Order_Report_Cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd; pd.set_option('display.max_columns', None)\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>amazon_order_id</th>\n",
       "      <th>purchase_date</th>\n",
       "      <th>order_status</th>\n",
       "      <th>order_total</th>\n",
       "      <th>currency_code</th>\n",
       "      <th>buyer_name</th>\n",
       "      <th>is_fba</th>\n",
       "      <th>do_not_solicit</th>\n",
       "      <th>add_name</th>\n",
       "      <th>add_line_1</th>\n",
       "      <th>add_line_2</th>\n",
       "      <th>add_city</th>\n",
       "      <th>add_state</th>\n",
       "      <th>add_postal</th>\n",
       "      <th>add_country</th>\n",
       "      <th>add_phone</th>\n",
       "      <th>shipment_level</th>\n",
       "      <th>shipment_datetime</th>\n",
       "      <th>shipment_delivery_datetime</th>\n",
       "      <th>number_items</th>\n",
       "      <th>total_item_quantity</th>\n",
       "      <th>skus</th>\n",
       "      <th>asins</th>\n",
       "      <th>order_notes</th>\n",
       "      <th>buyer_email</th>\n",
       "      <th>buyer_opted_out</th>\n",
       "      <th>repeat_buyer</th>\n",
       "      <th>emails_sent</th>\n",
       "      <th>buyer_first_name</th>\n",
       "      <th>buyer_last_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>112-9520688-0214660</td>\n",
       "      <td>2020-12-13 04:04 PM PST</td>\n",
       "      <td>Returned</td>\n",
       "      <td>32.61</td>\n",
       "      <td>USD</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>BUFFALO</td>\n",
       "      <td>NY</td>\n",
       "      <td>14221-5812</td>\n",
       "      <td>US</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>Expedited</td>\n",
       "      <td>2020-12-15 11:59 PM PST</td>\n",
       "      <td>2020-12-17 11:59 PM PST</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2B-97HE-0LXQ</td>\n",
       "      <td>B08CSKBQVC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>k717f9js0ctgdf4@marketplace.amazon.com</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>111-9763412-1465054</td>\n",
       "      <td>2020-12-13 07:50 PM PST</td>\n",
       "      <td>Delivered</td>\n",
       "      <td>32.66</td>\n",
       "      <td>USD</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>WALLA WALLA</td>\n",
       "      <td>WA</td>\n",
       "      <td>99362-4556</td>\n",
       "      <td>US</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>SecondDay</td>\n",
       "      <td>2020-12-15 11:59 PM PST</td>\n",
       "      <td>2020-12-17 11:59 PM PST</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2B-97HE-0LXQ</td>\n",
       "      <td>B08CSKBQVC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>r7xkc1hmm359z7d@marketplace.amazon.com</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>111-0582021-8828235</td>\n",
       "      <td>2020-12-13 08:42 PM PST</td>\n",
       "      <td>Delivered</td>\n",
       "      <td>31.79</td>\n",
       "      <td>USD</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>WEST CHESTER</td>\n",
       "      <td>PA</td>\n",
       "      <td>19380-4033</td>\n",
       "      <td>US</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>Expedited</td>\n",
       "      <td>2020-12-13 11:59 PM PST</td>\n",
       "      <td>2020-12-15 11:59 PM PST</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2B-97HE-0LXQ</td>\n",
       "      <td>B08CSKBQVC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>k0vbmjyzw185g0d@marketplace.amazon.com</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>114-5298494-2101848</td>\n",
       "      <td>2020-12-13 09:08 PM PST</td>\n",
       "      <td>Delivered</td>\n",
       "      <td>29.99</td>\n",
       "      <td>USD</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>PANAMA CITY</td>\n",
       "      <td>FL</td>\n",
       "      <td>32404-6031</td>\n",
       "      <td>US</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>Expedited</td>\n",
       "      <td>2020-12-15 11:59 PM PST</td>\n",
       "      <td>2020-12-17 11:59 PM PST</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2B-97HE-0LXQ</td>\n",
       "      <td>B08CSKBQVC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8z1tgry607b6gg7@marketplace.amazon.com</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>114-7045356-6018634</td>\n",
       "      <td>2020-12-13 10:34 PM PST</td>\n",
       "      <td>Delivered</td>\n",
       "      <td>33.06</td>\n",
       "      <td>USD</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>PASADENA</td>\n",
       "      <td>CA</td>\n",
       "      <td>91105-1316</td>\n",
       "      <td>US</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>Expedited</td>\n",
       "      <td>2020-12-14 11:59 PM PST</td>\n",
       "      <td>2020-12-16 11:59 PM PST</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2B-97HE-0LXQ</td>\n",
       "      <td>B08CSKBQVC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>876z35txc7nd69h@marketplace.amazon.com</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       amazon_order_id            purchase_date order_status  order_total  \\\n",
       "0  112-9520688-0214660  2020-12-13 04:04 PM PST     Returned        32.61   \n",
       "1  111-9763412-1465054  2020-12-13 07:50 PM PST    Delivered        32.66   \n",
       "2  111-0582021-8828235  2020-12-13 08:42 PM PST    Delivered        31.79   \n",
       "3  114-5298494-2101848  2020-12-13 09:08 PM PST    Delivered        29.99   \n",
       "4  114-7045356-6018634  2020-12-13 10:34 PM PST    Delivered        33.06   \n",
       "\n",
       "  currency_code     buyer_name  is_fba  do_not_solicit       add_name  \\\n",
       "0           USD  NOT_AVAILABLE       1               0  NOT_AVAILABLE   \n",
       "1           USD  NOT_AVAILABLE       1               0  NOT_AVAILABLE   \n",
       "2           USD  NOT_AVAILABLE       1               0  NOT_AVAILABLE   \n",
       "3           USD  NOT_AVAILABLE       1               0  NOT_AVAILABLE   \n",
       "4           USD  NOT_AVAILABLE       1               0  NOT_AVAILABLE   \n",
       "\n",
       "      add_line_1     add_line_2      add_city add_state  add_postal  \\\n",
       "0  NOT_AVAILABLE  NOT_AVAILABLE       BUFFALO        NY  14221-5812   \n",
       "1  NOT_AVAILABLE  NOT_AVAILABLE   WALLA WALLA        WA  99362-4556   \n",
       "2  NOT_AVAILABLE  NOT_AVAILABLE  WEST CHESTER        PA  19380-4033   \n",
       "3  NOT_AVAILABLE  NOT_AVAILABLE   PANAMA CITY        FL  32404-6031   \n",
       "4  NOT_AVAILABLE  NOT_AVAILABLE      PASADENA        CA  91105-1316   \n",
       "\n",
       "  add_country      add_phone shipment_level        shipment_datetime  \\\n",
       "0          US  NOT_AVAILABLE      Expedited  2020-12-15 11:59 PM PST   \n",
       "1          US  NOT_AVAILABLE      SecondDay  2020-12-15 11:59 PM PST   \n",
       "2          US  NOT_AVAILABLE      Expedited  2020-12-13 11:59 PM PST   \n",
       "3          US  NOT_AVAILABLE      Expedited  2020-12-15 11:59 PM PST   \n",
       "4          US  NOT_AVAILABLE      Expedited  2020-12-14 11:59 PM PST   \n",
       "\n",
       "  shipment_delivery_datetime  number_items  total_item_quantity          skus  \\\n",
       "0    2020-12-17 11:59 PM PST             1                    1  2B-97HE-0LXQ   \n",
       "1    2020-12-17 11:59 PM PST             1                    1  2B-97HE-0LXQ   \n",
       "2    2020-12-15 11:59 PM PST             1                    1  2B-97HE-0LXQ   \n",
       "3    2020-12-17 11:59 PM PST             1                    1  2B-97HE-0LXQ   \n",
       "4    2020-12-16 11:59 PM PST             1                    1  2B-97HE-0LXQ   \n",
       "\n",
       "        asins  order_notes                             buyer_email  \\\n",
       "0  B08CSKBQVC          NaN  k717f9js0ctgdf4@marketplace.amazon.com   \n",
       "1  B08CSKBQVC          NaN  r7xkc1hmm359z7d@marketplace.amazon.com   \n",
       "2  B08CSKBQVC          NaN  k0vbmjyzw185g0d@marketplace.amazon.com   \n",
       "3  B08CSKBQVC          NaN  8z1tgry607b6gg7@marketplace.amazon.com   \n",
       "4  B08CSKBQVC          NaN  876z35txc7nd69h@marketplace.amazon.com   \n",
       "\n",
       "   buyer_opted_out  repeat_buyer  emails_sent buyer_first_name buyer_last_name  \n",
       "0              0.0           0.0            0    NOT_AVAILABLE   NOT_AVAILABLE  \n",
       "1              0.0           1.0            0    NOT_AVAILABLE   NOT_AVAILABLE  \n",
       "2              0.0           0.0            0    NOT_AVAILABLE   NOT_AVAILABLE  \n",
       "3              0.0           0.0            0    NOT_AVAILABLE   NOT_AVAILABLE  \n",
       "4              0.0           0.0            0    NOT_AVAILABLE   NOT_AVAILABLE  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/report_orders.csv\")\n",
    "pop = pd.read_csv(\"data/population.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "US               5321\n",
       "NOT_AVAILABLE      46\n",
       "HK                 11\n",
       "CO                  8\n",
       "IL                  6\n",
       "TW                  2\n",
       "NG                  1\n",
       "VI                  1\n",
       "QA                  1\n",
       "CR                  1\n",
       "CL                  1\n",
       "SG                  1\n",
       "AU                  1\n",
       "NO                  1\n",
       "TH                  1\n",
       "Name: add_country, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['add_country'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['amazon_order_id', 'purchase_date', 'order_total', 'add_city', 'add_state', 'add_postal',\n",
    "       'add_country', 'number_items', 'total_item_quantity', 'asins','repeat_buyer']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns={\"asins\": \"Product\"})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Product'] = df['Product'].replace({'B08CSKBQVC': 'Rainforest', 'B08MWVS7T7': 'Floral', 'B08CSKBQVC B08MWVS7T7': 'Both'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['add_state'].str.contains('Texas'), 'add_state'] = \"TX\"\n",
    "df.loc[df['add_state'].str.contains('New York'), 'add_state'] = \"NY\"\n",
    "df.loc[df['add_state'].str.contains('CALIFORNIA'), 'add_state'] = \"CA\"\n",
    "\n",
    "df.loc[df['add_state'].str.contains('Florida'), 'add_state'] = \"FL\"\n",
    "df.loc[df['add_state'].str.contains('California'), 'add_state'] = \"CA\"\n",
    "df.loc[df['add_state'].str.contains('FLORIDA'), 'add_state'] = \"FL\"\n",
    "\n",
    "df.loc[df['add_state'].str.contains('TEXAS'), 'add_state'] = \"TX\"\n",
    "df.loc[df['add_state'].str.contains('Mi'), 'add_state'] = \"MI\"\n",
    "df.loc[df['add_state'].str.contains('Colorado'), 'add_state'] = \"CO\"\n",
    "\n",
    "df.loc[df['add_state'].str.contains('OHIO'), 'add_state'] = \"OH\"\n",
    "df.loc[df['add_state'].str.contains('WASHINGTON'), 'add_state'] = \"WA\"\n",
    "df.loc[df['add_state'].str.contains('New york'), 'add_state'] = \"NY\"\n",
    "\n",
    "df.loc[df['add_state'].str.contains('NEW YORK'), 'add_state'] = \"NY\"\n",
    "df.loc[df['add_state'].str.contains('Illinois'), 'add_state'] = \"IL\"\n",
    "df.loc[df['add_state'].str.contains('MICHIGAN'), 'add_state'] = \"MI\"\n",
    "\n",
    "df.loc[df['add_state'].str.contains('Pa'), 'add_state'] = \"PA\"\n",
    "df.loc[df['add_state'].str.contains('ct'), 'add_state'] = \"CT\"\n",
    "df.loc[df['add_state'].str.contains('Wi'), 'add_state'] = \"WI\"\n",
    "\n",
    "df.loc[df['add_state'].str.contains('New Jersey'), 'add_state'] = \"NJ\"\n",
    "df.loc[df['add_state'].str.contains('Fl'), 'add_state'] = \"FL\"\n",
    "df.loc[df['add_state'].str.contains('Nc'), 'add_state'] = \"NC\"\n",
    "\n",
    "df.loc[df['add_state'].str.contains('Nj'), 'add_state'] = \"NJ\"\n",
    "df.loc[df['add_state'].str.contains('Ohio'), 'add_state'] = \"OH\"\n",
    "df.loc[df['add_state'].str.contains('UTAH'), 'add_state'] = \"UT\"\n",
    "df.loc[df['add_state'].str.contains('Utah'), 'add_state'] = \"UT\"\n",
    "df.loc[df['add_state'].str.contains('Tennessee'), 'add_state'] = \"TN\"\n",
    "df.loc[df['add_state'].str.contains('Massachusetts'), 'add_state'] = \"MA\"\n",
    "df.loc[df['add_state'].str.contains('Va'), 'add_state'] = \"VA\"\n",
