwDBgxQaGhoqevj4uI0fvz4Sp9p11x/ymrXXLKS1Z+y2jWXrGS1a65dWc/HOTsAAMDROLIDAAAcjXN2fsOJEye0YMECrVu3TtnZ2XK5XIqNjVWnTp109913KyIiwidmktW+uWQlqz9ltWsuWclq11y7sv4ab2OVYfv27erevbtOnjyppKQkxcbGyhijnJwcrV69WhEREVq2bJmaN29eqTPJyu+ArGTld0BWf5xrV9YSyvllhJeF5ORk88c//tEUFhaWWFdYWGjuvvtuk5ycXOkzyWrfXLKS1Z+y2jWXrGS1a65dWc9H2SlDWFiY2bZtW6nrt2zZYsLCwip9pl1z/SmrXXPJSlZ/ymrXXLKS1a65dmU9Hycol6F69eratWtXqet3796t6tWrV/pMu+b6U1a75pKVrP6U1a65ZCWrXXPtylpCheuSg40fP95ER0ebl156yWzevNlkZWWZ7Oxss3nzZvPSSy+Z6tWrm2effbbSZ5LVvrlkJas/ZbVrLlnJatdcu7Kej7LzGyZNmmTi4+ONy+UyAQEBJiAgwLhcLhMfH28mT57sMzPJat9cspLVn7LaNZesZLVrrl1Zf41PY12kPXv2KDs7W9Iv3+jYsGFDn5xp11x/ymrXXLKS1Z+y2jWXrGS1a65dWSU+eg4AAByOE5R/Q0FBgTIzM7V9+/YS606dOqX09HSfmElW++aSlaz+lNWuuWQlq11z7crqwStvhjnUzp07TWJiovt9xKSkJPPjjz+612dnZ5uAgIBKn0lW++aSlaz+lNWuuWQlq11z7cp6Po7slOGJJ55Qq1atlJOTo507dyoqKkqdOnXS/v37fWomWe2bS1ay+lNWu+aSlax2zbUrawkVrksOVrt2bfPNN994LBs+fLipX7+++f7778vVOO2YSVb75pKVrP6U1a65ZCWrXXPtyno+LgRahoKCAgUFef6KXn31VQUEBCgpKUkLFizwiZlktW8uWcnqT1ntmktWsto1166sJVS4LjlYu3btTHp6+gXXjRgxwlSrVs1y47RjJlntm0tWsvpTVrvmkpWsds21K+v5KDtlSEtLM7179y51/SOPPGJcLlelz7Rrrj9ltWsuWcnqT1ntmktWsto1166s5+N7dgAAgKPxaSwAAOBolB0AAOBolB0AAOBolB0AAOBolB0AuATmzp2ratWqVXYM4LJE2QEAL2vQoIFefvnlyo4B4P9H2QHgd4qKilRcXFzZMQD4CcoOgApJT09XTEyMCgsLPZb3799f999/vyTp448/Vps2bRQaGqpGjRrp2Wef1dmzZ93bTp06Va1atVJERITq1aun4cOH6/jx4+71594C+uSTT9S8eXOFhIRo3759ZeYaNGiQ+vXrp7S0NMXGxqpatWrun/v444+rRo0aqlu3rmbPnu3xuC1btujmm29WWFiYYmJi9NBDD3lkOTf3r3/9q+Lj4xUTE6MRI0bozJkzkqTk5GTt27dPjz76qFwul1wul8f8pUuX6qqrrlLVqlXVq1cvZWVlWfhtAygPyg6ACvnDH/6goqIiffTRR+5lP/30kz755BM98MADWrp0qe6991796U9/0vbt2/X6669r7ty5mjBhgnv7gIAA/e1vf9PWrVs1b948rVy5UmPHjvX4OSdPntTEiRP11ltvadu2bapdu/ZvZlu5cqV+/PFHrVmzRlOnTlVqaqpuu+02Va9eXV988YWGDRumYcOG6cCBA+6f0atXL1WvXl0bNmzQP//5T61YsUIjR470mJuRkaHvv/9eGRkZmjdvnubOnau5c+dKkt5//33VrVtXzz33nLKysjzKzMmTJ/XXv/5V8+fP15o1a7R//36NGTPG8u8cgEUV/g5mAJe9Rx55xOMr319++WXTqFEjU1xcbG666SaTlpbmsf38+fNNfHx8qfP+8Y9/mJiYGPf9OXPmGElm8+bNF51p4MCBJjEx0RQVFbmXXXHFFeamm25y3z979qyJiIgw//u//2uMMeaNN94w1atXN8ePH3dvs2TJEhMQEGCys7M95p49e9a9zR/+8Adz1113ue8nJiaaadOmeeQ59xx2797tXvbqq6+a2NjYi35OAMqHq54DqLAHH3xQ7dq106FDh1SnTh3NmTNHgwYNksvl0saNG7VhwwaPIzlFRUU6deqUTp48qfDwcGVkZCgtLU3bt29XXl6ezp49q1OnTunEiROKiIiQJAUHB+vqq6+2lKtFixYKCPh/B7BjY2PVsmVL9/3AwEDFxMQoJydHkrRjxw61bt3a/TMlqVOnTiouLtbOnTsVGxvrnhsYGOjeJj4+Xlu2bPnNPOHh4WrcuLHH4879bAD2oewAqLBrr71WrVu3Vnp6unr27KktW7bo448/liQVFxfr2Wef1R133FHicaGhodq3b59uueUWDRs2TM8//7xq1KihzMxMDRkyxH0ejCSFhYWVOP/lt1SpUsXjvsvluuCycyc7G2NK/Rm/Xl7WDKt5DJcnBGxH2QHgFUOHDtW0adN06NAhdevWTfXq1ZMkXXfdddq5c6eaNGlywcd9+eWXOnv2rKZMmeI+CvOPf/zjkuX+tebNm2vevHkeR5TWrl2rgIAANWvW7KLnBAcHq6ioyK6YACziBGUAXnHPPffo0KFDevPNNzV48GD38meeeUbp6elKTU3Vtm3btGPHDi1atEj/8z//I0lq3Lixzp49q+nTp+uHH37Q/Pnz9dprr1XacwgNDdXAgQO1detWZWRkaNSoUbrvvvvcb2FdjAYNGmjNmjU6dOiQfvrpJxsTA7gYlB0AXhEVFaX+/furatWq6tevn3t5z5499cknn2j58uVq166dbrjhBk2dOlWJiYmSpGuuuUZTp07V5MmT1bJlS73zzjuaOHFipTyH8PBwLV26VEeOHFG7du30+9//Xl27dtWMGTMszXnuuee0d+9eNW7cWLVq1bIpLYCL5TK8YQzAS7p3766rrrpKf/vb3yo7CgC4UXYAVNiRI0e0bNky3XPPPdq+fbuuuOKKyo4EAG6coAygwq677jodPXpUkydPvmRFp2rVqqWu+9e//qWbbrrpkuQA4Ps4sgPAL+3evbvUdXXq1FFYWNglTAPAl1F2AACAo/FpLAAA4GiUHQAA4GiUHQAA4GiUHQAA4GiUHQAA4GiUHQAA4GiUHQAA4GiUHQAA4Gj/H3FpSEpxLA3WAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cust_month_retention_summ.plot.bar(x = 'year_month',y = 'count')\n",
    "plt.ylabel('customer count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e04547e7",
   "metadata": {},
   "source": [
    "##### d. How the revenues from existing/new customers on month on month basis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e16e3648",
   "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>product_id</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>shipping_limit_date</th>\n",
       "      <th>price</th>\n",
       "      <th>freight_value</th>\n",
       "      <th>tot_cost</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>order_purchase_timestamp</th>\n",
       "      <th>order_approved_at</th>\n",
       "      <th>order_delivered_carrier_date</th>\n",
       "      <th>order_delivered_customer_date</th>\n",
       "      <th>order_estimated_delivery_date</th>\n",
       "      <th>year_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00010242fe8c5a6d1ba2dd792cb16214</td>\n",
       "      <td>1</td>\n",
       "      <td>4244733e06e7ecb4970a6e2683c13e61</td>\n",
       "      <td>48436dade18ac8b2bce089ec2a041202</td>\n",
       "      <td>2017-09-19 09:45:00</td>\n",
       "      <td>58.90</td>\n",
       "      <td>13.29</td>\n",
       "      <td>72.19</td>\n",
       "      <td>3ce436f183e68e07877b285a838db11a</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-09-13 08:59:00</td>\n",
       "      <td>2017-09-13 09:45:00</td>\n",
       "      <td>2017-09-19 18:34:00</td>\n",
       "      <td>2017-09-20 23:43:00</td>\n",
       "      <td>2017-09-29</td>\n",
       "      <td>2017-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00018f77f2f0320c557190d7a144bdd3</td>\n",
       "      <td>1</td>\n",
       "      <td>e5f2d52b802189ee658865ca93d83a8f</td>\n",
       "      <td>dd7ddc04e1b6c2c614352b383efe2d36</td>\n",
       "      <td>2017-05-03 11:05:00</td>\n",
       "      <td>239.90</td>\n",
       "      <td>19.93</td>\n",
       "      <td>259.83</td>\n",
       "      <td>f6dd3ec061db4e3987629fe6b26e5cce</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-04-26 10:53:00</td>\n",
       "      <td>2017-04-26 11:05:00</td>\n",
       "      <td>2017-05-04 14:35:00</td>\n",
       "      <td>2017-05-12 16:04:00</td>\n",
       "      <td>2017-05-15</td>\n",
       "      <td>2017-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000229ec398224ef6ca0657da4fc703e</td>\n",
       "      <td>1</td>\n",
       "      <td>c777355d18b72b67abbeef9df44fd0fd</td>\n",
       "      <td>5b51032eddd242adc84c38acab88f23d</td>\n",
       "      <td>2018-01-18 14:48:00</td>\n",
       "      <td>199.00</td>\n",
       "      <td>17.87</td>\n",
       "      <td>216.87</td>\n",
       "      <td>6489ae5e4333f3693df5ad4372dab6d3</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-01-14 14:33:00</td>\n",
       "      <td>2018-01-14 14:48:00</td>\n",
       "      <td>2018-01-16 12:36:00</td>\n",
       "      <td>2018-01-22 13:19:00</td>\n",
       "      <td>2018-02-05</td>\n",
       "      <td>2018-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00024acbcdf0a6daa1e931b038114c75</td>\n",
       "      <td>1</td>\n",
       "      <td>7634da152a4610f1595efa32f14722fc</td>\n",
       "      <td>9d7a1d34a5052409006425275ba1c2b4</td>\n",
       "      <td>2018-08-15 10:10:00</td>\n",
       "      <td>12.99</td>\n",
       "      <td>12.79</td>\n",
       "      <td>25.78</td>\n",
       "      <td>d4eb9395c8c0431ee92fce09860c5a06</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-08-08 10:00:00</td>\n",
       "      <td>2018-08-08 10:10:00</td>\n",
       "      <td>2018-08-10 13:28:00</td>\n",
       "      <td>2018-08-14 13:32:00</td>\n",
       "      <td>2018-08-20</td>\n",
       "      <td>2018-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00042b26cf59d7ce69dfabb4e55b4fd9</td>\n",
       "      <td>1</td>\n",
       "      <td>ac6c3623068f30de03045865e4e10089</td>\n",
       "      <td>df560393f3a51e74553ab94004ba5c87</td>\n",
       "      <td>2017-02-13 13:57:00</td>\n",
       "      <td>199.90</td>\n",
       "      <td>18.14</td>\n",
       "      <td>218.04</td>\n",
       "      <td>58dbd0b2d70206bf40e62cd34e84d795</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-02-04 13:57:00</td>\n",
       "      <td>2017-02-04 14:10:00</td>\n",
       "      <td>2017-02-16 09:46:00</td>\n",
       "      <td>2017-03-01 16:42:00</td>\n",
       "      <td>2017-03-17</td>\n",
       "      <td>2017-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           order_id  Quantity  \\\n",
       "0  00010242fe8c5a6d1ba2dd792cb16214         1   \n",
       "1  00018f77f2f0320c557190d7a144bdd3         1   \n",
       "2  000229ec398224ef6ca0657da4fc703e         1   \n",
       "3  00024acbcdf0a6daa1e931b038114c75         1   \n",
       "4  00042b26cf59d7ce69dfabb4e55b4fd9         1   \n",
       "\n",
       "                         product_id                         seller_id  \\\n",
       "0  4244733e06e7ecb4970a6e2683c13e61  48436dade18ac8b2bce089ec2a041202   \n",
       "1  e5f2d52b802189ee658865ca93d83a8f  dd7ddc04e1b6c2c614352b383efe2d36   \n",
       "2  c777355d18b72b67abbeef9df44fd0fd  5b51032eddd242adc84c38acab88f23d   \n",
       "3  7634da152a4610f1595efa32f14722fc  9d7a1d34a5052409006425275ba1c2b4   \n",
       "4  ac6c3623068f30de03045865e4e10089  df560393f3a51e74553ab94004ba5c87   \n",
       "\n",
       "  shipping_limit_date   price  freight_value  tot_cost  \\\n",
       "0 2017-09-19 09:45:00   58.90          13.29     72.19   \n",
       "1 2017-05-03 11:05:00  239.90          19.93    259.83   \n",
       "2 2018-01-18 14:48:00  199.00          17.87    216.87   \n",
       "3 2018-08-15 10:10:00   12.99          12.79     25.78   \n",
       "4 2017-02-13 13:57:00  199.90          18.14    218.04   \n",
       "\n",
       "                        customer_id order_status order_purchase_timestamp  \\\n",
       "0  3ce436f183e68e07877b285a838db11a    delivered      2017-09-13 08:59:00   \n",
       "1  f6dd3ec061db4e3987629fe6b26e5cce    delivered      2017-04-26 10:53:00   \n",
       "2  6489ae5e4333f3693df5ad4372dab6d3    delivered      2018-01-14 14:33:00   \n",
       "3  d4eb9395c8c0431ee92fce09860c5a06    delivered      2018-08-08 10:00:00   \n",
       "4  58dbd0b2d70206bf40e62cd34e84d795    delivered      2017-02-04 13:57:00   \n",
       "\n",
       "    order_approved_at order_delivered_carrier_date  \\\n",
       "0 2017-09-13 09:45:00          2017-09-19 18:34:00   \n",
       "1 2017-04-26 11:05:00          2017-05-04 14:35:00   \n",
       "2 2018-01-14 14:48:00          2018-01-16 12:36:00   \n",
       "3 2018-08-08 10:10:00          2018-08-10 13:28:00   \n",
       "4 2017-02-04 14:10:00          2017-02-16 09:46:00   \n",
       "\n",
       "  order_delivered_customer_date order_estimated_delivery_date year_month  \n",
       "0           2017-09-20 23:43:00                    2017-09-29    2017-09  \n",
       "1           2017-05-12 16:04:00                    2017-05-15    2017-04  \n",
       "2           2018-01-22 13:19:00                    2018-02-05    2018-01  \n",
       "3           2018-08-14 13:32:00                    2018-08-20    2018-08  \n",
       "4           2017-03-01 16:42:00                    2017-03-17    2017-02  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_item_month = pd.merge(left = order_items, right = order, how = 'left', on = 'order_id')\n",
    "order_item_month.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1a61f5db",
   "metadata": {},
   "outputs": [],
   "source": [
    "order_item_month['net_price'] = order_item_month.price + order_item_month.freight_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "01251c47",
   "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>net_price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-09</th>\n",
       "      <td>259.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10</th>\n",
       "      <td>52857.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12</th>\n",
       "      <td>19.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01</th>\n",
       "      <td>125855.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02</th>\n",
       "      <td>270360.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            net_price\n",
       "year_month           \n",
       "2016-09        259.11\n",
       "2016-10      52857.48\n",
       "2016-12         19.62\n",
       "2017-01     125855.62\n",
       "2017-02     270360.88"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rev_month = order_item_month.groupby('year_month')[['net_price']].sum()\n",
    "rev_month.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ec0d4057",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rev_month.plot(kind = 'bar', figsize=(8,6))\n",
    "plt.ylabel('Total Amount')\n",
    "plt.title('Total Revenue on month on month basis')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c41a31a",
   "metadata": {},
   "source": [
    "##### e. Understand the trends/seasonality of sales, quantity by category, location, month, week, day, time, channel, payment method etc…¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "fe8da6d2",
   "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>product_id</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>shipping_limit_date</th>\n",
       "      <th>price</th>\n",
       "      <th>freight_value</th>\n",
       "      <th>tot_cost</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>order_purchase_timestamp</th>\n",
       "      <th>order_approved_at</th>\n",
       "      <th>order_delivered_carrier_date</th>\n",
       "      <th>order_delivered_customer_date</th>\n",
       "      <th>order_estimated_delivery_date</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00010242fe8c5a6d1ba2dd792cb16214</td>\n",
       "      <td>1</td>\n",
       "      <td>4244733e06e7ecb4970a6e2683c13e61</td>\n",
       "      <td>48436dade18ac8b2bce089ec2a041202</td>\n",
       "      <td>2017-09-19 09:45:00</td>\n",
       "      <td>58.90</td>\n",
       "      <td>13.29</td>\n",
       "      <td>72.19</td>\n",
       "      <td>3ce436f183e68e07877b285a838db11a</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-09-13 08:59:00</td>\n",
       "      <td>2017-09-13 09:45:00</td>\n",
       "      <td>2017-09-19 18:34:00</td>\n",
       "      <td>2017-09-20 23:43:00</td>\n",
       "      <td>2017-09-29</td>\n",
       "      <td>Sep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00018f77f2f0320c557190d7a144bdd3</td>\n",
       "      <td>1</td>\n",
       "      <td>e5f2d52b802189ee658865ca93d83a8f</td>\n",
       "      <td>dd7ddc04e1b6c2c614352b383efe2d36</td>\n",
       "      <td>2017-05-03 11:05:00</td>\n",
       "      <td>239.90</td>\n",
       "      <td>19.93</td>\n",
       "      <td>259.83</td>\n",
       "      <td>f6dd3ec061db4e3987629fe6b26e5cce</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-04-26 10:53:00</td>\n",
       "      <td>2017-04-26 11:05:00</td>\n",
       "      <td>2017-05-04 14:35:00</td>\n",
       "      <td>2017-05-12 16:04:00</td>\n",
       "      <td>2017-05-15</td>\n",
       "      <td>Apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000229ec398224ef6ca0657da4fc703e</td>\n",
       "      <td>1</td>\n",
       "      <td>c777355d18b72b67abbeef9df44fd0fd</td>\n",
       "      <td>5b51032eddd242adc84c38acab88f23d</td>\n",
       "      <td>2018-01-18 14:48:00</td>\n",
       "      <td>199.00</td>\n",
       "      <td>17.87</td>\n",
       "      <td>216.87</td>\n",
       "      <td>6489ae5e4333f3693df5ad4372dab6d3</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-01-14 14:33:00</td>\n",
       "      <td>2018-01-14 14:48:00</td>\n",
       "      <td>2018-01-16 12:36:00</td>\n",
       "      <td>2018-01-22 13:19:00</td>\n",
       "      <td>2018-02-05</td>\n",
       "      <td>Jan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00024acbcdf0a6daa1e931b038114c75</td>\n",
       "      <td>1</td>\n",
       "      <td>7634da152a4610f1595efa32f14722fc</td>\n",
       "      <td>9d7a1d34a5052409006425275ba1c2b4</td>\n",
       "      <td>2018-08-15 10:10:00</td>\n",
       "      <td>12.99</td>\n",
       "      <td>12.79</td>\n",
       "      <td>25.78</td>\n",
       "      <td>d4eb9395c8c0431ee92fce09860c5a06</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-08-08 10:00:00</td>\n",
       "      <td>2018-08-08 10:10:00</td>\n",
       "      <td>2018-08-10 13:28:00</td>\n",
       "      <td>2018-08-14 13:32:00</td>\n",
       "      <td>2018-08-20</td>\n",
       "      <td>Aug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00042b26cf59d7ce69dfabb4e55b4fd9</td>\n",
       "      <td>1</td>\n",
       "      <td>ac6c3623068f30de03045865e4e10089</td>\n",
       "      <td>df560393f3a51e74553ab94004ba5c87</td>\n",
       "      <td>2017-02-13 13:57:00</td>\n",
       "      <td>199.90</td>\n",
       "      <td>18.14</td>\n",
       "      <td>218.04</td>\n",
       "      <td>58dbd0b2d70206bf40e62cd34e84d795</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-02-04 13:57:00</td>\n",
       "      <td>2017-02-04 14:10:00</td>\n",
       "      <td>2017-02-16 09:46:00</td>\n",
       "      <td>2017-03-01 16:42:00</td>\n",
       "      <td>2017-03-17</td>\n",
       "      <td>Feb</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           order_id  Quantity  \\\n",
       "0  00010242fe8c5a6d1ba2dd792cb16214         1   \n",
       "1  00018f77f2f0320c557190d7a144bdd3         1   \n",
       "2  000229ec398224ef6ca0657da4fc703e         1   \n",
       "3  00024acbcdf0a6daa1e931b038114c75         1   \n",
