data_cleaning_reduce_merge.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'matplotlib'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-7b664e8a2624>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mfunctools\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import os\n",
    "from functools import reduce"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gather data from csv files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get list of files in directory\n",
    "csv_files = [csv for csv in os.listdir() if csv.startswith('cart')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create list of df\n",
    "df_list= [pd.read_csv(x) for x in csv_files] # , index_col='Item'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[                                                  Item  04-03-2020\n",
       " 5733                   #10 6MM Bamboo Needles 35MM1 ea        4.98\n",
       " 5469                   #11 8MM Bamboo Needles 35MM1 ea        5.48\n",
       " 5577            #3/4/6/7 Crochet Hooks, Value Pack1 ea        8.98\n",
       " 5716                       #6 4MM Circular Needles1 ea        4.48\n",
       " 3053                        $10 Popcorn Chicken1.13 kg       10.00\n",
       " ...                                                ...         ...\n",
       " 7118                                uSee Eye Patch1 ea        3.98\n",
       " 6999                 uSwim Soft Silicone Ear Plugs4 ea        4.48\n",
       " 7158                     uSwim Soft Wax Ear Plugs12 ea        6.98\n",
       " 6984  uTemp 10 Second Flex Tip Digital Thermometer1 ea       14.98\n",
       " 7186                      uTravel Travel Ear Plugs2 ea        7.48\n",
       " \n",
       " [6695 rows x 2 columns],\n",
       "                                         Item  04-07-2020\n",
       " 5378         #11 8MM Bamboo Needles 35MM1 ea        5.48\n",
       " 5692  #2/4/5/8 Crochet Hooks, Value Pack1 ea        8.98\n",
       " 5665             #6 4MM Circular Needles1 ea        4.48\n",
       " 5313             #6 4MM Knitting Needles1 ea        4.98\n",
       " 5444             #8 5MM Circular Needles1 ea        4.48\n",
       " ...                                      ...         ...\n",
       " 6877           uHear Soft Foam Ear Plugs8 ea        3.98\n",
       " 7070              uMedPlan Pill Splitter1 ea        8.48\n",
       " 7273         uMedPlan Pill Tote Key Ring1 ea        4.48\n",
       " 6878      uSee Deluxe Contact Lens Cases1 ea        3.48\n",
       " 6777       uSwim Soft Silicone Ear Plugs4 ea        4.48\n",
       " \n",
       " [6728 rows x 2 columns],\n",
       "                                           Item  03-16-2020\n",
       " 552           #8 5MM Knitting Needles 35MM1 ea        4.98\n",
       " 1103         10' Offset Umbrella Warm Grey1 ea      129.00\n",
       " 1123        11.5\" Square Square Tray, Blue1 ea        2.00\n",
       " 228     12\" PC Spinach & Pumpernickel Tray1 ea       12.00\n",
       " 1083                 2\" Chip Brush 5 pack5x1ea        3.98\n",
       " ...                                        ...         ...\n",
       " 562                    ZiplocSandwich Bags4 ea       11.98\n",
       " 1212                                  Zucchini        1.53\n",
       " 188     two-biteCinnamon Rolls, Large Tub510 g        6.98\n",
       " 1010             uSwim Soft Wax Ear Plugs12 ea        6.98\n",
       " 984   uTherm Children's Animal Thermometer1 ea       14.98\n",
       " \n",
       " [1149 rows x 2 columns],\n",
       "                                                     Item  04-11-2020\n",
       " 22186          \"It's a boy\" Cake Topper Blue Glitter1 ea        2.50\n",
       " 23623         \"It's a girl\" Cake Topper Pink Glitter1 ea        2.50\n",
       " 15484                    #10 6MM Bamboo Needles 35MM1 ea        4.98\n",
       " 15251                  #10 6MM Knitting Needles 35MM1 ea        5.48\n",
       " 15513                    #11 8MM Bamboo Needles 35MM1 ea        5.48\n",
       " ...                                                  ...         ...\n",
