myntra_data_eda.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Myntra Dataset EDA\n",
    "\n",
    "The myntra dataset will be used to create the list of 100 products that our company \"High Value Imports, Inc.\" will be looking to asses seizure risk.\n",
    "\n",
    "Rather than manually typing and creating a list of 100 products, we'll create them as a subset from the myntra data-set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-2-62e95400b3a4>:1: DtypeWarning: Columns (9) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  myntra_raw_df = pd.read_csv(\"myntra-fashion-clothin.csv\")\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        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>URL</th>\n",
       "      <th>Product_id</th>\n",
       "      <th>BrandName</th>\n",
       "      <th>Category</th>\n",
       "      <th>Individual_category</th>\n",
       "      <th>category_by_Gender</th>\n",
       "      <th>Description</th>\n",
       "      <th>DiscountPrice (in Rs)</th>\n",
       "      <th>OriginalPrice (in Rs)</th>\n",
       "      <th>DiscountOffer</th>\n",
       "      <th>SizeOption</th>\n",
       "      <th>Ratings</th>\n",
       "      <th>Reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://www.myntra.com/jeans/roadster/roadster...</td>\n",
       "      <td>2296012</td>\n",
       "      <td>Roadster</td>\n",
       "      <td>Bottom Wear</td>\n",
       "      <td>jeans</td>\n",
       "      <td>Men</td>\n",
       "      <td>roadster men navy blue slim fit mid rise clean...</td>\n",
       "      <td>824.0</td>\n",
       "      <td>1499.0</td>\n",
       "      <td>45% OFF</td>\n",
       "      <td>28, 30, 32, 34, 36</td>\n",
       "      <td>3.9</td>\n",
       "      <td>999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://www.myntra.com/track-pants/locomotive/...</td>\n",
       "      <td>13780156</td>\n",
       "      <td>LOCOMOTIVE</td>\n",
       "      <td>Bottom Wear</td>\n",
       "      <td>track-pants</td>\n",
       "      <td>Men</td>\n",
       "      <td>locomotive men black  white solid slim fit tra...</td>\n",
       "      <td>517.0</td>\n",
       "      <td>1149.0</td>\n",
       "      <td>55% OFF</td>\n",
       "      <td>S, M, L, XL</td>\n",
       "      <td>4.0</td>\n",
       "      <td>999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://www.myntra.com/shirts/roadster/roadste...</td>\n",
       "      <td>11895958</td>\n",
       "      <td>Roadster</td>\n",
       "      <td>Topwear</td>\n",
       "      <td>shirts</td>\n",
       "      <td>Men</td>\n",
       "      <td>roadster men navy white  black geometric print...</td>\n",
       "      <td>629.0</td>\n",
       "      <td>1399.0</td>\n",
       "      <td>55% OFF</td>\n",
       "      <td>38, 40, 42, 44, 46, 48</td>\n",
       "      <td>4.3</td>\n",
       "      <td>999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://www.myntra.com/shapewear/zivame/zivame...</td>\n",
       "      <td>4335679</td>\n",
       "      <td>Zivame</td>\n",
       "      <td>Lingerie &amp; Sleep Wear</td>\n",
       "      <td>shapewear</td>\n",
       "      <td>Women</td>\n",
       "      <td>zivame women black saree shapewear zi3023core0...</td>\n",
       "      <td>893.0</td>\n",
       "      <td>1295.0</td>\n",
       "      <td>31% OFF</td>\n",
       "      <td>S, M, L, XL, XXL</td>\n",
       "      <td>4.2</td>\n",
       "      <td>999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://www.myntra.com/tshirts/roadster/roadst...</td>\n",
       "      <td>11690882</td>\n",
       "      <td>Roadster</td>\n",
       "      <td>Western</td>\n",
       "      <td>tshirts</td>\n",
       "      <td>Women</td>\n",
       "      <td>roadster women white solid v neck pure cotton ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>599.0</td>\n",
       "      <td>35% OFF</td>\n",
       "      <td>XS, S, M, L, XL</td>\n",
       "      <td>4.2</td>\n",
       "      <td>999.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 URL  Product_id   BrandName  \\\n",
       "0  https://www.myntra.com/jeans/roadster/roadster...     2296012    Roadster   \n",
       "1  https://www.myntra.com/track-pants/locomotive/...    13780156  LOCOMOTIVE   \n",
       "2  https://www.myntra.com/shirts/roadster/roadste...    11895958    Roadster   \n",
       "3  https://www.myntra.com/shapewear/zivame/zivame...     4335679      Zivame   \n",
       "4  https://www.myntra.com/tshirts/roadster/roadst...    11690882    Roadster   \n",
       "\n",
       "                Category Individual_category category_by_Gender  \\\n",
       "0            Bottom Wear               jeans                Men   \n",
       "1            Bottom Wear         track-pants                Men   \n",
       "2                Topwear              shirts                Men   \n",
       "3  Lingerie & Sleep Wear           shapewear              Women   \n",
       "4                Western             tshirts              Women   \n",
       "\n",
       "                                         Description  DiscountPrice (in Rs)  \\\n",
       "0  roadster men navy blue slim fit mid rise clean...                  824.0   \n",
       "1  locomotive men black  white solid slim fit tra...                  517.0   \n",
       "2  roadster men navy white  black geometric print...                  629.0   \n",
       "3  zivame women black saree shapewear zi3023core0...                  893.0   \n",
       "4  roadster women white solid v neck pure cotton ...                    NaN   \n",
       "\n",
       "   OriginalPrice (in Rs) DiscountOffer              SizeOption  Ratings  \\\n",
       "0                 1499.0       45% OFF      28, 30, 32, 34, 36      3.9   \n",
       "1                 1149.0       55% OFF             S, M, L, XL      4.0   \n",
       "2                 1399.0       55% OFF  38, 40, 42, 44, 46, 48      4.3   \n",
       "3                 1295.0       31% OFF        S, M, L, XL, XXL      4.2   \n",
       "4                  599.0       35% OFF         XS, S, M, L, XL      4.2   \n",
