Sales_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "983c3198",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "905dcfd1",
   "metadata": {},
   "source": [
    "## Merge the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "324deb13",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"filepath.csv\")\n",
    "\n",
    "files = [file for file in os.listdir('folderpath')]\n",
    "\n",
    "final_df = pd.DataFrame()\n",
    "\n",
    "for file in files:\n",
    "    df=pd.read_csv(\"folderpath\"+file)\n",
    "    final_df = pd.concat([final_df,df])\n",
    "    \n",
    "final_df.to_csv('all_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9038bf39",
   "metadata": {},
   "outputs": [],
   "source": [
    "fdf=pd.read_csv('path_to_all_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "be9914b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>176558</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>2</td>\n",
       "      <td>11.95</td>\n",
       "      <td>04/19/19 08:46</td>\n",
       "      <td>917 1st St, Dallas, TX 75001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176559</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>04/07/19 22:30</td>\n",
       "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186845</th>\n",
       "      <td>259353</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>3</td>\n",
       "      <td>2.99</td>\n",
       "      <td>09/17/19 20:56</td>\n",
       "      <td>840 Highland St, Los Angeles, CA 90001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186846</th>\n",
       "      <td>259354</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700</td>\n",
       "      <td>09/01/19 16:00</td>\n",
       "      <td>216 Dogwood St, San Francisco, CA 94016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186847</th>\n",
       "      <td>259355</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700</td>\n",
       "      <td>09/23/19 07:39</td>\n",
       "      <td>220 12th St, San Francisco, CA 94016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186848</th>\n",
       "      <td>259356</td>\n",
       "      <td>34in Ultrawide Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>379.99</td>\n",
       "      <td>09/19/19 17:30</td>\n",
       "      <td>511 Forest St, San Francisco, CA 94016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186849</th>\n",
       "      <td>259357</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>11.95</td>\n",
       "      <td>09/30/19 00:18</td>\n",
       "      <td>250 Meadow St, San Francisco, CA 94016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>186850 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Order ID                     Product Quantity Ordered Price Each  \\\n",
       "0        176558        USB-C Charging Cable                2      11.95   \n",
       "1           NaN                         NaN              NaN        NaN   \n",
       "2        176559  Bose SoundSport Headphones                1      99.99   \n",
       "3        176560                Google Phone                1        600   \n",
       "4        176560            Wired Headphones                1      11.99   \n",
       "...         ...                         ...              ...        ...   \n",
       "186845   259353      AAA Batteries (4-pack)                3       2.99   \n",
       "186846   259354                      iPhone                1        700   \n",
       "186847   259355                      iPhone                1        700   \n",
       "186848   259356      34in Ultrawide Monitor                1     379.99   \n",
       "186849   259357        USB-C Charging Cable                1      11.95   \n",
       "\n",
       "            Order Date                         Purchase Address  \n",
       "0       04/19/19 08:46             917 1st St, Dallas, TX 75001  \n",
       "1                  NaN                                      NaN  \n",
       "2       04/07/19 22:30        682 Chestnut St, Boston, MA 02215  \n",
       "3       04/12/19 14:38     669 Spruce St, Los Angeles, CA 90001  \n",
       "4       04/12/19 14:38     669 Spruce St, Los Angeles, CA 90001  \n",
       "...                ...                                      ...  \n",
       "186845  09/17/19 20:56   840 Highland St, Los Angeles, CA 90001  \n",
       "186846  09/01/19 16:00  216 Dogwood St, San Francisco, CA 94016  \n",
       "186847  09/23/19 07:39     220 12th St, San Francisco, CA 94016  \n",
       "186848  09/19/19 17:30   511 Forest St, San Francisco, CA 94016  \n",
       "186849  09/30/19 00:18   250 Meadow St, San Francisco, CA 94016  \n",
       "\n",
       "[186850 rows x 6 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d4e55e4",
   "metadata": {},
   "source": [
    "Clean the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "87d26682",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order ID            545\n",
       "Product             545\n",
       "Quantity Ordered    545\n",
       "Price Each          545\n",
       "Order Date          545\n",
       "Purchase Address    545\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdf.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c4d833eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order ID            545\n",
       "Product             545\n",
       "Quantity Ordered    545\n",
       "Price Each          545\n",
       "Order Date          545\n",
       "Purchase Address    545\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdf.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "aa1c6d2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1433</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1553</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185176</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185438</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186042</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186548</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186826</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>545 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Order ID Product Quantity Ordered Price Each Order Date  \\\n",
       "1           NaN     NaN              NaN        NaN        NaN   \n",
       "356         NaN     NaN              NaN        NaN        NaN   \n",
       "735         NaN     NaN              NaN        NaN        NaN   \n",
       "1433        NaN     NaN              NaN        NaN        NaN   \n",
       "1553        NaN     NaN              NaN        NaN        NaN   \n",
       "...         ...     ...              ...        ...        ...   \n",
       "185176      NaN     NaN              NaN        NaN        NaN   \n",
       "185438      NaN     NaN              NaN        NaN        NaN   \n",
       "186042      NaN     NaN              NaN        NaN        NaN   \n",
       "186548      NaN     NaN              NaN        NaN        NaN   \n",
       "186826      NaN     NaN              NaN        NaN        NaN   \n",
       "\n",
       "       Purchase Address  \n",
       "1                   NaN  \n",
       "356                 NaN  \n",
       "735                 NaN  \n",
       "1433                NaN  \n",
       "1553                NaN  \n",
       "...                 ...  \n",
       "185176              NaN  \n",
       "185438              NaN  \n",
       "186042              NaN  \n",
       "186548              NaN  \n",
       "186826              NaN  \n",
       "\n",
       "[545 rows x 6 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_df=fdf[fdf.isna().any(axis=1)]\n",
    "null_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "69586517",
   "metadata": {},
   "outputs": [],
   "source": [
    "fdf=fdf.dropna(how='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "936cbbd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order ID            0\n",
       "Product             0\n",
       "Quantity Ordered    0\n",
       "Price Each          0\n",
       "Order Date          0\n",
       "Purchase Address    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdf.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e03b3147",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order ID            0\n",
       "Product             0\n",
       "Quantity Ordered    0\n",
       "Price Each          0\n",
       "Order Date          0\n",
       "Purchase Address    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdf.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49b3364a",
   "metadata": {},
   "source": [
    "#### Question 1: What was the best month for sales How much money was earned in that month?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f860d125",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdf.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "33d665f5",
   "metadata": {},
   "outputs": [
    {
     "ename": "ParserError",
     "evalue": "Unknown string format: Order Date",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/arrays/datetimes.py\u001b[0m in \u001b[0;36mobjects_to_datetime64ns\u001b[0;34m(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object, allow_mixed)\u001b[0m\n\u001b[1;32m   2186\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2187\u001b[0;31m             \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtz_parsed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconversion\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime_to_datetime64\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"K\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2188\u001b[0m             \u001b[0;31m# If tzaware, these values represent unix timestamps, so we\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/_libs/tslibs/conversion.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.conversion.datetime_to_datetime64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Unrecognized value type: <class 'str'>",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mParserError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/m6/qgmvxk1d17g01z7vr6rqxmh00000gn/T/ipykernel_1459/3513543160.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Order Date'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Order Date'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mfdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Order Month'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Order Date'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmonth\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/tools/datetimes.py\u001b[0m in \u001b[0;36mto_datetime\u001b[0;34m(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)\u001b[0m\n\u001b[1;32m    885\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcache_array\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    886\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 887\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert_listlike\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    888\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    889\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mABCDataFrame\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mabc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMutableMapping\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/tools/datetimes.py\u001b[0m in \u001b[0;36m_convert_listlike_datetimes\u001b[0;34m(arg, format, name, tz, unit, errors, infer_datetime_format, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[1;32m    399\u001b[0m     \u001b[0;32massert\u001b[0m \u001b[0mformat\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0minfer_datetime_format\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    400\u001b[0m     \u001b[0mutc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtz\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"utc\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 401\u001b[0;31m     result, tz_parsed = objects_to_datetime64ns(\n\u001b[0m\u001b[1;32m    402\u001b[0m         \u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    403\u001b[0m         \u001b[0mdayfirst\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdayfirst\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/arrays/datetimes.py\u001b[0m in \u001b[0;36mobjects_to_datetime64ns\u001b[0;34m(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object, allow_mixed)\u001b[0m\n\u001b[1;32m   2191\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"i8\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtz_parsed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2192\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2193\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2194\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2195\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtz_parsed\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/arrays/datetimes.py\u001b[0m in \u001b[0;36mobjects_to_datetime64ns\u001b[0;34m(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object, allow_mixed)\u001b[0m\n\u001b[1;32m   2173\u001b[0m     \u001b[0morder\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mLiteral\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"F\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"C\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"F\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_contiguous\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"C\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2174\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2175\u001b[0;31m         result, tz_parsed = tslib.array_to_datetime(\n\u001b[0m\u001b[1;32m   2176\u001b[0m             \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"K\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2177\u001b[0m             \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/_libs/tslib.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/_libs/tslib.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/_libs/tslib.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslib._array_to_datetime_object\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/_libs/tslib.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslib._array_to_datetime_object\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/_libs/tslibs/parsing.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.parsing.parse_datetime_string\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/dateutil/parser/_parser.py\u001b[0m in \u001b[0;36mparse\u001b[0;34m(timestr, parserinfo, **kwargs)\u001b[0m\n\u001b[1;32m   1366\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparserinfo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimestr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1367\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1368\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mDEFAULTPARSER\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimestr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1369\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1370\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/dateutil/parser/_parser.py\u001b[0m in \u001b[0;36mparse\u001b[0;34m(self, timestr, default, ignoretz, tzinfos, **kwargs)\u001b[0m\n\u001b[1;32m    641\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    642\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 643\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mParserError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Unknown string format: %s\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimestr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    644\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    645\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mParserError\u001b[0m: Unknown string format: Order Date"
