Expolatory Analysis on 12 month sales/TwelveMonthSalesInsights.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sales Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge data of each month in single CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "files = [file for file in os.listdir(\"Sales_Data\")]   # generate a list of files present in Sales_Data folder\n",
    "\n",
    "all_months_data = pd.DataFrame()     # create a empty data frame of name all_months_data\n",
    "\n",
    "for file in files:\n",
    "    df = pd.read_csv(\"Sales_Data/\" + file)     # loop through files vairable and get a file\n",
    "    all_months_data = pd.concat([all_months_data,df])    # merge those files data into one data frame inshort merging dataframes\n",
    "    \n",
    "all_months_data.to_csv(\"all_data.csv\",index=False)   # transfer dataframe data to a new file\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read in updated file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>176558</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>2</td>\n",
       "      <td>11.95</td>\n",
       "      <td>04/19/19 08:46</td>\n",
       "      <td>917 1st St, Dallas, TX 75001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176559</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>04/07/19 22:30</td>\n",
       "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order ID                     Product Quantity Ordered Price Each  \\\n",
       "0   176558        USB-C Charging Cable                2      11.95   \n",
       "1      NaN                         NaN              NaN        NaN   \n",
       "2   176559  Bose SoundSport Headphones                1      99.99   \n",
       "3   176560                Google Phone                1        600   \n",
       "4   176560            Wired Headphones                1      11.99   \n",
       "\n",
       "       Order Date                      Purchase Address  \n",
       "0  04/19/19 08:46          917 1st St, Dallas, TX 75001  \n",
       "1             NaN                                   NaN  \n",
       "2  04/07/19 22:30     682 Chestnut St, Boston, MA 02215  \n",
       "3  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001  \n",
       "4  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data = pd.read_csv(\"all_data.csv\")\n",
    "all_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean Up the date\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop rows of NAN (null values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>176558</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>2</td>\n",
       "      <td>11.95</td>\n",
       "      <td>04/19/19 08:46</td>\n",
       "      <td>917 1st St, Dallas, TX 75001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176559</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>04/07/19 22:30</td>\n",
       "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>176561</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/30/19 09:27</td>\n",
       "      <td>333 8th St, Los Angeles, CA 90001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order ID                     Product Quantity Ordered Price Each  \\\n",
       "0   176558        USB-C Charging Cable                2      11.95   \n",
       "2   176559  Bose SoundSport Headphones                1      99.99   \n",
       "3   176560                Google Phone                1        600   \n",
       "4   176560            Wired Headphones                1      11.99   \n",
       "5   176561            Wired Headphones                1      11.99   \n",
       "\n",
       "       Order Date                      Purchase Address  \n",
       "0  04/19/19 08:46          917 1st St, Dallas, TX 75001  \n",
       "2  04/07/19 22:30     682 Chestnut St, Boston, MA 02215  \n",
       "3  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001  \n",
       "4  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001  \n",
       "5  04/30/19 09:27     333 8th St, Los Angeles, CA 90001  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nan_df = all_data[all_data.isna().any(axis=1)]   # finding nan(null values), if all_data has any null value horizontally. horizontally here axis=1 means to check horizontally of the table\n",
    "nan_df.head()\n",
    "\n",
    "all_data = all_data.dropna(how='all') # drop that row whose all values filled with nan\n",
    "all_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### *Get rid of text in order date column*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = all_data[all_data['Order Date'].str[0:2] != 'Or']  # delete those rows which having duplicate data all over there rows\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convert Column to the correct type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make int\n",
    "all_data['Quantity Ordered'] = pd.to_numeric(all_data['Quantity Ordered']) # below i have use astype() to convert that column into int but here i have use to_numeric() function\n",
    "#Make float\n",
    "all_data['Price Each'] = pd.to_numeric(all_data['Price Each']) # same to_numeric() has applied with price each column too"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Augment data with Additional columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add column month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>176558</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>2</td>\n",
       "      <td>11.95</td>\n",
       "      <td>04/19/19 08:46</td>\n",
       "      <td>917 1st St, Dallas, TX 75001</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176559</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>04/07/19 22:30</td>\n",
