Sales_Data_Analytics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3ded2d59",
   "metadata": {},
   "source": [
    "# Sales Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cb0163f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe9ac580",
   "metadata": {},
   "source": [
    "Merging all the files into a single csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "80147223",
   "metadata": {},
   "outputs": [],
   "source": [
    "path= './Sales_Data'\n",
    "\n",
    "files= [file for file in os.listdir('./Sales_Data')]\n",
    "\n",
    "combine_df = pd.DataFrame()\n",
    "\n",
    "for file in files:\n",
    "    df=pd.read_csv('./Sales_Data/' + file)\n",
    "    combine_df=pd.concat([combine_df,df])\n",
    "    \n",
    "combine_df.to_csv('combine_df.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dd0d6179",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\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": [
    "new_df=pd.read_csv('combine_df.csv')\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cad5e358",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order ID            545\n",
       "Product             545\n",
       "Quantity Ordered    545\n",
       "Price Each          545\n",
       "Order Date          545\n",
       "Purchase Address    545\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afd176da",
   "metadata": {},
   "source": [
    "### Cleaning the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efdf9c3e",
   "metadata": {},
   "source": [
    "#### Drop rows containing NAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "308e6423",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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>356</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>735</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>1433</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>1553</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Order ID Product Quantity Ordered Price Each Order Date Purchase Address\n",
       "1         NaN     NaN              NaN        NaN        NaN              NaN\n",
       "356       NaN     NaN              NaN        NaN        NaN              NaN\n",
       "735       NaN     NaN              NaN        NaN        NaN              NaN\n",
       "1433      NaN     NaN              NaN        NaN        NaN              NaN\n",
       "1553      NaN     NaN              NaN        NaN        NaN              NaN"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nan= new_df[new_df.isna().any(axis=1)]\n",
    "df_nan.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ca47c020",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "    </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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.dropna(how='any',inplace=True)\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9dac3901",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "    </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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = new_df[new_df['Order Date'].str[0:2] != 'Or']\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa40f7ff",
   "metadata": {},
   "source": [
    "#### Changing the datatype of columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1d6fc1bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df['Quantity Ordered'] = new_df['Quantity Ordered'].astype('int32')\n",
    "new_df['Price Each'] = new_df['Price Each'].astype('float')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92f4d9f4",
   "metadata": {},
   "source": [
    "### Augment data with new column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7939d824",
   "metadata": {},
   "source": [
    "#### Adding the column month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d0ab1c75",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df['Month']= new_df['Order Date'].str[0:2]\n",
    "new_df['Month']=new_df['Month'].astype('int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bc2c37da",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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",
       "  </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  \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  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99289535",
