{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with groups of rows of a data frame\n",
    "\n",
    "### Bogumił Kamiński"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "using DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "using CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "using Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "using FreqTables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "using Pipe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This part of the tutorial assumes that you have run *3. Working with text files* part so that you have auto2.csv file in your working directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 200)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ENV[\"LINES\"], ENV[\"COLUMNS\"] = 15, 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>mpg</th><th>cylinders</th><th>displacement</th><th>horsepower</th><th>weight</th><th>acceleration</th><th>year</th><th>origin</th><th>name</th><th>brand</th></tr><tr><th></th><th>Float64</th><th>Float64</th><th>Float64</th><th>Float64</th><th>Float64</th><th>Float64</th><th>Float64</th><th>Float64</th><th>String</th><th>String</th></tr></thead><tbody><p>392 rows × 10 columns</p><tr><th>1</th><td>18.0</td><td>8.0</td><td>307.0</td><td>130.0</td><td>3504.0</td><td>12.0</td><td>70.0</td><td>1.0</td><td>chevrolet chevelle malibu</td><td>chevrolet</td></tr><tr><th>2</th><td>15.0</td><td>8.0</td><td>350.0</td><td>165.0</td><td>3693.0</td><td>11.5</td><td>70.0</td><td>1.0</td><td>buick skylark 320</td><td>buick</td></tr><tr><th>3</th><td>18.0</td><td>8.0</td><td>318.0</td><td>150.0</td><td>3436.0</td><td>11.0</td><td>70.0</td><td>1.0</td><td>plymouth satellite</td><td>plymouth</td></tr><tr><th>4</th><td>16.0</td><td>8.0</td><td>304.0</td><td>150.0</td><td>3433.0</td><td>12.0</td><td>70.0</td><td>1.0</td><td>amc rebel sst</td><td>amc</td></tr><tr><th>5</th><td>17.0</td><td>8.0</td><td>302.0</td><td>140.0</td><td>3449.0</td><td>10.5</td><td>70.0</td><td>1.0</td><td>ford torino</td><td>ford</td></tr><tr><th>6</th><td>15.0</td><td>8.0</td><td>429.0</td><td>198.0</td><td>4341.0</td><td>10.0</td><td>70.0</td><td>1.0</td><td>ford galaxie 500</td><td>ford</td></tr><tr><th>7</th><td>14.0</td><td>8.0</td><td>454.0</td><td>220.0</td><td>4354.0</td><td>9.0</td><td>70.0</td><td>1.0</td><td>chevrolet impala</td><td>chevrolet</td></tr><tr><th>8</th><td>14.0</td><td>8.0</td><td>440.0</td><td>215.0</td><td>4312.0</td><td>8.5</td><td>70.0</td><td>1.0</td><td>plymouth fury iii</td><td>plymouth</td></tr><tr><th>9</th><td>14.0</td><td>8.0</td><td>455.0</td><td>225.0</td><td>4425.0</td><td>10.0</td><td>70.0</td><td>1.0</td><td>pontiac catalina</td><td>pontiac</td></tr><tr><th>10</th><td>15.0</td><td>8.0</td><td>390.0</td><td>190.0</td><td>3850.0</td><td>8.5</td><td>70.0</td><td>1.0</td><td>amc ambassador dpl</td><td>amc</td></tr><tr><th>11</th><td>15.0</td><td>8.0</td><td>383.0</td><td>170.0</td><td>3563.0</td><td>10.0</td><td>70.0</td><td>1.0</td><td>dodge challenger se</td><td>dodge</td></tr><tr><th>12</th><td>14.0</td><td>8.0</td><td>340.0</td><td>160.0</td><td>3609.0</td><td>8.0</td><td>70.0</td><td>1.0</td><td>plymouth 'cuda 340</td><td>plymouth</td></tr><tr><th>13</th><td>15.0</td><td>8.0</td><td>400.0</td><td>150.0</td><td>3761.0</td><td>9.5</td><td>70.0</td><td>1.0</td><td>chevrolet monte carlo</td><td>chevrolet</td></tr><tr><th>14</th><td>14.0</td><td>8.0</td><td>455.0</td><td>225.0</td><td>3086.0</td><td>10.0</td><td>70.0</td><td>1.0</td><td>buick estate wagon (sw)</td><td>buick</td></tr><tr><th>15</th><td>24.0</td><td>4.0</td><td>113.0</td><td>95.0</td><td>2372.0</td><td>15.0</td><td>70.0</td><td>3.0</td><td>toyota corona mark ii</td><td>toyota</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccccccccc}\n",
       "\t& mpg & cylinders & displacement & horsepower & weight & acceleration & year & origin & name & brand\\\\\n",
       "\t\\hline\n",
       "\t& Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & String & String\\\\\n",
       "\t\\hline\n",
       "\t1 & 18.0 & 8.0 & 307.0 & 130.0 & 3504.0 & 12.0 & 70.0 & 1.0 & chevrolet chevelle malibu & chevrolet \\\\\n",
       "\t2 & 15.0 & 8.0 & 350.0 & 165.0 & 3693.0 & 11.5 & 70.0 & 1.0 & buick skylark 320 & buick \\\\\n",
       "\t3 & 18.0 & 8.0 & 318.0 & 150.0 & 3436.0 & 11.0 & 70.0 & 1.0 & plymouth satellite & plymouth \\\\\n",
       "\t4 & 16.0 & 8.0 & 304.0 & 150.0 & 3433.0 & 12.0 & 70.0 & 1.0 & amc rebel sst & amc \\\\\n",
       "\t5 & 17.0 & 8.0 & 302.0 & 140.0 & 3449.0 & 10.5 & 70.0 & 1.0 & ford torino & ford \\\\\n",
       "\t6 & 15.0 & 8.0 & 429.0 & 198.0 & 4341.0 & 10.0 & 70.0 & 1.0 & ford galaxie 500 & ford \\\\\n",
       "\t7 & 14.0 & 8.0 & 454.0 & 220.0 & 4354.0 & 9.0 & 70.0 & 1.0 & chevrolet impala & chevrolet \\\\\n",
       "\t8 & 14.0 & 8.0 & 440.0 & 215.0 & 4312.0 & 8.5 & 70.0 & 1.0 & plymouth fury iii & plymouth \\\\\n",
       "\t9 & 14.0 & 8.0 & 455.0 & 225.0 & 4425.0 & 10.0 & 70.0 & 1.0 & pontiac catalina & pontiac \\\\\n",
       "\t10 & 15.0 & 8.0 & 390.0 & 190.0 & 3850.0 & 8.5 & 70.0 & 1.0 & amc ambassador dpl & amc \\\\\n",
       "\t11 & 15.0 & 8.0 & 383.0 & 170.0 & 3563.0 & 10.0 & 70.0 & 1.0 & dodge challenger se & dodge \\\\\n",
       "\t12 & 14.0 & 8.0 & 340.0 & 160.0 & 3609.0 & 8.0 & 70.0 & 1.0 & plymouth 'cuda 340 & plymouth \\\\\n",
       "\t13 & 15.0 & 8.0 & 400.0 & 150.0 & 3761.0 & 9.5 & 70.0 & 1.0 & chevrolet monte carlo & chevrolet \\\\\n",
       "\t14 & 14.0 & 8.0 & 455.0 & 225.0 & 3086.0 & 10.0 & 70.0 & 1.0 & buick estate wagon (sw) & buick \\\\\n",
       "\t15 & 24.0 & 4.0 & 113.0 & 95.0 & 2372.0 & 15.0 & 70.0 & 3.0 & toyota corona mark ii & toyota \\\\\n",
       "\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "392×10 DataFrame\n",
       "│ Row │ mpg     │ cylinders │ displacement │ horsepower │ weight  │ acceleration │ year    │ origin  │ name                      │ brand     │\n",
       "│     │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m   │ \u001b[90mFloat64\u001b[39m      │ \u001b[90mFloat64\u001b[39m    │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m      │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m │ \u001b[90mString\u001b[39m                    │ \u001b[90mString\u001b[39m    │\n",
       "├─────┼─────────┼───────────┼──────────────┼────────────┼─────────┼──────────────┼─────────┼─────────┼───────────────────────────┼───────────┤\n",
       "│ 1   │ 18.0    │ 8.0       │ 307.0        │ 130.0      │ 3504.0  │ 12.0         │ 70.0    │ 1.0     │ chevrolet chevelle malibu │ chevrolet │\n",
       "│ 2   │ 15.0    │ 8.0       │ 350.0        │ 165.0      │ 3693.0  │ 11.5         │ 70.0    │ 1.0     │ buick skylark 320         │ buick     │\n",
       "│ 3   │ 18.0    │ 8.0       │ 318.0        │ 150.0      │ 3436.0  │ 11.0         │ 70.0    │ 1.0     │ plymouth satellite        │ plymouth  │\n",
       "⋮\n",
       "│ 389 │ 44.0    │ 4.0       │ 97.0         │ 52.0       │ 2130.0  │ 24.6         │ 82.0    │ 2.0     │ vw pickup                 │ vw        │\n",
       "│ 390 │ 32.0    │ 4.0       │ 135.0        │ 84.0       │ 2295.0  │ 11.6         │ 82.0    │ 1.0     │ dodge rampage             │ dodge     │\n",
       "│ 391 │ 28.0    │ 4.0       │ 120.0        │ 79.0       │ 2625.0  │ 18.6         │ 82.0    │ 1.0     │ ford ranger               │ ford      │\n",
       "│ 392 │ 31.0    │ 4.0       │ 119.0        │ 82.0       │ 2720.0  │ 19.4         │ 82.0    │ 1.0     │ chevy s-10                │ chevy     │"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = CSV.File(\"auto2.csv\") |> DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to group our data frame by `:brand` column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p><b>GroupedDataFrame with 37 groups based on key: brand</b></p><p><i>First Group (43 rows): brand = \"chevrolet\"</i></p><table class=\"data-frame\"><thead><tr><th></th><th>mpg</th><th>cylinders</th><th>displacement</th><th>horsepower</th><th>weight</th><th>acceleration</th><th>year</th><th>origin</th><th>name</th><th>brand</th></tr><tr><th></th><th>Float64</th><th>Float64</th><th>Float64</th><th>Float64</th><th>Float64</th><th>Float64</th><th>Float64</th><th>Float64</th><th>String</th><th>String</th></tr></thead><tbody><tr><th>1</th><td>18.0</td><td>8.0</td><td>307.0</td><td>130.0</td><td>3504.0</td><td>12.0</td><td>70.0</td><td>1.0</td><td>chevrolet chevelle malibu</td><td>chevrolet</td></tr><tr><th>2</th><td>14.0</td><td>8.0</td><td>454.0</td><td>220.0</td><td>4354.0</td><td>9.0</td><td>70.0</td><td>1.0</td><td>chevrolet impala</td><td>chevrolet</td></tr><tr><th>3</th><td>15.0</td><td>8.0</td><td>400.0</td><td>150.0</td><td>3761.0</td><td>9.5</td><td>70.0</td><td>1.0</td><td>chevrolet monte carlo</td><td>chevrolet</td></tr><tr><th>4</th><td>28.0</td><td>4.0</td><td>140.0</td><td>90.0</td><td>2264.0</td><td>15.5</td><td>71.0</td><td>1.0</td><td>chevrolet vega 2300</td><td>chevrolet</td></tr><tr><th>5</th><td>17.0</td><td>6.0</td><td>250.0</td><td>100.0</td><td>3329.0</td><td>15.5</td><td>71.0</td><td>1.0</td><td>chevrolet chevelle malibu</td><td>chevrolet</td></tr><tr><th>6</th><td>14.0</td><td>8.0</td><td>350.0</td><td>165.0</td><td>4209.0</td><td>12.0</td><td>71.0</td><td>1.0</td><td>chevrolet impala</td><td>chevrolet</td></tr><tr><th>7</th><td>22.0</td><td>4.0</td><td>140.0</td><td>72.0</td><td>2408.0</td><td>19.0</td><td>71.0</td><td>1.0</td><td>chevrolet vega (sw)</td><td>chevrolet</td></tr><tr><th>8</th><td>20.0</td><td>4.0</td><td>140.0</td><td>90.0</td><td>2408.0</td><td>19.5</td><td>72.0</td><td>1.0</td><td>chevrolet vega</td><td>chevrolet</td></tr><tr><th>9</th><td>13.0</td><td>8.0</td><td>350.0</td><td>165.0</td><td>4274.0</td><td>12.0</td><td>72.0</td><td>1.0</td><td>chevrolet impala</td><td>chevrolet</td></tr><tr><th>10</th><td>13.0</td><td>8.0</td><td>307.0</td><td>130.0</td><td>4098.0</td><td>14.0</td><td>72.0</td><td>1.0</td><td>chevrolet chevelle concours (sw)</td><td>chevrolet</td></tr><tr><th>11</th><td>13.0</td><td>8.0</td><td>350.0</td><td>145.0</td><td>3988.0</td><td>13.0</td><td>73.0</td><td>1.0</td><td>chevrolet malibu</td><td>chevrolet</td></tr><tr><th>12</th><td>13.0</td><td>8.0</td><td>400.0</td><td>150.0</td><td>4464.0</td><td>12.0</td><td>73.0</td><td>1.0</td><td>chevrolet caprice classic</td><td>chevrolet</td></tr><tr><th>13</th><td>16.0</td><td>6.0</td><td>250.0</td><td>100.0</td><td>3278.0</td><td>18.0</td><td>73.0</td><td>1.0</td><td>chevrolet nova custom</td><td>chevrolet</td></tr><tr><th>14</th><td>11.0</td><td>8.0</td><td>400.0</td><td>150.0</td><td>4997.0</td><td>14.0</td><td>73.0</td><td>1.0</td><td>chevrolet impala</td><td>chevrolet</td></tr><tr><th>15</th><td>21.0</td><td>4.0</td><td>140.0</td><td>72.0</td><td>2401.0</td><td>19.5</td><td>73.0</td><td>1.0</td><td>chevrolet vega</td><td>chevrolet</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td></tr></tbody></table><p>&vellip;</p><p><i>Last Group (1 row): brand = \"nissan\"</i></p><table class=\"data-frame\"><thead><tr><th></th><th>mpg</th><th>cylinders</th><th>displacement</th><th>horsepower</th><th>weight</th><th>acceleration</th><th>year</th><th>origin</th><th>name</th><th>brand</th></tr><tr><th></th><th>Float64</th><th>Float64</th><th>Float64</th><th>Float64</th><th>Float64</th><th>Float64</th><th>Float64</th><th>Float64</th><th>String</th><th>String</th></tr></thead><tbody><tr><th>1</th><td>36.0</td><td>4.0</td><td>120.0</td><td>88.0</td><td>2160.0</td><td>14.5</td><td>82.0</td><td>3.0</td><td>nissan stanza xe</td><td>nissan</td></tr></tbody></table>"