       "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>176561</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/30/19 09:27</td>\n",
       "      <td>333 8th St, Los Angeles, CA 90001</td>\n",
       "      <td>4</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186845</th>\n",
       "      <td>259353</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>3</td>\n",
       "      <td>2.99</td>\n",
       "      <td>09/17/19 20:56</td>\n",
       "      <td>840 Highland St, Los Angeles, CA 90001</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186846</th>\n",
       "      <td>259354</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>09/01/19 16:00</td>\n",
       "      <td>216 Dogwood St, San Francisco, CA 94016</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186847</th>\n",
       "      <td>259355</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>09/23/19 07:39</td>\n",
       "      <td>220 12th St, San Francisco, CA 94016</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186848</th>\n",
       "      <td>259356</td>\n",
       "      <td>34in Ultrawide Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>379.99</td>\n",
       "      <td>09/19/19 17:30</td>\n",
       "      <td>511 Forest St, San Francisco, CA 94016</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186849</th>\n",
       "      <td>259357</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>11.95</td>\n",
       "      <td>09/30/19 00:18</td>\n",
       "      <td>250 Meadow St, San Francisco, CA 94016</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>185950 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Order ID                     Product  Quantity Ordered  Price Each  \\\n",
       "0        176558        USB-C Charging Cable                 2       11.95   \n",
       "2        176559  Bose SoundSport Headphones                 1       99.99   \n",
       "3        176560                Google Phone                 1      600.00   \n",
       "4        176560            Wired Headphones                 1       11.99   \n",
       "5        176561            Wired Headphones                 1       11.99   \n",
       "...         ...                         ...               ...         ...   \n",
       "186845   259353      AAA Batteries (4-pack)                 3        2.99   \n",
       "186846   259354                      iPhone                 1      700.00   \n",
       "186847   259355                      iPhone                 1      700.00   \n",
       "186848   259356      34in Ultrawide Monitor                 1      379.99   \n",
       "186849   259357        USB-C Charging Cable                 1       11.95   \n",
       "\n",
       "            Order Date                         Purchase Address  Month  \n",
       "0       04/19/19 08:46             917 1st St, Dallas, TX 75001      4  \n",
       "2       04/07/19 22:30        682 Chestnut St, Boston, MA 02215      4  \n",
       "3       04/12/19 14:38     669 Spruce St, Los Angeles, CA 90001      4  \n",
       "4       04/12/19 14:38     669 Spruce St, Los Angeles, CA 90001      4  \n",
       "5       04/30/19 09:27        333 8th St, Los Angeles, CA 90001      4  \n",
       "...                ...                                      ...    ...  \n",
       "186845  09/17/19 20:56   840 Highland St, Los Angeles, CA 90001      9  \n",
       "186846  09/01/19 16:00  216 Dogwood St, San Francisco, CA 94016      9  \n",
       "186847  09/23/19 07:39     220 12th St, San Francisco, CA 94016      9  \n",
       "186848  09/19/19 17:30   511 Forest St, San Francisco, CA 94016      9  \n",
       "186849  09/30/19 00:18   250 Meadow St, San Francisco, CA 94016      9  \n",
       "\n",
       "[185950 rows x 7 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data['Month'] = all_data['Order Date'].str[0:2] # adding a column here by graping the value from string\n",
    "all_data['Month'] = all_data['Month'].astype('int32') #converting that string into integer value \n",
    "all_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add Column Month(Alternative Method)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>Month</th>\n",
       "      <th>Month 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>176558</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>2</td>\n",
       "      <td>11.95</td>\n",
       "      <td>04/19/19 08:46</td>\n",
       "      <td>917 1st St, Dallas, TX 75001</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176559</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>04/07/19 22:30</td>\n",
       "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>176561</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/30/19 09:27</td>\n",
       "      <td>333 8th St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order ID                     Product  Quantity Ordered  Price Each  \\\n",
       "0   176558        USB-C Charging Cable                 2       11.95   \n",
       "2   176559  Bose SoundSport Headphones                 1       99.99   \n",
       "3   176560                Google Phone                 1      600.00   \n",
       "4   176560            Wired Headphones                 1       11.99   \n",
       "5   176561            Wired Headphones                 1       11.99   \n",
       "\n",
       "       Order Date                      Purchase Address  Month  Month 2  \n",
       "0  04/19/19 08:46          917 1st St, Dallas, TX 75001      4        4  \n",
       "2  04/07/19 22:30     682 Chestnut St, Boston, MA 02215      4        4  \n",
