13_extras.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to DataFrames\n",
    "**[Bogumił Kamiński](http://bogumilkaminski.pl/about/), February 13, 2023**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "using DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "using CategoricalArrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extras - selected functionalities of selected packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "using FreqTables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4×3 Named Matrix{Int64}\n",
       "a ╲ b │  x   y   z\n",
       "──────┼───────────\n",
       "'a'   │ 72  94  88\n",
       "'b'   │ 76  91  69\n",
       "'c'   │ 78  74  88\n",
       "'d'   │ 97  85  88"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = DataFrame(a=rand('a':'d', 1000), b=rand([\"x\", \"y\", \"z\"], 1000))\n",
    "ft = freqtable(df, :a, :b) # observe that dimensions are sorted if possible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(72, 69)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ft[1,1], ft['b', \"z\"] # you can index the result using numbers or names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4×3 Named Matrix{Float64}\n",
       "a ╲ b │        x         y         z\n",
       "──────┼─────────────────────────────\n",
       "'a'   │ 0.283465  0.370079  0.346457\n",
       "'b'   │ 0.322034  0.385593  0.292373\n",
       "'c'   │    0.325  0.308333  0.366667\n",
       "'d'   │ 0.359259  0.314815  0.325926"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop(ft, margins=1) # getting proportions - 1 means we want to calculate them in rows (first dimension)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4×3 Named Matrix{Float64}\n",
       "a ╲ b │        x         y         z\n",
       "──────┼─────────────────────────────\n",
       "'a'   │  0.22291  0.273256  0.264264\n",
       "'b'   │ 0.235294  0.264535  0.207207\n",
       "'c'   │ 0.241486  0.215116  0.264264\n",
       "'d'   │  0.30031  0.247093  0.264264"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop(ft, margins=2) # and columns are normalized to 1.0 now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-element Named Vector{Int64}\n",
       "Dim1  │ \n",
       "──────┼──\n",
       "3     │ 2\n",
       "1     │ 4\n",
       "2     │ 4\n",
       "4     │ 0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = categorical(rand(1:3, 10))\n",
    "levels!(x, [3, 1, 2, 4]) # reordering levels and adding an extra level\n",
    "freqtable(x) # order is preserved and not-used level is shown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-element Named Vector{Int64}\n",
       "Dim1    │ \n",
       "────────┼──\n",
       "1       │ 2\n",
       "2       │ 1\n",
       "3       │ 1\n",
       "missing │ 1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freqtable([1,1,2,3,missing]) # by default missings are listed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3-element Named Vector{Int64}\n",
       "Dim1  │ \n",
       "──────┼──\n",
       "1     │ 2\n",
       "2     │ 1\n",
       "3     │ 1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freqtable([1,1,2,3,missing], skipmissing=true) # but we can skip them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2×2 Named Matrix{Int64}\n",
       "a ╲ b │   5    6\n",
       "──────┼─────────\n",
       "3     │ 227  256\n",
       "4     │ 263  254"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = DataFrame(a=rand(3:4, 1000), b=rand(5:6, 1000))\n",
    "ft = freqtable(df, :a, :b) # now dimensions are numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "LoadError",
     "evalue": "BoundsError: attempt to access 2×2 Matrix{Int64} at index [3, 5]",
     "output_type": "error",
     "traceback": [
      "BoundsError: attempt to access 2×2 Matrix{Int64} at index [3, 5]",
      "",
      "Stacktrace:",
      " [1] getindex",
      "   @ .\\essentials.jl:14 [inlined]",
      " [2] getindex(::NamedArrays.NamedMatrix{Int64, Matrix{Int64}, Tuple{OrderedCollections.OrderedDict{Int64, Int64}, OrderedCollections.OrderedDict{Int64, Int64}}}, ::Int64, ::Int64)",
      "   @ NamedArrays C:\\Users\\bogum\\.julia\\packages\\NamedArrays\\TuJLn\\src\\index.jl:17",
      " [3] top-level scope",
      "   @ In[12]:1"
     ]
    }
   ],
   "source": [
    "ft[3,5] # this is an error - standard array indexing takes precedence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "227"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ft[Name(3), Name(5)] # you have to use Name() wrapper"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataFramesMeta.jl - working on `DataFrame`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataFramesMeta.jl provides a more terse syntax due to the benefits of metaprogramming."