Wish_Ecommerce_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dc9ff6ef",
   "metadata": {},
   "source": [
    "# E-commerce website 'Wish' Summer Clothes Sale EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7d34ecdb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:44.750244Z",
     "start_time": "2022-07-04T09:42:43.247179Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import pandasql as ps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1708a8bd",
   "metadata": {},
   "source": [
    "## Reading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b0cd3d0d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:44.796277Z",
     "start_time": "2022-07-04T09:42:44.750244Z"
    }
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv(r\"C:\\Users\\Twinkle Sahni\\Downloads\\jfreex-summer-products-and-sales-performance-in-e-commerce-on-wish\\jfreex-summer-products-and-sales-performance-in-e-commerce-on-wish\\summer-products-with-rating-and-performance_2020-08.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "21c166fb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:44.843684Z",
     "start_time": "2022-07-04T09:42:44.799312Z"
    }
   },
   "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>title</th>\n",
       "      <th>title_orig</th>\n",
       "      <th>price</th>\n",
       "      <th>retail_price</th>\n",
       "      <th>currency_buyer</th>\n",
       "      <th>units_sold</th>\n",
       "      <th>uses_ad_boosts</th>\n",
       "      <th>rating</th>\n",
       "      <th>rating_count</th>\n",
       "      <th>rating_five_count</th>\n",
       "      <th>...</th>\n",
       "      <th>merchant_rating_count</th>\n",
       "      <th>merchant_rating</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>merchant_has_profile_picture</th>\n",
       "      <th>merchant_profile_picture</th>\n",
       "      <th>product_url</th>\n",
       "      <th>product_picture</th>\n",
       "      <th>product_id</th>\n",
       "      <th>theme</th>\n",
       "      <th>crawl_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020 Summer Vintage Flamingo Print  Pajamas Se...</td>\n",
       "      <td>2020 Summer Vintage Flamingo Print  Pajamas Se...</td>\n",
       "      <td>16.00</td>\n",
       "      <td>14</td>\n",
       "      <td>EUR</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>3.76</td>\n",
       "      <td>54</td>\n",
       "      <td>26.0</td>\n",
       "      <td>...</td>\n",
       "      <td>568</td>\n",
       "      <td>4.128521</td>\n",
       "      <td>595097d6a26f6e070cb878d1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.wish.com/c/5e9ae51d43d6a96e303acdb0</td>\n",
       "      <td>https://contestimg.wish.com/api/webimage/5e9ae...</td>\n",
       "      <td>5e9ae51d43d6a96e303acdb0</td>\n",
       "      <td>summer</td>\n",
       "      <td>2020-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SSHOUSE Summer Casual Sleeveless Soirée Party ...</td>\n",
       "      <td>Women's Casual Summer Sleeveless Sexy Mini Dress</td>\n",
       "      <td>8.00</td>\n",
       "      <td>22</td>\n",
       "      <td>EUR</td>\n",
       "      <td>20000</td>\n",
       "      <td>1</td>\n",
       "      <td>3.45</td>\n",
       "      <td>6135</td>\n",
       "      <td>2269.0</td>\n",
       "      <td>...</td>\n",
       "      <td>17752</td>\n",
       "      <td>3.899673</td>\n",
       "      <td>56458aa03a698c35c9050988</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.wish.com/c/58940d436a0d3d5da4e95a38</td>\n",
       "      <td>https://contestimg.wish.com/api/webimage/58940...</td>\n",
       "      <td>58940d436a0d3d5da4e95a38</td>\n",
       "      <td>summer</td>\n",
       "      <td>2020-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020 Nouvelle Arrivée Femmes Printemps et Été ...</td>\n",
       "      <td>2020 New Arrival Women Spring and Summer Beach...</td>\n",
       "      <td>8.00</td>\n",
       "      <td>43</td>\n",
       "      <td>EUR</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>3.57</td>\n",
       "      <td>14</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>295</td>\n",
       "      <td>3.989831</td>\n",
       "      <td>5d464a1ffdf7bc44ee933c65</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.wish.com/c/5ea10e2c617580260d55310a</td>\n",
       "      <td>https://contestimg.wish.com/api/webimage/5ea10...</td>\n",
       "      <td>5ea10e2c617580260d55310a</td>\n",
       "      <td>summer</td>\n",
       "      <td>2020-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hot Summer Cool T-shirt pour les femmes Mode T...</td>\n",
       "      <td>Hot Summer Cool T Shirt for Women Fashion Tops...</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8</td>\n",
       "      <td>EUR</td>\n",
       "      <td>5000</td>\n",
       "      <td>1</td>\n",
       "      <td>4.03</td>\n",
       "      <td>579</td>\n",
       "      <td>295.0</td>\n",
       "      <td>...</td>\n",
       "      <td>23832</td>\n",
       "      <td>4.020435</td>\n",
       "      <td>58cfdefdacb37b556efdff7c</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.wish.com/c/5cedf17ad1d44c52c59e4aca</td>\n",
       "      <td>https://contestimg.wish.com/api/webimage/5cedf...</td>\n",
       "      <td>5cedf17ad1d44c52c59e4aca</td>\n",
       "      <td>summer</td>\n",
       "      <td>2020-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Femmes Shorts d'été à lacets taille élastique ...</td>\n",
       "      <td>Women Summer Shorts Lace Up Elastic Waistband ...</td>\n",
       "      <td>2.72</td>\n",
       "      <td>3</td>\n",
       "      <td>EUR</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>3.10</td>\n",
       "      <td>20</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14482</td>\n",
       "      <td>4.001588</td>\n",
       "      <td>5ab3b592c3911a095ad5dadb</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.wish.com/c/5ebf5819ebac372b070b0e70</td>\n",
       "      <td>https://contestimg.wish.com/api/webimage/5ebf5...</td>\n",
       "      <td>5ebf5819ebac372b070b0e70</td>\n",
       "      <td>summer</td>\n",
       "      <td>2020-08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title  \\\n",
       "0  2020 Summer Vintage Flamingo Print  Pajamas Se...   \n",
       "1  SSHOUSE Summer Casual Sleeveless Soirée Party ...   \n",
       "2  2020 Nouvelle Arrivée Femmes Printemps et Été ...   \n",
       "3  Hot Summer Cool T-shirt pour les femmes Mode T...   \n",
       "4  Femmes Shorts d'été à lacets taille élastique ...   \n",
       "\n",
       "                                          title_orig  price  retail_price  \\\n",
       "0  2020 Summer Vintage Flamingo Print  Pajamas Se...  16.00            14   \n",
       "1   Women's Casual Summer Sleeveless Sexy Mini Dress   8.00            22   \n",
       "2  2020 New Arrival Women Spring and Summer Beach...   8.00            43   \n",
       "3  Hot Summer Cool T Shirt for Women Fashion Tops...   8.00             8   \n",
       "4  Women Summer Shorts Lace Up Elastic Waistband ...   2.72             3   \n",
       "\n",
       "  currency_buyer  units_sold  uses_ad_boosts  rating  rating_count  \\\n",
       "0            EUR         100               0    3.76            54   \n",
       "1            EUR       20000               1    3.45          6135   \n",
       "2            EUR         100               0    3.57            14   \n",
       "3            EUR        5000               1    4.03           579   \n",
       "4            EUR         100               1    3.10            20   \n",
       "\n",
       "   rating_five_count  ...  merchant_rating_count  merchant_rating  \\\n",
       "0               26.0  ...                    568         4.128521   \n",
       "1             2269.0  ...                  17752         3.899673   \n",
       "2                5.0  ...                    295         3.989831   \n",
       "3              295.0  ...                  23832         4.020435   \n",
       "4                6.0  ...                  14482         4.001588   \n",
       "\n",
       "                merchant_id  merchant_has_profile_picture  \\\n",
       "0  595097d6a26f6e070cb878d1                             0   \n",
       "1  56458aa03a698c35c9050988                             0   \n",
       "2  5d464a1ffdf7bc44ee933c65                             0   \n",
       "3  58cfdefdacb37b556efdff7c                             0   \n",
       "4  5ab3b592c3911a095ad5dadb                             0   \n",
       "\n",
       "   merchant_profile_picture                                      product_url  \\\n",
       "0                       NaN  https://www.wish.com/c/5e9ae51d43d6a96e303acdb0   \n",
       "1                       NaN  https://www.wish.com/c/58940d436a0d3d5da4e95a38   \n",
       "2                       NaN  https://www.wish.com/c/5ea10e2c617580260d55310a   \n",
       "3                       NaN  https://www.wish.com/c/5cedf17ad1d44c52c59e4aca   \n",
       "4                       NaN  https://www.wish.com/c/5ebf5819ebac372b070b0e70   \n",
       "\n",
       "                                     product_picture  \\\n",
       "0  https://contestimg.wish.com/api/webimage/5e9ae...   \n",
       "1  https://contestimg.wish.com/api/webimage/58940...   \n",
       "2  https://contestimg.wish.com/api/webimage/5ea10...   \n",
       "3  https://contestimg.wish.com/api/webimage/5cedf...   \n",
       "4  https://contestimg.wish.com/api/webimage/5ebf5...   \n",
       "\n",
       "                 product_id   theme crawl_month  \n",
       "0  5e9ae51d43d6a96e303acdb0  summer     2020-08  \n",
       "1  58940d436a0d3d5da4e95a38  summer     2020-08  \n",
       "2  5ea10e2c617580260d55310a  summer     2020-08  \n",
       "3  5cedf17ad1d44c52c59e4aca  summer     2020-08  \n",
       "4  5ebf5819ebac372b070b0e70  summer     2020-08  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d07ba184",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:44.899537Z",
     "start_time": "2022-07-04T09:42:44.845681Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape : (1573, 43)\n"
     ]
    }
   ],
   "source": [
    "print(\"Data shape :\",data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dbec200f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:44.952395Z",
     "start_time": "2022-07-04T09:42:44.902529Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1573 entries, 0 to 1572\n",
      "Data columns (total 43 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   title                         1573 non-null   object \n",
      " 1   title_orig                    1573 non-null   object \n",
      " 2   price                         1573 non-null   float64\n",
      " 3   retail_price                  1573 non-null   int64  \n",
      " 4   currency_buyer                1573 non-null   object \n",
      " 5   units_sold                    1573 non-null   int64  \n",
      " 6   uses_ad_boosts                1573 non-null   int64  \n",
      " 7   rating                        1573 non-null   float64\n",
      " 8   rating_count                  1573 non-null   int64  \n",
      " 9   rating_five_count             1528 non-null   float64\n",
      " 10  rating_four_count             1528 non-null   float64\n",
      " 11  rating_three_count            1528 non-null   float64\n",
      " 12  rating_two_count              1528 non-null   float64\n",
      " 13  rating_one_count              1528 non-null   float64\n",
      " 14  badges_count                  1573 non-null   int64  \n",
      " 15  badge_local_product           1573 non-null   int64  \n",
      " 16  badge_product_quality         1573 non-null   int64  \n",
      " 17  badge_fast_shipping           1573 non-null   int64  \n",
      " 18  tags                          1573 non-null   object \n",
      " 19  product_color                 1532 non-null   object \n",
      " 20  product_variation_size_id     1559 non-null   object \n",
      " 21  product_variation_inventory   1573 non-null   int64  \n",
      " 22  shipping_option_name          1573 non-null   object \n",
      " 23  shipping_option_price         1573 non-null   int64  \n",
      " 24  shipping_is_express           1573 non-null   int64  \n",
      " 25  countries_shipped_to          1573 non-null   int64  \n",
      " 26  inventory_total               1573 non-null   int64  \n",
      " 27  has_urgency_banner            473 non-null    float64\n",
      " 28  urgency_text                  473 non-null    object \n",
