Week 5/Data Merge and Cleaning-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merge all files in one "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "files = [file for file in os.listdir('../Datasets/Week5/')]\n",
    "\n",
    "dft = pd.DataFrame()\n",
    "\n",
    "for file in files:\n",
    "    df = pd.read_csv('../Datasets/Week5/'+file)\n",
    "    dft = pd.concat([dft,df])\n",
    "\n",
    "    \n",
    "# Save all data in one file, na_rep='<value to replace>' index=<bool>\n",
    "\n",
    "dft.to_csv('merged_data.csv', index=False, na_rep='(missing)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
       "      <td>0</td>\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",
       "      <td>1</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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  (missing)                   (missing)        (missing)  (missing)   \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       (missing)                             (missing)  \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": [
    "df = pd.read_csv('merged_data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clean up the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
       "      <td>1</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>356</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>735</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1433</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1553</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>185176</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>185438</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>186042</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>186548</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>186826</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "      <td>(missing)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>545 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Order ID    Product Quantity Ordered Price Each Order Date  \\\n",
       "1       (missing)  (missing)        (missing)  (missing)  (missing)   \n",
       "356     (missing)  (missing)        (missing)  (missing)  (missing)   \n",
       "735     (missing)  (missing)        (missing)  (missing)  (missing)   \n",
       "1433    (missing)  (missing)        (missing)  (missing)  (missing)   \n",
       "1553    (missing)  (missing)        (missing)  (missing)  (missing)   \n",
       "...           ...        ...              ...        ...        ...   \n",
       "185176  (missing)  (missing)        (missing)  (missing)  (missing)   \n",
       "185438  (missing)  (missing)        (missing)  (missing)  (missing)   \n",
       "186042  (missing)  (missing)        (missing)  (missing)  (missing)   \n",
       "186548  (missing)  (missing)        (missing)  (missing)  (missing)   \n",
       "186826  (missing)  (missing)        (missing)  (missing)  (missing)   \n",
       "\n",
       "       Purchase Address  \n",
       "1             (missing)  \n",
       "356           (missing)  \n",
       "735           (missing)  \n",
       "1433          (missing)  \n",
       "1553          (missing)  \n",
       "...                 ...  \n",
       "185176        (missing)  \n",
       "185438        (missing)  \n",
       "186042        (missing)  \n",
       "186548        (missing)  \n",
       "186826        (missing)  \n",
       "\n",
       "[545 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Product'] == '(missing)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['Product'] != '(missing)'] # Remove (missing) data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['Product'] != 'Product'] # Remove trash data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save and Load again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('merged_data.csv', index=False, na_rep='(missing)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\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",
       "      <td>4</td>\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",
       "1    176559  Bose SoundSport Headphones                 1       99.99   \n",
       "2    176560                Google Phone                 1      600.00   \n",
       "3    176560            Wired Headphones                 1       11.99   \n",
       "4    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",
       "1  04/07/19 22:30     682 Chestnut St, Boston, MA 02215  \n",
       "2  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001  \n",
       "3  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001  \n",
       "4  04/30/19 09:27     333 8th St, Los Angeles, CA 90001  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('merged_data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reorder the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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",
       "1    176559  Bose SoundSport Headphones                 1       99.99   \n",
       "2    176560                Google Phone                 1      600.00   \n",
       "3    176560            Wired Headphones                 1       11.99   \n",
       "4    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",
       "1  04/07/19 22:30     682 Chestnut St, Boston, MA 02215      4  \n",
       "2  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001      4  \n",
       "3  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001      4  \n",
       "4  04/30/19 09:27     333 8th St, Los Angeles, CA 90001      4  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Month'] = df['Order Date'].str[:2]\n",
    "df['Month'] = df['Month'].astype('int32')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order ID              int64\n",
       "Product              object\n",
       "Quantity Ordered      int64\n",
       "Price Each          float64\n",
       "Order Date           object\n",
       "Purchase Address     object\n",
       "Month                 int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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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>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\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>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\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>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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>30</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    176559  Bose SoundSport Headphones                 1       99.99   \n",
       "2    176560                Google Phone                 1      600.00   \n",
       "3    176560            Wired Headphones                 1       11.99   \n",
       "4    176561            Wired Headphones                 1       11.99   \n",
       "\n",
       "       Order Date                      Purchase Address  Month  Day  \n",
       "0  04/19/19 08:46          917 1st St, Dallas, TX 75001      4   19  \n",
       "1  04/07/19 22:30     682 Chestnut St, Boston, MA 02215      4    7  \n",
       "2  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001      4   12  \n",
       "3  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001      4   12  \n",
       "4  04/30/19 09:27     333 8th St, Los Angeles, CA 90001      4   30  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Day'] = df['Order Date'].str[3:5]\n",
    "df['Day'] = df['Day'].astype('int32')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>176561</td>\n",
       "      <td>Wired Headphones</td>\n",