       "4  00042b26cf59d7ce69dfabb4e55b4fd9         1   \n",
       "\n",
       "                         product_id                         seller_id  \\\n",
       "0  4244733e06e7ecb4970a6e2683c13e61  48436dade18ac8b2bce089ec2a041202   \n",
       "1  e5f2d52b802189ee658865ca93d83a8f  dd7ddc04e1b6c2c614352b383efe2d36   \n",
       "2  c777355d18b72b67abbeef9df44fd0fd  5b51032eddd242adc84c38acab88f23d   \n",
       "3  7634da152a4610f1595efa32f14722fc  9d7a1d34a5052409006425275ba1c2b4   \n",
       "4  ac6c3623068f30de03045865e4e10089  df560393f3a51e74553ab94004ba5c87   \n",
       "\n",
       "  shipping_limit_date   price  freight_value  tot_cost  \\\n",
       "0 2017-09-19 09:45:00   58.90          13.29     72.19   \n",
       "1 2017-05-03 11:05:00  239.90          19.93    259.83   \n",
       "2 2018-01-18 14:48:00  199.00          17.87    216.87   \n",
       "3 2018-08-15 10:10:00   12.99          12.79     25.78   \n",
       "4 2017-02-13 13:57:00  199.90          18.14    218.04   \n",
       "\n",
       "                        customer_id order_status order_purchase_timestamp  \\\n",
       "0  3ce436f183e68e07877b285a838db11a    delivered      2017-09-13 08:59:00   \n",
       "1  f6dd3ec061db4e3987629fe6b26e5cce    delivered      2017-04-26 10:53:00   \n",
       "2  6489ae5e4333f3693df5ad4372dab6d3    delivered      2018-01-14 14:33:00   \n",
       "3  d4eb9395c8c0431ee92fce09860c5a06    delivered      2018-08-08 10:00:00   \n",
       "4  58dbd0b2d70206bf40e62cd34e84d795    delivered      2017-02-04 13:57:00   \n",
       "\n",
       "    order_approved_at order_delivered_carrier_date  \\\n",
       "0 2017-09-13 09:45:00          2017-09-19 18:34:00   \n",
       "1 2017-04-26 11:05:00          2017-05-04 14:35:00   \n",
       "2 2018-01-14 14:48:00          2018-01-16 12:36:00   \n",
       "3 2018-08-08 10:10:00          2018-08-10 13:28:00   \n",
       "4 2017-02-04 14:10:00          2017-02-16 09:46:00   \n",
       "\n",
       "  order_delivered_customer_date order_estimated_delivery_date Month  \n",
       "0           2017-09-20 23:43:00                    2017-09-29   Sep  \n",
       "1           2017-05-12 16:04:00                    2017-05-15   Apr  \n",
       "2           2018-01-22 13:19:00                    2018-02-05   Jan  \n",
       "3           2018-08-14 13:32:00                    2018-08-20   Aug  \n",
       "4           2017-03-01 16:42:00                    2017-03-17   Feb  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ord_item_orders['Month'] = ord_item_orders.order_purchase_timestamp.apply(lambda x:pd.Timestamp.strftime(x, format = '%b'))\n",
    "ord_item_orders.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "84214387",
   "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>order_id</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>product_id</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>shipping_limit_date</th>\n",
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       "      <th>customer_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>...</th>\n",
       "      <th>order_estimated_delivery_date</th>\n",
       "      <th>Month</th>\n",
       "      <th>product_category_name</th>\n",
       "      <th>product_name_lenght</th>\n",
       "      <th>product_description_lenght</th>\n",
       "      <th>product_photos_qty</th>\n",
       "      <th>product_weight_g</th>\n",
       "      <th>product_length_cm</th>\n",
       "      <th>product_height_cm</th>\n",
       "      <th>product_width_cm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00010242fe8c5a6d1ba2dd792cb16214</td>\n",
       "      <td>1</td>\n",
       "      <td>4244733e06e7ecb4970a6e2683c13e61</td>\n",
       "      <td>48436dade18ac8b2bce089ec2a041202</td>\n",
       "      <td>2017-09-19 09:45:00</td>\n",
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       "      <td>72.19</td>\n",
       "      <td>3ce436f183e68e07877b285a838db11a</td>\n",
       "      <td>delivered</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-09-29</td>\n",
       "      <td>Sep</td>\n",
       "      <td>Cool_Stuff</td>\n",
       "      <td>58.0</td>\n",
       "      <td>598.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00018f77f2f0320c557190d7a144bdd3</td>\n",
       "      <td>1</td>\n",
       "      <td>e5f2d52b802189ee658865ca93d83a8f</td>\n",
       "      <td>dd7ddc04e1b6c2c614352b383efe2d36</td>\n",
       "      <td>2017-05-03 11:05:00</td>\n",
       "      <td>239.90</td>\n",
       "      <td>19.93</td>\n",
       "      <td>259.83</td>\n",
       "      <td>f6dd3ec061db4e3987629fe6b26e5cce</td>\n",
       "      <td>delivered</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-05-15</td>\n",
       "      <td>Apr</td>\n",
       "      <td>Pet_Shop</td>\n",
       "      <td>56.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000229ec398224ef6ca0657da4fc703e</td>\n",
       "      <td>1</td>\n",
       "      <td>c777355d18b72b67abbeef9df44fd0fd</td>\n",
       "      <td>5b51032eddd242adc84c38acab88f23d</td>\n",
       "      <td>2018-01-18 14:48:00</td>\n",
       "      <td>199.00</td>\n",
       "      <td>17.87</td>\n",
       "      <td>216.87</td>\n",
       "      <td>6489ae5e4333f3693df5ad4372dab6d3</td>\n",
       "      <td>delivered</td>\n",
       "      <td>...</td>\n",
       "      <td>2018-02-05</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Furniture_Decor</td>\n",
       "      <td>59.0</td>\n",
       "      <td>695.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00024acbcdf0a6daa1e931b038114c75</td>\n",
       "      <td>1</td>\n",
       "      <td>7634da152a4610f1595efa32f14722fc</td>\n",
       "      <td>9d7a1d34a5052409006425275ba1c2b4</td>\n",
       "      <td>2018-08-15 10:10:00</td>\n",
       "      <td>12.99</td>\n",
       "      <td>12.79</td>\n",
       "      <td>25.78</td>\n",
       "      <td>d4eb9395c8c0431ee92fce09860c5a06</td>\n",
       "      <td>delivered</td>\n",
       "      <td>...</td>\n",
       "      <td>2018-08-20</td>\n",
       "      <td>Aug</td>\n",
       "      <td>Perfumery</td>\n",
       "      <td>42.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00042b26cf59d7ce69dfabb4e55b4fd9</td>\n",
       "      <td>1</td>\n",
       "      <td>ac6c3623068f30de03045865e4e10089</td>\n",
       "      <td>df560393f3a51e74553ab94004ba5c87</td>\n",
       "      <td>2017-02-13 13:57:00</td>\n",
       "      <td>199.90</td>\n",
       "      <td>18.14</td>\n",
       "      <td>218.04</td>\n",
       "      <td>58dbd0b2d70206bf40e62cd34e84d795</td>\n",
       "      <td>delivered</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-03-17</td>\n",
       "      <td>Feb</td>\n",
       "      <td>Garden_Tools</td>\n",
       "      <td>59.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           order_id  Quantity  \\\n",
       "0  00010242fe8c5a6d1ba2dd792cb16214         1   \n",
       "1  00018f77f2f0320c557190d7a144bdd3         1   \n",
       "2  000229ec398224ef6ca0657da4fc703e         1   \n",
       "3  00024acbcdf0a6daa1e931b038114c75         1   \n",
       "4  00042b26cf59d7ce69dfabb4e55b4fd9         1   \n",
       "\n",
       "                         product_id                         seller_id  \\\n",
       "0  4244733e06e7ecb4970a6e2683c13e61  48436dade18ac8b2bce089ec2a041202   \n",
       "1  e5f2d52b802189ee658865ca93d83a8f  dd7ddc04e1b6c2c614352b383efe2d36   \n",
       "2  c777355d18b72b67abbeef9df44fd0fd  5b51032eddd242adc84c38acab88f23d   \n",
       "3  7634da152a4610f1595efa32f14722fc  9d7a1d34a5052409006425275ba1c2b4   \n",
       "4  ac6c3623068f30de03045865e4e10089  df560393f3a51e74553ab94004ba5c87   \n",
       "\n",
       "  shipping_limit_date   price  freight_value  tot_cost  \\\n",
       "0 2017-09-19 09:45:00   58.90          13.29     72.19   \n",
       "1 2017-05-03 11:05:00  239.90          19.93    259.83   \n",
       "2 2018-01-18 14:48:00  199.00          17.87    216.87   \n",
       "3 2018-08-15 10:10:00   12.99          12.79     25.78   \n",
       "4 2017-02-13 13:57:00  199.90          18.14    218.04   \n",
       "\n",
       "                        customer_id order_status  ...  \\\n",
       "0  3ce436f183e68e07877b285a838db11a    delivered  ...   \n",
       "1  f6dd3ec061db4e3987629fe6b26e5cce    delivered  ...   \n",
       "2  6489ae5e4333f3693df5ad4372dab6d3    delivered  ...   \n",
       "3  d4eb9395c8c0431ee92fce09860c5a06    delivered  ...   \n",
       "4  58dbd0b2d70206bf40e62cd34e84d795    delivered  ...   \n",
       "\n",
       "  order_estimated_delivery_date Month product_category_name  \\\n",
       "0                    2017-09-29   Sep            Cool_Stuff   \n",
       "1                    2017-05-15   Apr              Pet_Shop   \n",
       "2                    2018-02-05   Jan       Furniture_Decor   \n",
       "3                    2018-08-20   Aug             Perfumery   \n",
       "4                    2017-03-17   Feb          Garden_Tools   \n",
       "\n",
       "  product_name_lenght product_description_lenght product_photos_qty  \\\n",
       "0                58.0                      598.0                4.0   \n",
       "1                56.0                      239.0                2.0   \n",
       "2                59.0                      695.0                2.0   \n",
       "3                42.0                      480.0                1.0   \n",
       "4                59.0                      409.0                1.0   \n",
       "\n",
       "  product_weight_g  product_length_cm  product_height_cm  product_width_cm  \n",
       "0            650.0               28.0                9.0              14.0  \n",
       "1          30000.0               50.0               30.0              40.0  \n",
       "2           3050.0               33.0               13.0              33.0  \n",
       "3            200.0               16.0               10.0              15.0  \n",
       "4           3750.0               35.0               40.0              30.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ord_item_orders_prod = pd.merge(left = ord_item_orders, right = products , how = 'left', on ='product_id')\n",
    "ord_item_orders_prod.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "85e6fb9e",
   "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>Product_category</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Agro_Industry_And_Commerce</td>\n",
       "      <td>216</td>\n",
       "      <td>78384.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air_Conditioning</td>\n",
       "      <td>299</td>\n",
       "      <td>61036.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Art</td>\n",
       "      <td>213</td>\n",
       "      <td>28531.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arts_And_Craftmanship</td>\n",
       "      <td>26</td>\n",
       "      <td>2246.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audio</td>\n",
       "      <td>368</td>\n",
       "      <td>56573.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Stationery</td>\n",
       "      <td>2616</td>\n",
       "      <td>282457.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Tablets_Printing_Image</td>\n",
       "      <td>85</td>\n",
       "      <td>8895.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Telephony</td>\n",
       "      <td>4667</td>\n",
       "      <td>394394.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Toys</td>\n",
       "      <td>4269</td>\n",
       "      <td>572201.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Watches_Gifts</td>\n",
       "      <td>6226</td>\n",
       "      <td>1324087.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Product_category  Quantity       Sales\n",
       "0   Agro_Industry_And_Commerce       216    78384.51\n",
       "1             Air_Conditioning       299    61036.81\n",
       "2                          Art       213    28531.16\n",
       "3        Arts_And_Craftmanship        26     2246.03\n",
       "4                        Audio       368    56573.82\n",
       "..                         ...       ...         ...\n",
       "66                  Stationery      2616   282457.31\n",
       "67      Tablets_Printing_Image        85     8895.90\n",
       "68                   Telephony      4667   394394.69\n",
       "69                        Toys      4269   572201.56\n",
       "70               Watches_Gifts      6226  1324087.12\n",
       "\n",
       "[71 rows x 3 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#product category wise sales\n",
    "ord_item_orders_prod.groupby('product_category_name')[['Quantity','tot_cost']].sum().reset_index().rename(columns={'product_category_name':'Product_category','tot_cost':'Sales'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "bf2dc35e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>Month</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apr</td>\n",
       "      <td>11168</td>\n",
       "      <td>1595225.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aug</td>\n",
       "      <td>12802</td>\n",
       "      <td>1707337.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dec</td>\n",
       "      <td>6578</td>\n",
       "      <td>873415.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Feb</td>\n",
       "      <td>9938</td>\n",
       "      <td>1280715.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jan</td>\n",
       "      <td>9603</td>\n",
       "      <td>1265705.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jul</td>\n",
       "      <td>12165</td>\n",
       "      <td>1666881.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jun</td>\n",
       "      <td>11126</td>\n",
       "      <td>1546972.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mar</td>\n",
       "      <td>11640</td>\n",
       "      <td>1598458.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>May</td>\n",
       "      <td>12597</td>\n",
       "      <td>1761992.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nov</td>\n",
       "      <td>9152</td>\n",
       "      <td>1203947.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Oct</td>\n",
       "      <td>6043</td>\n",
       "      <td>840206.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Sep</td>\n",
       "      <td>5032</td>\n",
       "      <td>728545.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Month  Quantity       Sales\n",
       "0    Apr     11168  1595225.10\n",
       "1    Aug     12802  1707337.97\n",
       "2    Dec      6578   873415.01\n",
       "3    Feb      9938  1280715.00\n",
       "4    Jan      9603  1265705.89\n",
       "5    Jul     12165  1666881.02\n",
       "6    Jun     11126  1546972.08\n",
       "7    Mar     11640  1598458.95\n",
       "8    May     12597  1761992.52\n",
       "9    Nov      9152  1203947.83\n",
       "10   Oct      6043   840206.51\n",
       "11   Sep      5032   728545.99"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#monthly sales trend\n",
    "ord_item_orders_prod.groupby('Month')[['Quantity','tot_cost']].sum().reset_index().rename(columns={'tot_cost':'Sales'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "063a46f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>product_id</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>shipping_limit_date</th>\n",
       "      <th>price</th>\n",
       "      <th>freight_value</th>\n",
       "      <th>tot_cost</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>...</th>\n",
       "      <th>Month</th>\n",
       "      <th>product_category_name</th>\n",
       "      <th>product_name_lenght</th>\n",
       "      <th>product_description_lenght</th>\n",
       "      <th>product_photos_qty</th>\n",
       "      <th>product_weight_g</th>\n",
       "      <th>product_length_cm</th>\n",
       "      <th>product_height_cm</th>\n",
       "      <th>product_width_cm</th>\n",
       "      <th>Week_Num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00010242fe8c5a6d1ba2dd792cb16214</td>\n",
       "      <td>1</td>\n",
       "      <td>4244733e06e7ecb4970a6e2683c13e61</td>\n",
       "      <td>48436dade18ac8b2bce089ec2a041202</td>\n",
       "      <td>2017-09-19 09:45:00</td>\n",
       "      <td>58.90</td>\n",
       "      <td>13.29</td>\n",
       "      <td>72.19</td>\n",
       "      <td>3ce436f183e68e07877b285a838db11a</td>\n",
       "      <td>delivered</td>\n",
       "      <td>...</td>\n",
       "      <td>Sep</td>\n",
       "      <td>Cool_Stuff</td>\n",
       "      <td>58.0</td>\n",
       "      <td>598.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00018f77f2f0320c557190d7a144bdd3</td>\n",
       "      <td>1</td>\n",
       "      <td>e5f2d52b802189ee658865ca93d83a8f</td>\n",
       "      <td>dd7ddc04e1b6c2c614352b383efe2d36</td>\n",
       "      <td>2017-05-03 11:05:00</td>\n",
       "      <td>239.90</td>\n",
       "      <td>19.93</td>\n",