     ]
    }
   ],
   "source": [
    "fdf['Order Date']=pd.to_datetime(fdf['Order Date'])\n",
    "fdf['Order Month']=fdf['Order Date'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6c2c1291",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         04/19/19 08:46\n",
       "2         04/07/19 22:30\n",
       "3         04/12/19 14:38\n",
       "4         04/12/19 14:38\n",
       "5         04/30/19 09:27\n",
       "               ...      \n",
       "186845    09/17/19 20:56\n",
       "186846    09/01/19 16:00\n",
       "186847    09/23/19 07:39\n",
       "186848    09/19/19 17:30\n",
       "186849    09/30/19 00:18\n",
       "Name: Order Date, Length: 186305, dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdf['Order Date'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9614d7b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(fdf['Order Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "94dd859b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/m6/qgmvxk1d17g01z7vr6rqxmh00000gn/T/ipykernel_1459/3766196916.py:1: 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",
      "  fdf['Order Month']=fdf['Order Date'].str[:2]\n"
     ]
    }
   ],
   "source": [
    "fdf['Order Month']=fdf['Order Date'].str[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "17399496",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>Order Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>176558</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>2</td>\n",
       "      <td>11.95</td>\n",
       "      <td>04/19/19 08:46</td>\n",
       "      <td>917 1st St, Dallas, TX 75001</td>\n",
       "      <td>04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176559</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>04/07/19 22:30</td>\n",
       "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
       "      <td>04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>176561</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/30/19 09:27</td>\n",
       "      <td>333 8th St, Los Angeles, CA 90001</td>\n",
       "      <td>04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order ID                     Product Quantity Ordered Price Each  \\\n",
       "0   176558        USB-C Charging Cable                2      11.95   \n",
       "2   176559  Bose SoundSport Headphones                1      99.99   \n",
       "3   176560                Google Phone                1        600   \n",
       "4   176560            Wired Headphones                1      11.99   \n",
       "5   176561            Wired Headphones                1      11.99   \n",
       "\n",
       "       Order Date                      Purchase Address Order Month  \n",
       "0  04/19/19 08:46          917 1st St, Dallas, TX 75001          04  \n",
       "2  04/07/19 22:30     682 Chestnut St, Boston, MA 02215          04  \n",
       "3  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001          04  \n",
       "4  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001          04  \n",
       "5  04/30/19 09:27     333 8th St, Los Angeles, CA 90001          04  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "fdf34fa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['04', '05', 'Or', '08', '09', '12', '01', '02', '03', '07', '06',\n",
       "       '11', '10'], dtype=object)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdf['Order Month'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5f7fd19e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order ID            0\n",
       "Product             0\n",
       "Quantity Ordered    0\n",
       "Price Each          0\n",
       "Order Date          0\n",
       "Purchase Address    0\n",
       "Order Month         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdf.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d55464fb",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "invalid literal for int() with base 10: 'Or'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/m6/qgmvxk1d17g01z7vr6rqxmh00000gn/T/ipykernel_1459/290741855.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Order Month'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   5813\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5814\u001b[0m             \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5815\u001b[0;31m             \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5816\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5817\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    416\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    417\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 418\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    420\u001b[0m     def convert(\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, align_keys, ignore_failures, **kwargs)\u001b[0m\n\u001b[1;32m    325\u001b[0m                     \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 327\u001b[0;31m                     \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    328\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    329\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mignore_failures\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    589\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    590\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 591\u001b[0;31m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_array_safe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    592\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    593\u001b[0m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaybe_coerce_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_array_safe\u001b[0;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   1307\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1308\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1309\u001b[0;31m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1310\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1311\u001b[0m         \u001b[0;31m# e.g. astype_nansafe can fail on object-dtype of strings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_array\u001b[0;34m(values, dtype, copy)\u001b[0m\n\u001b[1;32m   1255\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1256\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1257\u001b[0;31m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1259\u001b[0m     \u001b[0;31m# in pandas we don't store numpy str dtypes, so convert to object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m   1172\u001b[0m         \u001b[0;31m# work around NumPy brokenness, #1987\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1173\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0missubdtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minteger\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1174\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype_intsafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1176\u001b[0m         \u001b[0;31m# if we have a datetime/timedelta array of objects\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.astype_intsafe\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'Or'"
     ]
    }
   ],
   "source": [
    "fdf['Order Month'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "62657166",
   "metadata": {},
   "outputs": [],
   "source": [
    "fdf=fdf[fdf['Order Date'].str[:2]!='Or']                    #removing rows which have errors('Or') in Order Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "28612378",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['04', '05', '08', '09', '12', '01', '02', '03', '07', '06', '11',\n",
       "       '10'], dtype=object)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdf['Order Month'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "bc4799ef",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "can't multiply sequence by non-int of type 'str'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/ops/array_ops.py\u001b[0m in \u001b[0;36m_na_arithmetic_op\u001b[0;34m(left, right, op, is_cmp)\u001b[0m\n\u001b[1;32m    165\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 166\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    167\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/computation/expressions.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(op, a, b, use_numexpr)\u001b[0m\n\u001b[1;32m    238\u001b[0m             \u001b[0;31m# error: \"None\" not callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 239\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0m_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    240\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_evaluate_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/computation/expressions.py\u001b[0m in \u001b[0;36m_evaluate_numexpr\u001b[0;34m(op, op_str, a, b)\u001b[0m\n\u001b[1;32m    127\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 128\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_evaluate_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/computation/expressions.py\u001b[0m in \u001b[0;36m_evaluate_standard\u001b[0;34m(op, op_str, a, b)\u001b[0m\n\u001b[1;32m     68\u001b[0m         \u001b[0m_store_test_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     70\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'str'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/m6/qgmvxk1d17g01z7vr6rqxmh00000gn/T/ipykernel_1459/653448566.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Sales'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Quantity Ordered'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Price Each'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/ops/common.py\u001b[0m in \u001b[0;36mnew_method\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     67\u001b[0m         \u001b[0mother\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mitem_from_zerodim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     70\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mnew_method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/arraylike.py\u001b[0m in \u001b[0;36m__mul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    106\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0munpack_zerodim_and_defer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"__mul__\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__mul__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_arith_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moperator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmul\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    109\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0munpack_zerodim_and_defer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"__rmul__\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_arith_method\u001b[0;34m(self, other, op)\u001b[0m\n\u001b[1;32m   5524\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5525\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5526\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marithmetic_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5527\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5528\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_construct_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mres_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/ops/array_ops.py\u001b[0m in \u001b[0;36marithmetic_op\u001b[0;34m(left, right, op)\u001b[0m\n\u001b[1;32m    222\u001b[0m         \u001b[0m_bool_arith_check\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 224\u001b[0;31m         \u001b[0mres_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_na_arithmetic_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mres_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/ops/array_ops.py\u001b[0m in \u001b[0;36m_na_arithmetic_op\u001b[0;34m(left, right, op, is_cmp)\u001b[0m\n\u001b[1;32m    171\u001b[0m             \u001b[0;31m# Don't do this for comparisons, as that will handle complex numbers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m             \u001b[0;31m#  incorrectly, see GH#32047\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 173\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_masked_arith_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    174\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m             \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/ops/array_ops.py\u001b[0m in \u001b[0;36m_masked_arith_op\u001b[0;34m(x, y, op)\u001b[0m\n\u001b[1;32m    110\u001b[0m         \u001b[0;31m# See GH#5284, GH#5035, GH#19448 for historical reference\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 112\u001b[0;31m             \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxrav\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myrav\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'str'"
     ]
    }
   ],
   "source": [