       " 20715  iPrep Pre-Workout Powerhouse Fruit Punch Flavo...       19.98\n",
       " 7939   nuPastathe Low Calorie Pasta Organic Konjac An...        4.48\n",
       " 11780  nuPastathe Low Calorie Pasta Organic Konjac Fe...        4.48\n",
       " 7930   nuPastathe Low Calorie Pasta Organic Konjac Sp...        4.48\n",
       " 2318                     two-biteBrownie, Large Tub608 g        5.00\n",
       " \n",
       " [16096 rows x 2 columns],\n",
       "                                              Item  03-20-2020\n",
       " 1102            3.5\" Plastic Square Bowl Red8x1ea        4.50\n",
       " 1110  3MCommand Picture & Frame Hanging Strip1 ea        8.98\n",
       " 181          6\" Chocolate Brownie Cheesecake600 g       12.00\n",
       " 1101             8 Lovely Pink 9oz Paper Cups1 ea        1.25\n",
       " 1034      A VogelA.Vogel Deep Sleep Tincture50 mL       15.98\n",
       " ...                                           ...         ...\n",
       " 211    Ziggy'sExtra Lean Cooked Ham (Thin Sliced)        2.38\n",
       " 216           Ziggy'sStuffed Mammoth Olives500 mL        6.48\n",
       " 555                ZiplocSandwich Mega Bags270 ea        8.98\n",
       " 1799                                     Zucchini        1.53\n",
       " 981             uSwim Soft Silicone Ear Plugs4 ea        4.48\n",
       " \n",
       " [1145 rows x 2 columns],\n",
       "                                         Item  03-26-2020\n",
       " 1086                         1  Gold \"2\"1 ea        6.00\n",
       " 1109       11.5\"Square Square Tray Clear1 ea        2.00\n",
       " 169               12 Grain Sliced Bread450 g        3.00\n",
       " 1101             3MClear Hooks, Medium2x12ea        3.98\n",
       " 1097         3MScotch 40 Bumpers 12.7 mm1 ea        7.98\n",
       " ...                                      ...         ...\n",
       " 1597  two-biteCinnamon Rolls, Large Tub510 g        6.98\n",
       " 996               uCare Nasal Aspirateur1 ea        3.98\n",
       " 1009          uHealth Latex Finger Cots24 ea        3.98\n",
       " 991   uMedPlan Bubble Look Ultra Planner1 ea        2.98\n",
       " 983                      uSwim Nose Clip1 ea        3.48\n",
       " \n",
       " [1147 rows x 2 columns],\n",
       "                                                    Item  04-08-2020\n",
       " 5193                        #6 4MM Circular Needles1 ea        4.48\n",
       " 5300                     #8 5MM Bamboo Needles 35MM1 ea        4.98\n",
       " 5206                        #8 5MM Circular Needles1 ea        4.48\n",
       " 5576                   #8 5MM Knitting Needles 35MM1 ea        4.98\n",
       " 4556  'Nana Pops Supersnacks Organic Fruit & Nut Sna...        6.98\n",
       " ...                                                 ...         ...\n",
       " 6625                            uCare Lice Comb Kit1 ea       12.98\n",
       " 6909                         uCare Nasal Aspirateur1 ea        3.98\n",
       " 6707                      uHear Soft Foam Ear Plugs8 ea        3.98\n",
       " 6531                         uMedPlan Pill Splitter1 ea        8.48\n",
       " 6994                                uSwim Nose Clip1 ea        3.48\n",
       " \n",
       " [6680 rows x 2 columns],\n",
       "                                                   Item  04-17-2020\n",
       " 821                                    1  Gold \"5\"1 ea        6.00\n",
       " 828                  10-Inch Round Roaster 4-7 lbs1 ea        9.98\n",
       " 794                               18ct Forks, Gold1 ea        1.25\n",
       " 808                  3MCommand Hooks Value Pack, S1 ea       10.98\n",
       " 810                         3MPainter's Tape 36 mm1 ea        7.98\n",
       " ..                                                 ...         ...\n",
       " 423  ZeroodlePremium Shirataki Penne With Oat Fiber...        4.48\n",
       " 216                         Ziggy'sChicken Strips200 g        5.00\n",
       " 221                   Ziggy'sPotato & Egg Salad1.25 kg        4.98\n",
