{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "39ad3312",
   "metadata": {},
   "source": [
    "Retail Analysis examples\n",
    "======================\n",
    "Simple TenFrame examples for some basic analysis, to show how familiar it is for python users, and to demonstrate performance on a large dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "367cf2c0",
   "metadata": {},
   "source": [
    "Load iris\n",
    "----------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8c9e3979",
   "metadata": {},
   "outputs": [],
   "source": [
    "load_ext iris"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "276825b3",
   "metadata": {},
   "source": [
    "Import libraries\n",
    "------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a3f0aa89",
   "metadata": {},
   "outputs": [],
   "source": [
    "import py1010\n",
    "import tenFrame as tf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "tf.useiris(False)\n",
    "\n",
    "session = py1010.Session(\"1010data URL\", \"USERNAME\", \"PASSWORD\", py1010.POSSESS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "152b3023",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total sales rows: 5,997,373,902\n",
      "Rows with known customers: 836,621,077\n"
     ]
    }
   ],
   "source": [
    "sales = tf.TenFrame(session, \"retaildemo.retail.sales_detail\")\n",
    "# without downsampling, describe() below takes about 6 minutes.  Plan accordingly.\n",
    "# sales = sales.sample(frac=1e-3)\n",
    "print(f\"Total sales rows: {len(sales):,}\")\n",
    "# Sales linked with customer data, filtered to rows where such data exists.\n",
    "salescust = tf.merge(sales, \"retaildemo.retail.customers\", on=\"customer_key\")\n",
    "salescust.dropna(subset=[\"customer_key\"], inplace=True)\n",
    "print(f\"Rows with known customers: {len(salescust):,}\")\n",
    "# Sales linked with product data\n",
    "salesprod = tf.merge(sales, \"retaildemo.retail.products\", on=\"sku\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eb564e7",
   "metadata": {},
   "source": [
    "Overview of data\n",
    "--------------------\n",
    "We can just do the usual `describe()` method for an overview of the sales data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "166a02b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 32 ms, sys: 19.2 ms, total: 51.1 ms\n",
      "Wall time: 445 ms\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>qty</th>\n",
       "      <th>weight</th>\n",
       "      <th>xsales</th>\n",
       "      <th>price</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.997374e+09</td>\n",
       "      <td>5.997374e+09</td>\n",
       "      <td>5.997374e+09</td>\n",
       "      <td>5.997374e+09</td>\n",
       "      <td>5.997024e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.356727e+00</td>\n",
       "      <td>1.144611e+00</td>\n",
       "      <td>3.895354e+00</td>\n",
       "      <td>3.153982e+00</td>\n",
       "      <td>2.675612e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_s</th>\n",
       "      <td>1.252325e+00</td>\n",
       "      <td>8.808362e-01</td>\n",
       "      <td>4.493964e+00</td>\n",
       "      <td>3.159839e+00</td>\n",
       "      <td>3.375028e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-9.900000e+01</td>\n",
       "      <td>-1.066300e+02</td>\n",
       "      <td>-4.227208e+04</td>\n",
       "      <td>-9.717720e+03</td>\n",
       "      <td>-2.954677e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lquart</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.770000e+00</td>\n",
       "      <td>1.370000e+00</td>\n",
       "      <td>1.070000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.870000e+00</td>\n",
       "      <td>2.500000e+00</td>\n",
       "      <td>1.920000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uquart</th>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.420000e+00</td>\n",
       "      <td>4.650000e+00</td>\n",
       "      <td>3.870000e+00</td>\n",
       "      <td>3.090000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.900000e+01</td>\n",
       "      <td>1.200000e+02</td>\n",
       "      <td>8.997500e+02</td>\n",
       "      <td>1.999900e+02</td>\n",
       "      <td>2.705800e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 qty        weight        xsales         price          cost\n",
       "count   5.997374e+09  5.997374e+09  5.997374e+09  5.997374e+09  5.997024e+09\n",
       "mean    1.356727e+00  1.144611e+00  3.895354e+00  3.153982e+00  2.675612e+00\n",
       "std_s   1.252325e+00  8.808362e-01  4.493964e+00  3.159839e+00  3.375028e+00\n",
       "min    -9.900000e+01 -1.066300e+02 -4.227208e+04 -9.717720e+03 -2.954677e+04\n",
       "lquart  1.000000e+00  1.000000e+00  1.770000e+00  1.370000e+00  1.070000e+00\n",
       "median  2.000000e+00  1.000000e+00  2.870000e+00  2.500000e+00  1.920000e+00\n",
       "uquart  2.000000e+00  1.420000e+00  4.650000e+00  3.870000e+00  3.090000e+00\n",
