analyze-tech-gadgets-2019.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python Data Analysis of Tech Gadgets Sales with Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging Data Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 12 files in the directory: ./data.\n",
      "Here is the list of the files read: \n",
      "['gadget-sales-2019-01.csv', 'gadget-sales-2019-02.csv', 'gadget-sales-2019-03.csv', 'gadget-sales-2019-04.csv', 'gadget-sales-2019-05.csv', 'gadget-sales-2019-06.csv', 'gadget-sales-2019-07.csv', 'gadget-sales-2019-08.csv', 'gadget-sales-2019-09.csv', 'gadget-sales-2019-10.csv', 'gadget-sales-2019-11.csv', 'gadget-sales-2019-12.csv']\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</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>0</td>\n",
       "      <td>141234</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700</td>\n",
       "      <td>01/22/19 21:25</td>\n",
       "      <td>944 Walnut St, Boston, MA 02215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>141235</td>\n",
       "      <td>Lightning Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>01/28/19 14:15</td>\n",
       "      <td>185 Maple St, Portland, OR 97035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>141236</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>2</td>\n",
       "      <td>11.99</td>\n",
       "      <td>01/17/19 13:33</td>\n",
       "      <td>538 Adams St, San Francisco, CA 94016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>141237</td>\n",
       "      <td>27in FHD Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>149.99</td>\n",
       "      <td>01/05/19 20:33</td>\n",
       "      <td>738 10th St, Los Angeles, CA 90001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>141238</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>01/25/19 11:59</td>\n",
       "      <td>387 10th St, Austin, TX 73301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186845</th>\n",
       "      <td>186845</td>\n",
       "      <td>319666</td>\n",
       "      <td>Lightning Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>12/11/19 20:58</td>\n",
       "      <td>14 Madison St, San Francisco, CA 94016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186846</th>\n",
       "      <td>186846</td>\n",
       "      <td>319667</td>\n",
       "      <td>AA Batteries (4-pack)</td>\n",
       "      <td>2</td>\n",
       "      <td>3.84</td>\n",
       "      <td>12/01/19 12:01</td>\n",
       "      <td>549 Willow St, Los Angeles, CA 90001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186847</th>\n",
       "      <td>186847</td>\n",
       "      <td>319668</td>\n",
       "      <td>Vareebadd Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>400</td>\n",
       "      <td>12/09/19 06:43</td>\n",
       "      <td>273 Wilson St, Seattle, WA 98101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186848</th>\n",
       "      <td>186848</td>\n",
       "      <td>319669</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>12/03/19 10:39</td>\n",
       "      <td>778 River St, Dallas, TX 75001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186849</th>\n",
       "      <td>186849</td>\n",
       "      <td>319670</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>12/21/19 21:45</td>\n",
       "      <td>747 Chestnut St, Los Angeles, CA 90001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>186850 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         index Order ID                     Product Quantity Ordered  \\\n",
       "0            0   141234                      iPhone                1   \n",
       "1            1   141235    Lightning Charging Cable                1   \n",
       "2            2   141236            Wired Headphones                2   \n",
       "3            3   141237            27in FHD Monitor                1   \n",
       "4            4   141238            Wired Headphones                1   \n",
       "...        ...      ...                         ...              ...   \n",
       "186845  186845   319666    Lightning Charging Cable                1   \n",
       "186846  186846   319667       AA Batteries (4-pack)                2   \n",