       " 972                                           Zucchini        1.22\n",
       " 695   uTemp 10 Second Flex Tip Digital Thermometer1 ea       14.98\n",
       " \n",
       " [760 rows x 2 columns],\n",
       "                                                     Item  04-26-2020\n",
       " 24249          \"It's a boy\" Cake Topper Blue Glitter1 ea        2.50\n",
       " 24893         \"It's a girl\" Cake Topper Pink Glitter1 ea        2.50\n",
       " 15387                    #10 6MM Bamboo Needles 35MM1 ea        4.98\n",
       " 16593                  #10 6MM Knitting Needles 35MM1 ea        5.48\n",
       " 15741                    #11 8MM Bamboo Needles 35MM1 ea        5.48\n",
       " ...                                                  ...         ...\n",
       " 20391  uSwim Putty Buddies Floating Silicone Ear Plug...        6.48\n",
       " 20361                  uSwim Soft Silicone Ear Plugs4 ea        4.48\n",
       " 21670                      uSwim Soft Wax Ear Plugs12 ea        6.98\n",
       " 21511                 uTravel Anti-Nausea Wristbands1 ea       14.98\n",
       " 21685                       uTravel Travel Ear Plugs2 ea        7.48\n",
       " \n",
       " [16607 rows x 2 columns],\n",
       "                                        Item  03-27-2020\n",
       " 3067  $10 Chicken Fries, Fully Cooked1810 g        3.33\n",
       " 3170        $10 Pepperoni Pizza Bites1420 g        4.48\n",
       " 620              $10 Popcorn Chicken1.13 kg        4.98\n",
       " 2066     100% Whole Wheat Sliced Bread450 g        0.50\n",
       " 1889     100% Whole Wheat Sliced Bread450 g        2.97\n",
       " ...                                     ...         ...\n",
       " 2616       Ziggy'sSmoked Turkey Breast300 g        8.02\n",
       " 2424     Ziggy'sSmoked Turkey Pastrami300 g       34.44\n",
       " 2728   Ziggy'sZIGGY'S Brie Cheese Brie550 g       15.00\n",
       " 2401   Ziggy'sZIGGY'S Brie Cheese Brie550 g       10.98\n",
       " 73                                 Zucchini        1.53\n",
       " \n",
       " [4913 rows x 2 columns],\n",
       "                                                    Item  04-22-2020\n",
       " 24316        \"It's a girl\" Cake Topper Pink Glitter1 ea        2.50\n",
       " 16739                 #10 6MM Knitting Needles 35MM1 ea        5.48\n",
       " 16465                   #11 8MM Bamboo Needles 35MM1 ea        5.48\n",
       " 16009            #2/4/5/8 Crochet Hooks, Value Pack1 ea        8.98\n",
       " 16868            #3/4/6/7 Crochet Hooks, Value Pack1 ea        8.98\n",
       " ...                                                 ...         ...\n",
       " 20502                 uSwim Soft Silicone Ear Plugs4 ea        4.48\n",
       " 20670                     uSwim Soft Wax Ear Plugs12 ea        6.98\n",
       " 20998  uTemp 10 Second Flex Tip Digital Thermometer1 ea       14.98\n",
       " 21292          uTherm Children's Animal Thermometer1 ea       14.98\n",
       " 21514                uTravel Anti-Nausea Wristbands1 ea       14.98\n",
       " \n",
       " [16334 rows x 2 columns],\n",
       "                                                     Item  04-16-2020\n",
       " 25207          \"It's a boy\" Cake Topper Blue Glitter1 ea        2.50\n",
       " 24082         \"It's a girl\" Cake Topper Pink Glitter1 ea        2.50\n",
       " 17215                    #10 6MM Bamboo Needles 35MM1 ea        4.98\n",
       " 17370                  #10 6MM Knitting Needles 35MM1 ea        5.48\n",
       " 17327                        #6 4MM Circular Needles1 ea        4.48\n",
       " ...                                                  ...         ...\n",
       " 20600                                uSwim Nose Clip1 ea        3.48\n",
       " 21854  uSwim Putty Buddies Floating Silicone Ear Plug...        6.48\n",
       " 21501                      uSwim Soft Wax Ear Plugs12 ea        6.98\n",
       " 21324   uTemp 10 Second Flex Tip Digital Thermometer1 ea       14.98\n",
       " 20523                       uTravel Travel Ear Plugs2 ea        7.48\n",
       " \n",
       " [17042 rows x 2 columns],\n",