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       "      <td>04/30/19 09:27</td>\n",
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       "    </tr>\n",
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       "</div>"
      ],
      "text/plain": [
       "   Order ID                     Product  Quantity Ordered  Price Each  \\\n",
       "0    176558        USB-C Charging Cable                 2       11.95   \n",
       "1    176559  Bose SoundSport Headphones                 1       99.99   \n",
       "2    176560                Google Phone                 1      600.00   \n",
       "3    176560            Wired Headphones                 1       11.99   \n",
       "4    176561            Wired Headphones                 1       11.99   \n",
       "\n",
       "       Order Date                      Purchase Address  Month  Day  Year  \n",
       "0  04/19/19 08:46          917 1st St, Dallas, TX 75001      4   19    19  \n",
       "1  04/07/19 22:30     682 Chestnut St, Boston, MA 02215      4    7    19  \n",
       "2  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001      4   12    19  \n",
       "3  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001      4   12    19  \n",
       "4  04/30/19 09:27     333 8th St, Los Angeles, CA 90001      4   30    19  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Year'] = df['Order Date'].str[6:9]\n",
    "df['Year'] = df['Year'].astype('int32')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>11.99</td>\n",
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       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
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       "      <td>14:38</td>\n",
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       "      <td>4</td>\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>30</td>\n",
       "      <td>19</td>\n",
       "      <td>09:27</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",
       "1    176559  Bose SoundSport Headphones                 1       99.99   \n",
       "2    176560                Google Phone                 1      600.00   \n",
       "3    176560            Wired Headphones                 1       11.99   \n",
       "4    176561            Wired Headphones                 1       11.99   \n",
       "\n",
       "       Order Date                      Purchase Address  Month  Day  Year  \\\n",
       "0  04/19/19 08:46          917 1st St, Dallas, TX 75001      4   19    19   \n",
       "1  04/07/19 22:30     682 Chestnut St, Boston, MA 02215      4    7    19   \n",
       "2  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001      4   12    19   \n",
       "3  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001      4   12    19   \n",
       "4  04/30/19 09:27     333 8th St, Los Angeles, CA 90001      4   30    19   \n",
       "\n",
       "    Hour  \n",
       "0  08:46  \n",
       "1  22:30  \n",
       "2  14:38  \n",
       "3  14:38  \n",
       "4  09:27  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Hour'] = df['Order Date'].str[9:14]\n",
    "df['Hour'] = df['Hour'].astype('str')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Order Date'] = pd.to_datetime(df['Order Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order ID                     int64\n",
       "Product                     object\n",
       "Quantity Ordered             int64\n",
       "Price Each                 float64\n",
       "Order Date          datetime64[ns]\n",
       "Purchase Address            object\n",
       "Month                        int32\n",
       "Day                          int32\n",
       "Year                         int32\n",
       "Hour                        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>176561</td>\n",
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       "      <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",
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       "      <td>30</td>\n",
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       "      <td>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    176559  Bose SoundSport Headphones                 1       99.99   \n",
       "2    176560                Google Phone                 1      600.00   \n",
       "3    176560            Wired Headphones                 1       11.99   \n",
       "4    176561            Wired Headphones                 1       11.99   \n",
       "\n",
       "           Order Date                      Purchase Address  Month  Day  Year  \\\n",
       "0 2019-04-19 08:46:00          917 1st St, Dallas, TX 75001      4   19    19   \n",
       "1 2019-04-07 22:30:00     682 Chestnut St, Boston, MA 02215      4    7    19   \n",
       "2 2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001      4   12    19   \n",
       "3 2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001      4   12    19   \n",
       "4 2019-04-30 09:27:00     333 8th St, Los Angeles, CA 90001      4   30    19   \n",
       "\n",
       "    Hour           Street          City       Code  \n",
       "0  08:46       917 1st St        Dallas   TX 75001  \n",
       "1  22:30  682 Chestnut St        Boston   MA 02215  \n",
       "2  14:38    669 Spruce St   Los Angeles   CA 90001  \n",
       "3  14:38    669 Spruce St   Los Angeles   CA 90001  \n",
       "4  09:27       333 8th St   Los Angeles   CA 90001  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Street','City','Code']] = df['Purchase Address'].str.split(',', expand=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\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>Day</th>\n",
       "      <th>Year</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Street</th>\n",
       "      <th>City</th>\n",
       "      <th>Code</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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>19</td>\n",
       "      <td>19</td>\n",
       "      <td>08:46</td>\n",
       "      <td>917 1st St</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>TX 75001</td>\n",
       "      <td>23.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>176559</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>2019-04-07 22:30:00</td>\n",
       "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>22:30</td>\n",
       "      <td>682 Chestnut St</td>\n",
       "      <td>Boston</td>\n",
       "      <td>MA 02215</td>\n",
       "      <td>99.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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>12</td>\n",
       "      <td>19</td>\n",
       "      <td>14:38</td>\n",
       "      <td>669 Spruce St</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA 90001</td>\n",
       "      <td>600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\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>12</td>\n",
       "      <td>19</td>\n",
       "      <td>14:38</td>\n",
       "      <td>669 Spruce St</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA 90001</td>\n",
       "      <td>11.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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>30</td>\n",
       "      <td>19</td>\n",
       "      <td>09:27</td>\n",
       "      <td>333 8th St</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA 90001</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",
       "1    176559  Bose SoundSport Headphones                 1       99.99   \n",
       "2    176560                Google Phone                 1      600.00   \n",
       "3    176560            Wired Headphones                 1       11.99   \n",