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "using DataFramesMeta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>8×3 DataFrame</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">x</th><th style = \"text-align: left;\">y</th><th style = \"text-align: left;\">z</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Char\" style = \"text-align: left;\">Char</th><th title = \"Bool\" style = \"text-align: left;\">Bool</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: right;\">1</td><td style = \"text-align: left;\">a</td><td style = \"text-align: right;\">true</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: right;\">2</td><td style = \"text-align: left;\">b</td><td style = \"text-align: right;\">false</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">3</td><td style = \"text-align: left;\">c</td><td style = \"text-align: right;\">true</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">4</td><td style = \"text-align: right;\">4</td><td style = \"text-align: left;\">d</td><td style = \"text-align: right;\">false</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">5</td><td style = \"text-align: right;\">5</td><td style = \"text-align: left;\">e</td><td style = \"text-align: right;\">true</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">6</td><td style = \"text-align: right;\">6</td><td style = \"text-align: left;\">f</td><td style = \"text-align: right;\">false</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">7</td><td style = \"text-align: right;\">7</td><td style = \"text-align: left;\">g</td><td style = \"text-align: right;\">true</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">8</td><td style = \"text-align: right;\">8</td><td style = \"text-align: left;\">h</td><td style = \"text-align: right;\">false</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ccc}\n",
       "\t& x & y & z\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Char & Bool\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & a & 1 \\\\\n",
       "\t2 & 2 & b & 0 \\\\\n",
       "\t3 & 3 & c & 1 \\\\\n",
       "\t4 & 4 & d & 0 \\\\\n",
       "\t5 & 5 & e & 1 \\\\\n",
       "\t6 & 6 & f & 0 \\\\\n",
       "\t7 & 7 & g & 1 \\\\\n",
       "\t8 & 8 & h & 0 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m8×3 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m x     \u001b[0m\u001b[1m y    \u001b[0m\u001b[1m z     \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Char \u001b[0m\u001b[90m Bool  \u001b[0m\n",
       "─────┼────────────────────\n",
       "   1 │     1  a      true\n",
       "   2 │     2  b     false\n",
       "   3 │     3  c      true\n",
       "   4 │     4  d     false\n",
       "   5 │     5  e      true\n",
       "   6 │     6  f     false\n",
       "   7 │     7  g      true\n",
       "   8 │     8  h     false"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = DataFrame(x=1:8, y='a':'h', z=repeat([true,false], outer=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8-element Vector{Int64}:\n",
       " 2\n",
       " 2\n",
       " 4\n",
       " 4\n",
       " 6\n",
       " 6\n",
       " 8\n",
       " 8"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@with(df, :x + :z) # expressions with columns of DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8-element Vector{Char}:\n",
       " 'b': ASCII/Unicode U+0062 (category Ll: Letter, lowercase)\n",
       " 'e': ASCII/Unicode U+0065 (category Ll: Letter, lowercase)\n",
       " 'h': ASCII/Unicode U+0068 (category Ll: Letter, lowercase)\n",
       " 'k': ASCII/Unicode U+006B (category Ll: Letter, lowercase)\n",
       " 'g': ASCII/Unicode U+0067 (category Ll: Letter, lowercase)\n",
       " 'j': ASCII/Unicode U+006A (category Ll: Letter, lowercase)\n",
       " 'm': ASCII/Unicode U+006D (category Ll: Letter, lowercase)\n",
       " 'p': ASCII/Unicode U+0070 (category Ll: Letter, lowercase)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@with df begin # you can define code blocks\n",
    "    a = :x[:z]\n",
    "    b = :x[.!:z]\n",
    "    :y + [a; b]\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "LoadError",
     "evalue": "UndefVarError: `a` not defined",
     "output_type": "error",
     "traceback": [
      "UndefVarError: `a` not defined",
      ""
     ]
    }
   ],
   "source": [
    "a # @with creates hard scope so variables do not leak out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3-element BitVector:\n",
       " 1\n",
       " 0\n",
       " 0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = DataFrame(a = [:a, :b, :c])\n",
    "@with(df2, :a .== ^(:a)) # sometimes we want to work on a raw Symbol, ^() escapes it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3-element Vector{Int64}:\n",
       " 5\n",
       " 7\n",
       " 9"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_str = \"x\"\n",
    "y_str = \"y\"\n",
    "df2 = DataFrame(x=1:3, y=4:6, z=7:9)\n",
    "# $expression inderpolates the expression in-place; in particular this way you can use column names passed as strings\n",
    "@with(df2, $x_str + $y_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>2×3 DataFrame</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">x</th><th style = \"text-align: left;\">y</th><th style = \"text-align: left;\">z</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Char\" style = \"text-align: left;\">Char</th><th title = \"Bool\" style = \"text-align: left;\">Bool</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: right;\">1</td><td style = \"text-align: left;\">a</td><td style = \"text-align: right;\">true</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: right;\">3</td><td style = \"text-align: left;\">c</td><td style = \"text-align: right;\">true</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ccc}\n",
       "\t& x & y & z\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Char & Bool\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & a & 1 \\\\\n",
