DBMS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reports - Buyers and Sellers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import datetime\n",
    "import cufflinks as cf\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# iPython Settings\n",
    "%matplotlib inline\n",
    "sns.set(rc={'figure.figsize':(15,9)})\n",
    "cf.go_offline()\n",
    "init_notebook_mode(connected = True)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating Functions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def createMilkData(records, accs):\n",
    "    \n",
    "    min_date = pd.to_datetime('2015-01-01')\n",
    "    max_date = pd.to_datetime('2018-12-31')\n",
    "\n",
    "    d = (max_date - min_date).days + 1\n",
    "\n",
    "    df1_cols = [\"shift\", \"acc no\", \"fat(3-10%)\", \"clr(23-30)\", \"amount\", \"rate\", \"payment\"]\n",
    "    df1_rows = [int(i) + 1 for i in range(records)]\n",
    "    df1 = pd.DataFrame(np.random.randn(records, 7), df1_rows, df1_cols)\n",
    "    df1['date'] = min_date + pd.to_timedelta(pd.np.random.randint(d,size=records), unit='d')\n",
    "    df1['shift'] = df1[\"shift\"].apply(lambda x : int(x)%2)\n",
    "    df1['acc no'] = df1['acc no'].apply(lambda x : int(x*100)%accs + 1)\n",
    "    df1['fat(3-10%)'] = df1['fat(3-10%)'].apply(lambda x : float(round((x*100)%8 + 3, 2)))\n",
    "    df1['clr(23-30)'] = df1['clr(23-30)'].apply(lambda x : float(round((x*100)%8 + 23, 2)))\n",
    "    df1['amount'] = df1['amount'].apply(lambda x : int((x*1000)%500 + 500))\n",
    "    df1['snr'] = [int(i) for i in range (records)]\n",
    "    df1['snr'] = df1['snr'].apply(lambda x : float(df1['fat(3-10%)'].iloc[x])/5 + float(df1['clr(23-30)'].iloc[x])/4.0 + 0.6)\n",
    "    df1['rate'] = df1['rate'].apply(lambda x : round((x*100)%20 + 50, 2))\n",
    "    df1['payment'] = [int(i) for i in range (records)]\n",
    "    df1['payment'] = df1['payment'].apply(lambda x : df1['amount'].iloc[x]*df1['rate'].iloc[x])\n",
    "    df1['debt'] = pd.DataFrame([random.randint(0, 30000) for i in range(records)])\n",
    "    \n",
    "    return df1\n",
    "\n",
    "def createItemData(records, items, accs):\n",
    "    \n",
    "    min_date = pd.to_datetime('2015-01-01')\n",
    "    max_date = pd.to_datetime('2018-12-31')\n",
    "\n",
    "    d = (max_date - min_date).days + 1\n",
    "    \n",
    "    df_cols = ['acc no', 'item code', 'quantity', 'cost per unit', 'total amount', 'buy/sell']\n",
    "    df_rows = [int(i) + 1 for i in range(records)]\n",
    "    df = pd.DataFrame(np.random.randn(records, 6), df_rows, df_cols)\n",
    "    df['date'] = min_date + pd.to_timedelta(pd.np.random.randint(d,size=records), unit='d')\n",
    "    df['acc no'] = df['acc no'].apply(lambda x : int(x*100)%accs + 1)\n",
    "    #print(df['item code'])\n",
    "    df['item code'] = df['item code'].apply(lambda x : int(x*100)%items + 1)\n",
    "    df['quantity'] = df['quantity'].apply(lambda x : int((x*1000)%500 + 500))\n",
    "    #df['cost per unit'] = df['item code']\n",
    "    #df['cost per unit'] = df['item code'].apply(lambda x : int(x))\n",
    "    \n",
    "    items_list = [i + 1 for i in range(items)]\n",
    "    rate_list = [random.randint(50, 100) for i in range(items)]\n",
    "    ItemTranslation = dict(zip(items_list, rate_list))\n",
    "    df['cost per unit'] = df['item code'].apply(lambda x : ItemTranslation[x])\n",
    "    \n",
    "    df['total amount'] = [int(i) for i in range (records)] \n",
    "    df['total amount'] = df['total amount'].apply(lambda x : round(df['quantity'].iloc[x]*df['cost per unit'].iloc[x], 2))\n",
    "    df['buy/sell'] = df[\"buy/sell\"].apply(lambda x : int(x)%2)\n",