       "max     9.900000e+01  1.200000e+02  8.997500e+02  1.999900e+02  2.705800e+03"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "sales.describe(exclude=[\"transid\", \"date\", \"tme\", \"sku\", \"store\", \"customer_key\", \"promo_flag\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "697e62f3",
   "metadata": {},
   "source": [
    "More detailed overview\n",
    "----------------------------\n",
    "The following shows an overview at a slightly more granular level, with customer data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "82f57bc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 39.4 ms, sys: 11 ms, total: 50.5 ms\n",
      "Wall time: 617 ms\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>qty_count</th>\n",
       "      <th>qty_mean</th>\n",
       "      <th>qty_std_s</th>\n",
       "      <th>qty_min</th>\n",
       "      <th>qty_lquart</th>\n",
       "      <th>qty_median</th>\n",
       "      <th>qty_uquart</th>\n",
       "      <th>qty_max</th>\n",
       "      <th>weight_count</th>\n",
       "      <th>...</th>\n",
       "      <th>cost_uquart</th>\n",
       "      <th>cost_max</th>\n",
       "      <th>age_count</th>\n",
       "      <th>age_mean</th>\n",
       "      <th>age_std_s</th>\n",
       "      <th>age_min</th>\n",
       "      <th>age_lquart</th>\n",
       "      <th>age_median</th>\n",
       "      <th>age_uquart</th>\n",
       "      <th>age_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>176834985.0</td>\n",
       "      <td>1.310697</td>\n",
       "      <td>0.962674</td>\n",
       "      <td>-82.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>176834985.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.21</td>\n",
       "      <td>765.69</td>\n",
       "      <td>176834985.0</td>\n",
       "      <td>47.831535</td>\n",
       "      <td>10.575100</td>\n",
       "      <td>18</td>\n",
       "      <td>41</td>\n",
       "      <td>48</td>\n",
       "      <td>55</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>566190126.0</td>\n",
       "      <td>1.309058</td>\n",
       "      <td>0.958930</td>\n",
       "      <td>-89.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>566190126.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.20</td>\n",
       "      <td>824.12</td>\n",
       "      <td>566190126.0</td>\n",
       "      <td>47.765438</td>\n",
       "      <td>10.659013</td>\n",
       "      <td>18</td>\n",
       "      <td>41</td>\n",
       "      <td>48</td>\n",
       "      <td>55</td>\n",
       "      <td>96</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>93595966.0</td>\n",
       "      <td>1.398271</td>\n",
       "      <td>1.185352</td>\n",
       "      <td>-70.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>93595966.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.26</td>\n",
       "      <td>712.15</td>\n",
       "      <td>93595966.0</td>\n",
       "      <td>46.804938</td>\n",
       "      <td>10.590270</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "      <td>47</td>\n",
       "      <td>54</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender    qty_count  qty_mean  qty_std_s  qty_min  qty_lquart  qty_median  \\\n",
       "0      M  176834985.0  1.310697   0.962674    -82.0         1.0         1.0   \n",
       "1      F  566190126.0  1.309058   0.958930    -89.0         1.0         1.0   \n",
       "2   <NA>   93595966.0  1.398271   1.185352    -70.0         1.0         1.0   \n",
       "\n",
       "   qty_uquart  qty_max  weight_count  ...  cost_uquart  cost_max    age_count  \\\n",
       "0         1.0     99.0   176834985.0  ...         3.21    765.69  176834985.0   \n",
       "1         1.0     99.0   566190126.0  ...         3.20    824.12  566190126.0   \n",
       "2         1.0     99.0    93595966.0  ...         3.26    712.15   93595966.0   \n",
       "\n",
       "    age_mean  age_std_s  age_min  age_lquart  age_median  age_uquart  age_max  \n",
       "0  47.831535  10.575100       18          41          48          55       92  \n",
       "1  47.765438  10.659013       18          41          48          55       96  \n",
       "2  46.804938  10.590270       17          40          47          54       94  \n",
       "\n",
       "[3 rows x 49 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "salescust.groupby(\"gender\").describe(exclude=[\"transid\", \"date\", \"tme\", \"sku\", \"store\", \"customer_key\", \"promo_flag\", \"birthdate\", \"customerid\", \"tier\"])\n",
    "# (the short and very very wide format is what pandas does as well.)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb3e0742",
   "metadata": {},
   "source": [
    "Count of transactions by date\n",
    "-----------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7eea22bf",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<?xml version=\"1.0\" ?>\n",
      "<macro>\n",
      "\t<base table=\"retaildemo.retail.sales_detail\"/>\n",
      "\t<willbe value=\"g_cnt((date);())\" name=\"transid_cnt\"/>\n",
      "\t<sel value=\"g_first1(date;;)\"/>\n",