       "\t2 & 3 & c & 1 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m2×3 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m x     \u001b[0m\u001b[1m y    \u001b[0m\u001b[1m z    \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Char \u001b[0m\u001b[90m Bool \u001b[0m\n",
       "─────┼───────────────────\n",
       "   1 │     1  a     true\n",
       "   2 │     3  c     true"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@subset(df, :x .< 4, :z .== true) # a very useful macro for filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>8×3 DataFrame</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">x</th><th style = \"text-align: left;\">y</th><th style = \"text-align: left;\">z</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Char\" style = \"text-align: left;\">Char</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">2</td><td style = \"text-align: left;\">a</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">4</td><td style = \"text-align: left;\">b</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">3</td><td style = \"text-align: right;\">6</td><td style = \"text-align: left;\">c</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">4</td><td style = \"text-align: right;\">4</td><td style = \"text-align: right;\">8</td><td style = \"text-align: left;\">d</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">5</td><td style = \"text-align: right;\">5</td><td style = \"text-align: right;\">10</td><td style = \"text-align: left;\">e</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">6</td><td style = \"text-align: right;\">6</td><td style = \"text-align: right;\">12</td><td style = \"text-align: left;\">f</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">7</td><td style = \"text-align: right;\">7</td><td style = \"text-align: right;\">14</td><td style = \"text-align: left;\">g</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">8</td><td style = \"text-align: right;\">8</td><td style = \"text-align: right;\">16</td><td style = \"text-align: left;\">h</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ccc}\n",
       "\t& x & y & z\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Int64 & Char\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 2 & a \\\\\n",
       "\t2 & 2 & 4 & b \\\\\n",
       "\t3 & 3 & 6 & c \\\\\n",
       "\t4 & 4 & 8 & d \\\\\n",
       "\t5 & 5 & 10 & e \\\\\n",
       "\t6 & 6 & 12 & f \\\\\n",
       "\t7 & 7 & 14 & g \\\\\n",
       "\t8 & 8 & 16 & h \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m8×3 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m x     \u001b[0m\u001b[1m y     \u001b[0m\u001b[1m z    \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Char \u001b[0m\n",
       "─────┼────────────────────\n",
       "   1 │     1      2  a\n",
       "   2 │     2      4  b\n",
       "   3 │     3      6  c\n",
       "   4 │     4      8  d\n",
       "   5 │     5     10  e\n",
       "   6 │     6     12  f\n",
       "   7 │     7     14  g\n",
       "   8 │     8     16  h"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@select(df, :x, :y = 2*:x, :z=:y) # create a new DataFrame based on the old one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>8×3 DataFrame</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">x</th><th style = \"text-align: left;\">y</th><th style = \"text-align: left;\">z</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Bool\" style = \"text-align: left;\">Bool</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">true</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: right;\">4</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">false</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">6</td><td style = \"text-align: right;\">3</td><td style = \"text-align: right;\">true</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">4</td><td style = \"text-align: right;\">8</td><td style = \"text-align: right;\">4</td><td style = \"text-align: right;\">false</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">5</td><td style = \"text-align: right;\">10</td><td style = \"text-align: right;\">5</td><td style = \"text-align: right;\">true</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">6</td><td style = \"text-align: right;\">12</td><td style = \"text-align: right;\">6</td><td style = \"text-align: right;\">false</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">7</td><td style = \"text-align: right;\">14</td><td style = \"text-align: right;\">7</td><td style = \"text-align: right;\">true</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">8</td><td style = \"text-align: right;\">16</td><td style = \"text-align: right;\">8</td><td style = \"text-align: right;\">false</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ccc}\n",
       "\t& x & y & z\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Int64 & Bool\\\\\n",
       "\t\\hline\n",
       "\t1 & 2 & 1 & 1 \\\\\n",
       "\t2 & 4 & 2 & 0 \\\\\n",
       "\t3 & 6 & 3 & 1 \\\\\n",
       "\t4 & 8 & 4 & 0 \\\\\n",
       "\t5 & 10 & 5 & 1 \\\\\n",
       "\t6 & 12 & 6 & 0 \\\\\n",
       "\t7 & 14 & 7 & 1 \\\\\n",
       "\t8 & 16 & 8 & 0 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m8×3 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m x     \u001b[0m\u001b[1m y     \u001b[0m\u001b[1m z     \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Bool  \u001b[0m\n",