       "186847  186847   319668             Vareebadd Phone                1   \n",
       "186848  186848   319669            Wired Headphones                1   \n",
       "186849  186849   319670  Bose SoundSport Headphones                1   \n",
       "\n",
       "       Price Each      Order Date                        Purchase Address  \n",
       "0             700  01/22/19 21:25         944 Walnut St, Boston, MA 02215  \n",
       "1           14.95  01/28/19 14:15        185 Maple St, Portland, OR 97035  \n",
       "2           11.99  01/17/19 13:33   538 Adams St, San Francisco, CA 94016  \n",
       "3          149.99  01/05/19 20:33      738 10th St, Los Angeles, CA 90001  \n",
       "4           11.99  01/25/19 11:59           387 10th St, Austin, TX 73301  \n",
       "...           ...             ...                                     ...  \n",
       "186845      14.95  12/11/19 20:58  14 Madison St, San Francisco, CA 94016  \n",
       "186846       3.84  12/01/19 12:01    549 Willow St, Los Angeles, CA 90001  \n",
       "186847        400  12/09/19 06:43        273 Wilson St, Seattle, WA 98101  \n",
       "186848      11.99  12/03/19 10:39          778 River St, Dallas, TX 75001  \n",
       "186849      99.99  12/21/19 21:45  747 Chestnut St, Los Angeles, CA 90001  \n",
       "\n",
       "[186850 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Get the list of all files in the directory\n",
    "file_path_data = \"./data\"\n",
    "files = [file for file in os.listdir(file_path_data) if not file.startswith('.')] \n",
    "print(f\"There are {len(files)} files in the directory: {file_path_data}.\")\n",
    "print(f\"Here is the list of the files read: \\n{files}\")\n",
    "\n",
    "df_all_months = pd.DataFrame()\n",
    "\n",
    "## Combine data files\n",
    "for file in files:\n",
    "\t## Current spreadsheet\n",
    "\tcurrent_file = f\"{file_path_data}/{file}\"\n",
    "\ttry: \n",
    "\t\tdf_month = pd.read_csv(current_file)\n",
    "\texcept: \n",
    "\t\tprint(f\"An error occurred while reading the file: {current_file}\")\n",
    "\t\tsys.exit(1)\n",
    "\t## Concatenate Pandas DataFrame along the index axis \n",
    "\tdf_all_months = pd.concat([df_all_months, df_month])\n",
    "\n",
    "## Output the combined data frames to 1 file\n",
    "filename_output = \"all-gadget-sales-2019.csv\"\n",
    "file_path_output = f\"./output/{filename_output}\"\n",
    "df_all_months.to_csv(file_path_output, index=False)\n",
    "# print(df_all_months.head())\n",
    "\n",
    "## Read in the updated DataFrame in\n",
    "df_all_sales = pd.read_csv(file_path_output)\n",
    "df_all_sales.reset_index()\n",
    "# print(df_all_sales.head())\n",
    "\n",
    "## Check the new DataFrame\n",
    "# print(df_all_months.equals(df_all_sales))\n",
    "# print(df_all_months.index.values)\n",
    "# print(df_all_sales.index.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best way to find out what to clean is to perform operations and get errors. Based on the errors, we decide how to go about cleaning the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Order ID Product Quantity Ordered Price Each Order Date Purchase Address\n",
      "664       NaN     NaN              NaN        NaN        NaN              NaN\n",
      "678       NaN     NaN              NaN        NaN        NaN              NaN\n",
      "797       NaN     NaN              NaN        NaN        NaN              NaN\n",
      "876       NaN     NaN              NaN        NaN        NaN              NaN\n",
      "1299      NaN     NaN              NaN        NaN        NaN              NaN\n",
      "  Order ID                   Product Quantity Ordered Price Each  \\\n",
      "0   141234                    iPhone                1        700   \n",
      "1   141235  Lightning Charging Cable                1      14.95   \n",
      "2   141236          Wired Headphones                2      11.99   \n",
      "3   141237          27in FHD Monitor                1     149.99   \n",
      "4   141238          Wired Headphones                1      11.99   \n",
      "\n",
      "       Order Date                       Purchase Address  \n",
      "0  01/22/19 21:25        944 Walnut St, Boston, MA 02215  \n",