   "metadata": {},
   "source": [
    "#### Adding the column Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "33fa42b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</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>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>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>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>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>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>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   Sales  \n",
       "0  04/19/19 08:46          917 1st St, Dallas, TX 75001      4   23.90  \n",
       "2  04/07/19 22:30     682 Chestnut St, Boston, MA 02215      4   99.99  \n",
       "3  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001      4  600.00  \n",
       "4  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001      4   11.99  \n",
       "5  04/30/19 09:27     333 8th St, Los Angeles, CA 90001      4   11.99  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['Sales'] = new_df['Quantity Ordered'] * new_df['Price Each']\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "838f724e",
   "metadata": {},
   "source": [
    "#### Adding the column City"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "db024390",
   "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>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>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>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>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>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>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   Sales  \\\n",
       "0  04/19/19 08:46          917 1st St, Dallas, TX 75001      4   23.90   \n",
       "2  04/07/19 22:30     682 Chestnut St, Boston, MA 02215      4   99.99   \n",
       "3  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001      4  600.00   \n",
       "4  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001      4   11.99   \n",
       "5  04/30/19 09:27     333 8th St, Los Angeles, CA 90001      4   11.99   \n",
       "\n",
       "               City  \n",
       "0       Dallas (TX)  \n",
       "2       Boston (MA)  \n",
       "3  Los Angeles (CA)  \n",
       "4  Los Angeles (CA)  \n",
       "5  Los Angeles (CA)  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['City']=new_df['Purchase Address'].apply(lambda x: x.split(',')[1].strip() + ' (' +  x.split(',')[2].strip().split(' ')[0] + ')')\n",
    "\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8856151",
   "metadata": {},
   "source": [
    "#### Adding the column Hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e4a1e733",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df['Order Date']=pd.to_datetime(new_df['Order Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6b043c5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>Sales</th>\n",
       "      <th>City</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Minute</th>\n",
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       "  </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>23.90</td>\n",
       "      <td>Dallas (TX)</td>\n",
       "      <td>8</td>\n",
       "      <td>46</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176559</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
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       "      <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>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>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>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>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   Sales  \\\n",
       "0 2019-04-19 08:46:00          917 1st St, Dallas, TX 75001      4   23.90   \n",
       "2 2019-04-07 22:30:00     682 Chestnut St, Boston, MA 02215      4   99.99   \n",
       "3 2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001      4  600.00   \n",
       "4 2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001      4   11.99   \n",
       "5 2019-04-30 09:27:00     333 8th St, Los Angeles, CA 90001      4   11.99   \n",
       "\n",
       "               City  Hour  Minute  \n",
       "0       Dallas (TX)     8      46  \n",
       "2       Boston (MA)    22      30  \n",