       "─────┼─────────────────────\n",
       "   1 │     2      1   true\n",
       "   2 │     4      2  false\n",
       "   3 │     6      3   true\n",
       "   4 │     8      4  false\n",
       "   5 │    10      5   true\n",
       "   6 │    12      6  false\n",
       "   7 │    14      7   true\n",
       "   8 │    16      8  false"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@transform(df, :x = 2*:x, :y = :x) # create a new DataFrame adding columns based on the old one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>8×3 DataFrame</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">x</th><th style = \"text-align: left;\">y</th><th style = \"text-align: left;\">z</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Char\" style = \"text-align: left;\">Char</th><th title = \"Bool\" style = \"text-align: left;\">Bool</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: right;\">8</td><td style = \"text-align: left;\">h</td><td style = \"text-align: right;\">false</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: right;\">6</td><td style = \"text-align: left;\">f</td><td style = \"text-align: right;\">false</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">4</td><td style = \"text-align: left;\">d</td><td style = \"text-align: right;\">false</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">4</td><td style = \"text-align: right;\">2</td><td style = \"text-align: left;\">b</td><td style = \"text-align: right;\">false</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">5</td><td style = \"text-align: right;\">7</td><td style = \"text-align: left;\">g</td><td style = \"text-align: right;\">true</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">6</td><td style = \"text-align: right;\">5</td><td style = \"text-align: left;\">e</td><td style = \"text-align: right;\">true</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">7</td><td style = \"text-align: right;\">3</td><td style = \"text-align: left;\">c</td><td style = \"text-align: right;\">true</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">8</td><td style = \"text-align: right;\">1</td><td style = \"text-align: left;\">a</td><td style = \"text-align: right;\">true</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ccc}\n",
       "\t& x & y & z\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Char & Bool\\\\\n",
       "\t\\hline\n",
       "\t1 & 8 & h & 0 \\\\\n",
       "\t2 & 6 & f & 0 \\\\\n",
       "\t3 & 4 & d & 0 \\\\\n",
       "\t4 & 2 & b & 0 \\\\\n",
       "\t5 & 7 & g & 1 \\\\\n",
       "\t6 & 5 & e & 1 \\\\\n",
       "\t7 & 3 & c & 1 \\\\\n",
       "\t8 & 1 & a & 1 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m8×3 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m x     \u001b[0m\u001b[1m y    \u001b[0m\u001b[1m z     \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Char \u001b[0m\u001b[90m Bool  \u001b[0m\n",
       "─────┼────────────────────\n",
       "   1 │     8  h     false\n",
       "   2 │     6  f     false\n",
       "   3 │     4  d     false\n",
       "   4 │     2  b     false\n",
       "   5 │     7  g      true\n",
       "   6 │     5  e      true\n",
       "   7 │     3  c      true\n",
       "   8 │     1  a      true"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@orderby(df, :z, -:x) # sorting into a new data frame, less powerful than sort, but lightweight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "using Chain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>4×3 DataFrame</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">z</th><th style = \"text-align: left;\">x</th><th style = \"text-align: left;\">x²</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"Bool\" style = \"text-align: left;\">Bool</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: right;\">false</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">4</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: right;\">false</td><td style = \"text-align: right;\">4</td><td style = \"text-align: right;\">16</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">true</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">1</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">4</td><td style = \"text-align: right;\">true</td><td style = \"text-align: right;\">3</td><td style = \"text-align: right;\">9</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ccc}\n",
       "\t& z & x & x²\\\\\n",
       "\t\\hline\n",
       "\t& Bool & Int64 & Int64\\\\\n",
       "\t\\hline\n",
       "\t1 & 0 & 2 & 4 \\\\\n",
       "\t2 & 0 & 4 & 16 \\\\\n",
       "\t3 & 1 & 1 & 1 \\\\\n",
       "\t4 & 1 & 3 & 9 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m4×3 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m z     \u001b[0m\u001b[1m x     \u001b[0m\u001b[1m x²    \u001b[0m\n",
       "     │\u001b[90m Bool  \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\n",
       "─────┼─────────────────────\n",
       "   1 │ false      2      4\n",
       "   2 │ false      4     16\n",
       "   3 │  true      1      1\n",
       "   4 │  true      3      9"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@chain df begin # chaining of operations on DataFrame\n",