      "\t<colord cols=\"date,transid_cnt\"/>\n",
      "</macro>\n",
      "\n",
      "CPU times: user 18.3 ms, sys: 10.5 ms, total: 28.8 ms\n",
      "Wall time: 1.16 s\n"
     ]
    },
    {
     "data": {
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       "      <td>2012-12-31</td>\n",
       "      <td>3332409</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>1912203</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>2374098</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-03</td>\n",
       "      <td>2403905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-01-04</td>\n",
       "      <td>2512208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013-01-05</td>\n",
       "      <td>2792596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013-01-06</td>\n",
       "      <td>3134535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013-01-07</td>\n",
       "      <td>2659785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013-01-08</td>\n",
       "      <td>2425479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2013-01-09</td>\n",
       "      <td>2338966</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  transid_cnt\n",
       "0  2012-12-31      3332409\n",
       "1  2013-01-01      1912203\n",
       "2  2013-01-02      2374098\n",
       "3  2013-01-03      2403905\n",
       "4  2013-01-04      2512208\n",
       "5  2013-01-05      2792596\n",
       "6  2013-01-06      3134535\n",
       "7  2013-01-07      2659785\n",
       "8  2013-01-08      2425479\n",
       "9  2013-01-09      2338966"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "r = sales.groupby(\"date\").transid.cnt()\n",
    "r.printprettyXML()\n",
    "q = r.iloc[0]       # Something to make the time shown honest.\n",
    "r"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebf6e067",
   "metadata": {},
   "source": [
    "Total sales by date\n",
    "----------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "97fa288a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<?xml version=\"1.0\" ?>\n",
      "<macro>\n",
      "\t<base table=\"retaildemo.retail.sales_detail\"/>\n",
      "\t<willbe value=\"g_sum((date);();(xsales))\" name=\"xsales_sum\"/>\n",
      "\t<sel value=\"g_first1(date;;)\"/>\n",
      "\t<colord cols=\"date,xsales_sum\"/>\n",
      "</macro>\n",
      "\n",
      "CPU times: user 22.5 ms, sys: 11.3 ms, total: 33.8 ms\n",
      "Wall time: 1.33 s\n"
     ]
    },
    {
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       "      <th>1</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>6.713122e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>8.270888e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-03</td>\n",
       "      <td>8.443139e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-01-04</td>\n",
       "      <td>9.015836e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013-01-05</td>\n",
       "      <td>1.000415e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013-01-06</td>\n",
       "      <td>1.086893e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013-01-07</td>\n",
       "      <td>9.230439e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013-01-08</td>\n",
       "      <td>8.493609e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2013-01-09</td>\n",
       "      <td>8.253537e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date    xsales_sum\n",
       "0  2012-12-31  1.335217e+07\n",
       "1  2013-01-01  6.713122e+06\n",
       "2  2013-01-02  8.270888e+06\n",
       "3  2013-01-03  8.443139e+06\n",
       "4  2013-01-04  9.015836e+06\n",
       "5  2013-01-05  1.000415e+07\n",
       "6  2013-01-06  1.086893e+07\n",
       "7  2013-01-07  9.230439e+06\n",
       "8  2013-01-08  8.493609e+06\n",
       "9  2013-01-09  8.253537e+06"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "r = sales.groupby(\"date\")['xsales'].sum()\n",
    "r.printprettyXML()\n",
    "q = r.iloc[0]\n",
    "r"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0242e88",
   "metadata": {},
   "source": [
    "Transactions, total sales, and sales per transaction, by customer\n",
    "-----------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d5dea487",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<?xml version=\"1.0\" ?>\n",
      "<macro>\n",
      "\t<base table=\"retaildemo.retail.sales_detail\"/>\n",
      "\t<link table2=\"retaildemo.retail.customers\" col=\"customer_key\" type=\"exact\"/>\n",
      "\t<sel value=\"~(r_nacnt(customer_key;) >= 1)\"/>\n",
      "\t<tabu breaks=\"customer_key\">\n",
      "\t\t<tcol name=\"transid_count\" source=\"transid\" fun=\"valcnt\"/>\n",
      "\t\t<tcol name=\"xsales_sum\" source=\"xsales\" fun=\"sum\"/>\n",
      "\t</tabu>\n",
      "\t<willbe name=\"sales_per_trans\" value=\"((xsales_sum) / (transid_count))\"/>\n",
      "</macro>\n",
      "\n",
      "CPU times: user 40.5 ms, sys: 20.3 ms, total: 60.8 ms\n",
      "Wall time: 1.38 s\n"
     ]
    },
    {