      " 29  origin_country                1556 non-null   object \n",
      " 30  merchant_title                1573 non-null   object \n",
      " 31  merchant_name                 1569 non-null   object \n",
      " 32  merchant_info_subtitle        1572 non-null   object \n",
      " 33  merchant_rating_count         1573 non-null   int64  \n",
      " 34  merchant_rating               1573 non-null   float64\n",
      " 35  merchant_id                   1573 non-null   object \n",
      " 36  merchant_has_profile_picture  1573 non-null   int64  \n",
      " 37  merchant_profile_picture      226 non-null    object \n",
      " 38  product_url                   1573 non-null   object \n",
      " 39  product_picture               1573 non-null   object \n",
      " 40  product_id                    1573 non-null   object \n",
      " 41  theme                         1573 non-null   object \n",
      " 42  crawl_month                   1573 non-null   object \n",
      "dtypes: float64(9), int64(15), object(19)\n",
      "memory usage: 528.6+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cc22740",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "495ec286",
   "metadata": {},
   "source": [
    "### Dropping the irrelevant features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e7681151",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:44.973339Z",
     "start_time": "2022-07-04T09:42:44.954389Z"
    }
   },
   "outputs": [],
   "source": [
    "df = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0affff10",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:44.983311Z",
     "start_time": "2022-07-04T09:42:44.975334Z"
    }
   },
   "outputs": [],
   "source": [
    "df.drop(['currency_buyer', 'badges_count', 'badge_local_product', 'badge_product_quality', 'badge_fast_shipping',\n",
    "         'product_variation_inventory','has_urgency_banner', 'urgency_text',\n",
    "         'merchant_title','merchant_info_subtitle','merchant_has_profile_picture', \n",
    "         'merchant_profile_picture', 'product_url', 'product_picture', 'theme', 'crawl_month'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d88df70",
   "metadata": {},
   "source": [
    "### Cleaning the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "51382309",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:44.999269Z",
     "start_time": "2022-07-04T09:42:44.987302Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title                         0\n",
       "title_orig                    0\n",
       "price                         0\n",
       "retail_price                  0\n",
       "units_sold                    0\n",
       "uses_ad_boosts                0\n",
       "rating                        0\n",
       "rating_count                  0\n",
       "rating_five_count            45\n",
       "rating_four_count            45\n",
       "rating_three_count           45\n",
       "rating_two_count             45\n",
       "rating_one_count             45\n",
       "tags                          0\n",
       "product_color                41\n",
       "product_variation_size_id    14\n",
       "shipping_option_name          0\n",
       "shipping_option_price         0\n",
       "shipping_is_express           0\n",
       "countries_shipped_to          0\n",
       "inventory_total               0\n",
       "origin_country               17\n",
       "merchant_name                 4\n",
       "merchant_rating_count         0\n",
       "merchant_rating               0\n",
       "merchant_id                   0\n",
       "product_id                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26fea5c9",
   "metadata": {},
   "source": [
    "The rating count has zero null values while individual numerical rating count has 45 nulls, implying that for 45 products we do not have segmented ratings which will not be an issue in our analysis. Similarly, 41 items has no information about the color. We can drop these values from our dataset.\n",
    "Moreover, for 17 merchandise, the country for origin is not known and I think in this case, the null values can be filled using Central Tendency Measures, i.e., Mode. Missing merchant names can just be dopped off the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "81f17835",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:45.015226Z",
     "start_time": "2022-07-04T09:42:45.004258Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CN'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['origin_country'].mode().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "946d6601",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:45.030187Z",
     "start_time": "2022-07-04T09:42:45.016224Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       CN\n",
       "1       CN\n",
       "2       CN\n",
       "3       CN\n",
       "4       CN\n",
       "        ..\n",
       "1568    CN\n",
       "1569    CN\n",
       "1570    CN\n",
       "1571    CN\n",
       "1572    CN\n",
       "Name: origin_country, Length: 1573, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['origin_country'].replace('NaN', 'CN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a2e1caaa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:45.046144Z",
     "start_time": "2022-07-04T09:42:45.032183Z"
    }
   },
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7164a7b7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:45.077060Z",
     "start_time": "2022-07-04T09:42:45.048139Z"
    }
   },
   "outputs": [
    {
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       "       title  title_orig  price  retail_price  units_sold  uses_ad_boosts  \\\n",
       "nulls      0           0      0             0           0               0   \n",
       "\n",
       "       rating  rating_count  rating_five_count  rating_four_count  ...  \\\n",
       "nulls       0             0                  0                  0  ...   \n",
       "\n",
       "       shipping_option_price  shipping_is_express  countries_shipped_to  \\\n",
       "nulls                      0                    0                     0   \n",
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       "       inventory_total  origin_country  merchant_name  merchant_rating_count  \\\n",
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    "null1 = df.isnull().sum().to_frame(name='nulls').T\n",
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   "metadata": {},
   "source": [
    "## Analyzing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6d19e9e6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:45.121942Z",
     "start_time": "2022-07-04T09:42:45.078058Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2020 Summer Vintage Flamingo Print  Pajamas Se...</td>\n",
       "      <td>2020 Summer Vintage Flamingo Print  Pajamas Se...</td>\n",
       "      <td>16.00</td>\n",
       "      <td>14</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>3.76</td>\n",
       "      <td>54</td>\n",
       "      <td>26.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "      <td>CN</td>\n",
       "      <td>zgrdejia</td>\n",
       "      <td>568</td>\n",
       "      <td>4.128521</td>\n",
       "      <td>595097d6a26f6e070cb878d1</td>\n",
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       "      <td>SSHOUSE Summer Casual Sleeveless Soirée Party ...</td>\n",
       "      <td>Women's Casual Summer Sleeveless Sexy Mini Dress</td>\n",
       "      <td>8.00</td>\n",
       "      <td>22</td>\n",
       "      <td>20000</td>\n",
       "      <td>1</td>\n",
       "      <td>3.45</td>\n",
       "      <td>6135</td>\n",
       "      <td>2269.0</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>50</td>\n",
       "      <td>CN</td>\n",
       "      <td>sarahouse</td>\n",
       "      <td>17752</td>\n",
       "      <td>3.899673</td>\n",
       "      <td>56458aa03a698c35c9050988</td>\n",
       "      <td>58940d436a0d3d5da4e95a38</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020 Nouvelle Arrivée Femmes Printemps et Été ...</td>\n",
       "      <td>2020 New Arrival Women Spring and Summer Beach...</td>\n",
       "      <td>8.00</td>\n",
       "      <td>43</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>3.57</td>\n",
       "      <td>14</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>50</td>\n",
       "      <td>CN</td>\n",
       "      <td>hxt520</td>\n",
       "      <td>295</td>\n",
       "      <td>3.989831</td>\n",
       "      <td>5d464a1ffdf7bc44ee933c65</td>\n",
       "      <td>5ea10e2c617580260d55310a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hot Summer Cool T-shirt pour les femmes Mode T...</td>\n",
       "      <td>Hot Summer Cool T Shirt for Women Fashion Tops...</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8</td>\n",
       "      <td>5000</td>\n",
       "      <td>1</td>\n",
       "      <td>4.03</td>\n",
       "      <td>579</td>\n",
       "      <td>295.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>50</td>\n",
       "      <td>CN</td>\n",
       "      <td>allenfan</td>\n",
       "      <td>23832</td>\n",
       "      <td>4.020435</td>\n",
       "      <td>58cfdefdacb37b556efdff7c</td>\n",
       "      <td>5cedf17ad1d44c52c59e4aca</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Femmes Shorts d'été à lacets taille élastique ...</td>\n",
       "      <td>Women Summer Shorts Lace Up Elastic Waistband ...</td>\n",
       "      <td>2.72</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>3.10</td>\n",
       "      <td>20</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>50</td>\n",
       "      <td>CN</td>\n",
       "      <td>happyhorses</td>\n",
       "      <td>14482</td>\n",
       "      <td>4.001588</td>\n",
       "      <td>5ab3b592c3911a095ad5dadb</td>\n",
       "      <td>5ebf5819ebac372b070b0e70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Plus la taille d'été femmes décontracté sans m...</td>\n",
       "      <td>Plus Size Summer Women Casual Sleeveless Rompe...</td>\n",
       "      <td>3.92</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "      <td>CN</td>\n",
       "      <td>zhoulinglinga</td>\n",
       "      <td>65</td>\n",
       "      <td>3.507692</td>\n",
       "      <td>5e4b9c3801ba9d210036fc5a</td>\n",
       "      <td>5ec645bafd107a02279c8c54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Women Fashion Loose Lace Blouse Blouse V Neck ...</td>\n",
       "      <td>Women Fashion Loose Lace Blouse V Neck Bat Sle...</td>\n",
       "      <td>7.00</td>\n",
       "      <td>6</td>\n",
       "      <td>50000</td>\n",
       "      <td>0</td>\n",
       "      <td>3.84</td>\n",
       "      <td>6742</td>\n",
       "      <td>3172.0</td>\n",
       "      <td>1352.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>50</td>\n",
       "      <td>CN</td>\n",
       "      <td>uniquelifashionshopbb657bfe91d211e598c7063a14d...</td>\n",
       "      <td>10194</td>\n",
       "      <td>4.076516</td>\n",
       "      <td>5652f4053a698c76dc9a3f37</td>\n",
       "      <td>5c63a337d5e2ce4bbb3152cf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Robe tunique ample femme Robe d'été Robe en je...</td>\n",
       "      <td>Women's Baggy Tunic Dress Summer Dress Denim D...</td>\n",
       "      <td>12.00</td>\n",
       "      <td>11</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>3.76</td>\n",
       "      <td>286</td>\n",
       "      <td>120.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>139</td>\n",
       "      <td>50</td>\n",
       "      <td>CN</td>\n",
       "      <td>soband</td>\n",
       "      <td>342</td>\n",
       "      <td>3.681287</td>\n",
       "      <td>5d45349676befe65691dcfbb</td>\n",
       "      <td>5e0ae5ebc2efb76ccf0a3391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Robe d'été décontractée à manches courtes pour...</td>\n",
       "      <td>Women's Summer Casual Dress Fashion Short Slee...</td>\n",
       "      <td>11.00</td>\n",
       "      <td>84</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>3.47</td>\n",
       "      <td>15</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>50</td>\n",
       "      <td>CN</td>\n",
       "      <td>chenxiangjunjun</td>\n",
       "      <td>330</td>\n",
       "      <td>3.803030</td>\n",
       "      <td>5d42980e8388970d32294ddc</td>\n",
       "      <td>5e6f1fb7fe4a5bb4b8bf36e5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Femmes d'été, plus la taille décontractée lâch...</td>\n",
       "      <td>Summer Women Plus Size Casual Loose V Neck Sho...</td>\n",