       "      <td>259.83</td>\n",
       "      <td>f6dd3ec061db4e3987629fe6b26e5cce</td>\n",
       "      <td>delivered</td>\n",
       "      <td>...</td>\n",
       "      <td>Apr</td>\n",
       "      <td>Pet_Shop</td>\n",
       "      <td>56.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000229ec398224ef6ca0657da4fc703e</td>\n",
       "      <td>1</td>\n",
       "      <td>c777355d18b72b67abbeef9df44fd0fd</td>\n",
       "      <td>5b51032eddd242adc84c38acab88f23d</td>\n",
       "      <td>2018-01-18 14:48:00</td>\n",
       "      <td>199.00</td>\n",
       "      <td>17.87</td>\n",
       "      <td>216.87</td>\n",
       "      <td>6489ae5e4333f3693df5ad4372dab6d3</td>\n",
       "      <td>delivered</td>\n",
       "      <td>...</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Furniture_Decor</td>\n",
       "      <td>59.0</td>\n",
       "      <td>695.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00024acbcdf0a6daa1e931b038114c75</td>\n",
       "      <td>1</td>\n",
       "      <td>7634da152a4610f1595efa32f14722fc</td>\n",
       "      <td>9d7a1d34a5052409006425275ba1c2b4</td>\n",
       "      <td>2018-08-15 10:10:00</td>\n",
       "      <td>12.99</td>\n",
       "      <td>12.79</td>\n",
       "      <td>25.78</td>\n",
       "      <td>d4eb9395c8c0431ee92fce09860c5a06</td>\n",
       "      <td>delivered</td>\n",
       "      <td>...</td>\n",
       "      <td>Aug</td>\n",
       "      <td>Perfumery</td>\n",
       "      <td>42.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00042b26cf59d7ce69dfabb4e55b4fd9</td>\n",
       "      <td>1</td>\n",
       "      <td>ac6c3623068f30de03045865e4e10089</td>\n",
       "      <td>df560393f3a51e74553ab94004ba5c87</td>\n",
       "      <td>2017-02-13 13:57:00</td>\n",
       "      <td>199.90</td>\n",
       "      <td>18.14</td>\n",
       "      <td>218.04</td>\n",
       "      <td>58dbd0b2d70206bf40e62cd34e84d795</td>\n",
       "      <td>delivered</td>\n",
       "      <td>...</td>\n",
       "      <td>Feb</td>\n",
       "      <td>Garden_Tools</td>\n",
       "      <td>59.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           order_id  Quantity  \\\n",
       "0  00010242fe8c5a6d1ba2dd792cb16214         1   \n",
       "1  00018f77f2f0320c557190d7a144bdd3         1   \n",
       "2  000229ec398224ef6ca0657da4fc703e         1   \n",
       "3  00024acbcdf0a6daa1e931b038114c75         1   \n",
       "4  00042b26cf59d7ce69dfabb4e55b4fd9         1   \n",
       "\n",
       "                         product_id                         seller_id  \\\n",
       "0  4244733e06e7ecb4970a6e2683c13e61  48436dade18ac8b2bce089ec2a041202   \n",
       "1  e5f2d52b802189ee658865ca93d83a8f  dd7ddc04e1b6c2c614352b383efe2d36   \n",
       "2  c777355d18b72b67abbeef9df44fd0fd  5b51032eddd242adc84c38acab88f23d   \n",
       "3  7634da152a4610f1595efa32f14722fc  9d7a1d34a5052409006425275ba1c2b4   \n",
       "4  ac6c3623068f30de03045865e4e10089  df560393f3a51e74553ab94004ba5c87   \n",
       "\n",
       "  shipping_limit_date   price  freight_value  tot_cost  \\\n",
       "0 2017-09-19 09:45:00   58.90          13.29     72.19   \n",
       "1 2017-05-03 11:05:00  239.90          19.93    259.83   \n",
       "2 2018-01-18 14:48:00  199.00          17.87    216.87   \n",
       "3 2018-08-15 10:10:00   12.99          12.79     25.78   \n",
       "4 2017-02-13 13:57:00  199.90          18.14    218.04   \n",
       "\n",
       "                        customer_id order_status  ... Month  \\\n",
       "0  3ce436f183e68e07877b285a838db11a    delivered  ...   Sep   \n",
       "1  f6dd3ec061db4e3987629fe6b26e5cce    delivered  ...   Apr   \n",
       "2  6489ae5e4333f3693df5ad4372dab6d3    delivered  ...   Jan   \n",
       "3  d4eb9395c8c0431ee92fce09860c5a06    delivered  ...   Aug   \n",
       "4  58dbd0b2d70206bf40e62cd34e84d795    delivered  ...   Feb   \n",
       "\n",
       "  product_category_name product_name_lenght product_description_lenght  \\\n",
       "0            Cool_Stuff                58.0                      598.0   \n",
       "1              Pet_Shop                56.0                      239.0   \n",
       "2       Furniture_Decor                59.0                      695.0   \n",
       "3             Perfumery                42.0                      480.0   \n",
       "4          Garden_Tools                59.0                      409.0   \n",
       "\n",
       "  product_photos_qty product_weight_g product_length_cm  product_height_cm  \\\n",
       "0                4.0            650.0              28.0                9.0   \n",
       "1                2.0          30000.0              50.0               30.0   \n",
       "2                2.0           3050.0              33.0               13.0   \n",
       "3                1.0            200.0              16.0               10.0   \n",
       "4                1.0           3750.0              35.0               40.0   \n",
       "\n",
       "   product_width_cm  Week_Num  \n",
       "0              14.0        37  \n",
       "1              40.0        17  \n",
       "2              33.0         2  \n",
       "3              15.0        32  \n",
       "4              30.0         5  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Adding Week Number in Table\n",
    "ord_item_orders_prod['Week_Num'] = ord_item_orders_prod['order_purchase_timestamp'].dt.isocalendar().week\n",
    "ord_item_orders_prod.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "eac798fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Week_Num</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1689</td>\n",
       "      <td>222465.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2172</td>\n",
       "      <td>295283.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2283</td>\n",
       "      <td>291854.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2352</td>\n",
       "      <td>319659.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2472</td>\n",
       "      <td>319828.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2422</td>\n",
       "      <td>330436.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2415</td>\n",
       "      <td>292971.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>2518</td>\n",
       "      <td>305029.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>2793</td>\n",
       "      <td>386203.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>2611</td>\n",
       "      <td>361025.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>2595</td>\n",
       "      <td>369251.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>2847</td>\n",
       "      <td>373469.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>2407</td>\n",
       "      <td>341987.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>2547</td>\n",
       "      <td>375069.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>2381</td>\n",
       "      <td>343404.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>2712</td>\n",
       "      <td>380528.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>2833</td>\n",
       "      <td>389554.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>3004</td>\n",
       "      <td>406560.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>3344</td>\n",
       "      <td>477222.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>3332</td>\n",
       "      <td>449154.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>2095</td>\n",
       "      <td>294959.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>2219</td>\n",
       "      <td>349830.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>2760</td>\n",
       "      <td>375102.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>2707</td>\n",
       "      <td>385306.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>2471</td>\n",
       "      <td>334496.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>2508</td>\n",
       "      <td>336570.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>2360</td>\n",
       "      <td>317345.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>2300</td>\n",
       "      <td>332464.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>3101</td>\n",
       "      <td>412769.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>3099</td>\n",
       "      <td>426046.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>3557</td>\n",
       "      <td>475326.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>3504</td>\n",
       "      <td>492260.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>3376</td>\n",
       "      <td>446924.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>2397</td>\n",
       "      <td>278394.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>1320</td>\n",
       "      <td>220551.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>1056</td>\n",
       "      <td>156458.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>1352</td>\n",
       "      <td>188214.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>1181</td>\n",
       "      <td>152250.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>1129</td>\n",
       "      <td>167846.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>1551</td>\n",
       "      <td>223440.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>1286</td>\n",
       "      <td>186037.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>1464</td>\n",
       "      <td>194735.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>1215</td>\n",
       "      <td>162950.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>1152</td>\n",
       "      <td>168055.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>1425</td>\n",
       "      <td>190619.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>1595</td>\n",
       "      <td>208515.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>3710</td>\n",
       "      <td>487980.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>2493</td>\n",
       "      <td>317982.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>1969</td>\n",
       "      <td>263162.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>1631</td>\n",
       "      <td>219345.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>51</td>\n",
       "      <td>1130</td>\n",
       "      <td>151156.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>52</td>\n",
       "      <td>1002</td>\n",
       "      <td>121341.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Week_Num  Quantity      Sales\n",
       "0          1      1689  222465.61\n",
       "1          2      2172  295283.44\n",
       "2          3      2283  291854.78\n",
       "3          4      2352  319659.00\n",
       "4          5      2472  319828.80\n",
       "5          6      2422  330436.00\n",
       "6          7      2415  292971.69\n",
       "7          8      2518  305029.81\n",
       "8          9      2793  386203.44\n",
       "9         10      2611  361025.20\n",
       "10        11      2595  369251.90\n",
       "11        12      2847  373469.46\n",
       "12        13      2407  341987.30\n",
       "13        14      2547  375069.60\n",
       "14        15      2381  343404.19\n",
       "15        16      2712  380528.56\n",
       "16        17      2833  389554.25\n",
       "17        18      3004  406560.04\n",
       "18        19      3344  477222.94\n",
       "19        20      3332  449154.17\n",
       "20        21      2095  294959.83\n",
       "21        22      2219  349830.05\n",
       "22        23      2760  375102.87\n",
       "23        24      2707  385306.58\n",
       "24        25      2471  334496.46\n",
       "25        26      2508  336570.47\n",
       "26        27      2360  317345.39\n",
       "27        28      2300  332464.40\n",
       "28        29      3101  412769.93\n",
       "29        30      3099  426046.17\n",
       "30        31      3557  475326.71\n",
       "31        32      3504  492260.52\n",
       "32        33      3376  446924.20\n",
       "33        34      2397  278394.90\n",
       "34        35      1320  220551.38\n",
       "35        36      1056  156458.74\n",
       "36        37      1352  188214.75\n",
       "37        38      1181  152250.28\n",
       "38        39      1129  167846.86\n",
       "39        40      1551  223440.34\n",
       "40        41      1286  186037.92\n",
       "41        42      1464  194735.51\n",
       "42        43      1215  162950.38\n",
       "43        44      1152  168055.96\n",
       "44        45      1425  190619.38\n",
       "45        46      1595  208515.58\n",
       "46        47      3710  487980.20\n",
       "47        48      2493  317982.11\n",
       "48        49      1969  263162.01\n",
       "49        50      1631  219345.85\n",
       "50        51      1130  151156.30\n",
       "51        52      1002  121341.66"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#weekly sales trend\n",
    "ord_item_orders_prod.groupby('Week_Num')[['Quantity','tot_cost']].sum().reset_index().rename(columns = {'tot_cost':'Sales'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "9b3a69dc",
   "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>product_id</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>shipping_limit_date</th>\n",
       "      <th>price</th>\n",
       "      <th>freight_value</th>\n",
       "      <th>tot_cost</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>order_purchase_timestamp</th>\n",
       "      <th>order_approved_at</th>\n",
       "      <th>order_delivered_carrier_date</th>\n",
       "      <th>order_delivered_customer_date</th>\n",
       "      <th>order_estimated_delivery_date</th>\n",
       "      <th>Month</th>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th>customer_zip_code_prefix</th>\n",
       "      <th>customer_city</th>\n",
       "      <th>customer_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00010242fe8c5a6d1ba2dd792cb16214</td>\n",
       "      <td>1</td>\n",
       "      <td>4244733e06e7ecb4970a6e2683c13e61</td>\n",
       "      <td>48436dade18ac8b2bce089ec2a041202</td>\n",
       "      <td>2017-09-19 09:45:00</td>\n",
       "      <td>58.90</td>\n",
       "      <td>13.29</td>\n",
       "      <td>72.19</td>\n",
       "      <td>3ce436f183e68e07877b285a838db11a</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-09-13 08:59:00</td>\n",
       "      <td>2017-09-13 09:45:00</td>\n",
       "      <td>2017-09-19 18:34:00</td>\n",
       "      <td>2017-09-20 23:43:00</td>\n",
       "      <td>2017-09-29</td>\n",
       "      <td>Sep</td>\n",
       "      <td>871766c5855e863f6eccc05f988b23cb</td>\n",
       "      <td>28013</td>\n",
       "      <td>Guntakal</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00018f77f2f0320c557190d7a144bdd3</td>\n",
       "      <td>1</td>\n",
       "      <td>e5f2d52b802189ee658865ca93d83a8f</td>\n",
       "      <td>dd7ddc04e1b6c2c614352b383efe2d36</td>\n",
       "      <td>2017-05-03 11:05:00</td>\n",
       "      <td>239.90</td>\n",
       "      <td>19.93</td>\n",
       "      <td>259.83</td>\n",
       "      <td>f6dd3ec061db4e3987629fe6b26e5cce</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-04-26 10:53:00</td>\n",
       "      <td>2017-04-26 11:05:00</td>\n",
       "      <td>2017-05-04 14:35:00</td>\n",
       "      <td>2017-05-12 16:04:00</td>\n",
       "      <td>2017-05-15</td>\n",
       "      <td>Apr</td>\n",
       "      <td>eb28e67c4c0b83846050ddfb8a35d051</td>\n",
       "      <td>15775</td>\n",
       "      <td>Kirandul</td>\n",
       "      <td>Chhattisgarh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000229ec398224ef6ca0657da4fc703e</td>\n",
       "      <td>1</td>\n",
       "      <td>c777355d18b72b67abbeef9df44fd0fd</td>\n",
       "      <td>5b51032eddd242adc84c38acab88f23d</td>\n",
       "      <td>2018-01-18 14:48:00</td>\n",
       "      <td>199.00</td>\n",
       "      <td>17.87</td>\n",
       "      <td>216.87</td>\n",
       "      <td>6489ae5e4333f3693df5ad4372dab6d3</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-01-14 14:33:00</td>\n",
       "      <td>2018-01-14 14:48:00</td>\n",
       "      <td>2018-01-16 12:36:00</td>\n",
       "      <td>2018-01-22 13:19:00</td>\n",
       "      <td>2018-02-05</td>\n",
       "      <td>Jan</td>\n",
       "      <td>3818d81c6709e39d06b2738a8d3a2474</td>\n",
       "      <td>35661</td>\n",
       "      <td>Kanker</td>\n",
       "      <td>Chhattisgarh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00024acbcdf0a6daa1e931b038114c75</td>\n",
       "      <td>1</td>\n",
       "      <td>7634da152a4610f1595efa32f14722fc</td>\n",
       "      <td>9d7a1d34a5052409006425275ba1c2b4</td>\n",
       "      <td>2018-08-15 10:10:00</td>\n",
       "      <td>12.99</td>\n",
       "      <td>12.79</td>\n",
       "      <td>25.78</td>\n",
