report-overall_sales.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d7aa06dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "The beginning of this notebook will import dataframes then create additional dataframes\n",
    "that will be used to compare 2021 and 2022, and combine the imported dataframes to make\n",
    "new insights (eg desirability)\n",
    "\n",
    "Afterwards there will be charts and graphs analyzing the data\n",
    "\"\"\"\n",
    "\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import datetime\n",
    "%matplotlib inline\n",
    "%matplotlib notebook\n",
    "\n",
    "\"\"\"\n",
    "Importing these dataframes created by the create_data_frames notebook:\n",
    "   -nameID weekly sales: item nameIDs on the rows, date of the delivery on the columns,\n",
    "   total income for that item on that week in the cells\n",
    "   -nameID weekly item count: item nameIDs on the rows, date of the delivery on the columns,\n",
    "   total count sold for that item on that week in the cells\n",
    "   -nameID weekly availability: same rows and columns, but the data is a 1 or 0 for\n",
    "   whether or not the item was available that week\n",
    "   -userID weekly sales: a dictionary where the key is the userID, and the value is\n",
    "   a nameID weekly sales frame for that specific user\n",
    "   -userID weekly item count: a dictionary where the key is the userID, and the value is\n",
    "   a nameID weekly item count frame for that specific user\n",
    "   -weekly delivery count: a dictionary where the key is week and the value is the number\n",
    "   of deliveries for that week\n",
    "   -userID weekly CSA sales: a multi-indexed dataframe where levelOne is the userID,\n",
    "   levelTwo is the point-of-sale (p/d/m) and each column is a date\n",
    "   -external weekly sales: the rows are the point-of-sale (eg. Cumberland, Frostburg)\n",
    "   and the columns are the dates\n",
    "   -userID renewal: rows are the userIDs and there's a column for 2021 and 2022 total renewal\n",
    "   amount and end of year balance\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "#read files\n",
    "ef1 = pd.ExcelFile('data_frames/nameID_weekly_sales_frame.xlsx')\n",
    "ef2 = pd.ExcelFile('data_frames/nameID_weekly_count_frame.xlsx')\n",
    "ef3 = pd.ExcelFile('data_frames/nameID_weekly_avail_frame.xlsx')\n",
    "ef4 = pd.ExcelFile('data_frames/weekly_delivery_count_frame.xlsx')\n",
    "ef5 = pd.ExcelFile('data_frames/userID_weekly_sales_frame_dictionary.xlsx')\n",
    "ef6 = pd.ExcelFile('data_frames/userID_weekly_count_frame_dictionary.xlsx')\n",
    "ef7 = pd.ExcelFile('data_frames/csa_profile.xlsx')\n",
    "ef8 = pd.ExcelFile('data_frames/weekly_external_sales.xlsx')\n",
    "ef9 = pd.ExcelFile('data_frames/csa_renewal.xlsx')\n",
    "\n",
    "\n",
    "\n",
    "#creating dataframes from the excel files with a single sheet\n",
    "nameID_weekly_sales_frame = ef1.parse(sheet_name='Sheet1', index_col=[0,1,2,3,4], parse_dates=True)\n",
    "nameID_weekly_count_frame = ef2.parse(sheet_name='Sheet1', index_col=[0,1,2,3,4], parse_dates=True)\n",
    "nameID_weekly_avail_frame = ef3.parse(sheet_name='Sheet1', index_col=[0,1,2,3,4], parse_dates=True)\n",
    "weekly_delivery_count_frame = ef4.parse(sheet_name='Sheet1', index_col=0, parse_dates=True)\n",
    "userID_weekly_csa_sales_frame = ef7.parse(sheet_name='Sheet1', header=[0,1], index_col=0, parse_dates=True)\n",
    "external_weekly_sales_frame = ef8.parse(sheet_name='Sheet1', index_col=0, parse_dates=True)\n",
    "userID_renewal_frame = ef9.parse(sheet_name='Sheet1', index_col=0)\n",
    "\n",
    "#set index for each dataframe as nameID, type, category, and subcategory\n",
    "#nameID_weekly_sales_frame = nameID_weekly_sales_frame.set_index(['Unnamed: 0', 'nameID', 'type', 'category', 'subcategory'])\n",
    "#nameID_weekly_count_frame = nameID_weekly_count_frame.set_index(['Unnamed: 0', 'nameID', 'type', 'category', 'subcategory'])\n",