       "3  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001      4        4  \n",
       "4  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001      4        4  \n",
       "5  04/30/19 09:27     333 8th St, Los Angeles, CA 90001      4        4  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data['Month 2'] = pd.to_datetime(all_data['Order Date']).dt.month\n",
    "all_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add a sales Column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>Month</th>\n",
       "      <th>Month 2</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>176558</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>2</td>\n",
       "      <td>11.95</td>\n",
       "      <td>04/19/19 08:46</td>\n",
       "      <td>917 1st St, Dallas, TX 75001</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>23.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176559</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>04/07/19 22:30</td>\n",
       "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>99.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>11.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>176561</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/30/19 09:27</td>\n",
       "      <td>333 8th St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>11.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order ID                     Product  Quantity Ordered  Price Each  \\\n",
       "0   176558        USB-C Charging Cable                 2       11.95   \n",
       "2   176559  Bose SoundSport Headphones                 1       99.99   \n",
       "3   176560                Google Phone                 1      600.00   \n",
       "4   176560            Wired Headphones                 1       11.99   \n",
       "5   176561            Wired Headphones                 1       11.99   \n",
       "\n",
       "       Order Date                      Purchase Address  Month  Month 2  \\\n",
       "0  04/19/19 08:46          917 1st St, Dallas, TX 75001      4        4   \n",
       "2  04/07/19 22:30     682 Chestnut St, Boston, MA 02215      4        4   \n",
       "3  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001      4        4   \n",
       "4  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001      4        4   \n",
       "5  04/30/19 09:27     333 8th St, Los Angeles, CA 90001      4        4   \n",
       "\n",
       "    Sales  \n",
       "0   23.90  \n",
       "2   99.99  \n",
       "3  600.00  \n",
       "4   11.99  \n",
       "5   11.99  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data['Sales'] = all_data['Quantity Ordered'] * all_data['Price Each'] #all_data has created a sales column where it has stored the multiplication value ordered and price of each item\n",
    "all_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Add a city Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>Month</th>\n",
       "      <th>Month 2</th>\n",
       "      <th>Sales</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>176558</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>2</td>\n",
       "      <td>11.95</td>\n",
       "      <td>04/19/19 08:46</td>\n",
       "      <td>917 1st St, Dallas, TX 75001</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>23.90</td>\n",
       "      <td>Dallas (TX)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176559</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>04/07/19 22:30</td>\n",
       "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>99.99</td>\n",
       "      <td>Boston (MA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>600.00</td>\n",
       "      <td>Los Angeles (CA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Los Angeles (CA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>176561</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/30/19 09:27</td>\n",
       "      <td>333 8th St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Los Angeles (CA)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order ID                     Product  Quantity Ordered  Price Each  \\\n",
       "0   176558        USB-C Charging Cable                 2       11.95   \n",
       "2   176559  Bose SoundSport Headphones                 1       99.99   \n",
       "3   176560                Google Phone                 1      600.00   \n",
       "4   176560            Wired Headphones                 1       11.99   \n",
       "5   176561            Wired Headphones                 1       11.99   \n",
       "\n",
       "       Order Date                      Purchase Address  Month  Month 2  \\\n",
       "0  04/19/19 08:46          917 1st St, Dallas, TX 75001      4        4   \n",
       "2  04/07/19 22:30     682 Chestnut St, Boston, MA 02215      4        4   \n",
       "3  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001      4        4   \n",
       "4  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001      4        4   \n",
       "5  04/30/19 09:27     333 8th St, Los Angeles, CA 90001      4        4   \n",
       "\n",
       "    Sales               City  \n",
       "0   23.90        Dallas (TX)  \n",
       "2   99.99        Boston (MA)  \n",
       "3  600.00   Los Angeles (CA)  \n",
       "4   11.99   Los Angeles (CA)  \n",
       "5   11.99   Los Angeles (CA)  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# as we see that cities in data are so into it and we need to get that city name\n",
    "# so we use apply() here\n",
    "\n",
    "def get_city(address):\n",
    "    return address.split(',')[1]\n",
    "\n",
    "def get_state(address):\n",