    "    df['debt'] = pd.DataFrame([random.randint(0, 30000) for i in range(records)])\n",
    "    \n",
    "    return df\n",
    "\n",
    "def filterByDate(df1, sd, sm, sy, ed, em, ey):\n",
    "    \n",
    "    return df1[(df1['date']>datetime.date(sy,sm,sd)) & (df1['date']<datetime.date(ey,em,ed))] \n",
    "\n",
    "def stdError(df, column):\n",
    "    #mean +- std/sqrt(count)\n",
    "    mean = df[column].describe()['mean']\n",
    "    std_int = df[column].describe()['std']/(df[column].describe()['count']**0.5)\n",
    "    \n",
    "    return (int(mean - std_int), int(mean + std_int))\n",
    "\n",
    "def sellerReportMilkGranular(df1, sd, sm, sy, ed, em, ey, acc_no):\n",
    "    \n",
    "    df = filterByDate(df1, sd, sm, sy, ed, em, ey)\n",
    "    df = df[df['acc no'] == int(acc_no)]\n",
    "    snr = stdError(df, 'snr')\n",
    "    total_amt = df['amount'].sum()\n",
    "    avg_amount = stdError(df, 'amount')\n",
    "    #iters = df.describe()['count']\n",
    "    morning_shift = df[df['shift'] == 0]['shift'].describe()['count']\n",
    "    evening_shift = df[df['shift'] == 1]['shift'].describe()['count']\n",
    "    iters = morning_shift + evening_shift\n",
    "    avg_debt = stdError(df, 'debt')\n",
    "    avg_rate = stdError(df, 'rate')\n",
    "    total_pay = df['payment'].sum()\n",
    "    avg_payment = stdError(df, 'payment')\n",
    "    \n",
    "    return f\"\"\"\n",
    "    Account Number: {acc_no}\\n\n",
    "    SNF: {snr}\\n\n",
    "    Total Amount: {total_amt}\\n\n",
    "    Average Amount: {avg_amount}\\n\n",
    "    Total Visits: {iters}\\n\n",
    "    Morning Shifts: {morning_shift}\\n\n",
    "    Evening Shifts: {evening_shift}\\n\n",
    "    Average Debt: {avg_debt}\\n\n",
    "    Average Rate: {avg_rate}\\n\n",
    "    Total Payment: {total_pay}\\n\n",
    "    Average Payment: {avg_payment}\\n\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    #return [snr, (total_amt, avg_amount), (iters, morning_shift, evening_shift), avg_debt, avg_rate, (total_pay, avg_payment)]\n",
    "    \n",
    "\n",
    "def sellerReportMilk(df1, sd, sm, sy, ed, em, ey):\n",
    "    \n",
    "    df = filterByDate(df1, sd, sm, sy, ed, em, ey)\n",
    "    snr = stdError(df, 'snr')\n",
    "    total_amt = df['amount'].sum()\n",
    "    avg_amount = stdError(df, 'amount')\n",
    "    #iters = df.describe()['count']\n",
    "    morning_shift = df[df['shift'] == 0]['shift'].describe()['count']\n",
    "    evening_shift = df[df['shift'] == 1]['shift'].describe()['count']\n",
    "    iters = morning_shift + evening_shift\n",
    "    avg_debt = stdError(df, 'debt')\n",
    "    avg_rate = stdError(df, 'rate')\n",
    "    total_pay = df['payment'].sum()\n",
    "    avg_payment = stdError(df, 'payment')\n",
    "    \n",
    "    return f\"\"\"\n",
    "    SNF: {snr}\\n\n",
    "    Total Amount: {total_amt}\\n\n",
    "    Average Amount: {avg_amount}\\n\n",
    "    Total Visits: {iters}\\n\n",
    "    Morning Shifts: {morning_shift}\\n\n",
    "    Evening Shifts: {evening_shift}\\n\n",
    "    Average Debt: {avg_debt}\\n\n",
    "    Average Rate: {avg_rate}\\n\n",
    "    Total Payment: {total_pay}\\n\n",
    "    Average Payment: {avg_payment}\\n\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    #return [snr, (total_amt, avg_amount), (iters, morning_shift, evening_shift), avg_debt, avg_rate, (total_pay, avg_payment)]\n",
    "\n",
    "\n",
    "\n",
    "def sellerReportMilkByShiftVisualGranular(df, acc_no):\n",
    "    #sns.countplot(x = 'shift', data = df[df['acc no'] == int(acc_no)])\n",
    "    \n",
    "    return None\n",
    "\n",
    "def sellerReportMilkByCount(df):\n",
    "    #sns.countplot(x = 'acc no', data = df)\n",