       "      <td>d4eb9395c8c0431ee92fce09860c5a06</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-08-08 10:00:00</td>\n",
       "      <td>2018-08-08 10:10:00</td>\n",
       "      <td>2018-08-10 13:28:00</td>\n",
       "      <td>2018-08-14 13:32:00</td>\n",
       "      <td>2018-08-20</td>\n",
       "      <td>Aug</td>\n",
       "      <td>af861d436cfc08b2c2ddefd0ba074622</td>\n",
       "      <td>12952</td>\n",
       "      <td>Patancheru</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00042b26cf59d7ce69dfabb4e55b4fd9</td>\n",
       "      <td>1</td>\n",
       "      <td>ac6c3623068f30de03045865e4e10089</td>\n",
       "      <td>df560393f3a51e74553ab94004ba5c87</td>\n",
       "      <td>2017-02-13 13:57:00</td>\n",
       "      <td>199.90</td>\n",
       "      <td>18.14</td>\n",
       "      <td>218.04</td>\n",
       "      <td>58dbd0b2d70206bf40e62cd34e84d795</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-02-04 13:57:00</td>\n",
       "      <td>2017-02-04 14:10:00</td>\n",
       "      <td>2017-02-16 09:46:00</td>\n",
       "      <td>2017-03-01 16:42:00</td>\n",
       "      <td>2017-03-17</td>\n",
       "      <td>Feb</td>\n",
       "      <td>64b576fb70d441e8f1b2d7d446e483c5</td>\n",
       "      <td>13226</td>\n",
       "      <td>Changanassery</td>\n",
       "      <td>Kerala</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           order_id  Quantity  \\\n",
       "0  00010242fe8c5a6d1ba2dd792cb16214         1   \n",
       "1  00018f77f2f0320c557190d7a144bdd3         1   \n",
       "2  000229ec398224ef6ca0657da4fc703e         1   \n",
       "3  00024acbcdf0a6daa1e931b038114c75         1   \n",
       "4  00042b26cf59d7ce69dfabb4e55b4fd9         1   \n",
       "\n",
       "                         product_id                         seller_id  \\\n",
       "0  4244733e06e7ecb4970a6e2683c13e61  48436dade18ac8b2bce089ec2a041202   \n",
       "1  e5f2d52b802189ee658865ca93d83a8f  dd7ddc04e1b6c2c614352b383efe2d36   \n",
       "2  c777355d18b72b67abbeef9df44fd0fd  5b51032eddd242adc84c38acab88f23d   \n",
       "3  7634da152a4610f1595efa32f14722fc  9d7a1d34a5052409006425275ba1c2b4   \n",
       "4  ac6c3623068f30de03045865e4e10089  df560393f3a51e74553ab94004ba5c87   \n",
       "\n",
       "  shipping_limit_date   price  freight_value  tot_cost  \\\n",
       "0 2017-09-19 09:45:00   58.90          13.29     72.19   \n",
       "1 2017-05-03 11:05:00  239.90          19.93    259.83   \n",
       "2 2018-01-18 14:48:00  199.00          17.87    216.87   \n",
       "3 2018-08-15 10:10:00   12.99          12.79     25.78   \n",
       "4 2017-02-13 13:57:00  199.90          18.14    218.04   \n",
       "\n",
       "                        customer_id order_status order_purchase_timestamp  \\\n",
       "0  3ce436f183e68e07877b285a838db11a    delivered      2017-09-13 08:59:00   \n",
       "1  f6dd3ec061db4e3987629fe6b26e5cce    delivered      2017-04-26 10:53:00   \n",
       "2  6489ae5e4333f3693df5ad4372dab6d3    delivered      2018-01-14 14:33:00   \n",
       "3  d4eb9395c8c0431ee92fce09860c5a06    delivered      2018-08-08 10:00:00   \n",
       "4  58dbd0b2d70206bf40e62cd34e84d795    delivered      2017-02-04 13:57:00   \n",
       "\n",
       "    order_approved_at order_delivered_carrier_date  \\\n",
       "0 2017-09-13 09:45:00          2017-09-19 18:34:00   \n",
       "1 2017-04-26 11:05:00          2017-05-04 14:35:00   \n",
       "2 2018-01-14 14:48:00          2018-01-16 12:36:00   \n",
       "3 2018-08-08 10:10:00          2018-08-10 13:28:00   \n",
       "4 2017-02-04 14:10:00          2017-02-16 09:46:00   \n",
       "\n",
       "  order_delivered_customer_date order_estimated_delivery_date Month  \\\n",
       "0           2017-09-20 23:43:00                    2017-09-29   Sep   \n",
       "1           2017-05-12 16:04:00                    2017-05-15   Apr   \n",
       "2           2018-01-22 13:19:00                    2018-02-05   Jan   \n",
       "3           2018-08-14 13:32:00                    2018-08-20   Aug   \n",
       "4           2017-03-01 16:42:00                    2017-03-17   Feb   \n",
       "\n",
       "                 customer_unique_id  customer_zip_code_prefix  customer_city  \\\n",
       "0  871766c5855e863f6eccc05f988b23cb                     28013       Guntakal   \n",
       "1  eb28e67c4c0b83846050ddfb8a35d051                     15775       Kirandul   \n",
       "2  3818d81c6709e39d06b2738a8d3a2474                     35661         Kanker   \n",
       "3  af861d436cfc08b2c2ddefd0ba074622                     12952     Patancheru   \n",
       "4  64b576fb70d441e8f1b2d7d446e483c5                     13226  Changanassery   \n",
       "\n",
       "   customer_state  \n",
       "0  Andhra Pradesh  \n",
       "1    Chhattisgarh  \n",
       "2    Chhattisgarh  \n",
       "3  Andhra Pradesh  \n",
       "4          Kerala  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ord_item_orders_cust = pd.merge(left= ord_item_orders, right= cust,how ='left',on ='customer_id')\n",
    "ord_item_orders_cust.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "606b20d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_zip_code_prefix</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1003</td>\n",
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       "      <td>98.97</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1004</td>\n",
       "      <td>2</td>\n",
       "      <td>198.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1005</td>\n",
       "      <td>6</td>\n",
       "      <td>570.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1006</td>\n",
       "      <td>2</td>\n",
       "      <td>868.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1007</td>\n",
       "      <td>4</td>\n",
       "      <td>505.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>14971</th>\n",
       "      <td>99960</td>\n",
       "      <td>1</td>\n",
       "      <td>141.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14972</th>\n",
       "      <td>99965</td>\n",
       "      <td>2</td>\n",
       "      <td>227.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14973</th>\n",
       "      <td>99970</td>\n",
       "      <td>1</td>\n",
       "      <td>252.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14974</th>\n",
       "      <td>99980</td>\n",
       "      <td>4</td>\n",
       "      <td>271.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14975</th>\n",
       "      <td>99990</td>\n",
       "      <td>1</td>\n",
       "      <td>270.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14976 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       customer_zip_code_prefix  Quantity   Sales\n",
       "0                          1003         1   98.97\n",
       "1                          1004         2  198.11\n",
       "2                          1005         6  570.72\n",
       "3                          1006         2  868.05\n",
       "4                          1007         4  505.55\n",
       "...                         ...       ...     ...\n",
       "14971                     99960         1  141.63\n",
       "14972                     99965         2  227.59\n",
       "14973                     99970         1  252.74\n",
       "14974                     99980         4  271.76\n",
       "14975                     99990         1  270.45\n",
       "\n",
       "[14976 rows x 3 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#location wise sales\n",
    "ord_item_orders_cust.groupby('customer_zip_code_prefix')[['Quantity','tot_cost']].sum().reset_index().rename(columns={'tot_cost':'Sales'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f13b04f",
   "metadata": {},
   "source": [
    "##### f. Popular Products by month, seller, state, category.¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "908e8f89",
   "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>product_category_name</th>\n",
       "      <th>product_id</th>\n",
       "      <th>Quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14154</th>\n",
       "      <td>Furniture_Decor</td>\n",
       "      <td>aca2eb7d00ea1a7b8ebd4e68314663af</td>\n",
       "      <td>529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5002</th>\n",
       "      <td>Bed_Bath_Table</td>\n",
       "      <td>99a4788cb24856965c36a24e339b6058</td>\n",
       "      <td>519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15387</th>\n",
       "      <td>Garden_Tools</td>\n",
       "      <td>422879e10f46682990de24d770e7f83d</td>\n",
       "      <td>511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7999</th>\n",
       "      <td>Computers_Accessories</td>\n",
       "      <td>d1c427060a0f73f6b889a5c7c61f2ac4</td>\n",
       "      <td>348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31448</th>\n",
       "      <td>Watches_Gifts</td>\n",
       "      <td>53b36df67ebb7c41585e8d54d6772e08</td>\n",
       "      <td>328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>Arts_And_Craftmanship</td>\n",
       "      <td>b9976e9c22fb1540bd71d1bcd2989475</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22144</th>\n",
       "      <td>Music</td>\n",
       "      <td>4befae07dba4384f6d8237ad1e7f8294</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21673</th>\n",
       "      <td>La_Cuisine</td>\n",
       "      <td>33900d427fa4bd3f509cdacda72e84a3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11650</th>\n",
       "      <td>Fashion_Childrens_Clothes</td>\n",
       "      <td>57bdf3098169cccdb62221bd3e089cbd</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24372</th>\n",
       "      <td>Security_And_Services</td>\n",
       "      <td>6c7a0a349ad11817745e3ad58abd5c79</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           product_category_name                        product_id  Quantity\n",
       "14154            Furniture_Decor  aca2eb7d00ea1a7b8ebd4e68314663af       529\n",
       "5002              Bed_Bath_Table  99a4788cb24856965c36a24e339b6058       519\n",
       "15387               Garden_Tools  422879e10f46682990de24d770e7f83d       511\n",
       "7999       Computers_Accessories  d1c427060a0f73f6b889a5c7c61f2ac4       348\n",
       "31448              Watches_Gifts  53b36df67ebb7c41585e8d54d6772e08       328\n",
       "...                          ...                               ...       ...\n",
       "267        Arts_And_Craftmanship  b9976e9c22fb1540bd71d1bcd2989475         5\n",
       "22144                      Music  4befae07dba4384f6d8237ad1e7f8294         4\n",
       "21673                 La_Cuisine  33900d427fa4bd3f509cdacda72e84a3         3\n",
       "11650  Fashion_Childrens_Clothes  57bdf3098169cccdb62221bd3e089cbd         3\n",
       "24372      Security_And_Services  6c7a0a349ad11817745e3ad58abd5c79         1\n",
       "\n",
       "[71 rows x 3 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Most Popular Products by Category\n",
    "ord_item_orders_prod.groupby(['product_category_name','product_id'])['Quantity'].sum().reset_index().sort_values(by = 'Quantity', ascending = False).drop_duplicates(subset = 'product_category_name', keep = 'first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "48348438",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_state</th>\n",
       "      <th>product_id</th>\n",
       "      <th>Quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16353</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>aca2eb7d00ea1a7b8ebd4e68314663af</td>\n",
       "      <td>347</td>\n",
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       "      <th>34289</th>\n",
       "      <td>Gujarat</td>\n",
       "      <td>53759a2ecddad2bb87a079a1f1519f73</td>\n",
       "      <td>52</td>\n",
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       "    <tr>\n",
       "      <th>26503</th>\n",
       "      <td>Chhattisgarh</td>\n",
       "      <td>422879e10f46682990de24d770e7f83d</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43557</th>\n",
       "      <td>Karnataka</td>\n",
       "      <td>99a4788cb24856965c36a24e339b6058</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30616</th>\n",
       "      <td>Delhi</td>\n",
       "      <td>422879e10f46682990de24d770e7f83d</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47486</th>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>99a4788cb24856965c36a24e339b6058</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49461</th>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>99a4788cb24856965c36a24e339b6058</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41114</th>\n",
       "      <td>Jammu &amp; Kashmir</td>\n",
       "      <td>89b190a046022486c635022524a974a8</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52665</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>53759a2ecddad2bb87a079a1f1519f73</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38162</th>\n",
       "      <td>Haryana</td>\n",
       "      <td>2b4609f8948be18874494203496bc318</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45661</th>\n",
       "      <td>Kerala</td>\n",
       "      <td>aca2eb7d00ea1a7b8ebd4e68314663af</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55127</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>bf44071ef18f5c9ded039681c68b1996</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24307</th>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>03e1c946c0ddfc58724ff262aef08dff</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54037</th>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>3dd2a17168ec895c781a9191c1e95ad7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51536</th>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>3dd2a17168ec895c781a9191c1e95ad7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51237</th>\n",
       "      <td>Punjab</td>\n",
       "      <td>d1c427060a0f73f6b889a5c7c61f2ac4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50532</th>\n",
       "      <td>Orissa</td>\n",
       "      <td>aca2eb7d00ea1a7b8ebd4e68314663af</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39873</th>\n",
       "      <td>Himachal Pradesh</td>\n",
       "      <td>422879e10f46682990de24d770e7f83d</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54851</th>\n",
       "      <td>Uttaranchal</td>\n",
       "      <td>f919da4f716dc149cef4551a322001fd</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32584</th>\n",
       "      <td>Goa</td>\n",
       "      <td>2a5806f10d0f00e5ad032dd2e3c8806e</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          customer_state                        product_id  Quantity\n",
       "16353     Andhra Pradesh  aca2eb7d00ea1a7b8ebd4e68314663af       347\n",
       "34289            Gujarat  53759a2ecddad2bb87a079a1f1519f73        52\n",
       "26503       Chhattisgarh  422879e10f46682990de24d770e7f83d        41\n",
       "43557          Karnataka  99a4788cb24856965c36a24e339b6058        24\n",
       "30616              Delhi  422879e10f46682990de24d770e7f83d        22\n",
       "47486     Madhya Pradesh  99a4788cb24856965c36a24e339b6058        18\n",
       "49461        Maharashtra  99a4788cb24856965c36a24e339b6058        15\n",
       "41114    Jammu & Kashmir  89b190a046022486c635022524a974a8        15\n",
       "52665         Tamil Nadu  53759a2ecddad2bb87a079a1f1519f73        13\n",
       "38162            Haryana  2b4609f8948be18874494203496bc318        13\n",
       "45661             Kerala  aca2eb7d00ea1a7b8ebd4e68314663af        13\n",
       "55127        West Bengal  bf44071ef18f5c9ded039681c68b1996        12\n",
       "24307  Arunachal Pradesh  03e1c946c0ddfc58724ff262aef08dff        12\n",
       "54037      Uttar Pradesh  3dd2a17168ec895c781a9191c1e95ad7         8\n",
       "51536          Rajasthan  3dd2a17168ec895c781a9191c1e95ad7         7\n",
       "51237             Punjab  d1c427060a0f73f6b889a5c7c61f2ac4         6\n",
       "50532             Orissa  aca2eb7d00ea1a7b8ebd4e68314663af         6\n",
       "39873   Himachal Pradesh  422879e10f46682990de24d770e7f83d         6\n",
       "54851        Uttaranchal  f919da4f716dc149cef4551a322001fd         3\n",
       "32584                Goa  2a5806f10d0f00e5ad032dd2e3c8806e         1"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Most Popular Products by State\n",
    "ord_item_orders_cust.groupby(['customer_state','product_id'])['Quantity'].sum().reset_index().sort_values(by = 'Quantity', ascending = False).drop_duplicates(subset = 'customer_state', keep = 'first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6d9606ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seller_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>Quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21025</th>\n",
       "      <td>955fee9216a65b617aa5c0531780ce60</td>\n",
       "      <td>aca2eb7d00ea1a7b8ebd4e68314663af</td>\n",