    "df.loc[df['add_state'].str.contains('MINNESOTA'), 'add_state'] = \"MN\"\n",
    "df.loc[df['add_state'].str.contains('Tx'), 'add_state'] = \"TX\"\n",
    "\n",
    "df.loc[df['add_state'].str.contains('Ca'), 'add_state'] = \"CA\"\n",
    "df.loc[df['add_state'].str.contains('ca'), 'add_state'] = \"CA\"\n",
    "df.loc[df['add_state'].str.contains('Washington'), 'add_state'] = \"WA\"\n",
    "df.loc[df['add_state'].str.contains('fl'), 'add_state'] = \"FL\"\n",
    "df.loc[df['add_state'].str.contains('Oregon'), 'add_state'] = \"OR\"\n",
    "df.loc[df['add_state'].str.contains('WISCONSIN'), 'add_state'] = \"WI\"\n",
    "df.loc[df['add_state'].str.contains('Wa'), 'add_state'] = \"WA\"\n",
    "df.loc[df['add_state'].str.contains('Ny'), 'add_state'] = \"NY\"\n",
    "df.loc[df['add_state'].str.contains('MAINE'), 'add_state'] = \"ME\"\n",
    "\n",
    "df.loc[df['add_state'].str.contains('ARIZONA'), 'add_state'] = \"AZ\"\n",
    "df.loc[df['add_state'].str.contains('Arizona'), 'add_state'] = \"AZ\"\n",
    "df.loc[df['add_state'].str.contains('ny'), 'add_state'] = \"NY\"\n",
    "df.loc[df['add_state'].str.contains('Co'), 'add_state'] = \"CO\"\n",
    "df.loc[df['add_state'].str.contains('Indiana'), 'add_state'] = \"IN\"\n",
    "df.loc[df['add_state'].str.contains('Iowa'), 'add_state'] = \"IA\"\n",
    "df.loc[df['add_state'].str.contains('COLORADO'), 'add_state'] = \"CO\"\n",
    "df.loc[df['add_state'].str.contains('Maryland'), 'add_state'] = \"MD\"\n",
    "df.loc[df['add_state'].str.contains('Ma'), 'add_state'] = \"MA\"\n",
    "df.loc[df['add_state'].str.contains('tx'), 'add_state'] = \"TX\"\n",
    "df.loc[df['add_state'].str.contains('Ut'), 'add_state'] = \"UT\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns={\"add_state\": \"Abbrev\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>amazon_order_id</th>\n",
       "      <th>purchase_date</th>\n",
       "      <th>order_total</th>\n",
       "      <th>add_city</th>\n",
       "      <th>Abbrev</th>\n",
       "      <th>add_postal</th>\n",
       "      <th>add_country</th>\n",
       "      <th>number_items</th>\n",
       "      <th>total_item_quantity</th>\n",
       "      <th>Product</th>\n",
       "      <th>repeat_buyer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>112-9520688-0214660</td>\n",
       "      <td>2020-12-13 04:04 PM PST</td>\n",
       "      <td>32.61</td>\n",
       "      <td>BUFFALO</td>\n",
       "      <td>NY</td>\n",
       "      <td>14221-5812</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>111-9763412-1465054</td>\n",
       "      <td>2020-12-13 07:50 PM PST</td>\n",
       "      <td>32.66</td>\n",
       "      <td>WALLA WALLA</td>\n",
       "      <td>WA</td>\n",
       "      <td>99362-4556</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>111-0582021-8828235</td>\n",
       "      <td>2020-12-13 08:42 PM PST</td>\n",
       "      <td>31.79</td>\n",
       "      <td>WEST CHESTER</td>\n",
       "      <td>PA</td>\n",
       "      <td>19380-4033</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>114-5298494-2101848</td>\n",
       "      <td>2020-12-13 09:08 PM PST</td>\n",
       "      <td>29.99</td>\n",
       "      <td>PANAMA CITY</td>\n",
       "      <td>FL</td>\n",
       "      <td>32404-6031</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>114-7045356-6018634</td>\n",
       "      <td>2020-12-13 10:34 PM PST</td>\n",
       "      <td>33.06</td>\n",
       "      <td>PASADENA</td>\n",
       "      <td>CA</td>\n",
       "      <td>91105-1316</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>113-6338118-2538635</td>\n",
       "      <td>2020-12-14 05:21 AM PST</td>\n",
       "      <td>32.09</td>\n",
       "      <td>BROWNSBURG</td>\n",
       "      <td>IN</td>\n",
       "      <td>46112-9234</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>114-0981175-7351430</td>\n",
       "      <td>2020-12-14 05:45 AM PST</td>\n",
       "      <td>29.99</td>\n",
       "      <td>KANSAS CITY</td>\n",
       "      <td>MO</td>\n",
       "      <td>64110-1628</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>112-8562237-5146647</td>\n",
       "      <td>2020-12-14 06:01 AM PST</td>\n",
       "      <td>31.98</td>\n",
       "      <td>MAHWAH</td>\n",
       "      <td>NJ</td>\n",
       "      <td>07430-2567</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>112-1988159-4756231</td>\n",
       "      <td>2020-12-14 06:30 AM PST</td>\n",
       "      <td>31.79</td>\n",
       "      <td>TAYLOR</td>\n",
       "      <td>MI</td>\n",
       "      <td>48180-2054</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>111-9367418-0112249</td>\n",
       "      <td>2020-12-14 06:54 AM PST</td>\n",
       "      <td>32.65</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>NY</td>\n",
       "      <td>11209-4909</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>112-2596731-9364200</td>\n",
       "      <td>2020-12-14 08:30 AM PST</td>\n",
       "      <td>32.46</td>\n",
       "      <td>HASLET</td>\n",
       "      <td>TX</td>\n",
       "      <td>76052-4892</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>111-7721673-0274613</td>\n",
       "      <td>2020-12-14 08:56 AM PST</td>\n",
       "      <td>31.79</td>\n",
       "      <td>ARLINGTON</td>\n",
       "      <td>VA</td>\n",
       "      <td>22209-1127</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>111-2942922-3350650</td>\n",
       "      <td>2020-12-14 09:55 AM PST</td>\n",
       "      <td>32.76</td>\n",
       "      <td>Chattanooga</td>\n",
       "      <td>TN</td>\n",
       "      <td>37412</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>111-2879347-4500257</td>\n",
       "      <td>2020-12-14 11:36 AM PST</td>\n",
       "      <td>32.60</td>\n",
       "      <td>TUCSON</td>\n",
       "      <td>AZ</td>\n",
       "      <td>85719-5109</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>112-5244593-7641823</td>\n",
       "      <td>2020-12-14 02:53 PM PST</td>\n",
       "      <td>32.69</td>\n",
       "      <td>POULSBO</td>\n",
       "      <td>WA</td>\n",
       "      <td>98370-6565</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>112-7671164-1161025</td>\n",
       "      <td>2020-12-14 04:18 PM PST</td>\n",
       "      <td>31.49</td>\n",
       "      <td>Dousman</td>\n",
       "      <td>WI</td>\n",
       "      <td>53118</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>114-9778111-7625051</td>\n",
       "      <td>2020-12-14 04:33 PM PST</td>\n",
       "      <td>32.46</td>\n",
       "      <td>AUSTIN</td>\n",
       "      <td>TX</td>\n",
       "      <td>78723-5390</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>112-1235840-1754610</td>\n",
       "      <td>2020-12-14 05:50 PM PST</td>\n",
       "      <td>32.50</td>\n",
       "      <td>BRIARCLIFF MANOR</td>\n",
       "      <td>NY</td>\n",
       "      <td>10510-1027</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>113-6338508-3681812</td>\n",
       "      <td>2020-12-14 06:17 PM PST</td>\n",
       "      <td>29.99</td>\n",
       "      <td>ATLANTIC BEACH</td>\n",
       "      <td>FL</td>\n",
       "      <td>32233-6960</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>114-4458180-6141040</td>\n",
       "      <td>2020-12-14 11:32 PM PST</td>\n",
       "      <td>31.79</td>\n",
       "      <td>MARIETTA</td>\n",
       "      <td>GA</td>\n",
       "      <td>30066</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>114-5232999-3116263</td>\n",
       "      <td>2020-12-15 04:54 AM PST</td>\n",
       "      <td>34.33</td>\n",
       "      <td>FORT HUACHUCA</td>\n",
       "      <td>AZ</td>\n",
       "      <td>85613-1421</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>111-1060530-7456226</td>\n",
       "      <td>2020-12-15 06:20 AM PST</td>\n",
       "      <td>31.86</td>\n",
       "      <td>PLAINFIELD</td>\n",
       "      <td>IL</td>\n",
       "      <td>60586-8637</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>113-8155155-7993035</td>\n",
       "      <td>2020-12-15 06:21 AM PST</td>\n",
       "      <td>32.16</td>\n",
       "      <td>FUQUAY VARINA</td>\n",
       "      <td>NC</td>\n",
       "      <td>27526-2421</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>114-1484300-8721032</td>\n",
       "      <td>2020-12-15 07:12 AM PST</td>\n",
       "      <td>29.99</td>\n",
       "      <td>TAMPA</td>\n",
       "      <td>FL</td>\n",
       "      <td>33606-2471</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>113-8445378-8589031</td>\n",
       "      <td>2020-12-15 10:46 AM PST</td>\n",
       "      <td>31.86</td>\n",
       "      <td>WEST ROXBURY</td>\n",
       "      <td>MA</td>\n",
       "      <td>02132-2439</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>112-2274841-3145863</td>\n",
       "      <td>2020-12-15 11:30 AM PST</td>\n",
       "      <td>33.14</td>\n",
       "      <td>LONOKE</td>\n",
       "      <td>AR</td>\n",
       "      <td>72086-3622</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>111-6331056-7902669</td>\n",
       "      <td>2020-12-15 12:02 PM PST</td>\n",
       "      <td>32.09</td>\n",
       "      <td>GREENSBORO</td>\n",
       "      <td>GA</td>\n",
       "      <td>30642-3911</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>113-2222798-8942609</td>\n",
       "      <td>2020-12-15 12:43 PM PST</td>\n",
       "      <td>31.79</td>\n",
       "      <td>DULUTH</td>\n",
       "      <td>GA</td>\n",
       "      <td>30097-5035</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>111-9802738-5384267</td>\n",
       "      <td>2020-12-15 01:59 PM PST</td>\n",
       "      <td>29.99</td>\n",
       "      <td>NORTH POLE</td>\n",
       "      <td>AK</td>\n",
       "      <td>99705-5373</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>114-2980695-9285827</td>\n",
       "      <td>2020-12-15 03:42 PM PST</td>\n",
       "      <td>32.65</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>NY</td>\n",
       "      <td>11204-5302</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>112-1574791-0355465</td>\n",
       "      <td>2020-12-15 05:19 PM PST</td>\n",
       "      <td>32.43</td>\n",
       "      <td>RHINEBECK</td>\n",
       "      <td>NY</td>\n",
       "      <td>12572-2163</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>114-5231919-4406648</td>\n",
       "      <td>2020-12-15 06:06 PM PST</td>\n",
       "      <td>29.99</td>\n",
       "      <td>WELLINGTON</td>\n",
       "      <td>FL</td>\n",
       "      <td>33414-8674</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>114-1157807-2505844</td>\n",
       "      <td>2020-12-15 07:35 PM PST</td>\n",
       "      <td>32.66</td>\n",
       "      <td>SPOKANE</td>\n",
       "      <td>WA</td>\n",
       "      <td>99223-1544</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>114-2262918-4781012</td>\n",
       "      <td>2020-12-15 07:48 PM PST</td>\n",
       "      <td>32.38</td>\n",
       "      <td>Caruthers</td>\n",
       "      <td>CA</td>\n",
       "      <td>93609</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>113-1179689-6449026</td>\n",
       "      <td>2020-12-15 11:10 PM PST</td>\n",
       "      <td>32.76</td>\n",
       "      <td>OAKLAND</td>\n",
       "      <td>CA</td>\n",
       "      <td>94605-1123</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>113-8518211-5389837</td>\n",
       "      <td>2020-12-16 05:36 AM PST</td>\n",
       "      <td>32.61</td>\n",
       "      <td>clarence center</td>\n",
       "      <td>new york</td>\n",
       "      <td>14032</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>113-2088026-5539446</td>\n",
       "      <td>2020-12-16 07:02 AM PST</td>\n",
       "      <td>32.01</td>\n",
       "      <td>BRISTOLVILLE</td>\n",
       "      <td>OH</td>\n",
       "      <td>44402-9718</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>112-5654946-6761040</td>\n",
       "      <td>2020-12-16 07:10 AM PST</td>\n",
       "      <td>31.79</td>\n",
       "      <td>PLEASANT GAP</td>\n",
       "      <td>PA</td>\n",
       "      <td>16823-7417</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>112-4327268-1121862</td>\n",
       "      <td>2020-12-16 07:13 AM PST</td>\n",