    "    return address.split(',')[2].split(' ')[1]\n",
    "\n",
    "# added a city column by applying lambda function \n",
    "#lambda function has x value which is getting us to each cell\n",
    "#get_city function is spliting the column on ',' and getting us value availabe on 1 index\n",
    "# same concept goes with add a city columns\n",
    "all_data['City'] = all_data['Purchase Address'].apply(lambda x: get_city(x) + ' (' + get_state(x) +')' )\n",
    "\n",
    "all_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Question 1: What was the best month for sales? How much was earned that month? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data['Sales'] = all_data['Quantity Ordered'].astype('int') * all_data['Price Each'].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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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>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Month 2</th>\n",
       "      <th>Sales</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>1</th>\n",
       "      <td>10903</td>\n",
       "      <td>1.811768e+06</td>\n",
       "      <td>9709</td>\n",
       "      <td>1.822257e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13449</td>\n",
       "      <td>2.188885e+06</td>\n",
       "      <td>23950</td>\n",
       "      <td>2.202022e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17005</td>\n",
       "      <td>2.791208e+06</td>\n",
       "      <td>45459</td>\n",
       "      <td>2.807100e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20558</td>\n",
       "      <td>3.367671e+06</td>\n",
       "      <td>73116</td>\n",
       "      <td>3.390670e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18667</td>\n",
       "      <td>3.135125e+06</td>\n",
       "      <td>82830</td>\n",
       "      <td>3.152607e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15253</td>\n",
       "      <td>2.562026e+06</td>\n",
       "      <td>81324</td>\n",
       "      <td>2.577802e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16072</td>\n",
       "      <td>2.632540e+06</td>\n",
       "      <td>100051</td>\n",
       "      <td>2.647776e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13448</td>\n",
       "      <td>2.230345e+06</td>\n",
       "      <td>95688</td>\n",
       "      <td>2.244468e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>13109</td>\n",
       "      <td>2.084992e+06</td>\n",
       "      <td>104589</td>\n",
       "      <td>2.097560e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>22703</td>\n",
       "      <td>3.715555e+06</td>\n",
       "      <td>202820</td>\n",
       "      <td>3.736727e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>19798</td>\n",
       "      <td>3.180601e+06</td>\n",
       "      <td>193303</td>\n",
       "      <td>3.199603e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>28114</td>\n",
       "      <td>4.588415e+06</td>\n",
       "      <td>299808</td>\n",
       "      <td>4.613443e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Quantity Ordered    Price Each  Month 2         Sales\n",
       "Month                                                       \n",
       "1                 10903  1.811768e+06     9709  1.822257e+06\n",
       "2                 13449  2.188885e+06    23950  2.202022e+06\n",
       "3                 17005  2.791208e+06    45459  2.807100e+06\n",
       "4                 20558  3.367671e+06    73116  3.390670e+06\n",
       "5                 18667  3.135125e+06    82830  3.152607e+06\n",
       "6                 15253  2.562026e+06    81324  2.577802e+06\n",
       "7                 16072  2.632540e+06   100051  2.647776e+06\n",
       "8                 13448  2.230345e+06    95688  2.244468e+06\n",
       "9                 13109  2.084992e+06   104589  2.097560e+06\n",
       "10                22703  3.715555e+06   202820  3.736727e+06\n",
       "11                19798  3.180601e+06   193303  3.199603e+06\n",
       "12                28114  4.588415e+06   299808  4.613443e+06"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = all_data.groupby('Month').sum() #addition of columns are done w.r.t months\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "months = range(1,13) # 13 is excluded\n",
    "\n",
    "plt.bar(months, results['Sales']) # months on y-axis and results on x-axis\n",
    "plt.xticks(months)  # ticks are showing here\n",
    "plt.ylabel('Sales in USD($)')\n",
    "plt.xlabel('Month Number')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Question 2: What city had the highest number of sales?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Month</th>\n",
       "      <th>Month 2</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Atlanta (GA)</th>\n",
       "      <td>16602</td>\n",
       "      <td>2.779908e+06</td>\n",
       "      <td>104794</td>\n",
       "      <td>104794</td>\n",
       "      <td>2.795499e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austin (TX)</th>\n",
       "      <td>11153</td>\n",
       "      <td>1.809874e+06</td>\n",
       "      <td>69829</td>\n",
       "      <td>69829</td>\n",
       "      <td>1.819582e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boston (MA)</th>\n",
       "      <td>22528</td>\n",
       "      <td>3.637410e+06</td>\n",
       "      <td>141112</td>\n",
       "      <td>141112</td>\n",
       "      <td>3.661642e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dallas (TX)</th>\n",
       "      <td>16730</td>\n",
       "      <td>2.752628e+06</td>\n",
       "      <td>104620</td>\n",
       "      <td>104620</td>\n",