      ],
      "text/latex": [
       "GroupedDataFrame with 37 groups based on key: brand\n",
       "\n",
       "First Group (43 rows): brand = \"chevrolet\"\n",
       "\n",
       "\\begin{tabular}{r|cccccccccc}\n",
       "\t& mpg & cylinders & displacement & horsepower & weight & acceleration & year & origin & name & brand\\\\\n",
       "\t\\hline\n",
       "\t& Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & String & String\\\\\n",
       "\t\\hline\n",
       "\t1 & 18.0 & 8.0 & 307.0 & 130.0 & 3504.0 & 12.0 & 70.0 & 1.0 & chevrolet chevelle malibu & chevrolet \\\\\n",
       "\t2 & 14.0 & 8.0 & 454.0 & 220.0 & 4354.0 & 9.0 & 70.0 & 1.0 & chevrolet impala & chevrolet \\\\\n",
       "\t3 & 15.0 & 8.0 & 400.0 & 150.0 & 3761.0 & 9.5 & 70.0 & 1.0 & chevrolet monte carlo & chevrolet \\\\\n",
       "\t4 & 28.0 & 4.0 & 140.0 & 90.0 & 2264.0 & 15.5 & 71.0 & 1.0 & chevrolet vega 2300 & chevrolet \\\\\n",
       "\t5 & 17.0 & 6.0 & 250.0 & 100.0 & 3329.0 & 15.5 & 71.0 & 1.0 & chevrolet chevelle malibu & chevrolet \\\\\n",
       "\t6 & 14.0 & 8.0 & 350.0 & 165.0 & 4209.0 & 12.0 & 71.0 & 1.0 & chevrolet impala & chevrolet \\\\\n",
       "\t7 & 22.0 & 4.0 & 140.0 & 72.0 & 2408.0 & 19.0 & 71.0 & 1.0 & chevrolet vega (sw) & chevrolet \\\\\n",
       "\t8 & 20.0 & 4.0 & 140.0 & 90.0 & 2408.0 & 19.5 & 72.0 & 1.0 & chevrolet vega & chevrolet \\\\\n",
       "\t9 & 13.0 & 8.0 & 350.0 & 165.0 & 4274.0 & 12.0 & 72.0 & 1.0 & chevrolet impala & chevrolet \\\\\n",
       "\t10 & 13.0 & 8.0 & 307.0 & 130.0 & 4098.0 & 14.0 & 72.0 & 1.0 & chevrolet chevelle concours (sw) & chevrolet \\\\\n",
       "\t11 & 13.0 & 8.0 & 350.0 & 145.0 & 3988.0 & 13.0 & 73.0 & 1.0 & chevrolet malibu & chevrolet \\\\\n",
       "\t12 & 13.0 & 8.0 & 400.0 & 150.0 & 4464.0 & 12.0 & 73.0 & 1.0 & chevrolet caprice classic & chevrolet \\\\\n",
       "\t13 & 16.0 & 6.0 & 250.0 & 100.0 & 3278.0 & 18.0 & 73.0 & 1.0 & chevrolet nova custom & chevrolet \\\\\n",
       "\t14 & 11.0 & 8.0 & 400.0 & 150.0 & 4997.0 & 14.0 & 73.0 & 1.0 & chevrolet impala & chevrolet \\\\\n",
       "\t15 & 21.0 & 4.0 & 140.0 & 72.0 & 2401.0 & 19.5 & 73.0 & 1.0 & chevrolet vega & chevrolet \\\\\n",
       "\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ \\\\\n",
       "\\end{tabular}\n",
       "\n",
       "$\\dots$\n",
       "\n",
       "Last Group (1 row): brand = \"nissan\"\n",
       "\n",
       "\\begin{tabular}{r|cccccccccc}\n",
       "\t& mpg & cylinders & displacement & horsepower & weight & acceleration & year & origin & name & brand\\\\\n",
       "\t\\hline\n",
       "\t& Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & String & String\\\\\n",
       "\t\\hline\n",
       "\t1 & 36.0 & 4.0 & 120.0 & 88.0 & 2160.0 & 14.5 & 82.0 & 3.0 & nissan stanza xe & nissan \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "GroupedDataFrame with 37 groups based on key: brand\n",
       "First Group (43 rows): brand = \"chevrolet\"\n",
       "│ Row │ mpg     │ cylinders │ displacement │ horsepower │ weight  │ acceleration │ year    │ origin  │ name                      │ brand     │\n",
       "│     │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m   │ \u001b[90mFloat64\u001b[39m      │ \u001b[90mFloat64\u001b[39m    │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m      │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m │ \u001b[90mString\u001b[39m                    │ \u001b[90mString\u001b[39m    │\n",
       "├─────┼─────────┼───────────┼──────────────┼────────────┼─────────┼──────────────┼─────────┼─────────┼───────────────────────────┼───────────┤\n",
       "│ 1   │ 18.0    │ 8.0       │ 307.0        │ 130.0      │ 3504.0  │ 12.0         │ 70.0    │ 1.0     │ chevrolet chevelle malibu │ chevrolet │\n",
       "│ 2   │ 14.0    │ 8.0       │ 454.0        │ 220.0      │ 4354.0  │ 9.0          │ 70.0    │ 1.0     │ chevrolet impala          │ chevrolet │\n",
       "│ 3   │ 15.0    │ 8.0       │ 400.0        │ 150.0      │ 3761.0  │ 9.5          │ 70.0    │ 1.0     │ chevrolet monte carlo     │ chevrolet │\n",
       "⋮\n",
       "│ 40  │ 28.0    │ 4.0       │ 112.0        │ 88.0       │ 2605.0  │ 19.6         │ 82.0    │ 1.0     │ chevrolet cavalier        │ chevrolet │\n",
       "│ 41  │ 27.0    │ 4.0       │ 112.0        │ 88.0       │ 2640.0  │ 18.6         │ 82.0    │ 1.0     │ chevrolet cavalier wagon  │ chevrolet │\n",
       "│ 42  │ 34.0    │ 4.0       │ 112.0        │ 88.0       │ 2395.0  │ 18.0         │ 82.0    │ 1.0     │ chevrolet cavalier 2-door │ chevrolet │\n",
       "│ 43  │ 27.0    │ 4.0       │ 151.0        │ 90.0       │ 2950.0  │ 17.3         │ 82.0    │ 1.0     │ chevrolet camaro          │ chevrolet │\n",
       "⋮\n",
       "Last Group (1 row): brand = \"nissan\"\n",
       "│ Row │ mpg     │ cylinders │ displacement │ horsepower │ weight  │ acceleration │ year    │ origin  │ name             │ brand  │\n",
       "│     │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m   │ \u001b[90mFloat64\u001b[39m      │ \u001b[90mFloat64\u001b[39m    │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m      │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m │ \u001b[90mString\u001b[39m           │ \u001b[90mString\u001b[39m │\n",
       "├─────┼─────────┼───────────┼──────────────┼────────────┼─────────┼──────────────┼─────────┼─────────┼──────────────────┼────────┤\n",
       "│ 1   │ 36.0    │ 4.0       │ 120.0        │ 88.0       │ 2160.0  │ 14.5         │ 82.0    │ 3.0     │ nissan stanza xe │ nissan │"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf = groupby(df, :brand)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that it is easy (and fast) to find an appropriate group by passing a `Tuple` of values of grouping columns (in our case it is just one column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>mpg</th><th>cylinders</th><th>displacement</th><th>horsepower</th><th>weight</th><th>acceleration</th><th>year</th><th>origin</th><th>name</th><th>brand</th></tr><tr><th></th><th>Float64</th><th>Float64</th><th>Float64</th><th>Float64</th><th>Float64</th><th>Float64</th><th>Float64</th><th>Float64</th><th>String</th><th>String</th></tr></thead><tbody><p>48 rows × 10 columns</p><tr><th>1</th><td>17.0</td><td>8.0</td><td>302.0</td><td>140.0</td><td>3449.0</td><td>10.5</td><td>70.0</td><td>1.0</td><td>ford torino</td><td>ford</td></tr><tr><th>2</th><td>15.0</td><td>8.0</td><td>429.0</td><td>198.0</td><td>4341.0</td><td>10.0</td><td>70.0</td><td>1.0</td><td>ford galaxie 500</td><td>ford</td></tr><tr><th>3</th><td>21.0</td><td>6.0</td><td>200.0</td><td>85.0</td><td>2587.0</td><td>16.0</td><td>70.0</td><td>1.0</td><td>ford maverick</td><td>ford</td></tr><tr><th>4</th><td>10.0</td><td>8.0</td><td>360.0</td><td>215.0</td><td>4615.0</td><td>14.0</td><td>70.0</td><td>1.0</td><td>ford f250</td><td>ford</td></tr><tr><th>5</th><td>19.0</td><td>6.0</td><td>250.0</td><td>88.0</td><td>3302.0</td><td>15.5</td><td>71.0</td><td>1.0</td><td>ford torino 500</td><td>ford</td></tr><tr><th>6</th><td>14.0</td><td>8.0</td><td>351.0</td><td>153.0</td><td>4154.0</td><td>13.5</td><td>71.0</td><td>1.0</td><td>ford galaxie 500</td><td>ford</td></tr><tr><th>7</th><td>13.0</td><td>8.0</td><td>400.0</td><td>170.0</td><td>4746.0</td><td>12.0</td><td>71.0</td><td>1.0</td><td>ford country squire (sw)</td><td>ford</td></tr><tr><th>8</th><td>18.0</td><td>6.0</td><td>250.0</td><td>88.0</td><td>3139.0</td><td>14.5</td><td>71.0</td><td>1.0</td><td>ford mustang</td><td>ford</td></tr><tr><th>9</th><td>21.0</td><td>4.0</td><td>122.0</td><td>86.0</td><td>2226.0</td><td>16.5</td><td>72.0</td><td>1.0</td><td>ford pinto runabout</td><td>ford</td></tr><tr><th>10</th><td>14.0</td><td>8.0</td><td>351.0</td><td>153.0</td><td>4129.0</td><td>13.0</td><td>72.0</td><td>1.0</td><td>ford galaxie 500</td><td>ford</td></tr><tr><th>11</th><td>13.0</td><td>8.0</td><td>302.0</td><td>140.0</td><td>4294.0</td><td>16.0</td><td>72.0</td><td>1.0</td><td>ford gran torino (sw)</td><td>ford</td></tr><tr><th>12</th><td>22.0</td><td>4.0</td><td>122.0</td><td>86.0</td><td>2395.0</td><td>16.0</td><td>72.0</td><td>1.0</td><td>ford pinto (sw)</td><td>ford</td></tr><tr><th>13</th><td>14.0</td><td>8.0</td><td>302.0</td><td>137.0</td><td>4042.0</td><td>14.5</td><td>73.0</td><td>1.0</td><td>ford gran torino</td><td>ford</td></tr><tr><th>14</th><td>13.0</td><td>8.0</td><td>351.0</td><td>158.0</td><td>4363.0</td><td>13.0</td><td>73.0</td><td>1.0</td><td>ford ltd</td><td>ford</td></tr><tr><th>15</th><td>18.0</td><td>6.0</td><td>250.0</td><td>88.0</td><td>3021.0</td><td>16.5</td><td>73.0</td><td>1.0</td><td>ford maverick</td><td>ford</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccccccccc}\n",
       "\t& mpg & cylinders & displacement & horsepower & weight & acceleration & year & origin & name & brand\\\\\n",
       "\t\\hline\n",
       "\t& Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & String & String\\\\\n",
       "\t\\hline\n",
       "\t1 & 17.0 & 8.0 & 302.0 & 140.0 & 3449.0 & 10.5 & 70.0 & 1.0 & ford torino & ford \\\\\n",
       "\t2 & 15.0 & 8.0 & 429.0 & 198.0 & 4341.0 & 10.0 & 70.0 & 1.0 & ford galaxie 500 & ford \\\\\n",
       "\t3 & 21.0 & 6.0 & 200.0 & 85.0 & 2587.0 & 16.0 & 70.0 & 1.0 & ford maverick & ford \\\\\n",
       "\t4 & 10.0 & 8.0 & 360.0 & 215.0 & 4615.0 & 14.0 & 70.0 & 1.0 & ford f250 & ford \\\\\n",
       "\t5 & 19.0 & 6.0 & 250.0 & 88.0 & 3302.0 & 15.5 & 71.0 & 1.0 & ford torino 500 & ford \\\\\n",
       "\t6 & 14.0 & 8.0 & 351.0 & 153.0 & 4154.0 & 13.5 & 71.0 & 1.0 & ford galaxie 500 & ford \\\\\n",
       "\t7 & 13.0 & 8.0 & 400.0 & 170.0 & 4746.0 & 12.0 & 71.0 & 1.0 & ford country squire (sw) & ford \\\\\n",
       "\t8 & 18.0 & 6.0 & 250.0 & 88.0 & 3139.0 & 14.5 & 71.0 & 1.0 & ford mustang & ford \\\\\n",