       "\n",
       "   Reviews  \n",
       "0    999.0  \n",
       "1    999.0  \n",
       "2    999.0  \n",
       "3    999.0  \n",
       "4    999.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myntra_raw_df = pd.read_csv(\"myntra-fashion-clothing.csv\")\n",
    "myntra_raw_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "URL\n",
      "Product_id\n",
      "BrandName\n",
      "Category\n",
      "Individual_category\n",
      "category_by_Gender\n",
      "Description\n",
      "DiscountPrice (in Rs)\n",
      "OriginalPrice (in Rs)\n",
      "DiscountOffer\n",
      "SizeOption\n",
      "Ratings\n",
      "Reviews\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\".join(myntra_raw_df.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_id</th>\n",
       "      <th>category</th>\n",
       "      <th>individual_category</th>\n",
       "      <th>brand_name</th>\n",
       "      <th>description</th>\n",
       "      <th>price_rs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2296012</td>\n",
       "      <td>Bottom Wear</td>\n",
       "      <td>jeans</td>\n",
       "      <td>Roadster</td>\n",
       "      <td>roadster men navy blue slim fit mid rise clean...</td>\n",
       "      <td>1499.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13780156</td>\n",
       "      <td>Bottom Wear</td>\n",
       "      <td>track-pants</td>\n",
       "      <td>LOCOMOTIVE</td>\n",
       "      <td>locomotive men black  white solid slim fit tra...</td>\n",
       "      <td>1149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11895958</td>\n",
       "      <td>Topwear</td>\n",
       "      <td>shirts</td>\n",
       "      <td>Roadster</td>\n",
       "      <td>roadster men navy white  black geometric print...</td>\n",
       "      <td>1399.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4335679</td>\n",
       "      <td>Lingerie &amp; Sleep Wear</td>\n",
       "      <td>shapewear</td>\n",
       "      <td>Zivame</td>\n",
       "      <td>zivame women black saree shapewear zi3023core0...</td>\n",
       "      <td>1295.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11690882</td>\n",
       "      <td>Western</td>\n",
       "      <td>tshirts</td>\n",
       "      <td>Roadster</td>\n",
       "      <td>roadster women white solid v neck pure cotton ...</td>\n",
       "      <td>599.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_id               category individual_category  brand_name  \\\n",
       "0     2296012            Bottom Wear               jeans    Roadster   \n",
       "1    13780156            Bottom Wear         track-pants  LOCOMOTIVE   \n",
       "2    11895958                Topwear              shirts    Roadster   \n",
       "3     4335679  Lingerie & Sleep Wear           shapewear      Zivame   \n",
       "4    11690882                Western             tshirts    Roadster   \n",
       "\n",
       "                                         description  price_rs  \n",
       "0  roadster men navy blue slim fit mid rise clean...    1499.0  \n",
       "1  locomotive men black  white solid slim fit tra...    1149.0  \n",
       "2  roadster men navy white  black geometric print...    1399.0  \n",
       "3  zivame women black saree shapewear zi3023core0...    1295.0  \n",
       "4  roadster women white solid v neck pure cotton ...     599.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myntra_df = myntra_raw_df[[\"Product_id\", \"Category\", \"Individual_category\",  \"BrandName\", \"Description\", \"OriginalPrice (in Rs)\"]].copy()\n",
    "myntra_df.rename(columns={\n",
    "    'Product_id': 'product_id',\n",
    "    'BrandName': 'brand_name',\n",
    "    'Category': 'category',\n",
    "    'Individual_category': 'individual_category',\n",
    "    \"Description\": \"description\",\n",
    "    'OriginalPrice (in Rs)': 'price_rs'\n",
    "}, inplace=True)\n",
    "\n",
    "myntra_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2088"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brands = myntra_df[\"brand_name\"].unique()\n",
    "len(brands)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "brand_name\n",
       "Pothys                   16005\n",
       "Roadster                 10935\n",
       "KALINI                    9589\n",
       "HERE&NOW                  6515\n",
       "HRX by Hrithik Roshan     5297\n",
       "                         ...  \n",
       "PIVOTO                       1\n",
       "PEONY SMART WORLD            1\n",
       "OFFIRA TEX WORLD             1\n",
       "Sztori Garfield              1\n",
       "Geonaute By Decathlon        1\n",
       "Name: count, Length: 2088, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brands = myntra_df.brand_name.value_counts()\n",
    "brands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "brand_name\n",
       "Pothys                   16005\n",
       "Roadster                 10935\n",
       "KALINI                    9589\n",
       "HERE&NOW                  6515\n",
       "HRX by Hrithik Roshan     5297\n",
       "                         ...  \n",
       "Swiss Club                 101\n",
       "LELA                       101\n",
       "Lux Cozi                   101\n",
       "Mizzific                   101\n",
       "PAPA BRANDS                101\n",
       "Name: count, Length: 840, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = brands[brands > 100]\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Van Heusen Flex\n",
      "LELA\n",
      "MANGO\n",
      "RAJGRANTH\n",
      "TWIN BIRDS\n",
      "Puma\n",
      "Quechua By Decathlon\n",
      "Sangria\n",
      "ORIGIN BY ZALORA\n",
      "Athena\n",
      "BRINNS\n",
      "beebelle\n",
      "CODE by Lifestyle\n",
      "BharatSthali\n",
      "Triveni\n",
      "Camey\n",
      "SERONA FABRICS\n",
      "ADORENITE\n",
      "Louis Philippe Sport\n",
      "Saree mall\n",
      "SAPPER\n",
      "Thara Sarees\n",
      "La Mode\n",
      "Duke\n",
      "Anug by SOJANYA\n",
      "Purple State\n",