       "      <td>5.78</td>\n",
       "      <td>22</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "      <td>3.60</td>\n",
       "      <td>687</td>\n",
       "      <td>287.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>50</td>\n",
       "      <td>CN</td>\n",
       "      <td>luoweiclothe</td>\n",
       "      <td>5534</td>\n",
       "      <td>3.999819</td>\n",
       "      <td>5ba2251b4315d12ebce873fa</td>\n",
       "      <td>5ccfaf238a8d535cec2dfb47</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>10 rows × 27 columns</p>\n",
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      "text/plain": [
       "                                               title  \\\n",
       "0  2020 Summer Vintage Flamingo Print  Pajamas Se...   \n",
       "1  SSHOUSE Summer Casual Sleeveless Soirée Party ...   \n",
       "2  2020 Nouvelle Arrivée Femmes Printemps et Été ...   \n",
       "3  Hot Summer Cool T-shirt pour les femmes Mode T...   \n",
       "4  Femmes Shorts d'été à lacets taille élastique ...   \n",
       "5  Plus la taille d'été femmes décontracté sans m...   \n",
       "6  Women Fashion Loose Lace Blouse Blouse V Neck ...   \n",
       "7  Robe tunique ample femme Robe d'été Robe en je...   \n",
       "8  Robe d'été décontractée à manches courtes pour...   \n",
       "9  Femmes d'été, plus la taille décontractée lâch...   \n",
       "\n",
       "                                          title_orig  price  retail_price  \\\n",
       "0  2020 Summer Vintage Flamingo Print  Pajamas Se...  16.00            14   \n",
       "1   Women's Casual Summer Sleeveless Sexy Mini Dress   8.00            22   \n",
       "2  2020 New Arrival Women Spring and Summer Beach...   8.00            43   \n",
       "3  Hot Summer Cool T Shirt for Women Fashion Tops...   8.00             8   \n",
       "4  Women Summer Shorts Lace Up Elastic Waistband ...   2.72             3   \n",
       "5  Plus Size Summer Women Casual Sleeveless Rompe...   3.92             9   \n",
       "6  Women Fashion Loose Lace Blouse V Neck Bat Sle...   7.00             6   \n",
       "7  Women's Baggy Tunic Dress Summer Dress Denim D...  12.00            11   \n",
       "8  Women's Summer Casual Dress Fashion Short Slee...  11.00            84   \n",
       "9  Summer Women Plus Size Casual Loose V Neck Sho...   5.78            22   \n",
       "\n",
       "   units_sold  uses_ad_boosts  rating  rating_count  rating_five_count  \\\n",
       "0         100               0    3.76            54               26.0   \n",
       "1       20000               1    3.45          6135             2269.0   \n",
       "2         100               0    3.57            14                5.0   \n",
       "3        5000               1    4.03           579              295.0   \n",
       "4         100               1    3.10            20                6.0   \n",
       "5          10               0    5.00             1                1.0   \n",
       "6       50000               0    3.84          6742             3172.0   \n",
       "7        1000               0    3.76           286              120.0   \n",
       "8         100               1    3.47            15                6.0   \n",
       "9        5000               0    3.60           687              287.0   \n",
       "\n",
       "   rating_four_count  ...  shipping_option_price  shipping_is_express  \\\n",
       "0                8.0  ...                      4                    0   \n",
       "1             1027.0  ...                      2                    0   \n",
       "2                4.0  ...                      3                    0   \n",
       "3              119.0  ...                      2                    0   \n",
       "4                4.0  ...                      1                    0   \n",
       "5                0.0  ...                      1                    0   \n",
       "6             1352.0  ...                      2                    0   \n",
       "7               56.0  ...                      3                    0   \n",
       "8                2.0  ...                      2                    0   \n",
       "9              128.0  ...                      2                    0   \n",
       "\n",
       "   countries_shipped_to inventory_total origin_country  \\\n",
       "0                    34              50             CN   \n",
       "1                    41              50             CN   \n",
       "2                    36              50             CN   \n",
       "3                    41              50             CN   \n",
       "4                    35              50             CN   \n",
       "5                    40              50             CN   \n",
       "6                    31              50             CN   \n",
       "7                   139              50             CN   \n",
       "8                    36              50             CN   \n",
       "9                    33              50             CN   \n",
       "\n",
       "                                       merchant_name merchant_rating_count  \\\n",
       "0                                           zgrdejia                   568   \n",
       "1                                          sarahouse                 17752   \n",
       "2                                             hxt520                   295   \n",
       "3                                           allenfan                 23832   \n",
       "4                                        happyhorses                 14482   \n",
       "5                                      zhoulinglinga                    65   \n",
       "6  uniquelifashionshopbb657bfe91d211e598c7063a14d...                 10194   \n",
       "7                                             soband                   342   \n",
       "8                                    chenxiangjunjun                   330   \n",
       "9                                       luoweiclothe                  5534   \n",
       "\n",
       "   merchant_rating               merchant_id                product_id  \n",
       "0         4.128521  595097d6a26f6e070cb878d1  5e9ae51d43d6a96e303acdb0  \n",
       "1         3.899673  56458aa03a698c35c9050988  58940d436a0d3d5da4e95a38  \n",
       "2         3.989831  5d464a1ffdf7bc44ee933c65  5ea10e2c617580260d55310a  \n",
       "3         4.020435  58cfdefdacb37b556efdff7c  5cedf17ad1d44c52c59e4aca  \n",
       "4         4.001588  5ab3b592c3911a095ad5dadb  5ebf5819ebac372b070b0e70  \n",
       "5         3.507692  5e4b9c3801ba9d210036fc5a  5ec645bafd107a02279c8c54  \n",
       "6         4.076516  5652f4053a698c76dc9a3f37  5c63a337d5e2ce4bbb3152cf  \n",
       "7         3.681287  5d45349676befe65691dcfbb  5e0ae5ebc2efb76ccf0a3391  \n",
       "8         3.803030  5d42980e8388970d32294ddc  5e6f1fb7fe4a5bb4b8bf36e5  \n",
       "9         3.999819  5ba2251b4315d12ebce873fa  5ccfaf238a8d535cec2dfb47  \n",
       "\n",
       "[10 rows x 27 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ccf44f43",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:45.183776Z",
     "start_time": "2022-07-04T09:42:45.123937Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
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       "      <th>50%</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>1457.0</td>\n",
       "      <td>8.366575</td>\n",
       "      <td>3.893274</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.860000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>11.00000</td>\n",
       "      <td>4.900000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retail_price</th>\n",
       "      <td>1457.0</td>\n",
       "      <td>23.431023</td>\n",
       "      <td>30.868310</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>26.00000</td>\n",
       "      <td>2.520000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>units_sold</th>\n",
       "      <td>1457.0</td>\n",
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       "      <td>9037.937218</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>5000.00000</td>\n",
       "      <td>1.000000e+05</td>\n",
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       "    <tr>\n",
       "      <th>uses_ad_boosts</th>\n",
       "      <td>1457.0</td>\n",
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       "      <td>1.00000</td>\n",
       "      <td>1.000000e+00</td>\n",
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       "    <tr>\n",
       "      <th>rating</th>\n",
       "      <td>1457.0</td>\n",
       "      <td>3.778758</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>3.520000</td>\n",
       "      <td>3.830000</td>\n",
       "      <td>4.08000</td>\n",
       "      <td>5.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating_count</th>\n",
       "      <td>1457.0</td>\n",
       "      <td>886.133837</td>\n",
       "      <td>1928.281899</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>876.00000</td>\n",
       "      <td>2.074400e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating_five_count</th>\n",
       "      <td>1457.0</td>\n",
       "      <td>430.795470</td>\n",
       "      <td>956.840252</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>400.00000</td>\n",
       "      <td>1.154800e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating_four_count</th>\n",
       "      <td>1457.0</td>\n",
       "      <td>174.533288</td>\n",
       "      <td>388.128840</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>168.00000</td>\n",
       "      <td>4.152000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating_three_count</th>\n",
       "      <td>1457.0</td>\n",
       "      <td>129.252574</td>\n",
       "      <td>295.739698</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>128.00000</td>\n",
       "      <td>3.658000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating_two_count</th>\n",
       "      <td>1457.0</td>\n",
       "      <td>60.535347</td>\n",
       "      <td>139.752775</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>62.00000</td>\n",
       "      <td>2.003000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating_one_count</th>\n",
       "      <td>1457.0</td>\n",
       "      <td>91.017159</td>\n",
       "      <td>194.803068</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>94.00000</td>\n",
       "      <td>2.559000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shipping_option_price</th>\n",
       "      <td>1457.0</td>\n",
       "      <td>2.345916</td>\n",
       "      <td>1.005363</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>1.200000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shipping_is_express</th>\n",
       "      <td>1457.0</td>\n",
       "      <td>0.001373</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>countries_shipped_to</th>\n",
       "      <td>1457.0</td>\n",
       "      <td>40.258065</td>\n",
       "      <td>19.852979</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>43.00000</td>\n",
       "      <td>1.400000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inventory_total</th>\n",
       "      <td>1457.0</td>\n",
       "      <td>49.849005</td>\n",
       "      <td>2.381935</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.00000</td>\n",
       "      <td>5.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>merchant_rating_count</th>\n",
       "      <td>1457.0</td>\n",
       "      <td>23800.970487</td>\n",
       "      <td>69927.107078</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2044.000000</td>\n",
       "      <td>7874.000000</td>\n",
       "      <td>23161.00000</td>\n",
       "      <td>2.174765e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>merchant_rating</th>\n",
       "      <td>1457.0</td>\n",
       "      <td>4.028146</td>\n",
       "      <td>0.196138</td>\n",
       "      <td>2.941176</td>\n",
       "      <td>3.917353</td>\n",
       "      <td>4.039474</td>\n",
       "      <td>4.15528</td>\n",
       "      <td>4.577519e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        count          mean           std       min  \\\n",
       "price                  1457.0      8.366575      3.893274  1.000000   \n",
       "retail_price           1457.0     23.431023     30.868310  1.000000   \n",
       "units_sold             1457.0   4339.216884   9037.937218  2.000000   \n",
       "uses_ad_boosts         1457.0      0.437886      0.496297  0.000000   \n",
       "rating                 1457.0      3.778758      0.482481  1.000000   \n",
