Repo1 Sales Analysis.ipynb

{
 "cells": [
  {
   "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": [
    "#### Task 1: Merging 12 months of sales data into a single csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"D:\\Self\\Online Course\\Solve real world Data science task\\Pandas-Data-Science-Tasks-master\\Pandas-Data-Science-Tasks-master\\SalesAnalysis\\Sales_Data\\Sales_April_2019.csv\")\n",
    "    \n",
    "files = [file for file in os.listdir(\"D:\\Self\\Online Course\\Solve real world Data science task\\Pandas-Data-Science-Tasks-master\\Pandas-Data-Science-Tasks-master\\SalesAnalysis\\Sales_Data\")]\n",
    "\n",
    "all_months_data = pd.DataFrame() #Creating empty dataframe called 'all_month_data'\n",
    "\n",
    "for file in files:\n",
    "    df = pd.read_csv(\"D:\\Self\\Online Course\\Solve real world Data science task\\Pandas-Data-Science-Tasks-master\\Pandas-Data-Science-Tasks-master\\SalesAnalysis\\Sales_Data/\"+file)\n",
    "    all_months_data = pd.concat([all_months_data, df]) #Merging to the previous empty dataframe\n",
    "\n",
    "#Checking the result\n",
    "all_months_data.to_csv(\"all_data.csv\", index=False) #single csv file contain 12 months data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reading an updated dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
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       "\n",
       "    .dataframe thead th {\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",
       "    </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": [
    "#### Task 2: Add \"Month\" and \"Sales\" Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Removing Nan Values in our data\n",
    "all_data=all_data.dropna(how='all')\n",
    "\n",
    "#Removing rows based on condition, finding 'Or' and delete it\n",
    "all_data = all_data[all_data['Order Date'].str[0:2]!='Or']\n",
    "\n",
    "#Add \"Month\" Column\n",
    "all_data['Month'] = all_data['Order Date'].str[0:2] #Get the first 2 characters.\n",
    "all_data['Month'] = all_data['Month'].astype('int32') #turning the data from string to integer\n",
    "\n",
    "#Convert 'Quantity Ordered' and 'Price Each' to numeric\n",
    "all_data['Quantity Ordered'] = pd.to_numeric(all_data['Quantity Ordered']) #Becoming integer\n",
    "all_data['Price Each'] = pd.to_numeric(all_data['Price Each']) #Becoming float\n",
    "\n",
    "#Add \"Sales\" Column\n",
    "all_data['Sales'] = all_data['Quantity Ordered']*all_data['Price Each']\n",
    "all_data.head()"
   ]
  },
  {
   "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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#all_data.groupby('Month').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing our results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": [
    "months = range(1,13) #For x axes\n",
    "results = all_data.groupby('Month').sum()\n",
    "\n",
    "plt.bar(months, results['Sales'])\n",
    "plt.xticks(months)\n",
    "labels, location = plt.yticks()\n",
    "plt.yticks(labels, (labels/1000000).astype(int)) #Scaling in million USD\n",
    "plt.ylabel('Sales in million USD')\n",
    "plt.xlabel('Month Number')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2: What city sold the most product?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Task 3: Add a \"City\" Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Function\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",
    "#Extract the city and the state\n",
    "all_data['City'] = all_data['Purchase Address'].apply(lambda x: get_city(x) + ' ' + get_state(x))\n",
    "\n",
    "all_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Month</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",
       "    </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>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>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>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>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>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>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>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>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>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>2.747755e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Quantity Ordered    Price Each   Month         Sales\n",
       "City                                                                   \n",
       " Atlanta GA                   16602  2.779908e+06  104794  2.795499e+06\n",
       " Austin TX                    11153  1.809874e+06   69829  1.819582e+06\n",
       " Boston MA                    22528  3.637410e+06  141112  3.661642e+06\n",
       " Dallas TX                    16730  2.752628e+06  104620  2.767975e+06\n",
       " Los Angeles CA               33289  5.421435e+06  208325  5.452571e+06\n",
       " New York City NY             27932  4.635371e+06  175741  4.664317e+06\n",
       " Portland ME                   2750  4.471893e+05   17144  4.497583e+05\n",
       " Portland OR                  11303  1.860558e+06   70621  1.870732e+06\n",
       " San Francisco CA             50239  8.211462e+06  315520  8.262204e+06\n",
       " Seattle WA                   16553  2.733296e+06  104941  2.747755e+06"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results2 = all_data.groupby('City').sum()\n",
    "results2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "#We've already import the matplotlib\n",
    "\n",
    "#Fixing the cities order\n",
    "#cities = all_data['City'].unique()\n",
    "cities = [city for city, df in all_data.groupby('City')]\n",
    "\n",
    "plt.bar(cities, results2['Sales'])\n",
    "plt.xticks(cities, rotation='vertical', size = 8)\n",
    "labels, location = plt.yticks()\n",