    "    @subset(:x .< 5)\n",
    "    @orderby(:z)\n",
    "    @transform(:x² = :x .^ 2)\n",
    "    @select(:z, :x, :x²)\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataFramesMeta - working on grouped `DataFrame`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>12×2 DataFrame</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">a</th><th style = \"text-align: left;\">b</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Char\" style = \"text-align: left;\">Char</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: right;\">1</td><td style = \"text-align: left;\">a</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: right;\">2</td><td style = \"text-align: left;\">b</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">3</td><td style = \"text-align: left;\">c</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">4</td><td style = \"text-align: right;\">4</td><td style = \"text-align: left;\">d</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">5</td><td style = \"text-align: right;\">5</td><td style = \"text-align: left;\">a</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">6</td><td style = \"text-align: right;\">6</td><td style = \"text-align: left;\">b</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">7</td><td style = \"text-align: right;\">7</td><td style = \"text-align: left;\">c</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">8</td><td style = \"text-align: right;\">8</td><td style = \"text-align: left;\">d</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">9</td><td style = \"text-align: right;\">9</td><td style = \"text-align: left;\">a</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">10</td><td style = \"text-align: right;\">10</td><td style = \"text-align: left;\">b</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">11</td><td style = \"text-align: right;\">11</td><td style = \"text-align: left;\">c</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">12</td><td style = \"text-align: right;\">12</td><td style = \"text-align: left;\">d</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& a & b\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Char\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & a \\\\\n",
       "\t2 & 2 & b \\\\\n",
       "\t3 & 3 & c \\\\\n",
       "\t4 & 4 & d \\\\\n",
       "\t5 & 5 & a \\\\\n",
       "\t6 & 6 & b \\\\\n",
       "\t7 & 7 & c \\\\\n",
       "\t8 & 8 & d \\\\\n",
       "\t9 & 9 & a \\\\\n",
       "\t10 & 10 & b \\\\\n",
       "\t11 & 11 & c \\\\\n",
       "\t12 & 12 & d \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m12×2 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m a     \u001b[0m\u001b[1m b    \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Char \u001b[0m\n",
       "─────┼─────────────\n",
       "   1 │     1  a\n",
       "   2 │     2  b\n",
       "   3 │     3  c\n",
       "   4 │     4  d\n",
       "   5 │     5  a\n",
       "   6 │     6  b\n",
       "   7 │     7  c\n",
       "   8 │     8  d\n",
       "   9 │     9  a\n",
       "  10 │    10  b\n",
       "  11 │    11  c\n",
       "  12 │    12  d"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = DataFrame(a = 1:12, b = repeat('a':'d', outer=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p><b>GroupedDataFrame with 4 groups based on key: b</b></p><div><div style = \"float: left;\"><span>First Group (3 rows): b = &apos;a&apos;: ASCII/Unicode U+0061 (category Ll: Letter, lowercase)</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">a</th><th style = \"text-align: left;\">b</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Char\" style = \"text-align: left;\">Char</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: right;\">1</td><td style = \"text-align: left;\">a</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: right;\">5</td><td style = \"text-align: left;\">a</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">9</td><td style = \"text-align: left;\">a</td></tr></tbody></table></div><p>&vellip;</p><div><div style = \"float: left;\"><span>Last Group (3 rows): b = &apos;d&apos;: ASCII/Unicode U+0064 (category Ll: Letter, lowercase)</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">a</th><th style = \"text-align: left;\">b</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Char\" style = \"text-align: left;\">Char</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: right;\">4</td><td style = \"text-align: left;\">d</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: right;\">8</td><td style = \"text-align: left;\">d</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">12</td><td style = \"text-align: left;\">d</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "GroupedDataFrame with 4 groups based on key: b\n",
       "\n",
       "First Group (3 rows): b = 'a': ASCII/Unicode U+0061 (category Ll: Letter, lowercase)\n",
       "\n",
       "\\begin{tabular}{r|cc}\n",
       "\t& a & b\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Char\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & a \\\\\n",
       "\t2 & 5 & a \\\\\n",
       "\t3 & 9 & a \\\\\n",
       "\\end{tabular}\n",
       "\n",
       "$\\dots$\n",
       "\n",
       "Last Group (3 rows): b = 'd': ASCII/Unicode U+0064 (category Ll: Letter, lowercase)\n",
       "\n",
       "\\begin{tabular}{r|cc}\n",