       "      <td>32.43</td>\n",
       "      <td>NEW WINDSOR</td>\n",
       "      <td>NY</td>\n",
       "      <td>12553-6843</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>114-6909399-2548245</td>\n",
       "      <td>2020-12-16 07:36 AM PST</td>\n",
       "      <td>31.79</td>\n",
       "      <td>Byron Center</td>\n",
       "      <td>MI</td>\n",
       "      <td>49315-8835</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>112-6352294-8384227</td>\n",
       "      <td>2020-12-16 09:33 AM PST</td>\n",
       "      <td>32.01</td>\n",
       "      <td>RICHFIELD</td>\n",
       "      <td>OH</td>\n",
       "      <td>44286-9734</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>114-0776169-8514627</td>\n",
       "      <td>2020-12-16 11:36 AM PST</td>\n",
       "      <td>32.31</td>\n",
       "      <td>OCEANSIDE</td>\n",
       "      <td>CA</td>\n",
       "      <td>92058-6626</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>111-2389245-7851443</td>\n",
       "      <td>2020-12-16 12:01 PM PST</td>\n",
       "      <td>32.30</td>\n",
       "      <td>WASHOUGAL</td>\n",
       "      <td>WA</td>\n",
       "      <td>98671-9582</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>112-0682410-2143417</td>\n",
       "      <td>2020-12-16 07:13 PM PST</td>\n",
       "      <td>32.24</td>\n",
       "      <td>DURHAM</td>\n",
       "      <td>NC</td>\n",
       "      <td>27707-5418</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>114-0851129-7212232</td>\n",
       "      <td>2020-12-17 05:05 AM PST</td>\n",
       "      <td>31.89</td>\n",
       "      <td>FAIRFIELD</td>\n",
       "      <td>CT</td>\n",
       "      <td>06824-4547</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>114-3972674-4044228</td>\n",
       "      <td>2020-12-17 09:27 AM PST</td>\n",
       "      <td>31.89</td>\n",
       "      <td>Milford</td>\n",
       "      <td>CT</td>\n",
       "      <td>6460</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>113-1231341-1197830</td>\n",
       "      <td>2020-12-17 10:43 AM PST</td>\n",
       "      <td>32.76</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>CA</td>\n",
       "      <td>95111</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>114-9566257-4512234</td>\n",
       "      <td>2020-12-17 11:37 AM PST</td>\n",
       "      <td>33.11</td>\n",
       "      <td>EVERETT</td>\n",
       "      <td>WA</td>\n",
       "      <td>98208-7116</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>113-5577499-9405069</td>\n",
       "      <td>2020-12-17 08:08 PM PST</td>\n",
       "      <td>31.89</td>\n",
       "      <td>WOODBRIDGE</td>\n",
       "      <td>CT</td>\n",
       "      <td>06525-1134</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>114-5057005-3701031</td>\n",
       "      <td>2020-12-17 08:10 PM PST</td>\n",
       "      <td>31.86</td>\n",
       "      <td>GERMANTOWN HILLS</td>\n",
       "      <td>IL</td>\n",
       "      <td>61548-9440</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>112-1474500-4317059</td>\n",
       "      <td>2020-12-17 08:22 PM PST</td>\n",
       "      <td>31.79</td>\n",
       "      <td>MCLEAN</td>\n",
       "      <td>VA</td>\n",
       "      <td>22102-3424</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>111-2601689-4911461</td>\n",
       "      <td>2020-12-17 08:47 PM PST</td>\n",
       "      <td>31.79</td>\n",
       "      <td>RESTON</td>\n",
       "      <td>VA</td>\n",
       "      <td>20194-2000</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>114-1981733-6489060</td>\n",
       "      <td>2020-12-17 09:04 PM PST</td>\n",
       "      <td>33.06</td>\n",
       "      <td>ARCADIA</td>\n",
       "      <td>CA</td>\n",
       "      <td>91007-7118</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>114-7899449-2921826</td>\n",
       "      <td>2020-12-17 09:57 PM PST</td>\n",
       "      <td>32.46</td>\n",
       "      <td>Cedar Park</td>\n",
       "      <td>TX</td>\n",
       "      <td>78613</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>114-3948442-8321808</td>\n",
       "      <td>2020-12-18 05:50 AM PST</td>\n",
       "      <td>31.86</td>\n",
       "      <td>MISSOURI CITY</td>\n",
       "      <td>TX</td>\n",
       "      <td>77459-7271</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>114-5832017-8645825</td>\n",
       "      <td>2020-12-18 06:17 AM PST</td>\n",
       "      <td>31.86</td>\n",
       "      <td>WAREHAM</td>\n",
       "      <td>MA</td>\n",
       "      <td>02571-1457</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>113-1898748-2151420</td>\n",
       "      <td>2020-12-18 08:14 AM PST</td>\n",
       "      <td>31.86</td>\n",
       "      <td>LIBERTYVILLE</td>\n",
       "      <td>IL</td>\n",
       "      <td>60048-1950</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>114-0452611-8498644</td>\n",
       "      <td>2020-12-18 01:48 PM PST</td>\n",
       "      <td>32.46</td>\n",
       "      <td>BRENTWOOD</td>\n",
       "      <td>CA</td>\n",
       "      <td>94513-2325</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>113-7192414-0482630</td>\n",
       "      <td>2020-12-18 01:55 PM PST</td>\n",
       "      <td>32.76</td>\n",
       "      <td>Oakland</td>\n",
       "      <td>CA</td>\n",
       "      <td>94605</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        amazon_order_id            purchase_date  order_total  \\\n",
       "0   112-9520688-0214660  2020-12-13 04:04 PM PST        32.61   \n",
       "1   111-9763412-1465054  2020-12-13 07:50 PM PST        32.66   \n",
       "2   111-0582021-8828235  2020-12-13 08:42 PM PST        31.79   \n",
       "3   114-5298494-2101848  2020-12-13 09:08 PM PST        29.99   \n",
       "4   114-7045356-6018634  2020-12-13 10:34 PM PST        33.06   \n",
       "5   113-6338118-2538635  2020-12-14 05:21 AM PST        32.09   \n",
       "6   114-0981175-7351430  2020-12-14 05:45 AM PST        29.99   \n",
       "7   112-8562237-5146647  2020-12-14 06:01 AM PST        31.98   \n",
       "8   112-1988159-4756231  2020-12-14 06:30 AM PST        31.79   \n",
       "9   111-9367418-0112249  2020-12-14 06:54 AM PST        32.65   \n",
       "10  112-2596731-9364200  2020-12-14 08:30 AM PST        32.46   \n",
       "11  111-7721673-0274613  2020-12-14 08:56 AM PST        31.79   \n",
       "12  111-2942922-3350650  2020-12-14 09:55 AM PST        32.76   \n",
       "13  111-2879347-4500257  2020-12-14 11:36 AM PST        32.60   \n",
       "14  112-5244593-7641823  2020-12-14 02:53 PM PST        32.69   \n",
       "15  112-7671164-1161025  2020-12-14 04:18 PM PST        31.49   \n",
       "16  114-9778111-7625051  2020-12-14 04:33 PM PST        32.46   \n",
       "17  112-1235840-1754610  2020-12-14 05:50 PM PST        32.50   \n",
       "18  113-6338508-3681812  2020-12-14 06:17 PM PST        29.99   \n",
       "19  114-4458180-6141040  2020-12-14 11:32 PM PST        31.79   \n",
       "20  114-5232999-3116263  2020-12-15 04:54 AM PST        34.33   \n",
       "21  111-1060530-7456226  2020-12-15 06:20 AM PST        31.86   \n",
       "22  113-8155155-7993035  2020-12-15 06:21 AM PST        32.16   \n",
       "23  114-1484300-8721032  2020-12-15 07:12 AM PST        29.99   \n",
       "24  113-8445378-8589031  2020-12-15 10:46 AM PST        31.86   \n",
       "25  112-2274841-3145863  2020-12-15 11:30 AM PST        33.14   \n",
       "26  111-6331056-7902669  2020-12-15 12:02 PM PST        32.09   \n",
       "27  113-2222798-8942609  2020-12-15 12:43 PM PST        31.79   \n",
       "28  111-9802738-5384267  2020-12-15 01:59 PM PST        29.99   \n",
       "29  114-2980695-9285827  2020-12-15 03:42 PM PST        32.65   \n",
       "30  112-1574791-0355465  2020-12-15 05:19 PM PST        32.43   \n",
       "31  114-5231919-4406648  2020-12-15 06:06 PM PST        29.99   \n",
       "32  114-1157807-2505844  2020-12-15 07:35 PM PST        32.66   \n",
       "33  114-2262918-4781012  2020-12-15 07:48 PM PST        32.38   \n",
       "34  113-1179689-6449026  2020-12-15 11:10 PM PST        32.76   \n",
       "35  113-8518211-5389837  2020-12-16 05:36 AM PST        32.61   \n",
       "36  113-2088026-5539446  2020-12-16 07:02 AM PST        32.01   \n",
       "37  112-5654946-6761040  2020-12-16 07:10 AM PST        31.79   \n",
       "38  112-4327268-1121862  2020-12-16 07:13 AM PST        32.43   \n",
       "39  114-6909399-2548245  2020-12-16 07:36 AM PST        31.79   \n",
       "40  112-6352294-8384227  2020-12-16 09:33 AM PST        32.01   \n",
       "41  114-0776169-8514627  2020-12-16 11:36 AM PST        32.31   \n",
       "42  111-2389245-7851443  2020-12-16 12:01 PM PST        32.30   \n",
       "43  112-0682410-2143417  2020-12-16 07:13 PM PST        32.24   \n",
       "44  114-0851129-7212232  2020-12-17 05:05 AM PST        31.89   \n",
       "45  114-3972674-4044228  2020-12-17 09:27 AM PST        31.89   \n",
       "46  113-1231341-1197830  2020-12-17 10:43 AM PST        32.76   \n",
       "47  114-9566257-4512234  2020-12-17 11:37 AM PST        33.11   \n",
       "48  113-5577499-9405069  2020-12-17 08:08 PM PST        31.89   \n",
       "49  114-5057005-3701031  2020-12-17 08:10 PM PST        31.86   \n",
       "50  112-1474500-4317059  2020-12-17 08:22 PM PST        31.79   \n",
       "51  111-2601689-4911461  2020-12-17 08:47 PM PST        31.79   \n",
       "52  114-1981733-6489060  2020-12-17 09:04 PM PST        33.06   \n",
       "53  114-7899449-2921826  2020-12-17 09:57 PM PST        32.46   \n",
       "54  114-3948442-8321808  2020-12-18 05:50 AM PST        31.86   \n",
       "55  114-5832017-8645825  2020-12-18 06:17 AM PST        31.86   \n",
       "56  113-1898748-2151420  2020-12-18 08:14 AM PST        31.86   \n",
       "57  114-0452611-8498644  2020-12-18 01:48 PM PST        32.46   \n",
       "58  113-7192414-0482630  2020-12-18 01:55 PM PST        32.76   \n",
       "\n",
       "            add_city    Abbrev  add_postal add_country  number_items  \\\n",
       "0            BUFFALO        NY  14221-5812          US             1   \n",
       "1        WALLA WALLA        WA  99362-4556          US             1   \n",
       "2       WEST CHESTER        PA  19380-4033          US             1   \n",
       "3        PANAMA CITY        FL  32404-6031          US             1   \n",
       "4           PASADENA        CA  91105-1316          US             1   \n",
       "5         BROWNSBURG        IN  46112-9234          US             1   \n",
       "6        KANSAS CITY        MO  64110-1628          US             1   \n",
       "7             MAHWAH        NJ  07430-2567          US             1   \n",
       "8             TAYLOR        MI  48180-2054          US             1   \n",
       "9           BROOKLYN        NY  11209-4909          US             1   \n",
       "10            HASLET        TX  76052-4892          US             1   \n",
       "11         ARLINGTON        VA  22209-1127          US             1   \n",
       "12       Chattanooga        TN       37412          US             1   \n",
       "13            TUCSON        AZ  85719-5109          US             1   \n",
       "14           POULSBO        WA  98370-6565          US             1   \n",
       "15           Dousman        WI       53118          US             1   \n",
       "16            AUSTIN        TX  78723-5390          US             1   \n",
       "17  BRIARCLIFF MANOR        NY  10510-1027          US             1   \n",
       "18    ATLANTIC BEACH        FL  32233-6960          US             1   \n",
       "19          MARIETTA        GA       30066          US             1   \n",
       "20     FORT HUACHUCA        AZ  85613-1421          US             1   \n",
       "21        PLAINFIELD        IL  60586-8637          US             1   \n",
       "22     FUQUAY VARINA        NC  27526-2421          US             1   \n",
       "23             TAMPA        FL  33606-2471          US             1   \n",
       "24      WEST ROXBURY        MA  02132-2439          US             1   \n",
       "25            LONOKE        AR  72086-3622          US             1   \n",
       "26        GREENSBORO        GA  30642-3911          US             1   \n",