      "1  01/28/19 14:15       185 Maple St, Portland, OR 97035  \n",
      "2  01/17/19 13:33  538 Adams St, San Francisco, CA 94016  \n",
      "3  01/05/19 20:33     738 10th St, Los Angeles, CA 90001  \n",
      "4  01/25/19 11:59          387 10th St, Austin, TX 73301  \n"
     ]
    }
   ],
   "source": [
    "## Find rows with all NA values\n",
    "## isna() - returns a boolean DataFrame that indicates whether each element is NA\n",
    "## any(axis=1) - reduces columns of the boolean DataFrame to only a boolean Series\n",
    "df_nan = df_all_sales[df_all_sales.isna().any(axis=1)]\n",
    "print(df_nan.head())\n",
    "\n",
    "## Drop rows with all NA values\n",
    "df_all_sales.dropna(how='all', inplace=True)\n",
    "print(df_all_sales.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we created the `Month` column, we took the first 2 letters in the `Order Date` column and converted the `Month` column to the integer type, which resulted in `ValueError`. So, we came up here to perform some data cleaning operations to get rid of the text \"Or\" in `Order Date` column before we attempt the data conversion again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Order ID                   Product Quantity Ordered Price Each  \\\n",
      "0   141234                    iPhone                1        700   \n",
      "1   141235  Lightning Charging Cable                1      14.95   \n",
      "2   141236          Wired Headphones                2      11.99   \n",
      "3   141237          27in FHD Monitor                1     149.99   \n",
      "4   141238          Wired Headphones                1      11.99   \n",
      "\n",
      "       Order Date                       Purchase Address  \n",
      "0  01/22/19 21:25        944 Walnut St, Boston, MA 02215  \n",
      "1  01/28/19 14:15       185 Maple St, Portland, OR 97035  \n",
      "2  01/17/19 13:33  538 Adams St, San Francisco, CA 94016  \n",
      "3  01/05/19 20:33     738 10th St, Los Angeles, CA 90001  \n",
      "4  01/25/19 11:59          387 10th St, Austin, TX 73301  \n"
     ]
    }
   ],
   "source": [
    "## Select rows by negating a condition\n",
    "df_all_sales = df_all_sales[df_all_sales['Order Date'].str[0:2] != 'Or']\n",
    "print(df_all_sales.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modifying DataFrame Column Data Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Order ID            object\n",
      "Product             object\n",
      "Quantity Ordered    object\n",
      "Price Each          object\n",
      "Order Date          object\n",
      "Purchase Address    object\n",
      "dtype: object\n",
      "Order ID             object\n",
      "Product              object\n",
      "Quantity Ordered      int64\n",
      "Price Each          float64\n",
      "Order Date           object\n",
      "Purchase Address     object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "## Show the data type of each column\n",
    "print(df_all_sales.dtypes)\n",
    "## Modify the data type of each column in DataFrame\n",
    "# df_all_sales['Quantity Ordered'] = pd.to_numeric(df_all_sales['Quantity Ordered'])\n",
    "# df_all_sales['Price Each'] = pd.to_numeric(df_all_sales['Price Each'])\n",
    "## errors='coerce' - invalid parsing will be set as NaN\n",
    "df_all_sales[['Quantity Ordered', 'Price Each']] = df_all_sales[['Quantity Ordered', 'Price Each']].apply(pd.to_numeric, errors='coerce')\n",
    "print(df_all_sales.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Augmenting Data with Additional Columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) `Month` column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Order ID                   Product  Quantity Ordered  Price Each  \\\n",
      "0   141234                    iPhone                 1      700.00   \n",
      "1   141235  Lightning Charging Cable                 1       14.95   \n",
      "2   141236          Wired Headphones                 2       11.99   \n",
      "3   141237          27in FHD Monitor                 1      149.99   \n",
      "4   141238          Wired Headphones                 1       11.99   \n",
      "\n",
      "       Order Date                       Purchase Address  Month  \n",
      "0  01/22/19 21:25        944 Walnut St, Boston, MA 02215      1  \n",