      "Sand Dune\n",
      "Ethnic basket\n",
      "ZHEIA\n",
      "Srey trends\n",
      "Allen Solly Tribe\n",
      "Wacoal\n",
      "iki chic\n",
      "JC Mode\n",
      "Sugathari\n",
      "FLAMBOYANT\n",
      "Charukriti\n",
      "GRITSTONES\n",
      "ONLY\n",
      "Amante\n",
      "Antony Morato\n",
      "kipek\n",
      "BASIICS by La Intimo\n",
      "Sera\n",
      "BE AWARA\n",
      "ATTIITUDE\n",
      "The Mom Store\n",
      "Ed Hardy\n",
      "SHANGRILA\n",
      "Marie Claire\n",
      "Trend Arrest\n",
      "Turtle\n",
      "DIVA WALK EXCLUSIVE\n",
      "La Aimee\n",
      "AV2\n",
      "Van Heusen\n",
      "ETC\n",
      "HIGHLANDER\n",
      "PERFECT WEAR\n",
      "Just Wow\n",
      "883 Police\n",
      "Kook N Keech\n",
      "Hangup\n",
      "NEU LOOK FASHION\n",
      "RIVI\n",
      "Lady Lyka\n",
      "CINOCCI\n",
      "Adobe\n",
      "RARE\n",
      "Crocodile\n",
      "Harpa\n",
      "ADA\n",
      "Beverly Hills Polo Club\n",
      "Invincible\n",
      "Monte Carlo\n",
      "ANI\n",
      "SIRIL\n",
      "Soch\n",
      "OM SAI LATEST CREATION\n",
      "Texco\n",
      "Vami\n",
      "Florence\n",
      "Molcha\n",
      "STALK\n",
      "V Dot\n",
      "Forca\n",
      "HPS Sports\n",
      "J Hampstead\n",
      "TULIP 21\n",
      "Saanjh\n",
      "V2 Value & Variety\n",
      "W\n",
      "BYFORD by Pantaloons\n",
      "mod & shy\n",
      "FEMEA\n",
      "LY2\n",
      "I Know\n",
      "Mizzific\n",
      "Wolfpack\n",
      "Lounge Dreams\n",
      "DAMENSCH\n",
      "Leading Lady\n",
      "Kook N Keech Marvel\n",
      "Enamor\n",
      "SALWAR STUDIO\n",
      "WoowZerz\n",
      "Fame Forever by Lifestyle\n",
      "RedRound\n",
      "METTLE\n",
      "Luxure by Louis Philippe\n",
      "FREESOUL\n",
      "AD By Arvind\n",
      "NAMASKAR\n",
      "TOM BURG\n",
      "elleven\n",
      "SHECZZAR\n",
      "Crimsoune Club\n",
      "PAUSE SPORT\n",
      "RAMRAJ COTTON\n",
      "Pierre Carlo\n",
      "indus route by Pantaloons\n",
      "max\n",
      "SCORPIUS\n",
      "SELECTED\n",
      "Octave\n",
      "LINDBERGH\n",
      "JoE Hazel\n",
      "SF JEANS by Pantaloons\n",
      "ZALORA OCCASION\n",
      "Manyavar\n",
      "Yuris\n",
      "GRIFFEL\n",
      "ONN\n",
      "Peter England\n",
      "Flambeur\n",
      "Dollar\n",
      "EROTISSCH\n",
      "Difference of Opinion\n",
      "Awadhi\n",
      "ManQ CASUAL\n",
      "BOMBAY SELECTIONS\n",
      "Darzi\n",
      "Fab Dadu\n",
      "Code 61\n",
      "VASTRANAND\n",
      "Revolution\n",
      "COVER STORY\n",
      "FABNEST\n",
      "Selvia\n",
      "plusS\n",
      "DOLCE CRUDO\n",
      "Moda Rapido\n",
      "SIRIKIT\n",
      "Woodland\n",
      "PERFECTBLUE\n",
      "Cation\n",
      "Heelium\n",
      "Callino London\n",
      "Rangriti\n",
      "Dollar Missy\n",
      "Ayaany\n",
      "BROADSTAR\n",
      "Laceandme\n",
      "Women Touch\n",
      "Shaily\n",
      "Sztori\n",
      "FRENCH FLEXIOUS\n",
      "Pavechas\n",
      "Bodycare\n",
      "Ginger by Lifestyle\n",
      "Missguided\n",
      "IQRAAR\n",
      "LE BOURGEOIS\n",
      "PATRORNA\n",
      "TABARD\n",
      "ADIDAS Originals\n",
      "FableStreet\n",
      "SINGLE\n",
      "CHIMPAAANZEE\n",
      "Lilots\n",
      "bigbanana\n",
      "Latin Quarters\n",
      "TRUNDZ\n",
      "Dynamocks\n",
      "THE MILLION CLUB\n",
      "OTORVA\n",
      "inocenCia\n",
      "Leeza Store\n",
      "SHFFL\n",
      "MAKCLAN\n",
      "MONJOLIKA FASHION\n",
      "DIVASTRI\n",
      "BRATMA\n",
      "SHOWOFF\n",
      "BLACK RADIO\n",
      "Mesmore\n",
      "Zelocity by Zivame\n",
      "SOUNDARYA\n",
      "Bitz\n",
      "SHAYE\n",
      "American Crew\n",
      "EXTRA LOVE BY LIBAS\n",
      "Kanvin\n",
      "Ethnicity\n",
      "Suta\n",
      "ARISE\n",
      "ABELINO\n",
      "Llak Jeans\n",
      "Inner Sense\n",
      "Peter England Elite\n",
      "Klamotten\n",
      "PAPA BRANDS\n",
      "SG LEMAN\n",
      "Wildcraft\n",
      "Wintage\n",
      "DECHEN\n",
      "Cantabil\n",
      "FLAWLESS\n",
      "Gipsy\n",
      "Uptownie Lite\n",
      "Sonari\n",
      "Modeve\n",
      "FERANOID\n",
      "The Souled Store\n",
      "COBB\n",
      "Jaipur Kurti\n",
      "Vbuyz\n",
      "MBE\n",
      "Mint & Oak\n",
      "Sanwara\n",
      "Head\n",
      "Celio\n",
      "Ruhaans\n",
      "Ira Soleil\n",
      "Molly & Michel\n",
      "Raymond\n",
      "Bruun & Stengade\n",
      "VividArtsy\n",
      "Smugglerz\n",
      "KRAFT INDIA\n",
      "Yaadleen\n",
      "URBANIC\n",
      "PROTEENS\n",
      "ZIYAA\n",
      "MAXENCE\n",
      "Blacksmith\n",
      "Unnati Silks\n",
      "Saaki\n",
      "DIVYANK\n",
      "FREECULTR\n",
      "Silvertraq\n",
      "Ahalyaa\n",
      "Imfashini\n",
      "Bhama Couture\n",
      "INVICTUS\n",
      "NOT YET by us\n",
      "Varanga\n",
      "AMERICAN EAGLE OUTFITTERS\n",
      "Antheaa\n",
      "Sweet Dreams\n",
      "V&M\n",
      "M&H Our Water\n",
      "Gallus\n",
      "De Moza\n",
      "AHIKA\n",
      "WROGN\n",
      "The Label Life\n",
      "aaliya\n",
      "Emmyrobe\n",
      "UnderJeans by Spykar\n",
      "JAINISH\n",
      "ANVI Be Yourself\n",
      "Fort Collins\n",
      "Flying Machine\n",
      "Indietoga\n",
      "Netram\n",
      "Secret Wish\n",
      "SPARROWHAWK\n",
      "FREAKINS\n",
      "Annabelle by Pantaloons\n",
      "Ajile by Pantaloons\n",
      "I like me\n",
      "LAMAAYA\n",
      "Lady Love\n",
      "Kaifoo\n",
      "Park Avenue\n",
      "Bonjour\n",
      "INDYA\n",
      "KETCH\n",
      "SG RAJASAHAB\n",
      "Louis Philippe\n",
      "Mode by Red Tape\n",
      "Anubhutee\n",
      "MELOSO\n",
      "EthnoVogue\n",
      "Ethnix by Raymond\n",
      "Rex Straut Jeans\n",
      "Park Avenue Woman\n",
      "Kuons Avenue\n",
      "Fitkin\n",
      "NEVA\n",
      "Qurvii\n",
      "KALINI\n",
      "Tommy Hilfiger\n",
      "IC4\n",
      "The Vanca\n",
      "Xpose\n",
      "Laabha\n",
      "Silk Land\n",
      "Canary London\n",
      "The Indian Garage Co\n",