       "27            DULUTH        GA  30097-5035          US             1   \n",
       "28        NORTH POLE        AK  99705-5373          US             1   \n",
       "29          BROOKLYN        NY  11204-5302          US             1   \n",
       "30         RHINEBECK        NY  12572-2163          US             1   \n",
       "31        WELLINGTON        FL  33414-8674          US             1   \n",
       "32           SPOKANE        WA  99223-1544          US             1   \n",
       "33         Caruthers        CA       93609          US             1   \n",
       "34           OAKLAND        CA  94605-1123          US             1   \n",
       "35   clarence center  new york       14032          US             1   \n",
       "36      BRISTOLVILLE        OH  44402-9718          US             1   \n",
       "37      PLEASANT GAP        PA  16823-7417          US             1   \n",
       "38       NEW WINDSOR        NY  12553-6843          US             1   \n",
       "39      Byron Center        MI  49315-8835          US             1   \n",
       "40         RICHFIELD        OH  44286-9734          US             1   \n",
       "41         OCEANSIDE        CA  92058-6626          US             1   \n",
       "42         WASHOUGAL        WA  98671-9582          US             1   \n",
       "43            DURHAM        NC  27707-5418          US             1   \n",
       "44         FAIRFIELD        CT  06824-4547          US             1   \n",
       "45           Milford        CT        6460          US             1   \n",
       "46          San Jose        CA       95111          US             1   \n",
       "47           EVERETT        WA  98208-7116          US             1   \n",
       "48        WOODBRIDGE        CT  06525-1134          US             1   \n",
       "49  GERMANTOWN HILLS        IL  61548-9440          US             1   \n",
       "50            MCLEAN        VA  22102-3424          US             1   \n",
       "51            RESTON        VA  20194-2000          US             1   \n",
       "52           ARCADIA        CA  91007-7118          US             1   \n",
       "53        Cedar Park        TX       78613          US             1   \n",
       "54     MISSOURI CITY        TX  77459-7271          US             1   \n",
       "55           WAREHAM        MA  02571-1457          US             1   \n",
       "56      LIBERTYVILLE        IL  60048-1950          US             1   \n",
       "57         BRENTWOOD        CA  94513-2325          US             1   \n",
       "58           Oakland        CA       94605          US             1   \n",
       "\n",
       "    total_item_quantity     Product  repeat_buyer  \n",
       "0                     1  Rainforest           0.0  \n",
       "1                     1  Rainforest           1.0  \n",
       "2                     1  Rainforest           0.0  \n",
       "3                     1  Rainforest           0.0  \n",
       "4                     1  Rainforest           0.0  \n",
       "5                     1  Rainforest           0.0  \n",
       "6                     1  Rainforest           0.0  \n",
       "7                     1  Rainforest           0.0  \n",
       "8                     1  Rainforest           0.0  \n",
       "9                     1  Rainforest           0.0  \n",
       "10                    1  Rainforest           0.0  \n",
       "11                    1  Rainforest           NaN  \n",
       "12                    1  Rainforest           0.0  \n",
       "13                    1  Rainforest           0.0  \n",
       "14                    1  Rainforest           0.0  \n",
       "15                    1  Rainforest           0.0  \n",
       "16                    1  Rainforest           NaN  \n",
       "17                    1  Rainforest           1.0  \n",
       "18                    1  Rainforest           0.0  \n",
       "19                    1  Rainforest           0.0  \n",
       "20                    1  Rainforest           0.0  \n",
       "21                    1  Rainforest           0.0  \n",
       "22                    1  Rainforest           0.0  \n",
       "23                    1  Rainforest           NaN  \n",
       "24                    1  Rainforest           0.0  \n",
       "25                    1  Rainforest           0.0  \n",
       "26                    1  Rainforest           NaN  \n",
       "27                    1  Rainforest           0.0  \n",
       "28                    1  Rainforest           0.0  \n",
       "29                    1  Rainforest           NaN  \n",
       "30                    1  Rainforest           0.0  \n",
       "31                    1  Rainforest           0.0  \n",
       "32                    1  Rainforest           0.0  \n",
       "33                    1  Rainforest           0.0  \n",
       "34                    1  Rainforest           NaN  \n",
       "35                    1  Rainforest           0.0  \n",
       "36                    1  Rainforest           0.0  \n",
       "37                    1  Rainforest           0.0  \n",
       "38                    1  Rainforest           0.0  \n",
       "39                    1  Rainforest           NaN  \n",
       "40                    1  Rainforest           0.0  \n",
       "41                    1  Rainforest           0.0  \n",
       "42                    1  Rainforest           0.0  \n",
       "43                    1  Rainforest           0.0  \n",
       "44                    1  Rainforest           0.0  \n",
       "45                    1  Rainforest           0.0  \n",
       "46                    1  Rainforest           0.0  \n",
       "47                    1  Rainforest           0.0  \n",
       "48                    1  Rainforest           0.0  \n",
       "49                    1  Rainforest           1.0  \n",
       "50                    1  Rainforest           0.0  \n",
       "51                    1  Rainforest           0.0  \n",
       "52                    1  Rainforest           0.0  \n",
       "53                    1  Rainforest           0.0  \n",
       "54                    1  Rainforest           0.0  \n",
       "55                    1  Rainforest           0.0  \n",
       "56                    1  Rainforest           0.0  \n",
       "57                    1  Rainforest           0.0  \n",
       "58                    1  Rainforest           0.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usa_df = df.loc[df['add_country'] == 'US']\n",
    "usa_df.head(59)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_total</th>\n",
       "      <th>number_items</th>\n",
       "      <th>total_item_quantity</th>\n",
       "      <th>repeat_buyer</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abbrev</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <td>30642.20</td>\n",
       "      <td>967</td>\n",
       "      <td>993</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>13608.07</td>\n",
       "      <td>433</td>\n",
       "      <td>441</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL</th>\n",
       "      <td>12382.52</td>\n",
       "      <td>401</td>\n",
       "      <td>415</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>10869.13</td>\n",
       "      <td>333</td>\n",
       "      <td>349</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WA</th>\n",
       "      <td>8439.29</td>\n",
       "      <td>262</td>\n",
       "      <td>273</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nevada</th>\n",
       "      <td>27.08</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OREGON</th>\n",
       "      <td>24.99</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oh</th>\n",
       "      <td>32.24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oklahoma</th>\n",
       "      <td>32.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wa</th>\n",
       "      <td>33.02</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          order_total  number_items  total_item_quantity  repeat_buyer\n",
       "Abbrev                                                                \n",
       "CA           30642.20           967                  993          84.0\n",
       "NY           13608.07           433                  441          45.0\n",
       "FL           12382.52           401                  415          35.0\n",
       "TX           10869.13           333                  349          20.0\n",
       "WA            8439.29           262                  273          20.0\n",
       "...               ...           ...                  ...           ...\n",
       "Nevada          27.08             1                    1           0.0\n",
       "OREGON          24.99             1                    1           0.0\n",
       "Oh              32.24             1                    1           0.0\n",
       "Oklahoma        32.61             1                    1           0.0\n",
       "wa              33.02             1                    1           0.0\n",
       "\n",
       "[102 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_df = usa_df.groupby(by=[\"Abbrev\"]).sum()\n",
    "state_df.sort_values(by='number_items', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_df = state_df[state_df['number_items'] >= 5]\n",
    "index = state_df.index\n",
    "number_of_rows = len(index)\n",
    "number_of_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_df = pd.merge(state_df,pop,on='Abbrev',how='left')\n",
    "state_df=state_df.drop([0, 1])"
   ]
  },
  {
   "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>Abbrev</th>\n",
       "      <th>order_total</th>\n",
       "      <th>number_items</th>\n",
       "      <th>total_item_quantity</th>\n",
       "      <th>repeat_buyer</th>\n",
       "      <th>State Name</th>\n",
       "      <th>Population</th>\n",
       "      <th>Orders per capita (100,000)</th>\n",
       "      <th>Totals per capita (100,000)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>VT</td>\n",
       "      <td>907.24</td>\n",
       "      <td>30</td>\n",
       "      <td>31</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>623989.0</td>\n",
       "      <td>48.077771</td>\n",
       "      <td>1453.935887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>WA</td>\n",
       "      <td>8439.29</td>\n",
       "      <td>262</td>\n",
       "      <td>273</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Washington</td>\n",
       "      <td>7614893.0</td>\n",
       "      <td>34.406262</td>\n",
       "      <td>1108.261140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MA</td>\n",
       "      <td>7260.07</td>\n",
       "      <td>228</td>\n",
       "      <td>237</td>\n",
       "      <td>23.0</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>6892503.0</td>\n",
       "      <td>33.079420</td>\n",
       "      <td>1053.328522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CT</td>\n",
       "      <td>3735.57</td>\n",
       "      <td>117</td>\n",
       "      <td>120</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>3565287.0</td>\n",
       "      <td>32.816432</td>\n",
       "      <td>1047.761372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>OR</td>\n",
       "      <td>3893.79</td>\n",
       "      <td>134</td>\n",
       "      <td>136</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>4217737.0</td>\n",
       "      <td>31.770592</td>\n",
       "      <td>923.194120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CO</td>\n",
       "      <td>5259.67</td>\n",
       "      <td>168</td>\n",
       "      <td>173</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>5758736.0</td>\n",
       "      <td>29.173069</td>\n",
       "      <td>913.337580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DC</td>\n",
       "      <td>609.30</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>705749.0</td>\n",
       "      <td>26.921753</td>\n",
       "      <td>863.338099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>MT</td>\n",
       "      <td>719.74</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Montana</td>\n",
       "      <td>1068778.0</td>\n",