       "\t9 & 21.0 & 4.0 & 122.0 & 86.0 & 2226.0 & 16.5 & 72.0 & 1.0 & ford pinto runabout & ford \\\\\n",
       "\t10 & 14.0 & 8.0 & 351.0 & 153.0 & 4129.0 & 13.0 & 72.0 & 1.0 & ford galaxie 500 & ford \\\\\n",
       "\t11 & 13.0 & 8.0 & 302.0 & 140.0 & 4294.0 & 16.0 & 72.0 & 1.0 & ford gran torino (sw) & ford \\\\\n",
       "\t12 & 22.0 & 4.0 & 122.0 & 86.0 & 2395.0 & 16.0 & 72.0 & 1.0 & ford pinto (sw) & ford \\\\\n",
       "\t13 & 14.0 & 8.0 & 302.0 & 137.0 & 4042.0 & 14.5 & 73.0 & 1.0 & ford gran torino & ford \\\\\n",
       "\t14 & 13.0 & 8.0 & 351.0 & 158.0 & 4363.0 & 13.0 & 73.0 & 1.0 & ford ltd & ford \\\\\n",
       "\t15 & 18.0 & 6.0 & 250.0 & 88.0 & 3021.0 & 16.5 & 73.0 & 1.0 & ford maverick & ford \\\\\n",
       "\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "48×10 SubDataFrame\n",
       "│ Row │ mpg     │ cylinders │ displacement │ horsepower │ weight  │ acceleration │ year    │ origin  │ name                 │ brand  │\n",
       "│     │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m   │ \u001b[90mFloat64\u001b[39m      │ \u001b[90mFloat64\u001b[39m    │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m      │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m │ \u001b[90mString\u001b[39m               │ \u001b[90mString\u001b[39m │\n",
       "├─────┼─────────┼───────────┼──────────────┼────────────┼─────────┼──────────────┼─────────┼─────────┼──────────────────────┼────────┤\n",
       "│ 1   │ 17.0    │ 8.0       │ 302.0        │ 140.0      │ 3449.0  │ 10.5         │ 70.0    │ 1.0     │ ford torino          │ ford   │\n",
       "│ 2   │ 15.0    │ 8.0       │ 429.0        │ 198.0      │ 4341.0  │ 10.0         │ 70.0    │ 1.0     │ ford galaxie 500     │ ford   │\n",
       "│ 3   │ 21.0    │ 6.0       │ 200.0        │ 85.0       │ 2587.0  │ 16.0         │ 70.0    │ 1.0     │ ford maverick        │ ford   │\n",
       "⋮\n",
       "│ 45  │ 24.0    │ 4.0       │ 140.0        │ 92.0       │ 2865.0  │ 16.4         │ 82.0    │ 1.0     │ ford fairmont futura │ ford   │\n",
       "│ 46  │ 22.0    │ 6.0       │ 232.0        │ 112.0      │ 2835.0  │ 14.7         │ 82.0    │ 1.0     │ ford granada l       │ ford   │\n",
       "│ 47  │ 27.0    │ 4.0       │ 140.0        │ 86.0       │ 2790.0  │ 15.6         │ 82.0    │ 1.0     │ ford mustang gl      │ ford   │\n",
       "│ 48  │ 28.0    │ 4.0       │ 120.0        │ 79.0       │ 2625.0  │ 18.6         │ 82.0    │ 1.0     │ ford ranger          │ ford   │"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf[(\"ford\",)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `combine` function we can easily calculate some aggregates by group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>brand</th><th>mpg_mean</th></tr><tr><th></th><th>String</th><th>Float64</th></tr></thead><tbody><p>37 rows × 2 columns</p><tr><th>1</th><td>chevrolet</td><td>20.4721</td></tr><tr><th>2</th><td>buick</td><td>19.1824</td></tr><tr><th>3</th><td>plymouth</td><td>21.7032</td></tr><tr><th>4</th><td>amc</td><td>18.0704</td></tr><tr><th>5</th><td>ford</td><td>19.475</td></tr><tr><th>6</th><td>pontiac</td><td>20.0125</td></tr><tr><th>7</th><td>dodge</td><td>22.0607</td></tr><tr><th>8</th><td>toyota</td><td>28.372</td></tr><tr><th>9</th><td>datsun</td><td>31.113</td></tr><tr><th>10</th><td>volkswagen</td><td>29.1067</td></tr><tr><th>11</th><td>peugeot</td><td>23.6875</td></tr><tr><th>12</th><td>audi</td><td>26.7143</td></tr><tr><th>13</th><td>saab</td><td>23.9</td></tr><tr><th>14</th><td>bmw</td><td>23.75</td></tr><tr><th>15</th><td>chevy</td><td>18.0</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& brand & mpg\\_mean\\\\\n",
       "\t\\hline\n",
       "\t& String & Float64\\\\\n",
       "\t\\hline\n",
       "\t1 & chevrolet & 20.4721 \\\\\n",
       "\t2 & buick & 19.1824 \\\\\n",
       "\t3 & plymouth & 21.7032 \\\\\n",
       "\t4 & amc & 18.0704 \\\\\n",
       "\t5 & ford & 19.475 \\\\\n",
       "\t6 & pontiac & 20.0125 \\\\\n",
       "\t7 & dodge & 22.0607 \\\\\n",
       "\t8 & toyota & 28.372 \\\\\n",
       "\t9 & datsun & 31.113 \\\\\n",
       "\t10 & volkswagen & 29.1067 \\\\\n",
       "\t11 & peugeot & 23.6875 \\\\\n",
       "\t12 & audi & 26.7143 \\\\\n",
       "\t13 & saab & 23.9 \\\\\n",
       "\t14 & bmw & 23.75 \\\\\n",
       "\t15 & chevy & 18.0 \\\\\n",
       "\t$\\dots$ & $\\dots$ & $\\dots$ \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "37×2 DataFrame\n",
       "│ Row │ brand     │ mpg_mean │\n",
       "│     │ \u001b[90mString\u001b[39m    │ \u001b[90mFloat64\u001b[39m  │\n",
       "├─────┼───────────┼──────────┤\n",
       "│ 1   │ chevrolet │ 20.4721  │\n",
       "│ 2   │ buick     │ 19.1824  │\n",
       "│ 3   │ plymouth  │ 21.7032  │\n",
       "⋮\n",
       "│ 34  │ mercedes  │ 25.4     │\n",
       "│ 35  │ vokswagen │ 29.8     │\n",
       "│ 36  │ triumph   │ 35.0     │\n",
       "│ 37  │ nissan    │ 36.0     │"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brand_mpg = combine(gdf, :mpg => mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `:mpg => mean` syntax means that we pass a column `:mpg` to be processed by the `mean` function for each group.\n",
    "\n",
    "We could have added a target column name to the generated column in the following way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>brand</th><th>mean_mpg</th></tr><tr><th></th><th>String</th><th>Float64</th></tr></thead><tbody><p>37 rows × 2 columns</p><tr><th>1</th><td>chevrolet</td><td>20.4721</td></tr><tr><th>2</th><td>buick</td><td>19.1824</td></tr><tr><th>3</th><td>plymouth</td><td>21.7032</td></tr><tr><th>4</th><td>amc</td><td>18.0704</td></tr><tr><th>5</th><td>ford</td><td>19.475</td></tr><tr><th>6</th><td>pontiac</td><td>20.0125</td></tr><tr><th>7</th><td>dodge</td><td>22.0607</td></tr><tr><th>8</th><td>toyota</td><td>28.372</td></tr><tr><th>9</th><td>datsun</td><td>31.113</td></tr><tr><th>10</th><td>volkswagen</td><td>29.1067</td></tr><tr><th>11</th><td>peugeot</td><td>23.6875</td></tr><tr><th>12</th><td>audi</td><td>26.7143</td></tr><tr><th>13</th><td>saab</td><td>23.9</td></tr><tr><th>14</th><td>bmw</td><td>23.75</td></tr><tr><th>15</th><td>chevy</td><td>18.0</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& brand & mean\\_mpg\\\\\n",
       "\t\\hline\n",
       "\t& String & Float64\\\\\n",
       "\t\\hline\n",
       "\t1 & chevrolet & 20.4721 \\\\\n",
       "\t2 & buick & 19.1824 \\\\\n",
       "\t3 & plymouth & 21.7032 \\\\\n",
       "\t4 & amc & 18.0704 \\\\\n",
       "\t5 & ford & 19.475 \\\\\n",
       "\t6 & pontiac & 20.0125 \\\\\n",
       "\t7 & dodge & 22.0607 \\\\\n",
       "\t8 & toyota & 28.372 \\\\\n",
       "\t9 & datsun & 31.113 \\\\\n",
       "\t10 & volkswagen & 29.1067 \\\\\n",
       "\t11 & peugeot & 23.6875 \\\\\n",
       "\t12 & audi & 26.7143 \\\\\n",
       "\t13 & saab & 23.9 \\\\\n",
       "\t14 & bmw & 23.75 \\\\\n",
       "\t15 & chevy & 18.0 \\\\\n",
       "\t$\\dots$ & $\\dots$ & $\\dots$ \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "37×2 DataFrame\n",
       "│ Row │ brand     │ mean_mpg │\n",
       "│     │ \u001b[90mString\u001b[39m    │ \u001b[90mFloat64\u001b[39m  │\n",
       "├─────┼───────────┼──────────┤\n",
       "│ 1   │ chevrolet │ 20.4721  │\n",
       "│ 2   │ buick     │ 19.1824  │\n",
       "│ 3   │ plymouth  │ 21.7032  │\n",
       "⋮\n",
       "│ 34  │ mercedes  │ 25.4     │\n",
       "│ 35  │ vokswagen │ 29.8     │\n",
       "│ 36  │ triumph   │ 35.0     │\n",
       "│ 37  │ nissan    │ 36.0     │"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine(gdf, :mpg => mean => :mean_mpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us see how one can sort a data frame. In order to see alll rows of a data frame we temporarily change the number of rows printed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ENV[\"LINES\"] = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>brand</th><th>mpg_mean</th></tr><tr><th></th><th>String</th><th>Float64</th></tr></thead><tbody><p>37 rows × 2 columns</p><tr><th>1</th><td>vw</td><td>39.0167</td></tr><tr><th>2</th><td>nissan</td><td>36.0</td></tr><tr><th>3</th><td>triumph</td><td>35.0</td></tr><tr><th>4</th><td>honda</td><td>33.7615</td></tr><tr><th>5</th><td>datsun</td><td>31.113</td></tr><tr><th>6</th><td>mazda</td><td>30.86</td></tr><tr><th>7</th><td>subaru</td><td>30.525</td></tr><tr><th>8</th><td>vokswagen</td><td>29.8</td></tr><tr><th>9</th><td>renault</td><td>29.6667</td></tr><tr><th>10</th><td>volkswagen</td><td>29.1067</td></tr><tr><th>11</th><td>fiat</td><td>28.9125</td></tr><tr><th>12</th><td>toyota</td><td>28.372</td></tr><tr><th>13</th><td>audi</td><td>26.7143</td></tr><tr><th>14</th><td>maxda</td><td>26.05</td></tr><tr><th>15</th><td>opel</td><td>25.75</td></tr><tr><th>16</th><td>mercedes</td><td>25.4</td></tr><tr><th>17</th><td>capri</td><td>25.0</td></tr><tr><th>18</th><td>saab</td><td>23.9</td></tr><tr><th>19</th><td>bmw</td><td>23.75</td></tr><tr><th>20</th><td>peugeot</td><td>23.6875</td></tr><tr><th>21</th><td>mercedes-benz</td><td>23.25</td></tr><tr><th>22</th><td>toyouta</td><td>23.0</td></tr><tr><th>23</th><td>dodge</td><td>22.0607</td></tr><tr><th>24</th><td>plymouth</td><td>21.7032</td></tr><tr><th>25</th><td>volvo</td><td>21.1167</td></tr><tr><th>26</th><td>oldsmobile</td><td>21.1</td></tr><tr><th>27</th><td>chevrolet</td><td>20.4721</td></tr><tr><th>28</th><td>pontiac</td><td>20.0125</td></tr><tr><th>29</th><td>cadillac</td><td>19.75</td></tr><tr><th>30</th><td>ford</td><td>19.475</td></tr><tr><th>31</th><td>buick</td><td>19.1824</td></tr><tr><th>32</th><td>mercury</td><td>19.1182</td></tr><tr><th>33</th><td>amc</td><td>18.0704</td></tr><tr><th>34</th><td>chevy</td><td>18.0</td></tr><tr><th>35</th><td>chrysler</td><td>17.2667</td></tr><tr><th>36</th><td>chevroelt</td><td>16.0</td></tr><tr><th>37</th><td>hi</td><td>9.0</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& brand & mpg\\_mean\\\\\n",
       "\t\\hline\n",
       "\t& String & Float64\\\\\n",
       "\t\\hline\n",
       "\t1 & vw & 39.0167 \\\\\n",
       "\t2 & nissan & 36.0 \\\\\n",
       "\t3 & triumph & 35.0 \\\\\n",
       "\t4 & honda & 33.7615 \\\\\n",
       "\t5 & datsun & 31.113 \\\\\n",
       "\t6 & mazda & 30.86 \\\\\n",
       "\t7 & subaru & 30.525 \\\\\n",
       "\t8 & vokswagen & 29.8 \\\\\n",
       "\t9 & renault & 29.6667 \\\\\n",
       "\t10 & volkswagen & 29.1067 \\\\\n",
       "\t11 & fiat & 28.9125 \\\\\n",
       "\t12 & toyota & 28.372 \\\\\n",
       "\t13 & audi & 26.7143 \\\\\n",
       "\t14 & maxda & 26.05 \\\\\n",
       "\t15 & opel & 25.75 \\\\\n",