       "rating_count           1457.0    886.133837   1928.281899  1.000000   \n",
       "rating_five_count      1457.0    430.795470    956.840252  0.000000   \n",
       "rating_four_count      1457.0    174.533288    388.128840  0.000000   \n",
       "rating_three_count     1457.0    129.252574    295.739698  0.000000   \n",
       "rating_two_count       1457.0     60.535347    139.752775  0.000000   \n",
       "rating_one_count       1457.0     91.017159    194.803068  0.000000   \n",
       "shipping_option_price  1457.0      2.345916      1.005363  1.000000   \n",
       "shipping_is_express    1457.0      0.001373      0.037037  0.000000   \n",
       "countries_shipped_to   1457.0     40.258065     19.852979  6.000000   \n",
       "inventory_total        1457.0     49.849005      2.381935  1.000000   \n",
       "merchant_rating_count  1457.0  23800.970487  69927.107078  3.000000   \n",
       "merchant_rating        1457.0      4.028146      0.196138  2.941176   \n",
       "\n",
       "                               25%          50%          75%           max  \n",
       "price                     5.860000     8.000000     11.00000  4.900000e+01  \n",
       "retail_price              7.000000    10.000000     26.00000  2.520000e+02  \n",
       "units_sold              100.000000  1000.000000   5000.00000  1.000000e+05  \n",
       "uses_ad_boosts            0.000000     0.000000      1.00000  1.000000e+00  \n",
       "rating                    3.520000     3.830000      4.08000  5.000000e+00  \n",
       "rating_count             27.000000   166.000000    876.00000  2.074400e+04  \n",
       "rating_five_count        11.000000    79.000000    400.00000  1.154800e+04  \n",
       "rating_four_count         5.000000    31.000000    168.00000  4.152000e+03  \n",
       "rating_three_count        4.000000    24.000000    128.00000  3.658000e+03  \n",
       "rating_two_count          2.000000    11.000000     62.00000  2.003000e+03  \n",
       "rating_one_count          3.000000    20.000000     94.00000  2.559000e+03  \n",
       "shipping_option_price     2.000000     2.000000      3.00000  1.200000e+01  \n",
       "shipping_is_express       0.000000     0.000000      0.00000  1.000000e+00  \n",
       "countries_shipped_to     31.000000    40.000000     43.00000  1.400000e+02  \n",
       "inventory_total          50.000000    50.000000     50.00000  5.000000e+01  \n",
       "merchant_rating_count  2044.000000  7874.000000  23161.00000  2.174765e+06  \n",
       "merchant_rating           3.917353     4.039474      4.15528  4.577519e+00  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ec87e0a6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:45.198737Z",
     "start_time": "2022-07-04T09:42:45.184773Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title                         object\n",
       "title_orig                    object\n",
       "price                        float64\n",
       "retail_price                   int64\n",
       "units_sold                     int64\n",
       "uses_ad_boosts                 int64\n",
       "rating                       float64\n",
       "rating_count                   int64\n",
       "rating_five_count            float64\n",
       "rating_four_count            float64\n",
       "rating_three_count           float64\n",
       "rating_two_count             float64\n",
       "rating_one_count             float64\n",
       "tags                          object\n",
       "product_color                 object\n",
       "product_variation_size_id     object\n",
       "shipping_option_name          object\n",
       "shipping_option_price          int64\n",
       "shipping_is_express            int64\n",
       "countries_shipped_to           int64\n",
       "inventory_total                int64\n",
       "origin_country                object\n",
       "merchant_name                 object\n",
       "merchant_rating_count          int64\n",
       "merchant_rating              float64\n",
       "merchant_id                   object\n",
       "product_id                    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "79b74a59",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:45.230651Z",
     "start_time": "2022-07-04T09:42:45.199733Z"
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       "      <td>-0.001503</td>\n",
       "      <td>0.228983</td>\n",
       "      <td>0.130975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uses_ad_boosts</th>\n",
       "      <td>-0.081578</td>\n",
       "      <td>-0.023671</td>\n",
       "      <td>-0.021297</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.050130</td>\n",
       "      <td>-0.044384</td>\n",
       "      <td>-0.044013</td>\n",
       "      <td>-0.044705</td>\n",
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       "      <td>0.029562</td>\n",
       "      <td>-0.032637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating</th>\n",
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       "      <td>-0.050130</td>\n",
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       "      <td>0.118200</td>\n",
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       "      <td>-0.012981</td>\n",
       "      <td>0.077416</td>\n",
       "      <td>-0.003748</td>\n",
       "      <td>0.041287</td>\n",
       "      <td>-0.039254</td>\n",
       "      <td>0.066285</td>\n",
       "      <td>0.268140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating_count</th>\n",
       "      <td>-0.001058</td>\n",
       "      <td>0.045911</td>\n",
       "      <td>0.903303</td>\n",
       "      <td>-0.044384</td>\n",
       "      <td>0.111166</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.984322</td>\n",
       "      <td>0.996203</td>\n",
       "      <td>0.981064</td>\n",
       "      <td>0.946772</td>\n",
       "      <td>0.910329</td>\n",
       "      <td>-0.012638</td>\n",
       "      <td>-0.016851</td>\n",
       "      <td>-0.013031</td>\n",
       "      <td>0.001657</td>\n",
       "      <td>0.222432</td>\n",
       "      <td>0.154170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating_five_count</th>\n",
       "      <td>0.001092</td>\n",
       "      <td>0.045099</td>\n",
       "      <td>0.879508</td>\n",
       "      <td>-0.044013</td>\n",
       "      <td>0.154079</td>\n",
       "      <td>0.984322</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.980754</td>\n",
       "      <td>0.933823</td>\n",
       "      <td>0.877409</td>\n",
       "      <td>0.830386</td>\n",
       "      <td>-0.008116</td>\n",
       "      <td>-0.016524</td>\n",
       "      <td>-0.013013</td>\n",
       "      <td>-0.007735</td>\n",
       "      <td>0.228045</td>\n",
       "      <td>0.175754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating_four_count</th>\n",
       "      <td>0.002409</td>\n",
       "      <td>0.052829</td>\n",
       "      <td>0.894473</td>\n",
       "      <td>-0.044705</td>\n",
       "      <td>0.118200</td>\n",
       "      <td>0.996203</td>\n",
       "      <td>0.980754</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.977404</td>\n",
       "      <td>0.937133</td>\n",
       "      <td>0.895174</td>\n",
       "      <td>-0.009597</td>\n",
       "      <td>-0.016534</td>\n",
       "      <td>-0.011980</td>\n",
       "      <td>0.007850</td>\n",
       "      <td>0.222418</td>\n",
       "      <td>0.155769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating_three_count</th>\n",
       "      <td>-0.011791</td>\n",
       "      <td>0.039877</td>\n",
       "      <td>0.900029</td>\n",
       "      <td>-0.042433</td>\n",
       "      <td>0.067480</td>\n",
       "      <td>0.981064</td>\n",
       "      <td>0.933823</td>\n",
       "      <td>0.977404</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.985659</td>\n",
       "      <td>0.951736</td>\n",
       "      <td>-0.024530</td>\n",
       "      <td>-0.016209</td>\n",
       "      <td>-0.008669</td>\n",
       "      <td>0.011409</td>\n",
       "      <td>0.212992</td>\n",
       "      <td>0.122915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating_two_count</th>\n",
       "      <td>-0.011023</td>\n",
       "      <td>0.036334</td>\n",
       "      <td>0.876017</td>\n",
       "      <td>-0.044041</td>\n",
       "      <td>0.025949</td>\n",
       "      <td>0.946772</td>\n",
       "      <td>0.877409</td>\n",
       "      <td>0.937133</td>\n",
       "      <td>0.985659</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.981110</td>\n",
       "      <td>-0.025653</td>\n",
       "      <td>-0.015800</td>\n",
       "      <td>-0.011345</td>\n",
       "      <td>0.013761</td>\n",
       "      <td>0.192236</td>\n",
       "      <td>0.103005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating_one_count</th>\n",
       "      <td>0.005178</td>\n",
       "      <td>0.041074</td>\n",
       "      <td>0.844462</td>\n",
       "      <td>-0.038070</td>\n",
       "      <td>-0.012981</td>\n",
       "      <td>0.910329</td>\n",
       "      <td>0.830386</td>\n",
       "      <td>0.895174</td>\n",
       "      <td>0.951736</td>\n",
       "      <td>0.981110</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010470</td>\n",
       "      <td>-0.016757</td>\n",
       "      <td>-0.019907</td>\n",
       "      <td>0.011561</td>\n",
       "      <td>0.177238</td>\n",
       "      <td>0.091939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shipping_option_price</th>\n",
       "      <td>0.877472</td>\n",
       "      <td>0.264417</td>\n",
       "      <td>-0.032804</td>\n",
       "      <td>-0.068404</td>\n",
       "      <td>0.077416</td>\n",
       "      <td>-0.012638</td>\n",
       "      <td>-0.008116</td>\n",
       "      <td>-0.009597</td>\n",
       "      <td>-0.024530</td>\n",
       "      <td>-0.025653</td>\n",
       "      <td>-0.010470</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.263915</td>\n",
       "      <td>-0.047145</td>\n",
       "      <td>-0.201021</td>\n",
       "      <td>-0.027795</td>\n",
       "      <td>0.037440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shipping_is_express</th>\n",
       "      <td>0.191794</td>\n",
       "      <td>0.001284</td>\n",
       "      <td>-0.017396</td>\n",
       "      <td>-0.032723</td>\n",
       "      <td>-0.003748</td>\n",
       "      <td>-0.016851</td>\n",
       "      <td>-0.016524</td>\n",
       "      <td>-0.016534</td>\n",
       "      <td>-0.016209</td>\n",
       "      <td>-0.015800</td>\n",
       "      <td>-0.016757</td>\n",
       "      <td>0.263915</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001386</td>\n",
       "      <td>-0.371341</td>\n",
       "      <td>-0.002814</td>\n",
       "      <td>0.047609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>countries_shipped_to</th>\n",
       "      <td>-0.017899</td>\n",
       "      <td>0.041622</td>\n",
       "      <td>-0.023151</td>\n",
       "      <td>-0.026952</td>\n",
       "      <td>0.041287</td>\n",
       "      <td>-0.013031</td>\n",
       "      <td>-0.013013</td>\n",
       "      <td>-0.011980</td>\n",
       "      <td>-0.008669</td>\n",
       "      <td>-0.011345</td>\n",
       "      <td>-0.019907</td>\n",
       "      <td>-0.047145</td>\n",
       "      <td>0.001386</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.007890</td>\n",
       "      <td>0.136923</td>\n",
       "      <td>0.141096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inventory_total</th>\n",
       "      <td>-0.215626</td>\n",
       "      <td>-0.007091</td>\n",
       "      <td>-0.001503</td>\n",
       "      <td>0.034473</td>\n",
       "      <td>-0.039254</td>\n",
       "      <td>0.001657</td>\n",
       "      <td>-0.007735</td>\n",
       "      <td>0.007850</td>\n",
       "      <td>0.011409</td>\n",
       "      <td>0.013761</td>\n",
       "      <td>0.011561</td>\n",
       "      <td>-0.201021</td>\n",
       "      <td>-0.371341</td>\n",
       "      <td>-0.007890</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011380</td>\n",
       "      <td>-0.063439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>merchant_rating_count</th>\n",
       "      <td>-0.016289</td>\n",
       "      <td>0.050782</td>\n",
       "      <td>0.228983</td>\n",
       "      <td>0.029562</td>\n",
       "      <td>0.066285</td>\n",
       "      <td>0.222432</td>\n",
       "      <td>0.228045</td>\n",
       "      <td>0.222418</td>\n",