       "      <td>25.262496</td>\n",
       "      <td>673.423293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ID</td>\n",
       "      <td>1318.90</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>1787065.0</td>\n",
       "      <td>24.621376</td>\n",
       "      <td>738.025757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CA</td>\n",
       "      <td>30642.20</td>\n",
       "      <td>967</td>\n",
       "      <td>993</td>\n",
       "      <td>84.0</td>\n",
       "      <td>California</td>\n",
       "      <td>39512223.0</td>\n",
       "      <td>24.473440</td>\n",
       "      <td>775.511922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ME</td>\n",
       "      <td>958.59</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Maine</td>\n",
       "      <td>1344212.0</td>\n",
       "      <td>23.805769</td>\n",
       "      <td>713.124120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>NY</td>\n",
       "      <td>13608.07</td>\n",
       "      <td>433</td>\n",
       "      <td>441</td>\n",
       "      <td>45.0</td>\n",
       "      <td>New York</td>\n",
       "      <td>19453561.0</td>\n",
       "      <td>22.258136</td>\n",
       "      <td>699.515631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>NH</td>\n",
       "      <td>934.68</td>\n",
       "      <td>29</td>\n",
       "      <td>32</td>\n",
       "      <td>1.0</td>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>1359711.0</td>\n",
       "      <td>21.328062</td>\n",
       "      <td>687.410781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>NJ</td>\n",
       "      <td>5817.50</td>\n",
       "      <td>189</td>\n",
       "      <td>192</td>\n",
       "      <td>15.0</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>8882190.0</td>\n",
       "      <td>21.278536</td>\n",
       "      <td>654.962346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>UT</td>\n",
       "      <td>1981.59</td>\n",
       "      <td>63</td>\n",
       "      <td>64</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Utah</td>\n",
       "      <td>3205958.0</td>\n",
       "      <td>19.650912</td>\n",
       "      <td>618.096057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FL</td>\n",
       "      <td>12382.52</td>\n",
       "      <td>401</td>\n",
       "      <td>415</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Florida</td>\n",
       "      <td>21477737.0</td>\n",
       "      <td>18.670496</td>\n",
       "      <td>576.528151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>MD</td>\n",
       "      <td>3533.91</td>\n",
       "      <td>112</td>\n",
       "      <td>119</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>6045680.0</td>\n",
       "      <td>18.525625</td>\n",
       "      <td>584.534742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DE</td>\n",
       "      <td>574.79</td>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Delaware</td>\n",
       "      <td>973764.0</td>\n",
       "      <td>17.458029</td>\n",
       "      <td>590.276494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>MI</td>\n",
       "      <td>5185.01</td>\n",
       "      <td>166</td>\n",
       "      <td>176</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>9986857.0</td>\n",
       "      <td>16.621846</td>\n",
       "      <td>519.183363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>VA</td>\n",
       "      <td>4340.09</td>\n",
       "      <td>138</td>\n",
       "      <td>142</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>8535519.0</td>\n",
       "      <td>16.167734</td>\n",
       "      <td>508.474060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AZ</td>\n",
       "      <td>3628.29</td>\n",
       "      <td>117</td>\n",
       "      <td>119</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>7278717.0</td>\n",
       "      <td>16.074261</td>\n",
       "      <td>498.479334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>RI</td>\n",
       "      <td>492.04</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>1059361.0</td>\n",
       "      <td>15.103444</td>\n",
       "      <td>464.468675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NC</td>\n",
       "      <td>4895.63</td>\n",
       "      <td>156</td>\n",
       "      <td>162</td>\n",
       "      <td>14.0</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>10488084.0</td>\n",
       "      <td>14.874023</td>\n",
       "      <td>466.780205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>IL</td>\n",
       "      <td>5885.50</td>\n",
       "      <td>180</td>\n",
       "      <td>187</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>12671821.0</td>\n",
       "      <td>14.204746</td>\n",
       "      <td>464.455740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>NV</td>\n",
       "      <td>1438.94</td>\n",
       "      <td>43</td>\n",
       "      <td>46</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>3080156.0</td>\n",
       "      <td>13.960332</td>\n",
       "      <td>467.164650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>WI</td>\n",
       "      <td>2581.17</td>\n",
       "      <td>79</td>\n",
       "      <td>83</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>5822434.0</td>\n",
       "      <td>13.568209</td>\n",
       "      <td>443.314600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>SD</td>\n",
       "      <td>386.16</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>South Dakota</td>\n",
       "      <td>884659.0</td>\n",
       "      <td>13.564549</td>\n",
       "      <td>436.507174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>PA</td>\n",
       "      <td>5468.86</td>\n",
       "      <td>173</td>\n",
       "      <td>179</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>12801989.0</td>\n",
       "      <td>13.513525</td>\n",
       "      <td>427.188306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>MN</td>\n",
       "      <td>2424.87</td>\n",
       "      <td>73</td>\n",
       "      <td>78</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>5639632.0</td>\n",
       "      <td>12.944107</td>\n",
       "      <td>429.969544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HI</td>\n",
       "      <td>444.16</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Hawaii</td>\n",
       "      <td>1415872.0</td>\n",
       "      <td>12.006735</td>\n",
       "      <td>313.700674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>TX</td>\n",
       "      <td>10869.13</td>\n",
       "      <td>333</td>\n",
       "      <td>349</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>28995881.0</td>\n",
       "      <td>11.484390</td>\n",
       "      <td>374.850828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>WY</td>\n",
       "      <td>184.64</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>578759.0</td>\n",
       "      <td>10.367009</td>\n",
       "      <td>319.027436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>GA</td>\n",
       "      <td>3551.09</td>\n",
       "      <td>110</td>\n",
       "      <td>117</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>10617423.0</td>\n",
       "      <td>10.360329</td>\n",
       "      <td>334.458748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>OH</td>\n",
       "      <td>3585.09</td>\n",
       "      <td>113</td>\n",
       "      <td>118</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>11689100.0</td>\n",
       "      <td>9.667126</td>\n",
       "      <td>306.703681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>NE</td>\n",
       "      <td>677.80</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>1934408.0</td>\n",
       "      <td>9.305172</td>\n",
       "      <td>350.391438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>MO</td>\n",
       "      <td>1779.59</td>\n",
       "      <td>57</td>\n",
       "      <td>60</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>6137428.0</td>\n",
       "      <td>9.287278</td>\n",
       "      <td>289.956966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>NM</td>\n",
       "      <td>588.50</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>2096829.0</td>\n",
       "      <td>9.061302</td>\n",
       "      <td>280.661895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>TN</td>\n",
       "      <td>1890.61</td>\n",
       "      <td>61</td>\n",
       "      <td>62</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>6829174.0</td>\n",
       "      <td>8.932266</td>\n",
       "      <td>276.843144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>OK</td>\n",
       "      <td>1065.38</td>\n",
       "      <td>33</td>\n",
       "      <td>35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>3956971.0</td>\n",
       "      <td>8.339712</td>\n",
       "      <td>269.241296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>KS</td>\n",
       "      <td>771.13</td>\n",
       "      <td>24</td>\n",
       "      <td>26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>2913314.0</td>\n",
       "      <td>8.238041</td>\n",
       "      <td>264.691688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>IN</td>\n",
       "      <td>1690.46</td>\n",
       "      <td>55</td>\n",
       "      <td>56</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>6732219.0</td>\n",
       "      <td>8.169669</td>\n",
       "      <td>251.099972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>SC</td>\n",
       "      <td>1356.69</td>\n",
       "      <td>42</td>\n",
       "      <td>46</td>\n",
       "      <td>4.0</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>5148714.0</td>\n",
       "      <td>8.157377</td>\n",
       "      <td>263.500750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>ND</td>\n",
       "      <td>170.68</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North Dakota</td>\n",
       "      <td>762062.0</td>\n",
       "      <td>7.873375</td>\n",
       "      <td>223.971278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>IA</td>\n",
       "      <td>809.30</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>3155070.0</td>\n",
       "      <td>7.606804</td>\n",
       "      <td>256.507780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>LA</td>\n",
       "      <td>984.93</td>\n",
       "      <td>31</td>\n",
       "      <td>32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>4648794.0</td>\n",
       "      <td>6.668396</td>\n",
       "      <td>211.867852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>KY</td>\n",
       "      <td>805.33</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>4467673.0</td>\n",
       "      <td>5.819584</td>\n",
       "      <td>180.257150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL</td>\n",
       "      <td>917.70</td>\n",
       "      <td>27</td>\n",
       "      <td>30</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>4903185.0</td>\n",
       "      <td>5.506625</td>\n",
       "      <td>187.164058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>WV</td>\n",
       "      <td>292.41</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>West Virginia</td>\n",
       "      <td>1792147.0</td>\n",
       "      <td>5.021909</td>\n",
       "      <td>163.161839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AR</td>\n",
       "      <td>449.73</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>3017804.0</td>\n",
       "      <td>4.307768</td>\n",
       "      <td>149.025583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>MS</td>\n",
       "      <td>217.53</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>2976149.0</td>\n",
       "      <td>2.352033</td>\n",
       "      <td>73.091099</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Abbrev  order_total  number_items  total_item_quantity  repeat_buyer  \\\n",
       "47     VT       907.24            30                   31           2.0   \n",
       "48     WA      8439.29           262                  273          20.0   \n",
       "20     MA      7260.07           228                  237          23.0   \n",
       "7      CT      3735.57           117                  120          10.0   \n",
       "38     OR      3893.79           134                  136          13.0   \n",