      "SPYKAR\n",
      "THE BEAR HOUSE\n",
      "Kotty\n",
      "YASH GALLERY\n",
      "LAASA  SPORTS\n",
      "Popnetic\n",
      "Laa Calcutta\n",
      "FEVER\n",
      "QUIERO\n",
      "Excalibur\n",
      "Napra\n",
      "Parx\n",
      "Safaa\n",
      "MASCLN SASSAFRAS\n",
      "ARDEUR\n",
      "Aarika\n",
      "STREET 9\n",
      "Greenfibre\n",
      "Da Intimo\n",
      "Anahi\n",
      "Okane\n",
      "Go Colors\n",
      "Ennoble\n",
      "Miss Poem\n",
      "Karmic Vision\n",
      "Nautica\n",
      "Van Heusen Woman\n",
      "Huetrap\n",
      "Ruggers\n",
      "Grancy\n",
      "Khushal K\n",
      "Richlook\n",
      "Pothys\n",
      "LYKKEIN\n",
      "IVOC\n",
      "SmileyWorld\n",
      "Tokyo Talkies\n",
      "The Pink Moon\n",
      "Amrutam Fab\n",
      "Vastraa Fusion\n",
      "Linen Club\n",
      "Kappa\n",
      "CAVALLO by Linen Club\n",
      "Balenzia\n",
      "Belle Fille\n",
      "PinkLoom\n",
      "SECRETS BY ZEROKAATA\n",
      "Oxemberg\n",
      "Poshak Hub\n",
      "Saffron Threads\n",
      "Benstoke\n",
      "Dollar Bigboss\n",
      "Zastraa\n",
      "Joven\n",
      "Chhabra 555\n",
      "La Zoire\n",
      "PANIT\n",
      "SHAVYA\n",
      "PURYS\n",
      "Hypernation\n",
      "UNITED LIBERTY\n",
      "Aawari\n",
      "SIMON CARTER LONDON\n",
      "Saree Swarg\n",
      "HERE&NOW\n",
      "Taavi\n",
      "Cayman\n",
      "XYXX\n",
      "MomToBe\n",
      "U.S. Polo Assn. Women\n",
      "mezmoda\n",
      "Peter England Casuals\n",
      "U.S. Polo Assn. Denim Co.\n",
      "Parcel Yard\n",
      "SASSAFRAS\n",
      "her by invictus\n",
      "BLANC9\n",
      "Fleurt\n",
      "Tikhi Imli\n",
      "Voi Jeans\n",
      "AADVIKA\n",
      "rock.it\n",
      "TREEMODA\n",
      "PURVAJA\n",
      "Ode by House of Pataudi\n",
      "TATTVA\n",
      "N-Gal\n",
      "French Connection\n",
      "ATHLISIS\n",
      "People\n",
      "ARMISTO\n",
      "Lebami\n",
      "Nite Flite\n",
      "1 Stop Fashion\n",
      "RARE RABBIT\n",
      "JerfSports\n",
      "Hancock\n",
      "HARBORNBAY\n",
      "WTFUNK\n",
      "EVERDION\n",
      "I Saw It First\n",
      "Cherokee\n",
      "ZALORA ACTIVE\n",
      "3PIN\n",
      "GERUA\n",
      "THE WEAVE TRAVELLER\n",
      "DIXCY SCOTT MAXIMUS\n",
      "Karigari\n",
      "Red Tape\n",
      "fabGLOBAL\n",
      "BEAT LONDON by PEPE JEANS\n",
      "studio rasa\n",
      "Sringam\n",
      "SWAGG INDIA\n",
      "Calvin Klein Underwear\n",
      "Indo Era\n",
      "Juniper\n",
      "RANGMAYEE\n",
      "Pootlu\n",
      "Zivame\n",
      "Miss Chase\n",
      "Shae by SASSAFRAS\n",
      "Carlton London\n",
      "URBAN SCOTTISH\n",
      "Veni Vidi Vici\n",
      "Candyskin\n",
      "cape canary\n",
      "VAIRAGEE\n",
      "KOI SLEEPWEAR\n",
      "Indian Terrain\n",
      "Mr Bowerbird\n",
      "MERCHANT MARINE\n",
      "Jean Cafe\n",
      "Biba\n",
      "VAN HEUSEN DENIM LABS\n",
      "Kraus Jeans\n",
      "Fluffalump\n",
      "Luni\n",
      "Urbano Plus\n",
      "Allen Cooper\n",
      "Shaftesbury London\n",
      "U&F\n",
      "Global Desi\n",
      "Anouk\n",
      "MBeautiful\n",
      "Kvsfab\n",
      "Urban Ranger by pantaloons\n",
      "JISORA\n",
      "Slumber Jill\n",
      "Masculino Latino\n",
      "one8 x PUMA\n",
      "MANGO MAN\n",
      "Swiss Club\n",
      "Friskers\n",
      "Black Panther\n",
      "Columbia\n",
      "The Chennai Silks\n",
      "MARC LOUIS\n",
      "XIN\n",
      "Urbano Fashion\n",
      "ANTI CULTURE\n",
      "WESTCLO\n",
      "Tinted\n",
      "FUAARK\n",
      "OFF LIMITS\n",
      "Solemio\n",
      "Geroo Jaipur\n",
      "Rajnandini\n",
      "Force NXT\n",
      "Arrow\n",
      "Jockey\n",
      "Domyos By Decathlon\n",
      "HRX by Hrithik Roshan\n",
      "Iris\n",
      "ROOTED\n",
      "Kalt\n",
      "Wrangler\n",
      "Purple Feather\n",
      "Bellofox\n",
      "Bushirt\n",
      "Manthan\n",
      "Belliskey\n",
      "Berrylush\n",
      "Desi Weavess\n",
      "COUPER & COLL\n",
      "SOJANYA\n",
      "Coucou by Zivame\n",
      "AGIL ATHLETICA\n",
      "V-Mart\n",
      "KASSUALLY\n",
      "YOONOY\n",
      "Berrys Intimatess\n",
      "Calvin Klein Jeans\n",
      "Lakshita\n",
      "JADE BLUE\n",
      "IMARA\n",
      "Obaan\n",
      "Street Armor by Pantaloons\n",
      "New Balance\n",
      "VIMAL JONNEY\n",
      "LA LOFT\n",
      "Jack & Jones\n",
      "Madame\n",
      "VRSALES\n",
      "MAGRE\n",
      "Chennis\n",
      "Rosaline by Zivame\n",
      "BEATITUDE\n",
      "InWeave\n",
      "BUY NEW TREND\n",
      "Martini\n",
      "Almo Wear\n",
      "CL SPORT\n",
      "H&M\n",
      "Be Indi\n",
      "SORATIA\n",
      "Yashika\n",
      "GANT\n",
      "VIMAL\n",
      "Pretty Awesome\n",
      "Roadster\n",
      "JUMP USA\n",
      "Forever New\n",
      "Ducati\n",
      "Kook N Keech Disney\n",
      "Mayra\n",
      "Femella\n",
      "COTTON ON\n",
      "Snitch\n",
      "WROGN ACTIVE\n",
      "Van Heusen Sport\n",
      "Enchanted Drapes\n",
      "Vero Moda\n",
      "KENDALL & KYLIE\n",
      "Banarasi Style\n",
      "GRACIT\n",
      "RG DESIGNERS\n",
      "Readiprint Fashions\n",
      "Aujjessa\n",
      "PrettyCat\n",
      "mokshi\n",
      "Soie\n",
      "Breakbounce\n",
      "THE NKS PLUS\n",
      "saubhagya\n",
      "SHUBHVASTRA\n",
      "Sports52 wear\n",
      "Off Label\n",
      "ZALORA WORK\n",
      "ColorPlus\n",
      "DIXCY SCOTT\n",
      "BEVERLY BLUES\n",
      "MIMOSA\n",
      "Tulsattva\n",
      "Invictus Indoor\n",
      "Floret\n",
      "SHOPGARB\n",
      "hummel\n",
      "DEEBACO\n",
      "DEVOILER\n",
      "Waahiba\n",
      "Bitterlime\n",
      "HOUSE OF KKARMA\n",
      "Saadgi\n",
      "SheWill\n",
      "AND\n",
      "C9 AIRWEAR\n",
      "La Intimo\n",
      "TAG 7 PLUS\n",
      "am ma\n",
      "Zink London\n",
      "FOREVER 21\n",
      "TAG 7\n",
      "Pepe Jeans\n",
      "Fabindia\n",
      "ZOLA\n",
      "The Dry State\n",
      "FASHOR\n",
      "Lenissa\n",
      "Rodzen\n",
      "9teenAGAIN\n",