       "      <td>2.767975e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Los Angeles (CA)</th>\n",
       "      <td>33289</td>\n",
       "      <td>5.421435e+06</td>\n",
       "      <td>208325</td>\n",
       "      <td>208325</td>\n",
       "      <td>5.452571e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York City (NY)</th>\n",
       "      <td>27932</td>\n",
       "      <td>4.635371e+06</td>\n",
       "      <td>175741</td>\n",
       "      <td>175741</td>\n",
       "      <td>4.664317e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portland (ME)</th>\n",
       "      <td>2750</td>\n",
       "      <td>4.471893e+05</td>\n",
       "      <td>17144</td>\n",
       "      <td>17144</td>\n",
       "      <td>4.497583e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portland (OR)</th>\n",
       "      <td>11303</td>\n",
       "      <td>1.860558e+06</td>\n",
       "      <td>70621</td>\n",
       "      <td>70621</td>\n",
       "      <td>1.870732e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Francisco (CA)</th>\n",
       "      <td>50239</td>\n",
       "      <td>8.211462e+06</td>\n",
       "      <td>315520</td>\n",
       "      <td>315520</td>\n",
       "      <td>8.262204e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seattle (WA)</th>\n",
       "      <td>16553</td>\n",
       "      <td>2.733296e+06</td>\n",
       "      <td>104941</td>\n",
       "      <td>104941</td>\n",
       "      <td>2.747755e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Quantity Ordered    Price Each   Month  Month 2  \\\n",
       "City                                                                   \n",
       " Atlanta (GA)                   16602  2.779908e+06  104794   104794   \n",
       " Austin (TX)                    11153  1.809874e+06   69829    69829   \n",
       " Boston (MA)                    22528  3.637410e+06  141112   141112   \n",
       " Dallas (TX)                    16730  2.752628e+06  104620   104620   \n",
       " Los Angeles (CA)               33289  5.421435e+06  208325   208325   \n",
       " New York City (NY)             27932  4.635371e+06  175741   175741   \n",
       " Portland (ME)                   2750  4.471893e+05   17144    17144   \n",
       " Portland (OR)                  11303  1.860558e+06   70621    70621   \n",
       " San Francisco (CA)             50239  8.211462e+06  315520   315520   \n",
       " Seattle (WA)                   16553  2.733296e+06  104941   104941   \n",
       "\n",
       "                            Sales  \n",
       "City                               \n",
       " Atlanta (GA)        2.795499e+06  \n",
       " Austin (TX)         1.819582e+06  \n",
       " Boston (MA)         3.661642e+06  \n",
       " Dallas (TX)         2.767975e+06  \n",
       " Los Angeles (CA)    5.452571e+06  \n",
       " New York City (NY)  4.664317e+06  \n",
       " Portland (ME)       4.497583e+05  \n",
       " Portland (OR)       1.870732e+06  \n",
       " San Francisco (CA)  8.262204e+06  \n",
       " Seattle (WA)        2.747755e+06  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = all_data.groupby('City').sum()  #groupby on city and sum all the columns\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "cities = [city for city, df in all_data.groupby('City')] #this is getting the same value of cities which we grouped by if we don't do this then there will be no consistency among data in graph\n",
    "\n",
    "plt.bar(cities, results['Sales'])\n",
    "plt.xticks(cities,rotation='vertical',size=8)\n",
    "plt.ylabel('Sales in USD ($)')\n",
    "plt.xlabel('City name')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Question 3: What time should we display advertisements to maximize likelihood of customer's buying product?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add hour column\n",
    "#Order date was in string so we converted in date time. Before we get the our month using slicing\n",
    "all_data['Order Date'] = pd.to_datetime(all_data['Order Date'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>Month</th>\n",
       "      <th>Month 2</th>\n",
       "      <th>Sales</th>\n",
       "      <th>City</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>176558</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>2</td>\n",
       "      <td>11.95</td>\n",
       "      <td>2019-04-19 08:46:00</td>\n",
       "      <td>917 1st St, Dallas, TX 75001</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>23.90</td>\n",
       "      <td>Dallas (TX)</td>\n",
       "      <td>8</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176559</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>2019-04-07 22:30:00</td>\n",
       "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>99.99</td>\n",
       "      <td>Boston (MA)</td>\n",
       "      <td>22</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>2019-04-12 14:38:00</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>600.00</td>\n",
       "      <td>Los Angeles (CA)</td>\n",
       "      <td>14</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>2019-04-12 14:38:00</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Los Angeles (CA)</td>\n",
       "      <td>14</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>176561</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>2019-04-30 09:27:00</td>\n",
       "      <td>333 8th St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Los Angeles (CA)</td>\n",
       "      <td>9</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order ID                     Product  Quantity Ordered  Price Each  \\\n",