       "\t& a & b\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Char\\\\\n",
       "\t\\hline\n",
       "\t1 & 4 & d \\\\\n",
       "\t2 & 8 & d \\\\\n",
       "\t3 & 12 & d \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "GroupedDataFrame with 4 groups based on key: b\n",
       "First Group (3 rows): b = 'a': ASCII/Unicode U+0061 (category Ll: Letter, lowercase)\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m a     \u001b[0m\u001b[1m b    \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Char \u001b[0m\n",
       "─────┼─────────────\n",
       "   1 │     1  a\n",
       "   2 │     5  a\n",
       "   3 │     9  a\n",
       "⋮\n",
       "Last Group (3 rows): b = 'd': ASCII/Unicode U+0064 (category Ll: Letter, lowercase)\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m a     \u001b[0m\u001b[1m b    \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Char \u001b[0m\n",
       "─────┼─────────────\n",
       "   1 │     4  d\n",
       "   2 │     8  d\n",
       "   3 │    12  d"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = groupby(df, :b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "using Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>4×4 DataFrame</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">b</th><th style = \"text-align: left;\">first</th><th style = \"text-align: left;\">last</th><th style = \"text-align: left;\">mean</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"Char\" style = \"text-align: left;\">Char</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: left;\">a</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">5.0</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: left;\">b</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">10</td><td style = \"text-align: right;\">6.0</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: left;\">c</td><td style = \"text-align: right;\">3</td><td style = \"text-align: right;\">11</td><td style = \"text-align: right;\">7.0</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">4</td><td style = \"text-align: left;\">d</td><td style = \"text-align: right;\">4</td><td style = \"text-align: right;\">12</td><td style = \"text-align: right;\">8.0</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& b & first & last & mean\\\\\n",
       "\t\\hline\n",
       "\t& Char & Int64 & Int64 & Float64\\\\\n",
       "\t\\hline\n",
       "\t1 & a & 1 & 9 & 5.0 \\\\\n",
       "\t2 & b & 2 & 10 & 6.0 \\\\\n",
       "\t3 & c & 3 & 11 & 7.0 \\\\\n",
       "\t4 & d & 4 & 12 & 8.0 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m4×4 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m b    \u001b[0m\u001b[1m first \u001b[0m\u001b[1m last  \u001b[0m\u001b[1m mean    \u001b[0m\n",
       "     │\u001b[90m Char \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Float64 \u001b[0m\n",
       "─────┼─────────────────────────────\n",
       "   1 │ a         1      9      5.0\n",
       "   2 │ b         2     10      6.0\n",
       "   3 │ c         3     11      7.0\n",
       "   4 │ d         4     12      8.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@by(df, :b, :first = first(:a), :last = last(:a), :mean = mean(:a)) # groupby+combine in one shot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>4×4 DataFrame</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">b</th><th style = \"text-align: left;\">first</th><th style = \"text-align: left;\">last</th><th style = \"text-align: left;\">mean</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"Char\" style = \"text-align: left;\">Char</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: left;\">a</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">5.0</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: left;\">b</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">10</td><td style = \"text-align: right;\">6.0</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: left;\">c</td><td style = \"text-align: right;\">3</td><td style = \"text-align: right;\">11</td><td style = \"text-align: right;\">7.0</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">4</td><td style = \"text-align: left;\">d</td><td style = \"text-align: right;\">4</td><td style = \"text-align: right;\">12</td><td style = \"text-align: right;\">8.0</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& b & first & last & mean\\\\\n",
       "\t\\hline\n",
       "\t& Char & Int64 & Int64 & Float64\\\\\n",
       "\t\\hline\n",
       "\t1 & a & 1 & 9 & 5.0 \\\\\n",
       "\t2 & b & 2 & 10 & 6.0 \\\\\n",
       "\t3 & c & 3 & 11 & 7.0 \\\\\n",
       "\t4 & d & 4 & 12 & 8.0 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m4×4 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m b    \u001b[0m\u001b[1m first \u001b[0m\u001b[1m last  \u001b[0m\u001b[1m mean    \u001b[0m\n",
       "     │\u001b[90m Char \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Float64 \u001b[0m\n",
       "─────┼─────────────────────────────\n",
       "   1 │ a         1      9      5.0\n",
       "   2 │ b         2     10      6.0\n",
       "   3 │ c         3     11      7.0\n",