       "\t16 & mercedes & 25.4 \\\\\n",
       "\t17 & capri & 25.0 \\\\\n",
       "\t18 & saab & 23.9 \\\\\n",
       "\t19 & bmw & 23.75 \\\\\n",
       "\t20 & peugeot & 23.6875 \\\\\n",
       "\t21 & mercedes-benz & 23.25 \\\\\n",
       "\t22 & toyouta & 23.0 \\\\\n",
       "\t23 & dodge & 22.0607 \\\\\n",
       "\t24 & plymouth & 21.7032 \\\\\n",
       "\t25 & volvo & 21.1167 \\\\\n",
       "\t26 & oldsmobile & 21.1 \\\\\n",
       "\t27 & chevrolet & 20.4721 \\\\\n",
       "\t28 & pontiac & 20.0125 \\\\\n",
       "\t29 & cadillac & 19.75 \\\\\n",
       "\t30 & ford & 19.475 \\\\\n",
       "\t31 & buick & 19.1824 \\\\\n",
       "\t32 & mercury & 19.1182 \\\\\n",
       "\t33 & amc & 18.0704 \\\\\n",
       "\t34 & chevy & 18.0 \\\\\n",
       "\t35 & chrysler & 17.2667 \\\\\n",
       "\t36 & chevroelt & 16.0 \\\\\n",
       "\t37 & hi & 9.0 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "37×2 DataFrame\n",
       "│ Row │ brand         │ mpg_mean │\n",
       "│     │ \u001b[90mString\u001b[39m        │ \u001b[90mFloat64\u001b[39m  │\n",
       "├─────┼───────────────┼──────────┤\n",
       "│ 1   │ vw            │ 39.0167  │\n",
       "│ 2   │ nissan        │ 36.0     │\n",
       "│ 3   │ triumph       │ 35.0     │\n",
       "│ 4   │ honda         │ 33.7615  │\n",
       "│ 5   │ datsun        │ 31.113   │\n",
       "│ 6   │ mazda         │ 30.86    │\n",
       "│ 7   │ subaru        │ 30.525   │\n",
       "│ 8   │ vokswagen     │ 29.8     │\n",
       "│ 9   │ renault       │ 29.6667  │\n",
       "│ 10  │ volkswagen    │ 29.1067  │\n",
       "│ 11  │ fiat          │ 28.9125  │\n",
       "│ 12  │ toyota        │ 28.372   │\n",
       "│ 13  │ audi          │ 26.7143  │\n",
       "│ 14  │ maxda         │ 26.05    │\n",
       "│ 15  │ opel          │ 25.75    │\n",
       "│ 16  │ mercedes      │ 25.4     │\n",
       "│ 17  │ capri         │ 25.0     │\n",
       "│ 18  │ saab          │ 23.9     │\n",
       "│ 19  │ bmw           │ 23.75    │\n",
       "│ 20  │ peugeot       │ 23.6875  │\n",
       "│ 21  │ mercedes-benz │ 23.25    │\n",
       "│ 22  │ toyouta       │ 23.0     │\n",
       "│ 23  │ dodge         │ 22.0607  │\n",
       "│ 24  │ plymouth      │ 21.7032  │\n",
       "│ 25  │ volvo         │ 21.1167  │\n",
       "│ 26  │ oldsmobile    │ 21.1     │\n",
       "│ 27  │ chevrolet     │ 20.4721  │\n",
       "│ 28  │ pontiac       │ 20.0125  │\n",
       "│ 29  │ cadillac      │ 19.75    │\n",
       "│ 30  │ ford          │ 19.475   │\n",
       "│ 31  │ buick         │ 19.1824  │\n",
       "│ 32  │ mercury       │ 19.1182  │\n",
       "│ 33  │ amc           │ 18.0704  │\n",
       "│ 34  │ chevy         │ 18.0     │\n",
       "│ 35  │ chrysler      │ 17.2667  │\n",
       "│ 36  │ chevroelt     │ 16.0     │\n",
       "│ 37  │ hi            │ 9.0      │"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sort!(brand_mpg, :mpg_mean, rev=true) # sort! performs an in-place operation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A typical data cleaning task is to check its consistency. In this case it would be making sure that each brand has a unique origin. We will do it in several ways."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we use FreqTables.jl:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37×3 Named Array{Int64,2}\n",
       "brand ╲ origin │ 1.0  2.0  3.0\n",
       "───────────────┼──────────────\n",
       "amc            │  27    0    0\n",
       "audi           │   0    7    0\n",
       "bmw            │   0    2    0\n",
       "buick          │  17    0    0\n",
       "cadillac       │   2    0    0\n",
       "capri          │   1    0    0\n",
       "chevroelt      │   1    0    0\n",
       "chevrolet      │  43    0    0\n",
       "chevy          │   3    0    0\n",
       "chrysler       │   6    0    0\n",
       "datsun         │   0    0   23\n",
       "dodge          │  28    0    0\n",
       "fiat           │   0    8    0\n",
       "ford           │  48    0    0\n",
       "hi             │   1    0    0\n",
       "honda          │   0    0   13\n",
       "maxda          │   0    0    2\n",
       "mazda          │   0    0   10\n",
       "mercedes       │   0    1    0\n",
       "mercedes-benz  │   0    2    0\n",
       "mercury        │  11    0    0\n",
       "nissan         │   0    0    1\n",
       "oldsmobile     │  10    0    0\n",
       "opel           │   0    4    0\n",
       "peugeot        │   0    8    0\n",
       "plymouth       │  31    0    0\n",
       "pontiac        │  16    0    0\n",
       "renault        │   0    3    0\n",
       "saab           │   0    4    0\n",
       "subaru         │   0    0    4\n",
       "toyota         │   0    0   25\n",
       "toyouta        │   0    0    1\n",
       "triumph        │   0    1    0\n",
       "vokswagen      │   0    1    0\n",
       "volkswagen     │   0   15    0\n",
       "volvo          │   0    6    0\n",
       "vw             │   0    6    0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freqtable(df, :brand, :origin)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us create a chain of operations using Pipe.jl. Here `_` denotes the position of the argument passed from the previous step in the chain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>brand</th><th>origin_function</th></tr><tr><th></th><th>String</th><th>Int64</th></tr></thead><tbody><p>37 rows × 2 columns</p><tr><th>1</th><td>chevrolet</td><td>1</td></tr><tr><th>2</th><td>buick</td><td>1</td></tr><tr><th>3</th><td>plymouth</td><td>1</td></tr><tr><th>4</th><td>amc</td><td>1</td></tr><tr><th>5</th><td>ford</td><td>1</td></tr><tr><th>6</th><td>pontiac</td><td>1</td></tr><tr><th>7</th><td>dodge</td><td>1</td></tr><tr><th>8</th><td>toyota</td><td>1</td></tr><tr><th>9</th><td>datsun</td><td>1</td></tr><tr><th>10</th><td>volkswagen</td><td>1</td></tr><tr><th>11</th><td>peugeot</td><td>1</td></tr><tr><th>12</th><td>audi</td><td>1</td></tr><tr><th>13</th><td>saab</td><td>1</td></tr><tr><th>14</th><td>bmw</td><td>1</td></tr><tr><th>15</th><td>chevy</td><td>1</td></tr><tr><th>16</th><td>hi</td><td>1</td></tr><tr><th>17</th><td>mercury</td><td>1</td></tr><tr><th>18</th><td>opel</td><td>1</td></tr><tr><th>19</th><td>fiat</td><td>1</td></tr><tr><th>20</th><td>oldsmobile</td><td>1</td></tr><tr><th>21</th><td>chrysler</td><td>1</td></tr><tr><th>22</th><td>mazda</td><td>1</td></tr><tr><th>23</th><td>volvo</td><td>1</td></tr><tr><th>24</th><td>renault</td><td>1</td></tr><tr><th>25</th><td>toyouta</td><td>1</td></tr><tr><th>26</th><td>maxda</td><td>1</td></tr><tr><th>27</th><td>honda</td><td>1</td></tr><tr><th>28</th><td>subaru</td><td>1</td></tr><tr><th>29</th><td>chevroelt</td><td>1</td></tr><tr><th>30</th><td>capri</td><td>1</td></tr><tr><th>31</th><td>vw</td><td>1</td></tr><tr><th>32</th><td>mercedes-benz</td><td>1</td></tr><tr><th>33</th><td>cadillac</td><td>1</td></tr><tr><th>34</th><td>mercedes</td><td>1</td></tr><tr><th>35</th><td>vokswagen</td><td>1</td></tr><tr><th>36</th><td>triumph</td><td>1</td></tr><tr><th>37</th><td>nissan</td><td>1</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& brand & origin\\_function\\\\\n",
       "\t\\hline\n",
       "\t& String & Int64\\\\\n",
       "\t\\hline\n",
       "\t1 & chevrolet & 1 \\\\\n",
       "\t2 & buick & 1 \\\\\n",
       "\t3 & plymouth & 1 \\\\\n",
       "\t4 & amc & 1 \\\\\n",
       "\t5 & ford & 1 \\\\\n",
       "\t6 & pontiac & 1 \\\\\n",
       "\t7 & dodge & 1 \\\\\n",
       "\t8 & toyota & 1 \\\\\n",
       "\t9 & datsun & 1 \\\\\n",
       "\t10 & volkswagen & 1 \\\\\n",
       "\t11 & peugeot & 1 \\\\\n",
       "\t12 & audi & 1 \\\\\n",
       "\t13 & saab & 1 \\\\\n",
       "\t14 & bmw & 1 \\\\\n",
       "\t15 & chevy & 1 \\\\\n",
       "\t16 & hi & 1 \\\\\n",
       "\t17 & mercury & 1 \\\\\n",
       "\t18 & opel & 1 \\\\\n",
       "\t19 & fiat & 1 \\\\\n",
       "\t20 & oldsmobile & 1 \\\\\n",
       "\t21 & chrysler & 1 \\\\\n",
       "\t22 & mazda & 1 \\\\\n",
       "\t23 & volvo & 1 \\\\\n",
       "\t24 & renault & 1 \\\\\n",
       "\t25 & toyouta & 1 \\\\\n",
       "\t26 & maxda & 1 \\\\\n",
       "\t27 & honda & 1 \\\\\n",
       "\t28 & subaru & 1 \\\\\n",
       "\t29 & chevroelt & 1 \\\\\n",
       "\t30 & capri & 1 \\\\\n",
       "\t31 & vw & 1 \\\\\n",
       "\t32 & mercedes-benz & 1 \\\\\n",
       "\t33 & cadillac & 1 \\\\\n",
       "\t34 & mercedes & 1 \\\\\n",
       "\t35 & vokswagen & 1 \\\\\n",
       "\t36 & triumph & 1 \\\\\n",
       "\t37 & nissan & 1 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "37×2 DataFrame\n",
       "│ Row │ brand         │ origin_function │\n",
       "│     │ \u001b[90mString\u001b[39m        │ \u001b[90mInt64\u001b[39m           │\n",
       "├─────┼───────────────┼─────────────────┤\n",
       "│ 1   │ chevrolet     │ 1               │\n",
       "│ 2   │ buick         │ 1               │\n",
       "│ 3   │ plymouth      │ 1               │\n",
       "│ 4   │ amc           │ 1               │\n",
       "│ 5   │ ford          │ 1               │\n",
       "│ 6   │ pontiac       │ 1               │\n",
       "│ 7   │ dodge         │ 1               │\n",
       "│ 8   │ toyota        │ 1               │\n",
       "│ 9   │ datsun        │ 1               │\n",
       "│ 10  │ volkswagen    │ 1               │\n",
       "│ 11  │ peugeot       │ 1               │\n",
       "│ 12  │ audi          │ 1               │\n",
       "│ 13  │ saab          │ 1               │\n",
       "│ 14  │ bmw           │ 1               │\n",
       "│ 15  │ chevy         │ 1               │\n",
       "│ 16  │ hi            │ 1               │\n",
       "│ 17  │ mercury       │ 1               │\n",
       "│ 18  │ opel          │ 1               │\n",
       "│ 19  │ fiat          │ 1               │\n",
       "│ 20  │ oldsmobile    │ 1               │\n",
       "│ 21  │ chrysler      │ 1               │\n",
       "│ 22  │ mazda         │ 1               │\n",
       "│ 23  │ volvo         │ 1               │\n",
       "│ 24  │ renault       │ 1               │\n",
       "│ 25  │ toyouta       │ 1               │\n",
       "│ 26  │ maxda         │ 1               │\n",
       "│ 27  │ honda         │ 1               │\n",
       "│ 28  │ subaru        │ 1               │\n",
       "│ 29  │ chevroelt     │ 1               │\n",
       "│ 30  │ capri         │ 1               │\n",
       "│ 31  │ vw            │ 1               │\n",
       "│ 32  │ mercedes-benz │ 1               │\n",
       "│ 33  │ cadillac      │ 1               │\n",
       "│ 34  │ mercedes      │ 1               │\n",