     "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>customer_key</th>\n",
       "      <th>transid_count</th>\n",
       "      <th>xsales_sum</th>\n",
       "      <th>sales_per_trans</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>776.0</td>\n",
       "      <td>3268.82</td>\n",
       "      <td>4.212397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1131.0</td>\n",
       "      <td>2518.92</td>\n",
       "      <td>2.227162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1754.0</td>\n",
       "      <td>5529.91</td>\n",
       "      <td>3.152742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1721.0</td>\n",
       "      <td>5131.60</td>\n",
       "      <td>2.981755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2743.0</td>\n",
       "      <td>8399.06</td>\n",
       "      <td>3.061998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>1961.0</td>\n",
       "      <td>9066.47</td>\n",
       "      <td>4.623391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>153.0</td>\n",
       "      <td>1339.69</td>\n",
       "      <td>8.756144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>198.0</td>\n",
       "      <td>523.50</td>\n",
       "      <td>2.643939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1177.0</td>\n",
       "      <td>4034.91</td>\n",
       "      <td>3.428131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>618.0</td>\n",
       "      <td>1840.90</td>\n",
       "      <td>2.978803</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_key  transid_count  xsales_sum  sales_per_trans\n",
       "0             1          776.0     3268.82         4.212397\n",
       "1             2         1131.0     2518.92         2.227162\n",
       "2             3         1754.0     5529.91         3.152742\n",
       "3             4         1721.0     5131.60         2.981755\n",
       "4             5         2743.0     8399.06         3.061998\n",
       "5             6         1961.0     9066.47         4.623391\n",
       "6             7          153.0     1339.69         8.756144\n",
       "7             8          198.0      523.50         2.643939\n",
       "8             9         1177.0     4034.91         3.428131\n",
       "9            10          618.0     1840.90         2.978803"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "r = salescust.groupby(\"customer_key\").agg({\"transid\":\"count\", \"xsales\":\"sum\"})\n",
    "r['sales_per_trans'] = r['xsales_sum'] / r['transid_count']\n",
    "r.printprettyXML()\n",
    "q = r.iloc[0]\n",
    "r"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a6796ce",
   "metadata": {},
   "source": [
    "Charting examples\n",
    "-----------------------\n",
    " 1. Bar chart on total sales by gender.\n",
    " 2. Histogram on age.\n",
    " 3. Bar chart on number of transactions by age group.\n",
    " 4. Bar chart on total sales by age group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7361a514",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 952 ms, sys: 449 ms, total: 1.4 s\n",
      "Wall time: 1min 48s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age_group'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "salescust.groupby(\"gender\")['xsales'].sum().plot(kind=\"bar\", x=\"gender\", y=\"xsales_sum\")\n",
    "salescust[['age']].plot(kind=\"hist\")\n",
    "salescust.groupby(\"age_group\").transid.cnt().sort_values(\"age_group\").plot(kind=\"bar\", x=\"age_group\", y=\"transid_cnt\")\n",
    "salescust.groupby(\"age_group\").xsales.sum().sort_values(\"age_group\").plot(kind=\"bar\", x=\"age_group\", y=\"xsales_sum\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55e6541f",
   "metadata": {},
   "source": [
    "Now, a line graph of total sales by date, and a histogram of total sales by date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fd76d846",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 234 ms, sys: 13 ms, total: 247 ms\n",
      "Wall time: 3.81 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('', <Axes: >)]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time \n",
    "sales.groupby(\"date\").xsales.sum().plot(kind=\"line\", x=\"date\", y=\"xsales_sum\")\n",
    "sales.groupby(\"date\").xsales.sum()[['xsales_sum']].plot(kind=\"hist\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d40e9b74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 124 ms, sys: 7.26 ms, total: 132 ms\n",
      "Wall time: 1.74 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='date'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "# Once you've done the aggregation by date, the data is small enough\n",
    "# that you could just bring it into pandas and work with it directly.\n",
    "rr = sales[sales.store == 937].groupby(\"date\").xsales.sum()\n",
    "rr2 = rr.merge(sales[sales.store == 728].groupby(\"date\").xsales.sum(), on=\"date\")\n",
    "rr2.plot(kind=\"line\", x=\"date\", y=[\"xsales_sum\", \"c1\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75d12f8e",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}