       "0   176558        USB-C Charging Cable                 2       11.95   \n",
       "2   176559  Bose SoundSport Headphones                 1       99.99   \n",
       "3   176560                Google Phone                 1      600.00   \n",
       "4   176560            Wired Headphones                 1       11.99   \n",
       "5   176561            Wired Headphones                 1       11.99   \n",
       "\n",
       "           Order Date                      Purchase Address  Month  Month 2  \\\n",
       "0 2019-04-19 08:46:00          917 1st St, Dallas, TX 75001      4        4   \n",
       "2 2019-04-07 22:30:00     682 Chestnut St, Boston, MA 02215      4        4   \n",
       "3 2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001      4        4   \n",
       "4 2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001      4        4   \n",
       "5 2019-04-30 09:27:00     333 8th St, Los Angeles, CA 90001      4        4   \n",
       "\n",
       "    Sales               City  Hour  Minute  \n",
       "0   23.90        Dallas (TX)     8      46  \n",
       "2   99.99        Boston (MA)    22      30  \n",
       "3  600.00   Los Angeles (CA)    14      38  \n",
       "4   11.99   Los Angeles (CA)    14      38  \n",
       "5   11.99   Los Angeles (CA)     9      27  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating an hour column by getting an hour value from Order Date column\n",
    "all_data['Hour'] = all_data['Order Date'].dt.hour\n",
    "# creating a minute column by getting a minute value from Order Date Column\n",
    "all_data['Minute'] = all_data['Order Date'].dt.minute\n",
    "all_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": [
    "import seaborn as sns\n",
    "#hours = [hour for hour, df in all_data.groupby('Hour')] #same method as we used previous to get the keys of data\n",
    "    \n",
    "#plt.plot(hours,all_data.groupby(['Hour']).count()) # count all the items by that specific hour\n",
    "\n",
    "sns.distplot(all_data['Hour'])\n",
    "\n",
    "plt.xticks(hours)\n",
    "#plt.xlabel('Hour')\n",
    "#plt.ylabel('Number of Orders')\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "# My recomendation is around 11am (11) or 7pm (19)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Question 4: What products are most often sold together?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-19-3de36d67b4ee>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['Grouped'] = df.groupby('Order ID')['Product'].transform(lambda x: ','.join(x)) # It created a new column grouped and join tow col order id and product and combine them over ,\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Grouped</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone,Wired Headphones</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>176574</td>\n",
       "      <td>Google Phone,USB-C Charging Cable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>176585</td>\n",
       "      <td>Bose SoundSport Headphones,Bose SoundSport Hea...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>176586</td>\n",
       "      <td>AAA Batteries (4-pack),Google Phone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>176672</td>\n",
       "      <td>Lightning Charging Cable,USB-C Charging Cable</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Order ID                                            Grouped\n",
       "3     176560                      Google Phone,Wired Headphones\n",
       "18    176574                  Google Phone,USB-C Charging Cable\n",
       "30    176585  Bose SoundSport Headphones,Bose SoundSport Hea...\n",
       "32    176586                AAA Batteries (4-pack),Google Phone\n",
       "119   176672      Lightning Charging Cable,USB-C Charging Cable"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://stackoverflow.com/questions/43348194/pandas-select-rows-if-id-appear-several-time\n",
    "df = all_data[all_data['Order ID'].duplicated(keep=False)] #keep=false means that all duplicates should be keep\n",
    " \n",
    "df['Grouped'] = df.groupby('Order ID')['Product'].transform(lambda x: ','.join(x)) # It created a new column grouped and join tow col order id and product and combine them over ,\n",
    "\n",
    "# Referenced: https://stackoverflow.com/questions/27298178/concatenate-strings-from-several-rows-using-pandas-groupby\n",
    "df = df[['Order ID','Grouped']].drop_duplicates() # there were two rows created over same data so we dropped additional row\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('iPhone', 'Lightning Charging Cable') 1005\n",
      "('Google Phone', 'USB-C Charging Cable') 987\n",
      "('iPhone', 'Wired Headphones') 447\n",
      "('Google Phone', 'Wired Headphones') 414\n",
      "('Vareebadd Phone', 'USB-C Charging Cable') 361\n",
      "('iPhone', 'Apple Airpods Headphones') 360\n",
      "('Google Phone', 'Bose SoundSport Headphones') 220\n",
      "('USB-C Charging Cable', 'Wired Headphones') 160\n",
      "('Vareebadd Phone', 'Wired Headphones') 143\n",
      "('Lightning Charging Cable', 'Wired Headphones') 92\n"
     ]
    }
   ],
   "source": [
    "from itertools import combinations\n",
    "from collections import Counter\n",
    "\n",
    "\n",
    "# we have list of comma seperated values \n",
    "count = Counter()\n",
    "\n",
    "for row in df['Grouped']:\n",
    "    row_list = row.split(',')\n",
    "    count.update(Counter(combinations(row_list,2)))  # we update the count based on sublists and in the end we get output as dictionary\n",
    "    \n",
    "for key,value in count.most_common(10):\n",