       "                                               Item  03-17-2020\n",
       " 1118            18ct Assorted Cutlery , Purple1 ea        1.25\n",
       " 165                 6\" Chocolate Truffle Cake600 g       12.00\n",
       " 1061                 A VogelEchinacea Lozenges30 g        3.98\n",
       " 174   ACEArtisan Crisps, Olive Oil & Sea Salt180 g        4.98\n",
       " 173     ACEBake Your Own White Demi-Baguettes400 g        4.28\n",
       " ...                                            ...         ...\n",
       " 218                       Ziggy'sGarlic Roast Beef        1.78\n",
       " 207                    Ziggy'sMozzarella Ball340 g        5.00\n",
       " 21            Ziggy'sOil & Vinegar Coleslaw1.25 kg        4.48\n",
       " 1759                                      Zucchini        1.53\n",
       " 1609        two-biteCinnamon Rolls, Large Tub510 g        6.98\n",
       " \n",
       " [1147 rows x 2 columns]]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Iterate over df_list and change Price label according to Date\n",
    "for df in df_list:\n",
    "    df.sort_values('Item', inplace=True)\n",
    "    df.drop_duplicates(inplace=True)\n",
    "    df['Price'] = [x.strip('$c') for x in df['Price']]\n",
    "    \n",
    "    for x in df['Price']:\n",
    "        df['Price'] = df['Price'].astype(float)\n",
    "\n",
    "    date = pd.to_datetime(df['Date']).max().strftime('%m-%d-%Y') # grab max date\n",
    "    df.rename(columns={'Price': date}, inplace=True) # change column label to date\n",
    "    df.drop(columns='Date', inplace=True) # Remove columns no longer needed\n",
    "\n",
    "df_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = reduce(\n",
    "    lambda left, right: pd.merge(left, right, on='Item'), \n",
    "    df_list\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f95668c5898>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(rot=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Item</th>\n",
       "      <th>04-03-2020</th>\n",
       "      <th>04-07-2020</th>\n",
       "      <th>03-16-2020</th>\n",
       "      <th>04-11-2020</th>\n",
       "      <th>03-20-2020</th>\n",
       "      <th>03-26-2020</th>\n",
       "      <th>04-08-2020</th>\n",
       "      <th>04-17-2020</th>\n",
       "      <th>04-26-2020</th>\n",
       "      <th>03-27-2020</th>\n",
       "      <th>04-22-2020</th>\n",
       "      <th>04-16-2020</th>\n",
       "      <th>03-17-2020</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Blueberries340 g</td>\n",
       "      <td>7.98</td>\n",
       "      <td>7.98</td>\n",
       "      <td>4.98</td>\n",
       "      <td>5.98</td>\n",
       "      <td>6.98</td>\n",
       "      <td>6.98</td>\n",
       "      <td>7.98</td>\n",
       "      <td>5.98</td>\n",
       "      <td>4.98</td>\n",
       "      <td>4.98</td>\n",
       "      <td>4.98</td>\n",
       "      <td>5.98</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Blueberries340 g</td>\n",
       "      <td>7.98</td>\n",
       "      <td>7.98</td>\n",
       "      <td>4.98</td>\n",
       "      <td>5.98</td>\n",
       "      <td>6.98</td>\n",
       "      <td>6.98</td>\n",
       "      <td>4.98</td>\n",
       "      <td>5.98</td>\n",
       "      <td>4.98</td>\n",
       "      <td>4.98</td>\n",
       "      <td>4.98</td>\n",
       "      <td>5.98</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Broccoli1 ea</td>\n",
       "      <td>1.98</td>\n",
       "      <td>3.48</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2.97</td>\n",
       "      <td>2.98</td>\n",
       "      <td>1.98</td>\n",
       "      <td>3.48</td>\n",
       "      <td>2.98</td>\n",
       "      <td>2.98</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2.98</td>\n",
       "      <td>2.98</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Broccoli1 ea</td>\n",
       "      <td>1.98</td>\n",
       "      <td>3.48</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2.97</td>\n",
       "      <td>2.98</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2.97</td>\n",
       "      <td>2.98</td>\n",