       "      <td>0.212992</td>\n",
       "      <td>0.192236</td>\n",
       "      <td>0.177238</td>\n",
       "      <td>-0.027795</td>\n",
       "      <td>-0.002814</td>\n",
       "      <td>0.136923</td>\n",
       "      <td>0.011380</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.159337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>merchant_rating</th>\n",
       "      <td>0.033238</td>\n",
       "      <td>0.064949</td>\n",
       "      <td>0.130975</td>\n",
       "      <td>-0.032637</td>\n",
       "      <td>0.268140</td>\n",
       "      <td>0.154170</td>\n",
       "      <td>0.175754</td>\n",
       "      <td>0.155769</td>\n",
       "      <td>0.122915</td>\n",
       "      <td>0.103005</td>\n",
       "      <td>0.091939</td>\n",
       "      <td>0.037440</td>\n",
       "      <td>0.047609</td>\n",
       "      <td>0.141096</td>\n",
       "      <td>-0.063439</td>\n",
       "      <td>0.159337</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          price  retail_price  units_sold  uses_ad_boosts  \\\n",
       "price                  1.000000      0.304725   -0.023270       -0.081578   \n",
       "retail_price           0.304725      1.000000    0.010911       -0.023671   \n",
       "units_sold            -0.023270      0.010911    1.000000       -0.021297   \n",
       "uses_ad_boosts        -0.081578     -0.023671   -0.021297        1.000000   \n",
       "rating                 0.075179      0.029740    0.095670       -0.050130   \n",
       "rating_count          -0.001058      0.045911    0.903303       -0.044384   \n",
       "rating_five_count      0.001092      0.045099    0.879508       -0.044013   \n",
       "rating_four_count      0.002409      0.052829    0.894473       -0.044705   \n",
       "rating_three_count    -0.011791      0.039877    0.900029       -0.042433   \n",
       "rating_two_count      -0.011023      0.036334    0.876017       -0.044041   \n",
       "rating_one_count       0.005178      0.041074    0.844462       -0.038070   \n",
       "shipping_option_price  0.877472      0.264417   -0.032804       -0.068404   \n",
       "shipping_is_express    0.191794      0.001284   -0.017396       -0.032723   \n",
       "countries_shipped_to  -0.017899      0.041622   -0.023151       -0.026952   \n",
       "inventory_total       -0.215626     -0.007091   -0.001503        0.034473   \n",
       "merchant_rating_count -0.016289      0.050782    0.228983        0.029562   \n",
       "merchant_rating        0.033238      0.064949    0.130975       -0.032637   \n",
       "\n",
       "                         rating  rating_count  rating_five_count  \\\n",
       "price                  0.075179     -0.001058           0.001092   \n",
       "retail_price           0.029740      0.045911           0.045099   \n",
       "units_sold             0.095670      0.903303           0.879508   \n",
       "uses_ad_boosts        -0.050130     -0.044384          -0.044013   \n",
       "rating                 1.000000      0.111166           0.154079   \n",
       "rating_count           0.111166      1.000000           0.984322   \n",
       "rating_five_count      0.154079      0.984322           1.000000   \n",
       "rating_four_count      0.118200      0.996203           0.980754   \n",
       "rating_three_count     0.067480      0.981064           0.933823   \n",
       "rating_two_count       0.025949      0.946772           0.877409   \n",
       "rating_one_count      -0.012981      0.910329           0.830386   \n",
       "shipping_option_price  0.077416     -0.012638          -0.008116   \n",
       "shipping_is_express   -0.003748     -0.016851          -0.016524   \n",
       "countries_shipped_to   0.041287     -0.013031          -0.013013   \n",
       "inventory_total       -0.039254      0.001657          -0.007735   \n",
       "merchant_rating_count  0.066285      0.222432           0.228045   \n",
       "merchant_rating        0.268140      0.154170           0.175754   \n",
       "\n",
       "                       rating_four_count  rating_three_count  \\\n",
       "price                           0.002409           -0.011791   \n",
       "retail_price                    0.052829            0.039877   \n",
       "units_sold                      0.894473            0.900029   \n",
       "uses_ad_boosts                 -0.044705           -0.042433   \n",
       "rating                          0.118200            0.067480   \n",
       "rating_count                    0.996203            0.981064   \n",
       "rating_five_count               0.980754            0.933823   \n",
       "rating_four_count               1.000000            0.977404   \n",
       "rating_three_count              0.977404            1.000000   \n",
       "rating_two_count                0.937133            0.985659   \n",
       "rating_one_count                0.895174            0.951736   \n",
       "shipping_option_price          -0.009597           -0.024530   \n",
       "shipping_is_express            -0.016534           -0.016209   \n",
       "countries_shipped_to           -0.011980           -0.008669   \n",
       "inventory_total                 0.007850            0.011409   \n",
       "merchant_rating_count           0.222418            0.212992   \n",
       "merchant_rating                 0.155769            0.122915   \n",
       "\n",
       "                       rating_two_count  rating_one_count  \\\n",
       "price                         -0.011023          0.005178   \n",
       "retail_price                   0.036334          0.041074   \n",
       "units_sold                     0.876017          0.844462   \n",
       "uses_ad_boosts                -0.044041         -0.038070   \n",
       "rating                         0.025949         -0.012981   \n",
       "rating_count                   0.946772          0.910329   \n",
       "rating_five_count              0.877409          0.830386   \n",
       "rating_four_count              0.937133          0.895174   \n",
       "rating_three_count             0.985659          0.951736   \n",
       "rating_two_count               1.000000          0.981110   \n",
       "rating_one_count               0.981110          1.000000   \n",
       "shipping_option_price         -0.025653         -0.010470   \n",
       "shipping_is_express           -0.015800         -0.016757   \n",
       "countries_shipped_to          -0.011345         -0.019907   \n",
       "inventory_total                0.013761          0.011561   \n",
       "merchant_rating_count          0.192236          0.177238   \n",
       "merchant_rating                0.103005          0.091939   \n",
       "\n",
       "                       shipping_option_price  shipping_is_express  \\\n",
       "price                               0.877472             0.191794   \n",
       "retail_price                        0.264417             0.001284   \n",
       "units_sold                         -0.032804            -0.017396   \n",
       "uses_ad_boosts                     -0.068404            -0.032723   \n",
       "rating                              0.077416            -0.003748   \n",
       "rating_count                       -0.012638            -0.016851   \n",
       "rating_five_count                  -0.008116            -0.016524   \n",
       "rating_four_count                  -0.009597            -0.016534   \n",
       "rating_three_count                 -0.024530            -0.016209   \n",
       "rating_two_count                   -0.025653            -0.015800   \n",
       "rating_one_count                   -0.010470            -0.016757   \n",
       "shipping_option_price               1.000000             0.263915   \n",
       "shipping_is_express                 0.263915             1.000000   \n",
       "countries_shipped_to               -0.047145             0.001386   \n",
       "inventory_total                    -0.201021            -0.371341   \n",
       "merchant_rating_count              -0.027795            -0.002814   \n",
       "merchant_rating                     0.037440             0.047609   \n",
       "\n",
       "                       countries_shipped_to  inventory_total  \\\n",
       "price                             -0.017899        -0.215626   \n",
       "retail_price                       0.041622        -0.007091   \n",
       "units_sold                        -0.023151        -0.001503   \n",
       "uses_ad_boosts                    -0.026952         0.034473   \n",
       "rating                             0.041287        -0.039254   \n",
       "rating_count                      -0.013031         0.001657   \n",
       "rating_five_count                 -0.013013        -0.007735   \n",
       "rating_four_count                 -0.011980         0.007850   \n",
       "rating_three_count                -0.008669         0.011409   \n",
       "rating_two_count                  -0.011345         0.013761   \n",
       "rating_one_count                  -0.019907         0.011561   \n",
       "shipping_option_price             -0.047145        -0.201021   \n",
       "shipping_is_express                0.001386        -0.371341   \n",
       "countries_shipped_to               1.000000        -0.007890   \n",
       "inventory_total                   -0.007890         1.000000   \n",
       "merchant_rating_count              0.136923         0.011380   \n",
       "merchant_rating                    0.141096        -0.063439   \n",
       "\n",
       "                       merchant_rating_count  merchant_rating  \n",
       "price                              -0.016289         0.033238  \n",
       "retail_price                        0.050782         0.064949  \n",
       "units_sold                          0.228983         0.130975  \n",
       "uses_ad_boosts                      0.029562        -0.032637  \n",
       "rating                              0.066285         0.268140  \n",
       "rating_count                        0.222432         0.154170  \n",
       "rating_five_count                   0.228045         0.175754  \n",
       "rating_four_count                   0.222418         0.155769  \n",
       "rating_three_count                  0.212992         0.122915  \n",
       "rating_two_count                    0.192236         0.103005  \n",
       "rating_one_count                    0.177238         0.091939  \n",
       "shipping_option_price              -0.027795         0.037440  \n",
       "shipping_is_express                -0.002814         0.047609  \n",
       "countries_shipped_to                0.136923         0.141096  \n",
       "inventory_total                     0.011380        -0.063439  \n",
       "merchant_rating_count               1.000000         0.159337  \n",
       "merchant_rating                     0.159337         1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = df.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2ddee32c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:46.368722Z",
     "start_time": "2022-07-04T09:42:45.231647Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "fig = sns.heatmap(df.corr(), annot=True,  cmap=sns.cubehelix_palette(as_cmap=True), ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ea2f688",
   "metadata": {},
   "source": [
    "As we can see from the heatmap, the correlation of our target variable, units sold, is the highest (0.9) with number of rating of a product followed by count of rating of a merchant. On the contrary, the correlation between units sold and price is found to be not so strong."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b58183e6",
   "metadata": {},
   "source": [
    "### Units Sold"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b080c394",
   "metadata": {},
   "source": [
    "Getting the distinct values of units_sold to group them for better analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d6653ca1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:46.446515Z",
     "start_time": "2022-07-04T09:42:46.370717Z"
    }
   },
   "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>units_sold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    units_sold\n",
       "0            2\n",
       "1            7\n",
       "2           10\n",
       "3           50\n",
       "4          100\n",
       "5         1000\n",
       "6         5000\n",
       "7        10000\n",
       "8        20000\n",
       "9        50000\n",
       "10      100000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1= \"\"\"\n",