       "│ 35  │ vokswagen     │ 1               │\n",
       "│ 36  │ triumph       │ 1               │\n",
       "│ 37  │ nissan        │ 1               │"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orig_brand = @pipe df |>\n",
    "                   groupby(_, :brand) |>\n",
    "                   combine(_, :origin => x -> length(unique(x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us make sure that we always get exactly one origin:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extrema(orig_brand.origin_function)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also could have grouped our data frame by two columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>origin</th><th>brand</th><th>nrow</th></tr><tr><th></th><th>Float64</th><th>String</th><th>Int64</th></tr></thead><tbody><p>37 rows × 3 columns</p><tr><th>1</th><td>1.0</td><td>chevrolet</td><td>43</td></tr><tr><th>2</th><td>1.0</td><td>buick</td><td>17</td></tr><tr><th>3</th><td>1.0</td><td>plymouth</td><td>31</td></tr><tr><th>4</th><td>1.0</td><td>amc</td><td>27</td></tr><tr><th>5</th><td>1.0</td><td>ford</td><td>48</td></tr><tr><th>6</th><td>1.0</td><td>pontiac</td><td>16</td></tr><tr><th>7</th><td>1.0</td><td>dodge</td><td>28</td></tr><tr><th>8</th><td>3.0</td><td>toyota</td><td>25</td></tr><tr><th>9</th><td>3.0</td><td>datsun</td><td>23</td></tr><tr><th>10</th><td>2.0</td><td>volkswagen</td><td>15</td></tr><tr><th>11</th><td>2.0</td><td>peugeot</td><td>8</td></tr><tr><th>12</th><td>2.0</td><td>audi</td><td>7</td></tr><tr><th>13</th><td>2.0</td><td>saab</td><td>4</td></tr><tr><th>14</th><td>2.0</td><td>bmw</td><td>2</td></tr><tr><th>15</th><td>1.0</td><td>chevy</td><td>3</td></tr><tr><th>16</th><td>1.0</td><td>hi</td><td>1</td></tr><tr><th>17</th><td>1.0</td><td>mercury</td><td>11</td></tr><tr><th>18</th><td>2.0</td><td>opel</td><td>4</td></tr><tr><th>19</th><td>2.0</td><td>fiat</td><td>8</td></tr><tr><th>20</th><td>1.0</td><td>oldsmobile</td><td>10</td></tr><tr><th>21</th><td>1.0</td><td>chrysler</td><td>6</td></tr><tr><th>22</th><td>3.0</td><td>mazda</td><td>10</td></tr><tr><th>23</th><td>2.0</td><td>volvo</td><td>6</td></tr><tr><th>24</th><td>2.0</td><td>renault</td><td>3</td></tr><tr><th>25</th><td>3.0</td><td>toyouta</td><td>1</td></tr><tr><th>26</th><td>3.0</td><td>maxda</td><td>2</td></tr><tr><th>27</th><td>3.0</td><td>honda</td><td>13</td></tr><tr><th>28</th><td>3.0</td><td>subaru</td><td>4</td></tr><tr><th>29</th><td>1.0</td><td>chevroelt</td><td>1</td></tr><tr><th>30</th><td>1.0</td><td>capri</td><td>1</td></tr><tr><th>31</th><td>2.0</td><td>vw</td><td>6</td></tr><tr><th>32</th><td>2.0</td><td>mercedes-benz</td><td>2</td></tr><tr><th>33</th><td>1.0</td><td>cadillac</td><td>2</td></tr><tr><th>34</th><td>2.0</td><td>mercedes</td><td>1</td></tr><tr><th>35</th><td>2.0</td><td>vokswagen</td><td>1</td></tr><tr><th>36</th><td>2.0</td><td>triumph</td><td>1</td></tr><tr><th>37</th><td>3.0</td><td>nissan</td><td>1</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ccc}\n",
       "\t& origin & brand & nrow\\\\\n",
       "\t\\hline\n",
       "\t& Float64 & String & Int64\\\\\n",
       "\t\\hline\n",
       "\t1 & 1.0 & chevrolet & 43 \\\\\n",
       "\t2 & 1.0 & buick & 17 \\\\\n",
       "\t3 & 1.0 & plymouth & 31 \\\\\n",
       "\t4 & 1.0 & amc & 27 \\\\\n",
       "\t5 & 1.0 & ford & 48 \\\\\n",
       "\t6 & 1.0 & pontiac & 16 \\\\\n",
       "\t7 & 1.0 & dodge & 28 \\\\\n",
       "\t8 & 3.0 & toyota & 25 \\\\\n",
       "\t9 & 3.0 & datsun & 23 \\\\\n",
       "\t10 & 2.0 & volkswagen & 15 \\\\\n",
       "\t11 & 2.0 & peugeot & 8 \\\\\n",
       "\t12 & 2.0 & audi & 7 \\\\\n",
       "\t13 & 2.0 & saab & 4 \\\\\n",
       "\t14 & 2.0 & bmw & 2 \\\\\n",
       "\t15 & 1.0 & chevy & 3 \\\\\n",
       "\t16 & 1.0 & hi & 1 \\\\\n",
       "\t17 & 1.0 & mercury & 11 \\\\\n",
       "\t18 & 2.0 & opel & 4 \\\\\n",
       "\t19 & 2.0 & fiat & 8 \\\\\n",
       "\t20 & 1.0 & oldsmobile & 10 \\\\\n",
       "\t21 & 1.0 & chrysler & 6 \\\\\n",
       "\t22 & 3.0 & mazda & 10 \\\\\n",
       "\t23 & 2.0 & volvo & 6 \\\\\n",
       "\t24 & 2.0 & renault & 3 \\\\\n",
       "\t25 & 3.0 & toyouta & 1 \\\\\n",
       "\t26 & 3.0 & maxda & 2 \\\\\n",
       "\t27 & 3.0 & honda & 13 \\\\\n",
       "\t28 & 3.0 & subaru & 4 \\\\\n",
       "\t29 & 1.0 & chevroelt & 1 \\\\\n",
       "\t30 & 1.0 & capri & 1 \\\\\n",
       "\t31 & 2.0 & vw & 6 \\\\\n",
       "\t32 & 2.0 & mercedes-benz & 2 \\\\\n",
       "\t33 & 1.0 & cadillac & 2 \\\\\n",
       "\t34 & 2.0 & mercedes & 1 \\\\\n",
       "\t35 & 2.0 & vokswagen & 1 \\\\\n",
       "\t36 & 2.0 & triumph & 1 \\\\\n",
       "\t37 & 3.0 & nissan & 1 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "37×3 DataFrame\n",
       "│ Row │ origin  │ brand         │ nrow  │\n",
       "│     │ \u001b[90mFloat64\u001b[39m │ \u001b[90mString\u001b[39m        │ \u001b[90mInt64\u001b[39m │\n",
       "├─────┼─────────┼───────────────┼───────┤\n",
       "│ 1   │ 1.0     │ chevrolet     │ 43    │\n",
       "│ 2   │ 1.0     │ buick         │ 17    │\n",
       "│ 3   │ 1.0     │ plymouth      │ 31    │\n",
       "│ 4   │ 1.0     │ amc           │ 27    │\n",
       "│ 5   │ 1.0     │ ford          │ 48    │\n",
       "│ 6   │ 1.0     │ pontiac       │ 16    │\n",
       "│ 7   │ 1.0     │ dodge         │ 28    │\n",
       "│ 8   │ 3.0     │ toyota        │ 25    │\n",
       "│ 9   │ 3.0     │ datsun        │ 23    │\n",
       "│ 10  │ 2.0     │ volkswagen    │ 15    │\n",
       "│ 11  │ 2.0     │ peugeot       │ 8     │\n",
       "│ 12  │ 2.0     │ audi          │ 7     │\n",
       "│ 13  │ 2.0     │ saab          │ 4     │\n",
       "│ 14  │ 2.0     │ bmw           │ 2     │\n",
       "│ 15  │ 1.0     │ chevy         │ 3     │\n",
       "│ 16  │ 1.0     │ hi            │ 1     │\n",
       "│ 17  │ 1.0     │ mercury       │ 11    │\n",
       "│ 18  │ 2.0     │ opel          │ 4     │\n",
       "│ 19  │ 2.0     │ fiat          │ 8     │\n",
       "│ 20  │ 1.0     │ oldsmobile    │ 10    │\n",
       "│ 21  │ 1.0     │ chrysler      │ 6     │\n",
       "│ 22  │ 3.0     │ mazda         │ 10    │\n",
       "│ 23  │ 2.0     │ volvo         │ 6     │\n",
       "│ 24  │ 2.0     │ renault       │ 3     │\n",
       "│ 25  │ 3.0     │ toyouta       │ 1     │\n",
       "│ 26  │ 3.0     │ maxda         │ 2     │\n",
       "│ 27  │ 3.0     │ honda         │ 13    │\n",
       "│ 28  │ 3.0     │ subaru        │ 4     │\n",
       "│ 29  │ 1.0     │ chevroelt     │ 1     │\n",
       "│ 30  │ 1.0     │ capri         │ 1     │\n",
       "│ 31  │ 2.0     │ vw            │ 6     │\n",
       "│ 32  │ 2.0     │ mercedes-benz │ 2     │\n",
       "│ 33  │ 1.0     │ cadillac      │ 2     │\n",
       "│ 34  │ 2.0     │ mercedes      │ 1     │\n",
       "│ 35  │ 2.0     │ vokswagen     │ 1     │\n",
       "│ 36  │ 2.0     │ triumph       │ 1     │\n",
       "│ 37  │ 3.0     │ nissan        │ 1     │"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "origin_brand2 = @pipe df |>\n",
    "                      groupby(_, [:origin, :brand]) |>\n",
    "                      combine(_, nrow)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that passing `nrow` as a combining operation gives us the number of rows per group."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can do the `unstack` operation on the result to get a similar table that we got with `freqtable`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>brand</th><th>1.0</th><th>2.0</th><th>3.0</th></tr><tr><th></th><th>String</th><th>Int64?</th><th>Int64?</th><th>Int64?</th></tr></thead><tbody><p>37 rows × 4 columns</p><tr><th>1</th><td>amc</td><td>27</td><td><em>missing</em></td><td><em>missing</em></td></tr><tr><th>2</th><td>audi</td><td><em>missing</em></td><td>7</td><td><em>missing</em></td></tr><tr><th>3</th><td>bmw</td><td><em>missing</em></td><td>2</td><td><em>missing</em></td></tr><tr><th>4</th><td>buick</td><td>17</td><td><em>missing</em></td><td><em>missing</em></td></tr><tr><th>5</th><td>cadillac</td><td>2</td><td><em>missing</em></td><td><em>missing</em></td></tr><tr><th>6</th><td>capri</td><td>1</td><td><em>missing</em></td><td><em>missing</em></td></tr><tr><th>7</th><td>chevroelt</td><td>1</td><td><em>missing</em></td><td><em>missing</em></td></tr><tr><th>8</th><td>chevrolet</td><td>43</td><td><em>missing</em></td><td><em>missing</em></td></tr><tr><th>9</th><td>chevy</td><td>3</td><td><em>missing</em></td><td><em>missing</em></td></tr><tr><th>10</th><td>chrysler</td><td>6</td><td><em>missing</em></td><td><em>missing</em></td></tr><tr><th>11</th><td>datsun</td><td><em>missing</em></td><td><em>missing</em></td><td>23</td></tr><tr><th>12</th><td>dodge</td><td>28</td><td><em>missing</em></td><td><em>missing</em></td></tr><tr><th>13</th><td>fiat</td><td><em>missing</em></td><td>8</td><td><em>missing</em></td></tr><tr><th>14</th><td>ford</td><td>48</td><td><em>missing</em></td><td><em>missing</em></td></tr><tr><th>15</th><td>hi</td><td>1</td><td><em>missing</em></td><td><em>missing</em></td></tr><tr><th>16</th><td>honda</td><td><em>missing</em></td><td><em>missing</em></td><td>13</td></tr><tr><th>17</th><td>maxda</td><td><em>missing</em></td><td><em>missing</em></td><td>2</td></tr><tr><th>18</th><td>mazda</td><td><em>missing</em></td><td><em>missing</em></td><td>10</td></tr><tr><th>19</th><td>mercedes</td><td><em>missing</em></td><td>1</td><td><em>missing</em></td></tr><tr><th>20</th><td>mercedes-benz</td><td><em>missing</em></td><td>2</td><td><em>missing</em></td></tr><tr><th>21</th><td>mercury</td><td>11</td><td><em>missing</em></td><td><em>missing</em></td></tr><tr><th>22</th><td>nissan</td><td><em>missing</em></td><td><em>missing</em></td><td>1</td></tr><tr><th>23</th><td>oldsmobile</td><td>10</td><td><em>missing</em></td><td><em>missing</em></td></tr><tr><th>24</th><td>opel</td><td><em>missing</em></td><td>4</td><td><em>missing</em></td></tr><tr><th>25</th><td>peugeot</td><td><em>missing</em></td><td>8</td><td><em>missing</em></td></tr><tr><th>26</th><td>plymouth</td><td>31</td><td><em>missing</em></td><td><em>missing</em></td></tr><tr><th>27</th><td>pontiac</td><td>16</td><td><em>missing</em></td><td><em>missing</em></td></tr><tr><th>28</th><td>renault</td><td><em>missing</em></td><td>3</td><td><em>missing</em></td></tr><tr><th>29</th><td>saab</td><td><em>missing</em></td><td>4</td><td><em>missing</em></td></tr><tr><th>30</th><td>subaru</td><td><em>missing</em></td><td><em>missing</em></td><td>4</td></tr><tr><th>31</th><td>toyota</td><td><em>missing</em></td><td><em>missing</em></td><td>25</td></tr><tr><th>32</th><td>toyouta</td><td><em>missing</em></td><td><em>missing</em></td><td>1</td></tr><tr><th>33</th><td>triumph</td><td><em>missing</em></td><td>1</td><td><em>missing</em></td></tr><tr><th>34</th><td>vokswagen</td><td><em>missing</em></td><td>1</td><td><em>missing</em></td></tr><tr><th>35</th><td>volkswagen</td><td><em>missing</em></td><td>15</td><td><em>missing</em></td></tr><tr><th>36</th><td>volvo</td><td><em>missing</em></td><td>6</td><td><em>missing</em></td></tr><tr><th>37</th><td>vw</td><td><em>missing</em></td><td>6</td><td><em>missing</em></td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& brand & 1.0 & 2.0 & 3.0\\\\\n",