      "1  01/28/19 14:15       185 Maple St, Portland, OR 97035      1  \n",
      "2  01/17/19 13:33  538 Adams St, San Francisco, CA 94016      1  \n",
      "3  01/05/19 20:33     738 10th St, Los Angeles, CA 90001      1  \n",
      "4  01/25/19 11:59          387 10th St, Austin, TX 73301      1  \n"
     ]
    }
   ],
   "source": [
    "## Add \"Month\" column\n",
    "## Method 1\n",
    "# df_all_sales['Month'] = df_all_sales['Order Date'].str[0:2]\n",
    "# df_all_sales['Month'] = df_all_sales['Month'].astype('int32')\n",
    "# print(df_all_sales.head())\n",
    "## Method 2\n",
    "df_all_sales['Month'] = pd.to_datetime(df_all_sales['Order Date'], format=r\"%m/%d/%y %H:%M\").dt.month\n",
    "print(df_all_sales.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2) `City` column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Order ID                   Product  Quantity Ordered  Price Each  \\\n",
      "0   141234                    iPhone                 1      700.00   \n",
      "1   141235  Lightning Charging Cable                 1       14.95   \n",
      "2   141236          Wired Headphones                 2       11.99   \n",
      "3   141237          27in FHD Monitor                 1      149.99   \n",
      "4   141238          Wired Headphones                 1       11.99   \n",
      "\n",
      "       Order Date                       Purchase Address  Month  \\\n",
      "0  01/22/19 21:25        944 Walnut St, Boston, MA 02215      1   \n",
      "1  01/28/19 14:15       185 Maple St, Portland, OR 97035      1   \n",
      "2  01/17/19 13:33  538 Adams St, San Francisco, CA 94016      1   \n",
      "3  01/05/19 20:33     738 10th St, Los Angeles, CA 90001      1   \n",
      "4  01/25/19 11:59          387 10th St, Austin, TX 73301      1   \n",
      "\n",
      "                 City  \n",
      "0         Boston (MA)  \n",
      "1       Portland (OR)  \n",
      "2  San Francisco (CA)  \n",
      "3    Los Angeles (CA)  \n",
      "4         Austin (TX)  \n"
     ]
    }
   ],
   "source": [
    "## Add \"City\" column\n",
    "def get_city(address):\n",
    "\treturn address.split(\",\")[-2].strip()\n",
    "\n",
    "def get_state(address):\n",
    "\treturn address.split(\",\")[-1].strip().split(\" \")[0]\n",
    "\n",
    "df_all_sales['City'] = df_all_sales['Purchase Address'].apply(lambda a: f\"{get_city(a)} ({get_state(a)})\")\n",
    "print(df_all_sales.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 01: What was the best month for sales? How much was earned that month?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all_sales['Sales'] = df_all_sales['Quantity Ordered'] * df_all_sales['Price Each']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13449</td>\n",
       "      <td>2188884.72</td>\n",
       "      <td>2202022.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17005</td>\n",
       "      <td>2791207.83</td>\n",
       "      <td>2807100.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20558</td>\n",
       "      <td>3367671.02</td>\n",
       "      <td>3390670.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18667</td>\n",
       "      <td>3135125.13</td>\n",
       "      <td>3152606.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15253</td>\n",
       "      <td>2562025.61</td>\n",
       "      <td>2577802.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16072</td>\n",
       "      <td>2632539.56</td>\n",
       "      <td>2647775.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13448</td>\n",
       "      <td>2230345.42</td>\n",
       "      <td>2244467.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>13109</td>\n",
       "      <td>2084992.09</td>\n",
       "      <td>2097560.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>22703</td>\n",
       "      <td>3715554.83</td>\n",
       "      <td>3736726.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>19798</td>\n",
       "      <td>3180600.68</td>\n",
       "      <td>3199603.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>28114</td>\n",
       "      <td>4588415.41</td>\n",
       "      <td>4613443.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Quantity Ordered  Price Each       Sales\n",