       "   4 │ d         4     12      8.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@combine(g, :first = first(:a), :last = last(:a), :mean = mean(:a)) # the same as by but on grouped DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>4×4 DataFrame</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">b</th><th style = \"text-align: left;\">a_first</th><th style = \"text-align: left;\">a_last</th><th style = \"text-align: left;\">a_mean</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"Char\" style = \"text-align: left;\">Char</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: left;\">a</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">5.0</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: left;\">b</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">10</td><td style = \"text-align: right;\">6.0</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: left;\">c</td><td style = \"text-align: right;\">3</td><td style = \"text-align: right;\">11</td><td style = \"text-align: right;\">7.0</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">4</td><td style = \"text-align: left;\">d</td><td style = \"text-align: right;\">4</td><td style = \"text-align: right;\">12</td><td style = \"text-align: right;\">8.0</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& b & a\\_first & a\\_last & a\\_mean\\\\\n",
       "\t\\hline\n",
       "\t& Char & Int64 & Int64 & Float64\\\\\n",
       "\t\\hline\n",
       "\t1 & a & 1 & 9 & 5.0 \\\\\n",
       "\t2 & b & 2 & 10 & 6.0 \\\\\n",
       "\t3 & c & 3 & 11 & 7.0 \\\\\n",
       "\t4 & d & 4 & 12 & 8.0 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m4×4 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m b    \u001b[0m\u001b[1m a_first \u001b[0m\u001b[1m a_last \u001b[0m\u001b[1m a_mean  \u001b[0m\n",
       "     │\u001b[90m Char \u001b[0m\u001b[90m Int64   \u001b[0m\u001b[90m Int64  \u001b[0m\u001b[90m Float64 \u001b[0m\n",
       "─────┼────────────────────────────────\n",
       "   1 │ a           1       9      5.0\n",
       "   2 │ b           2      10      6.0\n",
       "   3 │ c           3      11      7.0\n",
       "   4 │ d           4      12      8.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine(g, :a .=> [first, last, mean]) # similar in DataFrames.jl - we use auto-generated column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>12×4 DataFrame</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">a</th><th style = \"text-align: left;\">b</th><th style = \"text-align: left;\">center</th><th style = \"text-align: left;\">centered</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Char\" style = \"text-align: left;\">Char</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: right;\">1</td><td style = \"text-align: left;\">a</td><td style = \"text-align: right;\">5.0</td><td style = \"text-align: right;\">-4.0</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: right;\">2</td><td style = \"text-align: left;\">b</td><td style = \"text-align: right;\">6.0</td><td style = \"text-align: right;\">-4.0</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">3</td><td style = \"text-align: left;\">c</td><td style = \"text-align: right;\">7.0</td><td style = \"text-align: right;\">-4.0</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">4</td><td style = \"text-align: right;\">4</td><td style = \"text-align: left;\">d</td><td style = \"text-align: right;\">8.0</td><td style = \"text-align: right;\">-4.0</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">5</td><td style = \"text-align: right;\">5</td><td style = \"text-align: left;\">a</td><td style = \"text-align: right;\">5.0</td><td style = \"text-align: right;\">0.0</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">6</td><td style = \"text-align: right;\">6</td><td style = \"text-align: left;\">b</td><td style = \"text-align: right;\">6.0</td><td style = \"text-align: right;\">0.0</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">7</td><td style = \"text-align: right;\">7</td><td style = \"text-align: left;\">c</td><td style = \"text-align: right;\">7.0</td><td style = \"text-align: right;\">0.0</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">8</td><td style = \"text-align: right;\">8</td><td style = \"text-align: left;\">d</td><td style = \"text-align: right;\">8.0</td><td style = \"text-align: right;\">0.0</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">9</td><td style = \"text-align: right;\">9</td><td style = \"text-align: left;\">a</td><td style = \"text-align: right;\">5.0</td><td style = \"text-align: right;\">4.0</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">10</td><td style = \"text-align: right;\">10</td><td style = \"text-align: left;\">b</td><td style = \"text-align: right;\">6.0</td><td style = \"text-align: right;\">4.0</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">11</td><td style = \"text-align: right;\">11</td><td style = \"text-align: left;\">c</td><td style = \"text-align: right;\">7.0</td><td style = \"text-align: right;\">4.0</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">12</td><td style = \"text-align: right;\">12</td><td style = \"text-align: left;\">d</td><td style = \"text-align: right;\">8.0</td><td style = \"text-align: right;\">4.0</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& a & b & center & centered\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Char & Float64 & Float64\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & a & 5.0 & -4.0 \\\\\n",
       "\t2 & 2 & b & 6.0 & -4.0 \\\\\n",