    "fdf['Sales'] = fdf['Quantity Ordered'] * fdf['Price Each']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8ced8a72",
   "metadata": {},
   "outputs": [],
   "source": [
    "fdf['Quantity Ordered']=pd.to_numeric(fdf['Quantity Ordered'])          #convert to variable from str to numeric for calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b2088c8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "fdf['Price Each']=pd.to_numeric(fdf['Price Each'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "26e3708d",
   "metadata": {},
   "outputs": [],
   "source": [
    "fdf['Sales'] = fdf['Quantity Ordered'] * fdf['Price Each']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "7a02218c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>Order Month</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>176558</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>2</td>\n",
       "      <td>11.95</td>\n",
       "      <td>04/19/19 08:46</td>\n",
       "      <td>917 1st St, Dallas, TX 75001</td>\n",
       "      <td>04</td>\n",
       "      <td>23.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176559</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>04/07/19 22:30</td>\n",
       "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
       "      <td>04</td>\n",
       "      <td>99.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>04</td>\n",
       "      <td>600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>04</td>\n",
       "      <td>11.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>176561</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/30/19 09:27</td>\n",
       "      <td>333 8th St, Los Angeles, CA 90001</td>\n",
       "      <td>04</td>\n",
       "      <td>11.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order ID                     Product  Quantity Ordered  Price Each  \\\n",
       "0   176558        USB-C Charging Cable                 2       11.95   \n",
       "2   176559  Bose SoundSport Headphones                 1       99.99   \n",
       "3   176560                Google Phone                 1      600.00   \n",
       "4   176560            Wired Headphones                 1       11.99   \n",
       "5   176561            Wired Headphones                 1       11.99   \n",
       "\n",
       "       Order Date                      Purchase Address Order Month   Sales  \n",
       "0  04/19/19 08:46          917 1st St, Dallas, TX 75001          04   23.90  \n",
       "2  04/07/19 22:30     682 Chestnut St, Boston, MA 02215          04   99.99  \n",
       "3  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001          04  600.00  \n",
       "4  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001          04   11.99  \n",
       "5  04/30/19 09:27     333 8th St, Los Angeles, CA 90001          04   11.99  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c7224b1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "s1=fdf.groupby('Order Month')['Sales'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "bff98b39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order Month\n",
       "01    1822256.73\n",
       "02    2202022.42\n",
       "03    2807100.38\n",
       "04    3390670.24\n",
       "05    3152606.75\n",
       "06    2577802.26\n",
       "07    2647775.76\n",
       "08    2244467.88\n",
       "09    2097560.13\n",
       "10    3736726.88\n",
       "11    3199603.20\n",
       "12    4613443.34\n",
       "Name: Sales, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "7c6f379d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "a8df3233",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "months=range(1,13)\n",
    "\n",
    "plt.bar(months,s1)\n",
    "plt.xticks(months)\n",
    "plt.xlabel('Months')\n",
    "plt.ylabel('Sales')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae5e4828",
   "metadata": {},
   "source": [
    "### December had the most sales worth $4613443.34"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ac245dd",
   "metadata": {},
   "source": [
    "#### Question 2: which US city had the highest number of sales"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9e25cd3",
   "metadata": {},
   "source": [
    "Add city column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "73e87feb",
   "metadata": {},
   "outputs": [],
   "source": [
    "fdf['City']=fdf['Purchase Address'].apply(lambda x: x.split(',')[1])\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f1395e8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "fdf['State']=fdf['Purchase Address'].apply(lambda x: x.split(',')[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "751264e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "fdf['State']=fdf['State'].str[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "57f5e282",
   "metadata": {},
   "outputs": [],
   "source": [
    "fdf['City']=fdf['City']+\"(\"+fdf['State']+\")\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "65c71f03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>Order Month</th>\n",
       "      <th>Sales</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>176558</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>2</td>\n",
       "      <td>11.95</td>\n",
       "      <td>04/19/19 08:46</td>\n",
       "      <td>917 1st St, Dallas, TX 75001</td>\n",
       "      <td>04</td>\n",
       "      <td>23.90</td>\n",
       "      <td>Dallas(TX)</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176559</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>04/07/19 22:30</td>\n",
       "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
       "      <td>04</td>\n",
       "      <td>99.99</td>\n",
       "      <td>Boston(MA)</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>04</td>\n",
       "      <td>600.00</td>\n",
       "      <td>Los Angeles(CA)</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>04</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Los Angeles(CA)</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>176561</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/30/19 09:27</td>\n",
       "      <td>333 8th St, Los Angeles, CA 90001</td>\n",
       "      <td>04</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Los Angeles(CA)</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order ID                     Product  Quantity Ordered  Price Each  \\\n",
       "0   176558        USB-C Charging Cable                 2       11.95   \n",
       "2   176559  Bose SoundSport Headphones                 1       99.99   \n",
       "3   176560                Google Phone                 1      600.00   \n",
       "4   176560            Wired Headphones                 1       11.99   \n",
       "5   176561            Wired Headphones                 1       11.99   \n",
       "\n",
       "       Order Date                      Purchase Address Order Month   Sales  \\\n",
       "0  04/19/19 08:46          917 1st St, Dallas, TX 75001          04   23.90   \n",
       "2  04/07/19 22:30     682 Chestnut St, Boston, MA 02215          04   99.99   \n",
       "3  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001          04  600.00   \n",
       "4  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001          04   11.99   \n",
       "5  04/30/19 09:27     333 8th St, Los Angeles, CA 90001          04   11.99   \n",
       "\n",
       "               City State  \n",
       "0        Dallas(TX)    TX  \n",
       "2        Boston(MA)    MA  \n",
       "3   Los Angeles(CA)    CA  \n",
       "4   Los Angeles(CA)    CA  \n",
       "5   Los Angeles(CA)    CA  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "be42f873",
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</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>2795498.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austin(TX)</th>\n",
       "      <td>11153</td>\n",
       "      <td>1809873.61</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>3661642.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dallas(TX)</th>\n",
       "      <td>16730</td>\n",
       "      <td>2752627.82</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>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>4664317.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portland(ME)</th>\n",
       "      <td>2750</td>\n",
       "      <td>447189.25</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>1870732.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Francisco(CA)</th>\n",
       "      <td>50239</td>\n",
       "      <td>8211461.74</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>2747755.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Quantity Ordered  Price Each       Sales\n",
       "City                                                        \n",
       " Atlanta(GA)                   16602  2779908.20  2795498.58\n",
       " Austin(TX)                    11153  1809873.61  1819581.75\n",
       " Boston(MA)                    22528  3637409.77  3661642.01\n",
       " Dallas(TX)                    16730  2752627.82  2767975.40\n",
       " Los Angeles(CA)               33289  5421435.23  5452570.80\n",
       " New York City(NY)             27932  4635370.83  4664317.43\n",
       " Portland(ME)                   2750   447189.25   449758.27\n",
       " Portland(OR)                  11303  1860558.22  1870732.34\n",
       " San Francisco(CA)             50239  8211461.74  8262203.91\n",
       " Seattle(WA)                   16553  2733296.01  2747755.48"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cs=fdf.groupby(['City']).sum()\n",
    "cs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "3a52d5cc",
   "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": [
    "cities=[city for city, df in fdf.groupby('City')]            # to get list in same order as grouped data\n",
    "\n",
    "plt.bar(cities,cs['Sales'])\n",
    "plt.xticks(cities,rotation=\"vertical\")\n",
    "plt.xlabel('City')\n",
    "plt.ylabel('Sales')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18405a98",
   "metadata": {},
   "source": [
    "### San Francisco made the most sales in the US"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4fff100",
   "metadata": {},
   "source": [
    "#### Question 3: What time should we display the ads to maximixe likelihood of customers buying the product?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "f74b0478",
   "metadata": {},
   "outputs": [],
   "source": [
    "#change to datetime object\n",
    "fdf['Order Date']=pd.to_datetime(fdf['Order Date'])\n",
    "fdf['Hour']=fdf['Order Date'].dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c4593f1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>Order Month</th>\n",
       "      <th>Sales</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>176558</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>2</td>\n",
       "      <td>11.95</td>\n",
       "      <td>2019-04-19 08:46:00</td>\n",
       "      <td>917 1st St, Dallas, TX 75001</td>\n",
       "      <td>04</td>\n",
       "      <td>23.90</td>\n",
       "      <td>Dallas(TX)</td>\n",
       "      <td>TX</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176559</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>2019-04-07 22:30:00</td>\n",
       "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
       "      <td>04</td>\n",
       "      <td>99.99</td>\n",
       "      <td>Boston(MA)</td>\n",
       "      <td>MA</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>2019-04-12 14:38:00</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>04</td>\n",
       "      <td>600.00</td>\n",
       "      <td>Los Angeles(CA)</td>\n",
       "      <td>CA</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>2019-04-12 14:38:00</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>04</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Los Angeles(CA)</td>\n",
       "      <td>CA</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>176561</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>2019-04-30 09:27:00</td>\n",
       "      <td>333 8th St, Los Angeles, CA 90001</td>\n",
       "      <td>04</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Los Angeles(CA)</td>\n",
       "      <td>CA</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order ID                     Product  Quantity Ordered  Price Each  \\\n",
       "0   176558        USB-C Charging Cable                 2       11.95   \n",
       "2   176559  Bose SoundSport Headphones                 1       99.99   \n",
       "3   176560                Google Phone                 1      600.00   \n",
       "4   176560            Wired Headphones                 1       11.99   \n",
       "5   176561            Wired Headphones                 1       11.99   \n",
       "\n",
       "           Order Date                      Purchase Address Order Month  \\\n",
       "0 2019-04-19 08:46:00          917 1st St, Dallas, TX 75001          04   \n",
       "2 2019-04-07 22:30:00     682 Chestnut St, Boston, MA 02215          04   \n",
       "3 2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001          04   \n",
       "4 2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001          04   \n",
       "5 2019-04-30 09:27:00     333 8th St, Los Angeles, CA 90001          04   \n",
       "\n",
       "    Sales              City State  Hour  \n",
       "0   23.90        Dallas(TX)    TX     8  \n",
       "2   99.99        Boston(MA)    MA    22  \n",
       "3  600.00   Los Angeles(CA)    CA    14  \n",
       "4   11.99   Los Angeles(CA)    CA    14  \n",
       "5   11.99   Los Angeles(CA)    CA     9  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "007c118f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hour\n",
       "0      3910\n",
       "1      2350\n",
       "2      1243\n",
       "3       831\n",
       "4       854\n",
       "5      1321\n",
       "6      2482\n",
       "7      4011\n",
       "8      6256\n",
       "9      8748\n",
       "10    10944\n",
       "11    12411\n",
       "12    12587\n",
       "13    12129\n",
       "14    10984\n",
       "15    10175\n",
       "16    10384\n",
       "17    10899\n",
       "18    12280\n",
       "19    12905\n",
       "20    12228\n",
       "21    10921\n",
       "22     8822\n",
       "23     6275\n",