      "Indethnic\n",
      "Prakrti\n",
      "SANISA\n",
      "Vartah\n",
      "Indibelle\n",
      "pivot\n",
      "LABEL AARNA\n",
      "ether\n",
      "Nike\n",
      "Louis Philippe Jeans\n",
      "AURELIA\n",
      "Lee\n",
      "Sugr\n",
      "Proline Active\n",
      "Trendyol\n",
      "RAREISM\n",
      "Fleximaa\n",
      "PUMA Motorsport\n",
      "Taneira\n",
      "Jinfo\n",
      "Iti\n",
      "arangya\n",
      "Kazo\n",
      "Mitera\n",
      "MASH UNLIMITED\n",
      "hangup trend\n",
      "Masha\n",
      "Levis\n",
      "t-base\n",
      "Femme Luxe\n",
      "KISAH PLUS\n",
      "See Designs\n",
      "Red Chief\n",
      "berry blues\n",
      "Woods\n",
      "RANGMANCH BY PANTALOONS\n",
      "DILLINGER\n",
      "John Pride\n",
      "ZALORA BASICS\n",
      "Rapra The Label\n",
      "U.S. Polo Assn.\n",
      "Cottinfab\n",
      "evolove\n",
      "Dollar Socks\n",
      "Smarty Pants\n",
      "Nayo\n",
      "PERFKT-U\n",
      "Arhi\n",
      "Manu\n",
      "THANGAMAGAN\n",
      "Pistaa\n",
      "KOMLI\n",
      "Melange by Lifestyle\n",
      "Amydus\n",
      "Campus Sutra\n",
      "DRAAX Fashions\n",
      "Stylee LIFESTYLE\n",
      "MKH\n",
      "Kalamandir\n",
      "High Star\n",
      "KISAH\n",
      "DOOR74\n",
      "Chemistry\n",
      "Arrow New York\n",
      "NA-KD\n",
      "Span\n",
      "Basics\n",
      "ROMEO ROSSI\n",
      "Souminie\n",
      "Label Ritu Kumar\n",
      "Sethukrishna\n",
      "KLOTTHE\n",
      "Reebok Classic\n",
      "Meeranshi\n",
      "MISH\n",
      "AD & AV\n",
      "Orchid Blues\n",
      "FashionRack\n",
      "FirstKrush\n",
      "Urban Dog\n",
      "Yufta\n",
      "Myshka\n",
      "MANQ\n",
      "Softrose\n",
      "all about you\n",
      "Arrow Sport\n",
      "AASI - HOUSE OF NAYO\n",
      "Satrani\n",
      "Kalyani\n",
      "SATYAM WEAVES\n",
      "Blackberrys\n",
      "DENNISON\n",
      "Havida Sarees\n",
      "GROVERSONS Paris Beauty\n",
      "GOLDSTROMS\n",
      "Mast & Harbour\n",
      "Bewakoof\n",
      "FabAlley\n",
      "ASICS\n",
      "UrGear\n",
      "Maniac\n",
      "98 Degree North\n",
      "Style Quotient\n",
      "SOJANYA PLUS\n",
      "VAABA\n",
      "WEAVERS VILLA\n",
      "BROOWL\n",
      "Allen Solly\n",
      "SHUBHKALA\n",
      "109F\n",
      "FUGAZEE\n",
      "Prakhya\n",
      "AARA\n",
      "Pratham Blue\n",
      "armure\n",
      "MABISH by Sonal Jain\n",
      "DEYANN PLUS\n",
      "Abhishti\n",
      "Globus\n",
      "Enviously Young\n",
      "BULLMER\n",
      "MUSCLE TORQUE\n",
      "STUDIOACTIV\n",
      "Louis Philippe ATHPLAY\n",
      "MOHANLAL SONS\n",
      "House of Pataudi\n",
      "Souchii\n",
      "NEUDIS\n",
      "METAL\n",
      "KHADIO\n",
      "Boston Club\n",
      "Marks & Spencer\n",
      "Slazenger\n",
      "appulse\n",
      "Clovia\n",
      "Cultsport\n",
      "SANGAM PRINTS\n",
      "Honey by Pantaloons\n",
      "zebu\n",
      "FURO by Red Chief\n",
      "Aditi Wasan\n",
      "Mystere Paris\n",
      "Inddus\n",
      "Colt\n",
      "anayna\n",
      "LOCOMOTIVE\n",
      "Kryptic\n",
      "Allen Solly Sport\n",
      "Reebok\n",
      "QUANCIOUS\n",
      "Innocence\n",
      "RAJUBHAI HARGOVINDAS\n",
      "Ishin\n",
      "one8 by Virat Kohli\n",
      "Speedo\n",
      "Libas\n",
      "HRITIKA\n",
      "KSUT\n",
      "Allen Solly Woman\n",
      "Janasya\n",
      "Clt.s\n",
      "DressBerry\n",
      "Ms.Lingies\n",
      "beevee\n",
      "Harvard\n",
      "Somras\n",
      "Miaz Lifestyle\n",
      "QUARANTINE\n",
      "Claura\n",
      "Bene Kleed\n",
      "M&H Easy\n",
      "Prag & Co\n",
      "YOLOCLAN\n",
      "MADSTO\n",
      "Divena\n",
      "Tweens\n",
      "Aeropostale\n",
      "Deewa\n",
      "Lux Cozi\n",
      "Juelle\n",
      "DeFacto\n",
      "Dupatta Bazaar\n",
      "VASTRADO\n",
      "Eavan\n",
      "Dennis Lingo\n",
      "WAIMEA\n",
      "Fruit of the loom\n",
      "KAMI KUBI\n",
      "BoStreet\n",
      "Jompers\n",
      "StyleStone\n",
      "OXXO\n",
      "Chkokko\n",
      "Classic Polo\n",
      "FILA\n",
      "GRASS by Gitika Goyal\n",
      "IMYOUNG\n",
      "Triumph\n",
      "JC Collection\n",
      "AESTHETIC NATION\n",
      "GIORDANO\n",
      "Club York\n",
      "Free Authority\n",
      "Vishudh\n",
      "SAADHVI\n",
      "Rigo\n",
      "even\n",
      "ELLE\n",
      "Thomas Scott\n",
      "Hubberholme\n",
      "Freehand\n",
      "Besiva\n",
      "VASTRAMAY\n",
      "The Kaftan Company\n",
      "SPORTO\n",
      "CUKOO\n",
      "LAHEJA\n",
      "UNDER ARMOUR\n",
      "Bannos Swagger\n",
      "Oxolloxo\n",
      "Status Quo\n",
      "7Threads\n",
      "Alcis\n",
      "Clora Creation\n",
      "Matinique\n",
      "Maaesa\n",
      "Rute\n",
      "abof\n",
      "Yuuki\n",
      "Rodamo\n",
      "Zeyo\n",
      "bebe\n",
      "Dreamz by Pantaloons\n",
      "MOKSHA DESIGNS\n",
      "Neerus\n",
      "DEYANN\n",
      "Mufti\n",
      "MIRCHI FASHION\n",
      "Bossini\n",
      "DODO & MOA\n",
      "PETER ENGLAND UNIVERSITY\n",
      "United Colors of Benetton\n",
      "20Dresses\n",
      "AKKRITI BY PANTALOONS\n",
      "Nimayaa\n",
      "ADIDAS\n",
      "Being Human\n",
      "Maybell\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\".join(set(r.index)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After we inspect the brand list, we'll look for \"high end\" brands and then randomly select"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll define a few helper functions to perform sampling and product selection for our company:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "HIGH_END_BRANDS = {\"Nike\", \"H&M\", \"Calvin Klein Jeans\", \"Calvin Klein Underwear\", \"ADIDAS\"}\n",
    "SAMPLE_ITERATIONS = 5_000\n",
    "\n",
    "def select_high_end_products(df, n=40):\n",
    "    \"\"\"Selects a number of products from high end brands\"\"\"\n",
    "    if n <= 0 or n >= len(df):\n",
    "        return df.copy()\n",
    "    hep_df = df[df['brand_name'].str.contains(\"|\".join(HIGH_END_BRANDS))].head(n=int(n))\n",