       "      <td>2.98</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2.98</td>\n",
       "      <td>2.98</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Broccoli1 ea</td>\n",
       "      <td>1.98</td>\n",
       "      <td>3.48</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2.48</td>\n",
       "      <td>2.98</td>\n",
       "      <td>1.98</td>\n",
       "      <td>3.48</td>\n",
       "      <td>2.98</td>\n",
       "      <td>2.98</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2.98</td>\n",
       "      <td>2.98</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>White Mushrooms</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Zucchini</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.84</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Zucchini</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.53</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Zucchini</td>\n",
       "      <td>1.84</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.84</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Zucchini</td>\n",
       "      <td>1.84</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.53</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>85 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Item  04-03-2020  04-07-2020  03-16-2020  04-11-2020  \\\n",
       "0   Blueberries340 g        7.98        7.98        4.98        5.98   \n",
       "1   Blueberries340 g        7.98        7.98        4.98        5.98   \n",
       "2       Broccoli1 ea        1.98        3.48        1.98        2.97   \n",
       "3       Broccoli1 ea        1.98        3.48        1.98        2.97   \n",
       "4       Broccoli1 ea        1.98        3.48        1.98        2.48   \n",
       "..               ...         ...         ...         ...         ...   \n",
       "80   White Mushrooms        0.26        0.26        0.26        0.26   \n",
       "81          Zucchini        1.22        1.22        1.53        1.84   \n",
       "82          Zucchini        1.22        1.22        1.53        0.91   \n",
       "83          Zucchini        1.84        1.22        1.53        1.84   \n",
       "84          Zucchini        1.84        1.22        1.53        0.91   \n",
       "\n",
       "    03-20-2020  03-26-2020  04-08-2020  04-17-2020  04-26-2020  03-27-2020  \\\n",
       "0         6.98        6.98        7.98        5.98        4.98        4.98   \n",
       "1         6.98        6.98        4.98        5.98        4.98        4.98   \n",
       "2         2.98        1.98        3.48        2.98        2.98        1.98   \n",
       "3         2.98        1.98        2.97        2.98        2.98        1.98   \n",
       "4         2.98        1.98        3.48        2.98        2.98        1.98   \n",
       "..         ...         ...         ...         ...         ...         ...   \n",
       "80        0.26        0.26        0.26        0.26        0.26        0.26   \n",
       "81        1.53        1.53        1.22        1.22        1.22        1.53   \n",
       "82        1.53        1.53        1.22        1.22        1.22        1.53   \n",
       "83        1.53        1.53        1.22        1.22        1.22        1.53   \n",
       "84        1.53        1.53        1.22        1.22        1.22        1.53   \n",
       "\n",
       "    04-22-2020  04-16-2020  03-17-2020  \n",
       "0         4.98        5.98        4.98  \n",
       "1         4.98        5.98        4.98  \n",
       "2         2.98        2.98        1.98  \n",
       "3         2.98        2.98        1.98  \n",
       "4         2.98        2.98        1.98  \n",
       "..         ...         ...         ...  \n",
       "80        0.26        0.26        0.26  \n",
       "81        1.22        1.22        1.53  \n",
       "82        1.22        1.22        1.53  \n",
       "83        1.22        1.22        1.53  \n",
       "84        1.22        1.22        1.53  \n",
       "\n",
       "[85 rows x 14 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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