       "Month                                          \n",
       "1                 10903  1811768.38  1822256.73\n",
       "2                 13449  2188884.72  2202022.42\n",
       "3                 17005  2791207.83  2807100.38\n",
       "4                 20558  3367671.02  3390670.24\n",
       "5                 18667  3135125.13  3152606.75\n",
       "6                 15253  2562025.61  2577802.26\n",
       "7                 16072  2632539.56  2647775.76\n",
       "8                 13448  2230345.42  2244467.88\n",
       "9                 13109  2084992.09  2097560.13\n",
       "10                22703  3715554.83  3736726.88\n",
       "11                19798  3180600.68  3199603.20\n",
       "12                28114  4588415.41  4613443.34"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all_sales.groupby(['Month']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Visualize the results\n",
    "months = range(1,13)\n",
    "# print(months)\n",
    "\n",
    "plt.bar(months, df_all_sales.groupby(['Month']).sum()['Sales'], color='#008080')\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Sales in USD ($)')\n",
    "plt.xticks(months)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "December, USD$4613443.34"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 2: What city sold the most products?**"
   ]
  },
  {
   "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>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>2779908.20</td>\n",
       "      <td>104794</td>\n",
       "      <td>2795498.58</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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Quantity Ordered  Price Each   Month       Sales\n",
       "City                                                                \n",
       "Atlanta (GA)                   16602  2779908.20  104794  2795498.58\n",
       "Austin (TX)                    11153  1809873.61   69829  1819581.75\n",
       "Boston (MA)                    22528  3637409.77  141112  3661642.01\n",
       "Dallas (TX)                    16730  2752627.82  104620  2767975.40\n",
       "Los Angeles (CA)               33289  5421435.23  208325  5452570.80\n",
       "New York City (NY)             27932  4635370.83  175741  4664317.43\n",
       "Portland (ME)                   2750   447189.25   17144   449758.27\n",
       "Portland (OR)                  11303  1860558.22   70621  1870732.34\n",
       "San Francisco (CA)             50239  8211461.74  315520  8262203.91\n",
       "Seattle (WA)                   16553  2733296.01  104941  2747755.48"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all_sales.groupby(['City']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Atlanta (GA)', 'Austin (TX)', 'Boston (MA)', 'Dallas (TX)', 'Los Angeles (CA)', 'New York City (NY)', 'Portland (ME)', 'Portland (OR)', 'San Francisco (CA)', 'Seattle (WA)']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Visualize the results\n",
    "# cities = [x for x in df_all_sales.groupby(['City'])]\n",
    "# cities = [key for key, value in df_all_sales.groupby(['City'])]\n",
    "# cities = [city for city, df in df_all_sales.groupby(['City'])]\n",
    "cities = [city_and_df[0] for city_and_df in df_all_sales.groupby(['City'])]\n",
    "print(cities)\n",
    "\n",
    "plt.bar(cities, df_all_sales.groupby(['City']).sum()['Sales'], color='#008080')\n",
    "plt.xlabel('US City')\n",
    "plt.ylabel('Sales in USD ($)')\n",
    "plt.xticks(cities, rotation='vertical', size=8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "San Francisco"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 3: What time should we display advertisements to maximize the sales?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Order ID                   Product  Quantity Ordered  Price Each  \\\n",
      "0   141234                    iPhone                 1      700.00   \n",
      "1   141235  Lightning Charging Cable                 1       14.95   \n",
      "2   141236          Wired Headphones                 2       11.99   \n",
      "3   141237          27in FHD Monitor                 1      149.99   \n",
      "4   141238          Wired Headphones                 1       11.99   \n",
      "\n",