       "\t\\hline\n",
       "\t& String & Int64? & Int64? & Int64?\\\\\n",
       "\t\\hline\n",
       "\t1 & amc & 27 & \\emph{missing} & \\emph{missing} \\\\\n",
       "\t2 & audi & \\emph{missing} & 7 & \\emph{missing} \\\\\n",
       "\t3 & bmw & \\emph{missing} & 2 & \\emph{missing} \\\\\n",
       "\t4 & buick & 17 & \\emph{missing} & \\emph{missing} \\\\\n",
       "\t5 & cadillac & 2 & \\emph{missing} & \\emph{missing} \\\\\n",
       "\t6 & capri & 1 & \\emph{missing} & \\emph{missing} \\\\\n",
       "\t7 & chevroelt & 1 & \\emph{missing} & \\emph{missing} \\\\\n",
       "\t8 & chevrolet & 43 & \\emph{missing} & \\emph{missing} \\\\\n",
       "\t9 & chevy & 3 & \\emph{missing} & \\emph{missing} \\\\\n",
       "\t10 & chrysler & 6 & \\emph{missing} & \\emph{missing} \\\\\n",
       "\t11 & datsun & \\emph{missing} & \\emph{missing} & 23 \\\\\n",
       "\t12 & dodge & 28 & \\emph{missing} & \\emph{missing} \\\\\n",
       "\t13 & fiat & \\emph{missing} & 8 & \\emph{missing} \\\\\n",
       "\t14 & ford & 48 & \\emph{missing} & \\emph{missing} \\\\\n",
       "\t15 & hi & 1 & \\emph{missing} & \\emph{missing} \\\\\n",
       "\t16 & honda & \\emph{missing} & \\emph{missing} & 13 \\\\\n",
       "\t17 & maxda & \\emph{missing} & \\emph{missing} & 2 \\\\\n",
       "\t18 & mazda & \\emph{missing} & \\emph{missing} & 10 \\\\\n",
       "\t19 & mercedes & \\emph{missing} & 1 & \\emph{missing} \\\\\n",
       "\t20 & mercedes-benz & \\emph{missing} & 2 & \\emph{missing} \\\\\n",
       "\t21 & mercury & 11 & \\emph{missing} & \\emph{missing} \\\\\n",
       "\t22 & nissan & \\emph{missing} & \\emph{missing} & 1 \\\\\n",
       "\t23 & oldsmobile & 10 & \\emph{missing} & \\emph{missing} \\\\\n",
       "\t24 & opel & \\emph{missing} & 4 & \\emph{missing} \\\\\n",
       "\t25 & peugeot & \\emph{missing} & 8 & \\emph{missing} \\\\\n",
       "\t26 & plymouth & 31 & \\emph{missing} & \\emph{missing} \\\\\n",
       "\t27 & pontiac & 16 & \\emph{missing} & \\emph{missing} \\\\\n",
       "\t28 & renault & \\emph{missing} & 3 & \\emph{missing} \\\\\n",
       "\t29 & saab & \\emph{missing} & 4 & \\emph{missing} \\\\\n",
       "\t30 & subaru & \\emph{missing} & \\emph{missing} & 4 \\\\\n",
       "\t31 & toyota & \\emph{missing} & \\emph{missing} & 25 \\\\\n",
       "\t32 & toyouta & \\emph{missing} & \\emph{missing} & 1 \\\\\n",
       "\t33 & triumph & \\emph{missing} & 1 & \\emph{missing} \\\\\n",
       "\t34 & vokswagen & \\emph{missing} & 1 & \\emph{missing} \\\\\n",
       "\t35 & volkswagen & \\emph{missing} & 15 & \\emph{missing} \\\\\n",
       "\t36 & volvo & \\emph{missing} & 6 & \\emph{missing} \\\\\n",
       "\t37 & vw & \\emph{missing} & 6 & \\emph{missing} \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "37×4 DataFrame\n",
       "│ Row │ brand         │ 1.0     │ 2.0     │ 3.0     │\n",
       "│     │ \u001b[90mString\u001b[39m        │ \u001b[90mInt64?\u001b[39m  │ \u001b[90mInt64?\u001b[39m  │ \u001b[90mInt64?\u001b[39m  │\n",
       "├─────┼───────────────┼─────────┼─────────┼─────────┤\n",
       "│ 1   │ amc           │ 27      │ \u001b[90mmissing\u001b[39m │ \u001b[90mmissing\u001b[39m │\n",
       "│ 2   │ audi          │ \u001b[90mmissing\u001b[39m │ 7       │ \u001b[90mmissing\u001b[39m │\n",
       "│ 3   │ bmw           │ \u001b[90mmissing\u001b[39m │ 2       │ \u001b[90mmissing\u001b[39m │\n",
       "│ 4   │ buick         │ 17      │ \u001b[90mmissing\u001b[39m │ \u001b[90mmissing\u001b[39m │\n",
       "│ 5   │ cadillac      │ 2       │ \u001b[90mmissing\u001b[39m │ \u001b[90mmissing\u001b[39m │\n",
       "│ 6   │ capri         │ 1       │ \u001b[90mmissing\u001b[39m │ \u001b[90mmissing\u001b[39m │\n",
       "│ 7   │ chevroelt     │ 1       │ \u001b[90mmissing\u001b[39m │ \u001b[90mmissing\u001b[39m │\n",
       "│ 8   │ chevrolet     │ 43      │ \u001b[90mmissing\u001b[39m │ \u001b[90mmissing\u001b[39m │\n",
       "│ 9   │ chevy         │ 3       │ \u001b[90mmissing\u001b[39m │ \u001b[90mmissing\u001b[39m │\n",
       "│ 10  │ chrysler      │ 6       │ \u001b[90mmissing\u001b[39m │ \u001b[90mmissing\u001b[39m │\n",
       "│ 11  │ datsun        │ \u001b[90mmissing\u001b[39m │ \u001b[90mmissing\u001b[39m │ 23      │\n",
       "│ 12  │ dodge         │ 28      │ \u001b[90mmissing\u001b[39m │ \u001b[90mmissing\u001b[39m │\n",
       "│ 13  │ fiat          │ \u001b[90mmissing\u001b[39m │ 8       │ \u001b[90mmissing\u001b[39m │\n",
       "│ 14  │ ford          │ 48      │ \u001b[90mmissing\u001b[39m │ \u001b[90mmissing\u001b[39m │\n",
       "│ 15  │ hi            │ 1       │ \u001b[90mmissing\u001b[39m │ \u001b[90mmissing\u001b[39m │\n",
       "│ 16  │ honda         │ \u001b[90mmissing\u001b[39m │ \u001b[90mmissing\u001b[39m │ 13      │\n",
       "│ 17  │ maxda         │ \u001b[90mmissing\u001b[39m │ \u001b[90mmissing\u001b[39m │ 2       │\n",
       "│ 18  │ mazda         │ \u001b[90mmissing\u001b[39m │ \u001b[90mmissing\u001b[39m │ 10      │\n",
       "│ 19  │ mercedes      │ \u001b[90mmissing\u001b[39m │ 1       │ \u001b[90mmissing\u001b[39m │\n",
       "│ 20  │ mercedes-benz │ \u001b[90mmissing\u001b[39m │ 2       │ \u001b[90mmissing\u001b[39m │\n",
       "│ 21  │ mercury       │ 11      │ \u001b[90mmissing\u001b[39m │ \u001b[90mmissing\u001b[39m │\n",
       "│ 22  │ nissan        │ \u001b[90mmissing\u001b[39m │ \u001b[90mmissing\u001b[39m │ 1       │\n",
       "│ 23  │ oldsmobile    │ 10      │ \u001b[90mmissing\u001b[39m │ \u001b[90mmissing\u001b[39m │\n",
       "│ 24  │ opel          │ \u001b[90mmissing\u001b[39m │ 4       │ \u001b[90mmissing\u001b[39m │\n",
       "│ 25  │ peugeot       │ \u001b[90mmissing\u001b[39m │ 8       │ \u001b[90mmissing\u001b[39m │\n",
       "│ 26  │ plymouth      │ 31      │ \u001b[90mmissing\u001b[39m │ \u001b[90mmissing\u001b[39m │\n",
       "│ 27  │ pontiac       │ 16      │ \u001b[90mmissing\u001b[39m │ \u001b[90mmissing\u001b[39m │\n",
       "│ 28  │ renault       │ \u001b[90mmissing\u001b[39m │ 3       │ \u001b[90mmissing\u001b[39m │\n",
       "│ 29  │ saab          │ \u001b[90mmissing\u001b[39m │ 4       │ \u001b[90mmissing\u001b[39m │\n",
       "│ 30  │ subaru        │ \u001b[90mmissing\u001b[39m │ \u001b[90mmissing\u001b[39m │ 4       │\n",
       "│ 31  │ toyota        │ \u001b[90mmissing\u001b[39m │ \u001b[90mmissing\u001b[39m │ 25      │\n",
       "│ 32  │ toyouta       │ \u001b[90mmissing\u001b[39m │ \u001b[90mmissing\u001b[39m │ 1       │\n",
       "│ 33  │ triumph       │ \u001b[90mmissing\u001b[39m │ 1       │ \u001b[90mmissing\u001b[39m │\n",
       "│ 34  │ vokswagen     │ \u001b[90mmissing\u001b[39m │ 1       │ \u001b[90mmissing\u001b[39m │\n",
       "│ 35  │ volkswagen    │ \u001b[90mmissing\u001b[39m │ 15      │ \u001b[90mmissing\u001b[39m │\n",
       "│ 36  │ volvo         │ \u001b[90mmissing\u001b[39m │ 6       │ \u001b[90mmissing\u001b[39m │\n",
       "│ 37  │ vw            │ \u001b[90mmissing\u001b[39m │ 6       │ \u001b[90mmissing\u001b[39m │"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "origin_vs_brand = unstack(origin_brand2, :brand, :origin, :nrow)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The only difference is that missing combinations are given as `missing` and `freqtable` produced `0` there. It is easy to fix using broadcasting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>brand</th><th>1.0</th><th>2.0</th><th>3.0</th></tr><tr><th></th><th>String</th><th>Int64</th><th>Int64</th><th>Int64</th></tr></thead><tbody><p>37 rows × 4 columns</p><tr><th>1</th><td>amc</td><td>27</td><td>0</td><td>0</td></tr><tr><th>2</th><td>audi</td><td>0</td><td>7</td><td>0</td></tr><tr><th>3</th><td>bmw</td><td>0</td><td>2</td><td>0</td></tr><tr><th>4</th><td>buick</td><td>17</td><td>0</td><td>0</td></tr><tr><th>5</th><td>cadillac</td><td>2</td><td>0</td><td>0</td></tr><tr><th>6</th><td>capri</td><td>1</td><td>0</td><td>0</td></tr><tr><th>7</th><td>chevroelt</td><td>1</td><td>0</td><td>0</td></tr><tr><th>8</th><td>chevrolet</td><td>43</td><td>0</td><td>0</td></tr><tr><th>9</th><td>chevy</td><td>3</td><td>0</td><td>0</td></tr><tr><th>10</th><td>chrysler</td><td>6</td><td>0</td><td>0</td></tr><tr><th>11</th><td>datsun</td><td>0</td><td>0</td><td>23</td></tr><tr><th>12</th><td>dodge</td><td>28</td><td>0</td><td>0</td></tr><tr><th>13</th><td>fiat</td><td>0</td><td>8</td><td>0</td></tr><tr><th>14</th><td>ford</td><td>48</td><td>0</td><td>0</td></tr><tr><th>15</th><td>hi</td><td>1</td><td>0</td><td>0</td></tr><tr><th>16</th><td>honda</td><td>0</td><td>0</td><td>13</td></tr><tr><th>17</th><td>maxda</td><td>0</td><td>0</td><td>2</td></tr><tr><th>18</th><td>mazda</td><td>0</td><td>0</td><td>10</td></tr><tr><th>19</th><td>mercedes</td><td>0</td><td>1</td><td>0</td></tr><tr><th>20</th><td>mercedes-benz</td><td>0</td><td>2</td><td>0</td></tr><tr><th>21</th><td>mercury</td><td>11</td><td>0</td><td>0</td></tr><tr><th>22</th><td>nissan</td><td>0</td><td>0</td><td>1</td></tr><tr><th>23</th><td>oldsmobile</td><td>10</td><td>0</td><td>0</td></tr><tr><th>24</th><td>opel</td><td>0</td><td>4</td><td>0</td></tr><tr><th>25</th><td>peugeot</td><td>0</td><td>8</td><td>0</td></tr><tr><th>26</th><td>plymouth</td><td>31</td><td>0</td><td>0</td></tr><tr><th>27</th><td>pontiac</td><td>16</td><td>0</td><td>0</td></tr><tr><th>28</th><td>renault</td><td>0</td><td>3</td><td>0</td></tr><tr><th>29</th><td>saab</td><td>0</td><td>4</td><td>0</td></tr><tr><th>30</th><td>subaru</td><td>0</td><td>0</td><td>4</td></tr><tr><th>31</th><td>toyota</td><td>0</td><td>0</td><td>25</td></tr><tr><th>32</th><td>toyouta</td><td>0</td><td>0</td><td>1</td></tr><tr><th>33</th><td>triumph</td><td>0</td><td>1</td><td>0</td></tr><tr><th>34</th><td>vokswagen</td><td>0</td><td>1</td><td>0</td></tr><tr><th>35</th><td>volkswagen</td><td>0</td><td>15</td><td>0</td></tr><tr><th>36</th><td>volvo</td><td>0</td><td>6</td><td>0</td></tr><tr><th>37</th><td>vw</td><td>0</td><td>6</td><td>0</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& brand & 1.0 & 2.0 & 3.0\\\\\n",
       "\t\\hline\n",