       "Name: Order ID, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr=fdf.groupby('Hour')['Order ID'].count()\n",
    "hr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "02975aac",
   "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": [
    "hours = [hour for hour, df in fdf.groupby('Hour')]\n",
    "\n",
    "plt.plot(hours,hr)\n",
    "plt.xticks(hours)\n",
    "plt.xlabel('Hour')\n",
    "plt.ylabel('Number of Orders')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "226d9ade",
   "metadata": {},
   "source": [
    "### Ads would be most effective during hours 11-13 and 18-20"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96095f9b",
   "metadata": {},
   "source": [
    "#### Question 4: What products are most often sold together?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "b59554cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>Order Month</th>\n",
       "      <th>Sales</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>2019-04-12 14:38:00</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>04</td>\n",
       "      <td>600.00</td>\n",
       "      <td>Los Angeles(CA)</td>\n",
       "      <td>CA</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>2019-04-12 14:38:00</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>04</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Los Angeles(CA)</td>\n",
       "      <td>CA</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>176574</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>2019-04-03 19:42:00</td>\n",
       "      <td>20 Hill St, Los Angeles, CA 90001</td>\n",
       "      <td>04</td>\n",
       "      <td>600.00</td>\n",
       "      <td>Los Angeles(CA)</td>\n",
       "      <td>CA</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>176574</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>11.95</td>\n",
       "      <td>2019-04-03 19:42:00</td>\n",
       "      <td>20 Hill St, Los Angeles, CA 90001</td>\n",
       "      <td>04</td>\n",
       "      <td>11.95</td>\n",
       "      <td>Los Angeles(CA)</td>\n",
       "      <td>CA</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>176585</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>2019-04-07 11:31:00</td>\n",
       "      <td>823 Highland St, Boston, MA 02215</td>\n",
       "      <td>04</td>\n",
       "      <td>99.99</td>\n",
       "      <td>Boston(MA)</td>\n",
       "      <td>MA</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Order ID                     Product  Quantity Ordered  Price Each  \\\n",
       "3    176560                Google Phone                 1      600.00   \n",
       "4    176560            Wired Headphones                 1       11.99   \n",
       "18   176574                Google Phone                 1      600.00   \n",
       "19   176574        USB-C Charging Cable                 1       11.95   \n",
       "30   176585  Bose SoundSport Headphones                 1       99.99   \n",
       "\n",
       "            Order Date                      Purchase Address Order Month  \\\n",
       "3  2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001          04   \n",
       "4  2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001          04   \n",
       "18 2019-04-03 19:42:00     20 Hill St, Los Angeles, CA 90001          04   \n",
       "19 2019-04-03 19:42:00     20 Hill St, Los Angeles, CA 90001          04   \n",
       "30 2019-04-07 11:31:00     823 Highland St, Boston, MA 02215          04   \n",
       "\n",
       "     Sales              City State  Hour  \n",
       "3   600.00   Los Angeles(CA)    CA    14  \n",
       "4    11.99   Los Angeles(CA)    CA    14  \n",
       "18  600.00   Los Angeles(CA)    CA    19  \n",
       "19   11.95   Los Angeles(CA)    CA    19  \n",
       "30   99.99        Boston(MA)    MA    11  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp=fdf[fdf['Order ID'].duplicated(keep=False)]\n",
    "grp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "9a526d17",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/m6/qgmvxk1d17g01z7vr6rqxmh00000gn/T/ipykernel_1459/3046391935.py:1: 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",
      "  grp['prod']=fdf.groupby('Order ID')['Product'].transform(lambda x: ','.join(x))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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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>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>Order Month</th>\n",
       "      <th>Sales</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Hour</th>\n",
       "      <th>prod</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>2019-04-12 14:38:00</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>04</td>\n",
       "      <td>600.00</td>\n",
       "      <td>Los Angeles(CA)</td>\n",
       "      <td>CA</td>\n",
       "      <td>14</td>\n",
       "      <td>Google Phone,Wired Headphones</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>2019-04-12 14:38:00</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>04</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Los Angeles(CA)</td>\n",
       "      <td>CA</td>\n",
       "      <td>14</td>\n",
       "      <td>Google Phone,Wired Headphones</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>176574</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>2019-04-03 19:42:00</td>\n",
       "      <td>20 Hill St, Los Angeles, CA 90001</td>\n",
       "      <td>04</td>\n",
       "      <td>600.00</td>\n",
       "      <td>Los Angeles(CA)</td>\n",
       "      <td>CA</td>\n",
       "      <td>19</td>\n",
       "      <td>Google Phone,USB-C Charging Cable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>176574</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>11.95</td>\n",
       "      <td>2019-04-03 19:42:00</td>\n",
       "      <td>20 Hill St, Los Angeles, CA 90001</td>\n",
       "      <td>04</td>\n",
       "      <td>11.95</td>\n",
       "      <td>Los Angeles(CA)</td>\n",
       "      <td>CA</td>\n",
       "      <td>19</td>\n",
       "      <td>Google Phone,USB-C Charging Cable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>176585</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>2019-04-07 11:31:00</td>\n",
       "      <td>823 Highland St, Boston, MA 02215</td>\n",
       "      <td>04</td>\n",
       "      <td>99.99</td>\n",
       "      <td>Boston(MA)</td>\n",
       "      <td>MA</td>\n",
       "      <td>11</td>\n",
       "      <td>Bose SoundSport Headphones,Bose SoundSport Hea...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Order ID                     Product  Quantity Ordered  Price Each  \\\n",
       "3    176560                Google Phone                 1      600.00   \n",
       "4    176560            Wired Headphones                 1       11.99   \n",
       "18   176574                Google Phone                 1      600.00   \n",
       "19   176574        USB-C Charging Cable                 1       11.95   \n",
       "30   176585  Bose SoundSport Headphones                 1       99.99   \n",
       "\n",
       "            Order Date                      Purchase Address Order Month  \\\n",
       "3  2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001          04   \n",
       "4  2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001          04   \n",
       "18 2019-04-03 19:42:00     20 Hill St, Los Angeles, CA 90001          04   \n",
       "19 2019-04-03 19:42:00     20 Hill St, Los Angeles, CA 90001          04   \n",
       "30 2019-04-07 11:31:00     823 Highland St, Boston, MA 02215          04   \n",
       "\n",
       "     Sales              City State  Hour  \\\n",
       "3   600.00   Los Angeles(CA)    CA    14   \n",
       "4    11.99   Los Angeles(CA)    CA    14   \n",
       "18  600.00   Los Angeles(CA)    CA    19   \n",
       "19   11.95   Los Angeles(CA)    CA    19   \n",
       "30   99.99        Boston(MA)    MA    11   \n",
       "\n",
       "                                                 prod  \n",
       "3                       Google Phone,Wired Headphones  \n",
       "4                       Google Phone,Wired Headphones  \n",
       "18                  Google Phone,USB-C Charging Cable  \n",
       "19                  Google Phone,USB-C Charging Cable  \n",
       "30  Bose SoundSport Headphones,Bose SoundSport Hea...  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp['prod']=fdf.groupby('Order ID')['Product'].transform(lambda x: ','.join(x))\n",
    "grp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "41d11694",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>prod</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone,Wired Headphones</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>176574</td>\n",
       "      <td>Google Phone,USB-C Charging Cable</td>\n",
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       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>176585</td>\n",
       "      <td>Bose SoundSport Headphones,Bose SoundSport Hea...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>176586</td>\n",
       "      <td>AAA Batteries (4-pack),Google Phone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>176672</td>\n",
       "      <td>Lightning Charging Cable,USB-C Charging Cable</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Order ID                                               prod\n",
       "3     176560                      Google Phone,Wired Headphones\n",
       "18    176574                  Google Phone,USB-C Charging Cable\n",
       "30    176585  Bose SoundSport Headphones,Bose SoundSport Hea...\n",
       "32    176586                AAA Batteries (4-pack),Google Phone\n",
       "119   176672      Lightning Charging Cable,USB-C Charging Cable"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp=grp[['Order ID', 'prod']].drop_duplicates()\n",
    "grp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "1da5108b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({('iPhone', 'Lightning Charging Cable'): 1005, ('Google Phone', 'USB-C Charging Cable'): 987, ('iPhone', 'Wired Headphones'): 447, ('Google Phone', 'Wired Headphones'): 414, ('Vareebadd Phone', 'USB-C Charging Cable'): 361, ('iPhone', 'Apple Airpods Headphones'): 360, ('Google Phone', 'Bose SoundSport Headphones'): 220, ('USB-C Charging Cable', 'Wired Headphones'): 160, ('Vareebadd Phone', 'Wired Headphones'): 143, ('Lightning Charging Cable', 'Wired Headphones'): 92, ('Lightning Charging Cable', 'Apple Airpods Headphones'): 81, ('Vareebadd Phone', 'Bose SoundSport Headphones'): 80, ('USB-C Charging Cable', 'Bose SoundSport Headphones'): 77, ('Apple Airpods Headphones', 'Wired Headphones'): 69, ('Lightning Charging Cable', 'USB-C Charging Cable'): 58, ('Lightning Charging Cable', 'AA Batteries (4-pack)'): 55, ('Lightning Charging Cable', 'Lightning Charging Cable'): 54, ('Bose SoundSport Headphones', 'Wired Headphones'): 53, ('AA Batteries (4-pack)', 'Lightning Charging Cable'): 51, ('AAA Batteries (4-pack)', 'USB-C Charging Cable'): 50, ('Apple Airpods Headphones', 'AAA Batteries (4-pack)'): 48, ('AA Batteries (4-pack)', 'AAA Batteries (4-pack)'): 48, ('USB-C Charging Cable', 'USB-C Charging Cable'): 48, ('AAA Batteries (4-pack)', 'AAA Batteries (4-pack)'): 48, ('USB-C Charging Cable', 'AAA Batteries (4-pack)'): 45, ('Wired Headphones', 'USB-C Charging Cable'): 45, ('AA Batteries (4-pack)', 'Wired Headphones'): 44, ('AAA Batteries (4-pack)', 'Lightning Charging Cable'): 44, ('AAA Batteries (4-pack)', 'Wired Headphones'): 43, ('Wired Headphones', 'AAA Batteries (4-pack)'): 43, ('USB-C Charging Cable', 'Lightning Charging Cable'): 42, ('AA Batteries (4-pack)', 'Apple Airpods Headphones'): 41, ('AAA Batteries (4-pack)', 'AA Batteries (4-pack)'): 39, ('Wired Headphones', 'AA Batteries (4-pack)'): 39, ('Lightning Charging Cable', 'Bose SoundSport Headphones'): 39, ('USB-C Charging Cable', 'AA Batteries (4-pack)'): 38, ('Bose SoundSport Headphones', 'AAA Batteries (4-pack)'): 37, ('AA Batteries (4-pack)', 'USB-C Charging Cable'): 37, ('Wired Headphones', 'Lightning Charging Cable'): 37, ('Lightning Charging Cable', 'AAA Batteries (4-pack)'): 36, ('Apple Airpods Headphones', 'Lightning Charging Cable'): 35, ('Wired Headphones', 'Wired Headphones'): 35, ('AA Batteries (4-pack)', 'AA Batteries (4-pack)'): 35, ('USB-C Charging Cable', 'Apple Airpods Headphones'): 34, ('Bose SoundSport Headphones', 'Lightning Charging Cable'): 33, ('AAA Batteries (4-pack)', 'Apple Airpods Headphones'): 33, ('Apple Airpods Headphones', 'Bose SoundSport Headphones'): 32, ('Wired Headphones', 'Apple Airpods Headphones'): 31, ('USB-C Charging Cable', '27in FHD Monitor'): 31, ('Apple Airpods Headphones', 'USB-C Charging Cable'): 29, ('Apple Airpods Headphones', 'AA Batteries (4-pack)'): 29, ('AA Batteries (4-pack)', 'Bose SoundSport Headphones'): 28, ('Bose SoundSport Headphones', 'Bose SoundSport Headphones'): 27, ('Bose SoundSport Headphones', 'AA Batteries (4-pack)'): 27, ('Bose SoundSport Headphones', 'USB-C Charging Cable'): 25, ('Apple Airpods Headphones', 'Apple Airpods Headphones'): 24, ('AAA Batteries (4-pack)', '27in FHD Monitor'): 22, ('27in FHD Monitor', 'AAA Batteries (4-pack)'): 21, ('Wired Headphones', 'Bose SoundSport Headphones'): 21, ('AAA Batteries (4-pack)', 'Bose SoundSport Headphones'): 20, ('34in Ultrawide Monitor', 'AA Batteries (4-pack)'): 19, ('Lightning Charging Cable', '27in 4K Gaming Monitor'): 18, ('AA Batteries (4-pack)', 'iPhone'): 18, ('27in FHD Monitor', 'Lightning Charging Cable'): 18, ('Lightning Charging Cable', '27in FHD Monitor'): 18, ('34in Ultrawide Monitor', 'Lightning Charging Cable'): 18, ('Wired Headphones', '27in 4K Gaming Monitor'): 18, ('Bose SoundSport Headphones', 'Apple Airpods Headphones'): 18, ('iPhone', 'AAA Batteries (4-pack)'): 17, ('Wired Headphones', '34in Ultrawide Monitor'): 17, ('ThinkPad Laptop', 'AAA Batteries (4-pack)'): 16, ('Lightning Charging Cable', 'Google Phone'): 16, ('27in 4K Gaming Monitor', 'Lightning Charging Cable'): 16, ('34in Ultrawide Monitor', 'USB-C Charging Cable'): 15, ('27in FHD Monitor', 'AA Batteries (4-pack)'): 15, ('Wired Headphones', 'iPhone'): 15, ('AAA Batteries (4-pack)', '27in 4K Gaming Monitor'): 15, ('iPhone', 'USB-C Charging Cable'): 15, ('20in Monitor', 'USB-C Charging Cable'): 15, ('Lightning Charging Cable', '20in Monitor'): 15, ('27in 4K Gaming