    "SELECT DISTINCT units_sold\n",
    "FROM df\n",
    "order by units_sold\n",
    "\"\"\"\n",
    "query1 = ps.sqldf(q1, locals())\n",
    "query1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "28c557df",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:46.461474Z",
     "start_time": "2022-07-04T09:42:46.448510Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       5\n",
       "2       1\n",
       "3       3\n",
       "4       1\n",
       "       ..\n",
       "1568    4\n",
       "1569    1\n",
       "1570    1\n",
       "1571    1\n",
       "1572    1\n",
       "Name: units_sold_group, Length: 1457, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['units_sold_group'] = df['units_sold'].apply(lambda x: 1 if x <= 100 else 2 if x == 1000 else 3 if x == 5000 else 4 \\\n",
    "                                                if x == 10000 else 5 if x == 20000 else 6 if x == 50000 else 7)\n",
    "df.units_sold_group"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d277fb6",
   "metadata": {},
   "source": [
    "Groups of units_sold:\n",
    "1. Less than 100\n",
    "2. Between 101 and 1000\n",
    "3. Between 1001 and 5000\n",
    "4. Between 5001 and 10000\n",
    "5. Between 10001 and 20000\n",
    "6. Between 20001 and 50000\n",
    "7. Between 50001 and 100000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a2edd9b",
   "metadata": {},
   "source": [
    "#### Discount"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a59091e",
   "metadata": {},
   "source": [
    "Due to lack of complete information, we will consider the price as discounted price while retail price is the maximum retail price. So, the difference between the two can be accounted a discount given on each product during the summer sale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "10c5ea8a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:46.477435Z",
     "start_time": "2022-07-04T09:42:46.463469Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0   -14.29\n",
      "1    63.64\n",
      "2    81.40\n",
      "3     0.00\n",
      "4     9.33\n",
      "5    56.44\n",
      "6   -16.67\n",
      "7    -9.09\n",
      "8    86.90\n",
      "9    73.73\n",
      "Name: discount_percn, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "df['discount'] = df['retail_price'] - df['price']\n",
    "df['discount_percn'] = round(df['discount']/df['retail_price'] * 100, 2)\n",
    "print(df['discount_percn'].head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "54b42bd3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:46.492391Z",
     "start_time": "2022-07-04T09:42:46.479427Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.014288410297255607"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_sale_discount = df['units_sold'].corr(df['discount'])\n",
    "corr_sale_discount"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1d16382",
   "metadata": {},
   "source": [
    "The discount feature appears to be only weakly positively correlated with number of units sold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bb72028a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:46.849360Z",
     "start_time": "2022-07-04T09:42:46.493388Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Units Sold')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (15, 10))\n",
    "sns.barplot(x = df.units_sold_group, y = df.discount_percn, ax = ax)\n",
    "\n",
    "units_sold_order = ['0.1k-1k', '1k-5k', '5k-10k', '10k-20k', '20k-50k', '50k-100k', '100k+']\n",
    "ax.set_xticklabels(units_sold_order)\n",
    "ax.set_ylabel('Discount (%)', fontweight = 'bold')\n",
    "ax.set_xlabel('Units Sold', fontweight = 'bold')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddd96c90",
   "metadata": {},
   "source": [
    "We can conclude from the above diagram that higher the discount, higher is the number of clothes sold during the sale as this has been also confirmed by the positive correlation between the two. 100k+ units have a discount of nearly 65% while number of products selling between 1k-50k having a discount range of 20%-30%. This notion confirms the price sensitive behaviour of buyers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30b11ff8",
   "metadata": {},
   "source": [
    "#### User Ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e60a28b5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T07:22:39.592555Z",
     "start_time": "2022-07-04T07:22:39.573606Z"
    }
   },
   "source": [
    "In this section, we look at 5 most rated and 5 least rated products along with graphical relationship between ratings and number of products sold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3a01c224",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:46.912120Z",
     "start_time": "2022-07-04T09:42:46.851282Z"
    }
   },
   "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>Product_Name</th>\n",
       "      <th>Ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Femmes Camisole extensible Spaghetti Strap Long Débardeur Slip Mini Dress 4Color SIZE XXL est disponible maintenant</td>\n",
       "      <td>20744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nouvelle arrivée femmes été sexy robe de soirée élégante sans manches col roulé mini robe</td>\n",
       "      <td>18393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018 New Fashion Women's Tops Sexy Strappy Sleeveless Dentelle sans manches Crop Cops</td>\n",
       "      <td>17980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018 Mode d'été Femmes Débardeurs Débardeurs Femmes Sexy Culottes sans manches Casual Style Femmes Coton Imprimé Dentelle Coutures Irrégulières Blouse Tops（S-5XL)</td>\n",
       "      <td>17444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Femmes sexy Boho à capuche moulante sans manches Sexy Party Cocktail Mini robe mode MEIfang</td>\n",
       "      <td>14568</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                         Product_Name  \\\n",
       "0                                                 Femmes Camisole extensible Spaghetti Strap Long Débardeur Slip Mini Dress 4Color SIZE XXL est disponible maintenant   \n",
       "1                                                                           Nouvelle arrivée femmes été sexy robe de soirée élégante sans manches col roulé mini robe   \n",
       "2                                                                               2018 New Fashion Women's Tops Sexy Strappy Sleeveless Dentelle sans manches Crop Cops   \n",
       "3  2018 Mode d'été Femmes Débardeurs Débardeurs Femmes Sexy Culottes sans manches Casual Style Femmes Coton Imprimé Dentelle Coutures Irrégulières Blouse Tops（S-5XL)   \n",
       "4                                                                         Femmes sexy Boho à capuche moulante sans manches Sexy Party Cocktail Mini robe mode MEIfang   \n",
       "\n",
       "   Ratings  \n",
       "0    20744  \n",
       "1    18393  \n",
       "2    17980  \n",
       "3    17444  \n",
       "4    14568  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q2 = \"\"\"\n",
    "SELECT title as Product_Name, rating_count as Ratings\n",
    "from df\n",
    "order by Ratings desc\n",
    "limit 5 \n",
    "\"\"\"\n",
    "\n",
    "query2 = ps.sqldf(q2, locals())\n",
    "pd.set_option('display.max_colwidth', None)\n",
    "query2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "244bc573",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:47.037312Z",
     "start_time": "2022-07-04T09:42:46.914114Z"
    },
    "scrolled": true
   },
   "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>Product_Name</th>\n",
       "      <th>Ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Plus la taille d'été femmes décontracté sans manches barboteuses combinaisons combinaison de couleur unie jarretelles pantalons lâche salopette</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dames d'été Tie Dye Imprimer manches courtes hors épaule Casual Plus Size Dress</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S-5XL Nouvelle Mode Femmes Tops Blouse Sans Manches Col En V Solide Couleur Débardeur Femmes D'été Chemise Tops Plus La Taille Causal Lâche</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S-5XL Nouvelle Mode Femmes Tops Blouse Sans Manches Col En V Solide Couleur Débardeur Femmes D'été Chemise Tops Plus La Taille Causal Lâche</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Anti-buée de natation d'été masque complet plongée sous-marine plongée avec tuba pour GoPro (S / M, L / XL)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                      Product_Name  \\\n",
       "0  Plus la taille d'été femmes décontracté sans manches barboteuses combinaisons combinaison de couleur unie jarretelles pantalons lâche salopette   \n",
       "1                                                                  Dames d'été Tie Dye Imprimer manches courtes hors épaule Casual Plus Size Dress   \n",
       "2      S-5XL Nouvelle Mode Femmes Tops Blouse Sans Manches Col En V Solide Couleur Débardeur Femmes D'été Chemise Tops Plus La Taille Causal Lâche   \n",
       "3      S-5XL Nouvelle Mode Femmes Tops Blouse Sans Manches Col En V Solide Couleur Débardeur Femmes D'été Chemise Tops Plus La Taille Causal Lâche   \n",
       "4                                      Anti-buée de natation d'été masque complet plongée sous-marine plongée avec tuba pour GoPro (S / M, L / XL)   \n",
       "\n",
       "   Ratings  \n",
       "0        1  \n",
       "1        1  \n",
       "2        1  \n",
       "3        1  \n",
       "4        1  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q3 = \"\"\"\n",
    "SELECT title as Product_Name, rating_count as Ratings\n",
    "from df\n",
    "order by Ratings\n",
    "limit 5\n",
    "\"\"\"\n",
    "\n",
    "query3 = ps.sqldf(q3, locals())\n",
    "query3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a83fba08",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T10:26:01.483053Z",
     "start_time": "2022-07-04T10:26:01.134828Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Units Sold')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (15, 10))\n",
    "sns.barplot(x = df.units_sold_group, y = df.rating_count, ax = ax)\n",
    "\n",
    "units_sold_order = ['0.1k-1k', '1k-5k', '5k-10k', '10k-20k', '20k-50k', '50k-100k', '100k+']\n",
    "ax.set_xticklabels(units_sold_order)\n",
    "ax.set_ylabel('Rating Count', fontweight = 'bold')\n",
    "ax.set_xlabel('Units Sold', fontweight = 'bold')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c312364c",
   "metadata": {},
   "source": [
    "#### Ad Boosts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3df02e84",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:47.414795Z",
     "start_time": "2022-07-04T09:42:47.353960Z"
    }
   },
   "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>Sale_with_adboosts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>819</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sale_with_adboosts\n",
       "0                 638\n",
       "1                 819"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q4 = \"\"\"\n",
    "SELECT count(units_sold) as Sale_with_adboosts\n",
    "from df\n",
    "where uses_ad_boosts = 1\n",
    "UNION\n",
    "SELECT count(units_sold) as Sale_without_adboosts\n",
    "from df\n",
    "where uses_ad_boosts = 0\n",
    "\"\"\"\n",
    "query4 = ps.sqldf(q4, locals())\n",
    "query4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "24056181",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:47.902114Z",
     "start_time": "2022-07-04T09:42:47.417789Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Units Sold')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (10, 12))\n",
    "sns.barplot(x = 'units_sold_group', y='price', data=df, hue='uses_ad_boosts')\n",
    "\n",
    "units_sold_order = ['0.1k-1k', '1k-5k', '5k-10k', '10k-20k', '20k-50k', '50k-100k', '100k+']\n",
    "ax.set_xticklabels(units_sold_order)\n",
    "ax.set_ylabel('Price (Euro)', fontweight = 'bold')\n",
    "ax.set_xlabel('Units Sold', fontweight = 'bold')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c934333c",
   "metadata": {},
   "source": [
    "Insight: Ad boost has no significant impact on the sale of the products. This is a signal for the marketing team work on better and compelling campaigns. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5c2d5f9",
   "metadata": {},
   "source": [
    "### Product Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "264d6864",
   "metadata": {},