    "#nameID_weekly_avail_frame = nameID_weekly_avail_frame.set_index(['Unnamed: 0', 'nameID', 'type', 'category', 'subcategory'])\n",
    "\n",
    "#creating dictionary of dataframes where the key is the userID\n",
    "#and the values is a dataframe similar to nameID_weekly_sales_frame\n",
    "#and nameID_weekly_count_frame, but for that specific user\n",
    "#also setting index for each dataframe like above\n",
    "userID_weekly_sales_frame_dictionary = {}\n",
    "userID_weekly_count_frame_dictionary = {}\n",
    "\n",
    "for userID in ef5.sheet_names:\n",
    "    userID_weekly_sales_frame_dictionary[userID] = ef5.parse(sheet_name=userID, index_col=[0,1,2,3,4], parse_dates=True)\n",
    "    #userID_weekly_sales_frame_dictionary[userID] = userID_weekly_sales_frame_dictionary[userID].set_index(['Unnamed: 0', 'nameID', 'type', 'category', 'subcategory'])\n",
    "    \n",
    "for userID in ef6.sheet_names:\n",
    "    userID_weekly_count_frame_dictionary[userID] = ef6.parse(sheet_name=userID, index_col=[0,1,2,3,4], parse_dates=True)    \n",
    "    #userID_weekly_count_frame_dictionary[userID] = userID_weekly_count_frame_dictionary[userID].set_index(['Unnamed: 0', 'nameID', 'type', 'category', 'subcategory'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aa3abc30",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weekly_csa_sales_sum = userID_weekly_csa_sales_frame.sum(axis=0)\n",
    "\n",
    "\n",
    "y1m = {'d' : [0,0,0,0,0,0,0,0,0,0,0,0],\n",
    "     'p' : [0,0,0,0,0,0,0,0,0,0,0,0],\n",
    "     'm' : [0,0,0,0,0,0,0,0,0,0,0,0]}\n",
    "\n",
    "\n",
    "y2m = {'d' : [0,0,0,0,0,0,0,0,0,0,0,0],\n",
    "     'p' : [0,0,0,0,0,0,0,0,0,0,0,0],\n",
    "     'm' : [0,0,0,0,0,0,0,0,0,0,0,0]}\n",
    "\n",
    "\n",
    "\n",
    "for week, val in weekly_csa_sales_sum.items():\n",
    "    if week[0].year == 2021:\n",
    "        y1m[week[1]][(week[0].month-1)] += val\n",
    "    elif week[0].year == 2022:\n",
    "        y2m[week[1]][(week[0].month-1)] += val\n",
    "\n",
    "        \n",
    "names = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "\n",
    "\n",
    "y1mdp = [x + y for x, y in zip(y1m['d'], y1m['p'])]\n",
    "y1mTotal = [x + y for x, y in zip(y1mdp, y1m['m'])]\n",
    "\n",
    "            \n",
    "ind = np.arange(12)\n",
    "\n",
    "\n",
    "width = 0.3\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (10,10))\n",
    "\n",
    "y1md = ax.bar(ind, y1m['d'], width, color = 'm', label = 'DELIVERY')\n",
    "\n",
    "y1mp = ax.bar(ind, y1m['p'], width, bottom = y1m['d'], color = 'b', label = 'PICK-UP')\n",
    "\n",
    "y1mm = ax.bar(ind, y1m['m'], width, bottom = y1mdp, color = 'r', label = 'MARKET')\n",
    "\n",
    "\n",
    "##################################\n",
    "\n",
    "y2mdp = [x + y for x, y in zip(y2m['d'], y2m['p'])]\n",
    "y2mTotal = [x + y for x, y in zip(y2mdp, y2m['m'])]\n",
    "\n",
    "y2md = ax.bar(ind + width + 0.1, y2m['d'], width, color = 'm', label = '_DELIVERY')\n",
    "\n",
    "y2mp = ax.bar(ind + width + 0.1, y2m['p'], width, bottom = y2m['d'], color = 'b', label = '_PICK-UP')\n",
    "\n",
    "y2mm = ax.bar(ind + width + 0.1, y2m['m'], width, bottom = y2mdp, color = 'r', label = '_MARKET')\n",
    "\n",
    "\n",
    "allBars = [y1md,y1mp,y1mm,y2md,y2mp,y2mm]\n",
    "\n",
    "ax.legend(loc=2)\n",
    "    \n",
    "\n",
    "annot = ax.annotate(\"\", xy=(0,0), xytext=(-20,20),textcoords=\"offset points\",\n",
    "                    bbox=dict(boxstyle=\"round\", fc=\"black\", ec=\"b\", lw=2),\n",
    "                    arrowprops=dict(arrowstyle=\"->\"))\n",
    "annot.set_visible(False)\n",
    "\n",
    "def update_annot(bar):\n",
    "    x = bar.get_x()\n",
    "    rX = round(x - int(x))\n",
    "    week = int(round(x))\n",
    "    if rX == 1:\n",
    "        name = names[week] + \" 2021\"\n",
    "        otherName = \"2022\"\n",