Monitor', 'AAA Batteries (4-pack)'): 15, ('Lightning Charging Cable', '34in Ultrawide Monitor'): 15, ('Google Phone', 'AA Batteries (4-pack)'): 14, ('Apple Airpods Headphones', 'Google Phone'): 14, ('USB-C Charging Cable', 'iPhone'): 14, ('Bose SoundSport Headphones', '27in FHD Monitor'): 14, ('AA Batteries (4-pack)', '27in 4K Gaming Monitor'): 14, ('AAA Batteries (4-pack)', 'iPhone'): 14, ('iPhone', 'AA Batteries (4-pack)'): 14, ('AA Batteries (4-pack)', 'Flatscreen TV'): 13, ('AA Batteries (4-pack)', '34in Ultrawide Monitor'): 13, ('AAA Batteries (4-pack)', '34in Ultrawide Monitor'): 13, ('Apple Airpods Headphones', 'iPhone'): 13, ('Wired Headphones', 'Macbook Pro Laptop'): 13, ('Apple Airpods Headphones', '27in 4K Gaming Monitor'): 12, ('Apple Airpods Headphones', '27in FHD Monitor'): 12, ('27in FHD Monitor', 'Bose SoundSport Headphones'): 12, ('27in FHD Monitor', 'USB-C Charging Cable'): 12, ('Google Phone', 'Lightning Charging Cable'): 12, ('Apple Airpods Headphones', 'Macbook Pro Laptop'): 12, ('27in 4K Gaming Monitor', 'USB-C Charging Cable'): 12, ('Macbook Pro Laptop', 'USB-C Charging Cable'): 12, ('Wired Headphones', '27in FHD Monitor'): 12, ('20in Monitor', 'Wired Headphones'): 12, ('Lightning Charging Cable', 'Flatscreen TV'): 12, ('27in FHD Monitor', 'Apple Airpods Headphones'): 12, ('USB-C Charging Cable', 'Google Phone'): 12, ('27in 4K Gaming Monitor', 'AA Batteries (4-pack)'): 12, ('34in Ultrawide Monitor', 'AAA Batteries (4-pack)'): 12, ('AAA Batteries (4-pack)', 'Google Phone'): 11, ('AAA Batteries (4-pack)', 'Macbook Pro Laptop'): 11, ('USB-C Charging Cable', '27in 4K Gaming Monitor'): 11, ('USB-C Charging Cable', 'ThinkPad Laptop'): 11, ('34in Ultrawide Monitor', 'Wired Headphones'): 11, ('20in Monitor', 'Lightning Charging Cable'): 11, ('AA Batteries (4-pack)', '27in FHD Monitor'): 11, ('Bose SoundSport Headphones', '34in Ultrawide Monitor'): 11, ('ThinkPad Laptop', 'Lightning Charging Cable'): 11, ('Google Phone', 'AAA Batteries (4-pack)'): 11, ('USB-C Charging Cable', '34in Ultrawide Monitor'): 11, ('Macbook Pro Laptop', 'Lightning Charging Cable'): 11, ('AA Batteries (4-pack)', 'Google Phone'): 11, ('AAA Batteries (4-pack)', 'ThinkPad Laptop'): 11, ('Macbook Pro Laptop', 'Bose SoundSport Headphones'): 11, ('27in 4K Gaming Monitor', 'Wired Headphones'): 11, ('Flatscreen TV', 'AAA Batteries (4-pack)'): 11, ('Flatscreen TV', 'Lightning Charging Cable'): 10, ('Wired Headphones', 'ThinkPad Laptop'): 10, ('USB-C Charging Cable', '20in Monitor'): 10, ('27in 4K Gaming Monitor', 'Apple Airpods Headphones'): 10, ('USB-C Charging Cable', 'Flatscreen TV'): 10, ('27in FHD Monitor', 'Wired Headphones'): 10, ('AA Batteries (4-pack)', '20in Monitor'): 10, ('AAA Batteries (4-pack)', 'Flatscreen TV'): 10, ('Lightning Charging Cable', 'iPhone'): 10, ('Bose SoundSport Headphones', 'Flatscreen TV'): 10, ('Lightning Charging Cable', 'Macbook Pro Laptop'): 10, ('Bose SoundSport Headphones', '27in 4K Gaming Monitor'): 10, ('Apple Airpods Headphones', 'ThinkPad Laptop'): 9, ('Wired Headphones', 'Google Phone'): 9, ('27in 4K Gaming Monitor', 'Bose SoundSport Headphones'): 9, ('20in Monitor', 'Bose SoundSport Headphones'): 9, ('Macbook Pro Laptop', 'AA Batteries (4-pack)'): 9, ('ThinkPad Laptop', 'USB-C Charging Cable'): 9, ('ThinkPad Laptop', 'Bose SoundSport Headphones'): 9, ('Vareebadd Phone', 'AA Batteries (4-pack)'): 9, ('USB-C Charging Cable', 'Macbook Pro Laptop'): 9, ('27in FHD Monitor', '27in FHD Monitor'): 9, ('AA Batteries (4-pack)', 'ThinkPad Laptop'): 9, ('Lightning Charging Cable', 'ThinkPad Laptop'): 9, ('AA Batteries (4-pack)', 'Macbook Pro Laptop'): 8, ('Flatscreen TV', 'AA Batteries (4-pack)'): 8, ('Apple Airpods Headphones', 'Flatscreen TV'): 8, ('ThinkPad Laptop', 'AA Batteries (4-pack)'): 8, ('AAA Batteries (4-pack)', '20in Monitor'): 8, ('34in Ultrawide Monitor', 'Apple Airpods Headphones'): 8, ('Bose SoundSport Headphones', 'Google Phone'): 8, ('20in Monitor', 'Apple Airpods Headphones'): 7, ('Macbook Pro Laptop', 'Apple Airpods Headphones'): 7, ('Wired Headphones', 'Flatscreen TV'): 7, ('Wired Headphones', '20in Monitor'): 7, ('Macbook Pro Laptop', 'Wired Headphones'): 7, ('USB-C Charging Cable', 'Vareebadd Phone'): 7, ('Google Phone', '27in FHD Monitor'): 7, ('Macbook Pro Laptop', 'AAA Batteries (4-pack)'): 7, ('34in Ultrawide Monitor', 'iPhone'): 7, ('34in Ultrawide Monitor', '34in Ultrawide Monitor'): 7, ('Flatscreen TV', 'USB-C Charging Cable'): 7, ('Bose SoundSport Headphones', 'iPhone'): 7, ('ThinkPad Laptop', 'Apple Airpods Headphones'): 7, ('Google Phone', 'Apple Airpods Headphones'): 7, ('Macbook Pro Laptop', '27in 4K Gaming Monitor'): 7, ('iPhone', '27in 4K Gaming Monitor'): 6, ('Flatscreen TV', 'Flatscreen TV'): 6, ('Apple Airpods Headphones', '34in Ultrawide Monitor'): 6, ('iPhone', '34in Ultrawide Monitor'): 6, ('Vareebadd Phone', 'Apple Airpods Headphones'): 6, ('27in 4K Gaming Monitor', '34in Ultrawide Monitor'): 6, ('27in 4K Gaming Monitor', 'Macbook Pro Laptop'): 6, ('Bose SoundSport Headphones', '20in Monitor'): 6, ('iPhone', 'Flatscreen TV'): 6, ('Apple Airpods Headphones', '20in Monitor'): 6, ('Apple Airpods Headphones', 'Vareebadd Phone'): 6, ('Wired Headphones', 'Vareebadd Phone'): 6, ('34in Ultrawide Monitor', 'Bose SoundSport Headphones'): 6, ('Google Phone', 'iPhone'): 6, ('27in FHD Monitor', 'Macbook Pro Laptop'): 6, ('20in Monitor', 'AA Batteries (4-pack)'): 6, ('iPhone', 'Bose SoundSport Headphones'): 5, ('27in 4K Gaming Monitor', '27in 4K Gaming Monitor'): 5, ('Flatscreen TV', '34in Ultrawide Monitor'): 5, ('27in 4K Gaming Monitor', 'Google Phone'): 5, ('27in FHD Monitor', '34in Ultrawide Monitor'): 5, ('Flatscreen TV', 'Apple Airpods Headphones'): 5, ('34in Ultrawide Monitor', '27in FHD Monitor'): 5, ('Macbook Pro Laptop', '34in Ultrawide Monitor'): 4, ('iPhone', 'Vareebadd Phone'): 4, ('Bose SoundSport Headphones', 'ThinkPad Laptop'): 4, ('20in Monitor', 'Macbook Pro Laptop'): 4, ('Vareebadd Phone', '34in Ultrawide Monitor'): 4, ('Flatscreen TV', 'Wired Headphones'): 4, ('Flatscreen TV', '27in FHD Monitor'): 4, ('LG Dryer', 'AA Batteries (4-pack)'): 4, ('Flatscreen TV', 'Macbook Pro Laptop'): 4, ('27in FHD Monitor', '27in 4K Gaming Monitor'): 4, ('ThinkPad Laptop', 'Flatscreen TV'): 4, ('Flatscreen TV', 'iPhone'): 4, ('27in 4K Gaming Monitor', 'ThinkPad Laptop'): 4, ('Vareebadd Phone', 'Google Phone'): 4, ('Macbook Pro Laptop', 'Google Phone'): 4, ('27in 4K Gaming Monitor', '27in FHD Monitor'): 4, ('Lightning Charging Cable', 'LG Washing Machine'): 4, ('27in FHD Monitor', 'ThinkPad Laptop'): 4, ('ThinkPad Laptop', 'Wired Headphones'): 4, ('iPhone', 'ThinkPad Laptop'): 4, ('Bose SoundSport Headphones', 'Macbook Pro Laptop'): 4, ('AAA Batteries (4-pack)', 'Vareebadd Phone'): 4, ('LG Washing Machine', 'AAA Batteries (4-pack)'): 4, ('Macbook Pro Laptop', 'ThinkPad Laptop'): 3, ('ThinkPad Laptop', 'Google Phone'): 3, ('34in Ultrawide Monitor', 'Macbook Pro Laptop'): 3, ('Lightning Charging Cable', 'Vareebadd Phone'): 3, ('Google Phone', 'ThinkPad Laptop'): 3, ('20in Monitor', '20in Monitor'): 3, ('ThinkPad Laptop', 'iPhone'): 3, ('Vareebadd Phone', 'Flatscreen TV'): 3, ('34in Ultrawide Monitor', 'Flatscreen TV'): 3, ('Macbook Pro Laptop', 'Macbook Pro Laptop'): 3, ('34in Ultrawide Monitor', 'ThinkPad Laptop'): 3, ('Macbook Pro Laptop', 'iPhone'): 3, ('Vareebadd Phone', 'iPhone'): 3, ('Wired Headphones', 'LG Washing Machine'): 3, ('Google Phone', '34in Ultrawide Monitor'): 3, ('Macbook Pro Laptop', '27in FHD Monitor'): 3, ('Flatscreen TV', 'Bose SoundSport Headphones'): 3, ('AA Batteries (4-pack)', 'Vareebadd Phone'): 3, ('27in FHD Monitor', '20in Monitor'): 3, ('iPhone', 'Google Phone'): 3, ('27in 4K Gaming Monitor', 'iPhone'): 3, ('Google Phone', 'Google Phone'): 3, ('Flatscreen TV', 'Google Phone'): 3, ('Google Phone', 'Macbook Pro Laptop'): 3, ('27in 4K Gaming Monitor', 'Flatscreen TV'): 3, ('Apple Airpods Headphones', 'LG Dryer'): 3, ('20in Monitor', 'AAA Batteries (4-pack)'): 3, ('iPhone', 'Macbook Pro Laptop'): 3, ('34in Ultrawide Monitor', 'Google Phone'): 2, ('Macbook Pro Laptop', '20in Monitor'): 2, ('Lightning Charging Cable', 'LG Dryer'): 2, ('Flatscreen TV', '27in 4K Gaming Monitor'): 2, ('ThinkPad Laptop', 'Macbook Pro Laptop'): 2, ('Macbook Pro Laptop', 'LG Washing Machine'): 2, ('20in Monitor', '27in FHD Monitor'): 2, ('ThinkPad Laptop', 'ThinkPad Laptop'): 2, ('Bose SoundSport Headphones', 'Vareebadd Phone'): 2, ('Vareebadd Phone', 'ThinkPad Laptop'): 2, ('20in Monitor', 'ThinkPad Laptop'): 2, ('iPhone', 'iPhone'): 2, ('27in FHD Monitor', 'LG Dryer'): 2, ('Vareebadd Phone', '27in 4K Gaming Monitor'): 2, ('27in 4K Gaming Monitor', '20in Monitor'): 2, ('LG Washing Machine', 'Lightning Charging Cable'): 2, ('LG Washing Machine', 'Bose SoundSport Headphones'): 2, ('AA Batteries (4-pack)', 'LG Dryer'): 2, ('Vareebadd Phone', 'AAA Batteries (4-pack)'): 2, ('iPhone', '20in Monitor'): 2, ('20in Monitor', 'Google Phone'): 2, ('Flatscreen TV', 'ThinkPad Laptop'): 2, ('ThinkPad Laptop', '27in FHD Monitor'): 2, ('27in FHD Monitor', 'Flatscreen TV'): 2, ('Google Phone', '20in Monitor'): 2, ('27in 4K Gaming Monitor', 'Vareebadd Phone'): 1, ('27in FHD Monitor', 'iPhone'): 1, ('Vareebadd Phone', 'Lightning Charging Cable'): 1, ('Google Phone', 'Vareebadd Phone'): 1, ('20in Monitor', 'iPhone'): 1, ('LG Dryer', 'Vareebadd Phone'): 1, ('Macbook Pro Laptop', 'Flatscreen TV'): 1, ('ThinkPad Laptop', 'Vareebadd Phone'): 1, ('Google Phone', 'Flatscreen TV'): 1, ('LG Washing Machine', 'Google Phone'): 1, ('LG Washing Machine', 'Wired Headphones'): 1, ('LG Dryer', 'Flatscreen TV'): 1, ('27in FHD Monitor', 'LG Washing Machine'): 1, ('LG Dryer', '27in FHD Monitor'): 1, ('20in Monitor', '34in Ultrawide Monitor'): 1, ('34in Ultrawide Monitor', '20in Monitor'): 1, ('34in Ultrawide Monitor', 'LG Washing Machine'): 1, ('Google Phone', '27in 4K Gaming Monitor'): 1, ('LG Washing Machine', 'iPhone'): 1, ('LG Dryer', 'Wired Headphones'): 1, ('27in FHD Monitor', 'Vareebadd Phone'): 1, ('LG Washing Machine', '27in 4K Gaming Monitor'): 1, ('LG Washing Machine', 'Apple Airpods Headphones'): 1, ('27in 4K Gaming Monitor', 'LG Dryer'): 1, ('20in Monitor', 'LG Washing Machine'): 1, ('LG Dryer', 'Google Phone'): 1, ('Vareebadd Phone', '27in FHD Monitor'): 1, ('ThinkPad Laptop', '27in 4K Gaming Monitor'): 1, ('20in Monitor', 'Flatscreen TV'): 1, ('USB-C Charging Cable', 'LG Dryer'): 1, ('LG Washing Machine', '20in Monitor'): 1, ('Flatscreen TV', '20in Monitor'): 1, ('27in FHD Monitor', 'Google Phone'): 1, ('iPhone', '27in FHD Monitor'): 1, ('LG Dryer', 'AAA Batteries (4-pack)'): 1, ('ThinkPad Laptop', '34in Ultrawide Monitor'): 1, ('iPhone', 'LG Washing Machine'): 1, ('AAA Batteries (4-pack)', 'LG Dryer'): 1, ('LG Dryer', '27in 4K Gaming Monitor'): 1, ('LG Dryer', 'Lightning Charging Cable'): 1, ('ThinkPad Laptop', 'LG Dryer'): 1, ('LG Washing Machine', 'AA Batteries (4-pack)'): 1})\n"
     ]
    }
   ],
   "source": [
    "from itertools import combinations\n",
    "from collections import Counter\n",
    "\n",
    "count=Counter()\n",
    "\n",
    "for row in grp['prod']:\n",
    "    row_list=row.split(',')\n",
    "    count.update(Counter(combinations(row_list,2)))\n",
    "    \n",
    "print(count)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "883f90cf",
   "metadata": {},
   "source": [
    "### Products most commonly sold together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "bae5b060",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(('iPhone', 'Lightning Charging Cable'), 1005),\n",
       " (('Google Phone', 'USB-C Charging Cable'), 987),\n",
       " (('iPhone', 'Wired Headphones'), 447),\n",
       " (('Google Phone', 'Wired Headphones'), 414),\n",
       " (('Vareebadd Phone', 'USB-C Charging Cable'), 361),\n",
       " (('iPhone', 'Apple Airpods Headphones'), 360),\n",
       " (('Google Phone', 'Bose SoundSport Headphones'), 220),\n",
       " (('USB-C Charging Cable', 'Wired Headphones'), 160),\n",
       " (('Vareebadd Phone', 'Wired Headphones'), 143),\n",
       " (('Lightning Charging Cable', 'Wired Headphones'), 92)]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count.most_common(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c62c9688",
   "metadata": {},
   "source": [
    "#### Question 5: Which product sold the most? Why do you think it sold the most?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "2540176a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product\n",
       "AAA Batteries (4-pack)        31017\n",
       "AA Batteries (4-pack)         27635\n",
       "USB-C Charging Cable          23975\n",
       "Lightning Charging Cable      23217\n",
       "Wired Headphones              20557\n",
       "Apple Airpods Headphones      15661\n",
       "Bose SoundSport Headphones    13457\n",
       "27in FHD Monitor               7550\n",
       "iPhone                         6849\n",
       "27in 4K Gaming Monitor         6244\n",
       "34in Ultrawide Monitor         6199\n",
       "Google Phone                   5532\n",
       "Flatscreen TV                  4819\n",
       "Macbook Pro Laptop             4728\n",
       "ThinkPad Laptop                4130\n",
       "20in Monitor                   4129\n",
       "Vareebadd Phone                2068\n",
       "LG Washing Machine              666\n",
       "LG Dryer                        646\n",
       "Name: Quantity Ordered, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_prod = fdf.groupby('Product')['Quantity Ordered'].sum().sort_values(ascending=False)\n",