   "source": [
    "#### Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1768d772",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:47.963228Z",
     "start_time": "2022-07-04T09:42:47.904110Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['M',\n",
       " 'XS',\n",
       " 'S',\n",
       " 'SIZE-XS',\n",
       " 'M.',\n",
       " 'XXS',\n",
       " 'L',\n",
       " 'XXL',\n",
       " 'S.',\n",
       " 'S',\n",
       " 'CHOOSE A SIZE',\n",
       " 'XS.',\n",
       " '32/L',\n",
       " 'SUIT-S',\n",
       " 'XXXXXL',\n",
       " 'EU 35',\n",
       " '4',\n",
       " 'SIZE S.',\n",
       " '1M BY 3M',\n",
       " '3XL',\n",
       " 'SIZE S',\n",
       " 'XL',\n",
       " 'WOMEN SIZE 36',\n",
       " 'US 6.5 (EU 37)',\n",
       " 'XXXS',\n",
       " 'SIZE XS',\n",
       " '26(WAIST 72CM 28INCH)',\n",
       " 'SIZE XXS',\n",
       " '29',\n",
       " '100 CM',\n",
       " 'ONE SIZE',\n",
       " 'SIZE-4XL',\n",
       " '1',\n",
       " 'S/M(CHILD)',\n",
       " '2PCS',\n",
       " 'XXXL',\n",
       " 'S..',\n",
       " '30 CM',\n",
       " '5XL',\n",
       " '33',\n",
       " 'SIZE M',\n",
       " '100 X 100CM(39.3 X 39.3INCH)',\n",
       " '2XL',\n",
       " '4XL',\n",
       " 'SIZE XXS',\n",
       " 'SIZE S',\n",
       " '35',\n",
       " '34',\n",
       " 'SIZE-XXS',\n",
       " 'S(BUST 88CM)',\n",
       " 'S (WAIST58-62CM)',\n",
       " '20PCS',\n",
       " 'US-S',\n",
       " 'SIZE -XXS',\n",
       " 'X   L',\n",
       " 'XXXXL',\n",
       " '25',\n",
       " 'SIZEL',\n",
       " 'SIZE-S',\n",
       " 'PACK OF 1',\n",
       " 'S DIAMETER 30CM',\n",
       " '6XL',\n",
       " 'AU PLUG LOW QUALITY',\n",
       " '5PAIRS',\n",
       " '25-S',\n",
       " 'SIZE/S',\n",
       " 'SIZE-5XL',\n",
       " 'DAUGHTER 24M',\n",
       " 'BABY FLOAT BOAT',\n",
       " '60',\n",
       " 'US5.5-EU35',\n",
       " '10PCS',\n",
       " 'SIZE-XXS',\n",
       " 'WOMEN SIZE 37',\n",
       " '4-5 YEARS',\n",
       " 'SIZE4XL',\n",
       " 'FIRST  GENERATION',\n",
       " '80 X 200 CM',\n",
       " 'EU39(US8)',\n",
       " 'L.',\n",
       " '36',\n",
       " '04-3XL',\n",
       " 'PANTS-S',\n",
       " 'FLOATING CHAIR FOR KID',\n",
       " '20PCS-10PAIRS',\n",
       " 'SIZE--S',\n",
       " '5',\n",
       " '1 PC - XL',\n",
       " '40 CM',\n",
       " 'SIZE S']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q5 = \"\"\"\n",
    "SELECT distinct(product_variation_size_id)\n",
    "from df\n",
    "\"\"\"\n",
    "query5 = ps.sqldf(q5, locals())\n",
    "query5['product_variation_size_id'] = query5['product_variation_size_id'].apply(lambda x: x.upper())\n",
    "query5 = list(query5['product_variation_size_id'])\n",
    "query5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8423ba18",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:47.994171Z",
     "start_time": "2022-07-04T09:42:47.965223Z"
    }
   },
   "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>product_variation_size_id</th>\n",
       "      <th>product_size_per</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>630</td>\n",
       "      <td>0.432395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XS</th>\n",
       "      <td>332</td>\n",
       "      <td>0.227865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>196</td>\n",
       "      <td>0.134523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XXS</th>\n",
       "      <td>106</td>\n",
       "      <td>0.072752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTHER</th>\n",
       "      <td>89</td>\n",
       "      <td>0.061084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L</th>\n",
       "      <td>50</td>\n",
       "      <td>0.034317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XXL</th>\n",
       "      <td>36</td>\n",
       "      <td>0.024708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XL</th>\n",
       "      <td>18</td>\n",
       "      <td>0.012354</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       product_variation_size_id  product_size_per\n",
       "S                            630          0.432395\n",
       "XS                           332          0.227865\n",
       "M                            196          0.134523\n",
       "XXS                          106          0.072752\n",
       "OTHER                         89          0.061084\n",
       "L                             50          0.034317\n",
       "XXL                           36          0.024708\n",
       "XL                            18          0.012354"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xxs_list = []\n",
    "xs_list = []\n",
    "s_list = []\n",
    "m_list = []\n",
    "l_list = []\n",
    "xl_list = []\n",
    "xxl_list = []\n",
    "\n",
    "for i in query5:\n",
    "    if (i == 'XXS') or (i == 'XXXS') or (i == 'SIZE XXS') or (i == 'SIZE-XXS') or (i == 'SIZE -XXS') or (i=='XS.') or (i == 'SizeXS.'):\n",
    "        xxs_list.append(i)\n",
    "    elif (i == 'XS') or (i == 'XS.') or (i == 'SIZE XS') or (i == 'SIZE-XS'):\n",
    "        xs_list.append(i)\n",
    "    elif (i == 'S') or (i == 'S.') or (i == 'SUIT-S') or (i == 'SIZE S.') or (i == 'SIZE S') or (i == 'S..') \\\n",
    "    or (i == 'S(BUST 88CM)') or (i == 'S (WAIST58-62CM)') or (i == 'SIZE-S') or (i == 'S DIAMETER 30CM') or (i == '25-S') \\\n",
    "    or (i == 'SIZE/S') or (i == 'PANTS-S') or (i == 'SIZE--S'):\n",
    "        s_list.append(i)\n",
    "    elif (i == 'M') or (i == 'M.') or (i == 'SIZE M'):\n",
    "        m_list.append(i)\n",
    "    elif (i == 'L') or (i == '32/L') or (i == 'L.') or (i == 'SIZEL'):\n",
    "        l_list.append(i)\n",
    "    elif (i == 'XL') or (i == 'X   L'):\n",
    "        xl_list.append(i)\n",
    "    elif (i == 'XXL') or (i == 'XXXXXL') or (i == '3XL') or (i == 'SIZE-4XL') or (i == 'XXXL') or (i == '5XL') \\\n",
    "    or (i == '2XL') or (i == '4XL') or (i == 'XXXXL') or (i == '6XL') or (i == 'SIZE-5XL') or (i == 'SIZE4XL') \\\n",
    "    or (i == '04-3XL') or (i == '1 PC - XL'):\n",
    "        xxl_list.append(i)\n",
    "        \n",
    "def size(tipe):\n",
    "    if tipe in xxs_list:\n",
    "        return 'XXS'\n",
    "    elif tipe in xs_list:\n",
    "        return 'XS'\n",
    "    elif tipe in s_list:\n",
    "        return 'S'\n",
    "    elif tipe in m_list:\n",
    "        return 'M'\n",
    "    elif tipe in l_list:\n",
    "        return 'L'\n",
    "    elif tipe in xl_list:\n",
    "        return 'XL'\n",
    "    elif tipe in xxl_list:\n",
    "        return 'XXL'\n",
    "    else:\n",
    "        return 'OTHER'\n",
    "    \n",
    "df['product_variation_size_id'] = df['product_variation_size_id'].apply(size)  \n",
    "\n",
    "Product_size = pd.DataFrame(df['product_variation_size_id'].value_counts())\n",
    "Product_size['product_size_per'] = Product_size['product_variation_size_id']/Product_size['product_variation_size_id'].sum()\n",
    "Product_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "608b50bf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:48.134285Z",
     "start_time": "2022-07-04T09:42:47.996142Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10, 8))\n",
    "plt.bar(Product_size.index, Product_size.product_variation_size_id)\n",
    "plt.xlabel(\"Sizes\")\n",
    "plt.ylabel(\"Number of products sold\")\n",
    "plt.title(\"Number of units old by sizes\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7430d38",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T08:13:42.889866Z",
     "start_time": "2022-07-04T08:13:42.870893Z"
    }
   },
   "source": [
    "Insight: S is the most sold size followed by XS and M during the summer sale."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b821f895",
   "metadata": {},
   "source": [
    "#### Color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "73dd75e7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:48.196915Z",
     "start_time": "2022-07-04T09:42:48.136080Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['white',\n",
       " 'green',\n",
       " 'leopardprint',\n",
       " 'black',\n",
       " 'yellow',\n",
       " 'navyblue',\n",
       " 'blue',\n",
       " 'beige',\n",
       " 'grey',\n",
       " 'lightblue',\n",
       " 'orange',\n",
       " 'red',\n",
       " 'Black',\n",
       " 'armygreen',\n",
       " 'pink',\n",
       " 'khaki',\n",
       " 'red & blue',\n",
       " 'blue & pink',\n",
       " 'RED',\n",
       " 'white & green',\n",
       " 'winered',\n",
       " 'black & green',\n",
       " 'purple',\n",
       " 'whitefloral',\n",
       " 'floral',\n",
       " 'fluorescentgreen',\n",
       " 'orange & camouflage',\n",
       " 'lightyellow',\n",
       " 'coolblack',\n",
       " 'multicolor',\n",
       " 'camouflage',\n",
       " 'lightpink',\n",
       " 'pink & black',\n",
       " 'silver',\n",
       " 'Army green',\n",
       " 'lightgreen',\n",
       " 'mintgreen',\n",
       " 'pink & grey',\n",
       " 'gray',\n",
       " 'coffee',\n",
       " 'rose',\n",
       " 'light green',\n",
       " 'leopard',\n",
       " 'black & white',\n",
       " 'orange-red',\n",
       " 'dustypink',\n",
       " 'White',\n",
       " 'star',\n",
       " 'white & black',\n",
       " 'apricot',\n",
       " 'burgundy',\n",
       " 'claret',\n",
       " 'pink & white',\n",
       " 'rosered',\n",
       " 'navy blue',\n",
       " 'wine red',\n",
       " 'lightred',\n",
       " 'Pink',\n",
       " 'skyblue',\n",
       " 'coralred',\n",
       " 'brown',\n",
       " 'lakeblue',\n",
       " 'darkblue',\n",
       " 'camel',\n",
       " 'pink & blue',\n",
       " 'nude',\n",
       " 'Blue',\n",
       " 'lightpurple',\n",
       " 'army',\n",
       " 'black & stripe',\n",
       " 'Rose red',\n",
       " 'greysnakeskinprint',\n",
       " 'denimblue',\n",
       " 'applegreen',\n",
       " 'offwhite',\n",
       " 'lightgray',\n",
       " 'gray & white',\n",
       " 'brown & yellow',\n",
       " 'winered & yellow',\n",
       " 'army green',\n",
       " 'whitestripe',\n",
       " 'rainbow',\n",
       " 'lightgrey',\n",
       " 'watermelonred',\n",
       " 'prussianblue',\n",
       " 'navyblue & white',\n",
       " 'white & red',\n",
       " 'wine',\n",
       " 'ivory',\n",
       " 'black & yellow',\n",
       " 'jasper',\n",
       " 'lightkhaki',\n",
       " 'offblack',\n",
       " 'violet',\n",
       " 'navy',\n",
       " 'black & blue',\n",
       " 'rosegold',\n",
       " 'gold']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q6 = \"\"\"\n",
    "SELECT distinct product_color\n",
    "from df\n",
    "\"\"\"\n",
    "query6 = ps.sqldf(q6, locals())\n",
    "query6 = list(query6['product_color'])\n",
    "query6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e2721726",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:48.227832Z",
     "start_time": "2022-07-04T09:42:48.199909Z"
    }
   },
   "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>product_color</th>\n",
       "      <th>product_color_per</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>593</td>\n",
       "      <td>0.407001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>288</td>\n",
       "      <td>0.197666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>240</td>\n",
       "      <td>0.164722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Green</th>\n",
       "      <td>124</td>\n",
       "      <td>0.085106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blue</th>\n",
       "      <td>113</td>\n",
       "      <td>0.077557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Red</th>\n",
       "      <td>99</td>\n",
       "      <td>0.067948</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       product_color  product_color_per\n",
       "Other            593           0.407001\n",
       "Black            288           0.197666\n",
       "White            240           0.164722\n",
       "Green            124           0.085106\n",
       "Blue             113           0.077557\n",
       "Red               99           0.067948"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "red_list = []\n",
    "green_list = []\n",
    "white_list = []\n",
    "black_list = []\n",
    "blue_list = []\n",
    "yellow_list = []\n",
    "grey_list = []\n",
    "purple_list = []\n",
    "\n",
    "for i in query6:\n",
    "    if (i == 'red') or (i == 'RED') or (i == 'wine red') or (i == 'rose red') or (i == \"lightred\") or (i == 'coralred') \\\n",
    "    or (i == 'watermelonred') and (i != 'white & red') and (i != 'red & blue'):\n",