       "4    176561            Wired Headphones                 1       11.99   \n",
       "\n",
       "           Order Date                      Purchase Address  Month  Day  Year  \\\n",
       "0 2019-04-19 08:46:00          917 1st St, Dallas, TX 75001      4   19    19   \n",
       "1 2019-04-07 22:30:00     682 Chestnut St, Boston, MA 02215      4    7    19   \n",
       "2 2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001      4   12    19   \n",
       "3 2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001      4   12    19   \n",
       "4 2019-04-30 09:27:00     333 8th St, Los Angeles, CA 90001      4   30    19   \n",
       "\n",
       "    Hour           Street          City       Code   Sales  \n",
       "0  08:46       917 1st St        Dallas   TX 75001   23.90  \n",
       "1  22:30  682 Chestnut St        Boston   MA 02215   99.99  \n",
       "2  14:38    669 Spruce St   Los Angeles   CA 90001  600.00  \n",
       "3  14:38    669 Spruce St   Los Angeles   CA 90001   11.99  \n",
       "4  09:27       333 8th St   Los Angeles   CA 90001   11.99  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Sales'] = df['Price Each'] * df['Quantity Ordered']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save again with all data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('merged_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validate all our data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
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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>Day</th>\n",
       "      <th>Year</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Street</th>\n",
       "      <th>City</th>\n",
       "      <th>Code</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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>19</td>\n",
       "      <td>19</td>\n",
       "      <td>08:46</td>\n",
       "      <td>917 1st St</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>TX 75001</td>\n",
       "      <td>23.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>176559</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>2019-04-07 22:30:00</td>\n",
       "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>22:30</td>\n",
       "      <td>682 Chestnut St</td>\n",
       "      <td>Boston</td>\n",
       "      <td>MA 02215</td>\n",
       "      <td>99.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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>12</td>\n",
       "      <td>19</td>\n",
       "      <td>14:38</td>\n",
       "      <td>669 Spruce St</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA 90001</td>\n",
       "      <td>600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\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>12</td>\n",
       "      <td>19</td>\n",
       "      <td>14:38</td>\n",
       "      <td>669 Spruce St</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA 90001</td>\n",
       "      <td>11.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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>30</td>\n",
       "      <td>19</td>\n",
       "      <td>09:27</td>\n",
       "      <td>333 8th St</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA 90001</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",
       "1    176559  Bose SoundSport Headphones                 1       99.99   \n",
       "2    176560                Google Phone                 1      600.00   \n",
       "3    176560            Wired Headphones                 1       11.99   \n",
       "4    176561            Wired Headphones                 1       11.99   \n",
       "\n",
       "            Order Date                      Purchase Address  Month  Day  \\\n",
       "0  2019-04-19 08:46:00          917 1st St, Dallas, TX 75001      4   19   \n",
       "1  2019-04-07 22:30:00     682 Chestnut St, Boston, MA 02215      4    7   \n",
       "2  2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001      4   12   \n",
       "3  2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001      4   12   \n",
       "4  2019-04-30 09:27:00     333 8th St, Los Angeles, CA 90001      4   30   \n",
       "\n",
       "   Year   Hour           Street          City       Code   Sales  \n",
       "0    19  08:46       917 1st St        Dallas   TX 75001   23.90  \n",
       "1    19  22:30  682 Chestnut St        Boston   MA 02215   99.99  \n",
       "2    19  14:38    669 Spruce St   Los Angeles   CA 90001  600.00  \n",
       "3    19  14:38    669 Spruce St   Los Angeles   CA 90001   11.99  \n",
       "4    19  09:27       333 8th St   Los Angeles   CA 90001   11.99  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('merged_data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order ID              int64\n",
       "Product              object\n",
       "Quantity Ordered      int64\n",
       "Price Each          float64\n",
       "Order Date           object\n",
       "Purchase Address     object\n",
       "Month                 int64\n",
       "Day                   int64\n",
       "Year                  int64\n",
       "Hour                 object\n",
       "Street               object\n",
       "City                 object\n",
       "Code                 object\n",
       "Sales               float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Year</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Atlanta</td>\n",
       "      <td>3423838407</td>\n",
       "      <td>16602</td>\n",
       "      <td>2.779908e+06</td>\n",
       "      <td>104794</td>\n",
       "      <td>234837</td>\n",
       "      <td>339887</td>\n",
       "      <td>2.795499e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Austin</td>\n",
       "      <td>2280982185</td>\n",
       "      <td>11153</td>\n",
       "      <td>1.809874e+06</td>\n",
       "      <td>69829</td>\n",
       "      <td>156782</td>\n",
       "      <td>226417</td>\n",
       "      <td>1.819582e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Boston</td>\n",
       "      <td>4598265261</td>\n",
       "      <td>22528</td>\n",
       "      <td>3.637410e+06</td>\n",
       "      <td>141112</td>\n",
       "      <td>312376</td>\n",
       "      <td>457374</td>\n",
       "      <td>3.661642e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Dallas</td>\n",
       "      <td>3415643578</td>\n",
       "      <td>16730</td>\n",
       "      <td>2.752628e+06</td>\n",
       "      <td>104620</td>\n",
       "      <td>234435</td>\n",
       "      <td>332542</td>\n",
       "      <td>2.767975e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>6811084693</td>\n",
       "      <td>33289</td>\n",
       "      <td>5.421435e+06</td>\n",
       "      <td>208325</td>\n",
       "      <td>469607</td>\n",
       "      <td>666418</td>\n",
       "      <td>5.452571e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>New York City</td>\n",
       "      <td>5736333884</td>\n",
       "      <td>27932</td>\n",
       "      <td>4.635371e+06</td>\n",
       "      <td>175741</td>\n",
       "      <td>392706</td>\n",
       "      <td>559468</td>\n",
       "      <td>4.664317e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Portland</td>\n",
       "      <td>2868861092</td>\n",
       "      <td>14053</td>\n",
       "      <td>2.307747e+06</td>\n",
       "      <td>87765</td>\n",
       "      <td>197521</td>\n",
       "      <td>279607</td>\n",
       "      <td>2.320491e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>San Francisco</td>\n",