    "pop_prod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "6bea6e9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wbIePZElgge5SSZIkyUyjl49kIcI/siCwRNv4/USUVJIkSZL09JGcC5wr6VDbv5/COSVJkiTTiL5l5IGFJR0MzG5/v+3NJ2tSSTKZZCn6JBkuVRTJccBBwPeAh/u8N0mSJJlhVFEkD9k+cNJnkiRJkkxLqoT//lTSeyStIGmZ1mPSZ5YkSZJMC6qsSHYpfz/cNmbgGcOfTpIkSTLdqNKzfdWpmEiSJEkyPamyIkHSOsBawBNbY7Z/OFmTSpIkSaYPfRWJpE8RVXzXAn4BbAVcQLS9TZIkSWY4VZzt2wIvBf5s+63AusDCkzqrJEmSZNpQRZH8y/YjwEOSlgTuIh3tSZIkSaGKj2SupKWB7wKXAn8HLpnMSSVJkiTThypRW+8pTw+SdCqwpO2rJndaSZIkyXShirP9Rd3GbJ83OVNKkiRJphNVTFvtiYhPBDYiTFxZtDFJkiSpZNp6Tfu2pJWBL03ajJIkSZJpRZWorU5uB9YZ9kSSJEmS6UkVH8n+RG0tCMWzHnDlJM4pSZIkmUZUCv9te/4QcJTtX03SfJIkSZJpRtXGVquX5zfa/vckzidJkiSZZkzoI5H0BEnfAG4DfgAcBtws6aPl9fWnZIZJkiTJfE0vZ/tXgcWB2bY3sL0+8GzgGZIOBE7otWNJ35d0l6Rr2saWkXS6pN+Wv09qe21vSTdJulHSK9rGN5B0dXntW5JUxheWdEwZv1jS7IE+gSRJkqQRvRTJK4F32J7XGrB9P/BuYAdgxz77PhTYsmPso8CZttcAzizbSFqr7HPtInOApAWKzIHAbsAa5dHa567AvbZXB74O7NdnPkmSJMkk0EuRPGLbnYO2Hwbutn1Rrx2XzPe/dgy/jjCRUf5u3TZ+tO1/274FuAnYSNIKREmWC8tcftgh09rXj4GXtlYrSZIkydTRS5FcJ2nnzkFJOwHXD3i85W3fAVD+LlfGVyR8MS1uL2Mrlued4+NkbD8E3Ac8udtBJe0maa6kuXffffeAU0+SJEm60Stqa3fgBElvI0qiGNgQWAR4/ZDn0W0l4R7jvWQeO2gfDBwMMGfOnK7vSZIkSQZjQkVi+4/A8yRtTvguBJxi+8wGx7tT0gq27yhmq7vK+O3Aym3vWwn4Uxlfqct4u8ztkhYEluKxprQkSZJkkulbIsX2Wbb3t/2thkoE4GRgl/J8F+CktvEdSiTWqoRT/ZJi/ponaePi/9i5Q6a1r22Bs7r5dJIkSZLJpUpC4kBIOoro9f4USbcDnwL2BY6VtCvwB2A7ANvXSjoWuI7Int+9OPUhosQOJUxqp5QHwCHA4ZJuIlYiO0zWuSRJkiQTM2mKxPZE4cEvneD9+wD7dBmfS5cikbYfoCiiJEmSZHQMUv03SZIkSR4lFUmSJEnSiFQkSZIkSSNSkSRJkiSNSEWSJEmSNCIVSZIkSdKIVCRJkiRJI1KRJEmSJI1IRZIkSZI0IhVJkiRJ0ohUJEmSJEkjJq3WVjI5zP7oz2vL3LrvqyZhJkmSJEGuSJIkSZJGpCJJkiRJGpGKJEmSJGlE+kiSJEmmIfOTvzRXJEmSJEkjUpEkSZIkjUhFkiRJkjQifSRTyPxk00ySJBkWqUhmGKnMkiQZNmnaSpIkSRqRiiRJkiRpRCqSJEmSpBEj8ZFIuhWYBzwMPGR7jqRlgGOA2cCtwBts31vevzewa3n/HrZ/WcY3AA4FFgF+Aexp21N5LklSl/RTJY83RrkieYnt9WzPKdsfBc60vQZwZtlG0lrADsDawJbAAZIWKDIHArsBa5THllM4/yRJkoT5y7T1OuCw8vwwYOu28aNt/9v2LcBNwEaSVgCWtH1hWYX8sE0mSZIkmSJGpUgMnCbpUkm7lbHlbd8BUP4uV8ZXBG5rk729jK1YnneOJ0mSJFPIqPJIXmD7T5KWA06XdEOP96rLmHuMP3YHoax2A1hllVXqzjVJkiTpwUgUie0/lb93SToR2Ai4U9IKtu8oZqu7yttvB1ZuE18J+FMZX6nLeLfjHQwcDDBnzpx0xieNSGd5c/IzfHwx5aYtSYtJWqL1HHg5cA1wMrBLedsuwEnl+cnADpIWlrQq4VS/pJi/5knaWJKAndtkkiRJkiliFCuS5YET49rPgsCPbJ8q6TfAsZJ2Bf4AbAdg+1pJxwLXAQ8Bu9t+uOzr3YyF/55SHkmSJMkUMuWKxPbNwLpdxv8CvHQCmX2AfbqMzwXWGfYckyRJkupk0cYapF03SeYP8rc4fzE/5ZEkSZIk05BUJEmSJEkjUpEkSZIkjUhFkiRJkjQiFUmSJEnSiFQkSZIkSSNSkSRJkiSNSEWSJEmSNCIVSZIkSdKIVCRJkiRJI1KRJEmSJI1IRZIkSZI0IhVJkiRJ0ois/pskSVKTrD48nlyRJEmSJI1IRZIkSZI0IhVJkiRJ0ohUJEmSJEkjUpEkSZIkjUhFkiRJkjQiFUmSJEnSiFQkSZIkSSMyITFJZhiZTJcMm1QkSTLNSEWQzG9Me9OWpC0l3SjpJkkfHfV8kiRJZhrTWpFIWgD4X2ArYC1gR0lrjXZWSZIkM4tprUiAjYCbbN9s+0HgaOB1I55TkiTJjEK2Rz2HgZG0LbCl7beX7TcDz7P93o737QbsVjafCdw4CdN5CnDPDJafH+Yw0+XnhzlMd/n5YQ6jlp+Ip9tettsL093Zri5jj9GMtg8GDp7UiUhzbc+ZqfLzwxxmuvz8MIfpLj8/zGHU8oMw3U1btwMrt22vBPxpRHNJkiSZkUx3RfIbYA1Jq0paCNgBOHnEc0qSJJlRTGvTlu2HJL0X+CWwAPB929eOaDpNTWfTXX5+mMNMl58f5jDd5eeHOYxavjbT2tmeJEmSjJ7pbtpKkiRJRkwqkiRJkqQRqUhGgKRZkjZpIC9JK/d/Z/J4pnyP3jDqeSRJKpIBKD/gawaVt/0I8NUG8gZ+Mqh80/l32d9ipVzNILJPkrS2pGdIqvV9LMedVZ6vKem1kp4w2fKSvt3kRqBtP5K0k6RPlu1VJG1UVb58j97b942TOIci83RJLyvPF5G0RA3ZJ0r6gKQTJB0v6f2SnlhD/hmSfirpHkl3STpJ0jPqzH8I57CmpDNbvylJz5H0iRryX5K0pKQnlP3cI2mnAc5hEUnPrCs3DFKRDED5AV8paZUGuzlN0jaSuiVVVuEiSRsOIth0/kURvVHSzyXdBdwA3CHpWklflrRGH/mlJH1M0tXARcB3gGOB30s6TtJLKk7lPOCJklYEzgTeChxa41QGlf8t8FVJt0raT9J6NY7ZzgHA84Edy/Y8onZcHU6X9CFJK0tapvWYqjlIegfwY+J/CJHL9ZMax/8hsDawP/Bt4NnA4TXkf0R8d54KPA04DjiqhvwwzuG7wN7AfwBsX0WkIlTl5bbvB15N5MatCXy4hjySXgNcAZxatteTNHWpELbzMcADOIv40Z1J5K6cDJxcQ34e8Ajx5bu/bN9fQ/464CHgd8BVwNXAVVMxf+Bc4H+A5wCz2saXAbYBjgd26iF/OvBmYOkur20AfAPYtcI8Lit/3wd8pDy/vMZn0FT+6cD/Ay4Hrgc+Caw5wPEvbxu7sub38JYuj5unag7ExWuhDvmra8g/5lg1j39xl7GLan6GTc/hN10+wytqyF9b/n6XKPk0yPfgUmCpjjlUvh40fUzrPJIR85kmwrYrL50nYKuG8k3m/zLb/+kctP1XQokc38tEZHuLHq9dSvwoqiBJzwfeBOxaxup8pxvJ2/49sB+wn6T1ge8DnyJymqrwn2ISdJnMssTNRWVsr1rn/ZMwh3/bfrC1sJa0IF3KFPXgckkb276oyD8P+FUN+bMV7SOOLsfdHvh5a1VWvpOTfQ73SFqNsc9wW+COGvI/lXQD8C/gPeV/8EANeYCHbN83uIGjIVOlsR6PD2B5Yjn6amC5AeRfC3ylPF49gPy6hI38vcC6I5j/Y1YNwL415D/bsb0AcGQN+RcTK6n/V7afAXxrCuWfALwGOBL4M3AMsHUN+TeV498O7EMUE92u5v9gUeATwMFle40636WmcwC+BHyMMG9uAZwI7FND/npCcd1aHo8A11JxhU33FVmtldkQzuEZwBnAP4E/AhcAs2v+H58ELFCeLwY8tab8IcAbCevEGoSp8KA6+2jyyITEAVFEy3wZOIcoHvlC4MO2f1xRfl9gQ+IiBGGjvtR2peZckvYE3gGcUIZeT1xM9p+K+Zd9nAIcYfvIsn0AsLDtXXtLPip/KHCj7S9KWpiwb19m+9NV51D2s5jtf9SRaSIvaQvi//Uq4BLibvgng8xB0rOAlxL/gzNtX19T/hhiBbez7XUkLQJcaHu9qZiDIlhhV+DlRf6XwPdc8cIi6em9Xnes+iaVpufQtp/FCFPvvJpyiwIfAFaxvVvxMT7T9s9q7uPjjD+Hz9muu7IZiFQkAyLpSmAL23eV7WWBM2yvW1H+KmA9h+O71aTrctvPqSH//NbFq3yJL6wh32j+RWYR4m72+4Sp7a+296ohL0KRXg28BDjF9tdryD+fuBNb3PYqktYF3mn7PZMpL+kC4DDgeFcznfTa1wLEyvBRk5rtP9SQn2t7jqTLba9fxq6s+X9sNIemlM/9hWXzfNtX1pB9AvBu4EVl6BzgO+5iep0syk3QNsBsxn+Gn60o3/hmYNSkj2RwZrUuwoW/UD8KbmmgdSFaqqasgIfbth+me1n9iRh4/h1RQW8nIlx+BXxW0jL9Lq6Sntu2+U0iWuZXwLmSnmv7sirzIJzyr6AU6rR9paQX9ZQYjvxitr9b4zhdkfQ+wqdyJ2P/PxNBDFV5sFx4Wvb51YB/T/YcSsTdhHehNW5oOlfWR0iqvLIGDiRMjAeU7TeXsbdXOPZQzgE4CbiPUAaVP/s2VrO9vaQdy3H/VTeaU9KawId4rDLbfID51CYVyeCcKumXjIUabg+cUkP+i4Sj8Wzix/siwk5blR8AF0s6sWxvTawMqtJk/pcSP0C1/X1VeZiwGfeiM4fmXqJV8leLfOUvv+3bOn5zD0/03iHKD2sZvydhwvhLg318mgj5XFnSkcALgLdMwRxeXfP9E7Er0YyutbLeD7iQsPFXYcOO1ddZZbVdhWGdw0q2t2wg3+hmoHAccBDwPWr+BoZBKpIBsf1hSf8NbEpcSA+2fWIfsXb5oySdQ/hJRDh8/1xD/mtFvnX8t9q+fCrm74aRQrar5on04zZFYqAVbQT2IJy3ky2/nKQPTPSi7a9VPT5xJzswtk+TdCmwMfF/3NN2ne54A82h3Xch6alE22sTobCVv8c0X1k/LGk1278rc3kGFS+kQzyHX0v6L9tX15Bp51M0uxmAiNo6cMDjNyZ9JAMiaT/b/6/fWA/5M22/tN9YD/nDbb+531gP+UbzL+/fnYiy+lvZfhKwo+0DegqOyX8B+FKH/AdtV8oKlvQUwjT2MuLicxpxIa10dz2ovKQ7CPNJ1wue7Uqh1ZIOIVo//5y2O9AaighJPyZWoqe2/G11aDoHSW8n8mfOIj6PFxPReJVWx0Uh70JESkGsrA+r6iuT9FJidX5zOf7TgbfZPquK/JDO4TpgdSJS7N9lH65hGkPSkxm7Gbio5s0Akj4N3EV8ju3/x0Y+vMrHT0UyGJIus/3cjrGr+n15FOUfFgXOBjZj7GK0JOFsfvYgxy8O06ttrzWZ8+94/xWdDsF2p28F+ce8t9u85jeGNUdJn+o2XlURlX28jMjI35gwbxxq+4apmoOkG4FNWsq3XBB/bbtyqY7iM2utjM+rs7Iujm4IZSgihBfbdfxEjc5hosgz14g4U1RXeDrj/Rvn1ZC/pfsUXLtczCCkaasmkt4NvAd4RomcarEE1RKp3gnsRZRzaHcq30+F0hSS9iZ8KYtIup8xRfQgFRraDGH+7cySJJe7kaLMFqohv4CkhVs/+mInXriPzKOUSLN38FgH49smWX4oWV+ti7WirpNt/32AfZwBnCFpKSIk+XRJtxFZ0kf0i16qo7Qm4HaiQkKLeYS5rBJtq+jLuoxV4cKi1B/9Lku6DKij6Budg+3fN4w824/wUV7LWDKoiRI+VefQNDG1EalI6vMjwin9RaA952NelWWk7W8C35T0vhqRKe3yXwS+KOmLtveuK0/D+XfwS+BYSQcRX/x3UWr9VOQI4ExJPyjybyPCaqtyEnA+kQw2iINxUPlK5sd+SFqHqCu1TNm+hwgBrdXls9xB70RELF1OhFRvSpiMNptA5hu295L0U7oED9h+bZ9jtnxEfySCPk4q+3kdkVtTlbU79rsAUSanJ8WnsSJxQ7U+41f2i1Y58LDOYQiRZ1sTAQ+DRHy15jDSMOg0bdVE0pK279cEhfEqhL5ubvus4ujuJn9Ct/E2+WfZvkHjQ2jb5XuGzjadf8e+ZhErrFYy22lEIlfli7Kkrdrlbf+yhuxjTGt1aCrfFEm/Bj5u++yyvRnwBduVKwtLOgF4FqGQDrV9R9trc23PmUBuA9uXSnpxt9dtn9vnuF1NYm3yPVc67StrIiN83Mq6302SpF0Ih/QcYG7bS/OIz6Hn76jso9E5tO2naU7XKUQ1gdor0rZ9fI8Ig27diL0ZeNh23zDoYZCKpCaSfmb71cUm2Qp9bdHXJinpM7Y/Ve7CO3E/s0q509lNETbcTb5n6GzT+c9PSPo8Ycv+xSjkm6IuiYPdxnrIzwI+4YqJb/MjDVbWLfltbB8/zDkNMIeriTDkB8r2E4nIr/+qKH88Ue7oTMY7yveoMYdG36WmpCJJBkZRyuGLRA7Ioz0kqiojSRsT+QLPJnwrCwD/sL1kRfl5RF2iByklvOPwUyPfFEUO0GWMlU3fCZhje+sa+7jQ9vMbzOEFRC5Ky9Hbijiq+j9cFvgIYaJq/w5UygWSJKK8z6bEjc35tn9SY/5PJsJnW/IXEBFXlfNihnAO3SLPDrX9jYryu3Qbt13ZzFv8Qtt5fBj0j6cqcCV9JA1oEmmhhmUVyj426SL/wxryjSJFiLDLTwFfB15CRA/VcUR/m+jbcBxhotiZCKOshBtWUG4qX8yT+wHLEefdughXVURvI6own1BkzyM+wzqcJmkb4AQPdld4CPB+Isl0ED/TkUSxylcTPrJdgLtryP8v8T9vJca+S9IWtnevKH808bltU7bfVObzshpzaHQOjpyuc4n8j0Fyug5T5DGtWYZuHMC38WGiEnJ7GHTd79LA5IpkQNoiLa5j7Afofk7KNvlTGSur8OgP2HalzomSDgdWI3optB+/0nK46fzLPi61vYGkq1vLeEnn235hP9ny3ladqEfDjiX9uqaP4LW0ORhdo9BdU3lJNwGvcc1Ci8OkbVX1MFGGvJYyk3Sx7ec1OH7rO9D+PzzXdlffSxf5a4F1WkqwmOuutr12b8nxx+8Ym9A3NBnnUN4/cL2y4hs7jKh+LGBlYJeaN3Wtm9NHw6CbOO/rkiuSwdmaZpEWTcsqzAHWGvAuFIYQKQI8UH74v5X0XiL6Zbka8v8sd2JXSPoS0cNhsarCemwF5T0lberqFZQbyQN3NlEiGkJ9pEFXVW3BGmdL+jKxKmq3z1etd9a6c75D0quAPxEdBqtyI7AK0Mq5WJm2UN4KnC1pB6JLIsC2RHJlHRqdg5rXTPsq0SXxxrK/NYkVWt/otQ42YOy7tK6kWhaKJuSKZECaRlpIOhjY3wOWVZB0HLBHe5ROTflhRIpsSJQUWRr4HFF48ksuTYoqyD+d+PEtRJhXlgIOsH1TRflhVFBuIv9NosXrTxh/Ee4bMVTkryTqI3WuSis19lI0YNqKiNqCWF3+0vZDFWS7BWu0TaGyf+DVRAj1yoS/a0ngM7YrtXktJqENGQu33ZCotfXPMpF+YcitFVkr/2IW0CrnX2llNoRzuImoFzZQzTR1SQTuNtZnH40sFE1JRTIgTSMt1LCsQrkQrEf8ANuPX9W01jhSpG1fS4ZovT4MRXYh4kJowjb8YA3Zq4DNXEKWFSHN59RUJE3kB4q8a5N/jFmmKpKeRlRHuIPIHRGwPqHYXmL7T4Psd6rRBOHHLdwnDHl+oPwWt6iiwCeQ/z7x/W8FXbwJWNB2ZR+HpOtpZqFoRCqSAWkaaaGGZRUm+gFW/eENKVJkDuFwb5lX7iPqHFW9o34VcUf+O+JCuCrRD6RSFWJF2e19iQtqq4Ly3raPngr5QdFYDs8eDFgfSdEU7IrOyCBJewAb2O76/+2yn4HqnRVT5M22D+oYfz/R3a9yzbamlDmvwfiIq77+habnoLGExrVpVq9sYWB32srEECvzOmVeGlkompKKpAFNIy3UoKxCkV+eMAUAXOLx/UWqyDed/1XA7rbPL9ubEj+Aqnf0NxBtYW8q26sBP7f9rN6S4/axAmMVlC92vaqtjeSLLftAYHlHQ6LnAK+1/fk+ct3qIrWwK4TeSrphos9J0o2uXifqcg9Q76ysqNdxR6HI4jO7yvY6FY/fNAT87UQp/JUIs87GRDJgX9Nc03PQkBIam6CxygRL0MBC0ZR0tg9It0gLSZUjLdSwrIIe2yp3f0l1Wv02mn9hXkuJANi+oNisq3JXhz/kZuIOvQ6zgHuI7/KaktaseQ5N5L9LhF1+B8D2VZJ+BPRUJB5OXaR/9XjtnzX2M2i9M3degMvgI1KtpkzdQsDXqCG/J3EjcJHtlyjaBle9gDc6B9ufUeSgPB24qbWqq4sem8vT2n+VXJ6vDHLMYZOKZHCaRlo0bejzcSKbdlyrXKBqz/VhRIpcIuk7Rc5EOPE5rYgg94/8uVbSL4iIGwPbAb9RKR/Tz2mthsXumsoDi9q+pOOaU8XRvQZxE7A60Wb4Q7b/WPGYLZZS9zI7IpzFVRm03tk/Ja1h+7fjDh7n1kvJPQbbN0lawFFa5weK0jFVecD2A5IoCvEGSVUrDzc6B0m7Egm5vwNWlbRbVQd9BwPn8tg+V9LWlO+Sa5QYGiapSAbnCa2LMIDt/1MUTqtK04Y+TVv9Np0/xFIaIvSxnU2gUqfDJxJRWy1/z91EAcPXFPl+0U9b0yyEuan8PcUc18qB2JZwfvfj+8APCYX1WuLmoWvttR6cS3xO3ahTNfZLihIfrXpnn6t4MfokcIqizEzLJzYH2Juobl2VRiHgwO2SliYi506XdC8RvluFpufwfmBt23crMsmPpLRtrsl9Vf2CnUg6gPDR/Br4nKSNbH9ukH01IX0kA9I00kLNyyp8mYhTb2+Ve1VVJ+cwIkVGjZqHYDeVfwZRun8Tol3wLcBOtm/tIzeuWGQVn8T8iKJ68YeBli/hGuArrhHSru4h4P/rUuqj5nxeXORPqerva3IOnf+3uv9HjeXyvIHwDdXO5ZF0DbCu7YclLUr4WgeKBGxCKpIBGVKkxcANfYr8NoyVZTjPNVr9DmP+Hfv7me2Be2DX+RFK2p9QgisyQAhzU/ku+1uMWCFW8g+VIIMdGVuBHgm8sbVd5QIyLJo6uydpTsfY3r6B/B9srzLMOU1wnLuIEi0tdmjfrvA9bJzL01SZDYtUJFOMJijf3sJT1Bpz2HSL/pkseU0QutzCfUKYm8q37Wd54AvA02xvJWktopz4IX3khpIMOAwkzaVLvTPbH5+qOXSZUyNFIOk22ysPc04THGco36OGc/gn0ApYEZGUeFN5btdIamxC+khqovFdBR9DhX/cPURHtpZTdlwZd6BfGfp5dGlEBNVqLA1h/hNRazXVhcplLdp/oBogobGpfBuHEnk0rYvu/xHF/3oqEtsvqXGMSaehs3t+ZErujoelKNSsgnGl1tyTTSqS+jxC/LN/BPyUmhEqhBlhM6Kt7VHABa63LDyTyF4+ATjGNfpCF5rOfyKq1qeaiEplRdqR9Eoi9PbRhEZJdRIaG8kDT7F9rKJJE7YfkjRIBd3aKKoJLN+KOJK0HdEkCqJMyp0Vd9XU2T0QmqAxG/F/6Bv0obFkwG7yiw86rxExcAXjAX7/k0KatgagxKrvSETNXEdclE9zxRIJJUZ9s7KPjYjOggfa7pWo1i6/FBHlswMR+XQMcHRVs9gQ5t9pnhMR9bI+8Z3q1yWy8yIiou3ta4p8JR+BGiY0DkH+HOLHf7rt5xZ/w36uUTV2UBS12n5t+9CyfRPRQnkR4CHb76q4n6cTuTtPYLB6Z9/qMnwfMNf2ST3kepn3+q7aNMRkwEHPYVioQQVjSRfY3rSLpaJuS4NGpCJpiKTtiZ4K+9n+ck3ZpQll8DngY7a/W1N+FhGttT/RorVSSYaOfdSev6RHGKvW2mIlwmRn9+8S+QhwEW0ObiIj+SLqFQw8z/aL2rYFnNs+NsnyzyU++3WIaJ9lgW1t16leOxCSLgee21rNtvuYWheXyZ5DOdbBhGnwuDK0DZGXszJRfmSvqZhHE0Z9DpK+QrQLbq9gvLbtnspyfiIVyQAoGkLtQHR2u5f4ApxYJYy0RPi8jlAAyzJmorqtxvE3IVYULyTsqce4LcN8Mudf5D9ELLs/3AqTlHSLK2ZsK/It3kcor1/UlW/bz4FENnB7QuONhNmwSkLjwPKKSsF7EIqk1QOiVpmZorjeBDzD9mclrULUeLqkjyhq6wFTttexfU15fo37l/cYiq9M0llEYutDZXtBYoW9BZEgt1aV/YySQc+hLfqvK1Wj/zS+p4wYoILxqElFUhNF2esliIvPj4FxZpwKZp1/AL8l/CM30fFFrHDxuxX4G2FXPYuOTOp+ZqGm82/bz0pEZ8TbCEfhlf1WIh3yixMrsZWADxJVd2v1i1f36rst7D5VeIcgf47tzXq9p4/8gYTPanPbz1YUHzzN9oZ9RFGUoH+FO2qDlZuEU/opAklX0MNXVtX2LulGYCPb95XtpYiaZc9qGsk3VQx6Dm1RWy8g2k0fU7a3Ay61/f5Jnvp8QyqSmpQLeetD62aT7GfWOZSJ72IqXbw6jj8u6qufWajp/Lvs7zVE1NJs20+tI1vk1yMU0tq26zTFGjmS9iF8CscwdgdZOQ9EJea/wyx1pe11K8juRNSZ+iBjEXPPJWov7e8KDY2a+srKPnYFPsFYzbcXESHRRwGftv3hqvsaFU3Pofh7Xt5ajSoqRJxWwc/TM9+j6vdofiAVSdIYRaG/1VqmlQHkBSxh+/6K729kUhiiSaKbw7iOj+diIiv+N0WhLEtcgKrm02wJfIwokWHCrr+vByi30dDXtwIRNCKiCnWtXiga3+74XNs/rSAzUdQWUL2Ee9v+Bj6HsqJ5vsf62jyJKCLZs+ZX2/fniUQOz5Xl+M8hVkRT4ucaBhn+m9Sm3A3/yKVyqu1/Ec7m1uurASvYvmAC+U8QkUF/LfIG7m97fXOiIOJE/dPnlr9dTQoVTqGpPGXeTfNBvkWUyFmurG62Je6Mqx7/VODUznFJe7lCqZ0uvrL3M1aypw6ziDppCwKrS1rd1atgf5G4gLfaHe8haRPbe/cRbfXAeSZR/bdV4+o11Kg11sbA50D0tLm8TTG8mKjm25PW90fS0cBubf7GdYgWzNOGXJEktVGUwH8bcdG9lPgBPpGoQPpiIunyo+6oqtom/zrgI8ADwGVt8msQhSDPIKLQ7u4zj4FMCkOUH6gpVMc+nsVYwcQz3aAHfNs++2aGD9FX1rWCsqt36mza7vg0YBuX8jSSlgCOs71lFflhnEPZx1OB55XNun1txtVem2hsfiYVSTIQ5Qe/OXFXvwLhrL2ecPT+oeI+1ugif15Z4VSRH8ikMET5xzhiVa0p1KSWyVGFEiHD8pWVz/A5HrxGW9N2xzcQRQtb/VQWJgI/6jRHG+gchuXjkHQU4WM7gvhf7AQsbnvHOvMZJWnaakC5mC7P+GY0VS+i3cqG30eEG/Zs7lTCE7ciYt8hLsCn1nGSlv1sCqxh+wfFPr+4KyZFOsppnF4eA1FWLF1XLRUZyKQwRPlBm0JdSpdAibLdt0xOBfreHdqe3fAYLW4mkhkHLcX/Bcb+B4+2O64hfzjRF+dE4rxfT5Tor8Og5/DV8rerj4MoeVKFtwLvJoInIExzB9acy0jJFcmASHofEfZ6J+OXw1XvpH4OPJ/oFw6R6X4R0fr2s7YPn0DuaUXmDiJaR0RG+VOBl1R1Eioyg+cQ/TjWLPs9zvYLqsjPLzQxKTSVl/QRop/IDxhrCnWy7S/VmcMgqHfNtUVsT8lNoqTjGbCCsiKhdlvgfJq1S96AsYv2IFW0Bz6HIn80sE+nj8P2W+rMYzqTimRAFCUpnudqhdW6yf8UeLtLTSRFJdkDgbcTP4auCWUlfPiKTmeqpD2ADWz3rEja9v4rCAV0WVvo6VVVFeH8QjFHrUHcFQJQw0k6DPktieRMEf6Vyh3qJjCN3Af8vu7qclRoggq4rl5BeVx1gQbzWI7x/8NKloEi2/QcGvk4ion3i0TgR/s5NF2ZThlp2hqc24gf/aDM9vjCencBa9r+q6Re2dEbd7vTsf2tYuutyoO2LalVYmPSC/UNG0lvJ8wBKwFXEGVWLqR/Z8ZhyS9GKI9TFe1dnynpCa6e3X4AkftxFaGI/oswjzxZ0rtsn1ZxPyOj6sW2B6crKiV05uJUdfa/ljAxPY34Da0C3ECERFdiCOdwvaTvMd7HUSdo4geEdePrwEsIU1edbqkjp05r1mQ8NxP9yfeW9IHWo4b8+ZJ+JmmXckd0EnBeuTj9rYdcL0f0P2sc/1hFv/WlJb2DiJSqW+trT0lLKjhE0mWSXl5D/ktF/gmSzpR0jyK0uCp7EiaR35dIq/WJCLCpkj8PeKIijPYM4gJwaA35W4H1bc9xFO1bjwijfhkw6eYxCOd2l0eV6rvHlr9XS7qq81FjCm8jGqydx1gU4NyeEuP5HHED8H+OEjsvo5S4mcJzeCsR8bUn0aL3ujJWlUVsn0lYiH5v+9NUvJmZX8gVyeD8oTwWKo+67E4Uh2t1OPwhcHzJqegVfrrUBI56AZVr8tj+iqQtiPyNZwKftF3Xcf4229+U9AqibthbiburqnfSL7f9EUmvJwo+bkf4f46oKP+A7QckUZzeN5SVQVWaysv2PxWZ0fs7+p/Xsc8/y/a1rQ3b10la3/bN0pTdkF5GFCe8l/gOLQ3coej+9w7bE+XVtBzDA3fFBHDN+mpd+I/tv0iaJWmW7bMV4bxVGNY5PECsJr4+4C4eKP6i30p6L/BHYFpVeUhFMiCuUaZ6AnkT8fs/ril6LpF01Y1aiVhFcQwcdcXY8vuVwA9sX6l6V8DWne8rgaOKWa/O8W9XVFD+CWEiuReok1XdVF6Snk8UXty1jNX5Td2oqLfVas+6PfB/ihDWysUfG3IqUbDzlwBlRbklkV9yAGOBCOOwfUf5O1A/DEnPI/rdrwZcTdyUDJJD8zdF3bbzgCOLAqzkX2p6Di2G4OPYC1iUKAL6OeJGspKvc34hne01kfQN23sVZ/ljPjxXT8T6b2A/4s5DMDX9A3pE+0BMoPLxFUUPVwRWJaJeFiByADboKTgmvy+wNWGu24i4G/6Z7a4Xrz77ejFR9+pU1+tyOLC8pBcRGci/sr2fpGcAe9WI9lkEeA8RcSSikvMBRKLmoq5YjbkJ6tL3ojVWxWE8wffpPsI89UHbN090XCLM9zwi8u3ttl8xwPwXJT6vWYRCXwo4sk4QzKDn0CZ/AWM+jtdQfByuWQZe0mK2/9H/nfMfqUhqImkD25eWC89jsH1uxf3cBLym7l1YPz+MK9YYkvRZ4M9EHH6rnPkSdUJXy3J8PaJnw98ULUNXdI1+HIqoqfttP1z8Q0vUDMEdOBemqbyk7Wwf12+szz4WIkyLpmYZ+mGgyAw/k/Groi2IVclv3D+58jPEKu5HxPdoByIU/Ubg3Z6gOrI6Ejc7tyvMu3NFs6vt66rKD+Mc2uQvtb2B2kr7Szrf9gsrHv/5RHvmxW2vImld4J223zPI+YwE2/kYwYO4ix1E7lNtjz91bH+qxn4urjLWZx8iIlQ+WbZXIcpxV5VflKgtdXDZXoPoWFjns/gp4WiFiNyp/LkOQf6yKmM95DcjGoSdS9yZ3wK8aIq/h08heqpcTkSufZvwdy0ErD7g9+ii8vfKHnI3E10+W49x2xWOO5dQeAsTvrVfNvgMBjqHtvf+ilgRnQC8l0iKvLHO8Qk/1eVtY9dM5feg6SN9JDVR9CrYmzDJLFuG7yKirvZ1qbtUgbmSjiHs8+1JUD37kbjNNyNpaw/uq3lY0puIO1ET5cTr9hs/gNJPA/gsMA84noiEqsIPiCidTcr27USXuomKNXbyekouDIDtPylqLVVlIHlJWxF+nRU1vk3rklS0zxe+SgQc3Fj2uyZRurySaXAY2L6HaDLWjSrtdh+R9AbGfH3btu++h1ynr69928RFuRezPBYccpykOtnwnQx6Di32YryPY3Nq+jhs39bhH6z7WxwpqUjqcyzRUGozFxOMIjv6LcRFcIuK+1mSCNdtD5et8gNqp4ld8o3AN8vDxF3VG2vu43ku/TQAbN9bTDVVWc329pJ2LPL/qumsb5oLM6j8n4g74tcyvlrwPKKCblWe0FIiALb/r0ro7TApyutDwGzGl/qpGn76JuI7dADxPboI2Kn4f947kZDtOuGx3Vi6I3px3Ha/G7IOBjqHtmP9Bh419e7hUkCyBrcpup66/H72oF4eyshJRVKf2bbHhRcWhbKvpMo/jiH8kBph+1ai5W8T/qOoN9a6EC/LWLmYKjxYfqwt+dWoV++oMxfmbdTLhRlI3vaVwJWSfuRmPo25kg4h/FQQF7TKZeyHxHHAQcD3qHkXXP7377Y9URRh1zYCQ6LpigYYzjlImkOsrpco2/cRUWhV/5fvIhTZisSq/DQiPWDakM72mhTn5BnAYR5f3uQtwBa2X9ZH/iOOfIOuzZXcvynT1W1yqzNmfmhFffVrsdro+B37ehPhnH0ucBiln4YrOpsVeSyfIMImTyNyat5i+5wac9iCWNWJsJPXCmduIt807LOE+e7OWNTWeUSflkELINam5ShuIH9WjdXL0FFb0cy2sWVco4Jy03NQJC/ubvv8sr0p8X+cVuWGmpArkvpsD3wUOFdR3weicOPJwBsqyLeWrHWyd9tplDw1hOM/iu0jJV3KWD+NrV0jCs326ZIuIzKTBexZbPZ15tC0AnET+UalLWz/W9K3y/FHErUF/FTSe4iGVu2+uqoX4sslnUysbNpLnNQxLY2jm3LowQmSXudSm6yYmX9OPT9T03OY11IiRe6CElLck4lu5tr2U/mmbtTkimSaIWlj2xcNYT/PcJ/4+B6yS9q+XxP01eh3EZL0LEcWeddwT/fp4zBB3D9UzMVpKt+2n6Zhn5sRK7lby7FXBnZxjaKRTZHULdTZNVZVP5hA/m0V5b/