       "3  Los Angeles (CA)    14      38  \n",
       "4  Los Angeles (CA)    14      38  \n",
       "5  Los Angeles (CA)     9      27  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['Hour'] = new_df['Order Date'].dt.hour\n",
    "new_df['Minute']= new_df['Order Date'].dt.minute\n",
    "\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aff0155f",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f66ba4ad",
   "metadata": {},
   "source": [
    "<b> Question 1: <b> What was the best month for sales? How much was earned that month?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ef0ad0c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<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>Sales</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Minute</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</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>1</th>\n",
       "      <td>10903</td>\n",
       "      <td>1811768.38</td>\n",
       "      <td>1822256.73</td>\n",
       "      <td>139485</td>\n",
       "      <td>282440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13449</td>\n",
       "      <td>2188884.72</td>\n",
       "      <td>2202022.42</td>\n",
       "      <td>172669</td>\n",
       "      <td>354885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17005</td>\n",
       "      <td>2791207.83</td>\n",
       "      <td>2807100.38</td>\n",
       "      <td>218969</td>\n",
       "      <td>447559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20558</td>\n",
       "      <td>3367671.02</td>\n",
       "      <td>3390670.24</td>\n",
       "      <td>262259</td>\n",
       "      <td>544186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18667</td>\n",
       "      <td>3135125.13</td>\n",
       "      <td>3152606.75</td>\n",
       "      <td>238780</td>\n",
       "      <td>487899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15253</td>\n",
       "      <td>2562025.61</td>\n",
       "      <td>2577802.26</td>\n",
       "      <td>195528</td>\n",
       "      <td>402436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16072</td>\n",
       "      <td>2632539.56</td>\n",
       "      <td>2647775.76</td>\n",
       "      <td>206169</td>\n",
       "      <td>417349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13448</td>\n",
       "      <td>2230345.42</td>\n",
       "      <td>2244467.88</td>\n",
       "      <td>172289</td>\n",
       "      <td>353857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>13109</td>\n",
       "      <td>2084992.09</td>\n",
       "      <td>2097560.13</td>\n",
       "      <td>168513</td>\n",
       "      <td>341698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>22703</td>\n",
       "      <td>3715554.83</td>\n",
       "      <td>3736726.88</td>\n",
       "      <td>290650</td>\n",
       "      <td>598437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>19798</td>\n",
       "      <td>3180600.68</td>\n",
       "      <td>3199603.20</td>\n",
       "      <td>254865</td>\n",
       "      <td>518231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>28114</td>\n",
       "      <td>4588415.41</td>\n",
       "      <td>4613443.34</td>\n",
       "      <td>359978</td>\n",
       "      <td>733082</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Quantity Ordered  Price Each       Sales    Hour  Minute\n",
       "Month                                                          \n",
       "1                 10903  1811768.38  1822256.73  139485  282440\n",
       "2                 13449  2188884.72  2202022.42  172669  354885\n",
       "3                 17005  2791207.83  2807100.38  218969  447559\n",
       "4                 20558  3367671.02  3390670.24  262259  544186\n",
       "5                 18667  3135125.13  3152606.75  238780  487899\n",
       "6                 15253  2562025.61  2577802.26  195528  402436\n",
       "7                 16072  2632539.56  2647775.76  206169  417349\n",
       "8                 13448  2230345.42  2244467.88  172289  353857\n",
       "9                 13109  2084992.09  2097560.13  168513  341698\n",
       "10                22703  3715554.83  3736726.88  290650  598437\n",
       "11                19798  3180600.68  3199603.20  254865  518231\n",