       "      <td>529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9881</th>\n",
       "      <td>4a3ca9315b744ce9f8e9374361493884</td>\n",
       "      <td>99a4788cb24856965c36a24e339b6058</td>\n",
       "      <td>513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4645</th>\n",
       "      <td>1f50f920176fa81dab994f9023523100</td>\n",
       "      <td>422879e10f46682990de24d770e7f83d</td>\n",
       "      <td>511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22211</th>\n",
       "      <td>a1043bafd471dff536d0c462352beb48</td>\n",
       "      <td>d1c427060a0f73f6b889a5c7c61f2ac4</td>\n",
       "      <td>348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26771</th>\n",
       "      <td>cc419e0650a3c5ba77189a1882b7556a</td>\n",
       "      <td>154e7e31ebfa092203795c972e5804a6</td>\n",
       "      <td>283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15411</th>\n",
       "      <td>713e3a23ca5e47786eb6f0d99f94cfac</td>\n",
       "      <td>3fcd8dfe610c62edfb51de2630cd9ef4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15396</th>\n",
       "      <td>712efd3b68f9686dc856b84abd0f21ae</td>\n",
       "      <td>4466f429e81266e99d1935267aa13329</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15019</th>\n",
       "      <td>702835e4b785b67a084280efca355756</td>\n",
       "      <td>a9c404971d1a5b1cbc2e4070e02731fd</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15275</th>\n",
       "      <td>70ee69419916dc13e3aed619061f996c</td>\n",
       "      <td>dedb5adefe63796ce6edd624d0ebd8a8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15253</th>\n",
       "      <td>70c0fda4c313d87115644d39e62eec60</td>\n",
       "      <td>41e4e4c8ab0ec1fcde0df5679749a3e4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3095 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              seller_id                        product_id  \\\n",
       "21025  955fee9216a65b617aa5c0531780ce60  aca2eb7d00ea1a7b8ebd4e68314663af   \n",
       "9881   4a3ca9315b744ce9f8e9374361493884  99a4788cb24856965c36a24e339b6058   \n",
       "4645   1f50f920176fa81dab994f9023523100  422879e10f46682990de24d770e7f83d   \n",
       "22211  a1043bafd471dff536d0c462352beb48  d1c427060a0f73f6b889a5c7c61f2ac4   \n",
       "26771  cc419e0650a3c5ba77189a1882b7556a  154e7e31ebfa092203795c972e5804a6   \n",
       "...                                 ...                               ...   \n",
       "15411  713e3a23ca5e47786eb6f0d99f94cfac  3fcd8dfe610c62edfb51de2630cd9ef4   \n",
       "15396  712efd3b68f9686dc856b84abd0f21ae  4466f429e81266e99d1935267aa13329   \n",
       "15019  702835e4b785b67a084280efca355756  a9c404971d1a5b1cbc2e4070e02731fd   \n",
       "15275  70ee69419916dc13e3aed619061f996c  dedb5adefe63796ce6edd624d0ebd8a8   \n",
       "15253  70c0fda4c313d87115644d39e62eec60  41e4e4c8ab0ec1fcde0df5679749a3e4   \n",
       "\n",
       "       Quantity  \n",
       "21025       529  \n",
       "9881        513  \n",
       "4645        511  \n",
       "22211       348  \n",
       "26771       283  \n",
       "...         ...  \n",
       "15411         1  \n",
       "15396         1  \n",
       "15019         1  \n",
       "15275         1  \n",
       "15253         1  \n",
       "\n",
       "[3095 rows x 3 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Most Popular Product by Seller\n",
    "ord_item_orders_cust.groupby(['seller_id','product_id'])[['Quantity']].sum().reset_index().sort_values(by = 'Quantity', ascending = False).drop_duplicates(subset = 'seller_id', keep = 'first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "db1076cb",
   "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>Month</th>\n",
       "      <th>product_id</th>\n",
       "      <th>Quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24273</th>\n",
       "      <td>Jan</td>\n",
       "      <td>aca2eb7d00ea1a7b8ebd4e68314663af</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46058</th>\n",
       "      <td>May</td>\n",
       "      <td>53b36df67ebb7c41585e8d54d6772e08</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51336</th>\n",
       "      <td>Nov</td>\n",
       "      <td>422879e10f46682990de24d770e7f83d</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1887</th>\n",
       "      <td>Apr</td>\n",
       "      <td>53b36df67ebb7c41585e8d54d6772e08</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35893</th>\n",
       "      <td>Jun</td>\n",
       "      <td>99a4788cb24856965c36a24e339b6058</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20624</th>\n",
       "      <td>Feb</td>\n",
       "      <td>e53e557d5a159f5aa2c5e995dfdf244b</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29727</th>\n",
       "      <td>Jul</td>\n",
       "      <td>99a4788cb24856965c36a24e339b6058</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12169</th>\n",
       "      <td>Aug</td>\n",
       "      <td>e7cc48a9daff5436f63d3aad9426f28b</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42079</th>\n",
       "      <td>Mar</td>\n",
       "      <td>aca2eb7d00ea1a7b8ebd4e68314663af</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13708</th>\n",
       "      <td>Dec</td>\n",
       "      <td>422879e10f46682990de24d770e7f83d</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55565</th>\n",
       "      <td>Oct</td>\n",
       "      <td>422879e10f46682990de24d770e7f83d</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58722</th>\n",
       "      <td>Sep</td>\n",
       "      <td>422879e10f46682990de24d770e7f83d</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Month                        product_id  Quantity\n",
       "24273   Jan  aca2eb7d00ea1a7b8ebd4e68314663af       122\n",
       "46058   May  53b36df67ebb7c41585e8d54d6772e08       114\n",
       "51336   Nov  422879e10f46682990de24d770e7f83d        98\n",
       "1887    Apr  53b36df67ebb7c41585e8d54d6772e08        87\n",
       "35893   Jun  99a4788cb24856965c36a24e339b6058        79\n",
       "20624   Feb  e53e557d5a159f5aa2c5e995dfdf244b        78\n",
       "29727   Jul  99a4788cb24856965c36a24e339b6058        74\n",
       "12169   Aug  e7cc48a9daff5436f63d3aad9426f28b        68\n",
       "42079   Mar  aca2eb7d00ea1a7b8ebd4e68314663af        65\n",
       "13708   Dec  422879e10f46682990de24d770e7f83d        53\n",
       "55565   Oct  422879e10f46682990de24d770e7f83d        40\n",
       "58722   Sep  422879e10f46682990de24d770e7f83d        39"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Most Popular Product by Month\n",
    "ord_item_orders_cust.groupby(['Month','product_id'])[['Quantity']].sum().reset_index().sort_values(by = 'Quantity', ascending = False).drop_duplicates(subset = 'Month', keep = 'first')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bb0df13",
   "metadata": {},
   "source": [
    "##### g. Popular categories by state, month "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "5a8a60ae",
   "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>Month</th>\n",
       "      <th>product_category_name</th>\n",
       "      <th>Quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>Jul</td>\n",
       "      <td>Bed_Bath_Table</td>\n",
       "      <td>1337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Aug</td>\n",
       "      <td>Bed_Bath_Table</td>\n",
       "      <td>1292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>Jun</td>\n",
       "      <td>Bed_Bath_Table</td>\n",
       "      <td>1271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>May</td>\n",
       "      <td>Bed_Bath_Table</td>\n",
       "      <td>1217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>Mar</td>\n",
       "      <td>Bed_Bath_Table</td>\n",
       "      <td>1196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>Feb</td>\n",
       "      <td>Computers_Accessories</td>\n",
       "      <td>1146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Apr</td>\n",
       "      <td>Bed_Bath_Table</td>\n",
       "      <td>1141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>Nov</td>\n",
       "      <td>Bed_Bath_Table</td>\n",
       "      <td>1096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>Jan</td>\n",
       "      <td>Bed_Bath_Table</td>\n",
       "      <td>1023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673</th>\n",
       "      <td>Oct</td>\n",
       "      <td>Bed_Bath_Table</td>\n",
       "      <td>667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Dec</td>\n",
       "      <td>Bed_Bath_Table</td>\n",
       "      <td>607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>Sep</td>\n",
       "      <td>Bed_Bath_Table</td>\n",
       "      <td>576</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Month  product_category_name  Quantity\n",
       "339   Jul         Bed_Bath_Table      1337\n",
       "76    Aug         Bed_Bath_Table      1292\n",
       "407   Jun         Bed_Bath_Table      1271\n",
       "541   May         Bed_Bath_Table      1217\n",
       "473   Mar         Bed_Bath_Table      1196\n",
       "213   Feb  Computers_Accessories      1146\n",
       "7     Apr         Bed_Bath_Table      1141\n",
       "608   Nov         Bed_Bath_Table      1096\n",
       "270   Jan         Bed_Bath_Table      1023\n",
       "673   Oct         Bed_Bath_Table       667\n",
       "144   Dec         Bed_Bath_Table       607\n",
       "738   Sep         Bed_Bath_Table       576"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#most popular category by month\n",
    "ord_item_orders_prod.groupby(['Month','product_category_name'])[['Quantity',]].sum().reset_index().sort_values(by = 'Quantity',ascending = False).drop_duplicates(subset = 'Month',keep = 'first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "198c0974",
   "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>customer_state</th>\n",
       "      <th>product_category_name</th>\n",
       "      <th>Quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>Bed_Bath_Table</td>\n",
       "      <td>7911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>Gujarat</td>\n",
       "      <td>Bed_Bath_Table</td>\n",
       "      <td>869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Chhattisgarh</td>\n",
       "      <td>Bed_Bath_Table</td>\n",
       "      <td>729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>Karnataka</td>\n",
       "      <td>Bed_Bath_Table</td>\n",
       "      <td>420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>Bed_Bath_Table</td>\n",
       "      <td>364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>Delhi</td>\n",
       "      <td>Health_Beauty</td>\n",
       "      <td>337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>Haryana</td>\n",
       "      <td>Bed_Bath_Table</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691</th>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Bed_Bath_Table</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>901</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>Bed_Bath_Table</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>Jammu &amp; Kashmir</td>\n",
       "      <td>Bed_Bath_Table</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>Kerala</td>\n",
       "      <td>Bed_Bath_Table</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>Bed_Bath_Table</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>Health_Beauty</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>Sports_Leisure</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>Himachal Pradesh</td>\n",
       "      <td>Bed_Bath_Table</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>Orissa</td>\n",
       "      <td>Bed_Bath_Table</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>825</th>\n",
       "      <td>Punjab</td>\n",
       "      <td>Health_Beauty</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>Health_Beauty</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031</th>\n",
       "      <td>Uttaranchal</td>\n",
       "      <td>Telephony</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>Goa</td>\n",
       "      <td>Telephony</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         customer_state product_category_name  Quantity\n",
       "7        Andhra Pradesh        Bed_Bath_Table      7911\n",
       "272             Gujarat        Bed_Bath_Table       869\n",
       "132        Chhattisgarh        Bed_Bath_Table       729\n",
       "505           Karnataka        Bed_Bath_Table       420\n",
       "625      Madhya Pradesh        Bed_Bath_Table       364\n",
       "229               Delhi         Health_Beauty       337\n",
       "339             Haryana        Bed_Bath_Table       315\n",
       "691         Maharashtra        Bed_Bath_Table       240\n",
       "901          Tamil Nadu        Bed_Bath_Table       233\n",
       "448     Jammu & Kashmir        Bed_Bath_Table       193\n",
       "568              Kerala        Bed_Bath_Table       157\n",
       "77    Arunachal Pradesh        Bed_Bath_Table       119\n",
       "987       Uttar Pradesh         Health_Beauty       106\n",
       "890           Rajasthan        Sports_Leisure        98\n",
       "396    Himachal Pradesh        Bed_Bath_Table        89\n",
       "750              Orissa        Bed_Bath_Table        84\n",
       "825              Punjab         Health_Beauty        72\n",
       "1056        West Bengal         Health_Beauty        49\n",
       "1031        Uttaranchal             Telephony        12\n",
       "264                 Goa             Telephony         1"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Most Popular Category by State\n",
    "ord_item_orders_cust_prod = pd.merge(ord_item_orders_cust, products, how = 'left', on = 'product_id')\n",
    "ord_item_orders_cust_prod.groupby(['customer_state','product_category_name'])[['Quantity']].sum().reset_index().sort_values(by = 'Quantity', ascending = False) \\\n",
    "                        .drop_duplicates(subset = 'customer_state', keep = 'first')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df9239ed",
   "metadata": {},
   "source": [
    "##### h. List top 10 most expensive products sorted by price\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "13f76443",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>product_id</th>\n",
       "      <th>price</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>489ae2aa008f021502940f251d4cce7f</td>\n",
       "      <td>6735.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>69c590f7ffc7bf8db97190b6cb6ed62e</td>\n",
       "      <td>6729.00</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1bdf5e6731585cf01aa8169c7028d6ad</td>\n",
       "      <td>6499.00</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a6492cc69376c469ab6f61d8f44de961</td>\n",
       "      <td>4799.00</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c3ed642d592594bb648ff4a04cee2747</td>\n",
       "      <td>4690.00</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>259037a6a41845e455183f89c5035f18</td>\n",
       "      <td>4590.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>a1beef8f3992dbd4cd8726796aa69c53</td>\n",
       "      <td>4399.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6cdf8fc1d741c76586d8b6b15e9eef30</td>\n",
       "      <td>4099.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6902c1962dd19d540807d0ab8fade5c6</td>\n",
       "      <td>3999.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4ca7b91a31637bd24fb8e559d5e015e4</td>\n",
       "      <td>3999.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         product_id    price\n",
       "0  489ae2aa008f021502940f251d4cce7f  6735.00\n",
       "1  69c590f7ffc7bf8db97190b6cb6ed62e  6729.00\n",
       "2  1bdf5e6731585cf01aa8169c7028d6ad  6499.00\n",
       "3  a6492cc69376c469ab6f61d8f44de961  4799.00\n",
       "4  c3ed642d592594bb648ff4a04cee2747  4690.00\n",
       "5  259037a6a41845e455183f89c5035f18  4590.00\n",
       "6  a1beef8f3992dbd4cd8726796aa69c53  4399.87\n",
       "7  6cdf8fc1d741c76586d8b6b15e9eef30  4099.99\n",
       "8  6902c1962dd19d540807d0ab8fade5c6  3999.90\n",
       "9  4ca7b91a31637bd24fb8e559d5e015e4  3999.00"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_items.groupby('product_id')['price'].mean().reset_index().sort_values(by = 'price',ascending = False,ignore_index = True).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f4b952f",
   "metadata": {},
   "source": [
    "#### 2. Performing Customers/sellers Segmentation\n",
    "a. Divide the customers into groups based on the revenue generated¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "be43b305",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating decile grp for customer\n",
    "cust_grp = ord_item_orders_cust.groupby('customer_id')[['tot_cost']].sum().reset_index().rename(columns={'tot_cost':'Revenue'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "24999029",