       "6      CO      5259.67           168                  173          28.0   \n",
       "8      DC       609.30            19                   20           1.0   \n",
       "27     MT       719.74            27                   27           2.0   \n",
       "14     ID      1318.90            44                   44           3.0   \n",
       "5      CA     30642.20           967                  993          84.0   \n",
       "22     ME       958.59            32                   32           0.0   \n",
       "35     NY     13608.07           433                  441          45.0   \n",
       "31     NH       934.68            29                   32           1.0   \n",
       "32     NJ      5817.50           189                  192          15.0   \n",
       "45     UT      1981.59            63                   64           6.0   \n",
       "10     FL     12382.52           401                  415          35.0   \n",
       "21     MD      3533.91           112                  119           8.0   \n",
       "9      DE       574.79            17                   21           0.0   \n",
       "23     MI      5185.01           166                  176          15.0   \n",
       "46     VA      4340.09           138                  142          11.0   \n",
       "4      AZ      3628.29           117                  119          12.0   \n",
       "40     RI       492.04            16                   16           2.0   \n",
       "28     NC      4895.63           156                  162          14.0   \n",
       "15     IL      5885.50           180                  187          11.0   \n",
       "34     NV      1438.94            43                   46           6.0   \n",
       "49     WI      2581.17            79                   83           3.0   \n",
       "42     SD       386.16            12                   13           0.0   \n",
       "39     PA      5468.86           173                  179           4.0   \n",
       "24     MN      2424.87            73                   78           4.0   \n",
       "12     HI       444.16            17                   17           2.0   \n",
       "44     TX     10869.13           333                  349          20.0   \n",
       "51     WY       184.64             6                    6           0.0   \n",
       "11     GA      3551.09           110                  117           8.0   \n",
       "36     OH      3585.09           113                  118           9.0   \n",
       "30     NE       677.80            18                   22           2.0   \n",
       "25     MO      1779.59            57                   60           4.0   \n",
       "33     NM       588.50            19                   19           0.0   \n",
       "43     TN      1890.61            61                   62           6.0   \n",
       "37     OK      1065.38            33                   35           0.0   \n",
       "17     KS       771.13            24                   26           0.0   \n",
       "16     IN      1690.46            55                   56           4.0   \n",
       "41     SC      1356.69            42                   46           4.0   \n",
       "29     ND       170.68             6                    6           0.0   \n",
       "13     IA       809.30            24                   25           3.0   \n",
       "19     LA       984.93            31                   32           0.0   \n",
       "18     KY       805.33            26                   27           0.0   \n",
       "2      AL       917.70            27                   30           6.0   \n",
       "50     WV       292.41             9                   10           0.0   \n",
       "3      AR       449.73            13                   14           0.0   \n",
       "26     MS       217.53             7                    7           0.0   \n",
       "\n",
       "              State Name  Population  Orders per capita (100,000)  \\\n",
       "47               Vermont    623989.0                    48.077771   \n",
       "48            Washington   7614893.0                    34.406262   \n",
       "20         Massachusetts   6892503.0                    33.079420   \n",
       "7            Connecticut   3565287.0                    32.816432   \n",
       "38                Oregon   4217737.0                    31.770592   \n",
       "6               Colorado   5758736.0                    29.173069   \n",
       "8   District of Columbia    705749.0                    26.921753   \n",
       "27               Montana   1068778.0                    25.262496   \n",
       "14                 Idaho   1787065.0                    24.621376   \n",
       "5             California  39512223.0                    24.473440   \n",
       "22                 Maine   1344212.0                    23.805769   \n",
       "35              New York  19453561.0                    22.258136   \n",
       "31         New Hampshire   1359711.0                    21.328062   \n",
       "32            New Jersey   8882190.0                    21.278536   \n",
       "45                  Utah   3205958.0                    19.650912   \n",
       "10               Florida  21477737.0                    18.670496   \n",
       "21              Maryland   6045680.0                    18.525625   \n",
       "9               Delaware    973764.0                    17.458029   \n",
       "23              Michigan   9986857.0                    16.621846   \n",
       "46              Virginia   8535519.0                    16.167734   \n",
       "4                Arizona   7278717.0                    16.074261   \n",
       "40          Rhode Island   1059361.0                    15.103444   \n",
       "28        North Carolina  10488084.0                    14.874023   \n",
       "15              Illinois  12671821.0                    14.204746   \n",
       "34                Nevada   3080156.0                    13.960332   \n",
       "49             Wisconsin   5822434.0                    13.568209   \n",
       "42          South Dakota    884659.0                    13.564549   \n",
       "39          Pennsylvania  12801989.0                    13.513525   \n",
       "24             Minnesota   5639632.0                    12.944107   \n",
       "12                Hawaii   1415872.0                    12.006735   \n",
       "44                 Texas  28995881.0                    11.484390   \n",
       "51               Wyoming    578759.0                    10.367009   \n",
       "11               Georgia  10617423.0                    10.360329   \n",
       "36                  Ohio  11689100.0                     9.667126   \n",
       "30              Nebraska   1934408.0                     9.305172   \n",
       "25              Missouri   6137428.0                     9.287278   \n",
       "33            New Mexico   2096829.0                     9.061302   \n",
       "43             Tennessee   6829174.0                     8.932266   \n",
       "37              Oklahoma   3956971.0                     8.339712   \n",
       "17                Kansas   2913314.0                     8.238041   \n",
       "16               Indiana   6732219.0                     8.169669   \n",
       "41        South Carolina   5148714.0                     8.157377   \n",
       "29          North Dakota    762062.0                     7.873375   \n",
       "13                  Iowa   3155070.0                     7.606804   \n",
       "19             Louisiana   4648794.0                     6.668396   \n",
       "18              Kentucky   4467673.0                     5.819584   \n",
       "2                Alabama   4903185.0                     5.506625   \n",
       "50         West Virginia   1792147.0                     5.021909   \n",
       "3               Arkansas   3017804.0                     4.307768   \n",
       "26           Mississippi   2976149.0                     2.352033   \n",
       "\n",
       "    Totals per capita (100,000)  \n",
       "47                  1453.935887  \n",
       "48                  1108.261140  \n",
       "20                  1053.328522  \n",
       "7                   1047.761372  \n",
       "38                   923.194120  \n",
       "6                    913.337580  \n",
       "8                    863.338099  \n",
       "27                   673.423293  \n",
       "14                   738.025757  \n",
       "5                    775.511922  \n",
       "22                   713.124120  \n",
       "35                   699.515631  \n",
       "31                   687.410781  \n",
       "32                   654.962346  \n",
       "45                   618.096057  \n",
       "10                   576.528151  \n",
       "21                   584.534742  \n",
       "9                    590.276494  \n",
       "23                   519.183363  \n",
       "46                   508.474060  \n",
       "4                    498.479334  \n",
       "40                   464.468675  \n",
       "28                   466.780205  \n",
       "15                   464.455740  \n",
       "34                   467.164650  \n",
       "49                   443.314600  \n",
       "42                   436.507174  \n",
       "39                   427.188306  \n",
       "24                   429.969544  \n",
       "12                   313.700674  \n",
       "44                   374.850828  \n",
       "51                   319.027436  \n",
       "11                   334.458748  \n",
       "36                   306.703681  \n",
       "30                   350.391438  \n",
       "25                   289.956966  \n",
       "33                   280.661895  \n",
       "43                   276.843144  \n",
       "37                   269.241296  \n",
       "17                   264.691688  \n",
       "16                   251.099972  \n",
       "41                   263.500750  \n",
       "29                   223.971278  \n",
       "13                   256.507780  \n",
       "19                   211.867852  \n",
       "18                   180.257150  \n",
       "2                    187.164058  \n",
       "50                   163.161839  \n",
       "3                    149.025583  \n",
       "26                    73.091099  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_df['Orders per capita (100,000)'] = state_df['number_items']/state_df['Population']*1000000\n",
    "state_df['Totals per capita (100,000)'] = state_df['order_total']/state_df['Population']*1000000\n",
    "state_df.sort_values(by='Orders per capita (100,000)', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df[\"purchase_date\"].str.split(\"-\", expand = True) \n",
    "df[\"Month\"]= df1[1]\n",
    "df[\"Month#\"]= df1[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['Month'].str.contains('01'), 'Month'] = \"January\"\n",
    "df.loc[df['Month'].str.contains('02'), 'Month'] = \"February\"\n",
    "df.loc[df['Month'].str.contains('03'), 'Month'] = \"March\"\n",
    "df.loc[df['Month'].str.contains('04'), 'Month'] = \"April\"\n",
    "df.loc[df['Month'].str.contains('05'), 'Month'] = \"May\"\n",
    "df.loc[df['Month'].str.contains('06'), 'Month'] = \"June\"\n",
    "df.loc[df['Month'].str.contains('12'), 'Month'] = \"December\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(r'data\\df_cleaned.csv', index = False)\n",
    "state_df.to_csv(r'data\\state.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "amazon_order_id         object\n",
       "purchase_date           object\n",
       "order_total            float64\n",
       "add_city                object\n",
       "Abbrev                  object\n",
       "add_postal              object\n",
       "add_country             object\n",
       "number_items             int64\n",
       "total_item_quantity      int64\n",
       "Product                 object\n",
       "repeat_buyer           float64\n",
       "Month                   object\n",
       "Month#                  object\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "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>State Name</th>\n",
       "      <th>Abbrev</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>4903185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>AK</td>\n",