      "       Order Date                       Purchase Address  Month  \\\n",
      "0  01/22/19 21:25        944 Walnut St, Boston, MA 02215      1   \n",
      "1  01/28/19 14:15       185 Maple St, Portland, OR 97035      1   \n",
      "2  01/17/19 13:33  538 Adams St, San Francisco, CA 94016      1   \n",
      "3  01/05/19 20:33     738 10th St, Los Angeles, CA 90001      1   \n",
      "4  01/25/19 11:59          387 10th St, Austin, TX 73301      1   \n",
      "\n",
      "                 City   Sales  Hour  Minute  Count  \n",
      "0         Boston (MA)  700.00    21      25      1  \n",
      "1       Portland (OR)   14.95    14      15      1  \n",
      "2  San Francisco (CA)   23.98    13      33      1  \n",
      "3    Los Angeles (CA)  149.99    20      33      1  \n",
      "4         Austin (TX)   11.99    11      59      1  \n"
     ]
    }
   ],
   "source": [
    "## Add \"Hour\" and \"Minute\" columns\n",
    "df_all_sales['Hour'] = pd.to_datetime(df_all_sales['Order Date']).dt.hour\n",
    "df_all_sales['Minute'] = pd.to_datetime(df_all_sales['Order Date']).dt.minute\n",
    "df_all_sales['Count'] = 1\n",
    "print(df_all_sales.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Visualize the results\n",
    "# hours = [x for x in df_all_sales.groupby(['Hour'])]\n",
    "# hours = [key for key, value in df_all_sales.groupby(['Hour'])]\n",
    "# hours = [hr for hr, df in df_all_sales.groupby(['Hour'])]\n",
    "hours = [hr_and_df[0] for hr_and_df in df_all_sales.groupby(['Hour'])]\n",
    "\n",
    "## df_all_sales.groupby(['Hour']).count() - a DataFrame\n",
    "## df_all_sales.groupby(['Hour']).count()['Count'] - a Series\n",
    "# print(df_all_sales.groupby(['Hour']).count())\n",
    "plt.plot(hours, df_all_sales.groupby(['Hour']).count()['Count'], color='#008080', marker='o', markersize=4)\n",
    "plt.xlabel('Hour')\n",
    "plt.ylabel('Number of Orders')\n",
    "plt.xticks(hours)\n",
    "plt.grid(visible=True, which=\"major\", axis=\"both\", color=\"#90E4C1\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Slightly before 11:00 or 19:00"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 4: What products are most often sold together?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Asus\\AppData\\Local\\Temp\\ipykernel_21408\\4118753921.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_same_id['Grouped'] = df_same_id.groupby('Order ID')['Product'].transform(lambda x: ', '.join(x))\n"
     ]
    }
   ],
   "source": [
    "## Select the rows with duplicated \"Order ID\"\n",
    "## duplicated() - returns boolean Series denoting duplicate rows\n",
    "## keep=False - mark all duplicates as True  \n",
    "# print(df_all_sales)\n",
    "# print(df_all_sales['Order ID'].duplicated(keep=False))\n",
    "# df_same_id = df_all_sales[df_all_sales['Order ID'].duplicated(keep=False)]\n",
    "df_same_id = df_all_sales[df_all_sales.duplicated(subset=['Order ID'], keep=False)]\n",
    "# print(df_same_id)\n",
    "## Join product names with the same order ID\n",
    "# print(df_same_id.groupby('Order ID'))\n",
    "# print(df_same_id.groupby('Order ID')['Product'])\n",
    "df_same_id['Grouped'] = df_same_id.groupby('Order ID')['Product'].transform(lambda x: ', '.join(x))\n",
    "# print(df_same_id)\n",
    "df_grouped = df_same_id[['Order ID', 'Grouped']].drop_duplicates()\n",
    "# print(df_grouped)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following products are often sold together for N times.\n",
      "- iPhone & Lightning Charging Cable, 1005 times\n",
      "- Google Phone & USB-C Charging Cable, 987 times\n",
      "- iPhone & Wired Headphones, 447 times\n",
      "- Google Phone & Wired Headphones, 414 times\n",
      "- Vareebadd Phone & USB-C Charging Cable, 361 times\n",
      "- iPhone & Apple Airpods Headphones, 360 times\n",
      "- Google Phone & Bose SoundSport Headphones, 220 times\n",
      "- USB-C Charging Cable & Wired Headphones, 160 times\n",
      "- Vareebadd Phone & Wired Headphones, 143 times\n",
      "- Lightning Charging Cable & Wired Headphones, 92 times\n"
     ]
    }
   ],
   "source": [
    "## Similar to high school math combinations (nCr)\n",
    "from itertools import combinations\n",
    "from collections import Counter\n",
    "\n",