   "metadata": {},
   "outputs": [
    {
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       "                            customer_id  Revenue\n",
       "0      00012a2ce6f8dcda20d059ce98491703   114.74\n",
       "1      000161a058600d5901f007fab4c27140    67.41\n",
       "2      0001fd6190edaaf884bcaf3d49edf079   195.42\n",
       "3      0002414f95344307404f0ace7a26f1d5   179.35\n",
       "4      000379cdec625522490c315e70c7a9fb   107.01\n",
       "...                                 ...      ...\n",
       "98661  fffcb937e9dd47a13f05ecb8290f4d3e    91.91\n",
       "98662  fffecc9f79fd8c764f843e9951b11341    81.36\n",
       "98663  fffeda5b6d849fbd39689bb92087f431    63.13\n",
       "98664  ffff42319e9b2d713724ae527742af25   214.13\n",
       "98665  ffffa3172527f765de70084a7e53aae8    33.65\n",
       "\n",
       "[98666 rows x 2 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_grp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f1dff514",
   "metadata": {},
   "outputs": [],
   "source": [
    "cust_grp['decile_grp'] = pd.cut(cust_grp.Revenue,bins=10,labels= np.arange(1,11))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "fdf9c8c6",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                            customer_id  Revenue decile_grp\n",
       "0      00012a2ce6f8dcda20d059ce98491703   114.74          1\n",
       "1      000161a058600d5901f007fab4c27140    67.41          1\n",
       "2      0001fd6190edaaf884bcaf3d49edf079   195.42          1\n",
       "3      0002414f95344307404f0ace7a26f1d5   179.35          1\n",
       "4      000379cdec625522490c315e70c7a9fb   107.01          1\n",
       "...                                 ...      ...        ...\n",
       "98661  fffcb937e9dd47a13f05ecb8290f4d3e    91.91          1\n",
       "98662  fffecc9f79fd8c764f843e9951b11341    81.36          1\n",
       "98663  fffeda5b6d849fbd39689bb92087f431    63.13          1\n",
       "98664  ffff42319e9b2d713724ae527742af25   214.13          1\n",
       "98665  ffffa3172527f765de70084a7e53aae8    33.65          1\n",
       "\n",
       "[98666 rows x 3 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "cust_grp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "8403a441",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating decile grp for sellers\n",
    "seller_grp = ord_item_orders_cust.groupby('seller_id')[['tot_cost']].sum().reset_index().rename(columns={'tot_cost':'Revenue'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "f3b888e5",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                             seller_id   Revenue\n",
       "0     0015a82c2db000af6aaaf3ae2ecb0532   2748.06\n",
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       "4     003554e2dce176b5555353e4f3555ac8    139.38\n",
       "...                                ...       ...\n",
       "3090  ffcfefa19b08742c5d315f2791395ee5     79.52\n",
       "3091  ffdd9f82b9a447f6f8d4b91554cc7dd3   2716.83\n",
       "3092  ffeee66ac5d5a62fe688b9d26f83f534   2259.55\n",
       "3093  fffd5413c0700ac820c7069d66d98c89  12117.32\n",
       "3094  ffff564a4f9085cd26170f4732393726   1770.86\n",
       "\n",
       "[3095 rows x 2 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
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   "source": [
    "seller_grp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "bf3749c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "seller_grp['decile_grp'] = pd.cut(seller_grp.Revenue , bins=10, labels = np.arange(1,11))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "6a4515da",
   "metadata": {},
   "outputs": [
    {
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       "                             seller_id   Revenue decile_grp\n",
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       "4     003554e2dce176b5555353e4f3555ac8    139.38          1\n",
       "...                                ...       ...        ...\n",
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       "\n",
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    "seller_grp"
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  {
   "cell_type": "markdown",
   "id": "e59c58c9",
   "metadata": {},
   "source": [
    "##### 3. Cross-Selling (Which products are selling together)¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "b7f09308",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                                order_id order_purchase_timestamp  \\\n",
       "73      002f98c0f7efd42638ed6100ca699b42      2017-08-04 09:19:00   \n",
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       "136     005d9a5423d47281ac463a968b3936fb      2017-10-18 12:14:00   \n",
       "...                                  ...                      ...   \n",
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       "102355  ffd543c2b60842e148a86870dc60e212      2018-01-04 12:00:00   \n",
       "\n",
       "                              product_id  \n",
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       "74      880be32f4db1d9f6e2bec38fb6ac23ab  \n",
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       "84      13944d17b257432717fd260e69853140  \n",
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       "...                                  ...  \n",
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     "execution_count": 88,
     "metadata": {},
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   ],
   "source": [
    "# Cross Selling in terms of same Order_ID and at Same time\n",
    "cross_sell = ord_item_orders.loc[: , ['order_id','order_purchase_timestamp','product_id']]\n",
    "cross_sell = cross_sell[cross_sell.duplicated(subset = ['order_id','order_purchase_timestamp'], keep = False)]\n",
    "cross_sell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "7ad60748",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "                              order_id order_purchase_timestamp  \\\n",
       "0     002f98c0f7efd42638ed6100ca699b42      2017-08-04 09:19:00   \n",
       "1     00337fe25a3780b3424d9ad7c5a4b35e      2017-09-25 17:39:00   \n",
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       "...                                ...                      ...   \n",
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       "3235  ffd543c2b60842e148a86870dc60e212      2018-01-04 12:00:00   \n",
       "\n",
       "                            product_id  \n",
       "0     d41dc2f2979f52d75d78714b378d4068  \n",
       "1     1f9799a175f50c9fa725984775cac5c5  \n",
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       "3     4dcb49b9ca7e48d2f108d40caa77caa2  \n",
       "4     b6397895a17ce86decd60b898b459796  \n",
       "...                                ...  \n",
       "3231  e86b81dcac341ea01df0260077cdf082  \n",
       "3232  bd6e8cf9fe4122c385da2bcb9f979d5d  \n",
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       "3234  241e398aacc909372622952b2ec6f954  \n",
       "3235  3ce943997ff85cad84ec6770b35d6bcd  \n",
       "\n",
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      ]
     },
     "execution_count": 89,
     "metadata": {},
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   ],
   "source": [
    "cross_sell_1 = cross_sell.drop_duplicates(subset=['order_id','order_purchase_timestamp'], keep='first', ignore_index=True)\n",
    "cross_sell_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "b8a0a2bc",
   "metadata": {},
   "outputs": [
    {
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       "      <td>005d9a5423d47281ac463a968b3936fb</td>\n",
       "      <td>2017-10-18 12:14:00</td>\n",
       "      <td>4c3ae5db49258df0784827bdacf3b396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00946f674d880be1f188abc10ad7cf46</td>\n",
       "      <td>2017-12-09 19:11:00</td>\n",
       "      <td>9bb2d066e4b33b624cbdfec7d50b3dcb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0097f0545a302aafa32782f1734ff71c</td>\n",
       "      <td>2018-03-05 18:15:00</td>\n",
       "      <td>636598095d69a5718e67d2c9a3c7dde6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3231</th>\n",
       "      <td>ffb18bf111fa70edf316eb0390427986</td>\n",
       "      <td>2017-11-27 13:29:00</td>\n",
       "      <td>599dc392f7a23273471b068d72408224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3232</th>\n",
       "      <td>ffb8f7de8940249a3221252818937ecb</td>\n",
       "      <td>2018-07-22 12:58:00</td>\n",
       "      <td>bd0ac51dc93e62c4dbe6ca9d70a9b311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3233</th>\n",
       "      <td>ffb9a9cd00c74c11c24aa30b3d78e03b</td>\n",
       "      <td>2017-03-11 17:20:00</td>\n",
       "      <td>3321ad579f19476d0d668f726f8dffec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3234</th>\n",
       "      <td>ffc16cecff8dc037f60458f28d1c1ba5</td>\n",
       "      <td>2017-11-21 19:25:00</td>\n",
       "      <td>7c1043bb5837db0c6bc1953419a18628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3235</th>\n",
       "      <td>ffd543c2b60842e148a86870dc60e212</td>\n",
       "      <td>2018-01-04 12:00:00</td>\n",
       "      <td>b7d94dc0640c7025dc8e3b46b52d8239</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3236 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              order_id order_purchase_timestamp  \\\n",
       "0     002f98c0f7efd42638ed6100ca699b42      2017-08-04 09:19:00   \n",
       "1     00337fe25a3780b3424d9ad7c5a4b35e      2017-09-25 17:39:00   \n",
       "2     005d9a5423d47281ac463a968b3936fb      2017-10-18 12:14:00   \n",
       "3     00946f674d880be1f188abc10ad7cf46      2017-12-09 19:11:00   \n",
       "4     0097f0545a302aafa32782f1734ff71c      2018-03-05 18:15:00   \n",
       "...                                ...                      ...   \n",
       "3231  ffb18bf111fa70edf316eb0390427986      2017-11-27 13:29:00   \n",
       "3232  ffb8f7de8940249a3221252818937ecb      2018-07-22 12:58:00   \n",
       "3233  ffb9a9cd00c74c11c24aa30b3d78e03b      2017-03-11 17:20:00   \n",
       "3234  ffc16cecff8dc037f60458f28d1c1ba5      2017-11-21 19:25:00   \n",
       "3235  ffd543c2b60842e148a86870dc60e212      2018-01-04 12:00:00   \n",
       "\n",
       "                            product_id  \n",
       "0     880be32f4db1d9f6e2bec38fb6ac23ab  \n",
       "1     13944d17b257432717fd260e69853140  \n",
       "2     4c3ae5db49258df0784827bdacf3b396  \n",
       "3     9bb2d066e4b33b624cbdfec7d50b3dcb  \n",
       "4     636598095d69a5718e67d2c9a3c7dde6  \n",
       "...                                ...  \n",
       "3231  599dc392f7a23273471b068d72408224  \n",
       "3232  bd0ac51dc93e62c4dbe6ca9d70a9b311  \n",
       "3233  3321ad579f19476d0d668f726f8dffec  \n",
       "3234  7c1043bb5837db0c6bc1953419a18628  \n",
       "3235  b7d94dc0640c7025dc8e3b46b52d8239  \n",
       "\n",
       "[3236 rows x 3 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_sell_2 = cross_sell.drop_duplicates(subset=['order_id','order_purchase_timestamp'], keep='last', ignore_index=True)\n",
    "cross_sell_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "0d930d6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>order_id</th>\n",
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       "      <td>2017-10-18 12:14:00</td>\n",
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       "      <th>4</th>\n",
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       "      <th>3233</th>\n",
       "      <td>ffb9a9cd00c74c11c24aa30b3d78e03b</td>\n",
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       "      <td>fec565c4e3ad965c73fb1a21bb809257</td>\n",
       "      <td>3321ad579f19476d0d668f726f8dffec</td>\n",
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       "      <th>3234</th>\n",
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       "      <td>2017-11-21 19:25:00</td>\n",
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       "<p>3236 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              order_id order_purchase_timestamp  \\\n",
       "0     002f98c0f7efd42638ed6100ca699b42      2017-08-04 09:19:00   \n",
       "1     00337fe25a3780b3424d9ad7c5a4b35e      2017-09-25 17:39:00   \n",
       "2     005d9a5423d47281ac463a968b3936fb      2017-10-18 12:14:00   \n",
       "3     00946f674d880be1f188abc10ad7cf46      2017-12-09 19:11:00   \n",
       "4     0097f0545a302aafa32782f1734ff71c      2018-03-05 18:15:00   \n",
       "...                                ...                      ...   \n",
       "3231  ffb18bf111fa70edf316eb0390427986      2017-11-27 13:29:00   \n",
       "3232  ffb8f7de8940249a3221252818937ecb      2018-07-22 12:58:00   \n",
       "3233  ffb9a9cd00c74c11c24aa30b3d78e03b      2017-03-11 17:20:00   \n",
       "3234  ffc16cecff8dc037f60458f28d1c1ba5      2017-11-21 19:25:00   \n",
       "3235  ffd543c2b60842e148a86870dc60e212      2018-01-04 12:00:00   \n",
       "\n",
       "                          product_id_x                      product_id_y  \n",
       "0     d41dc2f2979f52d75d78714b378d4068  880be32f4db1d9f6e2bec38fb6ac23ab  \n",
       "1     1f9799a175f50c9fa725984775cac5c5  13944d17b257432717fd260e69853140  \n",
       "2     fb7a100ec8c7b34f60cec22b1a9a10e0  4c3ae5db49258df0784827bdacf3b396  \n",
       "3     4dcb49b9ca7e48d2f108d40caa77caa2  9bb2d066e4b33b624cbdfec7d50b3dcb  \n",
       "4     b6397895a17ce86decd60b898b459796  636598095d69a5718e67d2c9a3c7dde6  \n",
       "...                                ...                               ...  \n",
       "3231  e86b81dcac341ea01df0260077cdf082  599dc392f7a23273471b068d72408224  \n",
       "3232  bd6e8cf9fe4122c385da2bcb9f979d5d  bd0ac51dc93e62c4dbe6ca9d70a9b311  \n",
       "3233  fec565c4e3ad965c73fb1a21bb809257  3321ad579f19476d0d668f726f8dffec  \n",
       "3234  241e398aacc909372622952b2ec6f954  7c1043bb5837db0c6bc1953419a18628  \n",
       "3235  3ce943997ff85cad84ec6770b35d6bcd  b7d94dc0640c7025dc8e3b46b52d8239  \n",
       "\n",
       "[3236 rows x 4 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross selling of 2 Products\n",
    "cross_sell_final = pd.merge(cross_sell_1, cross_sell_2, on = ['order_id','order_purchase_timestamp'], how = 'inner')\n",
    "cross_sell_final"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e1e0d24",
   "metadata": {},
   "source": [
    "##### 4.a) How customers are paying?¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "15bb1469",
   "metadata": {},
   "outputs": [],
   "source": [
    "payment =  order_payments.drop_duplicates(subset = 'payment_type',keep= 'first',ignore_index = True)['payment_type'].reset_index().drop(columns= 'index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "012de8c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>debit_card</td>\n",
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      ],
      "text/plain": [
       "  payment_type\n",
       "0  credit_card\n",
       "1          UPI\n",
       "2      voucher\n",
       "3   debit_card\n",
       "4  not_defined"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "payment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8517fa3e",
   "metadata": {},
   "source": [
    "##### 4.b) Which payment channels are used by most customers?¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "ebbf13af",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>payment_sequential</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25e8ea4e93396b6fa0d3dd708e76c1bd</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>1</td>\n",
       "      <td>65.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ba78997921bbcdc1373bb41e913ab953</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>8</td>\n",
       "      <td>107.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42fdf880ba16b47b59251dd489d4441a</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2</td>\n",
       "      <td>128.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>103881</th>\n",
       "      <td>0406037ad97740d563a178ecc7a2075c</td>\n",
       "      <td>1</td>\n",
       "      <td>UPI</td>\n",
       "      <td>1</td>\n",
       "      <td>363.31</td>\n",
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       "    <tr>\n",
       "      <th>103882</th>\n",
       "      <td>7b905861d7c825891d6347454ea7863f</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2</td>\n",
       "      <td>96.80</td>\n",
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       "    <tr>\n",
       "      <th>103883</th>\n",
       "      <td>32609bbb3dd69b3c066a6860554a77bf</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>1</td>\n",
       "      <td>47.77</td>\n",
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       "    <tr>\n",
       "      <th>103884</th>\n",
       "      <td>b8b61059626efa996a60be9bb9320e10</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>5</td>\n",
       "      <td>369.54</td>\n",