       "12                28114  4588415.41  4613443.34  359978  733082"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month_data = new_df.groupby('Month').sum()\n",
    "month_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "81e960e5",
   "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_data.columns\n",
    "months= range(1,13)\n",
    "\n",
    "plt.bar(months,month_data['Sales'],color='Blue')\n",
    "plt.xticks(months,rotation='vertical',size=8)\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Sales in USD ($)*10^6')\n",
    "plt.title('Month wise Sales Analysis')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c565dea",
   "metadata": {},
   "source": [
    "#### Question 2: What city sold the most product?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a5cf7613",
   "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>Sales</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Minute</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Atlanta (GA)</th>\n",
       "      <td>16602</td>\n",
       "      <td>2779908.20</td>\n",
       "      <td>104794</td>\n",
       "      <td>2795498.58</td>\n",
       "      <td>214264</td>\n",
       "      <td>442932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austin (TX)</th>\n",
       "      <td>11153</td>\n",
       "      <td>1809873.61</td>\n",
       "      <td>69829</td>\n",
       "      <td>1819581.75</td>\n",
       "      <td>141946</td>\n",
       "      <td>289060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boston (MA)</th>\n",
       "      <td>22528</td>\n",
       "      <td>3637409.77</td>\n",
       "      <td>141112</td>\n",
       "      <td>3661642.01</td>\n",
       "      <td>288225</td>\n",
       "      <td>590442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dallas (TX)</th>\n",
       "      <td>16730</td>\n",
       "      <td>2752627.82</td>\n",
       "      <td>104620</td>\n",
       "      <td>2767975.40</td>\n",
       "      <td>214390</td>\n",
       "      <td>435155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Los Angeles (CA)</th>\n",
       "      <td>33289</td>\n",
       "      <td>5421435.23</td>\n",
       "      <td>208325</td>\n",
       "      <td>5452570.80</td>\n",
       "      <td>427444</td>\n",
       "      <td>866638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York City (NY)</th>\n",
       "      <td>27932</td>\n",
       "      <td>4635370.83</td>\n",
       "      <td>175741</td>\n",
       "      <td>4664317.43</td>\n",
       "      <td>357696</td>\n",
       "      <td>733598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portland (ME)</th>\n",
       "      <td>2750</td>\n",
       "      <td>447189.25</td>\n",
       "      <td>17144</td>\n",
       "      <td>449758.27</td>\n",
       "      <td>35211</td>\n",
       "      <td>72856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portland (OR)</th>\n",
       "      <td>11303</td>\n",
       "      <td>1860558.22</td>\n",
       "      <td>70621</td>\n",
       "      <td>1870732.34</td>\n",
       "      <td>144421</td>\n",
       "      <td>295533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Francisco (CA)</th>\n",
       "      <td>50239</td>\n",
       "      <td>8211461.74</td>\n",
       "      <td>315520</td>\n",
       "      <td>8262203.91</td>\n",
       "      <td>643265</td>\n",
       "      <td>1319477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seattle (WA)</th>\n",
       "      <td>16553</td>\n",
       "      <td>2733296.01</td>\n",
       "      <td>104941</td>\n",
       "      <td>2747755.48</td>\n",
       "      <td>213292</td>\n",
       "      <td>436368</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Quantity Ordered  Price Each   Month       Sales    Hour  \\\n",
       "City                                                                           \n",
       "Atlanta (GA)                   16602  2779908.20  104794  2795498.58  214264   \n",
       "Austin (TX)                    11153  1809873.61   69829  1819581.75  141946   \n",
       "Boston (MA)                    22528  3637409.77  141112  3661642.01  288225   \n",
       "Dallas (TX)                    16730  2752627.82  104620  2767975.40  214390   \n",
       "Los Angeles (CA)               33289  5421435.23  208325  5452570.80  427444   \n",