    "count = Counter()\n",
    "\n",
    "for row in df_grouped['Grouped']:\n",
    "\t## Grouped products\n",
    "\t# print(row)\n",
    "\t## Split grouped products into a list of products\n",
    "\trow_list_products = row.split(', ')\n",
    "\t# print(row_list_products)\n",
    "\t## Add the counts of elements in the iterable to those in the counter object\n",
    "\t# print(Counter(combinations(row_list_products, 2)))\n",
    "\tcount.update(Counter(combinations(row_list_products, 2)))\n",
    "\n",
    "print(\"The following products are often sold together for N times.\")\n",
    "\n",
    "for prod, freq in count.most_common(10):\n",
    "\tprint(f\"- {prod[0]} & {prod[1]}, {freq} times\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following products are often sold together for N times.\n",
    "- iPhone & Lightning Charging Cable, 1005 times\n",
    "- Google Phone & USB-C Charging Cable, 987 times\n",
    "- iPhone & Wired Headphones, 447 times\n",
    "- Google Phone & Wired Headphones, 414 times\n",
    "- Vareebadd Phone & USB-C Charging Cable, 361 times\n",
    "- iPhone & Apple Airpods Headphones, 360 times\n",
    "- Google Phone & Bose SoundSport Headphones, 220 times\n",
    "- USB-C Charging Cable & Wired Headphones, 160 times\n",
    "- Vareebadd Phone & Wired Headphones, 143 times\n",
    "- Lightning Charging Cable & Wired Headphones, 92 times"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 5: What product was sold the most? Why do you think it is the most popular product?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Get the sum of all ordered quantities of each product\n",
    "# print(df_all_sales)\n",
    "product_group = df_all_sales.groupby('Product')\n",
    "## Just show the \"Quantity Ordered\" column\n",
    "# print(product_group.sum())\n",
    "quantity_ordered = product_group.sum()['Quantity Ordered']\n",
    "# print(quantity_ordered)\n",
    "\n",
    "## Visualize the results\n",
    "products = [product for product, df in product_group]\n",
    "# print(products)\n",
    "plt.bar(products, quantity_ordered, color='#008080')\n",
    "plt.xlabel(\"Product\")\n",
    "plt.ylabel(\"Total Quantity Ordered\")\n",
    "plt.xticks(products, rotation='vertical', size=8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Asus\\AppData\\Local\\Temp\\ipykernel_21408\\2432056224.py:32: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax1.set_xticklabels(products, rotation='vertical', size=8)\n",
      "C:\\Users\\Asus\\AppData\\Local\\Temp\\ipykernel_21408\\2432056224.py:33: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## List all the price for each product\n",
    "# print(df_all_sales)\n",
    "# print(df_all_sales.groupby('Product').mean())\n",
    "products_prices = df_all_sales.groupby('Product').mean()['Price Each']\n",
    "\n",
    "## Test how to use subplots()\n",
    "# fig, ax = plt.subplots()\n",
    "# print(fig)\n",
    "# print(ax)\n",
    "# fig, axs = plt.subplots(2, 2)\n",
    "# print(fig)\n",
    "# print(axs)\n",
    "# fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "# print(fig)\n",
    "# print((ax1, ax2))\n",
    "\n",
    "## Visualize the results\n",
    "## Create a figure and an Axes object\n",
    "fig, ax1 = plt.subplots()\n",
    "## Create a twin Axes sharing the x-axis\n",
    "## Create a new Axes with an invisible x-axis and an independent y-axis\n",
    "ax2 = ax1.twinx()\n",
    "## Y-axis 1 shares the same scale as the previous bar chart\n",
    "ax1.bar(products, quantity_ordered, color='#008080')\n",
    "## Same x-axis\n",
    "## Y-axis 2 uses \"Price Each\" column series from above\n",
    "ax2.plot(products, products_prices, color='#7F00FF')\n",
    "## Set the label of x- and y-axes\n",
    "ax1.set_xlabel('Product Name', color='#483D8B')\n",
    "ax1.set_ylabel('Quantity Ordered', color='royalblue')\n",
    "ax2.set_ylabel('Price ($)', color='crimson')\n",
    "ax1.set_xticklabels(products, rotation='vertical', size=8)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AAA Batteries (4-pack). Probably because it's one of the cheapest products on the list. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.13 ('base')",
   "language": "python",
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