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       "    <tr>\n",
       "      <th>103885</th>\n",
       "      <td>28bbae6599b09d39ca406b747b6632b1</td>\n",
       "      <td>1</td>\n",
       "      <td>UPI</td>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103886 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                order_id  payment_sequential payment_type  \\\n",
       "0       b81ef226f3fe1789b1e8b2acac839d17                   1  credit_card   \n",
       "1       a9810da82917af2d9aefd1278f1dcfa0                   1  credit_card   \n",
       "2       25e8ea4e93396b6fa0d3dd708e76c1bd                   1  credit_card   \n",
       "3       ba78997921bbcdc1373bb41e913ab953                   1  credit_card   \n",
       "4       42fdf880ba16b47b59251dd489d4441a                   1  credit_card   \n",
       "...                                  ...                 ...          ...   \n",
       "103881  0406037ad97740d563a178ecc7a2075c                   1          UPI   \n",
       "103882  7b905861d7c825891d6347454ea7863f                   1  credit_card   \n",
       "103883  32609bbb3dd69b3c066a6860554a77bf                   1  credit_card   \n",
       "103884  b8b61059626efa996a60be9bb9320e10                   1  credit_card   \n",
       "103885  28bbae6599b09d39ca406b747b6632b1                   1          UPI   \n",
       "\n",
       "        payment_installments  payment_value  \n",
       "0                          8          99.33  \n",
       "1                          1          24.39  \n",
       "2                          1          65.71  \n",
       "3                          8         107.78  \n",
       "4                          2         128.45  \n",
       "...                      ...            ...  \n",
       "103881                     1         363.31  \n",
       "103882                     2          96.80  \n",
       "103883                     1          47.77  \n",
       "103884                     5         369.54  \n",
       "103885                     1         191.58  \n",
       "\n",
       "[103886 rows x 5 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_payments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "52cdbc43",
   "metadata": {},
   "outputs": [],
   "source": [
    "payment_channel = order_payments.groupby('payment_type')[['payment_type']].count().rename(columns={'payment_type':'No_of_transaction'}).sort_values(by = 'No_of_transaction',ascending = False).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "06016222",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>payment_type</th>\n",
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       "      <th>0</th>\n",
       "      <td>credit_card</td>\n",
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       "      <td>UPI</td>\n",
       "      <td>19784</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>voucher</td>\n",
       "      <td>5775</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>1529</td>\n",
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      ],
      "text/plain": [
       "  payment_type  No_of_transaction\n",
       "0  credit_card              76795\n",
       "1          UPI              19784\n",
       "2      voucher               5775\n",
       "3   debit_card               1529\n",
       "4  not_defined                  3"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "payment_channel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ca8c979",
   "metadata": {},
   "source": [
    "##### 5. Customer satisfaction towards category & product\n",
    "a. Which categories (top 10) are maximum rated & minimum rated?¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "206f537d",
   "metadata": {},
   "outputs": [],
   "source": [
    "order_items_prod = pd.merge(order_items , products, how = 'left',on = 'product_id')\n",
    "order_review_cat= pd.merge(order_review, order_items_prod, how = 'left', on = 'order_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "56a04130",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>product_id</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>shipping_limit_date</th>\n",
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       "      <th>product_description_lenght</th>\n",
       "      <th>product_photos_qty</th>\n",
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       "      <th>product_height_cm</th>\n",
       "      <th>product_width_cm</th>\n",
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       "      <td>58.90</td>\n",
       "      <td>13.29</td>\n",
       "      <td>72.19</td>\n",
       "      <td>Cool_Stuff</td>\n",
       "      <td>58.0</td>\n",
       "      <td>598.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
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       "    <tr>\n",
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       "      <td>00018f77f2f0320c557190d7a144bdd3</td>\n",
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       "      <td>dd7ddc04e1b6c2c614352b383efe2d36</td>\n",
       "      <td>2017-05-03 11:05:00</td>\n",
       "      <td>239.90</td>\n",
       "      <td>19.93</td>\n",
       "      <td>259.83</td>\n",
       "      <td>Pet_Shop</td>\n",
       "      <td>56.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>40.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>199.00</td>\n",
       "      <td>17.87</td>\n",
       "      <td>216.87</td>\n",
       "      <td>Furniture_Decor</td>\n",
       "      <td>59.0</td>\n",
       "      <td>695.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00024acbcdf0a6daa1e931b038114c75</td>\n",
       "      <td>1</td>\n",
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       "      <td>9d7a1d34a5052409006425275ba1c2b4</td>\n",
       "      <td>2018-08-15 10:10:00</td>\n",
       "      <td>12.99</td>\n",
       "      <td>12.79</td>\n",
       "      <td>25.78</td>\n",
       "      <td>Perfumery</td>\n",
       "      <td>42.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
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       "      <td>2017-02-13 13:57:00</td>\n",
       "      <td>199.90</td>\n",
       "      <td>18.14</td>\n",
       "      <td>218.04</td>\n",
       "      <td>Garden_Tools</td>\n",
       "      <td>59.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>30.0</td>\n",
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      "text/plain": [
       "                           order_id  Quantity  \\\n",
       "0  00010242fe8c5a6d1ba2dd792cb16214         1   \n",
       "1  00018f77f2f0320c557190d7a144bdd3         1   \n",
       "2  000229ec398224ef6ca0657da4fc703e         1   \n",
       "3  00024acbcdf0a6daa1e931b038114c75         1   \n",
       "4  00042b26cf59d7ce69dfabb4e55b4fd9         1   \n",
       "\n",
       "                         product_id                         seller_id  \\\n",
       "0  4244733e06e7ecb4970a6e2683c13e61  48436dade18ac8b2bce089ec2a041202   \n",
       "1  e5f2d52b802189ee658865ca93d83a8f  dd7ddc04e1b6c2c614352b383efe2d36   \n",
       "2  c777355d18b72b67abbeef9df44fd0fd  5b51032eddd242adc84c38acab88f23d   \n",
       "3  7634da152a4610f1595efa32f14722fc  9d7a1d34a5052409006425275ba1c2b4   \n",
       "4  ac6c3623068f30de03045865e4e10089  df560393f3a51e74553ab94004ba5c87   \n",
       "\n",
       "  shipping_limit_date   price  freight_value  tot_cost product_category_name  \\\n",
       "0 2017-09-19 09:45:00   58.90          13.29     72.19            Cool_Stuff   \n",
       "1 2017-05-03 11:05:00  239.90          19.93    259.83              Pet_Shop   \n",
       "2 2018-01-18 14:48:00  199.00          17.87    216.87       Furniture_Decor   \n",
       "3 2018-08-15 10:10:00   12.99          12.79     25.78             Perfumery   \n",
       "4 2017-02-13 13:57:00  199.90          18.14    218.04          Garden_Tools   \n",
       "\n",
       "   product_name_lenght  product_description_lenght  product_photos_qty  \\\n",
       "0                 58.0                       598.0                 4.0   \n",
       "1                 56.0                       239.0                 2.0   \n",
       "2                 59.0                       695.0                 2.0   \n",
       "3                 42.0                       480.0                 1.0   \n",
       "4                 59.0                       409.0                 1.0   \n",
       "\n",
       "   product_weight_g  product_length_cm  product_height_cm  product_width_cm  \n",
       "0             650.0               28.0                9.0              14.0  \n",
       "1           30000.0               50.0               30.0              40.0  \n",
       "2            3050.0               33.0               13.0              33.0  \n",
       "3             200.0               16.0               10.0              15.0  \n",
       "4            3750.0               35.0               40.0              30.0  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_items_prod.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "1edba60b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>4</td>\n",
       "      <td>2018-01-18</td>\n",
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       "      <td>2018-01-18 15:47:00</td>\n",
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       "      <td>13.63</td>\n",
       "      <td>383.63</td>\n",
       "      <td>Sports_Leisure</td>\n",
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       "      <td>858.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>5</td>\n",
       "      <td>2018-03-10</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>8e6d7754bc7e0f22c96d255ebda59eba</td>\n",
       "      <td>2018-03-06 12:48:00</td>\n",
       "      <td>79.79</td>\n",
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       "      <td>8.30</td>\n",
       "      <td>88.09</td>\n",
       "      <td>Computers_Accessories</td>\n",
       "      <td>47.0</td>\n",
       "      <td>493.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>228ce5500dc1d8e020d8d1322874b6f0</td>\n",
       "      <td>f9e4b658b201a9f2ecdecbb34bed034b</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-02-17</td>\n",
       "      <td>2018-02-18 14:36:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>d1c427060a0f73f6b889a5c7c61f2ac4</td>\n",
       "      <td>a1043bafd471dff536d0c462352beb48</td>\n",
       "      <td>2018-02-08 10:31:00</td>\n",
       "      <td>149.00</td>\n",
       "      <td>...</td>\n",
       "      <td>45.12</td>\n",
       "      <td>194.12</td>\n",
       "      <td>Computers_Accessories</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1893.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6550.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>e64fb393e7b32834bb789ff8bb30750e</td>\n",
       "      <td>658677c97b385a9be170737859d3511b</td>\n",
       "      <td>5</td>\n",
       "      <td>2017-04-21</td>\n",
       "      <td>2017-04-21 22:02:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52c80cedd4e90108bf4fa6a206ef6b03</td>\n",
       "      <td>a1043bafd471dff536d0c462352beb48</td>\n",
       "      <td>2017-04-13 17:55:00</td>\n",
       "      <td>179.99</td>\n",
       "      <td>...</td>\n",
       "      <td>42.85</td>\n",
       "      <td>222.84</td>\n",
       "      <td>Garden_Tools</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2188.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7650.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
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       "    <tr>\n",
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       "      <td>2018-03-02 10:26:00</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>989becdce12ebc39863c2bceab6f3ca1</td>\n",
       "      <td>2018-02-15 15:48:00</td>\n",
       "      <td>1199.00</td>\n",
       "      <td>...</td>\n",
       "      <td>134.25</td>\n",
       "      <td>1333.25</td>\n",
       "      <td>Sports_Leisure</td>\n",
       "      <td>48.0</td>\n",
       "      <td>562.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9850.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>50.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          review_id                          order_id  \\\n",
       "0  7bc2406110b926393aa56f80a40eba40  73fc7af87114b39712e6da79b0a377eb   \n",
       "1  80e641a11e56f04c1ad469d5645fdfde  a548910a1c6147796b98fdf73dbeba33   \n",
       "2  228ce5500dc1d8e020d8d1322874b6f0  f9e4b658b201a9f2ecdecbb34bed034b   \n",
       "3  e64fb393e7b32834bb789ff8bb30750e  658677c97b385a9be170737859d3511b   \n",
       "4  f7c4243c7fe1938f181bec41a392bdeb  8e6bfb81e283fa7e4f11123a3fb894f1   \n",
       "\n",
       "   review_score review_creation_date review_answer_timestamp  Quantity_x  \\\n",
       "0             4           2018-01-18     2018-01-18 21:46:00         2.0   \n",
       "1             5           2018-03-10     2018-03-11 03:05:00         1.0   \n",
       "2             5           2018-02-17     2018-02-18 14:36:00         1.0   \n",
       "3             5           2017-04-21     2017-04-21 22:02:00         1.0   \n",
       "4             5           2018-03-01     2018-03-02 10:26:00         1.0   \n",
       "\n",
       "                       product_id_x                       seller_id_x  \\\n",
       "0  fd25ab760bfbba13c198fa3b4f1a0cd3  6d803cb79cc31c41c4c789a75933b3c7   \n",
       "1  be0dbdc3d67d55727a65d4cd696ca73c  8e6d7754bc7e0f22c96d255ebda59eba   \n",
       "2  d1c427060a0f73f6b889a5c7c61f2ac4  a1043bafd471dff536d0c462352beb48   \n",
       "3  52c80cedd4e90108bf4fa6a206ef6b03  a1043bafd471dff536d0c462352beb48   \n",
       "4  3880d25d502b15b1de6fddc42ad1d67a  989becdce12ebc39863c2bceab6f3ca1   \n",
       "\n",
       "  shipping_limit_date_x  price_x  ...  freight_value_y  tot_cost_y  \\\n",
       "0   2018-01-18 15:47:00   185.00  ...            13.63      383.63   \n",
       "1   2018-03-06 12:48:00    79.79  ...             8.30       88.09   \n",
       "2   2018-02-08 10:31:00   149.00  ...            45.12      194.12   \n",
       "3   2017-04-13 17:55:00   179.99  ...            42.85      222.84   \n",
       "4   2018-02-15 15:48:00  1199.00  ...           134.25     1333.25   \n",
       "\n",
       "  product_category_name_y  product_name_lenght_y  \\\n",
       "0          Sports_Leisure                   42.0   \n",
       "1   Computers_Accessories                   47.0   \n",
       "2   Computers_Accessories                   59.0   \n",
       "3            Garden_Tools                   33.0   \n",
       "4          Sports_Leisure                   48.0   \n",
       "\n",
       "   product_description_lenght_y  product_photos_qty_y  product_weight_g_y  \\\n",
       "0                         858.0                   1.0              1300.0   \n",
       "1                         493.0                   1.0               245.0   \n",
       "2                        1893.0                   1.0              6550.0   \n",
       "3                        2188.0                   2.0              7650.0   \n",
       "4                         562.0                   5.0              9850.0   \n",
       "\n",
       "   product_length_cm_y  product_height_cm_y  product_width_cm_y  \n",
       "0                 30.0                 30.0                35.0  \n",
       "1                 19.0                 14.0                14.0  \n",
       "2                 20.0                 20.0                20.0  \n",
       "3                 20.0                 20.0                20.0  \n",
       "4                105.0                 35.0                50.0  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_review_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "e551b5b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product Categories with Minimum Avg Rating\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_category_name_y</th>\n",
       "      <th>review_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Security_And_Services</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Fashion_Male_Clothing</td>\n",
       "      <td>3.472973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Office_Furniture</td>\n",
       "      <td>3.530642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>La_Cuisine</td>\n",
       "      <td>3.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Fashio_Female_Clothing</td>\n",
       "      <td>3.644444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Home_Comfort_2</td>\n",
       "      <td>3.653846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Diapers_And_Hygiene</td>\n",
       "      <td>3.740741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Home_Confort</td>\n",
       "      <td>3.756646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bed_Bath_Table</td>\n",