       "      <td>10304443952</td>\n",
       "      <td>50239</td>\n",
       "      <td>8.211462e+06</td>\n",
       "      <td>315520</td>\n",
       "      <td>702669</td>\n",
       "      <td>1011172</td>\n",
       "      <td>8.262204e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Seattle</td>\n",
       "      <td>3406693974</td>\n",
       "      <td>16553</td>\n",
       "      <td>2.733296e+06</td>\n",
       "      <td>104941</td>\n",
       "      <td>229552</td>\n",
       "      <td>333781</td>\n",
       "      <td>2.747755e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Order ID  Quantity Ordered    Price Each   Month     Day  \\\n",
       "City                                                                          \n",
       " Atlanta         3423838407             16602  2.779908e+06  104794  234837   \n",
       " Austin          2280982185             11153  1.809874e+06   69829  156782   \n",
       " Boston          4598265261             22528  3.637410e+06  141112  312376   \n",
       " Dallas          3415643578             16730  2.752628e+06  104620  234435   \n",
       " Los Angeles     6811084693             33289  5.421435e+06  208325  469607   \n",
       " New York City   5736333884             27932  4.635371e+06  175741  392706   \n",
       " Portland        2868861092             14053  2.307747e+06   87765  197521   \n",
       " San Francisco  10304443952             50239  8.211462e+06  315520  702669   \n",
       " Seattle         3406693974             16553  2.733296e+06  104941  229552   \n",
       "\n",
       "                   Year         Sales  \n",
       "City                                   \n",
       " Atlanta         339887  2.795499e+06  \n",
       " Austin          226417  1.819582e+06  \n",
       " Boston          457374  3.661642e+06  \n",
       " Dallas          332542  2.767975e+06  \n",
       " Los Angeles     666418  5.452571e+06  \n",
       " New York City   559468  4.664317e+06  \n",
       " Portland        279607  2.320491e+06  \n",
       " San Francisco  1011172  8.262204e+06  \n",
       " Seattle         333781  2.747755e+06  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = df.groupby('City').sum()\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[' Atlanta',\n",
       " ' Austin',\n",
       " ' Boston',\n",
       " ' Dallas',\n",
       " ' Los Angeles',\n",
       " ' New York City',\n",
       " ' Portland',\n",
       " ' San Francisco',\n",
       " ' Seattle']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities = [city for city, df in df.groupby('City')]\n",
    "cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['20in Monitor',\n",
       " '27in 4K Gaming Monitor',\n",
       " '27in FHD Monitor',\n",
       " '34in Ultrawide Monitor',\n",
       " 'AA Batteries (4-pack)',\n",
       " 'AAA Batteries (4-pack)',\n",
       " 'Apple Airpods Headphones',\n",
       " 'Bose SoundSport Headphones',\n",
       " 'Flatscreen TV',\n",
       " 'Google Phone',\n",
       " 'LG Dryer',\n",
       " 'LG Washing Machine',\n",
       " 'Lightning Charging Cable',\n",
       " 'Macbook Pro Laptop',\n",
       " 'ThinkPad Laptop',\n",
       " 'USB-C Charging Cable',\n",
       " 'Vareebadd Phone',\n",
       " 'Wired Headphones',\n",
       " 'iPhone']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products = [product for product, df in df.groupby('Product')]\n",
    "products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product\n",
       "20in Monitor                   4129\n",
       "27in 4K Gaming Monitor         6244\n",
       "27in FHD Monitor               7550\n",
       "34in Ultrawide Monitor         6199\n",
       "AA Batteries (4-pack)         27635\n",
       "AAA Batteries (4-pack)        31017\n",
       "Apple Airpods Headphones      15661\n",
       "Bose SoundSport Headphones    13457\n",
       "Flatscreen TV                  4819\n",
       "Google Phone                   5532\n",
       "LG Dryer                        646\n",
       "LG Washing Machine              666\n",
       "Lightning Charging Cable      23217\n",
       "Macbook Pro Laptop             4728\n",
       "ThinkPad Laptop                4130\n",
       "USB-C Charging Cable          23975\n",
       "Vareebadd Phone                2068\n",
       "Wired Headphones              20557\n",
       "iPhone                         6849\n",
       "Name: Quantity Ordered, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantity_ordered = df.groupby('Product').sum()['Quantity Ordered']\n",
    "quantity_ordered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product       City          \n",
       "20in Monitor   Atlanta           342\n",
       "               Austin            230\n",
       "               Boston            394\n",
       "               Dallas            343\n",
       "               Los Angeles       658\n",
       "                                ... \n",
       "iPhone         Los Angeles      1118\n",
       "               New York City     881\n",
       "               Portland          450\n",
       "               San Francisco    1661\n",
       "               Seattle           546\n",
       "Name: Quantity Ordered, Length: 171, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_quantity_ordered = df.groupby(['Product', 'City']).sum()['Quantity Ordered']\n",
    "city_quantity_ordered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([(          '20in Monitor',       ' Atlanta'),\n",
       "            (          '20in Monitor',        ' Austin'),\n",
       "            (          '20in Monitor',        ' Boston'),\n",
       "            (          '20in Monitor',        ' Dallas'),\n",
       "            (          '20in Monitor',   ' Los Angeles'),\n",
       "            (          '20in Monitor', ' New York City'),\n",
       "            (          '20in Monitor',      ' Portland'),\n",
       "            (          '20in Monitor', ' San Francisco'),\n",
       "            (          '20in Monitor',       ' Seattle'),\n",
       "            ('27in 4K Gaming Monitor',       ' Atlanta'),\n",
       "            ...\n",
       "            (      'Wired Headphones',       ' Seattle'),\n",
       "            (                'iPhone',       ' Atlanta'),\n",
       "            (                'iPhone',        ' Austin'),\n",
       "            (                'iPhone',        ' Boston'),\n",
       "            (                'iPhone',        ' Dallas'),\n",
       "            (                'iPhone',   ' Los Angeles'),\n",
       "            (                'iPhone', ' New York City'),\n",
       "            (                'iPhone',      ' Portland'),\n",
       "            (                'iPhone', ' San Francisco'),\n",
       "            (                'iPhone',       ' Seattle')],\n",
       "           names=['Product', 'City'], length=171)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_quantity_ordered.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('20in Monitor', ' Atlanta')\n",
      "('20in Monitor', ' Austin')\n",
      "('20in Monitor', ' Boston')\n",
      "('20in Monitor', ' Dallas')\n",
      "('20in Monitor', ' Los Angeles')\n",
      "('20in Monitor', ' New York City')\n",
      "('20in Monitor', ' Portland')\n",
      "('20in Monitor', ' San Francisco')\n",
      "('20in Monitor', ' Seattle')\n",
      "('27in 4K Gaming Monitor', ' Atlanta')\n",
      "('27in 4K Gaming Monitor', ' Austin')\n",
      "('27in 4K Gaming Monitor', ' Boston')\n",
      "('27in 4K Gaming Monitor', ' Dallas')\n",
      "('27in 4K Gaming Monitor', ' Los Angeles')\n",
