Amzn_Popularity_to_Product_Sales.ipynb

{
 "cells": [
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   "source": [
    "# Questions to Explore: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "Does popularity bring in more product sales on Amazon?<br><br>\n",
    "Are popular products better than less popular ones (based on customer reviews)?<br><br>\n",
    "What are the salient words and phrases used in the reviews of popular products?<br><br>\n",
    "Do customer opinions change from early to late reviews?<br><br>\n",
    "After the product is launched, what is the trend of positive and negative reviews over time? <br><br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Manufacturer', 'ProductName', 'review_rating', 'verified_purchase',\n",
      "       'review_date', 'review_title', 'review_text'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Manufacturer</th>\n",
       "      <th>ProductName</th>\n",
       "      <th>review_rating</th>\n",
       "      <th>verified_purchase</th>\n",
       "      <th>review_date</th>\n",
       "      <th>review_title</th>\n",
       "      <th>review_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>AirPods Pro</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-10-30</td>\n",
       "      <td>Airpods Pro deliver😎</td>\n",
       "      <td>This airpods pro do exactly what apple said th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>AirPods Pro</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-10-30</td>\n",
       "      <td>List of Features/Changes compared to Airpods (v2)</td>\n",
       "      <td>Like iphone 11 Pro, Macbook Pro, Airpad Pro; t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Apple</td>\n",
       "      <td>AirPods Pro</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-10-30</td>\n",
       "      <td>Cool but expensive</td>\n",
       "      <td>First review yeh heard about these excited but...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>AirPods Pro</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-10-30</td>\n",
       "      <td>Lit</td>\n",
       "      <td>They fire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>AirPods Pro</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-10-31</td>\n",
       "      <td>Saved my marriage</td>\n",
       "      <td>With the new transparency mode, I can listen t...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Manufacturer  ProductName  review_rating  verified_purchase review_date  \\\n",
       "0        Apple  AirPods Pro              5                  0  2019-10-30   \n",
       "1        Apple  AirPods Pro              4                  0  2019-10-30   \n",
       "2        Apple  AirPods Pro              3                  0  2019-10-30   \n",
       "3        Apple  AirPods Pro              5                  0  2019-10-30   \n",
       "4        Apple  AirPods Pro              5                  1  2019-10-31   \n",
       "\n",
       "                                        review_title  \\\n",
       "0                               Airpods Pro deliver😎   \n",
       "1  List of Features/Changes compared to Airpods (v2)   \n",
       "2                                 Cool but expensive   \n",
       "3                                                Lit   \n",
       "4                                  Saved my marriage   \n",
       "\n",
       "                                         review_text  \n",
       "0  This airpods pro do exactly what apple said th...  \n",
       "1  Like iphone 11 Pro, Macbook Pro, Airpad Pro; t...  \n",
       "2  First review yeh heard about these excited but...  \n",
       "3                                          They fire  \n",
       "4  With the new transparency mode, I can listen t...  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'Google Trend.xlsx'\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "def GetMergedAmazonDataFrames():\n",
    "    files_to_use = [r'data\\AirPods Pro.csv', r'data\\Elite 75t.csv',r'data\\Free.csv',\n",
    "                   r'data\\Galaxy Buds Plus.csv',r'data\\M-200.csv',r'data\\NC700.csv',\n",
    "                   r'data\\Soundcore Liberty 2 Pro.csv',r'data\\Soundcore Liberty Air 2.csv',r'data\\Soundcore Life Q20.csv',\n",
    "                   r'data\\WF-1000XM3.csv']\n",
    "    amzn_df = pd.read_csv(files_to_use[0],  parse_dates=False)\n",
    "    for file in files_to_use[1:]:\n",
    "        df = pd.read_csv(file,  parse_dates=False)\n",
    "#         amzn_df = pd.merge(amzn_df, df, how='outer',on='review_date')\n",
    "        amzn_df = pd.concat([amzn_df,df],sort=False)\n",
    "        \n",
    "    amzn_df['review_date'] = pd.to_datetime(amzn_df['review_date'], format='%Y-%m-%d')\n",
    "    amzn_df = amzn_df[['Manufacturer', 'ProductName', 'review_rating', 'verified_purchase',\n",
    "       'review_date', 'review_title', 'review_text']]\n",
    "    return amzn_df\n",
    " \n",
    "# ORIGINALLY I WAS GOING TO APPEND INTO A DATA SET WITH DATE,COUNT,PRODUCTNAME format\n",
    "# def GetGoogleTrendsDF():\n",
    "#     sheets_in_xls = ['Momentum True Wireless 2','WH-1000XM3','AirPds Pro',' Noise Cancelling Headphones 70',\n",
    "#                     'Elite 75t','Soundcore Liberty Air 2','WF-1000XM3','Free','Soundcore Life Q20','Soundcore Liberty 2 Pro']\n",
    "    \n",
    "#     df= GetXLSSheetToDF(sheets_in_xls[0])\n",
    "#     df['ProductName'] = df['Week']\n",
    "#     df['ProductName'] = df['ProductName'].apply(lambda x: df.columns[1].replace(': (United States)',''))\n",
    "#     df.rename(columns={df.columns[1]: \"Count\"}, inplace=True) \n",
    "#     for sheetname in sheets_in_xls[1:]:\n",
    "#         next_df = GetXLSSheetToDF(sheetname)\n",
    "#         next_df['ProductName'] = next_df['Week']\n",
    "#         next_df['ProductName'] = next_df['ProductName'].apply(lambda x: next_df.columns[1].replace(': (United States)',''))\n",
    "#         next_df.rename(columns={next_df.columns[1]: \"Count\"}, inplace=True)\n",
    "        \n",
    "#     return df\n",
    "\n",
    "\n",
    "def GetXLSSheetToDF(sheetname):\n",
    "    return pd.read_excel('data\\Google Trend.xlsx', sheet_name=sheetname, parse_dates=False)\n",
    "\n",
    "def GetGoogleTrendsDF():\n",
    "    sheets_in_xls = ['Momentum True Wireless 2','WH-1000XM3','AirPds Pro',' Noise Cancelling Headphones 70',\n",
    "                    'Elite 75t','Soundcore Liberty Air 2','WF-1000XM3','Free','Soundcore Life Q20','Soundcore Liberty 2 Pro']\n",
    "    \n",
    "    df= GetXLSSheetToDF(sheets_in_xls[0])\n",
    "    df.rename(columns={df.columns[1]: df.columns[1].replace(': (United States)','')}, inplace=True) \n",
    "    \n",
    "    for sheetname in sheets_in_xls[1:]:\n",
    "        next_df = GetXLSSheetToDF(sheetname)\n",
    "        next_df.rename(columns={next_df.columns[1]: next_df.columns[1].replace(': (United States)','')}, inplace=True)\n",
    "        df = pd.merge(df, next_df, how='outer',on='Week')\n",
    "    df['Week'] = pd.to_datetime(df['Week'], format='%Y-%m-%d')\n",
    "    # TODO: it might not be bad to set na values to 0, but isn't necessary for this data...\n",
    "    return df\n",
    "    \n",
    "\n",
    "# weather_df['review_date'] = pd.to_datetime(weather_df['review_date'], format='%Y-%m-%d')\n",
    "# googleTrends_df\n",
    "airpods_amzn_df = GetMergedAmazonDataFrames()\n",
    "print(airpods_amzn_df.columns)\n",
    "airpods_amzn_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "googleTrends_df = GetGoogleTrendsDF()\n",
    "\n",
    "amzn_df = GetMergedAmazonDataFrames()\n",
    "\n",
    "googleTrends_df.head()"
   ]
  }
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