       "      <td>731545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>AZ</td>\n",
       "      <td>7278717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>AR</td>\n",
       "      <td>3017804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>CA</td>\n",
       "      <td>39512223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>CO</td>\n",
       "      <td>5758736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>CT</td>\n",
       "      <td>3565287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>DE</td>\n",
       "      <td>973764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>DC</td>\n",
       "      <td>705749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Florida</td>\n",
       "      <td>FL</td>\n",
       "      <td>21477737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>GA</td>\n",
       "      <td>10617423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>HI</td>\n",
       "      <td>1415872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Idaho</td>\n",
       "      <td>ID</td>\n",
       "      <td>1787065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>IL</td>\n",
       "      <td>12671821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>IN</td>\n",
       "      <td>6732219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Iowa</td>\n",
       "      <td>IA</td>\n",
       "      <td>3155070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Kansas</td>\n",
       "      <td>KS</td>\n",
       "      <td>2913314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Kentucky</td>\n",
       "      <td>KY</td>\n",
       "      <td>4467673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Louisiana</td>\n",
       "      <td>LA</td>\n",
       "      <td>4648794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Maine</td>\n",
       "      <td>ME</td>\n",
       "      <td>1344212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Maryland</td>\n",
       "      <td>MD</td>\n",
       "      <td>6045680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>MA</td>\n",
       "      <td>6892503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Michigan</td>\n",
       "      <td>MI</td>\n",
       "      <td>9986857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>MN</td>\n",
       "      <td>5639632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Mississippi</td>\n",
       "      <td>MS</td>\n",
       "      <td>2976149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Missouri</td>\n",
       "      <td>MO</td>\n",
       "      <td>6137428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Montana</td>\n",
       "      <td>MT</td>\n",
       "      <td>1068778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Nebraska</td>\n",
       "      <td>NE</td>\n",
       "      <td>1934408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>NV</td>\n",
       "      <td>3080156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>NH</td>\n",
       "      <td>1359711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>New Jersey</td>\n",
       "      <td>NJ</td>\n",
       "      <td>8882190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>New Mexico</td>\n",
       "      <td>NM</td>\n",
       "      <td>2096829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>19453561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>NC</td>\n",
       "      <td>10488084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>ND</td>\n",
       "      <td>762062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>OH</td>\n",
       "      <td>11689100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>OK</td>\n",
       "      <td>3956971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>OR</td>\n",
       "      <td>4217737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>PA</td>\n",
       "      <td>12801989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>RI</td>\n",
       "      <td>1059361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>South Carolina</td>\n",
       "      <td>SC</td>\n",
       "      <td>5148714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>South Dakota</td>\n",
       "      <td>SD</td>\n",
       "      <td>884659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Tennessee</td>\n",
       "      <td>TN</td>\n",
       "      <td>6829174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Texas</td>\n",
       "      <td>TX</td>\n",
       "      <td>28995881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Utah</td>\n",
       "      <td>UT</td>\n",
       "      <td>3205958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Vermont</td>\n",
       "      <td>VT</td>\n",
       "      <td>623989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>VA</td>\n",
       "      <td>8535519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Washington</td>\n",
       "      <td>WA</td>\n",
       "      <td>7614893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>WV</td>\n",
       "      <td>1792147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>WI</td>\n",
       "      <td>5822434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>WY</td>\n",
       "      <td>578759</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              State Name Abbrev  Population\n",
       "0                Alabama     AL     4903185\n",
       "1                 Alaska     AK      731545\n",
       "2                Arizona     AZ     7278717\n",
       "3               Arkansas     AR     3017804\n",
       "4             California     CA    39512223\n",
       "5               Colorado     CO     5758736\n",
       "6            Connecticut     CT     3565287\n",
       "7               Delaware     DE      973764\n",
       "8   District of Columbia     DC      705749\n",
       "9                Florida     FL    21477737\n",
       "10               Georgia     GA    10617423\n",
       "11                Hawaii     HI     1415872\n",
       "12                 Idaho     ID     1787065\n",
       "13              Illinois     IL    12671821\n",
       "14               Indiana     IN     6732219\n",
       "15                  Iowa     IA     3155070\n",
       "16                Kansas     KS     2913314\n",
       "17              Kentucky     KY     4467673\n",
       "18             Louisiana     LA     4648794\n",
       "19                 Maine     ME     1344212\n",
       "20              Maryland     MD     6045680\n",
       "21         Massachusetts     MA     6892503\n",
       "22              Michigan     MI     9986857\n",
       "23             Minnesota     MN     5639632\n",
       "24           Mississippi     MS     2976149\n",
       "25              Missouri     MO     6137428\n",
       "26               Montana     MT     1068778\n",
       "27              Nebraska     NE     1934408\n",
       "28                Nevada     NV     3080156\n",
       "29         New Hampshire     NH     1359711\n",
       "30            New Jersey     NJ     8882190\n",
       "31            New Mexico     NM     2096829\n",
       "32              New York     NY    19453561\n",
       "33        North Carolina     NC    10488084\n",
       "34          North Dakota     ND      762062\n",
       "35                  Ohio     OH    11689100\n",
       "36              Oklahoma     OK     3956971\n",
       "37                Oregon     OR     4217737\n",
       "38          Pennsylvania     PA    12801989\n",
       "39          Rhode Island     RI     1059361\n",
       "40        South Carolina     SC     5148714\n",
       "41          South Dakota     SD      884659\n",
       "42             Tennessee     TN     6829174\n",
       "43                 Texas     TX    28995881\n",
       "44                  Utah     UT     3205958\n",
       "45               Vermont     VT      623989\n",
       "46              Virginia     VA     8535519\n",
       "47            Washington     WA     7614893\n",
       "48         West Virginia     WV     1792147\n",
       "49             Wisconsin     WI     5822434\n",
       "50               Wyoming     WY      578759"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "month_df = df.groupby(by=[\"Month\"]).sum()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_total</th>\n",
       "      <th>number_items</th>\n",
       "      <th>total_item_quantity</th>\n",
       "      <th>repeat_buyer</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>April</th>\n",
       "      <td>24137.48</td>\n",
       "      <td>694</td>\n",
       "      <td>726</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>7357.06</td>\n",
       "      <td>222</td>\n",
       "      <td>230</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>23626.38</td>\n",
       "      <td>644</td>\n",
       "      <td>670</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>28617.22</td>\n",
       "      <td>839</td>\n",
       "      <td>865</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>27166.05</td>\n",
       "      <td>1023</td>\n",
       "      <td>1065</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>27689.24</td>\n",
       "      <td>835</td>\n",
       "      <td>859</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>38334.16</td>\n",
       "      <td>1384</td>\n",
       "      <td>1431</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          order_total  number_items  total_item_quantity  repeat_buyer\n",
       "Month                                                                 \n",
       "April        24137.48           694                  726          64.0\n",
       "December      7357.06           222                  230          16.0\n",
       "February     23626.38           644                  670          56.0\n",
       "January      28617.22           839                  865          68.0\n",
       "June         27166.05          1023                 1065          50.0\n",
       "March        27689.24           835                  859          75.0\n",
       "May          38334.16          1384                 1431         120.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "month_df = month_df.reindex([\"December\", \"January\", \"February\",\"March\", \"April\", \"May\",\"June\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>order_total</th>\n",
       "      <th>number_items</th>\n",
       "      <th>total_item_quantity</th>\n",
       "      <th>repeat_buyer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>December</td>\n",
       "      <td>7357.06</td>\n",
       "      <td>222</td>\n",
       "      <td>230</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>January</td>\n",
       "      <td>28617.22</td>\n",
       "      <td>839</td>\n",
       "      <td>865</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>February</td>\n",
       "      <td>23626.38</td>\n",
       "      <td>644</td>\n",
       "      <td>670</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>March</td>\n",
       "      <td>27689.24</td>\n",
       "      <td>835</td>\n",
       "      <td>859</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>April</td>\n",
       "      <td>24137.48</td>\n",
       "      <td>694</td>\n",
       "      <td>726</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>May</td>\n",
       "      <td>38334.16</td>\n",
       "      <td>1384</td>\n",
       "      <td>1431</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>June</td>\n",
       "      <td>27166.05</td>\n",
       "      <td>1023</td>\n",
       "      <td>1065</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Month  order_total  number_items  total_item_quantity  repeat_buyer\n",
       "0  December      7357.06           222                  230          16.0\n",
       "1   January     28617.22           839                  865          68.0\n",
       "2  February     23626.38           644                  670          56.0\n",
       "3     March     27689.24           835                  859          75.0\n",
       "4     April     24137.48           694                  726          64.0\n",
       "5       May     38334.16          1384                 1431         120.0\n",