    "    print(key,value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Question 5: What product sold the most? Why do you think it sold the most?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product\n",
       "20in Monitor                   4129\n",
       "27in 4K Gaming Monitor         6244\n",
       "27in FHD Monitor               7550\n",
       "34in Ultrawide Monitor         6199\n",
       "AA Batteries (4-pack)         27635\n",
       "AAA Batteries (4-pack)        31017\n",
       "Apple Airpods Headphones      15661\n",
       "Bose SoundSport Headphones    13457\n",
       "Flatscreen TV                  4819\n",
       "Google Phone                   5532\n",
       "LG Dryer                        646\n",
       "LG Washing Machine              666\n",
       "Lightning Charging Cable      23217\n",
       "Macbook Pro Laptop             4728\n",
       "ThinkPad Laptop                4130\n",
       "USB-C Charging Cable          23975\n",
       "Vareebadd Phone                2068\n",
       "Wired Headphones              20557\n",
       "iPhone                         6849\n",
       "Name: Quantity Ordered, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Referenced: https://stackoverflow.com/questions/14762181/adding-a-y-axis-label-to-secondary-y-axis-in-matplotlib\n",
    "product_group = all_data.groupby('Product') # data group on product\n",
    "quantity_ordered = product_group.sum()['Quantity Ordered'] # sum of the products on the base of quantity ordered\n",
    "\n",
    "quantity_ordered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": [
    "products = [product for product, df in product_group] # for mainting the consistency among data\n",
    "\n",
    "plt.bar(products, quantity_ordered)\n",
    "plt.ylabel('Quantity Ordered')\n",
    "plt.xlabel('Product')\n",
    "plt.xticks(products,rotation='vertical',size=8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product\n",
      "20in Monitor                   109.99\n",
      "27in 4K Gaming Monitor         389.99\n",
      "27in FHD Monitor               149.99\n",
      "34in Ultrawide Monitor         379.99\n",
      "AA Batteries (4-pack)            3.84\n",
      "AAA Batteries (4-pack)           2.99\n",
      "Apple Airpods Headphones       150.00\n",
      "Bose SoundSport Headphones      99.99\n",
      "Flatscreen TV                  300.00\n",
      "Google Phone                   600.00\n",
      "LG Dryer                       600.00\n",
      "LG Washing Machine             600.00\n",
      "Lightning Charging Cable        14.95\n",
      "Macbook Pro Laptop            1700.00\n",
      "ThinkPad Laptop                999.99\n",
      "USB-C Charging Cable            11.95\n",
      "Vareebadd Phone                400.00\n",
      "Wired Headphones                11.99\n",
      "iPhone                         700.00\n",
      "Name: Price Each, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# printing the price of individual item present in our database\n",
    "# we want to place this price over our chart of most sale item\n",
    "\n",
    "prices = all_data.groupby('Product').mean()['Price Each']\n",
    "\n",
    "print(prices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Adding a y-axis label to secondary y-axis in matplotlib\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax1.bar(products, quantity_ordered, color='g')\n",
    "ax2.plot(products,prices,'b-')  # b- shows that how the line is goint to be displayed\n",
    "\n",
    "ax1.set_xlabel('Product Name')\n",
    "ax1.set_ylabel('Quantity Ordered', color='g')\n",
    "ax2.set_ylabel('Price ($)', color='b')\n",
    "ax1.set_xticklabels(products, rotation='vertical',size=8)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 6: Show the individual month with sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>Month 2</th>\n",
       "      <th>City</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Minute</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th>Sales</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>2.99</th>\n",
       "      <td>304165</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>2020-01-01 04:13:00</td>\n",
       "      <td>825 Adams St, Portland, OR 97035</td>\n",
       "      <td>1</td>\n",
       "      <td>Portland (OR)</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.84</th>\n",
       "      <td>141253</td>\n",
       "      <td>AA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2019-01-17 00:09:00</td>\n",
       "      <td>385 11th St, Atlanta, GA 30301</td>\n",
       "      <td>1</td>\n",
       "      <td>Atlanta (GA)</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.98</th>\n",
       "      <td>141259</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>2</td>\n",
       "      <td>2.99</td>\n",
       "      <td>2019-01-20 17:26:00</td>\n",
       "      <td>920 Adams St, San Francisco, CA 94016</td>\n",
       "      <td>1</td>\n",
       "      <td>San Francisco (CA)</td>\n",
       "      <td>17</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.68</th>\n",
       "      <td>141412</td>\n",
       "      <td>AA Batteries (4-pack)</td>\n",
       "      <td>2</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2019-01-14 17:16:00</td>\n",
       "      <td>397 Dogwood St, Atlanta, GA 30301</td>\n",
       "      <td>1</td>\n",
       "      <td>Atlanta (GA)</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.97</th>\n",
       "      <td>315138</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>3</td>\n",
       "      <td>2.99</td>\n",
       "      <td>2020-01-01 00:51:00</td>\n",
       "      <td>257 Adams St, New York City, NY 10001</td>\n",