    "        weekTotal = round(y1mTotal[week])\n",
    "        mT = round(y1m['m'][week])\n",
    "        pT = round(y1m['p'][week])\n",
    "        dT = round(y1m['d'][week])\n",
    "        lastYearTotal = round(y2mTotal[week])\n",
    "        lmT = round(y2m['m'][week])\n",
    "        lpT = round(y2m['p'][week])\n",
    "        ldT = round(y2m['d'][week])\n",
    "    else:\n",
    "        name = names[week] + \" 2022\"\n",
    "        otherName = \"2021\"\n",
    "        weekTotal = round(y2mTotal[week])\n",
    "        mT = round(y2m['m'][week])\n",
    "        pT = round(y2m['p'][week])\n",
    "        dT = round(y2m['d'][week])\n",
    "        lastYearTotal = round(y1mTotal[week])\n",
    "        lmT = round(y1m['m'][week])\n",
    "        lpT = round(y1m['p'][week])\n",
    "        ldT = round(y1m['d'][week])\n",
    "    y = bar.get_y()+bar.get_height()\n",
    "    annot.xy = (x,y)\n",
    "    text1 = \"{} \\nMonth Total = {} \\nMarket = {} \\nPick-Up = {} \\nDelivery = {}\\n----------\".format(name,weekTotal, mT, pT, dT)\n",
    "    text2 = \"\\n{} Month Total = {} \\nMarket = {} \\nPick-up = {} \\nDelivery = {}\".format(otherName, lastYearTotal, lmT, lpT, ldT)\n",
    "    text = text1 + text2\n",
    "    annot.set_text(text)\n",
    "    annot.get_bbox_patch().set_facecolor(\"white\")\n",
    "    annot.get_bbox_patch().set_alpha(0.9)\n",
    "\n",
    "\n",
    "def on_click(event):\n",
    "    vis = annot.get_visible()\n",
    "    if event.inaxes == ax:\n",
    "        for bars in allBars:\n",
    "            for bar in bars:\n",
    "                cont, ind = bar.contains(event)\n",
    "                if cont:\n",
    "                    update_annot(bar)\n",
    "                    annot.set_visible(True)\n",
    "                    fig.canvas.draw_idle()\n",
    "                    return\n",
    "    if vis:\n",
    "        annot.set_visible(False)\n",
    "        fig.canvas.draw_idle()\n",
    "\n",
    "plt.connect('button_press_event', on_click)\n",
    "\n",
    "ax.set_xticks(ind, names)\n",
    "\n",
    "\n",
    "# function to show the plot\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8581d002",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y1csa = [round(x + y + z) for x, y, z in zip(y1m['d'], y1m['p'], y1m['m'])]\n",
    "\n",
    "y2csa = [round(x + y + z) for x, y, z in zip(y2m['d'], y2m['p'], y2m['m'])]\n",
    "\n",
    "y1wholesale = [0,0,0,0,0,0,0,0,0,0,0,0]\n",
    "\n",
    "y2wholesale = [0,0,0,0,0,0,0,0,0,0,0,0]\n",
    "\n",
    "for week, total in external_weekly_sales_frame.loc['Wholesale'].fillna(0).items():\n",
    "    if week.year == 2021:\n",
    "        y1wholesale[week.month-1]+=round(total)\n",
    "    elif week.year == 2022:\n",
    "        y2wholesale[week.month-1]+=round(total)   \n",
    " \n",
    "y1Overallmarket = [0,0,0,0,0,0,0,0,0,0,0,0]\n",
    "y2Overallmarket = [0,0,0,0,0,0,0,0,0,0,0,0]\n",
    "for week, total in external_weekly_sales_frame.loc['Cumberland':'Berkeley Springs'].fillna(0).items():\n",
    "    if week.year == 2021:\n",
    "        y1Overallmarket[week.month-1]+=round(total.sum())\n",
    "    elif week.year == 2022:\n",
    "        y2Overallmarket[week.month-1]+=round(total.sum())   \n",
    "\n",
    "y1Overall = {'c' : y1csa,\n",
    "          'w' : y1wholesale,\n",
    "            'm' : y1Overallmarket}\n",
    "\n",
    "y2Overall = {'c' : y2csa,\n",
    "          'w' : y2wholesale,\n",
    "            'm' : y2Overallmarket}\n",
    "\n",
    "\n",
    "names = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "\n",
    "\n",
    "y1Overallcw = [x + y for x, y in zip(y1Overall['c'], y1Overall['w'])]\n",
    "y1OverallTotal = [x + y for x, y in zip(y1Overallcw, y1Overall['m'])]\n",
    "\n",
    "            \n",
    "ind = np.arange(12)\n",
    "\n",
    "\n",
    "width = 0.3\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (10,10))\n",
    "\n",