    "    \n",
    "    for _ in range(SAMPLE_ITERATIONS):\n",
    "        results_df = hep_df.sample(n=int(n))\n",
    "\n",
    "    return results_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_regular_products(df, n=40):\n",
    "    \"\"\"Select a sample of random regular products (non-high end)\"\"\"\n",
    "    if n <= 0 or n >= len(df):\n",
    "        return df.copy()\n",
    "\n",
    "    return df[~df['brand_name'].str.contains(\"|\".join(HIGH_END_BRANDS))][:int(n)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_random_products(df, n=40):\n",
    "    \"\"\"Selects a number of random products\"\"\"\n",
    "    if n <= 0 or n >= len(df):\n",
    "        return df.copy()\n",
    "\n",
    "    for _ in range(10_000):\n",
    "        s = df.sample(n=int(n))\n",
    "\n",
    "    return s.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_id</th>\n",
       "      <th>category</th>\n",
       "      <th>individual_category</th>\n",
       "      <th>brand_name</th>\n",
       "      <th>description</th>\n",
       "      <th>price_rs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1315</th>\n",
       "      <td>12696136</td>\n",
       "      <td>Western</td>\n",
       "      <td>shorts</td>\n",
       "      <td>H&amp;M</td>\n",
       "      <td>hm women black cotton jersey cycling shorts</td>\n",
       "      <td>699.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3931</th>\n",
       "      <td>15746196</td>\n",
       "      <td>Topwear</td>\n",
       "      <td>sweatshirts</td>\n",
       "      <td>H&amp;M</td>\n",
       "      <td>hm men green solid relaxed fit hoodie</td>\n",
       "      <td>1499.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1721</th>\n",
       "      <td>13735306</td>\n",
       "      <td>Sports Wear</td>\n",
       "      <td>tights</td>\n",
       "      <td>Nike</td>\n",
       "      <td>nike women black solid essential as nsw 78 mid...</td>\n",
       "      <td>2195.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2425</th>\n",
       "      <td>15650812</td>\n",
       "      <td>Western</td>\n",
       "      <td>jeans</td>\n",
       "      <td>H&amp;M</td>\n",
       "      <td>hm women black solid clean look slim high ankl...</td>\n",
       "      <td>1499.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>15193768</td>\n",
       "      <td>Topwear</td>\n",
       "      <td>sweatshirts</td>\n",
       "      <td>H&amp;M</td>\n",
       "      <td>hm men white relaxed fit hoodie</td>\n",
       "      <td>1499.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      product_id     category individual_category brand_name  \\\n",
       "1315    12696136      Western              shorts        H&M   \n",
       "3931    15746196      Topwear         sweatshirts        H&M   \n",
       "1721    13735306  Sports Wear              tights       Nike   \n",
       "2425    15650812      Western               jeans        H&M   \n",
       "310     15193768      Topwear         sweatshirts        H&M   \n",
       "\n",
       "                                            description  price_rs  \n",
       "1315        hm women black cotton jersey cycling shorts     699.0  \n",
       "3931              hm men green solid relaxed fit hoodie    1499.0  \n",
       "1721  nike women black solid essential as nsw 78 mid...    2195.0  \n",
       "2425  hm women black solid clean look slim high ankl...    1499.0  \n",
       "310                     hm men white relaxed fit hoodie    1499.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the distribution of item types\n",
    "TOTAL_PRODUCTS = 100\n",
    "high_end_pct, regular_pct, random_pct = (.2, .4, .4)\n",
    "\n",
    "df = myntra_df.copy()\n",
    "\n",
    "high_end_df = select_high_end_products(myntra_df, n=high_end_pct * TOTAL_PRODUCTS)\n",
    "\n",
    "df = df[~df.product_id.isin(high_end_df.product_id)]\n",
    "regular_df = select_regular_products(myntra_df, n=regular_pct * TOTAL_PRODUCTS)\n",
    "\n",
    "df = df[~df.product_id.isin(pd.concat([high_end_df, regular_df]).product_id)]\n",
    "random_df = select_random_products(myntra_df, n=random_pct * TOTAL_PRODUCTS)\n",
    "\n",
    "selections_df = pd.concat(\n",
    "    [high_end_df, regular_df, random_df]\n",
    ")\n",
    "\n",
    "selections_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_id</th>\n",
       "      <th>category</th>\n",
       "      <th>individual_category</th>\n",
       "      <th>brand_name</th>\n",
       "      <th>description</th>\n",
       "      <th>price_rs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2296012</td>\n",
       "      <td>Bottom Wear</td>\n",
       "      <td>jeans</td>\n",
       "      <td>Roadster</td>\n",
       "      <td>roadster men navy blue slim fit mid rise clean...</td>\n",
       "      <td>1499.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13780156</td>\n",
       "      <td>Bottom Wear</td>\n",
       "      <td>track-pants</td>\n",
       "      <td>LOCOMOTIVE</td>\n",
       "      <td>locomotive men black  white solid slim fit tra...</td>\n",
       "      <td>1149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11895958</td>\n",
       "      <td>Topwear</td>\n",
       "      <td>shirts</td>\n",
       "      <td>Roadster</td>\n",
       "      <td>roadster men navy white  black geometric print...</td>\n",
       "      <td>1399.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4335679</td>\n",
       "      <td>Lingerie &amp; Sleep Wear</td>\n",
       "      <td>shapewear</td>\n",
       "      <td>Zivame</td>\n",
       "      <td>zivame women black saree shapewear zi3023core0...</td>\n",
       "      <td>1295.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11690882</td>\n",
       "      <td>Western</td>\n",
       "      <td>tshirts</td>\n",
       "      <td>Roadster</td>\n",
       "      <td>roadster women white solid v neck pure cotton ...</td>\n",
       "      <td>599.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_id               category individual_category  brand_name  \\\n",