    "    \n",
    "    return None\n",
    "\n",
    "def passbookRecordsMilk(df1, acc_no, sd, sm, sy, ed, em, ey):\n",
    "    \n",
    "    df = filterByDate(df1, sd, sm, sy, ed, em, ey)\n",
    "    df = df[df['acc no'] == int(acc_no)]\n",
    "    df = df.sort_values('date')\n",
    "    df.drop(columns = 'acc no', inplace = True)\n",
    "    \n",
    "    records = [tuple(x) for x in df.to_records(index=False)]\n",
    "    \n",
    "    return df\n",
    "    #return records\n",
    "\n",
    "def visualizeMilkGranular(df1, acc_no, sd, sm, sy, ed, em, ey):\n",
    "    \n",
    "    df = filterByDate(df1, sd, sm, sy, ed, em, ey)\n",
    "    df = df[df['acc no'] == int(acc_no)]\n",
    "    df.sort_values('date')\n",
    "    \n",
    "    #df['amount'].iplot(kind = 'hist')\n",
    "    df['payment'].iplot(kind = 'line')\n",
    "    df[['fat(3-10%)', 'clr(23-30)']].iplot(kind = 'box')\n",
    "    df.iplot(kind = \"scatter\", x = 'snr', y = \"rate\", mode = \"markers\")\n",
    "    #df.iplot(kind = 'bar', x = 'shift', y = 'amount')\n",
    "    \n",
    "    return None\n",
    "\n",
    "def visualizeMilk(df1, sd, sm, sy, ed, em, ey):\n",
    "    \n",
    "    df = filterByDate(df1, sd, sm, sy, ed, em, ey)\n",
    "    df.sort_values('date')\n",
    "    #df['amount'].iplot(kind = 'hist')\n",
    "    df['payment'].iplot(kind = 'line')\n",
    "    df[['fat(3-10%)', 'clr(23-30)']].iplot(kind = 'box')\n",
    "    df.iplot(kind = \"scatter\", x = 'snr', y = \"rate\", mode = \"markers\")\n",
    "    #df.iplot(kind = 'bar', x = 'acc no', y = 'amount')\n",
    "    \n",
    "    return None\n",
    "\n",
    "def sellerReportItemsGranular(df1, sd, sm, sy, ed, em, ey, acc_no):\n",
    "    \n",
    "    df = filterByDate(df1, sd, sm, sy, ed, em, ey)\n",
    "    df = df[df['acc no'] == int(acc_no)]\n",
    "    total_amt = df['quantity'].sum()\n",
    "    avg_amount = stdError(df, 'quantity')\n",
    "    #iters = df.describe()['count']\n",
    "    buy = df[df['buy/sell'] == 0]['buy/sell'].describe()['count']\n",
    "    sell = df[df['buy/sell'] == 1]['buy/sell'].describe()['count']\n",
    "    iters = buy + sell\n",
    "    avg_debt = stdError(df, 'debt')\n",
    "    avg_rate = stdError(df, 'cost per unit')\n",
    "    total_purchased = df[df['buy/sell'] == 0]['buy/sell'].sum()\n",
    "    total_sold = df[df['buy/sell'] == 1]['buy/sell'].sum()\n",
    "    \n",
    "    return f\"\"\"\n",
    "    Account Number: {acc_no}\\n\n",
    "    Total Amount: {total_amt}\\n\n",
    "    Average Amount: {avg_amount}\\n\n",
    "    Total Visits: {iters}\\n\n",
    "    Items Brought: {buy}\\n\n",
    "    Items Sold: {sell}\\n\n",
    "    Average Debt: {avg_debt}\\n\n",
    "    Average Transaction Rate: {avg_rate}\\n\n",
    "    Total Entities Purchased: {total_purchased}\\n\n",
    "    Total Entities Sold: {total_sold}\\n\n",
    "    \"\"\"\n",
    "    \n",
    "    #return [(total_amt, avg_amount), (iters, buy, sell), avg_debt, avg_rate, (total_purchased, total_sold)]\n",
    "\n",
    "def sellerReportItems(df1, sd, sm, sy, ed, em, ey):\n",
    "    \n",
    "    df = filterByDate(df1, sd, sm, sy, ed, em, ey)\n",
    "    total_amt = df['quantity'].sum()\n",
    "    avg_amount = stdError(df, 'quantity')\n",
    "    #iters = df.describe()['count']\n",
    "    buy = df[df['buy/sell'] == 0]['buy/sell'].describe()['count']\n",
    "    sell = df[df['buy/sell'] == 1]['buy/sell'].describe()['count']\n",
    "    iters = buy + sell\n",
    "    avg_debt = stdError(df, 'debt')\n",
    "    avg_rate = stdError(df, 'cost per unit')\n",
    "    total_purchased = df[df['buy/sell'] == 0]['buy/sell'].sum()\n",
    "    total_sold = df[df['buy/sell'] == 1]['buy/sell'].sum()\n",