       "\t& String & Int64 & Int64 & Int64\\\\\n",
       "\t\\hline\n",
       "\t1 & amc & 27 & 0 & 0 \\\\\n",
       "\t2 & audi & 0 & 7 & 0 \\\\\n",
       "\t3 & bmw & 0 & 2 & 0 \\\\\n",
       "\t4 & buick & 17 & 0 & 0 \\\\\n",
       "\t5 & cadillac & 2 & 0 & 0 \\\\\n",
       "\t6 & capri & 1 & 0 & 0 \\\\\n",
       "\t7 & chevroelt & 1 & 0 & 0 \\\\\n",
       "\t8 & chevrolet & 43 & 0 & 0 \\\\\n",
       "\t9 & chevy & 3 & 0 & 0 \\\\\n",
       "\t10 & chrysler & 6 & 0 & 0 \\\\\n",
       "\t11 & datsun & 0 & 0 & 23 \\\\\n",
       "\t12 & dodge & 28 & 0 & 0 \\\\\n",
       "\t13 & fiat & 0 & 8 & 0 \\\\\n",
       "\t14 & ford & 48 & 0 & 0 \\\\\n",
       "\t15 & hi & 1 & 0 & 0 \\\\\n",
       "\t16 & honda & 0 & 0 & 13 \\\\\n",
       "\t17 & maxda & 0 & 0 & 2 \\\\\n",
       "\t18 & mazda & 0 & 0 & 10 \\\\\n",
       "\t19 & mercedes & 0 & 1 & 0 \\\\\n",
       "\t20 & mercedes-benz & 0 & 2 & 0 \\\\\n",
       "\t21 & mercury & 11 & 0 & 0 \\\\\n",
       "\t22 & nissan & 0 & 0 & 1 \\\\\n",
       "\t23 & oldsmobile & 10 & 0 & 0 \\\\\n",
       "\t24 & opel & 0 & 4 & 0 \\\\\n",
       "\t25 & peugeot & 0 & 8 & 0 \\\\\n",
       "\t26 & plymouth & 31 & 0 & 0 \\\\\n",
       "\t27 & pontiac & 16 & 0 & 0 \\\\\n",
       "\t28 & renault & 0 & 3 & 0 \\\\\n",
       "\t29 & saab & 0 & 4 & 0 \\\\\n",
       "\t30 & subaru & 0 & 0 & 4 \\\\\n",
       "\t31 & toyota & 0 & 0 & 25 \\\\\n",
       "\t32 & toyouta & 0 & 0 & 1 \\\\\n",
       "\t33 & triumph & 0 & 1 & 0 \\\\\n",
       "\t34 & vokswagen & 0 & 1 & 0 \\\\\n",
       "\t35 & volkswagen & 0 & 15 & 0 \\\\\n",
       "\t36 & volvo & 0 & 6 & 0 \\\\\n",
       "\t37 & vw & 0 & 6 & 0 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "37×4 DataFrame\n",
       "│ Row │ brand         │ 1.0   │ 2.0   │ 3.0   │\n",
       "│     │ \u001b[90mString\u001b[39m        │ \u001b[90mInt64\u001b[39m │ \u001b[90mInt64\u001b[39m │ \u001b[90mInt64\u001b[39m │\n",
       "├─────┼───────────────┼───────┼───────┼───────┤\n",
       "│ 1   │ amc           │ 27    │ 0     │ 0     │\n",
       "│ 2   │ audi          │ 0     │ 7     │ 0     │\n",
       "│ 3   │ bmw           │ 0     │ 2     │ 0     │\n",
       "│ 4   │ buick         │ 17    │ 0     │ 0     │\n",
       "│ 5   │ cadillac      │ 2     │ 0     │ 0     │\n",
       "│ 6   │ capri         │ 1     │ 0     │ 0     │\n",
       "│ 7   │ chevroelt     │ 1     │ 0     │ 0     │\n",
       "│ 8   │ chevrolet     │ 43    │ 0     │ 0     │\n",
       "│ 9   │ chevy         │ 3     │ 0     │ 0     │\n",
       "│ 10  │ chrysler      │ 6     │ 0     │ 0     │\n",
       "│ 11  │ datsun        │ 0     │ 0     │ 23    │\n",
       "│ 12  │ dodge         │ 28    │ 0     │ 0     │\n",
       "│ 13  │ fiat          │ 0     │ 8     │ 0     │\n",
       "│ 14  │ ford          │ 48    │ 0     │ 0     │\n",
       "│ 15  │ hi            │ 1     │ 0     │ 0     │\n",
       "│ 16  │ honda         │ 0     │ 0     │ 13    │\n",
       "│ 17  │ maxda         │ 0     │ 0     │ 2     │\n",
       "│ 18  │ mazda         │ 0     │ 0     │ 10    │\n",
       "│ 19  │ mercedes      │ 0     │ 1     │ 0     │\n",
       "│ 20  │ mercedes-benz │ 0     │ 2     │ 0     │\n",
       "│ 21  │ mercury       │ 11    │ 0     │ 0     │\n",
       "│ 22  │ nissan        │ 0     │ 0     │ 1     │\n",
       "│ 23  │ oldsmobile    │ 10    │ 0     │ 0     │\n",
       "│ 24  │ opel          │ 0     │ 4     │ 0     │\n",
       "│ 25  │ peugeot       │ 0     │ 8     │ 0     │\n",
       "│ 26  │ plymouth      │ 31    │ 0     │ 0     │\n",
       "│ 27  │ pontiac       │ 16    │ 0     │ 0     │\n",
       "│ 28  │ renault       │ 0     │ 3     │ 0     │\n",
       "│ 29  │ saab          │ 0     │ 4     │ 0     │\n",
       "│ 30  │ subaru        │ 0     │ 0     │ 4     │\n",
       "│ 31  │ toyota        │ 0     │ 0     │ 25    │\n",
       "│ 32  │ toyouta       │ 0     │ 0     │ 1     │\n",
       "│ 33  │ triumph       │ 0     │ 1     │ 0     │\n",
       "│ 34  │ vokswagen     │ 0     │ 1     │ 0     │\n",
       "│ 35  │ volkswagen    │ 0     │ 15    │ 0     │\n",
       "│ 36  │ volvo         │ 0     │ 6     │ 0     │\n",
       "│ 37  │ vw            │ 0     │ 6     │ 0     │"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coalesce.(origin_vs_brand, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that column names of a data frame can be any sequence of characters as in this case:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-element Array{String,1}:\n",
       " \"brand\"\n",
       " \"1.0\"\n",
       " \"2.0\"\n",
       " \"3.0\""
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names(origin_vs_brand)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-element Array{Symbol,1}:\n",
       " :brand\n",
       " Symbol(\"1.0\")\n",
       " Symbol(\"2.0\")\n",
       " Symbol(\"3.0\")"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "propertynames(origin_vs_brand)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In such situations it is easiest to refer to such columns using strings, as `Symbol`s are not super easy to write when they have to represent sequences that are not allowed as variable names as you can see above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37-element Array{Union{Missing, Int64},1}:\n",
       " 27\n",
       "   missing\n",
       "   missing\n",
       " 17\n",
       "  2\n",
       "  1\n",
       "  1\n",
       " 43\n",
       "  3\n",
       "  6\n",
       "   missing\n",
       " 28\n",
       "   missing\n",
       " 48\n",
       "  1\n",
       "   missing\n",
       "   missing\n",
       "   missing\n",
       "   missing\n",
       "   missing\n",
       " 11\n",
       "   missing\n",
       " 10\n",
       "   missing\n",
       "   missing\n",
       " 31\n",
       " 16\n",
       "   missing\n",
       "   missing\n",
       "   missing\n",
       "   missing\n",
       "   missing\n",
       "   missing\n",
       "   missing\n",
       "   missing\n",
       "   missing\n",
       "   missing"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "origin_vs_brand.\"1.0\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally let us show that columns of a data frame can hold any data type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>origin</th><th>brand_function</th></tr><tr><th></th><th>Float64</th><th>Array…</th></tr></thead><tbody><p>3 rows × 2 columns</p><tr><th>1</th><td>1.0</td><td>[\"chevrolet\", \"buick\", \"plymouth\", \"amc\", \"ford\", \"pontiac\", \"dodge\", \"chevy\", \"hi\", \"mercury\", \"oldsmobile\", \"chrysler\", \"chevroelt\", \"capri\", \"cadillac\"]</td></tr><tr><th>2</th><td>3.0</td><td>[\"toyota\", \"datsun\", \"mazda\", \"toyouta\", \"maxda\", \"honda\", \"subaru\", \"nissan\"]</td></tr><tr><th>3</th><td>2.0</td><td>[\"volkswagen\", \"peugeot\", \"audi\", \"saab\", \"bmw\", \"opel\", \"fiat\", \"volvo\", \"renault\", \"vw\", \"mercedes-benz\", \"mercedes\", \"vokswagen\", \"triumph\"]</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& origin & brand\\_function\\\\\n",
       "\t\\hline\n",
       "\t& Float64 & Array…\\\\\n",
       "\t\\hline\n",
       "\t1 & 1.0 & [\"chevrolet\", \"buick\", \"plymouth\", \"amc\", \"ford\", \"pontiac\", \"dodge\", \"chevy\", \"hi\", \"mercury\", \"oldsmobile\", \"chrysler\", \"chevroelt\", \"capri\", \"cadillac\"] \\\\\n",
       "\t2 & 3.0 & [\"toyota\", \"datsun\", \"mazda\", \"toyouta\", \"maxda\", \"honda\", \"subaru\", \"nissan\"] \\\\\n",
       "\t3 & 2.0 & [\"volkswagen\", \"peugeot\", \"audi\", \"saab\", \"bmw\", \"opel\", \"fiat\", \"volvo\", \"renault\", \"vw\", \"mercedes-benz\", \"mercedes\", \"vokswagen\", \"triumph\"] \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "3×2 DataFrame\n",
       "│ Row │ origin  │ brand_function                                                                                                                                              │\n",
       "│     │ \u001b[90mFloat64\u001b[39m │ \u001b[90mArray{String,1}\u001b[39m                                                                                                                                             │\n",
       "├─────┼─────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
       "│ 1   │ 1.0     │ [\"chevrolet\", \"buick\", \"plymouth\", \"amc\", \"ford\", \"pontiac\", \"dodge\", \"chevy\", \"hi\", \"mercury\", \"oldsmobile\", \"chrysler\", \"chevroelt\", \"capri\", \"cadillac\"] │\n",
       "│ 2   │ 3.0     │ [\"toyota\", \"datsun\", \"mazda\", \"toyouta\", \"maxda\", \"honda\", \"subaru\", \"nissan\"]                                                                              │\n",
       "│ 3   │ 2.0     │ [\"volkswagen\", \"peugeot\", \"audi\", \"saab\", \"bmw\", \"opel\", \"fiat\", \"volvo\", \"renault\", \"vw\", \"mercedes-benz\", \"mercedes\", \"vokswagen\", \"triumph\"]             │"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "origin_brand3 = @pipe df |>\n",
    "                      groupby(_, :origin) |>\n",
    "                      combine(_, :brand => x -> Ref(unique(x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we used `Ref` to protect the return value of our function to be broadcasted (this is the same trick as in standard broadcasting in Julia). Otherwise we would get:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>origin</th><th>brand_unique</th></tr><tr><th></th><th>Float64</th><th>String</th></tr></thead><tbody><p>37 rows × 2 columns</p><tr><th>1</th><td>1.0</td><td>chevrolet</td></tr><tr><th>2</th><td>1.0</td><td>buick</td></tr><tr><th>3</th><td>1.0</td><td>plymouth</td></tr><tr><th>4</th><td>1.0</td><td>amc</td></tr><tr><th>5</th><td>1.0</td><td>ford</td></tr><tr><th>6</th><td>1.0</td><td>pontiac</td></tr><tr><th>7</th><td>1.0</td><td>dodge</td></tr><tr><th>8</th><td>1.0</td><td>chevy</td></tr><tr><th>9</th><td>1.0</td><td>hi</td></tr><tr><th>10</th><td>1.0</td><td>mercury</td></tr><tr><th>11</th><td>1.0</td><td>oldsmobile</td></tr><tr><th>12</th><td>1.0</td><td>chrysler</td></tr><tr><th>13</th><td>1.0</td><td>chevroelt</td></tr><tr><th>14</th><td>1.0</td><td>capri</td></tr><tr><th>15</th><td>1.0</td><td>cadillac</td></tr><tr><th>16</th><td>3.0</td><td>toyota</td></tr><tr><th>17</th><td>3.0</td><td>datsun</td></tr><tr><th>18</th><td>3.0</td><td>mazda</td></tr><tr><th>19</th><td>3.0</td><td>toyouta</td></tr><tr><th>20</th><td>3.0</td><td>maxda</td></tr><tr><th>21</th><td>3.0</td><td>honda</td></tr><tr><th>22</th><td>3.0</td><td>subaru</td></tr><tr><th>23</th><td>3.0</td><td>nissan</td></tr><tr><th>24</th><td>2.0</td><td>volkswagen</td></tr><tr><th>25</th><td>2.0</td><td>peugeot</td></tr><tr><th>26</th><td>2.0</td><td>audi</td></tr><tr><th>27</th><td>2.0</td><td>saab</td></tr><tr><th>28</th><td>2.0</td><td>bmw</td></tr><tr><th>29</th><td>2.0</td><td>opel</td></tr><tr><th>30</th><td>2.0</td><td>fiat</td></tr><tr><th>31</th><td>2.0</td><td>volvo</td></tr><tr><th>32</th><td>2.0</td><td>renault</td></tr><tr><th>33</th><td>2.0</td><td>vw</td></tr><tr><th>34</th><td>2.0</td><td>mercedes-benz</td></tr><tr><th>35</th><td>2.0</td><td>mercedes</td></tr><tr><th>36</th><td>2.0</td><td>vokswagen</td></tr><tr><th>37</th><td>2.0</td><td>triumph</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& origin & brand\\_unique\\\\\n",