       "      <td>3.759277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Home_Construction</td>\n",
       "      <td>3.779570</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_category_name_y  review_score\n",
       "61   Security_And_Services      2.500000\n",
       "30   Fashion_Male_Clothing      3.472973\n",
       "57        Office_Furniture      3.530642\n",
       "52              La_Cuisine      3.600000\n",
       "27  Fashio_Female_Clothing      3.644444\n",
       "46          Home_Comfort_2      3.653846\n",
       "23     Diapers_And_Hygiene      3.740741\n",
       "47            Home_Confort      3.756646\n",
       "7           Bed_Bath_Table      3.759277\n",
       "48       Home_Construction      3.779570"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_rev = order_review_cat.groupby('product_category_name_y')[['review_score']].mean().reset_index().sort_values(by = 'review_score')\n",
    "print(\"Product Categories with Minimum Avg Rating\")\n",
    "cat_rev.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "3aca2c3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product Categories with Maximum Avg Rating\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_category_name_y</th>\n",
       "      <th>review_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Cds_Dvds_Musicals</td>\n",
       "      <td>4.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Fashion_Childrens_Clothes</td>\n",
       "      <td>4.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Books_Imported</td>\n",
       "      <td>4.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Food_Drink</td>\n",
       "      <td>4.429104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Books_General_Interest</td>\n",
       "      <td>4.400347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Costruction_Tools_Tools</td>\n",
       "      <td>4.371134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Small_Appliances_Home_Oven_And_Coffee</td>\n",
       "      <td>4.320513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Luggage_Accessories</td>\n",
       "      <td>4.320327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Books_Technical</td>\n",
       "      <td>4.276978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Food</td>\n",
       "      <td>4.230277</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  product_category_name_y  review_score\n",
       "11                      Cds_Dvds_Musicals      4.666667\n",
       "29              Fashion_Childrens_Clothes      4.600000\n",
       "9                          Books_Imported      4.444444\n",
       "37                             Food_Drink      4.429104\n",
       "8                  Books_General_Interest      4.400347\n",
       "22                Costruction_Tools_Tools      4.371134\n",
       "64  Small_Appliances_Home_Oven_And_Coffee      4.320513\n",
       "53                    Luggage_Accessories      4.320327\n",
       "10                        Books_Technical      4.276978\n",
       "36                                   Food      4.230277"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Product Categories with Maximum Avg Rating\")\n",
    "cat_rev.sort_values(by = 'review_score',ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba6d52b9",
   "metadata": {},
   "source": [
    "##### b. Which products (top10) are maximum rated & minimum rated?¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "4c940b2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum Rated 10 products\n"
     ]
    },
    {
     "data": {
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       "      <th>31832</th>\n",
       "      <td>f744418ea0467967ce11d829d1f9419a</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           product_id_x  review_score\n",
       "11572  592cc6634d2c783d297effc0b828bc37           1.0\n",
       "24133  bba2c8c0d830b5d1eb20f4a343676de5           1.0\n",
       "2638   14086fd4907ab4135efce70c2cd3c013           1.0\n",
       "2641   141125957e8b78c5cf939c18077fa2f7           1.0\n",
       "2643   14132fa6062431ce5af633fd3368f0ee           1.0\n",
       "9221   46e477d89da734ac9f6b12073503711a           1.0\n",
       "2646   141a1bbd15437e6ce4765f29d126ef67           1.0\n",
       "16966  83634a7abf56905fc9ab415328908de2           1.0\n",
       "9212   46d8fdabc7206779c58d45a23ad08d18           1.0\n",
       "31832  f744418ea0467967ce11d829d1f9419a           1.0"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Minimum Rated 10 products')\n",
    "order_review_cat.groupby('product_id_x')[['review_score']].mean().reset_index().sort_values(by= 'review_score').head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "10c607ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum Rated 10 products\n"
     ]
    },
    {
     "data": {
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       "      <td>5.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           product_id_x  review_score\n",
       "0      00066f42aeeb9f3007548bb9d3f33c38           5.0\n",
       "17330  86743ff92eee3d16b7df59cddd583b8c           5.0\n",
       "17345  868ceb027ab706a4dee42e2220006b85           5.0\n",
       "17343  868969d3a93aeeab7bfcd4fc3d3d65de           5.0\n",
       "17342  868766ed2172644fdd977d6bd395a107           5.0\n",
       "17340  8684bd8f93b4f4038d07188a23811e93           5.0\n",
       "17335  867c96d2bb67aba6500a4c509cf76072           5.0\n",
       "17334  867b820367ec206b38a357f2c12454b7           5.0\n",
       "17333  867901d7e8488fb97f1fb538c09d476e           5.0\n",
       "17325  865bfa00c1dad8f4146d3c2765f051ca           5.0"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Maximum Rated 10 products')\n",
    "order_review_cat.groupby('product_id_x')['review_score'].mean().reset_index().sort_values(by = 'review_score', ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fe70f30",
   "metadata": {},
   "source": [
    "##### c. Average rating by location, seller, product, category, month etc.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "edf0066e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>product_id_x</th>\n",
       "      <th>review_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00066f42aeeb9f3007548bb9d3f33c38</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00088930e925c41fd95ebfe695fd2655</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0009406fd7479715e4bef61dd91f2462</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000b8f95fcb9e0096488278317764d19</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000d9be29b5207b54e86aa1b1ac54872</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32946</th>\n",
       "      <td>fff6177642830a9a94a0f2cba5e476d1</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32947</th>\n",
       "      <td>fff81cc3158d2725c0655ab9ba0f712c</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32948</th>\n",
       "      <td>fff9553ac224cec9d15d49f5a263411f</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32949</th>\n",
       "      <td>fffdb2d0ec8d6a61f0a0a0db3f25b441</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32950</th>\n",
       "      <td>fffe9eeff12fcbd74a2f2b007dde0c58</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32951 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           product_id_x  review_score\n",
       "0      00066f42aeeb9f3007548bb9d3f33c38           5.0\n",
       "1      00088930e925c41fd95ebfe695fd2655           4.0\n",
       "2      0009406fd7479715e4bef61dd91f2462           1.0\n",
       "3      000b8f95fcb9e0096488278317764d19           5.0\n",
       "4      000d9be29b5207b54e86aa1b1ac54872           5.0\n",
       "...                                 ...           ...\n",
       "32946  fff6177642830a9a94a0f2cba5e476d1           4.5\n",
       "32947  fff81cc3158d2725c0655ab9ba0f712c           4.0\n",
       "32948  fff9553ac224cec9d15d49f5a263411f           5.0\n",
       "32949  fffdb2d0ec8d6a61f0a0a0db3f25b441           5.0\n",
       "32950  fffe9eeff12fcbd74a2f2b007dde0c58           4.0\n",
       "\n",
       "[32951 rows x 2 columns]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Average rating by product\n",
    "order_review_cat.groupby('product_id_x')[['review_score']].mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "05f7e57e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>review_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apr</td>\n",
       "      <td>3.838670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aug</td>\n",
       "      <td>4.109224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dec</td>\n",
       "      <td>3.799247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Feb</td>\n",
       "      <td>3.957620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jan</td>\n",
       "      <td>3.955774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jul</td>\n",
       "      <td>4.142343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jun</td>\n",
       "      <td>4.097680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mar</td>\n",
       "      <td>3.718254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>May</td>\n",
       "      <td>4.081539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nov</td>\n",
       "      <td>3.975142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Oct</td>\n",
       "      <td>3.968890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Sep</td>\n",
       "      <td>4.102201</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Month  review_score\n",
       "0    Apr      3.838670\n",
       "1    Aug      4.109224\n",
       "2    Dec      3.799247\n",
       "3    Feb      3.957620\n",
       "4    Jan      3.955774\n",
       "5    Jul      4.142343\n",
       "6    Jun      4.097680\n",
       "7    Mar      3.718254\n",
       "8    May      4.081539\n",
       "9    Nov      3.975142\n",
       "10   Oct      3.968890\n",
       "11   Sep      4.102201"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Average rating by Month\n",
    "order_review_cat['Month']= order_review_cat.review_creation_date.dt.strftime(\"%b\")\n",
    "order_review_cat.groupby('Month')[['review_score']].mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "49469585",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seller_id_x</th>\n",
       "      <th>review_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0015a82c2db000af6aaaf3ae2ecb0532</td>\n",
       "      <td>3.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>001cca7ae9ae17fb1caed9dfb1094831</td>\n",
       "      <td>3.892157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>001e6ad469a905060d959994f1b41e4f</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>002100f778ceb8431b7a1020ff7ab48f</td>\n",
       "      <td>4.075758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>003554e2dce176b5555353e4f3555ac8</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3090</th>\n",
       "      <td>ffcfefa19b08742c5d315f2791395ee5</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3091</th>\n",
       "      <td>ffdd9f82b9a447f6f8d4b91554cc7dd3</td>\n",
       "      <td>4.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3092</th>\n",
       "      <td>ffeee66ac5d5a62fe688b9d26f83f534</td>\n",
       "      <td>4.214286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3093</th>\n",
       "      <td>fffd5413c0700ac820c7069d66d98c89</td>\n",
       "      <td>3.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3094</th>\n",
       "      <td>ffff564a4f9085cd26170f4732393726</td>\n",
       "      <td>2.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3095 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           seller_id_x  review_score\n",
       "0     0015a82c2db000af6aaaf3ae2ecb0532      3.666667\n",
       "1     001cca7ae9ae17fb1caed9dfb1094831      3.892157\n",
       "2     001e6ad469a905060d959994f1b41e4f      1.000000\n",
       "3     002100f778ceb8431b7a1020ff7ab48f      4.075758\n",
       "4     003554e2dce176b5555353e4f3555ac8      5.000000\n",
       "...                                ...           ...\n",
       "3090  ffcfefa19b08742c5d315f2791395ee5      1.000000\n",
       "3091  ffdd9f82b9a447f6f8d4b91554cc7dd3      4.333333\n",
       "3092  ffeee66ac5d5a62fe688b9d26f83f534      4.214286\n",
       "3093  fffd5413c0700ac820c7069d66d98c89      3.750000\n",
       "3094  ffff564a4f9085cd26170f4732393726      2.100000\n",
       "\n",
       "[3095 rows x 2 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Average Rating by seller\n",
    "order_review_cat.groupby('seller_id_x')[['review_score']].mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "fd53eeb2",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'customer_id'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_10080\\3517843666.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mord_items_orders_review\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morder_items\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder_review\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhow\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'left'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mon\u001b[0m\u001b[1;33m=\u001b[0m \u001b[1;34m'order_id'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mord_items_orders_review\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mord_items_orders_review_cust\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mleft\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mord_items_orders_review\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0mright\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcust\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhow\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'left'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mon\u001b[0m \u001b[1;33m=\u001b[0m\u001b[1;34m'customer_id'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0mord_items_orders_review_cust\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\merge.py\u001b[0m in \u001b[0;36mmerge\u001b[1;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[0;32m    105\u001b[0m     \u001b[0mvalidate\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    106\u001b[0m ) -> DataFrame:\n\u001b[1;32m--> 107\u001b[1;33m     op = _MergeOperation(\n\u001b[0m\u001b[0;32m    108\u001b[0m         \u001b[0mleft\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    109\u001b[0m         \u001b[0mright\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\merge.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, left, right, how, on, left_on, right_on, axis, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[0;32m    698\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mright_join_keys\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    699\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin_names\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 700\u001b[1;33m         ) = self._get_merge_keys()\n\u001b[0m\u001b[0;32m    701\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    702\u001b[0m         \u001b[1;31m# validate the merge keys dtypes. We may need to coerce\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\merge.py\u001b[0m in \u001b[0;36m_get_merge_keys\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1108\u001b[0m                         \u001b[0mright_keys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1109\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[0mlk\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1110\u001b[1;33m                         \u001b[0mleft_keys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_label_or_level_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1111\u001b[0m                         \u001b[0mjoin_names\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1112\u001b[0m                     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_get_label_or_level_values\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1838\u001b[0m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_level_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1839\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1840\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1841\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1842\u001b[0m         \u001b[1;31m# Check for duplicates\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'customer_id'"
     ]
    }
   ],
   "source": [
    "#Average Rating by location\n",
    "ord_items_orders_review = pd.merge(order_items, order_review, how = 'left', on= 'order_id')\n",
    "ord_items_orders_review\n",
    "ord_items_orders_review_cust = pd.merge(left = ord_items_orders_review , right = cust, how = 'left', on ='customer_id')\n",
    "ord_items_orders_review_cust"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c25f5bd4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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