      "('27in 4K Gaming Monitor', ' New York City')\n",
      "('27in 4K Gaming Monitor', ' Portland')\n",
      "('27in 4K Gaming Monitor', ' San Francisco')\n",
      "('27in 4K Gaming Monitor', ' Seattle')\n",
      "('27in FHD Monitor', ' Atlanta')\n",
      "('27in FHD Monitor', ' Austin')\n",
      "('27in FHD Monitor', ' Boston')\n",
      "('27in FHD Monitor', ' Dallas')\n",
      "('27in FHD Monitor', ' Los Angeles')\n",
      "('27in FHD Monitor', ' New York City')\n",
      "('27in FHD Monitor', ' Portland')\n",
      "('27in FHD Monitor', ' San Francisco')\n",
      "('27in FHD Monitor', ' Seattle')\n",
      "('34in Ultrawide Monitor', ' Atlanta')\n",
      "('34in Ultrawide Monitor', ' Austin')\n",
      "('34in Ultrawide Monitor', ' Boston')\n",
      "('34in Ultrawide Monitor', ' Dallas')\n",
      "('34in Ultrawide Monitor', ' Los Angeles')\n",
      "('34in Ultrawide Monitor', ' New York City')\n",
      "('34in Ultrawide Monitor', ' Portland')\n",
      "('34in Ultrawide Monitor', ' San Francisco')\n",
      "('34in Ultrawide Monitor', ' Seattle')\n",
      "('AA Batteries (4-pack)', ' Atlanta')\n",
      "('AA Batteries (4-pack)', ' Austin')\n",
      "('AA Batteries (4-pack)', ' Boston')\n",
      "('AA Batteries (4-pack)', ' Dallas')\n",
      "('AA Batteries (4-pack)', ' Los Angeles')\n",
      "('AA Batteries (4-pack)', ' New York City')\n",
      "('AA Batteries (4-pack)', ' Portland')\n",
      "('AA Batteries (4-pack)', ' San Francisco')\n",
      "('AA Batteries (4-pack)', ' Seattle')\n",
      "('AAA Batteries (4-pack)', ' Atlanta')\n",
      "('AAA Batteries (4-pack)', ' Austin')\n",
      "('AAA Batteries (4-pack)', ' Boston')\n",
      "('AAA Batteries (4-pack)', ' Dallas')\n",
      "('AAA Batteries (4-pack)', ' Los Angeles')\n",
      "('AAA Batteries (4-pack)', ' New York City')\n",
      "('AAA Batteries (4-pack)', ' Portland')\n",
      "('AAA Batteries (4-pack)', ' San Francisco')\n",
      "('AAA Batteries (4-pack)', ' Seattle')\n",
      "('Apple Airpods Headphones', ' Atlanta')\n",
      "('Apple Airpods Headphones', ' Austin')\n",
      "('Apple Airpods Headphones', ' Boston')\n",
      "('Apple Airpods Headphones', ' Dallas')\n",
      "('Apple Airpods Headphones', ' Los Angeles')\n",
      "('Apple Airpods Headphones', ' New York City')\n",
      "('Apple Airpods Headphones', ' Portland')\n",
      "('Apple Airpods Headphones', ' San Francisco')\n",
      "('Apple Airpods Headphones', ' Seattle')\n",
      "('Bose SoundSport Headphones', ' Atlanta')\n",
      "('Bose SoundSport Headphones', ' Austin')\n",
      "('Bose SoundSport Headphones', ' Boston')\n",
      "('Bose SoundSport Headphones', ' Dallas')\n",
      "('Bose SoundSport Headphones', ' Los Angeles')\n",
      "('Bose SoundSport Headphones', ' New York City')\n",
      "('Bose SoundSport Headphones', ' Portland')\n",
      "('Bose SoundSport Headphones', ' San Francisco')\n",
      "('Bose SoundSport Headphones', ' Seattle')\n",
      "('Flatscreen TV', ' Atlanta')\n",
      "('Flatscreen TV', ' Austin')\n",
      "('Flatscreen TV', ' Boston')\n",
      "('Flatscreen TV', ' Dallas')\n",
      "('Flatscreen TV', ' Los Angeles')\n",
      "('Flatscreen TV', ' New York City')\n",
      "('Flatscreen TV', ' Portland')\n",
      "('Flatscreen TV', ' San Francisco')\n",
      "('Flatscreen TV', ' Seattle')\n",
      "('Google Phone', ' Atlanta')\n",
      "('Google Phone', ' Austin')\n",
      "('Google Phone', ' Boston')\n",
      "('Google Phone', ' Dallas')\n",
      "('Google Phone', ' Los Angeles')\n",
      "('Google Phone', ' New York City')\n",
      "('Google Phone', ' Portland')\n",
      "('Google Phone', ' San Francisco')\n",
      "('Google Phone', ' Seattle')\n",
      "('LG Dryer', ' Atlanta')\n",
      "('LG Dryer', ' Austin')\n",
      "('LG Dryer', ' Boston')\n",
      "('LG Dryer', ' Dallas')\n",
      "('LG Dryer', ' Los Angeles')\n",
      "('LG Dryer', ' New York City')\n",
      "('LG Dryer', ' Portland')\n",
      "('LG Dryer', ' San Francisco')\n",
      "('LG Dryer', ' Seattle')\n",
      "('LG Washing Machine', ' Atlanta')\n",
      "('LG Washing Machine', ' Austin')\n",
      "('LG Washing Machine', ' Boston')\n",
      "('LG Washing Machine', ' Dallas')\n",
      "('LG Washing Machine', ' Los Angeles')\n",
      "('LG Washing Machine', ' New York City')\n",
      "('LG Washing Machine', ' Portland')\n",
      "('LG Washing Machine', ' San Francisco')\n",
      "('LG Washing Machine', ' Seattle')\n",
      "('Lightning Charging Cable', ' Atlanta')\n",
      "('Lightning Charging Cable', ' Austin')\n",
      "('Lightning Charging Cable', ' Boston')\n",
      "('Lightning Charging Cable', ' Dallas')\n",
      "('Lightning Charging Cable', ' Los Angeles')\n",
      "('Lightning Charging Cable', ' New York City')\n",
      "('Lightning Charging Cable', ' Portland')\n",
      "('Lightning Charging Cable', ' San Francisco')\n",
      "('Lightning Charging Cable', ' Seattle')\n",
      "('Macbook Pro Laptop', ' Atlanta')\n",
      "('Macbook Pro Laptop', ' Austin')\n",
      "('Macbook Pro Laptop', ' Boston')\n",
      "('Macbook Pro Laptop', ' Dallas')\n",
      "('Macbook Pro Laptop', ' Los Angeles')\n",
      "('Macbook Pro Laptop', ' New York City')\n",
      "('Macbook Pro Laptop', ' Portland')\n",
      "('Macbook Pro Laptop', ' San Francisco')\n",
      "('Macbook Pro Laptop', ' Seattle')\n",
      "('ThinkPad Laptop', ' Atlanta')\n",
      "('ThinkPad Laptop', ' Austin')\n",
      "('ThinkPad Laptop', ' Boston')\n",
      "('ThinkPad Laptop', ' Dallas')\n",
      "('ThinkPad Laptop', ' Los Angeles')\n",
      "('ThinkPad Laptop', ' New York City')\n",
      "('ThinkPad Laptop', ' Portland')\n",
      "('ThinkPad Laptop', ' San Francisco')\n",
      "('ThinkPad Laptop', ' Seattle')\n",
      "('USB-C Charging Cable', ' Atlanta')\n",
      "('USB-C Charging Cable', ' Austin')\n",
      "('USB-C Charging Cable', ' Boston')\n",
      "('USB-C Charging Cable', ' Dallas')\n",
      "('USB-C Charging Cable', ' Los Angeles')\n",
      "('USB-C Charging Cable', ' New York City')\n",
      "('USB-C Charging Cable', ' Portland')\n",
      "('USB-C Charging Cable', ' San Francisco')\n",
      "('USB-C Charging Cable', ' Seattle')\n",
      "('Vareebadd Phone', ' Atlanta')\n",
      "('Vareebadd Phone', ' Austin')\n",
      "('Vareebadd Phone', ' Boston')\n",
      "('Vareebadd Phone', ' Dallas')\n",
      "('Vareebadd Phone', ' Los Angeles')\n",
      "('Vareebadd Phone', ' New York City')\n",
      "('Vareebadd Phone', ' Portland')\n",
      "('Vareebadd Phone', ' San Francisco')\n",
      "('Vareebadd Phone', ' Seattle')\n",
      "('Wired Headphones', ' Atlanta')\n",
      "('Wired Headphones', ' Austin')\n",
      "('Wired Headphones', ' Boston')\n",
      "('Wired Headphones', ' Dallas')\n",
      "('Wired Headphones', ' Los Angeles')\n",
      "('Wired Headphones', ' New York City')\n",
      "('Wired Headphones', ' Portland')\n",
      "('Wired Headphones', ' San Francisco')\n",
      "('Wired Headphones', ' Seattle')\n",
      "('iPhone', ' Atlanta')\n",
      "('iPhone', ' Austin')\n",
      "('iPhone', ' Boston')\n",
      "('iPhone', ' Dallas')\n",
      "('iPhone', ' Los Angeles')\n",
      "('iPhone', ' New York City')\n",
      "('iPhone', ' Portland')\n",
      "('iPhone', ' San Francisco')\n",
      "('iPhone', ' Seattle')\n"
     ]
    }
   ],
   "source": [
    "for i in city_quantity_ordered.index:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['20in Monitor', ' Atlanta'],\n",
       " ['20in Monitor', ' Austin'],\n",
       " ['20in Monitor', ' Boston'],\n",