    "y1Overallc = ax.bar(ind, y1Overall['c'], width, color = 'm', label = 'CSA')\n",
    "\n",
    "y1Overallw = ax.bar(ind, y1Overall['w'], width, bottom = y1Overall['c'], color = 'b', label = 'WHOLESALE')\n",
    "\n",
    "y1Overallm = ax.bar(ind, y1Overall['m'], width, bottom = y1Overallcw, color = 'r', label = 'MARKET')\n",
    "\n",
    "\n",
    "##################################\n",
    "\n",
    "y2Overallcw = [x + y for x, y in zip(y2Overall['c'], y2Overall['w'])]\n",
    "y2OverallTotal = [x + y for x, y in zip(y2Overallcw, y2Overall['m'])]\n",
    "\n",
    "y2Overallc = ax.bar(ind + width + 0.1, y2Overall['c'], width, color = 'm', label = '_CSA')\n",
    "\n",
    "y2Overallw = ax.bar(ind + width + 0.1, y2Overall['w'], width, bottom = y2Overall['c'], color = 'b', label = '_WHOLESALE')\n",
    "\n",
    "y2Overallm = ax.bar(ind + width + 0.1, y2Overall['m'], width, bottom = y2Overallcw, color = 'r', label = '_MARKET')\n",
    "\n",
    "\n",
    "allBars = [y1Overallc,y1Overallw,y1Overallm,y2Overallc,y2Overallw,y2Overallm]\n",
    "\n",
    "ax.legend(loc=2)\n",
    "    \n",
    "\n",
    "annot = ax.annotate(\"\", xy=(0,0), xytext=(-20,20),textcoords=\"offset points\",\n",
    "                    bbox=dict(boxstyle=\"round\", fc=\"black\", ec=\"b\", lw=2),\n",
    "                    arrowprops=dict(arrowstyle=\"->\"))\n",
    "annot.set_visible(False)\n",
    "\n",
    "def update_annot(bar):\n",
    "    x = bar.get_x()\n",
    "    rX = round(x - int(x))\n",
    "    week = int(round(x))\n",
    "    if rX == 1:\n",
    "        name = names[week] + \" 2021\"\n",
    "        otherName = \"2022\"\n",
    "        weekTotal = round(y1OverallTotal[week])\n",
    "        mT = round(y1Overall['m'][week])\n",
    "        wT = round(y1Overall['w'][week])\n",
    "        cT = round(y1Overall['c'][week])\n",
    "        lastYearTotal = round(y2OverallTotal[week])\n",
    "        lmT = round(y2Overall['m'][week])\n",
    "        lwT = round(y2Overall['w'][week])\n",
    "        lcT = round(y2Overall['c'][week])\n",
    "    else:\n",
    "        name = names[week] + \" 2022\"\n",
    "        otherName = \"2021\"\n",
    "        weekTotal = round(y2OverallTotal[week])\n",
    "        mT = round(y2Overall['m'][week])\n",
    "        wT = round(y2Overall['w'][week])\n",
    "        cT = round(y2Overall['c'][week])\n",
    "        lastYearTotal = round(y1OverallTotal[week])\n",
    "        lmT = round(y1Overall['m'][week])\n",
    "        lwT = round(y1Overall['w'][week])\n",
    "        lcT = round(y1Overall['c'][week])\n",
    "    y = bar.get_y()+bar.get_height()\n",
    "    annot.xy = (x,y)\n",
    "    text1 = \"{} \\nMonth Total = {} \\nMarket = {} \\nWholesale = {} \\nCSA = {}\\n----------\".format(name,weekTotal, mT, wT, cT)\n",
    "    text2 = \"\\n{} Month Total = {} \\nMarket = {} \\nWholesale-up = {} \\nCSA = {}\".format(otherName, lastYearTotal, lmT, lwT, lcT)\n",
    "    text = text1 + text2\n",
    "    annot.set_text(text)\n",
    "    annot.get_bbox_patch().set_facecolor(\"white\")\n",
    "    annot.get_bbox_patch().set_alpha(0.9)\n",
    "\n",
    "\n",
    "def on_click(event):\n",
    "    vis = annot.get_visible()\n",
    "    if event.inaxes == ax:\n",
    "        for bars in allBars:\n",
    "            for bar in bars:\n",
    "                cont, ind = bar.contains(event)\n",
    "                if cont:\n",
    "                    update_annot(bar)\n",
    "                    annot.set_visible(True)\n",
    "                    fig.canvas.draw_idle()\n",
    "                    return\n",
    "    if vis:\n",
    "        annot.set_visible(False)\n",
    "        fig.canvas.draw_idle()\n",
    "\n",
    "plt.connect('button_press_event', on_click)\n",
    "\n",
    "ax.set_xticks(ind, names)\n",
    "\n",
    "# function to show the plot\n",
    "plt.show()\n",
    "    \n"
   ]
  }
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