f/l5FcuFJtl9aUf4dwKuIKhErEzd0H3KNOmWDnkPb9/fNhLP9KOJ7tT1wr+2P95Fvd8h/hrgpaZ9A0xpgU0YqkgFQdLVbkQgR/Efb+JaO9qdV9jEH+DjwdMY7OfuZph6Nt5d0oe3nD3AKSDqPOIffECaV813KYFeQ/ZntV5eLUPsXqFJTJEkH295NDXuel31d7oo9zoctL+lXwAuJaJ+ziNIW+7p6Y6xLgTd2Rm01MTVNNyR9DniK7Xcrcop+DnzXdreL+0T72J3Ie5lN5F/8elIm+9jjdvv+tpjS7/GoSUVSE0W59t0JE9F6hDnmpPJa5aQqRaXeDxPJVI86qN2nXEP7F24IF9GFiFDdzYB3EglRPbv3tckKWNk1ynV3yM8iuhNWKrDXYz+1EtmGKS9pQ+J7sDQR9rkU0Xq30opRXcr2dxubDCRtbvssda/bVtmsI+mJRHmYtRnvJ6q0Iin72I/47DYgFPHxFWTaE3NFrAquJvJhcMXE3LKvxufQlKbf41GTPpL6vIPo+/F3SbOBH0uabfub1LCPA3fbPnmA488qd26z2p4/etyqtu3iEHxheSxN5G6c30umHdtWdKUb6O7Z9iOSvkI095qWuIR9An+nXrXXFqOM2noxsYrqFq1UJwz9cKJs+yuIXKI3USF0tUOBXQL8T/lrSf9dQZF15vucOMF4FQY6hxYl2OYLwNNsbyVpLeIm6ZAB5jItyRVJTSRdZ3uttu3FCdPGdcDmrt7M5qVEEmBnV7aePyBFr+1H6K606ti2HyYc7l8EfuHB6lP9L3Bo2wW1rvxniF4cJ7jGF7HjIvQVIg/iUSp8hk3lu9ZZa5OvWm9t5FFbTWmtilsrKUUezC/7mXUm8Eu0qOxjGQaDnkOb/ClE4MXHba+raIV9ectv1kOu3Ve3KGNtIKak7t4wSUVSE0lnAR+wfUXb2ILA94E32V6g4n6OIHquX8v4Vr1T8gNSVL19AdEje8Myhwtt/0+NfVxH1Im6lYh2qRSC3CY/D1iMyF/4FxV/QE0vQkOQ71pnrW0HleqtzQ8UZbYNj01I/GxF+Utsb1R8bu8h6rddUvWGpilqnlDZ+Bwk/cb2hh1m5yuq3lQ+HkjTVn12pqMMhiP0cGdFcltV1u13xzKZOIos3kxEuqxElCmpm1W9VcM5DGKGaJzM2VQeuGVQ3xCApNcBK9n+37J9MWPldv6faxR9HAInEZVuL6VeMmiLg4t59X+IiKnFgU9WFZa0ElHr6wXE3fkFhN/x9oq7GDihso1G5wD8Q1GwtJVYuzHNuqdOO3JFMiIkfRf4ugesWDqE4/+OqG56AeEbuXhA81aT6rmtqsOr2v6cpJWBFWxfUnceU0lH5NzxtrepKf8rYAfbt5XtK4hcnMWIvi6VQl+HgaRrbK8zVcfrcvzTiaq7LT/RTsTKvlKpITVMqBwGJQx4f2AdosPlssC2rlEFe7qTK5LRsSmwSwmh/Tc1zUJDYA3bdcqZPAZJnwLmEOatHxArmiOIu8sqtBd9/BzhtP5fqhd9HBXt/qlBTDgLtZRI4QJH/4y/qH69sKb8WtJ/uWLodydNTWPAsh4f6nuopL1qTKFpQmXjc7B9WTF3PpP4bowisXSkpCIZHVs23YGiTtDyjP/yVzW5LKRoEdsk5LFp9d2mRR8boe7lNapkVXuC51V50rid2e2FAZdlCtBYqZ0FgbcWM+cgNzRNTWP3SNqJSOaDCECp3JSKsSq7H24bM/UUfKNzUDTX+gDwdNvvkLSGpGfarlrFetqTimREuOSLKMqsPLHP2x+DpPcRmbB30uasB6peABqFPBaaVt9tVPRR0nZER8N5kj5B1Pz6vPtkxrdxYZHpN9bJupLuJy66i5TnUD3a5mJJ77A9rkCkpHcSIbBTQdNSOy1Wst3kpuhtRA+Ur5ftX5WxSrh5z3dofg6tdgitUPZK7RA0xG6loyYVyYiQ9FqiH8XTiH4mTycu5GtX3MWewDNdo6VoB6vb3k5Rp+gwST8CfllzH92q536vhvy3CJPEcpL2IYo+Vo4aA/7H9nHFT/MKIpT3QCboM95CUY9pRUIJrM+YqWpJIgyzJ1Uj83rwfuAnkt5IWc0R+TgLE31uJp22G5nVgNsddb82I25EflhjV41MY2UFXSlcuh0NKaGy0OgcGLAdQivYRBN0Kx1wLiMhFcno+BxRrPCMEsP+EmJZX5XbaBYZ0rLh/k3SOsQXeXadHdj+iqJ67v2EffiTrlE91w2LPjIWpfMq4EDbJ0n6dAW5VxDVmlcilHnrR38/8LEaxx8I23cBm0janLEbh5/bPmuyj92F44E5klYn2r2eTDi/X9lLaFimsQZRW40TKodo3mvaDuEVtttvfg4skXyV216PHM8HbRpn4gOYW/5eSXR7g4hdryp/CPGj25uwz36AyG+pKv92wlb/IqLN6V1EnaI657BflbEe8odXGesh/zPgO8DviOz8hanQGrXIziKig0b+XRjlg9IaGPgI8L7y/PIKck/v9ahx/NOJqgALlsdbgNOn6NyHdQ5bEP1Q7gaOJPKqNqsh/2tiFbJA63sJ/HrU3406j1yRjI6/KbLizwOOlHQX9dq0/qE8FiqPyijqXN1v+95y/EGTx7YA/l/H2FZdxiZinBmv+EvqhHK+gQha+IojL2YFxjtdJ8RRouWdxA9/JvOfYpLZmbG7+yr5RMsRxRZPaR+U9Bqig2TPmnFtNIraahhx1fgcym/pSUSv+UHbIQyjW+lIyTySEVEc0/9i7A5kKeBID+7zqHv882y/aEDZdxMZwKsxvq/3EsSd1Jv6yO9NmJAWYXxZiAeJyq8f7SPfs7Ckq9cb+x/if3AM4/tQVA4dne4o6kK9i6hqcJSkVYHtbe/bR+4cognZrR3jqwMHu3p5kTOAQxkftfVWVy8jfypjEVePJiTa/moF2XMYzjkM/Ft6vJCKZJoh6Ru299IE9Z5cvc7TwBdRSUsRd2FfJJp8tZhX5yIs6Yu29676/ja5Vvl6AasA95bnSwN/cMVIHjXsxTGTUVsPli6vXWl73Yr7WYWI2mpFPP2KuKOvtKJpklA5xHNodEOiKPNyILC87XUkPQd4re3PV5GfH0jT1hRTcjeWsf3lsv1H4k5ewEdsH9hnF60M4K80nEorxLK9N3Sl+Hvb9wH3SXqo8wcv6XDbb644h406BySd2e9utKUoJB0EnGz7F2V7K6Bnq+Nu+5nJaPB2wYv0eK1yGLgHjNpqo0nE1VDOgQa/pcJ3CZPsdwBsX1WiKFORJBPyLsYnI95le0VFT4TTiDuTCbF9afnbtDDgs20/0D5Q5lCHTh/HglTwcZTjLAY8RePL4C9JhENXZUPb72pt2D5F0SipEooqr+8mAg4AzgG+45mVlTxou+AzSsj2J9xm1lBUdK4cfSbpGYRvYGPi4nsh8H736d4p6Roi56hJxNVQzmEINySL2r6kI2K4jr905KQimXpmdfhBjgOw/UAJIeyJosf5CUQnvd81mMeveWziXbexbnN41MfRkYz3IHBwhWO/E9iLUBqXMj789n8ryLe4pyQiHkFchHaiXlb0gYRj+YCy/eYy9vYa+5juLGL7TEkqq8tPSzqfjravXfggkTN0k6JWGMC6RGuCOp/fj4j/+evL9g6Ev6RnLhCRB7RejeN0Y1jnQAmh71zVVc3HuaeEDLfCh7cF7qhz/FGTPpIpRtJNtlfvMj4LuKmfSaHY9Y8nIpb+TPzojrH9p4rHbyXjHUFEhrSvBg6y/awa5zKQj6NN/n22928gvwxxwWutKM4DPlPDNv0YO3gd2/jjATVvF/wMxlam1/ZbSXSRv9jjcyiQdJHtjfvIDa2j4BDO4VNEl9G1gF8QkYsX2N62xvEPJipw3wvcQoSmV418GzmpSKYYSQcAf7X9iY7xzxOhiO/qLvno+9orz76QiHL5byIr/ijbPVcEknYhYvXnEHdeLeYRTarqZARTTFNrMP5O7Lwa8k3u5Fr7WBJ4xPbfa8pdBmzXWtmVH/SPh3WBmg6oYbvgBsdtRd59BPgbcDRxR749sLDtniZKSbcDE7bTdY1Wu00piY3rEvk36yo6Jn7PdrdkyU7ZBQjF/eESyTnL9rxJnvLQSUUyxZQvy/eICrdXluFHl9P9Lobd7sTKl3ELImyzUq8NSdu4Qm/sPvt4O1GqZSXgCsLOfWGNsMmmd3L/RZTzaF2U7gF2sX1NRfmXEj6Cm4mV2dOJ0NOzq8hPZyRdSSS0/hr4VWcI7BQcvz3yrpO+kXOS7iDMkF39ObY/03iSFdFYY6xLCT/TPOAa25XKHUk6q+pvZn4lFcmI6FhOX1fV3yHpaNs7NDjuB3q9XudOrtyJbQhcZHs9Sc8iTEvb15Af6E6uyP+aaG96dtneDPiC7U1qnMPCjJX/vsHTqM1tE8pKcJO2x2KEUvk1kQt08Qin15dhmraaUqwMHyP8Ox8k2iFcUeOm7qvEqv44xocP17IOjJJ0to+IYoetZYstcgMrkcIwi8E9UIIEUJRfv0FSJdt64V+ODPOHinnqLupl2S/WvnqwfY5qVCAu0WPvIXrDGDhf0kGd0WyPR8qq7RpKcISkpxAXwr2I0PKqLaO7JYfOqxr5Jml3IhH3b2X7ScCOtg/oKVgtsqzfsTeke2b7a4E/tiIke8h/G/iR7feUoYMUCZJLul5Tq2WIIJH2VUmlemHzC6lIpiHlzn9Foqvh39vGt7R9ai/ZIS/5b1f0fv8JcLqke4nSElWZW+S/S0Rv/Z16ZdRvLslg7d31KnVnLPyQMEO0HP47ln1tV2Mf05JiDl2fWI28gKhS8EfC7HphjV1dRrRrbk8KvUNR8ucd/S7G5T2PRuo5etK8g7FIuokYRhfJLxP+wk6uIxRsP3PTb4GvKkrzHEP4KK+oO4mqK5f5mTRtzUdIWsh92t1K2oNIfLqeCH/c0/ZJ5bW+y31J3+oYMuFbONv2BQ3m/mLCUXtqv3OYQH42Ne/kyt3rZ4gVhYiorU87aohVkZ+xUVuS/kF8h/4XOMcV2yN32c9BwIm2f1m2X07kSR0LfLMzIquL/FXAuq08jqLgrqrqX2iChpfZ/nRiNbcDETRyFHC07f/rI/cR21+StD/dq1TsUeX48wO5IpliJP1Pt4gURdmRkwjncy/eAWxg++/l4vtjSbNtf5Nqy/1ud4jLAF+WdIztb1TYxzgUHeL+Qfh6KikRRfLiw7at6NU+h6jiW5miMPYon90jA0S7XC5p41aEkqTnESU6ZgJvJ8qSvJ1I6PsNsRK50PYfa+xnjscnhZ4m6Qu2P1D8T/34JdHX5iDiYvouoOeqeogMKzv/98B+wH6K/jbfJ8LS+5kHWy0T5vZ813TA80EJ4pn0ILLX9+kYeyoRwfXJCvLXdWwvTvzwvkY4+Aad1yJUKB9e3vtaolT2ZUTfiluAi4i8ll0qyL8D+CtRvfgdwP8R4Z83Av+vxpw3BK4uc7m1fIYb1JC/nsiObsk/Alxb9nnVqL8rU/idXJS4gfk4Ya75fQ3Z04hqz63y6x8hSsMvQClR30d+FlFd4MdEftQ7gQWm6LwPAvahWGbaxj9DFG2sup8nEJWTjyy/gWOI3jr95PYq3+EFR/0daPxZjnoCM+1BLH1/BnytbK9BVNCt1AuESBpbr2NsQcLe/3DDuV1e8X1XAmuWH8HfgWeU8eWAqyvIX0sUfVyFWMk8pYwvSiSEVZ3vVcAL27Y3raMAGFI/iun6IO66Nwc+AZxCmDgvB75dYx9PIXxMlxMh4N8m+s4vRHThHPl59jn/o4iV8PHl8TvipmbxCvJbEKuPO4GfElW8F6tx/K8QUXJ/JcrzfIFo0rbMqD+buo/0kYwARY2no4kuhc8H9rJ9YkXZlYCHbP+5y2svsF3bNFPMTG8G/tvVkqgut71+eT7Oztz+WkX5cbboKvJt7/2V7Rf0G+sityjwH5fIohJp9kriTnzaRMo0QdLlhCJvmbR+RYRx10rqHMI8Bi0aOcw5DJTZLulsosTL8W7QekDSQoRpdxPievB84G+21xp0n1NN+kimmLY8jksIM8D5wKqtcffJ43CPFqRVlIikeTzWsfcvosPbO/vJF2YVR/cs4BGNL7w4q4J8q1f6LGAhjfVNF20XkwpcougZfxRjWdHnSHougO3LJpA7FdgV+K2i98SFhFni1ZI2dIOyL9OIXYjVY6M7SUUJ9A/x2MZSVRPsBi0aOTTcFoqvaNX86YpyLxnSFBYhShQtVR5/Isyr04ZckUwxJZt7QjyFGbmDIulWwp8waFZyz8zxqj/QPvvxRBez9lWUolrwMrZ3L3eGl3qCSJ7ksZQM+YN4bGOpfmG/LflLbW/Q8T853/YLJ2XC/eczZYmOkg4mVkLzgIsJP+NFrhh1OD+RK5IpZjooin7Ynt1QvvGdnKLI5YG2jx1kCm3PNyfyCbD9oKRHms5thvGQ+/fQ6cUD5X/5W0nvJXJZlhvO1AZiKldDqwALEwEOfwRuJ+qOTTtyRTIf8HiotTMKNGCLU0lHENE1fyQ6PK5q+58lOfJcz4A8kmFRTEF3AScS/UCAWt0BR1I0ssd8ZtmespsJSSJWJa1SNesQzvcLbfcr5T/fkIpkiikJWOOGiAioGwFcrSFPQuTkMECLU0Xflz2BFYDv276yjG8CrGb78F7yjwdKSZrlbf+2bG/HWF7FL23fWXE/3RIZ+5o35wdKiZztiaz8nxI+yxcSkVufs33PFM5lJaLCwCbAq4En2156qo7flFQkU4ykk4kGTp8nLoIiHO6bwqPJTVM1lwWA5RnvJP3DVB2/KdP5IjZqin3+17YPLds3ESHAixDmqp7tDIZw/JN7vW67SfvdqnM4loicXIwIR7+GUCibEiH2r57k4+/BWIma/xCRc60IuquncmXUlFQkI0DS64H3A1+xfbKkm6f64ifpfUS0zJ2E4xyqtyjttr/FiC53O9p+1QDyqxG1rnawvc4gc6hxrKvpUpKixUxYFZbw3+e2orY6QrIvsL1pH/nNbZ8l6b+7vd4vjFrS3cBtRMTdxXT4Jty8lXRfJF1je50S/n677ae2vTbppXIkfY2xMv7TqiNiJ+lsHwG2T5R0GvA5RU+PhUYwjT2BZ3p8299alCinVxKdFrckEroOqiG/AmFaeCPwHCKfYMcKco0uYoTpAKJmGYwVfXwT8M9+x3+csGBH6O+b254vXUH+xURybLe8oyqVa59KJPTtSPz/f04UPby2wrGHxYMAth+S1Fls9OEu7x8qtnu2dJhO5IpkxEhaF3i+7coX4CEd92xgC9sPDSDbugC8Ajib8FHsXzWaS1HddUeiIdax5XGS7VUryn/G9qck/aDLy7b9tor7GSih8fFACdt9RWdiq6QVgVOmclVWanLtSETPfdYN2i/XPO5dRGKwiBuao1svAW+wvfxUzOPxQCqSGYqkQ4iGTj9nfLRN38ZWJUT2fOAtLlVj65jnJD1I2II/aHtuXfk++16+hqP4CuC9LlWPi7P9ANvrNZ3H/I6knYhV6QeJ8iYAzyXKduzviu2OixLYhscmJH62ouyrCCUyGziZCH6oUzRyYBRtpyfE9mFTMY/HA2namrn8oTwWor5pbQOiZPYZkm4m7uQqNUIqPI3o+fE1RVfEY4nCdwOhqP67DWEieTbRq6UKuwLfL/IQMfyVVjPTHdtHSLqHCPpYmzBHXUsUDj2lp/B4TgLuIxISK3eXlHQYEep6CtFVs1J75GHSTVGUKg1/a5rxP9PIFUnSCEkvIO4otyGK9p1o++Aa8isRSmlHomjjibY/VkFuEaIK8RuJO+klgK2B8+pGu5RQWNm+r47c4xVJe7liO4GWw3qAYzzCWMh2+0VIhHlyybr7HGAOnwSOdXT2XJhQausBDwFvtH3GZM/h8UIqkhEyivBbSd+wvZekn9K9mc5AYZclO/llRNTVQHf1pW7TDv3MIpKOBF5ElDA/mnD63lTVx9K2n4HNMo9nJP3B9ioV33swYQqbVrWhACRdC6xj25J2I25mXkbkdR1me6ORTnAakaatETFR+C0RvTSZtCKUvjLMnTp6r/+DyI2pTQkf3gjo2VGvsA6RRHY9cIPthyUNckc0kFlmBlCnTMimwFtKTs+/GVtRTIcQ6gfbTFivILoaPgxcX0KCk4rkhzU6GoffDoJLMb32OH1Jy9m+a5D9SVqPMC+9gWhwdXwN2YHCh22vq+hb/0bCT3MXsISkp3ZGIfVhJdtb1nj/TKGOUt5q0mYx+fxb0jrEzdxLiCrGLRYdzZSmJ6lIRsdtxN3wlCJpmS7Dl6iUcq9SI6llgiJMAX8hwn/l6lV7O8OHDwc2sv3WamcBtm8APgl8UtKcsr9LJN1ue5OKu/m1pP+ajmaZpqh7OwGIFUWvFrSdfN52ew4Kkg5nfF7K/MpeRGfGZYGvt0UgvpKxSLakAukjGRFNwm8bHvcRoLMMy0pE5dFK5UXawn93tX1TGasT/tsofLjHfgW8qGpWtKTrgNWJldR0M8vMF6ij7Hrx+13tadSUKWlOrkhGR5Pw2yZ8hHAofrh1Jy7plpqO6m2IFcnZkk5lLKmrKk3Dh7tS7N11SmtMZ7PMSJG0N/AxoknZ/a1hIlu8ctTeKNFYk7kWJtoNX9C6wUmqkSuSGUgJuf06YV77FHDlIKuB4iDfmjArbQ4cRoTvnlZjH43Ch5sgqWtk0mRHzj2ekPRFT9OOkureZG4ZwuT6adtHd3k96UIqkilmssJvB5zLa4CPA7PbC9YNuK9liCTD7T1Ab5VhhA8PcMxW8cZWi99VgRttr91TMBlHKavydMaHUJ83uhk1o3yXz/AUdUp8PJCmralnUsJvB8H2TyWdAaw2hH39FfhOeQwiXzt8uGTFfwF4mu2tJK1F1C07pOIxx7XUVfR6r9q3PgEk7UuYKa9jrNChgWmrSGz/tfjbkoqkIpliuoXfTjWSNoop+DfEXfjLJa1i+xcjmMt6DBg+DBwK/IBYVQH8HxFBVkmRdGL7MkXHvqQ6ryfC2B83eTiSNifylJKKpCKZYiRdRpTYPsr270Zw/E8RTuYFJZ1OJACeA3xU0vq295mCOTQKH27jKbaPLY7fVjnwyuW/O5yts4hSK3fXnMNM52aiTtq0UyTq3pdmGeBPwM5TP6PpSyqSqedJRL+HsyX9mWjsc4ztzn4Ik8W2RD2hhYm+5SvZvl/Sl4kGQ5OuSIAbiPDf17SFD79/gP38Q9KTKRcDSRtTLzdnibbnDxGh2HVWRDMWSfsTn/s/gSskncn4MPY9RjW3GnR2QDTwF9v/6PbmZGJSkUw999r+EPAhSS8k7sovk3Q9sUqZ7Iilh0oZiH9K+p3t+wFs/6vkd0wFTcOHW3yAKD2+mqRfEYll21YVtv0ZAElLxKb/PsAcZipzy99Lif/BtMNT2Nb68U5GbU0xnQlcZWwBolvc9nWyuwc8/sXAS2z/U9KsVqXcUkr97KmMVBlS+PCCRGKniIir/9SQXYcIfmhl+98D7OIRlDRPkulMKpIpRtLRtncY4fEX7uYYlfQUYIVRlQupEz6sCVrstnD/Vrut/fwa+Ljts8v2ZsAXapRYmfFM4Ge4j1ixfH6qa8kloyEVSTLtUPcWuy1cNQ9F0pW21+03lkyMpC8RYb8/KkM7EKvD+4BNbXfr6Z48zkhFMgJK5doVgYvb7fKStrR96uhmNrOQdCJwGWO5PTsBc2xvPbJJTTPUo++9pKs7c3WSxyezRj2BmYakPYg+GO8DrpH0uraXvzCaWU1PJD1Z0rckXSbpUknfLFFcVXkb4aA/ATixPJ9UH9XjkMUlPdpDpuQoLV42HxrNlJKpJlckU0yxKT/f9t8lzSbKWB9u+5uSLre9/mhnOH0oeTDnAUeUoTcBm9l+2QD7yl7dA1ASOL9PKA8B9wNvJ/q/v8r2sSOcXjJFpCKZYiRd115iW9LihDK5Dtjc9nqjmtt0Q9KltjfoGJtre04fuW69utclbP3Zq3sAStSfbP9t1HNJpp7MI5l6/ixpPdtXAJSVyauJu7q0J9fjbEk7AK273m2JpMJ+bA98rjzfhTDxLkfp1Q2kIumDpJ1sH9FZir1Vomqy++ok8xepSKaenemwHdt+CNhZ0kAFD2cw7ySSElumrVlEtvsHiOitJSeQ6+zVfZSzV3ddFit/l+jyWpo5Zhhp2kpmHJIuIuz4dwI3Ahu0dWq8wfazRjm/6Y6kvWx/Y9TzSKaOvPtKpjUlOXFT4i74fNs/qSC2J9mrezL5APCNUU8imTpyRZJMWyQdQPRcP6oMbQ/8zvbuo5tVIuk22yuPeh7J1JErkmQ682JgnZa/Q9JhwEhKvCTjyLvTGUYqkmQ6cyOwCtCq4roycNXopjNzkDSP7gpDwCJTPJ1kxKRpK5m2SDoX2BC4pAxtCFwE/APA9mtHNLUkmVHkiiSZznyyibCkRYEPAqvYfoekNYi2sT8byuySZIaQK5LkcYOkFxCZ6ZWc7ZKOIRoz7Wx7HUmLABdmdYEkqUcWbUymNZLWk/QlSbcCnweuryG+mu0vAf+B6BLJYJ0ak2RGk6atZNohaU2i78WOwF+AY4jV9Utq7urBsgppRX2tRlvf8SRJqpGKJJmO3ACcD7zG9k0Akt4/wH4+BZwKrCzpSOAFwFuGNckkmSmkjySZdkh6PbEi2YRQBEcD37O96gD7ejKwMWHSusj2PcOca5LMBNJHkkw7bJ9oe3vgWcA5wPuB5SUdKOnlVfdTnPMP2P45sDTwMUlPn4QpJ8njmlyRJI8LJC0DbAdsb3vzijJXEX1IngP8kCjl/9+2XzxpE02SxyGpSJIZi6TLbD+3NLr6o+1DWmOjnluSTCfS2Z7MZOZJ2ht4M/BCSQsATxjxnJJk2pE+kmQmsz0R7vs2238GVgS+PNopJcn0I01byYxG0vJEjS6AS2zfNcr5JMl0JFckyYxF0huIgo/bAW8ALpa07WhnlSTTj1yRJDMWSVcCW7RWIZKWBc6wve5oZ5Yk04tckSQzmVkdpqy/kL+JJKlNRm0lM5lTJf2S8a16fzHC+STJtCRNW8mMRtJ/A5sSJVLOs33iiKeUJNOOVCRJAkh6CvAX5w8iSWqT9uBkxiFpY0nnSDpB0vqSrgGuAe6UtOWo55ck041ckSQzDklzgY8BSwEHA1vZvkjSs4CjbK8/0gkmyTQjVyTJTGRB26fZPg74s+2LAGzfMOJ5Jcm0JBVJMhN5pO35vzpeyyV6ktQkTVvJjEPSw8A/iEitRYB/tl4Cnmg7CzcmSQ1SkSRJkiSNSNNWkiRJ0ohUJEmSJEkjUpEkSZIkjUhFkiRDQNLDkq6QdI2k4yQt2mBf50iaM4Dc0pLeM+hxk2RQUpEkyXD4l+31bK8DPAi8q/3F0sZ3slkaSEWSTDmpSJJk+JwPrC5pM0lnS/oRcLWkJ0r6gaSrJV0u6SUAkhaRdLSkqyQdQ4QkU177e9vzbSUdWp4vL+lESVeWxybAvsBqZWWULYOTKSPLyCfJEJG0ILAVcGoZ2ghYx/Ytkj4IYPu/SjmW0yStCbwb+Kft50h6DnBZhUN9CzjX9uvLamdx4KPlWOsN96ySpDe5IkmS4bCIpCuAucAfgEPK+CW2bynPNwUOh0fLsfweWBN4EXBEGb8KuKrC8TYHDiwyD9u+bzinkST1yRVJkgyHf3WuBCRBZNA/OtRDfqLM4PbxJw40sySZZHJFkiRTx3nAmwCKSWsV4MaO8XWA57TJ3Cnp2ZJmAa9vGz+TMIkhaQFJSwLzgCUm+ySSpJNUJEkydRwALCDpauAY4C22/02YqBaXdBXwEeCSNpmPAj8DzgLuaBvfE3hJ2delwNq2/wL8qoQgp7M9mTKy1laSJEnSiFyRJEmSJI1IRZIkSZI0IhVJkiRJ0ohUJEmSJEkjUpEkSZIkjUhFkiRJkjQiFUmSJEnSiP8Pjj1HtTKG72cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot \n",
    "prod_grp=fdf.groupby('Product')\n",
    "quant_prod = prod_grp.sum()['Quantity Ordered']\n",
    "\n",
    "products = [product for product, df in prod_grp]\n",
    "\n",
    "plt.bar(products, quant_prod)\n",
    "plt.xticks(products, rotation='vertical')\n",
    "plt.ylabel('Quantity Ordered')\n",
    "plt.xlabel('Product')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5135d98f",
   "metadata": {},
   "source": [
    "#### Looks like cheaper itmes are sold most. Lets check this hypotheses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "28be7bfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product\n",
       "AAA Batteries (4-pack)           2.99\n",
       "AA Batteries (4-pack)            3.84\n",
       "USB-C Charging Cable            11.95\n",
       "Wired Headphones                11.99\n",
       "Lightning Charging Cable        14.95\n",
       "Bose SoundSport Headphones      99.99\n",
       "20in Monitor                   109.99\n",
       "27in FHD Monitor               149.99\n",
       "Apple Airpods Headphones       150.00\n",
       "Flatscreen TV                  300.00\n",
       "34in Ultrawide Monitor         379.99\n",
       "27in 4K Gaming Monitor         389.99\n",
       "Vareebadd Phone                400.00\n",
       "Google Phone                   600.00\n",
       "LG Washing Machine             600.00\n",
       "LG Dryer                       600.00\n",
       "iPhone                         700.00\n",
       "ThinkPad Laptop                999.99\n",
       "Macbook Pro Laptop            1700.00\n",
       "Name: Price Each, dtype: float64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prod_price= fdf.groupby('Product')['Price Each'].mean().sort_values(ascending=True)\n",
    "prod_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "47ff7514",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7fdb04305430>,\n",
       "  <matplotlib.axis.XTick at 0x7fdb04305a00>,\n",
       "  <matplotlib.axis.XTick at 0x7fdb0431a520>,\n",
       "  <matplotlib.axis.XTick at 0x7fdb0432e100>,\n",
       "  <matplotlib.axis.XTick at 0x7fdb0432e6a0>,\n",
       "  <matplotlib.axis.XTick at 0x7fdb04332130>,\n",
       "  <matplotlib.axis.XTick at 0x7fdb043328b0>,\n",
       "  <matplotlib.axis.XTick at 0x7fdb04332b50>,\n",
       "  <matplotlib.axis.XTick at 0x7fdb0432e520>,\n",
       "  <matplotlib.axis.XTick at 0x7fdb04336490>,\n",
       "  <matplotlib.axis.XTick at 0x7fdb04336b20>,\n",
       "  <matplotlib.axis.XTick at 0x7fdb0433c610>,\n",