       " ['20in Monitor', ' Dallas'],\n",
       " ['20in Monitor', ' Los Angeles'],\n",
       " ['20in Monitor', ' New York City'],\n",
       " ['20in Monitor', ' Portland'],\n",
       " ['20in Monitor', ' San Francisco'],\n",
       " ['20in Monitor', ' Seattle'],\n",
       " ['27in 4K Gaming Monitor', ' Atlanta'],\n",
       " ['27in 4K Gaming Monitor', ' Austin'],\n",
       " ['27in 4K Gaming Monitor', ' Boston'],\n",
       " ['27in 4K Gaming Monitor', ' Dallas'],\n",
       " ['27in 4K Gaming Monitor', ' Los Angeles'],\n",
       " ['27in 4K Gaming Monitor', ' New York City'],\n",
       " ['27in 4K Gaming Monitor', ' Portland'],\n",
       " ['27in 4K Gaming Monitor', ' San Francisco'],\n",
       " ['27in 4K Gaming Monitor', ' Seattle'],\n",
       " ['27in FHD Monitor', ' Atlanta'],\n",
       " ['27in FHD Monitor', ' Austin'],\n",
       " ['27in FHD Monitor', ' Boston'],\n",
       " ['27in FHD Monitor', ' Dallas'],\n",
       " ['27in FHD Monitor', ' Los Angeles'],\n",
       " ['27in FHD Monitor', ' New York City'],\n",
       " ['27in FHD Monitor', ' Portland'],\n",
       " ['27in FHD Monitor', ' San Francisco'],\n",
       " ['27in FHD Monitor', ' Seattle'],\n",
       " ['34in Ultrawide Monitor', ' Atlanta'],\n",
       " ['34in Ultrawide Monitor', ' Austin'],\n",
       " ['34in Ultrawide Monitor', ' Boston'],\n",
       " ['34in Ultrawide Monitor', ' Dallas'],\n",
       " ['34in Ultrawide Monitor', ' Los Angeles'],\n",
       " ['34in Ultrawide Monitor', ' New York City'],\n",
       " ['34in Ultrawide Monitor', ' Portland'],\n",
       " ['34in Ultrawide Monitor', ' San Francisco'],\n",
       " ['34in Ultrawide Monitor', ' Seattle'],\n",
       " ['AA Batteries (4-pack)', ' Atlanta'],\n",
       " ['AA Batteries (4-pack)', ' Austin'],\n",
       " ['AA Batteries (4-pack)', ' Boston'],\n",
       " ['AA Batteries (4-pack)', ' Dallas'],\n",
       " ['AA Batteries (4-pack)', ' Los Angeles'],\n",
       " ['AA Batteries (4-pack)', ' New York City'],\n",
       " ['AA Batteries (4-pack)', ' Portland'],\n",
       " ['AA Batteries (4-pack)', ' San Francisco'],\n",
       " ['AA Batteries (4-pack)', ' Seattle'],\n",
       " ['AAA Batteries (4-pack)', ' Atlanta'],\n",
       " ['AAA Batteries (4-pack)', ' Austin'],\n",
       " ['AAA Batteries (4-pack)', ' Boston'],\n",
       " ['AAA Batteries (4-pack)', ' Dallas'],\n",
       " ['AAA Batteries (4-pack)', ' Los Angeles'],\n",
       " ['AAA Batteries (4-pack)', ' New York City'],\n",
       " ['AAA Batteries (4-pack)', ' Portland'],\n",
       " ['AAA Batteries (4-pack)', ' San Francisco'],\n",
       " ['AAA Batteries (4-pack)', ' Seattle'],\n",
       " ['Apple Airpods Headphones', ' Atlanta'],\n",
       " ['Apple Airpods Headphones', ' Austin'],\n",
       " ['Apple Airpods Headphones', ' Boston'],\n",
       " ['Apple Airpods Headphones', ' Dallas'],\n",
       " ['Apple Airpods Headphones', ' Los Angeles'],\n",
       " ['Apple Airpods Headphones', ' New York City'],\n",
       " ['Apple Airpods Headphones', ' Portland'],\n",
       " ['Apple Airpods Headphones', ' San Francisco'],\n",
       " ['Apple Airpods Headphones', ' Seattle'],\n",
       " ['Bose SoundSport Headphones', ' Atlanta'],\n",
       " ['Bose SoundSport Headphones', ' Austin'],\n",
       " ['Bose SoundSport Headphones', ' Boston'],\n",
       " ['Bose SoundSport Headphones', ' Dallas'],\n",
       " ['Bose SoundSport Headphones', ' Los Angeles'],\n",
       " ['Bose SoundSport Headphones', ' New York City'],\n",
       " ['Bose SoundSport Headphones', ' Portland'],\n",
       " ['Bose SoundSport Headphones', ' San Francisco'],\n",
       " ['Bose SoundSport Headphones', ' Seattle'],\n",
       " ['Flatscreen TV', ' Atlanta'],\n",
       " ['Flatscreen TV', ' Austin'],\n",
       " ['Flatscreen TV', ' Boston'],\n",
       " ['Flatscreen TV', ' Dallas'],\n",
       " ['Flatscreen TV', ' Los Angeles'],\n",
       " ['Flatscreen TV', ' New York City'],\n",
       " ['Flatscreen TV', ' Portland'],\n",
       " ['Flatscreen TV', ' San Francisco'],\n",
       " ['Flatscreen TV', ' Seattle'],\n",
       " ['Google Phone', ' Atlanta'],\n",
       " ['Google Phone', ' Austin'],\n",
       " ['Google Phone', ' Boston'],\n",
       " ['Google Phone', ' Dallas'],\n",
       " ['Google Phone', ' Los Angeles'],\n",
       " ['Google Phone', ' New York City'],\n",
       " ['Google Phone', ' Portland'],\n",
       " ['Google Phone', ' San Francisco'],\n",
       " ['Google Phone', ' Seattle'],\n",
       " ['LG Dryer', ' Atlanta'],\n",
       " ['LG Dryer', ' Austin'],\n",
       " ['LG Dryer', ' Boston'],\n",
       " ['LG Dryer', ' Dallas'],\n",
       " ['LG Dryer', ' Los Angeles'],\n",
       " ['LG Dryer', ' New York City'],\n",
       " ['LG Dryer', ' Portland'],\n",
       " ['LG Dryer', ' San Francisco'],\n",
       " ['LG Dryer', ' Seattle'],\n",
       " ['LG Washing Machine', ' Atlanta'],\n",
       " ['LG Washing Machine', ' Austin'],\n",
       " ['LG Washing Machine', ' Boston'],\n",
       " ['LG Washing Machine', ' Dallas'],\n",
       " ['LG Washing Machine', ' Los Angeles'],\n",
       " ['LG Washing Machine', ' New York City'],\n",
       " ['LG Washing Machine', ' Portland'],\n",
       " ['LG Washing Machine', ' San Francisco'],\n",
       " ['LG Washing Machine', ' Seattle'],\n",
       " ['Lightning Charging Cable', ' Atlanta'],\n",
       " ['Lightning Charging Cable', ' Austin'],\n",
       " ['Lightning Charging Cable', ' Boston'],\n",
       " ['Lightning Charging Cable', ' Dallas'],\n",
       " ['Lightning Charging Cable', ' Los Angeles'],\n",
       " ['Lightning Charging Cable', ' New York City'],\n",
       " ['Lightning Charging Cable', ' Portland'],\n",
       " ['Lightning Charging Cable', ' San Francisco'],\n",
       " ['Lightning Charging Cable', ' Seattle'],\n",
       " ['Macbook Pro Laptop', ' Atlanta'],\n",
       " ['Macbook Pro Laptop', ' Austin'],\n",
       " ['Macbook Pro Laptop', ' Boston'],\n",
       " ['Macbook Pro Laptop', ' Dallas'],\n",
       " ['Macbook Pro Laptop', ' Los Angeles'],\n",
       " ['Macbook Pro Laptop', ' New York City'],\n",
       " ['Macbook Pro Laptop', ' Portland'],\n",
       " ['Macbook Pro Laptop', ' San Francisco'],\n",
       " ['Macbook Pro Laptop', ' Seattle'],\n",
       " ['ThinkPad Laptop', ' Atlanta'],\n",
       " ['ThinkPad Laptop', ' Austin'],\n",
       " ['ThinkPad Laptop', ' Boston'],\n",
       " ['ThinkPad Laptop', ' Dallas'],\n",
       " ['ThinkPad Laptop', ' Los Angeles'],\n",
       " ['ThinkPad Laptop', ' New York City'],\n",
       " ['ThinkPad Laptop', ' Portland'],\n",
       " ['ThinkPad Laptop', ' San Francisco'],\n",
       " ['ThinkPad Laptop', ' Seattle'],\n",
       " ['USB-C Charging Cable', ' Atlanta'],\n",
       " ['USB-C Charging Cable', ' Austin'],\n",
       " ['USB-C Charging Cable', ' Boston'],\n",
       " ['USB-C Charging Cable', ' Dallas'],\n",
       " ['USB-C Charging Cable', ' Los Angeles'],\n",
       " ['USB-C Charging Cable', ' New York City'],\n",
       " ['USB-C Charging Cable', ' Portland'],\n",
       " ['USB-C Charging Cable', ' San Francisco'],\n",
       " ['USB-C Charging Cable', ' Seattle'],\n",
       " ['Vareebadd Phone', ' Atlanta'],\n",
       " ['Vareebadd Phone', ' Austin'],\n",
       " ['Vareebadd Phone', ' Boston'],\n",
       " ['Vareebadd Phone', ' Dallas'],\n",
       " ['Vareebadd Phone', ' Los Angeles'],\n",
       " ['Vareebadd Phone', ' New York City'],\n",
       " ['Vareebadd Phone', ' Portland'],\n",
       " ['Vareebadd Phone', ' San Francisco'],\n",
       " ['Vareebadd Phone', ' Seattle'],\n",
       " ['Wired Headphones', ' Atlanta'],\n",