       "0     2296012            Bottom Wear               jeans    Roadster   \n",
       "1    13780156            Bottom Wear         track-pants  LOCOMOTIVE   \n",
       "2    11895958                Topwear              shirts    Roadster   \n",
       "3     4335679  Lingerie & Sleep Wear           shapewear      Zivame   \n",
       "4    11690882                Western             tshirts    Roadster   \n",
       "\n",
       "                                         description  price_rs  \n",
       "0  roadster men navy blue slim fit mid rise clean...    1499.0  \n",
       "1  locomotive men black  white solid slim fit tra...    1149.0  \n",
       "2  roadster men navy white  black geometric print...    1399.0  \n",
       "3  zivame women black saree shapewear zi3023core0...    1295.0  \n",
       "4  roadster women white solid v neck pure cotton ...     599.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products_df = myntra_df[myntra_df.product_id.isin(selections_df.product_id)].copy()\n",
    "products_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_id</th>\n",
       "      <th>price_rs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.257532e+07</td>\n",
       "      <td>2068.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.445671e+06</td>\n",
       "      <td>1703.078724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.864573e+06</td>\n",
       "      <td>399.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.053942e+07</td>\n",
       "      <td>999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.364865e+07</td>\n",
       "      <td>1499.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.603708e+07</td>\n",
       "      <td>2519.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.789931e+07</td>\n",
       "      <td>12000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         product_id      price_rs\n",
       "count  1.000000e+02    100.000000\n",
       "mean   1.257532e+07   2068.500000\n",
       "std    4.445671e+06   1703.078724\n",
       "min    1.864573e+06    399.000000\n",
       "25%    1.053942e+07    999.000000\n",
       "50%    1.364865e+07   1499.000000\n",
       "75%    1.603708e+07   2519.500000\n",
       "max    1.789931e+07  12000.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we'll convert the prices into us dollar for consistency, we'll use the Federal Reserve's currency rate:\n",
    "\n",
    "  * https://www.federalreserve.gov/releases/h10/current/\n",
    "\n",
    "For July 26 the dollar to rupee rate is:\n",
    "\n",
    "```\n",
    "83.7000\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "usd_to_rs = 83.7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we'll add a new column that has the price in USD:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "products_df['price_usd'] = products_df['price_rs'].apply(lambda x: x / usd_to_rs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 100 entries, 0 to 478251\n",
      "Data columns (total 7 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   product_id           100 non-null    int64  \n",
      " 1   category             100 non-null    object \n",
      " 2   individual_category  100 non-null    object \n",
      " 3   brand_name           100 non-null    object \n",
      " 4   description          100 non-null    object \n",
      " 5   price_rs             100 non-null    float64\n",
      " 6   price_usd            100 non-null    float64\n",
      "dtypes: float64(2), int64(1), object(4)\n",
      "memory usage: 6.2+ KB\n"
     ]
    }
   ],
   "source": [
    "products_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we'll look into manually updating or adding the categories that match CBP's dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FY</th>\n",
       "      <th>Trading Partner</th>\n",
       "      <th>Mode of Transportation</th>\n",
       "      <th>Centers of Excellence</th>\n",
       "      <th>Product</th>\n",
       "      <th>Unique Seizure ID</th>\n",
       "      <th>Count of Lines</th>\n",
       "      <th>MSRP</th>\n",
       "      <th>Line</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020</td>\n",
       "      <td>CN</td>\n",
       "      <td>Mail</td>\n",
       "      <td>Apparel, Footwear &amp; Textiles</td>\n",
       "      <td>Footwear</td>\n",
       "      <td>2.020010e+11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020</td>\n",
       "      <td>CN</td>\n",
       "      <td>Cargo</td>\n",
       "      <td>Industrial &amp; Manufactured Materials</td>\n",
       "      <td>Labels/Tags</td>\n",
       "      <td>2.020010e+11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2110.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021</td>\n",
       "      <td>Other Countries</td>\n",
       "      <td>Express Consignment</td>\n",
       "      <td>Consumer Products &amp; Mass Merchandising</td>\n",
       "      <td>Handbags/Wallets</td>\n",
       "      <td>2.021010e+11</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3865.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020</td>\n",
       "      <td>CN</td>\n",
       "      <td>Mail</td>\n",
       "      <td>Consumer Products &amp; Mass Merchandising</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>2.020010e+11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020</td>\n",
       "      <td>HK</td>\n",
       "      <td>Express Consignment</td>\n",
       "      <td>Consumer Products &amp; Mass Merchandising</td>\n",
       "      <td>Handbags/Wallets</td>\n",
       "      <td>2.020010e+11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>108560.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     FY  Trading Partner Mode of Transportation  \\\n",
       "0  2020               CN                   Mail   \n",
       "1  2020               CN                  Cargo   \n",
       "2  2021  Other Countries    Express Consignment   \n",
       "3  2020               CN                   Mail   \n",