       "6      June     27166.05          1023                 1065          50.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month_df = month_df.reset_index()\n",
    "month_df = month_df.set_index('Month')\n",
    "month_df = month_df.reset_index()\n",
    "month_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "month_df.loc[month_df['Month'].str.contains('01'), 'Month'] = \"January\"\n",
    "month_df.loc[month_df['Month'].str.contains('02'), 'Month'] = \"February\"\n",
    "month_df.loc[month_df['Month'].str.contains('03'), 'Month'] = \"March\"\n",
    "month_df.loc[month_df['Month'].str.contains('04'), 'Month'] = \"April\"\n",
    "month_df.loc[month_df['Month'].str.contains('05'), 'Month'] = \"May\"\n",
    "month_df.loc[month_df['Month'].str.contains('06'), 'Month'] = \"June\"\n",
    "month_df.loc[month_df['Month'].str.contains('12'), 'Month'] = \"December\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>order_total</th>\n",
       "      <th>number_items</th>\n",
       "      <th>total_item_quantity</th>\n",
       "      <th>repeat_buyer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>December</td>\n",
       "      <td>7357.06</td>\n",
       "      <td>222</td>\n",
       "      <td>230</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>January</td>\n",
       "      <td>28617.22</td>\n",
       "      <td>839</td>\n",
       "      <td>865</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>February</td>\n",
       "      <td>23626.38</td>\n",
       "      <td>644</td>\n",
       "      <td>670</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>March</td>\n",
       "      <td>27689.24</td>\n",
       "      <td>835</td>\n",
       "      <td>859</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>April</td>\n",
       "      <td>24137.48</td>\n",
       "      <td>694</td>\n",
       "      <td>726</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>May</td>\n",
       "      <td>38334.16</td>\n",
       "      <td>1384</td>\n",
       "      <td>1431</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>June</td>\n",
       "      <td>27166.05</td>\n",
       "      <td>1023</td>\n",
       "      <td>1065</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Month  order_total  number_items  total_item_quantity  repeat_buyer\n",
       "0  December      7357.06           222                  230          16.0\n",
       "1   January     28617.22           839                  865          68.0\n",
       "2  February     23626.38           644                  670          56.0\n",
       "3     March     27689.24           835                  859          75.0\n",
       "4     April     24137.48           694                  726          64.0\n",
       "5       May     38334.16          1384                 1431         120.0\n",
       "6      June     27166.05          1023                 1065          50.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "month_df['Month']= month_df['Month'].apply(str)\n",
    "\n",
    "ax = month_df.plot.bar(x='Month', y='order_total', rot=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>amazon_order_id</th>\n",
       "      <th>purchase_date</th>\n",
       "      <th>order_total</th>\n",
       "      <th>add_city</th>\n",
       "      <th>Abbrev</th>\n",
       "      <th>add_postal</th>\n",
       "      <th>add_country</th>\n",
       "      <th>number_items</th>\n",
       "      <th>total_item_quantity</th>\n",
       "      <th>Product</th>\n",
       "      <th>repeat_buyer</th>\n",
       "      <th>Month</th>\n",
       "      <th>Month#</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>112-9520688-0214660</td>\n",
       "      <td>2020-12-13 04:04 PM PST</td>\n",
       "      <td>32.61</td>\n",
       "      <td>BUFFALO</td>\n",
       "      <td>NY</td>\n",
       "      <td>14221-5812</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "      <td>December</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>111-9763412-1465054</td>\n",
       "      <td>2020-12-13 07:50 PM PST</td>\n",
       "      <td>32.66</td>\n",
       "      <td>WALLA WALLA</td>\n",
       "      <td>WA</td>\n",
       "      <td>99362-4556</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>1.0</td>\n",
       "      <td>December</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>111-0582021-8828235</td>\n",
       "      <td>2020-12-13 08:42 PM PST</td>\n",
       "      <td>31.79</td>\n",
       "      <td>WEST CHESTER</td>\n",
       "      <td>PA</td>\n",
       "      <td>19380-4033</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "      <td>December</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>114-5298494-2101848</td>\n",
       "      <td>2020-12-13 09:08 PM PST</td>\n",
       "      <td>29.99</td>\n",
       "      <td>PANAMA CITY</td>\n",
       "      <td>FL</td>\n",
       "      <td>32404-6031</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "      <td>December</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>114-7045356-6018634</td>\n",
       "      <td>2020-12-13 10:34 PM PST</td>\n",
       "      <td>33.06</td>\n",
       "      <td>PASADENA</td>\n",
       "      <td>CA</td>\n",
       "      <td>91105-1316</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainforest</td>\n",
       "      <td>0.0</td>\n",
       "      <td>December</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5398</th>\n",
       "      <td>114-4557145-9879435</td>\n",
       "      <td>2021-06-19 02:11 PM PDT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Floral</td>\n",
       "      <td>NaN</td>\n",
       "      <td>June</td>\n",
       "      <td>06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5399</th>\n",
       "      <td>112-8964536-1097055</td>\n",
       "      <td>2021-06-19 03:05 PM PDT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Floral</td>\n",
       "      <td>NaN</td>\n",
       "      <td>June</td>\n",
       "      <td>06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5400</th>\n",
       "      <td>111-4452161-5946602</td>\n",
       "      <td>2021-06-19 03:58 PM PDT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Floral</td>\n",
       "      <td>NaN</td>\n",
       "      <td>June</td>\n",
       "      <td>06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5401</th>\n",
       "      <td>112-9786763-5069819</td>\n",
       "      <td>2021-06-19 04:51 PM PDT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Floral</td>\n",
       "      <td>NaN</td>\n",
       "      <td>June</td>\n",
       "      <td>06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5402</th>\n",
       "      <td>111-4420317-4613050</td>\n",
       "      <td>2021-06-19 05:24 PM PDT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>NOT_AVAILABLE</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Floral</td>\n",
       "      <td>NaN</td>\n",
       "      <td>June</td>\n",
       "      <td>06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5403 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          amazon_order_id            purchase_date  order_total  \\\n",
       "0     112-9520688-0214660  2020-12-13 04:04 PM PST        32.61   \n",
       "1     111-9763412-1465054  2020-12-13 07:50 PM PST        32.66   \n",
       "2     111-0582021-8828235  2020-12-13 08:42 PM PST        31.79   \n",
       "3     114-5298494-2101848  2020-12-13 09:08 PM PST        29.99   \n",
       "4     114-7045356-6018634  2020-12-13 10:34 PM PST        33.06   \n",
       "...                   ...                      ...          ...   \n",
       "5398  114-4557145-9879435  2021-06-19 02:11 PM PDT          NaN   \n",
       "5399  112-8964536-1097055  2021-06-19 03:05 PM PDT          NaN   \n",
       "5400  111-4452161-5946602  2021-06-19 03:58 PM PDT          NaN   \n",
       "5401  112-9786763-5069819  2021-06-19 04:51 PM PDT          NaN   \n",
       "5402  111-4420317-4613050  2021-06-19 05:24 PM PDT          NaN   \n",
       "\n",
       "           add_city         Abbrev     add_postal    add_country  \\\n",
       "0           BUFFALO             NY     14221-5812             US   \n",
       "1       WALLA WALLA             WA     99362-4556             US   \n",
       "2      WEST CHESTER             PA     19380-4033             US   \n",
       "3       PANAMA CITY             FL     32404-6031             US   \n",
       "4          PASADENA             CA     91105-1316             US   \n",
       "...             ...            ...            ...            ...   \n",
       "5398  NOT_AVAILABLE  NOT_AVAILABLE  NOT_AVAILABLE  NOT_AVAILABLE   \n",
       "5399  NOT_AVAILABLE  NOT_AVAILABLE  NOT_AVAILABLE  NOT_AVAILABLE   \n",
       "5400  NOT_AVAILABLE  NOT_AVAILABLE  NOT_AVAILABLE  NOT_AVAILABLE   \n",
       "5401  NOT_AVAILABLE  NOT_AVAILABLE  NOT_AVAILABLE  NOT_AVAILABLE   \n",
       "5402  NOT_AVAILABLE  NOT_AVAILABLE  NOT_AVAILABLE  NOT_AVAILABLE   \n",
       "\n",
       "      number_items  total_item_quantity     Product  repeat_buyer     Month  \\\n",
       "0                1                    1  Rainforest           0.0  December   \n",
       "1                1                    1  Rainforest           1.0  December   \n",
       "2                1                    1  Rainforest           0.0  December   \n",
       "3                1                    1  Rainforest           0.0  December   \n",
       "4                1                    1  Rainforest           0.0  December   \n",
       "...            ...                  ...         ...           ...       ...   \n",
       "5398             1                    1      Floral           NaN      June   \n",
       "5399             1                    1      Floral           NaN      June   \n",
       "5400             1                    1      Floral           NaN      June   \n",
       "5401             1                    2      Floral           NaN      June   \n",
       "5402             1                    1      Floral           NaN      June   \n",
       "\n",
       "     Month#  \n",
       "0        12  \n",
       "1        12  \n",
       "2        12  \n",
       "3        12  \n",
       "4        12  \n",
       "...     ...  \n",
       "5398     06  \n",
       "5399     06  \n",
       "5400     06  \n",
       "5401     06  \n",
       "5402     06  \n",
       "\n",
       "[5403 rows x 13 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "floral_df = df.groupby(by=[\"Product\"]).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_total</th>\n",
       "      <th>number_items</th>\n",
       "      <th>total_item_quantity</th>\n",
       "      <th>repeat_buyer</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Both</th>\n",
       "      <td>14365.10</td>\n",
       "      <td>476</td>\n",
       "      <td>488</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Floral</th>\n",
       "      <td>53978.69</td>\n",
       "      <td>1741</td>\n",
       "      <td>1791</td>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rainforest</th>\n",
       "      <td>108583.80</td>\n",
       "      <td>3424</td>\n",
       "      <td>3567</td>\n",
       "      <td>276.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            order_total  number_items  total_item_quantity  repeat_buyer\n",
       "Product                                                                 \n",
       "Both           14365.10           476                  488          13.0\n",
       "Floral         53978.69          1741                 1791         160.0\n",
       "Rainforest    108583.80          3424                 3567         276.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "floral_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "month_df.to_csv(r'data\\month.csv', index = False)\n",
    "pop.to_csv(r'data\\pop.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Month\"]= df[\"Month\"].astype(str)\n",
    "month_df[\"Month\"]= month_df[\"Month\"].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month                   object\n",
       "order_total            float64\n",
       "number_items             int64\n",
       "total_item_quantity      int64\n",
       "repeat_buyer           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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