       "\t\\hline\n",
       "\t& Float64 & String\\\\\n",
       "\t\\hline\n",
       "\t1 & 1.0 & chevrolet \\\\\n",
       "\t2 & 1.0 & buick \\\\\n",
       "\t3 & 1.0 & plymouth \\\\\n",
       "\t4 & 1.0 & amc \\\\\n",
       "\t5 & 1.0 & ford \\\\\n",
       "\t6 & 1.0 & pontiac \\\\\n",
       "\t7 & 1.0 & dodge \\\\\n",
       "\t8 & 1.0 & chevy \\\\\n",
       "\t9 & 1.0 & hi \\\\\n",
       "\t10 & 1.0 & mercury \\\\\n",
       "\t11 & 1.0 & oldsmobile \\\\\n",
       "\t12 & 1.0 & chrysler \\\\\n",
       "\t13 & 1.0 & chevroelt \\\\\n",
       "\t14 & 1.0 & capri \\\\\n",
       "\t15 & 1.0 & cadillac \\\\\n",
       "\t16 & 3.0 & toyota \\\\\n",
       "\t17 & 3.0 & datsun \\\\\n",
       "\t18 & 3.0 & mazda \\\\\n",
       "\t19 & 3.0 & toyouta \\\\\n",
       "\t20 & 3.0 & maxda \\\\\n",
       "\t21 & 3.0 & honda \\\\\n",
       "\t22 & 3.0 & subaru \\\\\n",
       "\t23 & 3.0 & nissan \\\\\n",
       "\t24 & 2.0 & volkswagen \\\\\n",
       "\t25 & 2.0 & peugeot \\\\\n",
       "\t26 & 2.0 & audi \\\\\n",
       "\t27 & 2.0 & saab \\\\\n",
       "\t28 & 2.0 & bmw \\\\\n",
       "\t29 & 2.0 & opel \\\\\n",
       "\t30 & 2.0 & fiat \\\\\n",
       "\t31 & 2.0 & volvo \\\\\n",
       "\t32 & 2.0 & renault \\\\\n",
       "\t33 & 2.0 & vw \\\\\n",
       "\t34 & 2.0 & mercedes-benz \\\\\n",
       "\t35 & 2.0 & mercedes \\\\\n",
       "\t36 & 2.0 & vokswagen \\\\\n",
       "\t37 & 2.0 & triumph \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "37×2 DataFrame\n",
       "│ Row │ origin  │ brand_unique  │\n",
       "│     │ \u001b[90mFloat64\u001b[39m │ \u001b[90mString\u001b[39m        │\n",
       "├─────┼─────────┼───────────────┤\n",
       "│ 1   │ 1.0     │ chevrolet     │\n",
       "│ 2   │ 1.0     │ buick         │\n",
       "│ 3   │ 1.0     │ plymouth      │\n",
       "│ 4   │ 1.0     │ amc           │\n",
       "│ 5   │ 1.0     │ ford          │\n",
       "│ 6   │ 1.0     │ pontiac       │\n",
       "│ 7   │ 1.0     │ dodge         │\n",
       "│ 8   │ 1.0     │ chevy         │\n",
       "│ 9   │ 1.0     │ hi            │\n",
       "│ 10  │ 1.0     │ mercury       │\n",
       "│ 11  │ 1.0     │ oldsmobile    │\n",
       "│ 12  │ 1.0     │ chrysler      │\n",
       "│ 13  │ 1.0     │ chevroelt     │\n",
       "│ 14  │ 1.0     │ capri         │\n",
       "│ 15  │ 1.0     │ cadillac      │\n",
       "│ 16  │ 3.0     │ toyota        │\n",
       "│ 17  │ 3.0     │ datsun        │\n",
       "│ 18  │ 3.0     │ mazda         │\n",
       "│ 19  │ 3.0     │ toyouta       │\n",
       "│ 20  │ 3.0     │ maxda         │\n",
       "│ 21  │ 3.0     │ honda         │\n",
       "│ 22  │ 3.0     │ subaru        │\n",
       "│ 23  │ 3.0     │ nissan        │\n",
       "│ 24  │ 2.0     │ volkswagen    │\n",
       "│ 25  │ 2.0     │ peugeot       │\n",
       "│ 26  │ 2.0     │ audi          │\n",
       "│ 27  │ 2.0     │ saab          │\n",
       "│ 28  │ 2.0     │ bmw           │\n",
       "│ 29  │ 2.0     │ opel          │\n",
       "│ 30  │ 2.0     │ fiat          │\n",
       "│ 31  │ 2.0     │ volvo         │\n",
       "│ 32  │ 2.0     │ renault       │\n",
       "│ 33  │ 2.0     │ vw            │\n",
       "│ 34  │ 2.0     │ mercedes-benz │\n",
       "│ 35  │ 2.0     │ mercedes      │\n",
       "│ 36  │ 2.0     │ vokswagen     │\n",
       "│ 37  │ 2.0     │ triumph       │"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@pipe df |> groupby(_, :origin) |> combine(_, :brand => unique)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we check if the groups are pairwise disjoint (this is a bit tricky, but it is much fun to be able to write such code in Julia):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 vs 2 => String[]\n",
      "1 vs 3 => String[]\n",
      "2 vs 3 => String[]\n"
     ]
    }
   ],
   "source": [
    "for i in axes(origin_brand3, 1)\n",
    "    for j in i+1:nrow(origin_brand3)\n",
    "        println(\"$i vs $j => \", intersect(origin_brand3.brand_function[[i,j]]...))\n",
    "    end\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also easily flatten the `origin_brand3` data frame so that `brand_function` column is expanded to span multiple rows (the case we have seen above when we omitted writing `Ref` in `combine`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>origin</th><th>brand_function</th></tr><tr><th></th><th>Float64</th><th>String</th></tr></thead><tbody><p>37 rows × 2 columns</p><tr><th>1</th><td>1.0</td><td>chevrolet</td></tr><tr><th>2</th><td>1.0</td><td>buick</td></tr><tr><th>3</th><td>1.0</td><td>plymouth</td></tr><tr><th>4</th><td>1.0</td><td>amc</td></tr><tr><th>5</th><td>1.0</td><td>ford</td></tr><tr><th>6</th><td>1.0</td><td>pontiac</td></tr><tr><th>7</th><td>1.0</td><td>dodge</td></tr><tr><th>8</th><td>1.0</td><td>chevy</td></tr><tr><th>9</th><td>1.0</td><td>hi</td></tr><tr><th>10</th><td>1.0</td><td>mercury</td></tr><tr><th>11</th><td>1.0</td><td>oldsmobile</td></tr><tr><th>12</th><td>1.0</td><td>chrysler</td></tr><tr><th>13</th><td>1.0</td><td>chevroelt</td></tr><tr><th>14</th><td>1.0</td><td>capri</td></tr><tr><th>15</th><td>1.0</td><td>cadillac</td></tr><tr><th>16</th><td>3.0</td><td>toyota</td></tr><tr><th>17</th><td>3.0</td><td>datsun</td></tr><tr><th>18</th><td>3.0</td><td>mazda</td></tr><tr><th>19</th><td>3.0</td><td>toyouta</td></tr><tr><th>20</th><td>3.0</td><td>maxda</td></tr><tr><th>21</th><td>3.0</td><td>honda</td></tr><tr><th>22</th><td>3.0</td><td>subaru</td></tr><tr><th>23</th><td>3.0</td><td>nissan</td></tr><tr><th>24</th><td>2.0</td><td>volkswagen</td></tr><tr><th>25</th><td>2.0</td><td>peugeot</td></tr><tr><th>26</th><td>2.0</td><td>audi</td></tr><tr><th>27</th><td>2.0</td><td>saab</td></tr><tr><th>28</th><td>2.0</td><td>bmw</td></tr><tr><th>29</th><td>2.0</td><td>opel</td></tr><tr><th>30</th><td>2.0</td><td>fiat</td></tr><tr><th>31</th><td>2.0</td><td>volvo</td></tr><tr><th>32</th><td>2.0</td><td>renault</td></tr><tr><th>33</th><td>2.0</td><td>vw</td></tr><tr><th>34</th><td>2.0</td><td>mercedes-benz</td></tr><tr><th>35</th><td>2.0</td><td>mercedes</td></tr><tr><th>36</th><td>2.0</td><td>vokswagen</td></tr><tr><th>37</th><td>2.0</td><td>triumph</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& origin & brand\\_function\\\\\n",
       "\t\\hline\n",
       "\t& Float64 & String\\\\\n",
       "\t\\hline\n",
       "\t1 & 1.0 & chevrolet \\\\\n",
       "\t2 & 1.0 & buick \\\\\n",
       "\t3 & 1.0 & plymouth \\\\\n",
       "\t4 & 1.0 & amc \\\\\n",
       "\t5 & 1.0 & ford \\\\\n",
       "\t6 & 1.0 & pontiac \\\\\n",
       "\t7 & 1.0 & dodge \\\\\n",
       "\t8 & 1.0 & chevy \\\\\n",
       "\t9 & 1.0 & hi \\\\\n",
       "\t10 & 1.0 & mercury \\\\\n",
       "\t11 & 1.0 & oldsmobile \\\\\n",
       "\t12 & 1.0 & chrysler \\\\\n",
       "\t13 & 1.0 & chevroelt \\\\\n",
       "\t14 & 1.0 & capri \\\\\n",
       "\t15 & 1.0 & cadillac \\\\\n",
       "\t16 & 3.0 & toyota \\\\\n",
       "\t17 & 3.0 & datsun \\\\\n",
       "\t18 & 3.0 & mazda \\\\\n",
       "\t19 & 3.0 & toyouta \\\\\n",
       "\t20 & 3.0 & maxda \\\\\n",
       "\t21 & 3.0 & honda \\\\\n",
       "\t22 & 3.0 & subaru \\\\\n",
       "\t23 & 3.0 & nissan \\\\\n",
       "\t24 & 2.0 & volkswagen \\\\\n",
       "\t25 & 2.0 & peugeot \\\\\n",
       "\t26 & 2.0 & audi \\\\\n",
       "\t27 & 2.0 & saab \\\\\n",
       "\t28 & 2.0 & bmw \\\\\n",
       "\t29 & 2.0 & opel \\\\\n",
       "\t30 & 2.0 & fiat \\\\\n",
       "\t31 & 2.0 & volvo \\\\\n",
       "\t32 & 2.0 & renault \\\\\n",
       "\t33 & 2.0 & vw \\\\\n",
       "\t34 & 2.0 & mercedes-benz \\\\\n",
       "\t35 & 2.0 & mercedes \\\\\n",
       "\t36 & 2.0 & vokswagen \\\\\n",
       "\t37 & 2.0 & triumph \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "37×2 DataFrame\n",
       "│ Row │ origin  │ brand_function │\n",
       "│     │ \u001b[90mFloat64\u001b[39m │ \u001b[90mString\u001b[39m         │\n",
       "├─────┼─────────┼────────────────┤\n",
       "│ 1   │ 1.0     │ chevrolet      │\n",
       "│ 2   │ 1.0     │ buick          │\n",
       "│ 3   │ 1.0     │ plymouth       │\n",
       "│ 4   │ 1.0     │ amc            │\n",
       "│ 5   │ 1.0     │ ford           │\n",
       "│ 6   │ 1.0     │ pontiac        │\n",
       "│ 7   │ 1.0     │ dodge          │\n",
       "│ 8   │ 1.0     │ chevy          │\n",
       "│ 9   │ 1.0     │ hi             │\n",
       "│ 10  │ 1.0     │ mercury        │\n",
       "│ 11  │ 1.0     │ oldsmobile     │\n",
       "│ 12  │ 1.0     │ chrysler       │\n",
       "│ 13  │ 1.0     │ chevroelt      │\n",
       "│ 14  │ 1.0     │ capri          │\n",
       "│ 15  │ 1.0     │ cadillac       │\n",
       "│ 16  │ 3.0     │ toyota         │\n",
       "│ 17  │ 3.0     │ datsun         │\n",
       "│ 18  │ 3.0     │ mazda          │\n",
       "│ 19  │ 3.0     │ toyouta        │\n",
       "│ 20  │ 3.0     │ maxda          │\n",
       "│ 21  │ 3.0     │ honda          │\n",
       "│ 22  │ 3.0     │ subaru         │\n",
       "│ 23  │ 3.0     │ nissan         │\n",
       "│ 24  │ 2.0     │ volkswagen     │\n",
       "│ 25  │ 2.0     │ peugeot        │\n",
       "│ 26  │ 2.0     │ audi           │\n",
       "│ 27  │ 2.0     │ saab           │\n",
       "│ 28  │ 2.0     │ bmw            │\n",
       "│ 29  │ 2.0     │ opel           │\n",
       "│ 30  │ 2.0     │ fiat           │\n",
       "│ 31  │ 2.0     │ volvo          │\n",
       "│ 32  │ 2.0     │ renault        │\n",
       "│ 33  │ 2.0     │ vw             │\n",
       "│ 34  │ 2.0     │ mercedes-benz  │\n",
       "│ 35  │ 2.0     │ mercedes       │\n",
       "│ 36  │ 2.0     │ vokswagen      │\n",
       "│ 37  │ 2.0     │ triumph        │"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flatten(origin_brand3, :brand_function)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Julia 1.4.1",
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   "file_extension": ".jl",
   "mimetype": "application/julia",
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