       "  <matplotlib.axis.XTick at 0x7fdb0433cd30>,\n",
       "  <matplotlib.axis.XTick at 0x7fdb0433f700>,\n",
       "  <matplotlib.axis.XTick at 0x7fdb0433c880>,\n",
       "  <matplotlib.axis.XTick at 0x7fdb04336580>,\n",
       "  <matplotlib.axis.XTick at 0x7fdb0433f190>,\n",
       "  <matplotlib.axis.XTick at 0x7fdb04345430>,\n",
       "  <matplotlib.axis.XTick at 0x7fdb04345dc0>],\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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05nenLSKFJIQQncSWlw8EYMgHVhRYkk6nM787bREN2QkhRCfQWFPOtlcPYOBhqygbUltocfLGzO4CTgBGmlk18FM697vTFpFCEkKITmDrK+/BG8oY8sFlhRZlj3D3s1u41CnfnbaGhuyEEGIPaWooYWvVOCoPXkPFqG2FFqfHIoUkhBB7yPZ5Y2na0Y8hH1xeaFF6NFJIQgixB3iTseXlA6nYfyP9xmiTgj1BCkkIIfaAHYv3JbNpIEM/uAztc71nSCEJIUSeuMOWFw+mbPg2KsevKbQ4PR4pJCGEyJOnngo7ww45brl6R52AFJIQQuTJlVdC6aBaBh3xdqFF6RVIIQkhRB7Mng1//jMMnrSiR67qXYxIIQkhRB5cdRUMGQKDJ75ZaFF6DVJIQgjRQZYtgz/+Ec4/H0r6ZQotTq9BCkkIITrINddAWRl897uFlqR3IYUkhBAdYO1auPVW+NrXYP/9Cy1N70IKSQghOsANN0BdHfzwh4WWpPchhSSEEO1k27agkM44AyZMKLQ0vQ8pJCGEaCc33wwbN8K//muhJemdaD8kURDGTX2sw2FWXvHZLpBEiPbR0BCMGT7+cTj++EJL0ztps4dkZrea2Vozm59y+5mZvW1mc+LvtNS1S81sqZktNrNTUu7Hmtm8eO36uK0tcevbe6L7i2Y2rpPvUQgh9pi774a33lLvqCtpz5DdbcCpOdyvdfeJ8TcdwMwOByYDR8QwvzGz0uh/GjCFsOf6+FSc5wEb3f0Q4FrgyjzvRQghugT38CHskUfCaae17V/kR5sKyd2fBdq7ycfpwN3uXufuK4ClwHFmth8wxN2fd3cH7gDOSIW5PR7fB5yY9J6EEKIYmDED5s8PvSPVTl3Hnhg1XGRmc+OQ3l7RbTTwVspPdXQbHY+bu+8Sxt0zwGZgxB7IJYQQncqVV8LYsTB5cqEl6d3kq5CmAQcDE4HVwNXRPVfbwVtxby3MbpjZFDObZWazMhkt1yGE6HpeeAGefRa+9z0oLy+0NF2PmU1I2QfMMbMtZnZJPrYDHSUvKzt337kTlZndBDwaT6uBsSmvY4BV0X1MDvd0mGozKwOG0sIQobvfCNwIMHDgwJxKSwghOpOrroK99oJvfavQknQP7r6Y0Nkg2gC8DTwIfINgO/DLtP9mtgP7A382s0PdvbGjaefVQ4pzQglfBBILvEeAydFy7kCC8cJL7r4a2Gpmx8f5oa8DD6fCnBOPzwSejvNMQghRUBYvhoceggsvhEGDCi1NQTgRWObub7TiJ6ftQD6JtdlDMrO7gBOAkWZWDfwUOMHMJhKG1lYC3wZw9wVmdi+wEMgAF6a05AUEi71KYEb8AdwC/N7MlhJ6RhqlFUIUBb/8JfTrB//8z4WWpFMpM7NZqfMb4+hTLiYDd6XOLzKzrwOzgO+7+0aCHcALKT9pG4GOCdaWB3c/O4fzLa34vxy4PIf7LODIHO61wFltySGEEN3J6tVwxx1w3nmw996FlqZTybj7pLY8mVkF8AXg0ug0DbiM0BG5jGA78E06YAfQFlo6SAghcnDddZDJwA9+UGhJCsZngNmJzYC7r3H3RndvAm4iOyzXku1Ah5FCEkKIZmzeDNOmwVlnwUEHFVqagnE2qeG6jtoO5JOg1rITQohm/O53sGVL310myMwGACcT7QMiV+VhO9AhpJCEECLFzJnhQ9iTToJjjim0NIXB3XfQbIECd/9aK/5z2g50FA3ZCSEEUF8fNt379Kdh333h178utER9D/WQRI9FW1iIzmLxYvjqV2H2bLjgArj6aqisLLRUfQ8pJCFEn8Udbr0VLr4Y+vcPH8Gefnqhpeq7aMhOCNEn2bgRvvKVsCTQ8cfD3LlSRoVGCkkI0ef429/gqKPgwQeDAcPMmTA6r7UFRGcihSSE6DN4k/GTn8AJJ4QlgZ57Lph2l6gmLAo0hySE6BM0bKpk/Z+O5rJVcO65cP31MHhwoaUSaaSQhBC9nu0L92f9E2Epzbvu0kZ7xYoUkhCi19JUV8qGPx/J9vlj6Dd6AyM/P4fJkz9VaLFEC0ghCSF6JXWrhrLuT0eT2TyAoR9ZwtAPL8VKtNVaMSOFJIToVDZtgj/9CRrzWs2sY2ybNyane8PGgWx58SBKB9Wyz1efp/+YjV0vjNhjpJCEEJ1GYyN8/vPw9793V4pHtXhlwIRVDD91HqX9M90ljNhDpJCEEJ3GL38ZlNG0aXDqqV2f3kevfDr3hZImygbXdb0AolORQhJCdApz5sD//t9w5pnw7W+D5dpHtJMpG1rT9YmIbkOfgwkh9pjaWvjHf4SRI+G3v+0eZSR6H+ohCSH2mB//GBYsgBkzYMSItv0LkQv1kIQQe8TTT8M118B3vtM980ai9yKFJITIm02bwjI8hx4KV11VaGlET0dDdkKIvLn4Yli1KixSOnBgoaURPR31kIQQefHHP8Lvfx8s6447rtDSiN6AFJIQosOsWgXnnw8f+AD86EeFlkZ0Nma20szmmdkcM5sV3Yab2Uwzez3+75Xyf6mZLTWzxWZ2Sr7ptqmQzOxWM1trZvNTbh0WzMyOjTe41MyuNwuGoWbWz8zuie4vmtm4fG9GCNH1uMM3vwk1NaGHVF5eaIlEF/FJd5/o7pPi+VTgKXcfDzwVzzGzw4HJwBHAqcBvzKw0nwTb00O6LSaSJh/BpgFTgPHxl8R5HrDR3Q8BrgWuzOdGhBDdw7Rp8MQTYVWGCRMKLY3oRk4Hbo/HtwNnpNzvdvc6d18BLAXyGsRtUyG5+7PAhj0RzMz2A4a4+/Pu7sAdzcIkcd0HnJj0noQQxUXD+oH84AdwyilwwQWFlkbkSZmZzUr9puTw48CTZlaVur6Pu68GiP97R/fRwFupsNXRreOC5ROouWBmlhbshRyCNcTj5u5JmLdiXBkz2wyMANY1TzRmzBSAioqKPEUXQuSDNxrrHpvIoEq49VatxtCDyaSG4VriI+6+KtbtM83stVb85ioJee3z0dlGDS0J1prA7b4Zd7/R3Se5+6SyMlmsC9GdbH7+EOpXD+N3v4P99y+0NKIrcfdV8X8t8CBhCG5NHO0i/q+N3quBsangY4BV+aSbr0LqqGDV8bi5+y5hzKwMGMruQ4RCiAJSt2oom587hIFHVHPmmYWWRnQlZjbQzAYnx8CngfnAI8A50ds5wMPx+BFgcjRQO5BgI/BSPmnnq5A6JFgc3ttqZsfH+aGvNwuTxHUm8HScZxJCFAFN9aWse3QipYNrGX7ygkKLI7qefYC/m9mrBMXymLs/DlwBnGxmrwMnx3PcfQFwL7AQeBy40N3z2p6xzXEvM7sLOAEYaWbVwE+jIPea2XnAm8BZiWBmlgiWaSbYBQSLvUpgRvwB3AL83syWEnpGk/O5ESFE17DxmfeS2TiQfc5+gZJ+2uyut+Puy8mx86G7rwdObCHM5cDle5p2mwrJ3c9u4VKHBHP3WcCROdxriQpNCFFc1CwbxbZXxjHkA8vpf4BG0kXXopUahBA5aawpZ/2M91M+cgvDPr640OKIPoAUkhBiN9xhw+Pvo7GmgpGfexUrayq0SKIPIIUkhNiN7QtGs2PJfgz72GIq9tlSaHFEH0Ef8wghduIOmU0D2DDzCPqN2cCQ45YXWiTRh5BCEqKXUlMD69fDhg3hlxyn/9f+7Viaasppqq2gqbacxppyaCzFKjKM+OwcTGMoohuRQhKiyKmr212Z5FIwzd1qa1uOs18/GDECMg0DKOnfQPnwbZT0b6Cksp6SygYqx62jfFhN992kEEghCVFUvPwyXH45rFyZVS47drTsv7w8KJYRI2D4cDjoIJg0aVe3XP+VlSH8uKl/65b7EqI9SCEJUQSsXg2XXgq33w6jRsGHPgRHH51boaSPBw7UIqei9yCFJEQBqa2Fa6+Fn/88DM3967/Cj38MQ4YUWjIhuh8pJCEKgDs8+CD84AewYgWcfnrY8O6QQwotmRCFQwqpQIyb+liHw6y84rNdIInobl59FS65BJ55Bo44AmbOhJNOKrRUQhQeGXUK0U28+y6cfz4ccwzMnQu//jXMmSNlJESCekhCdDH19XDDDfCf/wnbtsFFF8FPfxqMEoQQWaSQhOgi3GH6dPje92DJEjjlFLjmGjj88EJLJkRxoiE7IbqARYvgtNPgc58L548+CjNmSBkJ0RrqIQnRidTUhO+JbrgBBg2Cq68OQ3QVFYWWTBQrMnDKIoUkRCfR0ABnnRWG6aZMgcsuCx+5CiHahxSSEJ1AUxN84xvw2GMwbVqwphNCdAzNIQmxh7iH74ruvBP+67+kjITIF/WQ8qAYxnyLQQYR+I//gF/9KljT/ehHhZZGiD3DzMYCdwD7Ak3Aje5+nZn9DPgn4N3o9UfuPj2GuRQ4D2gELnb3J/JJWwpJiD3g+uuDQjr33LD0jxY6Fb2ADPB9d59tZoOBKjObGa9d6+6/THs2s8OBycARwP7An83sUHdv7GjCGrITIk/++7/hu9+FM86Am26SMhK9A3df7e6z4/FWYBEwupUgpwN3u3udu68AlgLH5ZO2FJIQefDoo6FX9MlPwl13QZnGGkTPoczMZqV+U1ryaGbjgKOBF6PTRWY218xuNbO9otto4K1UsGpaV2AtIoUkRAd59tlg3n300fDww9C/f6ElEqJDZNx9Uup3Yy5PZjYIuB+4xN23ANOAg4GJwGrg6sRrjuCej2B71K4zs5XAVsJEVsbdJ5nZcOAeYBywEviyu2+M/nNOfJnZscBtQCUwHfiuu+d1Q0J0Ja+8Ap//PIwbB+986Ened3lDh8LLsET0BMysnKCM7nT3BwDcfU3q+k3Ao/G0GhibCj4GWJVPup3RQ/qku09090nxfCrwlLuPB56K580nvk4FfmNmpTHMNGAKMD7+Tu0EuYToVJL16IYOhSefhNIBHVNGQvQEzMyAW4BF7n5Nyn2/lLcvAvPj8SPAZDPrZ2YHEurwl/JJuytGvk8HTojHtwPPAP9GauILWGFmS4HjYi9riLs/D2BmdwBnADO6QDYh8qK6Gk4+ORzPnAljx7buX4gezEeArwHzzGxOdPsRcLaZTSQMx60Evg3g7gvM7F5gIcFC78J8LOxgzxWSA0+amQO/i2OR+7j76ijoajPbO/odDbyQCptMfDXE4+buuxEn36YAVGhxMNFNrFsHn/40bNwYNtWbMKHQEgnRdbj738k9LzS9lTCXA5fvadp7qpA+4u6rotKZaWavteK3pYmvdk+IRYV3I8DAgQM1xyS6nK1bw6rdK1bA44+HzfWEEF3DHikkd18V/9ea2YME2/M1ZrZf7B3tB6yN3lua+KqOx83dhehS2lrtwjMlrL3vA9S+OZxRX6rinBlrWfkJGSWk0YohojPJ26jBzAbGr3gxs4HApwmTXI8A50Rv5wAPx+OcE19xeG+rmR0fJ9O+ngojREHwJuPdPx1N7RsjGXHaXAYcsrbtQEKIPWJPekj7AA8GHUIZ8Ad3f9zMXgbuNbPzgDeBs6DNia8LyJp9z0AGDaKAuMP6x99HzZJ92evEBQw68u1CiyREnyBvheTuy4GjcrivB05sIUzOiS93nwUcma8sQnQW7rDpmfeyfd5Yhn54CUMmrSy0SEL0Gfrkgica9+5deKNRt2ovapaNomb53mQ2DWjR78BfZY9rGk7JEZnhmVIGH7OSoR99vQukFWn0Loo0fVIhiZ7P6tWwbe4YapbvTc3KkXhdOZQ00W/MBgaNW0f4EmF3pnz84J3HNz77Rk4/pUNqGHzMG1osVYhuRgpJ9Ai8CZ57LmwPPn16WMIHjqJ0UC0DJ6ym8qB36T9uHSX9Mq3G84srsgrpj1Nb+0pBCNHdSCGJoqVxewU1K0ZRs3wUtStG8ZFfQGkpfPjD8H/+D1y/5FnKR21VT0aIXoIUkigo7uD1ZTTWlNNUW07TjoowH7R8b+pXDwWMkoG1VB6yhv/3k7GcfDIMGxbC/nbq1kKKLoToZKSQRKfSuKOcxq2VQcHUVAQlU1NBY205554LGzbA+vXw9mufiNfKwZt9DmdOv/03MvRjS6g8aC0V+2zBDM46SwvICdGbkUISnUJTQwmbnxvPlpcOgqbdv7e2igx/WQPDh8OIEVAxagsllQ2UVDZQ2r8+HMf/8hHbKK3UStpC9DWkkMQes2Pp3myYeQSNWwYw8MhqBox/h5LKekr6N1Ba2UBJ/wasrGkXc91xU18poMRCiGJECknkTWZLfzb8+QhqXt+X8hFbGfnV5+k/dkOhxRJC9FCkkESHqa+HzS8cxObnxgMw7BOLGPKBFVipFmAXQuSPFJLoEH/9K3znO7Bp4WFUjn+H4ScupGxoTaHFEkL0AqSQRLto3F7BOefAHXfAe94Do/7hZa2ALYToVPLefkL0Ddxh65wDWHXzJ7jrLrj0Uli4ECkjIUSnox6SaJH6NUNY/8SR1K/ei34HrOeVx0dw2GGFlkoI0VuRQhK70VRXxqa/HcrW2eMoqaxnxOfmMPDwtznsMK2yLHoXWm28uJBC6gO4A27s2AENDcFKLrO1HzSV4I2GN5VAYwneZDSsG8SmZ99L47Z+DDr6Dfb6+GJK+re+YKkQovdgZqcC1wGlwM3ufkV3pS2F1AV4poSmurL4K6eprowHHoDNm7O/DU8dRlNdOZ74qw/+vKG0xXgP+EP2eNWmT7WQOLsoGG8yaAxxDvxF2uNJLaZTse8mRn1pFv3229yBuxZC9HTMrBT4NXAyUA28bGaPuPvC7khfCinFqls/hjdGO4/YqwBwN95zV+hpNDXBO5tPDBV/vI7bTv9NmZKdCiDNP9yz67mVH0BJvwwl/RqwfhlK+tdTNnQHVtYILaxefdKk7Fpu985a1+J9WGkTVtoEJR6OSxxKm7j0sxOoqIDycvjpo/OwkiYodawk+i91Ssoz9BuzAZO5ixB9keOApXFHcMzsbuB0QAqpuykfsS17Yg5G2OjN4JPHDqCkBMzg3llrd14PysPDFgjmWFkTJf0aKOmXCYomHj/+ww8xdCgMHQpDhsAh//5Eh+W79YqsQnp66twOh/+3f5uw8/iX1W92OLwQoldQZmazUuc3uvuN8Xg08FbqWjXwwW4TrLsS6gmMOr3l9dVuu2LMzuOnps7rcNwTJ+YjkRBCdDoZd5/UwrVc4zPdtgSLBmaEEEIkVAPpfV7GAKu6K3EpJCGEEAkvA+PN7EAzqwAmA490V+IashNCCAGAu2fM7CLgCYLZ963uvqC70pdCEqJA7OlHmfqoU3QF7j4dmF6ItDVkJ4QQoigoGoVkZqea2WIzW2pmUwstjxBCiO6lKBRS6uvgzwCHA2eb2eGFlUoIIUR3UhQKidTXwe5eDyRfBwshhOgjmHvht502szOBU939W/H8a8AH3f2iZv6mAFPi6TFAV2xVWgbsyWqiPT18McjQ18MXgww9PXwxyFDo8C1R6e7F0hnZhWKxsmvX18FxeYsbc/jtPEHMZrXyFXOvD18MMvT18MUgQ08PXwwyFDp8T6RYtGRBvw4WQghReIpFIRX062AhhBCFpyiG7Ar9dXAz9nRIsKeHLwYZ+nr4YpChp4cvBhkKHb7HURRGDUIIIUSxDNkJIYTo40ghCSGEKA7cvct+BMu5vwCLgAXAd1PX7gHmxN9KYE50/yzB6i5XmOHATOD1+L9XdJ8EXE8whJif8v8z4AdRjr8C24AG4G2gHngjyjEXWEcwNW+K/1fHOCcB17dxn1+MYWqizI2EXRc/DPxHvPZwSqYngdui+/QoU02UyYFfxf9TmqXjqd+mGP+yeP7DlL+NwOaYt4uAKalr25rFuRm4ELgE2ALcG92nAS/Ge6kFfpYKMyedz9HtEuDrwM3A4dHtB1G2kc38TgeGAW8mcgPjgPkxvVdTz2oxMC+WiV/G+Cal74VQfk4ATgP2Af4ALAe2As8DX0ylfS6wfzN5zgVuiOmdCTyTSsOBf4zPsyHK+HdgPTAyJcNRxDIcz5+KYRfEMJl43hCPF8V8bATWxHt+B3gt+lsMXAesIMz1vgs8mor/P4GT2vkergTeA/yOUF5ei/m7PCXXNkKZejc+tznR74vx+F1gacyfc+P5nHh/9wEDCGX7ZUJZTp7bX+NxA+HdagDOa1ZufgMMSsm3AHgWqIqybo5+P0MorxsJ7+szhPf0e8AXgKnN7vtnMb5fN3MfF5/J/lHe/XO9G9HtGWBHjvLyZ+CGZu6fic8rQ3hnGoB/iukl9c5C4LeEzsBtwHZiWWsuXzMZJrX2jHvLr6t7SBng++5+GHA8cGGyJJC7f8XdJ7r7ROB+4IEYZjbwhVxhgKnAU+4+nvDCT41xzYoPLbUH+S4YoQDcQFBO2wkv1EjgTkIhuZ9QyTQQKr9t7n6xu89y94vbuM+zCZVUHfAJQmHc7O7PESrIeuDQZmEmEl6ugwkv2HuAhwgV34XA/8R40yQV2pvAYOAXwH7R/Zspf3XA5TFvPwJcGa0XsxkSKHH3oe7+a+CHMc6PmdlA4NuEj5DfIlQKz8ZwhzWLo8zMymL6d7v7t9x9oZmNBU6Osu56E+6nufsmwjP5ZrPLNYSVOrYA+7n7BOAqwjM8jVBB5uLIeP0h4Fl3P4igwO4ifEaQLFF1LqEiaolc78S/ECq+v0bZbmX3j7LnAe8xs8FmdirwAWAD8A2CstpOyIvLo//X4/NpAJa4+1GE8vxuSo7fxHAnEyqznbj7T9z9z63cR3OuJZSz8e7+XuD9wGUEJdRIUDafIZSlvWOYMYTGzERg33iPCfdE96MI5fsrqWvLgf8FfD9erwO+ClxAaCD8e8rvZMIzupmQX+Pd/QjCcyqP+VFhZkcS3t+kPP6G8L5+GPgfd3/E3a9o4d6/kcvR3VcR8re18tAuUvLdSFA4g4AfAz8n1DMbCc/g/YTl0c7Y0zR7Jd2p/YCHgZObuRmhkI2P5ycQW4KEFs4bhNbjckJLcr94bT9gcaplsoHwoNcSKoxnCIXgEYKy+7/N5HiH8JLfFuP+T8KLtTL+zoxyrSC8sJvj9XqC4qshKNx5hErlEcLLXBX/awktyBpCoa+P8uyI501ke0RN8ZpHmZzQA9xEaOU3xjSc8HI3smtvKYkj7a+R8MLWpvxkUtcaY9xOqPybx+UpmZKWbVNMP+0nk0pjY7znT5HtWaV7dEsILdvGeJ5ca4zxJHHXxPhXAM8RKvFMDJ+kmaR/VHxe21Lx1RMaFdVklXgju/aCm+J9Lyfba0nyN8n3RP5NBEWYvp8mQoNpR47nsSim3ZSStYlQNubkyOd0XqfzpCYlf1rupPXdvNeVDruRUDabUmEbYh4ujHKkey5bYv7UE96N5FlsjWFq4/HyeC3pwW6Pv+T62zH9GWTL1/QY/5spGZfkuK+tUe71ZN+vJP9qCO/ghujWEGVtJJST2lQ83ux6Ol/T5ThdlhN/W1NuK4CXYjwNUa5EptcJCmgUoTG7Lvr/r+h+XIxzO9ne9fp4f/XxXlZE9x0puf9C6L3VxHvakJI3KWv3xfA1BIW9hFBHTif0+N4gKP7Ho5xXpeq+T8drs4E/AoOi+xWEcjEX+GVv7SHtxMzGAUezewv3Y8Aad389R7BhhJbZiYQHPIrw4HH31WRbct8kvCg74vl7gVOAm4DPAY3ufkmU4yCCAtsbeAz4IOFhnkd4aZJe1mnx/1uEyqU/oVVmQGW8XkpoRd4PjI7+9yIUrDLgohi2MvotI7R8K8kOzUAoWP0JBW+f6FZPGBb4FaHQ3hzdy9m1Fb+WbCXdwK4v2V5AP0KhhlAQidf/SPblS5QohBbrozH8sPi/Icq+ifBCEMO+Gu9rNeG5Zgh5f1nK37r4X0LI23djWhdE923AlwlKJVmxY2v8PyDG/dWYTvK8VxFenEbg9zHvyuP9r0vlSUWU/+cxbEmUcRmh0m2IfjOEZ59UNkNTckJ40RfGvFwDXBNlvTWm7cCfUvk7IcZtwCsxPov/M6Kf9YQymuTTfLKVj6fSS+fHNkIF51G+WdHdCIpgczx/lWzjZDPhvcgQnmEdsDH2blYRKj0jNMxKYz6OIDQG62IeDo75+k6893LCEN1bUcakh/yb1H2PJ/R8mgijAcT730KoiA+Jcd4UZTPCM1pNKLefivlZEq9VR7/lhM9D7iUo/gyhbkgad5fF+y0hW6knbInXLObjczHMs/HeiPf1ZPRXDxwb03TC+1AX00w+5L+O0PN5EzifbI//tZj2dkLvupHQAPhLPDZCg6GMUKYOjOHeR+hZVcTfDTGOzcB3Yn6eRphKmB7lrAZeiPnwMeDKKPc3YnxfMbOxZjaS0Ds9yd2PIZSf75nZcMK0wxHu/n6CUi0M3dQzGkSo9L6U49o0wrBecn4CoUIcRHhh7kxdawTGpM43Egr7czHMOEIF8+NUD2trjOfQtByEAvR3QkUwi1B4fkEoHPWEgvc8QRkmrZp1hMK4mdA6TtwuAe4gFJabCYWkhrCC+R+jvzmESvf+mNbj8X6SluZ8wkvxQHRfBFwa42kkq2hWEF6spEV3VvzfQLanlvhPwiQKakmz8+Q43fKfR/blfjdea9769pjPz8Tjxwmtto0xb2oJyqsuFde2eO2qGN890f2N+GySHkU94YVaSHgRk95bPaFyryfbikxa1VvJ9ka2ECrn6lTaSUsziac+Pq9qQhnZBvwt5t9LqTBJvrxEqKS82S+JsykVb3Lt1ei+MOZp0mhIlGUyb5jO921kW/mJwvSYt00ERbqM7DxFuseb9LqT86RHVE+2F7uBUNk0ABfHZ7Q++t+XbLlvJCiM9WR7PIlciczPEiran8frP4p5mfT0XifMBSXPMOmRbyG8b1tiXKtScS5KHa9M5Ucm3vOL8XoiY2PKb5KPmdR/JuZj+nklYWpTzytpzCV598OY1twoQ/LcHoj59lT0f0N0n0N4518jO7+1gGz5Xs6uPfW3473eG91uivWVx3Snpe7zvihrHUFZJ/c3J+bnjhjXrJjXrxHqtm3AYTHeGcBHCY3zdWTn7hcCtxCU4qvx+EtARa/tIZlZOaESvtPdH2h2rSxmwD3Ng8Uw8wiVUEKGMFSHme1HKAwfIlRonyIomBHsOi+xjTAcN4OgtBI5mgjzNCsIPbcKwuT1XoTWVQWhZXQyoeX4NkG51MWwZWR7IVPjfSTzHHXx+B9i+PIo42hCi7IfoYAkvaXSeH0kYZXzEkIr+7Iox4uEOR0I490VZHtJyZxDBdnWNoSX7w1C4V0e3R6K/+uj2/Px+itkewNDCS9AI6GyyBCeRXI9GRpZm0oruV+iXOXAkHicyPlS9HNOjOuJ6L63uy8h9Mwg5Ov/irI/QGgtEuM8PP4fQ5hjaAQGElq1iQJZRTCueInwbJO8eYjwkm8k5HdyPwnpRSybCMO6Sc/kxii7E174pHeWLP7bGOX5eDxPK/mlMXyilJMlsS4EDiOrJEcTelnEtEYSKiuI83dRrjVke+OPpmR+nWyF9QLh+S8iPKfkB6ES3UYod4Pjf5IXSY8oKUdDYxpJvkIoC8k9GtkK/mPxfEC8XkXoBSc9+u3xPv6HoOwqCM/h4phHEIwTFhOe08/INhR3RNkOJCjRVwnlI1GU78b46+I91xJGF7aTLZdNhBGRZCgwWbg0KYvJs94Y021KhU3wHMclhDrofkJj618I84wvA/Xu/h5CQ6yJ0MB8PsrbSO75Smsmc9L7fYtg9PEOYcRnIiGPl3iYb7d4718GfgLc5u6LYjyN8X4NmOlx7t7dD3f389w96WXeT5jbejyHXN1ClyokMzOC1l3k7tfk8HIS8Jq7VzdzP4qQuc83c99CmNuBULE97O7TotvThEp+PaGFkeZxQiE4kuwLZYQK5A5C4a4mGAAsIRSABsIQwsDofxihh5W8cBBeqHcIlWnSiq0mvGDlhBfniug+P6bzPkIBuTq6ryc7h7COUPCIcdQRKpZJhAliCBXLOSkZLoz/Awktnk/FextC6DHWE4YBGwhDRMT72E6YeCfmS5LuWQSlaIQK0wgvlJHt8Vi8v2ExzMiYV8mQz1Mp/5vjfy3hec4lO48G0N/M9o/3kVQQgwkWlR8nVEIN8XdfDPPfHgxZSshahpXH/DyQUDFsIAzxJq3eYYRKcEQMdxJhuO3UKPd7Ytwerz8VZYWQ9+vjPe0f0yGm5YSXfSTZhpARhnJLCGXgErKVTDIM9y9kJ9vLCI2OE8kqg01klWA12YqqnqyifyaV3r6ExlFS8cyKcQ2I+VoSr/8ToeH2AOHZJIrlTLJDt9XA56P7WIKiGxrT2hL/jyL0Kr9Atkz0j36bCGU2aSSkDWr6Ed6xfjH9FwmNLwjvUX+CkjyU8PwqovyD4m94jDMZpk0aQP3JPl8IIyeDCO8B0d/nY9jSmDfL4rVjyD6X/oSGxmjCO1UZw9YT3q2vEN6n5Dk9Gf3/gtBLnBjdh5KtX5MGBFHGZFh+COF5fdLMxkS38YTGc0WU9YkYPjFqKQEazewswjzQPmZ2FEEBHkpQ6BDKY3NeAD5iZocAmNkAMzvUzAYBQz1sXX5J6h66ny4eqvsooYDPJdtNPC11/Tbg/GZh/jkVJjE8OC1eW0RoYb1OqDCGNxvmG0eosH+QGrJbTXjZnOzwRNJKuiL6u5PQenuL7LjzzfF8JdkewWyyZqcfje6vxPRvi3FfRlB6yTBOMu7/C7JDEMsJprV17Dqxm1SeiwjDfX+LcaSNCWpiPqaHo9JDb+lho8ZUvMmQRXpSPD1pviKVRjLxmw6b9AxXpc63kJ23aYr3WUO2JbolJfuWmDfLUs+iqVlaSRobUmm8G+OqIau0kuGaJoKSX0mYS0oPpyVxJ3m2KcqQnrROGzWk596SX6ZZfOnrTuhFNTVLN/ndRLb3kzyLNYRylUzUN6bi3NAs/COpZ/tClDWZk0iMNV5Lpb+M7PxSknevp66vYVfjkW0Eq8HVZHtvSwhl+/8ShsGTsMmkewNBkSV5upWskcp2skYNDYRKegGhYZU2nFhPGBJLyllN6n6SobPk2aaHJJsIQ1FJAy79S+JP/HqUZ2WUb2vKb1J+0sPJacOHdal03yY0ohKjhndTMi4i9DZHkv105M2Y1gbC++SE+upPZEcqriXUJbVRvlpCGU7K37z4S+qhZCh3XSrcarIGC5n4bOfEdJJPJFak6tRHgRPi8acIymtu/H2BMOr0UjyfB5xTqCG7giSqX44HkbV2GUGoXPaN5yeQ+v6kWZjdvpvoZpmfIfu9zoNES8l8wue4dh3NvrOh2fcZOcLcBpyZOn8AmNBBmXbGQWhJ94/HBxMq1Obfhg1KHU8FrutIGnnm+w3Eb3mIRgXx+EPs+i1Ui9c6uRwMILsM2WTiN3cdCJ+U/bJYqX4xDxlae08GkP1eLKd8rZUtQk9rJaFnk5d87byHgr7PxfArQxQLj5rZMEJX/TJ3f6cN/8XGVEJLK5e1ZD78nGABmRfxu6uH3H1xm55bZgDwlzgPasD/c/cnmvn5rJldSqhM3yDMJXUZZlZFaP0nQ7gHAPeaWTKs9E8p761d60yOBW6IQ/Sb2P3bsrb4mZklQ6hPkp3r7CyOBSrNbC55ykcYqv07YTjtoU6UTaTQ4qpCCCGKgm77DkkIIYRoDSkkIYQQRYEUkhBCiKJACkkIIURRIIUkhBCiKPj/S7w3d43oQlsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax1=plt.subplots()\n",
    "\n",
    "ax2=ax1.twinx()\n",
    "\n",
    "ax1.bar(products, quant_prod)\n",
    "ax2.plot(products,prod_price,'b-')\n",
    "plt.xticks(products, rotation='vertical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d13cfda",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}