       "New York City (NY)             27932  4635370.83  175741  4664317.43  357696   \n",
       "Portland (ME)                   2750   447189.25   17144   449758.27   35211   \n",
       "Portland (OR)                  11303  1860558.22   70621  1870732.34  144421   \n",
       "San Francisco (CA)             50239  8211461.74  315520  8262203.91  643265   \n",
       "Seattle (WA)                   16553  2733296.01  104941  2747755.48  213292   \n",
       "\n",
       "                     Minute  \n",
       "City                         \n",
       "Atlanta (GA)         442932  \n",
       "Austin (TX)          289060  \n",
       "Boston (MA)          590442  \n",
       "Dallas (TX)          435155  \n",
       "Los Angeles (CA)     866638  \n",
       "New York City (NY)   733598  \n",
       "Portland (ME)         72856  \n",
       "Portland (OR)        295533  \n",
       "San Francisco (CA)  1319477  \n",
       "Seattle (WA)         436368  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_df = new_df.groupby('City').sum()\n",
    "city_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1249fac7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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E9F43K1k/KmmN6vLGwO3A3yR9qIvnN3COpMsl7TPC8+8jaaqkqdOnT5+TtkdExCi6mSa5J3BTdflzwI7Ay4EPdvHYDWy/DtgW+G9Js9WvsX247cm2J0+cOLG7VkdEREed6sEfCCwP/I+khYF1gK2BbYAlq5Wt59u+YLjH276z+n6vpNMoJYaHvW9ERIytTtUkvyDpDZShlsWBU2wfLGkBYJvRVrZW4/QL2H60urwVkJWwEREN6WaIZm9gCcp0x/2rY6sDP+nwuOWBiyRdCfwZ+LXt385tQyMiYs50miaJ7buBTw05dj1l0dNoj7sZeM08tS4iIuZap5Wsn6sWNY10+2aS3jL2zYqIiHnVqQf/V+BXkp4ArgCmA4tSZtGsQ1nl+pU6GxgREXOn00nW04HTJb0ceDPwIuAR4DhgH9v/rr+JERExNzqOwQPY/hvwt5rbEjFfyH6wMb/oZhZNRETMh5LgIyL6VBJ8RESf6qbY2KaSTpV0TfV1sqRN6m9aRETMi07z4LcHjgR+BbwbeA9wFnCkpO3qb15ERMytTrNo9gd2tn1l27FpkqYC36Mk+4iIGIc6DdGsMCS5A2D7KkqtmYiIGKc6JfjH5vK2iIjosU5DNKtJOmOY4wJeWkN7IiJijHRK8DuNctv/jmVDIiJibHWqRTOl/bqkhYC1gTtt31tnwyIiYt50miZ5mKRXVpeXBq4EjgH+Imm3BtoXERFzqdNJ1g1tX1Nd3gu40fargHWBT3YTQNKCkv4i6cx5aGdERMyhTgn+qbbLWwK/BLB9zxzE+Chw3Zw1KyIi5lWnBP+QpLdIei2lHvxvASRNAJ7X6cklrQxsT+f9WyMiYox1mkXzX8B3gRWA/dp67psDv+7i+b9NGcpZcm4bGBERc6fTLJobgW2GOX42cPZoj632ar3X9uWjFSeTtA+wD8Aqq6zSucUREdGVURO8pO8B7XvLGLgPOM/2RR2e+83AjlVRskWBpSQdZ3v39jvZPhw4HGDy5MnZxyYiYox0GqKZOsyx5wNfl3SC7W+P9EDbnwE+A1D14D8xNLlHRER9Og3RHD3ccUmHARdTxtgjImIc6mrT7aFs/1tzsPOw7fOB8+cmVkREzJ05TvDVFMk9gDvGvjkRETFWOp1kfZRZT7IC/BuYQplCGRER41SnMfjMX4+ImE/N1Rh8jB9zcCpkrjmTVyPmS51KFURExHwqCT4iok91PUQjaUHKRtvPPcb2bXU0KiIi5l1XCV7SvsCBwD+BGdVhA6+uqV0RETGPuu3BfxRYw/b9dTYmIiLGTrdj8LcDD9fZkIiIGFvd9uBvBs6X9GvgydZB29+spVURETHPuk3wt1VfC1dfERExznWV4G1/oe6GRETE2OpUi+bbtveT9Ctmr0mD7R1ra1lERMyTTj34Y6vv/1t3QyIiYmx1KjZ2efV9SjPNiYiIsVJbqQJJi0r6s6QrJV0jKeP4ERENqrOa5JPAZrb/JWkh4CJJv7F9SY0xIyKiMsc9eEkLSFqq0/1c/Ku6ulD1lcKzEREN6SrBS/q5pKUkLQ5cDVwraf8uHregpGnAvcDvbF86T62NiIiudduDX8v2I8DOwG+AVSn7so7K9rO21wFWBl4vae2h95G0j6SpkqZOnz6964ZHRMTouk3wC1Xj6DsDZ9h+mjkYbrH9EHAesM0wtx1ue7LtyRMnTuz2KSMiooNuE/yPgFuAxYELJL0EeGS0B0iaKGmZ6vLzgC2B6+e6pRERMUe6LVXwXeC7bYdulbRph4e9CDi62ihkAeBE22fOXTMjImJOdbvhx/LAV4AVbW8raS1gfeCIkR5j+yrgtWPSyoiImGPdDtEcBZwNrFhdvxHYr4b2zDWp/q+YVX7nEeNbtwl+OdsnUm3XZ/sZ4NnaWhURMU7Mzx2ZbhP8Y5JeQDVzRtIbyQ5PERHjWrelCj4GnAGsJumPwETgHbW1KiIi5lm3s2iukLQxsAYg4IZqLnxERIxTnTb8eNsIN60uCdun1tCmiIgYA5168DuMcpuBJPiIiHGq04YfezXVkIiIGFtd14OXtD3wSmDR1jHbB9fRqIiImHfdlgs+DNgV2JdyknUX4CU1tisiIuZRt/Pg32T7vcCDtr9AKVOwen3NioiIedVtgv939f1xSSsCT1OKiUVExDjV7Rj8mVXp368DV1Bm0Py4rkZFRMS863ah0xeri6dIOhNY1HZKFUREjGOjDtFIWk/SCm3X3wucCHxR0vPrblxERMy9TmPwPwKeApC0EXAocAyl0Njh9TYtIiLmRachmgVtP1Bd3hU43PYplKGaabW2LCIi5kmnHvyCklpvApsDf2i7rVMdmxdLOk/StZKukfTReWloRETMmU49+OOBKZLuo0yVvBBA0svoXA/+GeDjVSXKJYHLJf3O9rXz2uiIiOisUy2aL0s6lzLn/Rzbrm5agLKqdbTH3g3cXV1+VNJ1wEpAEnxERAM6TpO0fckwx26ckyCSJlE24L50mNv2AfYBWGWVVebkaSMiYhTdrmSda5KWAE4B9rP9yNDbbR9ue7LtyRMnTqy7ORERA6PWBC9pIUpy/1k2B4mIaFbX5YLnlCQBRwDX2f5mXXEiohlS/TGeO8sXY6LOHvybgT2AzSRNq762qzFez0j1f0VEzKnaevC2L6LUjo+IiB6o/SRrRET0RhJ8RESfSoKPiOhTSfAREX0qCT4iok8lwUdE9Kkk+IiIPpUEHxHRp5LgIyL6VBJ8RESfSoKPiOhTSfAREX0qCT4iok8lwUdE9Kkk+IiIPpUEHxHRp2pL8JKOlHSvpKvrihERESOrswd/FLBNjc8fERGjqC3B274AeKCu54+IiNH1fAxe0j6SpkqaOn369F43JyKib/Q8wds+3PZk25MnTpzY6+ZERPSNnif4iIioRxJ8RESfqnOa5PHAn4A1JN0hae+6YkVExOwm1PXEtner67kjIqKzDNFERPSpJPiIiD6VBB8R0aeS4CMi+lQSfEREn0qCj4joU0nwERF9Kgk+IqJP1bbQKSLGnlR/DLv+GNGM9OAjIvpUEnxERJ9Kgo+I6FNJ8BERfSoJPiKiTyXBR0T0qST4iIg+VWuCl7SNpBsk3STp03XGioiIWdW5Zd+CwP8B2wJrAbtJWquueBERMas6e/CvB26yfbPtp4BfADvVGC8iItrUWapgJeD2tut3AG8YeidJ+wD7VFf/JemGGtvUbjngvjl5wBguE0/swYo9x/ETO7HnwEtGuqHntWhsHw4c3nRcSVNtT246bmIPXuxex0/swYrdrs4hmjuBF7ddX7k6FhERDagzwV8GvFzSqpIWBt4FnFFjvIiIaFPbEI3tZyR9GDgbWBA40vY1dcWbC40PCyX2wMbudfzEHqzYz5FT/Dkioi9lJWtERJ9Kgo+I6FNJ8BERfarn8+CbImkJYAvgFdWh64FzbT/aYBtWALB9T0PxVgQ+CLwRWKg6/BTwZ+CHtmubtirpecC7ge2B51eHHwDOAn5m+9/9GLuKP3D/a+MhtqRlgTWqqzfYfrChuD3/e49kIE6ySvoGZWXZJcDN1eGXAm8C7rX98Rpjrwd8CHgRJcmIknTuAn5g+7IaYx8CHDd09pKkVwLvsf3ZGmP/CDgdmGL7serY4sAmwA62P9CnsQf1f62XsXehTMN+CrilOrwqsDBwvO2Taozds793V2z3/RcwcW5uG6PY+wLLDHN8GWDfHvwulgb26OHfYrEext6wgRgvHuW2vv1f63HsHak6q0OOC9ix5tg9yy3dfA1ED34oSW8C3ga8wfaGNcdaycMMhUiabHtqnbHbYr0IeCulsucDwB9dSkTUGfN84GDbf6iuLwD8J7B73b/ztjYsBGwJ7ACsCFzjGj+1VDEvBr5n+/g644wQe2Xbd1SXJ9h+prr8SvdgDYokATvZ/mXDcRcGtgLeanvvhmM3llu6as8gJHhJE4DNKUlueWB1yh//xgZin00Z8z7U9mOSJgFfBp6yvVfNsT8JbEQpEXEK8HHbW9cZsy32csCBlHIVpwF7AycBh9l+uubY76Qk9cUoC+12tb15nTHbYi8KfIwyHHQoM4cMsH3z8I8as9h/sL3ZaJdrjL0e8EnAlP/vNwHvBc62fVCdsav4SwJvoXRi1ge+Thmiq7V4YS9zSzcG5STrfcAJlB7lnZJ+09QfwPbWkt4BnCPpRsp43edtT2sg/JaUHvs5wEXAfg3EBMD2fZKOAH5IeeH/Cji87uRe+RJwIfBt25dL2rmBmADYfkLSDygv9M8D/6AMFRj4j5rDa4TLTfgWsCuwLHA+5W/+Ztsz6g4s6SzgMUpH4r+BE+v+hNqmZ7mlG4OS4HegvMMeJmkqsHhTgauNT1YEngGeqA4/20Rs21tKej5ljPIIYE1JewHn2/5HnbElHUWZubOb7Vsk7Q6cK+lHto+tM7bt1SW9GnibpAOAV0jaFLjY9pN1xpb0EcpH9ENsn11nrGEsK2kzSnKf5XIDsZ+shiLvlHSN7SMbiNnyJ8pMsfUpn1abfHPrWW7pxkAM0bST9FrKC3Ay5Sz3+2qO90fgGOAntp+V9FLgEOBR2++vM/YwbVkc2A7Y2fZ7ao61ke0Lhhx7HvAx21+uM/YwbVmN8jffzvamNcfaB/ixe/DCknTgSLfZ/kLNsR8CrqIk11e1XbbtjeqM3daGSZRkux1lL4oLbf+0idhV/EZzSzcGLsG3k7R63R+nhkt01fE32/5jzbHfO9Jtto+pOXbt476jxN4aeMj2pW3H3gAsbfucmmMfSxmOgZlDMwDYHvHvEWNL0vKUGTQ/rjnOycCllPNsUz1zWm7tuaUbA5HgJX0WuMD2RZKuBe6hDE9Nsf35mmP3MtHdD9xEGZu8i5kfXd1Agj8PaA0RzKLucVlJFwCbtWaRVMcmAH+ouzcpaejuOltR5of/te4EL2kGMA14qP0w5e9d+/+gpK2Ad1Lmwt8NnGz7tw3E/RZwqu0L245tCLzN9v+rOfaqwHrV1+soJ/avA/5s+7A6Y3djUBL8Ba0XtqTzWh/TJf3O9pY1x55GmVUxm9YUwhpjTwA2BXYGVqCMVZ5k+9Y641axHwT+wqy92EaSTfvfuJvjNcRfmDKD5N2Uk9s/cAOrOqsFP9sDzwP+AJxm+96641axP0lZXPR1yvacK1NOtN5i+9CaY18w3Bv3SMdrasMSwLqUvah3AWbYfmMTsUczKCdZn2m7/La2yws2EHtpYANm78ma8iKsTdWD/V01J30LyuyS5YBP1xm3Mq1Xn1yAe4YOgUnagPLJrVaSDqZMTf05ZdreE9XxBer+5OKyYvOktvn/v5Z0ru0m/t7b2d6k7frNwAckTaFMF63TSL3U2nuv1WSCBYCHKR2as4FvtX967KVBSfB3StrA9kWu6lNI2ogGXvCUHszBDcSZjaS3MWuPbtumenSU6YG98mHge5K+CEwHXkgZovpwA7E3pCSW3SjL52Hmp5i6P7ksSJl/vzPlE9uJ1VcTFpCk9pPLVXuaKGj4S0k/pHx6uJsya21/4JcNxL4GeCVlptLawL+BxylDoz03KEM0ywLfo4wN3sfMF/xHbN9fc+x9bX+vzhijxJ4B/BV4pDpkGprZIOmjwNG2HxpyfBngvba/W2f8KtYCwERgehPzsXutOufyd0odnnuY9QRvrdMWqxP6bwe+S3ltrQR8BDjF9tF1xq7ib8PM8f+7KEORtY//D2nDCyjDMx+klKx4foeH1G4gEnxLL17wKoWvzqDMPW8vfLUp8BbXWPiqlyRNpiw6WZGZJ/2WpcxT/r7ty2uMfShwrGcvsrY2pcjaZ2qMPdLfexPqL3Q24rS8hpLsWsA7mJlkT7N9dQNxF7b91JzeNkaxt2HmSdYXArdRZtT8ebjZc00biATf4xf8aKVrf2778RpjfwQ4ppe96CreRADb0xuKN7RMsiiVBi+l2TLJL6D0opv6e79wpCG40W7rg9g/ofyOz2NmaYhJlCGxZWz/Z42xDwIuoyT0Rv6/58SgJPieveB7qZe96GiepP+lfEK9hFkT3ZuAe2zv34+xq/gvo5zUfnl16EbgLNt/qznuzsDpQxe2SWpVsjy9zvidDESCj+Z70dEbKkW3NmdmovsbZf7/IyM/av6P3SuS3g68h9JhbE0/nkTpSP7M9ik9ahqQBB81k7SC7XuqoYu3UBaXNTWTJ6IR1USOl1VXb3JDu0l1kgTfEEmLUXo3y1DNia97Nel4UM3D3lzSNylTFrexvXFDsdexPa369LIn8Cvb1zcU+7uUMfdLmogXMZyB23Rb0iKSXixpFUmrNBj6bMo82QUoCb7Rcq6SlurRz71w9X0524fQ7M/9jer7FymrKxsrPEWp3rmjpN9L+oqkVzUYeyBJWlnSDySdXX1/ca/b1GuDstAJAEmfptQGeQVlvvCTlBWeTXiwSnCNk3Q48BLK1DVopjZ5y4VVXZqvqWyGUeuG10MsJmkRYBHbx0tqbEqq7SuBK6sk8w3KfgBXAUfYrmXxkaSfMsLqTdu1/r17GbvNUZRNZqZSpi0eTc2Ly9pVkzlWsn2ZpMXqnDHVrYFK8JTtw9aXdL7tTSSd0GDsCZJOA66meiHYPqCh2KvWXXNnJJ59i7xGdpSqHEdZ9HNg9ebS2OpaSf9N2V3oXuDHlFWtAn5HfatLD6q+fwaYAlwOvBZ4c03xxkvslkXbylNcVL25N0KloOHalL0HJlN2UNu2qfgjGbQE39rs4fGqVMFaDcb+aoOxhrqrWlna/uZSax2cFklbUJaNrwzMoHySaaQAlO3/k3Q5ZZroZUBTb6hQEvs7bLdq0Sxj+6Fq1kUtWkXkJK3ZtqDqRkkfqivmeIjd5leSfkOpRf8ayi5iTdnS9qYqBe1mqBSc67lBS/Afrd7VP06ZF//xugNKeq3tv9BMYbOR3Ew5ubtBdb32QmdtvkIZBjsD2ImyX2cjJH0f+BeldPBp1YKYrRoK/8Gq+FfL4cA7G5pdMUVlG7urgFcDTa6o7Fls21+tin9NAr5p+59NxQaekrQSYJVa9LXuHNatQUvwu1cLLq4DPiLpc5T9Suv0akqVuaE7rDeWZG1/QdIKlHKut9i+u4m4lcdsPyLJlMqK6zUYe81qBs951fXa32QlvZVSsXQtSa1ZUhOApeqO3WL7gOrvvQqlsmFjia6XsSV92fb/AP9U8SXbn2so/Ico51qWBb4N7NtQ3FENRIJX2cprNWALlX0qobzYWyV0a9NWA+RftluzOlDZ2q0Rkvan1L65EnityiYkX2so/NHV+Pd3Kb25MxuKC/AvlZ2cWtupPdxAzD8AV1BqkvyIMu7+NM1ULgVAZT/a91FNyZXU2InOXsam7MkKlGp6kt7UUFxs/52Z1UPHjYFI8JQZJBswc5ii9aIbegJwzFV1X14AvEPSqVXsBSlV55ra+X1H2899gpB0EdBIgrd9VHXxtOqrSXtTat8/DuwONPGm+ibbv5F0M7PP0GpqI+qjKSUqelGCo5exn6jO+VxMSfa1FRlrkXQhI88eauRc02gGIsHbnkIZG/wKpTb60jQ3H3tjSn3uScDnmVkHp8ntvJ6qejN/oWwI/HTdAdv+8Z/bJpDmShW31nc8QNlVqEmtKqW93PDhWuAy27X/ncdZ7L0os3g+BlxP+SRRq1bHSdLa7ZUzJa1Rd+xuDNRK1mo++CrM3J/UDX50fbHt26siROsBV7VmWDQRG/gUZZjqJuBrtm9vInYvVGPuz72htA7TzHaB6wLP2p7WdmwdYAHbV9QZuy3eVZTCX61NJ2p/Ux0nsVtlMRajlMU4v6myGBqy97Kkk2zv0kTs0Qxagq99D9ZRYreW7H+RUjd6ZdvbNxT740PH/23XOjwk6VhG/uha6+bTQ9qxEDDR9l0d7zw28c6lDIk91nZscUqZhF5tYTgQ1IOyGJL2oiwafBVl5lCrU3G77ffUGbsbAzFE06Zn88GZWRZiku09qnHwWvV4/L+p2QsjUtkAY3fghZJeB/yigV7Vgu3JHcD2Y9Unt0aobAC9K2X+f6vuUSPbRvYyNrOWxfiYpNoXGtn+KfBTSZ9sn7hQvfZ6btBq0bTPB9+Q2acu1uk2Sb8DzpY0AXi2gZgbUxLtJMr4/+eBT9DA+L/tW6vFL7dRquxtVLWnkUJjlfdXn9gesP0s5c2ubo+q1CZ/jqTVKfPxm3Jy9f2tzNw+bxBit8piHK/my2JsM+R6UxMoRjVQQzS9JmmC7Weq3tySTdXJ7vH4/0mUE15vB04FXmJ7j4ZinwfsQFlktT1wpu3Na445CfgJJbndTUlwy1PebG4d5aFj2YbzqlWVU2xvLOks29v1e+xeaFv3sCUz19RMAJ5ve2jSb9xADdFI2h34ALAmZYejB21Pbij2eZRVbu2HmxqTPYpSqvhgqvF/SsJrwkTbu0ja0PbnJJ3RUFwoJ5ZPpvy9T6ZMmayV7Vso6y1WoexNerft2+qOO8S9VQ/2rypFwJbs59iSvmP7o0OmLDYyY4tZ1z20eu2NrnsYzUD14CX9mTI/9lzKHOWf2N6zB+14NfA22wc1FK/Vqzq2Nf5ve4POjxyT2L+nvJkcCdxAKfi2bhOxB101XXQd4Ho3XNmwl7F7ofp5N2XWcw893+9hoHrwlGXzz0p6mvKHWKdH7bieUmnuoIbi3VqN/x/d4Ph/yzbVsNQ+lEqSRzQVuG26ZMvTlPMw37B90/CPmn9VM7RG6rHVWmitl7Hb2nCE7b2rywJ+bPv9TcQGTmDIUCSQBN+wQ6uPjwcD3we+01TgYRb+NHYSxvaeQ8b/d2wibjX/eweV2iR3A2e42Q3OL6W82KZRqgu+i/JCPJqay9iqVFW8ru36hrYvrDMm8PsRjjfxMb2XsVte+lzQUqpgtQZj93IocmS2+/6LMlto2K8etUfAhg3G2wz4I6VO90XAFg3EfBelXOsmwOqUj6+nA+9q8Oe+eMjv/OLq8vkNxD4deEV1eSfgxAZ/7mOHXP/OgMQ+CXg/pQz43sDJDcb+PbAI8DPKJ5bLm4o92teg9ODPZeSeRCMnOqsFN1tQes8voizprrtH1/IlYGvbj0pairJ94Eg9rrGyD7CdZ87WuVHSn4CzgF/UHLvlmCrmbZQTy0dLWhD4ZQOxdweOlfQX4MU0UIiq+sS0LjBZUmuF9gTKxht9G7vN+yj/dx+mDJc0tqCOHg5FjmYgErztTaFsPmH7ucQmqfYTjZLeSZmqtxglsa7umqfqDdcMZtZImUEzdXie9ZCpmLafkNTY+L/twyT9mDL//X6XufBQyrnWYshY9K2UhPNTyvmWuseiTamB8wAzz7M8QdlwvG69jF0aYD9erSRulYSYTHP16FdU2dVpCcrPvCdlumxPDdosmqH1Ik61/baaY95I6an/wPblvZgXrFJh70BmngM42Pbvao55C7OfZBKlJv+qdcZua8NWlGmxzxWXc/21aFoLudrPt1DFnlJn7Cq+gGPc0FqD8RK7in8mpYrlc3sPu7kVvL+nfHL4ge3NWmUTmog9moHowbfXi5B0ATPrRVxad2zbq7emRUo6gLJn46aU8eBGdn2pPrXUPSQz1EiV/Jpsx6HAW9xQHRooSbxKdL9xDxa62LakOyW9ntKTnVEdnzH6I+fv2BXZ/q+GYg21oO3r29a5jIsqAYPWg9/B9q+qywI2cP0zG4a2YTXKyrftWkNHNcZag7I14cOUjZ4PoSw8+brts+qMPR6oVA/9jO37exD7e5QT2pczM9Hd3FDs84Ycct2fXMZJ7JOB25m11lQjNfglfZ6yankTylDsP21/pYnYoxm0BD/biU7btW/60SvV1Mz9KfV3jqDM+38M+L3txna76ZXqBOuylMqC0Gzp2p8OOWQ3t7PRQFIpLjcLz9xRrYn4rwJeAdxg+6qm4o5mUIZoxsOJzl542vYlAJJusj29utxIHZpes71++/WqfEBTsfeqZuy8ELi37QRv7VS2pfwi5aTngsBB7ZML+jW27aMlLUL5nTdSvVOlSukNLhVE/wm8G1hX0n1NDg2OZCB68OPhRGcvtJ3oFLBH2+XGTnT2mqQ1KZUN1wcecUM1uiW9m7Lx8j8oC3C+b/u4hmJfzJBpsUPf7Po09qeBrSi96L8DT9oeum3iWMe8mDLUO0PSOZRPytOBT9neus7Y3RiIHvx4ONHZI+0fWX8/wuW+U53keyszN2HY0G170jbkw1XcZ6qhwQuARhI8vZkWOx5i72R7fUnn295E0gkNxHyySu4vAFawfQKApJ7vhwADkuABqjGxq2CWE50HUFZY9qUmpuWNU78Hjgf+w/a9knpxQnkG5TzP7dX3pmaSQNkD4LeSWlM1Pz8gsVudtcclbURZ0Vq3x6pZeutTVtJS1XtarIHYHQ3EEE0MlmocdmtKiYBlgDWAHWz/o4HYH6JsqLI2ZSx6WeBB4EC37dNaU+yFgXdQVu3eSKn908gbSy9jt7XhNZQVrC+lzB77te2za465JGXF7OOUMg3PVOd61rHd83o0SfDR16oTnZtQPrG9ru7xYEn7UhLdV5ueiirpNOBi4ErKDlpL296332NX8QUc7Qb3+50fJMFHjLHq5OK3KVsV3tI6XnfyGWaldmOrKXsZuy3mVynVQ9vXHjT6KWK8GZgx+IgmqJSj/iilwNjnaUvwDXippPal+au1rtuuuw5OL2O3vJ6yJSXMXK3e1K5p41J68BFjSNJFwPdasykajj3ihuZ1n3Dvcewv2/6f6vI7bZ9YZ7z5SRJ89C1J37a9n8pevB8HzrH9qZpjLtLnU2/HnfbhoaFDRYMuQzTRz15Vfd/a9mur0gW1SnLvicUkrUop8LWYpPadnRqp/zNepQcffasqfHUepdLfgZIubGrBkyQ5L65GDFP3p2Xg6/8kwUffkrQspcDaBZS6KG9oqnqopF9RNr74C2X64CW2H20idkRLEnz0LUkrA58FVqPUJjnE9u0Nxp8A7Ap8AljD9rhY3RiDIwk++la1y84BlHnR61F2smqqNvmplKXzV1F68H+2/e8mYke05CRr9LNFbV9cXb6oKmHQlLMobyqvoJQrWBro+dL1fiZpReDtlPIUrS0aG9myb7xKgo9+doak31B60a+h2QR7AnAb8CZKCdv1Go4/iE4HvklVVDAyRBN9TtLywCTKitI1bF/QUNzjgUuAPwLTbD/TRNxBJulk2+/odTvGkyT4GBiSzrG9VUOxJlCKjk0EfkApdHZZE7EHlaQLgMWBa6tDHvTiYxmiiajHscAUYDfb35N0CGU/4KjPHr1uwHiTBB99R9IXKYWmZjlMmS7ZlIm2D6v2A45m3EYpLrYiM3eSOqZ3zem9JPjoRyNtSdjkVoX3StoVeJ6ktwJ3Nxh7UJ1I2fDj7ZSywS9hwBN8xuAjalCVDX4/sCYl6fzY9hO9bVV/a9uLtfX9DNs79rpdvZQefMQYai90RZkL39rVaUVgoAtfNeCZaq3DnZIOAFbqdYN6LT34iDE0pPBV68X1euAVttOhqpGkCdWeqItT9uS91PadvW5XL+UfLmIM2d4LQNIClH1g/xP4A7BzD5vV1yS9DrjB9mOSXgjsRykdfElPGzYOLNDrBkT0E0lLSfoYcC6wCrCL7Y/YvqnHTetn3wdadX6Oo2z8fQ4wUhnhgZEhmogxJOkRynS9Myjlgp97gTW4N+lAkXSe7U0lvQA4z/arq+MDv7tThmgixtYOvW7AAHpM0l7A+sBJ8NxK4oEvz5wefETM1yQtCbwXeBw4tjrRugqwju2BLvCWBB8R0adykjUiok8lwUdE9Kkk+JjvSVpB0i8k/V3S5ZLOkrS6pBUlnVzdZx1J283h8+4paYakV7cdu1rSpDH+ESJqkQQf8zVJAk4Dzre9mu11gc8Ay9u+q20DiHWAOUrwlTuA/xmTxkY0LAk+5nebAk/bPqx1wPaVti+UNKnqcS8MHAzsKmmapF0l/U3SRCirTiXd1Lo+xJnAKyWtMfQGST+UNFXSNZK+0Hb8FkmHVLGmSnqdpLOrTxgfaLvf/pIuk3RV++MjxkoSfMzv1gYuH+0Otp8CDgBOsL2O7RMoKx7fU91lC+BK29OHefgM4GvAZ4e57X9sTwZeDWzcPpQD3GZ7HeBC4CjK7k5vBL4AIGkr4OWUOjXrAOtK2qjTDxsxJ5LgY1AdSZk7DfAfjL6s/efAGyWtOuT4OyVdAfwFeCWwVtttrfnXf6UUvXq0egN5UtIylI24t6oeewXwCkrCjxgzWcka87trKL3jOWL7dkn/lLQZpRf9nlHu+4ykbwCfah2rkv0ngPVsPyjpKGDRtoc9WX2f0Xa5dX0CZcehQ2z/aE7bHtGt9OBjfvcHYBFJ+7QOSHq1pA2H3O9RYMkhx35CGao5yfazHeIcRRnKaY3TLwU8BjwsaXlg2zls99nAf0haomrzSlUlxIgxkwQf8zWXpdhvBbaoTmJeAxwC3DPkrucBa7VOslbHzgCWoIuqg9U4/neBF1bXr6QMr1xPGcL54xy2+5zqcX+S9FfgZGZ/A4qYJylVEANL0mTgW7aH9vYj+kLG4GMgSfo08EFGGXuPmN+lBx8R0acyBh8R0aeS4CMi+lQSfEREn0qCj4joU0nwERF96v8D4MHwpEHbFHcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cities = [city for city , df in new_df.groupby(['City'])]\n",
    "\n",
    "plt.bar(cities,city_df['Sales'],color='Blue')\n",
    "plt.xticks(cities,rotation='vertical',size=8)\n",
    "plt.xlabel('City Name')\n",
    "plt.ylabel('Sales in USD ($)*10^6')\n",
    "plt.title('City wise Sales Analysis')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dcea33f",
   "metadata": {},
   "source": [
    "#### Question 3: What time should we display advertisements to maximize likelihood of customer's buying product?\n",
    "###### Conclusion: By drawing the output graph, the best time to display the advertisement is after 11 as the first peak is around 12 and after 6 in the                                 evening as the second peaks at around 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8dd69163",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      3910\n",
       "1      2350\n",
       "2      1243\n",
       "3       831\n",
       "4       854\n",
       "5      1321\n",
       "6      2482\n",
       "7      4011\n",
       "8      6256\n",
       "9      8748\n",
       "10    10944\n",
       "11    12411\n",
       "12    12587\n",
       "13    12129\n",
       "14    10984\n",
       "15    10175\n",
       "16    10384\n",
       "17    10899\n",
       "18    12280\n",
       "19    12905\n",
       "20    12228\n",
       "21    10921\n",
       "22     8822\n",
       "23     6275\n",
       "Name: Hour, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_freq = new_df['Hour'].value_counts().sort_index()\n",
    "time_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "043c33c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Time vs Frequency of Orders')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "time_freq.plot()\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Frequency of Orders')\n",
    "plt.title('Time vs Frequency of Orders')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89ab48ca",
   "metadata": {},
   "source": [
    "#### Question 4: What product sold the most? What might be the reason?\n",
    "###### Conclusion: The prices of the products such as Batteries , Cables and headphones were comparatively less than others "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2899a56f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices = new_df.groupby('Product').mean()['Price Each']\n",
    "prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0db9b89f",
   "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": [
    "product_group = new_df.groupby('Product')\n",
    "quantity_ordered = product_group.sum()['Quantity Ordered']\n",
    "\n",
    "keys = [pair for pair, df in product_group]\n",
    "plt.bar(keys, quantity_ordered, color='Blue')\n",
    "plt.xticks(keys, rotation='vertical', size=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "62839c25",
   "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": [
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "# Plot the bar graph\n",
    "ax1.bar(keys, quantity_ordered, color='Blue')\n",
    "ax1.set_ylabel('Frequency', color='Blue')\n",
    "plt.xticks(keys,rotation='vertical' , size=10)\n",
    "\n",
    "# Create a second y-axis for the line graph\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "# Plot the line graph\n",
    "ax2.plot(keys, prices, color='Red')\n",
    "ax2.set_ylabel('Prices', color='Red')\n",
    "\n",
    "# Set labels and title\n",
    "ax1.set_xlabel('Products',color='Blue')\n",
    "ax1.set_title('Charts showing Prices and Frequency of products')\n",
    "\n",
    "\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}