    "        red_list.append(i)\n",
    "    elif (i == 'green') or (i == 'applegreen') or (i == 'light green') or (i == 'mintgreen') or (i == 'lightgreen') \\\n",
    "    or (i == 'armygreen') and (i != 'white & green') and (i != 'black & green'):\n",
    "        green_list.append(i)\n",
    "    elif (i == 'white') or (i == 'White') or (i == 'whitefloral') or (i == 'offwhite') or (i == 'whitestripe'):\n",
    "        white_list.append(i)\n",
    "    elif (i == 'black')or (i == 'Black') or (i == 'Coolblack') or (i == 'Offblack'):\n",
    "        black_list.append(i)\n",
    "    elif (i == 'blue') or (i == 'Blue') or (i == ' navyblue') or (i == 'navy blue') or (i == 'lightblue') or (i == 'lakeblue') and \\\n",
    "          (i != 'navyblue & white') and (i != 'black & blue') and (i != 'blue & pink') and (i != 'pink & blue'):\n",
    "        blue_list.append(i)\n",
    "    elif (i == 'Yellow') or (i == 'Lightyellow'):\n",
    "        yellow_list.append(i)\n",
    "    elif (i == 'Grey') or (i == 'Greysnakeskinprint') or (i == 'Lightgrey') or (i == 'Gray') or (i == 'Lightgray'):\n",
    "        grey_list.append(i)\n",
    "    elif 'Purple' in i:\n",
    "        purple_list.append(i)\n",
    "        \n",
    "def color(tipe):\n",
    "    if tipe in red_list:\n",
    "        return 'Red'\n",
    "    elif tipe in green_list:\n",
    "        return 'Green'\n",
    "    elif tipe in white_list:\n",
    "        return 'White'\n",
    "    elif tipe in black_list:\n",
    "        return 'Black'\n",
    "    elif tipe in blue_list:\n",
    "        return 'Blue'\n",
    "    elif tipe in yellow_list:\n",
    "        return 'Yellow'\n",
    "    elif tipe in grey_list:\n",
    "        return 'Gray'\n",
    "    elif tipe in purple_list:\n",
    "        return 'Purple'\n",
    "    else:\n",
    "        return 'Other'\n",
    "        \n",
    "df['product_color'] = df['product_color'].apply(color)\n",
    "Product_color = pd.DataFrame(df['product_color'].value_counts())\n",
    "Product_color['product_color_per'] = Product_color['product_color']/Product_color['product_color'].sum()\n",
    "Product_color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "65ed31eb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:48.351504Z",
     "start_time": "2022-07-04T09:42:48.229828Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10, 8))\n",
    "plt.bar(Product_color.index, Product_color.product_color)\n",
    "plt.xlabel(\"Colors\")\n",
    "plt.ylabel(\"Number of products sold\")\n",
    "plt.title(\"Number of units old by colors\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de960210",
   "metadata": {},
   "source": [
    "Insight: From the sorted data, Black is the most demanded color (19.7%) followed by White (16.4%). The category of 'Other' colors include colors like Pink, Maroon, Gold, Brown etc."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0d64fa9",
   "metadata": {},
   "source": [
    "#### Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "615dec48",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:44:25.968834Z",
     "start_time": "2022-07-04T09:44:24.235208Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Woman    1369\n",
       "Man        88\n",
       "Name: pro_gender, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['title_orig'] = df['title_orig'].apply(lambda x: x.title())\n",
    "\n",
    "man_list = []\n",
    "\n",
    "def prod_gender(tipe):\n",
    "    for i in df['title_orig']:\n",
    "        if ('Man' in i) or ('Men' in i) and (i != 'Woman') :\n",
    "            man_list.append(i)\n",
    "    if tipe in man_list:\n",
    "        return 'Man'\n",
    "    else:\n",
    "        return 'Woman'\n",
    "\n",
    "\n",
    "df['pro_gender'] = df['title_orig'].apply(prod_gender)\n",
    "df['pro_gender'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e58886eb",
   "metadata": {},
   "source": [
    "Insight: Roughly 94% of all the products constitute women clothes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbf8db34",
   "metadata": {},
   "source": [
    "###  Merchant Rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4a24ec8d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:50.040060Z",
     "start_time": "2022-07-04T09:42:49.965150Z"
    }
   },
   "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>merchant_name</th>\n",
       "      <th>merchant_rating_count</th>\n",
       "      <th>merchant_rating</th>\n",
       "      <th>merchant_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>simplevalueltd</td>\n",
       "      <td>2174765</td>\n",
       "      <td>4.356188</td>\n",
       "      <td>5bd0a13aa8f4bb2739445be4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>gl</td>\n",
       "      <td>402743</td>\n",
       "      <td>4.156715</td>\n",
       "      <td>53facca81c105e3d5866e212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gl</td>\n",
       "      <td>402743</td>\n",
       "      <td>4.156715</td>\n",
       "      <td>53facca81c105e3d5866e212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bigcompany</td>\n",
       "      <td>366898</td>\n",
       "      <td>4.036081</td>\n",
       "      <td>53082ea15aefb07dfe1f2a4f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>shanghailalanginternationaltradecoltd</td>\n",
       "      <td>330405</td>\n",
       "      <td>4.067348</td>\n",
       "      <td>53f2beb39020ee03566d6e18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           merchant_name  merchant_rating_count  \\\n",
       "0                         simplevalueltd                2174765   \n",
       "1                                     gl                 402743   \n",
       "2                                     gl                 402743   \n",
       "3                             bigcompany                 366898   \n",
       "4  shanghailalanginternationaltradecoltd                 330405   \n",
       "\n",
       "   merchant_rating               merchant_id  \n",
       "0         4.356188  5bd0a13aa8f4bb2739445be4  \n",
       "1         4.156715  53facca81c105e3d5866e212  \n",
       "2         4.156715  53facca81c105e3d5866e212  \n",
       "3         4.036081  53082ea15aefb07dfe1f2a4f  \n",
       "4         4.067348  53f2beb39020ee03566d6e18  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q7 = \"\"\"\n",
    "SELECT merchant_name, merchant_rating_count, merchant_rating, merchant_id   \n",
    "from df\n",
    "order by merchant_rating_count desc \n",
    "limit 5\n",
    "\"\"\"\n",
    "\n",
    "query7 = ps.sqldf(q7, locals())\n",
    "query7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "49657df3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:50.101560Z",
     "start_time": "2022-07-04T09:42:50.042054Z"
    }
   },
   "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>title</th>\n",
       "      <th>units_sold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Women Sister Shirt Cute Cat Imprimé Couple d'été Débardeur Dessus Veste S-3XL</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                           title  \\\n",
       "0  Women Sister Shirt Cute Cat Imprimé Couple d'été Débardeur Dessus Veste S-3XL   \n",
       "\n",
       "   units_sold  \n",
       "0       10000  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q8 = \"\"\"\n",
    "SELECT title, units_sold\n",
    "from df\n",
    "where merchant_name = \"simplevalueltd\"\n",
    "order by units_sold\n",
    "\"\"\"\n",
    "\n",
    "query8 = ps.sqldf(q8, locals())\n",
    "query8"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a493bc7f",
   "metadata": {},
   "source": [
    "Insight: Merchant 'simplevalueltd' is the highest rated merchant with a rating of 4.35 and more than 2 million counts. Their top selling product is a Cat imprint shirt for women."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cfa0a0c1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:50.471451Z",
     "start_time": "2022-07-04T09:42:50.103526Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Units Sold')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (15, 10))\n",
    "sns.barplot(x = df.units_sold_group, y = df.merchant_rating_count, ax = ax)\n",
    "\n",
    "units_sold_order = ['0.1k-1k', '1k-5k', '5k-10k', '10k-20k', '20k-50k', '50k-100k', '100k+']\n",
    "ax.set_xticklabels(units_sold_order)\n",
    "ax.set_ylabel('Number of Merchant Ratings', fontweight = 'bold')\n",
    "ax.set_xlabel('Units Sold', fontweight = 'bold')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8db62400",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:32:27.660669Z",
     "start_time": "2022-07-04T09:32:27.643687Z"
    }
   },
   "source": [
    "Higher the number of Ratings of a Merchant, higher is the units sold for that merchant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "76187aeb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T09:42:50.794348Z",
     "start_time": "2022-07-04T09:42:50.473420Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Units Sold')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (15, 10))\n",
    "sns.barplot(x = df.units_sold_group, y = df.merchant_rating, ax = ax)\n",
    "\n",
    "units_sold_order = ['0.1k-1k', '1k-5k', '5k-10k', '10k-20k', '20k-50k', '50k-100k', '100k+']\n",
    "ax.set_xticklabels(units_sold_order)\n",
    "ax.set_ylabel('Number of Merchant Ratings', fontweight = 'bold')\n",
    "ax.set_xlabel('Units Sold', fontweight = 'bold')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "859ae94c",
   "metadata": {},
   "source": [
    "Products sold are the ones with merchant_rating of 4 or more. This is a indication of buyer awareness implying that customers tend to buy products from higher rated merchants only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "dac344e2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-04T10:48:15.206546Z",
     "start_time": "2022-07-04T10:48:15.107757Z"
    }
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_Name</th>\n",
       "      <th>Units_Sold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018 New Fashion Women's Tops Sexy Strappy Sleeveless Dentelle sans manches Crop Cops</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Femmes Camisole extensible Spaghetti Strap Long Débardeur Slip Mini Dress 4Color SIZE XXL est disponible maintenant</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nouveau Aeeival Femmes Vêtements À Manches Longues Automne Hiver Robe De Bal Robe Slim Fit Patchwork Casual Dentelle Robe Robe De Renda</td>\n",
       "      <td>100000</td>\n",
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       "      <th>3</th>\n",
       "      <td>T-shirt à manches courtes en mousseline de soie rouge et blanc d'été pour femme</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nouvelle arrivée femmes été sexy robe de soirée élégante sans manches col roulé mini robe</td>\n",
       "      <td>100000</td>\n",
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      "text/plain": [
       "                                                                                                                              Product_Name  \\\n",
       "0                                                    2018 New Fashion Women's Tops Sexy Strappy Sleeveless Dentelle sans manches Crop Cops   \n",
       "1                      Femmes Camisole extensible Spaghetti Strap Long Débardeur Slip Mini Dress 4Color SIZE XXL est disponible maintenant   \n",
       "2  Nouveau Aeeival Femmes Vêtements À Manches Longues Automne Hiver Robe De Bal Robe Slim Fit Patchwork Casual Dentelle Robe Robe De Renda   \n",
       "3                                                          T-shirt à manches courtes en mousseline de soie rouge et blanc d'été pour femme   \n",
       "4                                                Nouvelle arrivée femmes été sexy robe de soirée élégante sans manches col roulé mini robe   \n",
       "\n",
       "   Units_Sold  \n",
       "0      100000  \n",
       "1      100000  \n",
       "2      100000  \n",
       "3      100000  \n",
       "4      100000  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q9 = \"\"\"\n",
    "SELECT title as Product_Name, units_sold as Units_Sold\n",
    "from df\n",
    "order by units_sold desc\n",
    "limit 5 \n",
    "\"\"\"\n",
    "\n",
    "query9 = ps.sqldf(q9, locals())\n",
    "pd.set_option('display.max_colwidth', None)\n",
    "query9"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f1bc43f",
   "metadata": {},
   "outputs": [],
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