       "      <td>1</td>\n",
       "      <td>New York City (NY)</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">12</th>\n",
       "      <th>759.98</th>\n",
       "      <td>298137</td>\n",
       "      <td>34in Ultrawide Monitor</td>\n",
       "      <td>2</td>\n",
       "      <td>379.99</td>\n",
       "      <td>2019-12-18 16:07:00</td>\n",
       "      <td>587 Hickory St, San Francisco, CA 94016</td>\n",
       "      <td>12</td>\n",
       "      <td>San Francisco (CA)</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779.98</th>\n",
       "      <td>301666</td>\n",
       "      <td>27in 4K Gaming Monitor</td>\n",
       "      <td>2</td>\n",
       "      <td>389.99</td>\n",
       "      <td>2019-12-29 11:56:00</td>\n",
       "      <td>18 North St, Boston, MA 02215</td>\n",
       "      <td>12</td>\n",
       "      <td>Boston (MA)</td>\n",
       "      <td>11</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999.99</th>\n",
       "      <td>295676</td>\n",
       "      <td>ThinkPad Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>999.99</td>\n",
       "      <td>2019-12-28 17:19:00</td>\n",
       "      <td>410 Lincoln St, Los Angeles, CA 90001</td>\n",
       "      <td>12</td>\n",
       "      <td>Los Angeles (CA)</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1200.00</th>\n",
       "      <td>312867</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>2</td>\n",
       "      <td>600.00</td>\n",
       "      <td>2019-12-29 12:52:00</td>\n",
       "      <td>305 Hill St, Los Angeles, CA 90001</td>\n",
       "      <td>12</td>\n",
       "      <td>Los Angeles (CA)</td>\n",
       "      <td>12</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700.00</th>\n",
       "      <td>295665</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>2019-12-30 00:01:00</td>\n",
       "      <td>136 Church St, New York City, NY 10001</td>\n",
       "      <td>12</td>\n",
       "      <td>New York City (NY)</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>508 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Order ID                 Product  Quantity Ordered  Price Each  \\\n",
       "Month Sales                                                                    \n",
       "1     2.99      304165  AAA Batteries (4-pack)                 1        2.99   \n",
       "      3.84      141253   AA Batteries (4-pack)                 1        3.84   \n",
       "      5.98      141259  AAA Batteries (4-pack)                 2        2.99   \n",
       "      7.68      141412   AA Batteries (4-pack)                 2        3.84   \n",
       "      8.97      315138  AAA Batteries (4-pack)                 3        2.99   \n",
       "...                ...                     ...               ...         ...   \n",
       "12    759.98    298137  34in Ultrawide Monitor                 2      379.99   \n",
       "      779.98    301666  27in 4K Gaming Monitor                 2      389.99   \n",
       "      999.99    295676         ThinkPad Laptop                 1      999.99   \n",
       "      1200.00   312867            Google Phone                 2      600.00   \n",
       "      1700.00   295665      Macbook Pro Laptop                 1     1700.00   \n",
       "\n",
       "                       Order Date                         Purchase Address  \\\n",
       "Month Sales                                                                  \n",
       "1     2.99    2020-01-01 04:13:00         825 Adams St, Portland, OR 97035   \n",
       "      3.84    2019-01-17 00:09:00           385 11th St, Atlanta, GA 30301   \n",
       "      5.98    2019-01-20 17:26:00    920 Adams St, San Francisco, CA 94016   \n",
       "      7.68    2019-01-14 17:16:00        397 Dogwood St, Atlanta, GA 30301   \n",
       "      8.97    2020-01-01 00:51:00    257 Adams St, New York City, NY 10001   \n",
       "...                           ...                                      ...   \n",
       "12    759.98  2019-12-18 16:07:00  587 Hickory St, San Francisco, CA 94016   \n",
       "      779.98  2019-12-29 11:56:00            18 North St, Boston, MA 02215   \n",
       "      999.99  2019-12-28 17:19:00    410 Lincoln St, Los Angeles, CA 90001   \n",
       "      1200.00 2019-12-29 12:52:00       305 Hill St, Los Angeles, CA 90001   \n",
       "      1700.00 2019-12-30 00:01:00   136 Church St, New York City, NY 10001   \n",
       "\n",
       "               Month 2                 City  Hour  Minute  \n",
       "Month Sales                                                \n",
       "1     2.99           1        Portland (OR)     4      13  \n",
       "      3.84           1         Atlanta (GA)     0       9  \n",
       "      5.98           1   San Francisco (CA)    17      26  \n",
       "      7.68           1         Atlanta (GA)    17      16  \n",
       "      8.97           1   New York City (NY)     0      51  \n",
       "...                ...                  ...   ...     ...  \n",
       "12    759.98        12   San Francisco (CA)    16       7  \n",
       "      779.98        12          Boston (MA)    11      56  \n",
       "      999.99        12     Los Angeles (CA)    17      19  \n",
       "      1200.00       12     Los Angeles (CA)    12      52  \n",
       "      1700.00       12   New York City (NY)     0       1  \n",
       "\n",
       "[508 rows x 10 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "each_month = all_data.groupby(['Month', 'Sales']) \n",
    "each_month.first()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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