    "    \n",
    "    return f\"\"\"\n",
    "    Total Amount: {total_amt}\\n\n",
    "    Average Amount: {avg_amount}\\n\n",
    "    Total Visits: {iters}\\n\n",
    "    Items Brought: {buy}\\n\n",
    "    Items Sold: {sell}\\n\n",
    "    Average Debt: {avg_debt}\\n\n",
    "    Average Transaction Rate: {avg_rate}\\n\n",
    "    Total Entities Purchased: {total_purchased}\\n\n",
    "    Total Entities Sold: {total_sold}\\n\n",
    "    \"\"\"\n",
    "    \n",
    "    #return [(total_amt, avg_amount), (iters, buy, sell), avg_debt, avg_rate, (total_purchased, total_sold)]\n",
    "\n",
    "def passbookRecordsItemByAccount(df1, acc_no, sd, sm, sy, ed, em, ey):\n",
    "    \n",
    "    df = filterByDate(df1, sd, sm, sy, ed, em, ey)\n",
    "    #df = df[df['acc no'] == int(acc_no)]\n",
    "    df = df[df['acc no'] == acc_no]\n",
    "    df.sort_values('date')\n",
    "    df.drop(columns = acc_no, inplace = True)\n",
    "    \n",
    "    records = [tuple(x) for x in df.to_records(index=False)]\n",
    "    \n",
    "    return df\n",
    "    #return records\n",
    "\n",
    "def passbookRecordsItemByItem(df1, item_code, sd, sm, sy, ed, em, ey):\n",
    "    \n",
    "    df = filterByDate(df1, sd, sm, sy, ed, em, ey)\n",
    "    df = df[df['item code'] == int(item_code)]\n",
    "    df.sort_values('date')\n",
    "    df.drop(columns = item_code, inplace = True)\n",
    "    \n",
    "    records = [tuple(x) for x in df.to_records(index=False)]\n",
    "    \n",
    "    return df\n",
    "    #return records\n",
    "\n",
    "def visualizeItemGranularByItemID(df1, item_code, sd, sm, sy, ed, em, ey):\n",
    "    \n",
    "    df = filterByDate(df1, sd, sm, sy, ed, em, ey)\n",
    "    df = df[df['item code'] == int(item_code)]\n",
    "    df.sort_values('date')\n",
    "    \n",
    "    df.iplot(kind = 'bar', x = 'acc no', y = 'quantity')\n",
    "    #df[df['buy/sell'] == 0].iplot(kind = 'bar', x = 'acc no', y = 'quantity')\n",
    "    #df[df['buy/sell'] == 1].iplot(kind = 'bar', x = 'acc no', y = 'quantity')\n",
    "    \n",
    "    return None\n",
    "\n",
    "def visualizeItemGranularByAccId(df1, acc_no, sd, sm, sy, ed, em, ey):\n",
    "    \n",
    "    df = filterByDate(df1, sd, sm, sy, ed, em, ey)\n",
    "    df = df[df['acc no'] == int(acc_no)]\n",
    "    df.sort_values('date')\n",
    "    \n",
    "    df.iplot(kind = 'bar', x = 'item code', y = 'quantity')\n",
    "    #df[df['buy/sell'] == 0].iplot(kind = 'bar', x = 'item code', y = 'quantity')\n",
    "    #df[df['buy/sell'] == 1].iplot(kind = 'bar', x = 'item code', y = 'quantity')\n",
    "    \n",
    "def visualizeItems(df1, sd, sm, sy, ed, em, ey):\n",
    "    \n",
    "    df = filterByDate(df1, sd, sm, sy, ed, em, ey)\n",
    "    df.sort_values('date')\n",
    "    \n",
    "    df['total amount'].iplot(kind = 'line')\n",
    "    #df[df['buy/sell'] == 0]['total amount'].iplot(kind = 'line')\n",
    "    #df[df['buy/sell'] == 1]['total amount'].iplot(kind = 'line')\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "items = createItemData(200, 5, 20)\n",
    "milk = createMilkData(200, 10)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>acc no</th>\n",
       "      <th>item code</th>\n",
       "      <th>quantity</th>\n",
       "      <th>cost per unit</th>\n",
       "      <th>total amount</th>\n",
       "      <th>buy/sell</th>\n",
       "      <th>date</th>\n",
       "      <th>debt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>688</td>\n",
       "      <td>94</td>\n",
       "      <td>64672</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-05-07</td>\n",
       "      <td>21560.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>658</td>\n",