       " ['Wired Headphones', ' Austin'],\n",
       " ['Wired Headphones', ' Boston'],\n",
       " ['Wired Headphones', ' Dallas'],\n",
       " ['Wired Headphones', ' Los Angeles'],\n",
       " ['Wired Headphones', ' New York City'],\n",
       " ['Wired Headphones', ' Portland'],\n",
       " ['Wired Headphones', ' San Francisco'],\n",
       " ['Wired Headphones', ' Seattle'],\n",
       " ['iPhone', ' Atlanta'],\n",
       " ['iPhone', ' Austin'],\n",
       " ['iPhone', ' Boston'],\n",
       " ['iPhone', ' Dallas'],\n",
       " ['iPhone', ' Los Angeles'],\n",
       " ['iPhone', ' New York City'],\n",
       " ['iPhone', ' Portland'],\n",
       " ['iPhone', ' San Francisco'],\n",
       " ['iPhone', ' Seattle']]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = [list(c) for c in city_quantity_ordered.index[:][:]]\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product       City          \n",
       "20in Monitor   Atlanta           342\n",
       "               Austin            230\n",
       "               Boston            394\n",
       "               Dallas            343\n",
       "               Los Angeles       658\n",
       "                                ... \n",
       "iPhone         Los Angeles      1118\n",
       "               New York City     881\n",
       "               Portland          450\n",
       "               San Francisco    1661\n",
       "               Seattle           546\n",
       "Name: Quantity Ordered, Length: 171, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_quantity_ordered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ca0fEVX2M6UrgW8B/R8RjXedeBbwbuDUiTu9jTD8CfkhKZLd1KgyS1iIlsncBF0bEt/sY01HANyLiTws5PwVYMyKu7FdM+brrRMQilxEYzmNGOaYa3+efAb7U64NG0rLAm4FxEXH+qF970JK+2WiTtFxEPDPSx4wFSWt0fziaLcpAJ31Jm5GWcF6xcywivlUuIpC0PXAcsBGpT0UprHhJwZgmAP8ATKLRzxMRhxaMaXtgVkT8KTdlTAZOjohfF4zppcDsiPiLpB2AzYFvlUy6kn4J/Aw4IyL+p1QcjXjm0rWeVlNEjO9jOPPJfWpfAl4LPA/8BDiq301gXTGtC/wnMDEidstLzW83lt9kBzbpS/o4sAMp6V9KWsP/JxHxjsJx3UVqi50JvNARGBG/LxjTtcCPe8Q06l8tFyOmW0gd8ZsDZwGnA3tHxOsLxjQLmEL6cLyCtITIyyNij4IxLQPsChwKbAmcQ+qz+mWpmHJcnwB+S/rbidQfslpEfLpgTFcC/5VjgtTZvH9E7FIwpsuAM4APR8QWuWnnpoh45ZhdNCIG8ge4lTQk9eZ8f11SO3HpuK4vHUOPmGaVjqFHTDfm3x8DDmseqyCm/we8L9++qXRZNeLbAfgNMBe4CtimYCwLvM9Lv/d7vc9Lv/eBG/LvmxrHxjSmQR6n/1SkDrdnJY0n9Y4Xa0JpmC7pM5K2kzS581M4pkskFautLsRcSR8i1cZ+kHddW65wTM9I2g84iDQKCwrHJGkNSe+VdD1pNMj7SSNCPgx8t2Boz0naX9I4SctI2p/Gt8hCfifpgBzTuNxsWOwbdvYnSWuTm8QkbQuM6ZDWgR2nD8yQtAbwdVKzxR9JbZ+lvTr/bq6ZHcBOBWLpOAo4VtLTQKczMqJg+yvwTtKomMMi4reSNgQ+UzAegEOAI4D/iIhfSdoY6NuInYW4gdRksU/M399xnaSvF4oJ0t/u5PwTwE/zsZIOBb4MnESK6dp8rKSjSc2EL5X0U2ACMKZN0APbpt8kaRIwPiJuKRyKLeUkrUQaWjtaO7qNiKR9IuLcrmN7R8QFpWKyxZPb8V9O6vv4eYzxKLCBTfqSroqInYc61sd4DoiIb0s6utf5iPh8v2NqkvRW4HX57tURccmiHt+HeLYljbT4W9IkrXHAHyNi9YIxvQX4LLB8RGycx3d/IiLeWjCmGyNictexmRHxqlIx5RhqHBE2jTRa57F8f03SBKi+xyRp70WdH8sP7YFr3pG0IrAysE7+o3a2aBxPmupcyir592o9zhX95JV0Ammm6dn50FGSXhsRxxQM68ukWbDfIzWFHQhsUjAeSENttwGuBoiIWbmJp+8k7QrsBkyU1KwwjCcNRyztItKIsB9Svi2/Y/NoDK+NiEclbVUolrcs4lwATvqL4R+BfyEl+JnMS/pPAF8pFVREnJpv/jAifto8l8ekl7QHsGXMm2k6jTQ1vGTSJyLukTQu0ho3Z+ShpSU9GxGPS/Nt9VzqA/sR0vIBfwaaGxHNpfDfLVs5Ij5YOoguy0haMyIehRdmVBfJgRFxSI5h44j4VfPcWFckBi7pR8TJwMmS3hcRXyodTw9fIk00GupYv60BdBaeKtaE0vBkXppilqRPAw8x79tSKbdJehcwTtImpPVcinwQRcRNwE2Svh0RfykRwxAukbRHRFxaOpCGzwHXSjov358K/EfBeADOZ8H/++eR1uIaEwPbpg8g6TUs2KZYZEau0sqDryF9CzmpcWo88LaI2KJEXAB5GOIJwHTSN6PXAcdGxDkFY9qIVJtdjjQMcXXSYmt9W5uoR0wrk4ZCvpFUTlcAn4yIPxeI5ZyI2E/STfT4ttHdzt9veWbuKqTF6Z5h3szzkiPCyDNed8rxXBURdxSK42+AVwCfJs376BgP/L+IeMWYXXtQk76ks0ir1c1iXptiRMSRheJ5PWnyzBHA1xqn5pImjfV1lcZuktYjteuLNInmtyXjsUWTtH5EzM5LQywgCs/IrVFNy2hI2hPYC3grachmx1zgOxExZt8gBznp3wlsGpX9AyVt1BlPnafQrxpjsHzqYsZUzUgnSbey6LVbNu9jOABI+m8WHVPfR+9IegmwbkT8X9fx7YEHu9uJS8gDKTZh/rWv+rZ0eI94mstoXA78N+WX0Xhdd5lI2r673280DVybfsNtwF+R2oJr8ilJR5C+fcwEVpf0+Yjo+8SjSkc6vbnQdRfls6UD6OFk4KM9jj8DfAHYs7/hzE/S35Mm/a1P+ra9LfB/lJ2E+HxEPJuHS54cEV/KzWMlfYE+9/ENctJfB7hD0s9I7YpAmVpZl00j4ok8Lf1S0oYvMykz27S6kU5RcBXNhYmIH3VuVzQ5a+OImNV9MCJ+VmoYaZejSM2F10XEjrkN+/jCMXWW0TiQeUMmiyyj0ejjm9A1d2c8aU7KmBnkpH9c6QAWYjlJy5Ha874cEc9IKtIEVeNIJy18ad7iHYHNyVlA6clZKyzi3Mp9i2Lh/hwRf5aEpBUi4i5JLy8cU03LaCwPrErKwc25O08wxsswDGzSb9bOKnMqcB9wM3BNHqVStE0f+K2k1SJirqSPkL5a/ntE3NjvQCKi1+S1WhzHgpOzJhWK5UZJh0TEGc2DSlsDlm6yAJid1776PnClpEdJ2ygWk0fqHNm4/yvSqLUSsfwI+JGkM/v97XaQO3J7TeP/U+khY71IWjYini14/VsiYnNJrwU+RarNHhsRrx7iqWNOaYvLZkdg3/Yx7RHL9RHxakk3RcRW+dgthTqX1yMl1LmkpjlInZSrAXtGRDV9WXnk2urA5RHxdIHrnxsR+/QYJND59tj3v98LAaTlKv6NNHyz+T4fs76Pga3pU+c0fiR9bCGnPtHXQObXGdL6JuCUiLhI0nEF4+msBfQ5Un/DI6Sdxu4k/ecopabJWQ8Br5a0C7BZPnxiFN49K89y7XZr/r0q8yYA9tNR+XeNgwTOJi2B/WZS09NBwJyxvOAgJ/0ap/EDNDeyXpH0x76zUCwdv5F0KvAG4ERJK0DxvRY+SRrx8cOI2ErSjsB+hWN6H2ly1l9IO1RdQYqzmEgbn/d18/MhzCTVpptrVXTuBwX2tGh861kGeKgzmS53yq/b73i6rB0Rp0s6qtHkM6ZN04PcvHMNKYl9g7Rt20PAwSVnvvaSE+zFEbFrwRhWJi3edWtE3J2bDl5ZstYoaUZETJF0M7BVRDwv6WcRsU2pmGzpJmkG8JpOE1Ne5uOnEbF1wZiui4htJV0BfJHU73FeRPScdDcaBrmm/25SO/4/k6bxbwC8vWhEva1M4R29IuJJpQ22d1VavfHHpZsJgMckrUpaqfFsSY8Axfo9YKGTtB4HZgCnlliOoVY1TfhrWLbZpxART+fEX9K/S1od+ACpD3I8KV+NmYGt6deqqzNpHGmnnE+WHDIp6SjS2ued5VzfBpxWOKZVgKdIX8n3J3UEnh1lN5A/mfT36qxJ9E7St8iVSJv0vLtATG/s/oCW9A8RUWTXrDzhbxXgf0nLjjQn/F0WEX9bIi54YWP0L0XExfn+nsCRhT+I+m7gkn6N0/ib8hDNjmeBh0uO3IE0AgXYLiL+lO+vAvxf4VENazCv4/0XETGm+4YOh6RrIuJ1vY5Jun0sF8laREzXAR/sDFGW9AFg14h4Y79jydc/inkT/n7D/BP+vh4RXy4RV47tpaSO0xfnuB4ADoyyi/i9DDiFtKTGZpI2B94aEf8+ZtccwKS/0aLO1zTjMyfXvYB3RcSbCsZxK7B1o4NrReCGiHhlgViWB04jlcuvSP85NwIuBI4oMeSvEdudpIR6f76/IWkY4qbNYZx9jmkC8ANSot0V2IK0X27JchpHGvJbtJN7YXKzoSJibgWx/Ii0yuapjWHAt0XEZot+5pIbuDb9xmJmJ0bXJg6STiQte1BMTmp7kDaJ3o20nvbXFvmksXcGcL2kC/P9vYDTC8XyEdLU+A06/yklrUZaFuKj9F5vpl8+APwk938I2Bh4T/7wnlYioIiYk4e3Xkla42bvyJvhlBIRz0nag8Ijm7rlQRNvJy+3rrwZTkSUHC69cl46o3lsTL/5D1xNv0O99w4tMpEmX3sX0pDDXUnr1n+X1L44qUQ83SRNBl5LSmbXRNqko0QctwHbRMSTXcdXJa3jMmY1oOHIieNvSOV0V6nO2zzDtfmfdwXSYmtBmnDUa7x830g6HrgFuCAqSTKSLid1vM+ksYVjRHyuYEyXkQabfC8iJkt6B3BYROw+Ztes5O8xaiT9E/Ae0oiY5priqwHXRsT+heJ6njQS5eA8/RtJ90ZE0ZE7TXno5qbAryNiTCeILCKGhX4wS7q1RJNTVwxVbMyTm1CWAdYjtZ3PJ89NKUbzNlF5jtQhX8PaSWPabLIklJbIPo20+NqjpCbN/ceyGXrgmneA/wIuIy0n0NwrdG5ElJgN2PEq0gzhH0q6F/gOY7ya3lBys8AXSbMkP0JqQnkYmCTpgxFRoskiNP8yz01Fmy20kI15gL4n/ZzUn5N0YUSM2dZ6SyrqXEPpWkmvjIhbh35of0TEvcAbchPhMv3oZxi4mn4vtXSYNuLZntTU83ZSArkwIk4rEMfNpH1CVyc1OW0eEffm9W6uKtSRex8pufdK+lHym5Eq3JhH0imkUTF9XxxvKLlS0RntdHVEXFI4njuAvybVpv9CwbV3lFZsvaXRB/kxUj74NXBUjOEmOAOb9BfSYXpBRPx30cAalHbO2gXYNyIOKXD95sJh8zWdlBqNUjNJ3yON665pMbNbSYsK/pK0xEcnkZXeI/cE0nr6Z+dD+wEzI+KYhT9rzGPqObKvxIi+PEx62zwx8s3A50lltBUwdSxn6A9c806PDtOzSB2DfU+qQ8mjLK7IPyUsk5tSlgGe72pWKb32To1q3Jhnr4LXXpQ9gC07I4kkTSMt+Vws6Tdq1fOt3FounBcGK+wNnB4RM4GZkt4zlhceuKRPSqA/Bl7b6DA9uWxI1Vqd+XfMajYRDOZXwJE5rnQA3SLil5I2I428grSExu0lY2pYg3mraq5eMhCobuVW5RFpTwI7A19tnBvTD6RBTPrVdZjWqpbhokuL6NqYJ/fNvAsotmGPpH8mjVb7fj50rqSvRMRXF/G0fvgUcJOk6aRKxeuAD5UNqaqVW79A6s97ArgzImYASNqKMd7Xe2Db9KGeDlMbHEpbJL4L2IfUIXh+4aUFbiGtHPnHfH9V0tDkosuN5FjWI7XrC7g+In5bOJ6qVm6VNBF4EXBzoxlsPWC5GMPNggaxpv+CiPgp8FNJR5I7TEljYs2GLa+Psi+pAvF70sQ6RcSORQNLRJqU1fEMvUc+9UVuLz+WNErmVuBTEVF6O9COzsqt11DByq0R8Ru65lj0Y5DAQNf0zUZDY2LdYZ3FuWqZWCfp30gfRufnQ28DzomIzxaK53JSP9E1pA2CVouIg0vE0k0VrtxagpO+2RAkvY1U038NcDmpn+gbEbFx0cAySVsDf8e8JTRuKBjLrIjYsnF/geVQSsgzmK+IiDeUjqW0gW7eWRpI+iHpK/lXSk9eqVnJcoqIC4ELG5P83g+smydGXRgFNpyRdCnwnoi4Lyf5Yom+i7qG/o5r3i81Kz4vAvekpNWjgmW6S3JNvzBJLyatn7JtRHyldDy1qq2clDYAnwq8MyJ2KnD9fYB/J63u+emIeGaIp/RF5TOqzyWN3rmSxl7VEXFkqZhKcNLvo/wVc1pEHFA6lpq5nIYnf/P4GGnG+Vk01iaKiM+XiqtWkg7qdbzQGlPFuHmnj/JXzAmSlo+Cm1wMpXST09JSThV4hlRjXYG0imzRBelqJWnDiLi/bcl9YZz0++8+0jDSi5n/K2ZNNbMDyU0pBWO4j/rLqRhJu5HWa7kYmBxd+w/YfL4PTAaQdH5EvL1wPAvVjwpXa5J+6dprw4P5ZxlS7ayoXk0pEdGJcWaxwCorJ5g35C9P6nkZaTOVywq1p3+YtDBXLUsu1KzZv1B8mO0QxrzC1Zo2/Qo7AlcjdWz9sYJYrgDeUmNTSmXlNJM0NHJN4DpgBvBkFNqYx4anOWy0liGkUK7vaiBr+hXXXsmLY50FrJXv/w44sHCN7T4qa0qptJyUl8I9jLTV5aclFdlW0j7ChFUAABTvSURBVBbLFpKeINX4V8q3ofBuXqX6rgYy6VfeEXgacHRETAeQtAPwddLEn1Kqa0qhznKSpO1IszkPy8cG8v/QIImImhdcvI8+V7gG+Q17H5XVXrNVOokMICKuzm3FxUTE8VBXUwoVlhPwL6SVIi+MiNuV9jedPsRzxpSkQ0nLKd9dMg5bYn2vcA1sm76kj/c63klwpUi6kLRu/Vn50AHAlIgothlGd1MKULwppcZy6qjpw1HSJ0hr6W9Earr8MelDYFbRwLpUNJCiSv18Tw1s0u+o6T8oQJ6SfjzzNr24BjguIh4rGNO1wIe7mlL+MyKKNaVUWk6vJG2CvhapPXgO5fsZAJC0EvAPwL8CE2tr0qhtIEUtilS4ImIgf4DNSNuz/Tr/zAReUUFcU4dzrM8x3TycYy4nrgV2bNzfgbR2fcmYPgJcRqrhf5G0zv96hWMaB3y7ZAxLy0+J99Qg74Pa6QjcKCI2Aj5A6ggsrdfuQaV3FLpX0kclTco/HyFtEFJSjeW0QD8DULqfYW9gbeCHwAXAxVF44/aIeA6YIGn5knF0k7S3pLslPS7pCUlzGyN5Sun7e2qQO3Kr6giUtDtps+iJkr7YODWeghs5ZIeSmlIuyPevAQ4uEUjl5XSvpI8yfz9D0Q/HiJicmzBfS9oo6OuSHo6I1w7x1LF2H/UNpPg0aT7KnQVj6Nb399QgJ/3a/oM+SJrM81bmnyswl7RUb0lviK6VBiVNBb5XIJaay6maD8eO3Cb8d8DrgSnAA6SmntJqHAb8cGUJHwq8pwa2I7fGjkAASctGROka63x6zVIsPXOxxnLqRdJnI+JfC17/B6T39o+BG6KSJZY7ahpIIelk4K9Ia/H8pXM8Ii5Y6JPGPqapEfG9oY6N6jUHOOn3vTCHiOfciNhH0q3AAoUeBTaybjSl7EPa97VjPLBpFNgwusZyWhRJ90fEhoVjWAnYMCJ+XjKOpkqHAZ/R43BExKF9DyYrUeEa5KRfVe1V0noR8ZCkjXqdj4hfF4hpC2BL4BOkddk75gLTI+LRAjFVV06LIumBiNig4PXfAnwWWD4iNpa0JfCJiHhrqZhyXNUNA65JyQrXwLXp19oR2BlRUVPSioibgZslnV1LU0qN5aS0S1bPU/TeIaqfjgO2Aa4GiIhZkiaVC+cFVQ2kAJC0Imn5jFcAK3aOF6rpF+u7GrikT90dgUjaGzgReBHzkkZEgUWfOk0pwE2SqmpKqamcSO+joHeCL72207MR8bhU+rNnAbUNpIAUy13ArqRvt/sDRTp2S1a4Brl5p8qOQEn3UMmwsZqbUmoqp5pJOh24CjgGeDtwJLBcRBxROK7qBlJIuikitpJ0S0RsLmk54Ioos8dxsb6rgavp11x7zaoZNlZjU0pDNeVUufeRNlT5C3AOcAXwyaIRJTUNA+7ojGx6LHc0/xaYVCiWo/LvN/f7wgNX06+59grVDhurqSmlE1N15WTDV9tAinz9vwfOBzYHzgBWBT4aEaeWiqmEgUv6tat02Fh1TSk1llNNcvks7D9vRMRhCzk3pmocBlyzEhWugU36NdZeayXppxGxfek4bPgk9drce0PSmv/jImL9PocEVDsM+C3ALZ1v+ZI+Rur/+DVwVEQU62AuUeEa5KRfXe0VQNL6wJeA7Uk1tZ+Q3nizC8ZUXVNKjeVUK6XNXI4FXgecBJwehXeMq2kghaRbSEs6PynpzcDngf2ArUgrt+5aMLa+V7gGriO3odaOwDOA/wKm5vsH5GO7FIsoffV+Enhj41gwbz2QEmosp6pI+ltSJ+5WwGeAI0on2koHUkREPJlv7036UJwJzJT0ngLxNM2Q9F36WOEa5Jp+dbVXAEmzImLLoY61nctp0SR9j7TA2meBc4Hnmucj4g+F4qpuIEWu6b+GVLH5FfD2iJiRz90REZv2O6ZGbH3vuxrkmn6NtVeA30k6gDS8DtLXzN8XjKfWppTqyqmbym4BuDXpb/WvpL0iYN7ksQBe0ud40oXrHAb8BWAW8ARwZyPhbwWU3nvgkH5fc2Br+rWStCHwZWA70n/Oa0kJtuREqCtJTSnN2ZP7R0SxppQay6mbvAXgQtU2kELSxBzLzRHxfD62Hmki2/0lYsox9L3CNbBJv9Laa5XclDI0SeOAaRFxQOlYlga1DqSoTYkK1yA371TVEdi1+NsCumcv9lk1TSm1llNEPCdpgqTlS4+MWUrUOpCiNhMiotmuf6akfxnLCw5y0u97YQ7hCOA2Uqfbg5RfnbHpUFJTyknMa0opNQmq5nK6j/q2AKxV30elLKX6XuEa5KRfTe01W4/0reOdpCWevwucX2KySrfcpll0/fWGasuJOrcArFWtAylq0/cK1yC36VfbEZg7lfYDjgY+GBFnDfGUsYqjyqaUjlrKqZsq2gLQbHENbE2/strrCyRNJiWyXYDLmH/N/36rtimlsnICFtwCUFLxLQBr5YEUi1aywjVwNf1aa6+Sjicto3on8B3g8gpmT65NZU0pNZZTh7cAHL4ahwHXRNLTLKLCFRHTxuzaA5j0ixXmEHE9D9wLPNUJpXMqhVV2nf9amlJqLidJN0fEFkMdK6nwhLFmHNUPAy5ZViUrXIPYvFNrR+DGha+/UJU1pVRbTtS5BWC3A8kTxgrHUdtAil6KlVVE/B74GvC1RoXrdkljXuEauJp+Uy2111rV3JRSo9q2AKx5wlhtAylqLauuCtdM4HMRcceYXnNQk36Jwlza1NyUUiNJUyPie0Md63NMV5BmvnrC2BBqKquSFa6BS/quvQ7fwlZC7KhheGtNKt0C8FRgMlDFhLFaB1JAXWVVssI1iG36HyUV5hb55z8lgWuvC3BSH57GFoATu5LaeFK/UUm1TRirdhgwdZVVsb6rQazpV117lbQ9cBywEelDt/NhVGQp3FrVVE41bgHYrZYJYzUOA+5WS1mVMnBJv3aS7gLeT+pneGHji9ybb1mN5VTTFoAd3RPGgGomjNU2kKLmsuqnQWzeqd3jEXFZ6SCWAtWUU6VbAHacBhzdNWHs66SdooqpbBhwR5Vl1W+u6feZpBOAcaSFp5qrD95YMKZqmlIaMVVTTjVuAdhR24SxmgdS1FZWpTjp95mk6T0OR0Ts1PdgskqbUqorpxpJuhC4kfknjE2JiL0KxVPtMODayirH1PcK18Am/Rprr7WSdH1EvLp0HLWrbQvAHFNtE8aqHUhRW1nlmPpe4RrkpF9V7VXSARHxbUlH9zpfalw1VNeUUnM5VbcFYI0TxmpVY1mVqHANckduNR2B2Sr5d+nxwb103nRTGscCKNGUUnM51bgF4IeA7qTV65jVWVbTJX2GPla4BrmmX03t1QaDpJOBv6KCLQAbE8b2IY2F7xgPbBoR2/Q7plrVXFYl+q4GuaZfU+0VScsChwF7ARNzLA8CFwGnR8QzBWKqrimlxnJqqGkLwAeBGaSNgprDIeeSmjVtnmrLKiJ27Pc1B7amXxtJ5wCPAdOAzu5B6wMHAWtFxDsLxPSPEXGqpI/3Oh8RxxeIqbpyqlmNE8agzoEUNZVVyQrXwNX0a6y9ZpMj4uVdx2YD10n6RYmAIuLU/LvvyX0Rqiunjpq2AKx8whjA6fQYSFFCpWVVrO9q4JI+9XYEPippKmkdkucBJC1DWqekyLoklTalVFdODWeQtgCcmu8fkI+V2ALwqPz7zQWuPRw1DaSorqxKVrjcvNMnkiaRxnjvxLzktQYwHTgmIvq+A1ONTSk1llPH0rAFYC08kGLRSla4Bi7pV1p7nU9eiVAR8bvCcfy8R1NK59wvIuJl/Y6pK4YqyqlDaU/VM5l/C8BDImLngjFVN2Esx1XdjOqayqpkhWsQk351tddGbOOBCRHxy67jm0fELQXiuQ74HL2bUo4uNUu3tnJqXL+qLQBzTNVNGKtVTWVVssI1iG36VXYEStoH+ALwiKTlgIMj4oZ8+kzSjj79ti+p5vNVSd1NKfsWiKfWcgIgIu4nDfurSVUTxioeSAF1lVWxvqtBTPq1dgQeC7wq0mqN2wBnSTo2T+wpsrtQRNxH2uyipqaU6spJFW8BCMyQ9F0qmDCW1TqQAuoqq2IVrkFM+tXVXrNxEfEQQET8TNKOwCV5GGCxNrYKm1JqLKeatwCsacJYrcOAO6opq5IVroFr02+qqPaKpGuBdzeTq9K2bd8HXhsRKxSI6YWmFGC+phQV2vC70nKqfgvAWiwNAylqUarCtcxYvXBJksZLemlE/L6Z8CWVnLDyT3SVd0TMBXYDDi0S0bymlC2BQ0hNKXvnc6Vqs9WVU34ffS1PmT+Y9M3xdknvLhFPk6T1JV0o6RFJD0s6P38rKuUs0n7Cx5PWu3lTvr0F8O2CcVVVVrnCdRdwvqTbJW3dOH3mmF48Igbqh7So0oPALOB2YOvGuRsLxqXReMwox3Rr1/31SDMojyxVVjWWU+O6k4HP5PfW6aTFuoq8nxoxXUn6wF42/xwMXFkwnp8v4twvXFYvxDILWC/f3ob0AbB3vn/TWF57EGv6NdZeIS2h+r487O8FkpaXtJOkaaRhpf00V9JLO3citaXvAOwJvKLPsXRUV06Sjpc0k7TB949Iuy0dFhF39DOOhZgQEWdExLP550xgQsF4HpU0NQ+eANJACknvpPyM6prKar6+K2BH4MOSjmSM+64GsSO3xo5AmNc8cY6kjUlzCVYkzVr8H+CkiJjV55h6NqVI2o30jamEGsvpo6QtALfIP/8pCSi/BSDwO0kHMP+EsWLbXFLvQAqoq6zm5iboX0KqcClt1P59xrjCNXAduTV2BHbL48/XAZ6Kslu1KYZ4AwznMWOlonKqeQvA6iaMddQ0kALqKitJWwBPRsTdXceXA/aJiLPH7NoDmPSLFebSRtLVwPnARZEmHnWOL0/aR/QgYHr+Gmw2LBUOA65OyQrXICb9qmuvNZG0IqkpZX+gV1PKVwo0pdgw1DphrNJhwNWVVckK1yAm/atx7XWx1dKUYsMj6WkWMWEsIqYVimsWsHvMm1H9LeDYiLhA0k0RsVWBmKorq5IVrkFM+q692sCrdcKYpFsj4pWN++sBl5AWQDy4UE2/yrLq6HeFa+CSfpNrrzaaVOEWgDmuiaSRKEcDH4yIswrGUvVAiprKqpRBHLL5gkhTvh8qHYcNjGq2AOyQNJmUxHYBLmP+jb9LqHEYMFBlWRUx0DV9s9Ek6footMdAN0nHk7b/uxP4DnB5VLDpd40DKWotq1Kc9M2GSRVtASjpedKEsac6YXROUXDCWI0DKWotq1Kc9M2GSRVtAVjrhLEaB1LUWlalOOmb2ZjwQIo6OembDUF1bwFotlgGevSO2SipeQtAs8Ximr6ZWYu4pm82BFW8BWCtE8Zq5LJKXNM3G4Kkc0ijUKYBs/Ph9UnDD9eKiHcWjO0uekwYi4iSa+pXyWWVuKZvNrTJEfHyrmOzgesk/aJEQA2PR8RlhWNYWriscNI3G45HJU0lLdL1PKQtAEmLeJVetGu6pM9QwYSxpYDLCjfvmA1J0iTSFoA7MS/Jd7YAPCYiflUmsromjNXOZZU46Zsthtq2ADRbXG7eMRuG2rYA9ISx4XNZzc9J32wIzS0A89ICL2wBCJwJ9H1jEDxhbHG4rBrcvGM2hBq3ADRbUq7pmw1tXEQ8BBARP5O0I3CJpPWZt0xvX9U8Yaw2Lqv5uaZvNoQatwCsecJYbVxW83PSNxuCpC2AJyPi7q7jywH7RMTZBWL6eY8JY51zv4iIl/U7plq5rOa3zNAPMWu9W7oTPqQ9mDsJX5L6HNOjkqbmSWLkGJaR9E7KTxirjcuqwUnfbGjTJb1P0obNg5KWl7STpGmkpoJ+2hd4B/CwpF/k5SB+C+ydz9k8LqsGN++YDaHGLQC74vOEsWFyWTnpmy2WmrYArG3CWM1cVvO4ecdsMeR2/IcqSPj7AHcB50u6XdLWjdNnlomqTi6r+Tnpmy2djgVeFRFbAocAZ0naO5/rd6dy7VxWDZ6cZbZ0qm7CWMVcVg2u6ZstneZKemnnTk5qOwB7Aq8oFVSlXFYNrumbLZ3+ia5KW0TMlbQbsE+ZkKrlsmrw6B2zpZAkxRD/eYfzmDZwWc3PzTtmS6caJ4zVymXV4Jq+2VKo9gljNXFZzc9J32wpV9OEsdq5rJz0zcxaxW36ZmYt4qRvZtYiTvq21JP0nKRZkm6T9D1JK4/gtQ6W9OURPPfFizj/r5LuynHeLOnAfPwbkjbNt49dssjNhsdJ3wbBUxGxZURsBjwNHNE8qaQf7/WDgZ5JX9IRwC7ANjnO15HXfYmIv4+IO/JDnfRtTDnp26D5MfDXkiZJulPSV4EbgQ0k7Sfp1lzTPrHzBEmH5M01fgRs3zh+pqR3NO7/sXH73/Jr3SzphPy4KcDZ+VvHSl1xHQu8JyKeAIiIxyNiWn6tqyVNkXQCsFJ+/tmSPinpqMY1/0PSkaNYVtZCTvo2MCQtC+wO3JoPvRz4VkRsBTwDnAjsBGwJbC1pL0nrAceTkv0uwKbDuM7uwF7AqyNiC+DTEXEeMAPYP3/reKrx+NWA1brXcu8WEccw71vL/sDp5ElD+ZvKvkDf9+O1weK1d2wQrCSpM7nmx6Rk+WLg1xFxXT6+NXB1RMwBkHQ2qYmFruPfBYbaKPsNwBkR8SRARPxhiMeLJVjNMSLuk/R7SVsB6wI3RcTvF/d1zJqc9G0QPJXXSn9B3qf8T81Di3j+whLys+Rvw0ovuHzjtYadxCPiCUl/kvSSiLh3uM/LvkHqK/gr4JuL+VyzBbh5x9rieuD1ktaRNA7YD/hRPr6DpLXzbM2pjefcB7wq394TWC7f/h/g0M4oIUlr5eNzgdU6T5b0KUlvy3c/BXwlb9uHpPGSDu8R5zM5jo4Lgd1I31SuWPx/ttn8XNO3VoiIhyR9CJhOqqlfGhEXAUg6Dvg/4CFSp++4/LSvAxdJ+hlwFfmbQ0RcLmlLYIakp4FLSR21ZwJfk/QUsB3wSuDi/FqnAKsCN0h6htTH8LkeoZ4G3CLpxojYPyKeljQdeCwinhu1ArHW8jIMZmNE0hURsesIX2MZ0gfR1Ii4e3QiszZz847ZGBmFhL8pcA9wlRO+jRbX9M3MWsQ1fTOzFnHSNzNrESd9M7MWcdI3M2sRJ30zsxZx0jcza5H/D7HoXSdNSMsqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = city_quantity_ordered[:9].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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": [
    "fig2 = city_quantity_ordered[9:18].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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": [
    "fig2 = city_quantity_ordered[18:27].plot(kind='bar')"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}