       "4  2020               HK    Express Consignment   \n",
       "\n",
       "                    Centers of Excellence           Product  \\\n",
       "0            Apparel, Footwear & Textiles          Footwear   \n",
       "1     Industrial & Manufactured Materials       Labels/Tags   \n",
       "2  Consumer Products & Mass Merchandising  Handbags/Wallets   \n",
       "3  Consumer Products & Mass Merchandising    Sporting Goods   \n",
       "4  Consumer Products & Mass Merchandising  Handbags/Wallets   \n",
       "\n",
       "   Unique Seizure ID  Count of Lines      MSRP  Line  \n",
       "0       2.020010e+11             1.0    1350.0   NaN  \n",
       "1       2.020010e+11             1.0    2110.0   NaN  \n",
       "2       2.021010e+11             3.0    3865.0   NaN  \n",
       "3       2.020010e+11             1.0    4000.0   NaN  \n",
       "4       2.020010e+11             1.0  108560.0   NaN  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ipr_data import load_ipr\n",
    "\n",
    "ipr_data_df = load_ipr()\n",
    "ipr_data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17\n",
      "Footwear\n",
      "Labels/Tags\n",
      "Handbags/Wallets\n",
      "Sporting Goods\n",
      "Consumer Products\n",
      "Wearing Apparel/Accessories\n",
      "Watches/Jewelry\n",
      "Consumer Electronics\n",
      "Pharmaceuticals/Personal Care\n",
      "Other\n",
      "Automotive/Aerospace\n",
      "Cigarettes\n",
      "Toys\n",
      "Optical Media\n",
      "Computers/Accessories\n",
      "Food\n",
      "Ball Bearings\n"
     ]
    }
   ],
   "source": [
    "print(len(ipr_data_df['Product'].unique()))\n",
    "print(\"\\n\".join(ipr_data_df['Product'].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There seem to be only `17` categories, and of those `Wearing Apparel/Accessories` would be the more appropriate for our dataset, we'll assign that one to our product list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "products_df['product'] = 'Wearing Apparel/Accessories'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_id</th>\n",
       "      <th>category</th>\n",
       "      <th>individual_category</th>\n",
       "      <th>brand_name</th>\n",
       "      <th>description</th>\n",
       "      <th>price_rs</th>\n",
       "      <th>price_usd</th>\n",
       "      <th>product</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2296012</td>\n",
       "      <td>Bottom Wear</td>\n",
       "      <td>jeans</td>\n",
       "      <td>Roadster</td>\n",
       "      <td>roadster men navy blue slim fit mid rise clean...</td>\n",
       "      <td>1499.0</td>\n",
       "      <td>17.909200</td>\n",
       "      <td>Wearing Apparel/Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13780156</td>\n",
       "      <td>Bottom Wear</td>\n",
       "      <td>track-pants</td>\n",
       "      <td>LOCOMOTIVE</td>\n",
       "      <td>locomotive men black  white solid slim fit tra...</td>\n",
       "      <td>1149.0</td>\n",
       "      <td>13.727599</td>\n",
       "      <td>Wearing Apparel/Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11895958</td>\n",
       "      <td>Topwear</td>\n",
       "      <td>shirts</td>\n",
       "      <td>Roadster</td>\n",
       "      <td>roadster men navy white  black geometric print...</td>\n",
       "      <td>1399.0</td>\n",
       "      <td>16.714456</td>\n",
       "      <td>Wearing Apparel/Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4335679</td>\n",
       "      <td>Lingerie &amp; Sleep Wear</td>\n",
       "      <td>shapewear</td>\n",
       "      <td>Zivame</td>\n",
       "      <td>zivame women black saree shapewear zi3023core0...</td>\n",
       "      <td>1295.0</td>\n",
       "      <td>15.471924</td>\n",
       "      <td>Wearing Apparel/Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11690882</td>\n",
       "      <td>Western</td>\n",
       "      <td>tshirts</td>\n",
       "      <td>Roadster</td>\n",
       "      <td>roadster women white solid v neck pure cotton ...</td>\n",
       "      <td>599.0</td>\n",
       "      <td>7.156511</td>\n",
       "      <td>Wearing Apparel/Accessories</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_id               category individual_category  brand_name  \\\n",
       "0     2296012            Bottom Wear               jeans    Roadster   \n",
       "1    13780156            Bottom Wear         track-pants  LOCOMOTIVE   \n",
       "2    11895958                Topwear              shirts    Roadster   \n",
       "3     4335679  Lingerie & Sleep Wear           shapewear      Zivame   \n",
       "4    11690882                Western             tshirts    Roadster   \n",
       "\n",
       "                                         description  price_rs  price_usd  \\\n",
       "0  roadster men navy blue slim fit mid rise clean...    1499.0  17.909200   \n",
       "1  locomotive men black  white solid slim fit tra...    1149.0  13.727599   \n",
       "2  roadster men navy white  black geometric print...    1399.0  16.714456   \n",
       "3  zivame women black saree shapewear zi3023core0...    1295.0  15.471924   \n",
       "4  roadster women white solid v neck pure cotton ...     599.0   7.156511   \n",
       "\n",
       "                       product  \n",
       "0  Wearing Apparel/Accessories  \n",
       "1  Wearing Apparel/Accessories  \n",
       "2  Wearing Apparel/Accessories  \n",
       "3  Wearing Apparel/Accessories  \n",
       "4  Wearing Apparel/Accessories  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we'll write our product list as a CSV file so that it can be consistently loaded for analysis our actual project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "products_df.to_csv(\"hvi-products.csv\", index=False, columns=[\n",
    "    \"product_id\",\n",
    "    \"product\",\n",
    "    \"category\",\n",
    "    \"individual_category\",\n",
    "    \"brand_name\",\n",
    "    \"description\",\n",
    "    \"price_usd\"\n",
    "])"
   ]
  }
 ],
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