       "      <td>87</td>\n",
       "      <td>57246</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>15916.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>737</td>\n",
       "      <td>84</td>\n",
       "      <td>61908</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-08-15</td>\n",
       "      <td>20682.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>904</td>\n",
       "      <td>84</td>\n",
       "      <td>75936</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-12-13</td>\n",
       "      <td>28086.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>808</td>\n",
       "      <td>63</td>\n",
       "      <td>50904</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-10-18</td>\n",
       "      <td>15669.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   acc no  item code  quantity  cost per unit  total amount  buy/sell  \\\n",
       "1      13          3       688             94         64672         0   \n",
       "2      11          2       658             87         57246         1   \n",
       "3      18          4       737             84         61908         0   \n",
       "4       5          4       904             84         75936         0   \n",
       "5      10          5       808             63         50904         1   \n",
       "\n",
       "        date     debt  \n",
       "1 2016-05-07  21560.0  \n",
       "2 2015-01-15  15916.0  \n",
       "3 2015-08-15  20682.0  \n",
       "4 2015-12-13  28086.0  \n",
       "5 2015-10-18  15669.0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items.head()"
   ]
  },
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       "      <th></th>\n",
       "      <th>shift</th>\n",
       "      <th>acc no</th>\n",
       "      <th>fat(3-10%)</th>\n",
       "      <th>clr(23-30)</th>\n",
       "      <th>amount</th>\n",
       "      <th>rate</th>\n",
       "      <th>payment</th>\n",
       "      <th>date</th>\n",
       "      <th>snr</th>\n",
       "      <th>debt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>6.68</td>\n",
       "      <td>27.40</td>\n",
       "      <td>799</td>\n",
       "      <td>58.52</td>\n",
       "      <td>46757.48</td>\n",
       "      <td>2016-11-27</td>\n",
       "      <td>8.7860</td>\n",
       "      <td>12057.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6.03</td>\n",
       "      <td>26.07</td>\n",
       "      <td>818</td>\n",
       "      <td>60.73</td>\n",
       "      <td>49677.14</td>\n",
       "      <td>2016-05-13</td>\n",
       "      <td>8.3235</td>\n",
       "      <td>9169.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>4.57</td>\n",
       "      <td>26.01</td>\n",
       "      <td>936</td>\n",
       "      <td>65.97</td>\n",
       "      <td>61747.92</td>\n",
       "      <td>2017-11-07</td>\n",
       "      <td>8.0165</td>\n",
       "      <td>3404.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6.65</td>\n",
       "      <td>28.71</td>\n",
       "      <td>911</td>\n",
       "      <td>58.15</td>\n",
       "      <td>52974.65</td>\n",
       "      <td>2018-06-24</td>\n",
       "      <td>9.1075</td>\n",
       "      <td>1558.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4.83</td>\n",
       "      <td>25.21</td>\n",
       "      <td>603</td>\n",
       "      <td>52.84</td>\n",
       "      <td>31862.52</td>\n",
       "      <td>2017-03-18</td>\n",
       "      <td>7.8685</td>\n",
       "      <td>18510.0</td>\n",
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      "text/plain": [
       "   shift  acc no  fat(3-10%)  clr(23-30)  amount   rate   payment       date  \\\n",
       "1      0      10        6.68       27.40     799  58.52  46757.48 2016-11-27   \n",