    "plt.yticks(labels, (labels/1000000).astype(int)) #Scaling in million USD\n",
    "plt.ylabel('Sales in million 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": "markdown",
   "metadata": {},
   "source": [
    "#### Task 4: Aggregate the period in 24-hours distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Sales</th>\n",
       "      <th>City</th>\n",
       "      <th>Order_Date_DTO</th>\n",
       "      <th>Hour</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",
       "      <td>2019-04-19 08:46:00</td>\n",
       "      <td>8</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",
       "      <td>2019-04-07 22:30:00</td>\n",
       "      <td>22</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",
       "      <td>2019-04-12 14:38:00</td>\n",
       "      <td>14</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",
       "      <td>2019-04-12 14:38:00</td>\n",
       "      <td>14</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",
       "      <td>2019-04-30 09:27:00</td>\n",
       "      <td>9</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      Order_Date_DTO  Hour  \n",
       "0        Dallas TX 2019-04-19 08:46:00     8  \n",
       "2        Boston MA 2019-04-07 22:30:00    22  \n",
       "3   Los Angeles CA 2019-04-12 14:38:00    14  \n",
       "4   Los Angeles CA 2019-04-12 14:38:00    14  \n",
       "5   Los Angeles CA 2019-04-30 09:27:00     9  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create new column in date-time Object (DTO)\n",
    "all_data['Order_Date_DTO'] = pd.to_datetime(all_data['Order Date'])\n",
    "\n",
    "#Extraction the hours data\n",
    "all_data['Hour'] = all_data['Order_Date_DTO'].dt.hour\n",
    "\n",
    "all_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": [
    "#Plotting\n",
    "results3 = all_data.groupby(['Hour'])['Quantity Ordered'].count()\n",
    "hours = [hour for hour, df in all_data.groupby('Hour')]\n",
    "\n",
    "plt.plot(hours, results3)\n",
    "plt.xticks(hours)\n",
    "plt.xlabel('Hour')\n",
    "plt.ylabel('Number of Orders')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4: What products are most often sold together?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Task 5: Make a new column called \"Product Bundle\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-18-837801aa5e3d>:5: 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",
      "  new_all['Product_Bundle'] = new_all.groupby('Order ID')['Product'].transform(lambda x: ','.join(x))\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>Product_Bundle</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                                     Product_Bundle\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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Make a new dataframe to seperate the duplicated values of Order ID\n",
    "new_all = all_data[all_data['Order ID'].duplicated(keep=False)]\n",
    "\n",
    "#Joining few products with the same Order ID into the same line.\n",
    "new_all['Product_Bundle'] = new_all.groupby('Order ID')['Product'].transform(lambda x: ','.join(x))\n",
    "\n",
    "#Dropping the duplicate values\n",
    "new_all = new_all[['Order ID','Product_Bundle']].drop_duplicates()\n",
    "\n",
    "new_all.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Task 6: Counting the Product bundles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(('Google Phone', 'USB-C Charging Cable', 'Wired Headphones'), 87),\n",
       " (('iPhone', 'Lightning Charging Cable', 'Wired Headphones'), 62),\n",
       " (('iPhone', 'Lightning Charging Cable', 'Apple Airpods Headphones'), 47),\n",
       " (('Google Phone', 'USB-C Charging Cable', 'Bose SoundSport Headphones'), 35),\n",
       " (('Vareebadd Phone', 'USB-C Charging Cable', 'Wired Headphones'), 33),\n",
       " (('iPhone', 'Apple Airpods Headphones', 'Wired Headphones'), 27),\n",
       " (('Google Phone', 'Bose SoundSport Headphones', 'Wired Headphones'), 24),\n",
       " (('Vareebadd Phone', 'USB-C Charging Cable', 'Bose SoundSport Headphones'),\n",
       "  16),\n",
       " (('USB-C Charging Cable', 'Bose SoundSport Headphones', 'Wired Headphones'),\n",
       "  5),\n",
       " (('Vareebadd Phone', 'Bose SoundSport Headphones', 'Wired Headphones'), 5)]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Importing libraries\n",
    "from itertools import combinations\n",
    "from collections import Counter\n",
    "\n",
    "count = Counter()\n",
    "\n",
    "for row in new_all['Product_Bundle']:\n",
    "    row_list = row.split(',')\n",
    "    #count.update(Counter(combinations(row_list,2))) #Counting all the 2 products bundle\n",
    "    count.update(Counter(combinations(row_list,3))) #Counting all the 3 products bundle\n",
    "    \n",
    "count.most_common(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 5: What Product sold the most? Why do you think it did?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Task 7: Grouping by the product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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 = all_data.groupby('Product')\n",
    "\n",
    "#Visualizing\n",
    "quantity_ordered = product_group.sum()['Quantity Ordered']\n",
    "\n",
    "products = [product for product, df in product_group]\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": "markdown",
   "metadata": {},
   "source": [
    "#### Task 8: Overlaying a second y-axis on existing chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": [
    "prices = all_data.groupby('Product').mean()['Price Each'] \n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax1.bar(products, quantity_ordered, color='g')\n",
    "ax2.plot(products, prices, 'b-')\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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}