       "2      0       4        6.03       26.07     818  60.73  49677.14 2016-05-13   \n",
       "3      0      10        4.57       26.01     936  65.97  61747.92 2017-11-07   \n",
       "4      0       2        6.65       28.71     911  58.15  52974.65 2018-06-24   \n",
       "5      1       5        4.83       25.21     603  52.84  31862.52 2017-03-18   \n",
       "\n",
       "      snr     debt  \n",
       "1  8.7860  12057.0  \n",
       "2  8.3235   9169.0  \n",
       "3  8.0165   3404.0  \n",
       "4  9.1075   1558.0  \n",
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   ],
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     "text": [
      "\n",
      "    Account Number: 1\n",
      "\n",
      "    SNR: (8, 8)\n",
      "\n",
      "    Total Amount: 4220\n",
      "\n",
      "    Average Amount: (648, 758)\n",
      "\n",
      "    Total Visits: 6.0\n",
      "\n",
      "    Morning Shifts: 4.0\n",
      "\n",
      "    Evening Shifts: 2.0\n",
      "\n",
      "    Average Debt: (13832, 19546)\n",
      "\n",
      "    Average Rate: (58, 65)\n",
      "\n",
      "    Total Payment: 261460.87999999998\n",
      "\n",
      "    Average Payment: (39772, 47381)\n",
      "\n",
      "    \n",
      "    \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/ipykernel_launcher.py:58: FutureWarning:\n",
      "\n",
      "Comparing Series of datetimes with 'datetime.date'.  Currently, the\n",
      "'datetime.date' is coerced to a datetime. In the future pandas will\n",
      "not coerce, and a TypeError will be raised. To retain the current\n",
      "behavior, convert the 'datetime.date' to a datetime with\n",
      "'pd.Timestamp'.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(sellerReportMilkGranular(milk, 1, 1, 2017, 31, 12, 2017, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    SNF: (8, 8)\n",
      "\n",
      "    Total Amount: 38116\n",
      "\n",
      "    Average Amount: (715, 750)\n",
      "\n",
      "    Total Visits: 52.0\n",
      "\n",
      "    Morning Shifts: 44.0\n",
      "\n",
      "    Evening Shifts: 8.0\n",
      "\n",
      "    Average Debt: (13819, 16055)\n",
      "\n",
      "    Average Rate: (60, 61)\n",
      "\n",
      "    Total Payment: 2313132.6200000006\n",
      "\n",
      "    Average Payment: (43323, 45643)\n",
      "\n",
      "    \n",
      "    \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/ipykernel_launcher.py:58: FutureWarning:\n",
      "\n",
      "Comparing Series of datetimes with 'datetime.date'.  Currently, the\n",
      "'datetime.date' is coerced to a datetime. In the future pandas will\n",
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      "behavior, convert the 'datetime.date' to a datetime with\n",
      "'pd.Timestamp'.\n",
      "\n"
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   ],
   "source": [
    "print(sellerReportMilk(milk, 1, 1, 2017, 31, 12, 2017))"
   ]
  },
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   "execution_count": 27,
   "metadata": {},
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     "name": "stdout",
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     "text": [
      "     shift  fat(3-10%)  clr(23-30)  amount   rate   payment       date  \\\n",
      "43       1        6.70       29.76     856  69.81  59757.36 2017-02-25   \n",
      "190      0        9.69       26.93     516  66.62  34375.92 2017-03-28   \n",
      "19       0        3.74       29.47     826  50.37  41605.62 2017-04-05   \n",
      "33       0        9.04       25.90     758  63.94  48466.52 2017-10-08   \n",
      "35       1        4.44       23.14     591  69.33  40974.03 2017-10-24   \n",
      "15       0        7.60       25.01     673  53.91  36281.43 2017-10-30   \n",
      "\n",
      "        snr     debt  \n",
      "43   9.3800   8415.0  \n",
      "190  9.2705  20138.0  \n",
      "19   8.7155  16836.0  \n",
      "33   8.8830   9167.0  \n",
      "35   7.2730  26877.0  \n",
      "15   8.3725  18704.0  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/ipykernel_launcher.py:58: FutureWarning:\n",
      "\n",
      "Comparing Series of datetimes with 'datetime.date'.  Currently, the\n",
      "'datetime.date' is coerced to a datetime. In the future pandas will\n",
      "not coerce, and a TypeError will be raised. To retain the current\n",
      "behavior, convert the 'datetime.date' to a datetime with\n",
      "'pd.Timestamp'.\n",
      "\n"
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   ],
   "source": [
    "print(passbookRecordsMilk(milk, 1, 1, 1, 2017, 31, 12, 2017))"
   ]
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   "source": [
    "visualizeMilk(milk, 1, 1, 2017, 31, 12, 2017)"
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      "\n",
      "    Account Number: 1\n",
      "\n",
      "    Total Amount: 2209\n",
      "\n",
      "    Average Amount: (641, 831)\n",
      "\n",
      "    Total Visits: 3.0\n",
      "\n",
      "    Items Brought: 2.0\n",
      "\n",
      "    Items Sold: 1.0\n",
      "\n",
      "    Average Debt: (4225, 12383)\n",
      "\n",
      "    Average Transaction Rate: (81, 87)\n",
      "\n",
      "    Total Entities Purchased: 0\n",
      "\n",
      "    Total Entities Sold: 1\n",
      "\n",
      "    \n"
     ]
    },
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     "text": [
      "/usr/lib/python3/dist-packages/ipykernel_launcher.py:58: FutureWarning:\n",
      "\n",
      "Comparing Series of datetimes with 'datetime.date'.  Currently, the\n",
      "'datetime.date' is coerced to a datetime. In the future pandas will\n",
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      "behavior, convert the 'datetime.date' to a datetime with\n",
      "'pd.Timestamp'.\n",
      "\n"
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   "source": [
    "print(sellerReportItemsGranular(items, 1, 1, 2017, 31, 12, 2017, 1))"
   ]
  },
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   "execution_count": 39,
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      "\n",
      "    Total Amount: 37418\n",
      "\n",
      "    Average Amount: (742, 784)\n",
      "\n",
      "    Total Visits: 49.0\n",
      "\n",
      "    Items Brought: 35.0\n",
      "\n",
      "    Items Sold: 14.0\n",
      "\n",
      "    Average Debt: (14359, 16844)\n",
      "\n",
      "    Average Transaction Rate: (72, 77)\n",
      "\n",
      "    Total Entities Purchased: 0\n",
      "\n",
      "    Total Entities Sold: 14\n",
      "\n",
      "    \n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/ipykernel_launcher.py:58: FutureWarning:\n",
      "\n",
      "Comparing Series of datetimes with 'datetime.date'.  Currently, the\n",
      "'datetime.date' is coerced to a datetime. In the future pandas will\n",
      "not coerce, and a TypeError will be raised. To retain the current\n",
      "behavior, convert the 'datetime.date' to a datetime with\n",
      "'pd.Timestamp'.\n",
      "\n"
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   ],
   "source": [
    "print(sellerReportItems(items, 1, 1, 2017, 31, 12, 2017))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(passbookRecordsItemByAccount(items, 13, 1, 1, 2017, 31, 12, 2017))\n",
    "#passbookRecordsItemByItem(items, 1, 1, 1, 2017, 31, 12, 2017)"
   ]
  },
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