       "\t3 & 3 & c & 7.0 & -4.0 \\\\\n",
       "\t4 & 4 & d & 8.0 & -4.0 \\\\\n",
       "\t5 & 5 & a & 5.0 & 0.0 \\\\\n",
       "\t6 & 6 & b & 6.0 & 0.0 \\\\\n",
       "\t7 & 7 & c & 7.0 & 0.0 \\\\\n",
       "\t8 & 8 & d & 8.0 & 0.0 \\\\\n",
       "\t9 & 9 & a & 5.0 & 4.0 \\\\\n",
       "\t10 & 10 & b & 6.0 & 4.0 \\\\\n",
       "\t11 & 11 & c & 7.0 & 4.0 \\\\\n",
       "\t12 & 12 & d & 8.0 & 4.0 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m12×4 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m a     \u001b[0m\u001b[1m b    \u001b[0m\u001b[1m center  \u001b[0m\u001b[1m centered \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Char \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64  \u001b[0m\n",
       "─────┼────────────────────────────────\n",
       "   1 │     1  a         5.0      -4.0\n",
       "   2 │     2  b         6.0      -4.0\n",
       "   3 │     3  c         7.0      -4.0\n",
       "   4 │     4  d         8.0      -4.0\n",
       "   5 │     5  a         5.0       0.0\n",
       "   6 │     6  b         6.0       0.0\n",
       "   7 │     7  c         7.0       0.0\n",
       "   8 │     8  d         8.0       0.0\n",
       "   9 │     9  a         5.0       4.0\n",
       "  10 │    10  b         6.0       4.0\n",
       "  11 │    11  c         7.0       4.0\n",
       "  12 │    12  d         8.0       4.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# perform operations within a group and return ungrouped DataFrame\n",
    "@transform(g, :center = mean(:a), :centered = :a .- mean(:a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>12×2 DataFrame</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">a</th><th style = \"text-align: left;\">b</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Char\" style = \"text-align: left;\">Char</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: right;\">1</td><td style = \"text-align: left;\">a</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: right;\">5</td><td style = \"text-align: left;\">a</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">9</td><td style = \"text-align: left;\">a</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">4</td><td style = \"text-align: right;\">2</td><td style = \"text-align: left;\">b</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">5</td><td style = \"text-align: right;\">6</td><td style = \"text-align: left;\">b</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">6</td><td style = \"text-align: right;\">10</td><td style = \"text-align: left;\">b</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">7</td><td style = \"text-align: right;\">3</td><td style = \"text-align: left;\">c</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">8</td><td style = \"text-align: right;\">7</td><td style = \"text-align: left;\">c</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">9</td><td style = \"text-align: right;\">11</td><td style = \"text-align: left;\">c</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">10</td><td style = \"text-align: right;\">4</td><td style = \"text-align: left;\">d</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">11</td><td style = \"text-align: right;\">8</td><td style = \"text-align: left;\">d</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">12</td><td style = \"text-align: right;\">12</td><td style = \"text-align: left;\">d</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& a & b\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Char\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & a \\\\\n",
       "\t2 & 5 & a \\\\\n",
       "\t3 & 9 & a \\\\\n",
       "\t4 & 2 & b \\\\\n",
       "\t5 & 6 & b \\\\\n",
       "\t6 & 10 & b \\\\\n",
       "\t7 & 3 & c \\\\\n",
       "\t8 & 7 & c \\\\\n",
       "\t9 & 11 & c \\\\\n",
       "\t10 & 4 & d \\\\\n",
       "\t11 & 8 & d \\\\\n",
       "\t12 & 12 & d \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m12×2 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m a     \u001b[0m\u001b[1m b    \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Char \u001b[0m\n",
       "─────┼─────────────\n",
       "   1 │     1  a\n",
       "   2 │     5  a\n",
       "   3 │     9  a\n",
       "   4 │     2  b\n",
       "   5 │     6  b\n",
       "   6 │    10  b\n",
       "   7 │     3  c\n",
       "   8 │     7  c\n",
       "   9 │    11  c\n",
       "  10 │     4  d\n",
       "  11 │     8  d\n",
       "  12 │    12  d"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrame(g) # this is defined in DataFrames.jl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>12×2 DataFrame</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">a</th><th style = \"text-align: left;\">b</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Char\" style = \"text-align: left;\">Char</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: right;\">1</td><td style = \"text-align: left;\">a</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: right;\">2</td><td style = \"text-align: left;\">b</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">3</td><td style = \"text-align: left;\">c</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">4</td><td style = \"text-align: right;\">4</td><td style = \"text-align: left;\">d</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">5</td><td style = \"text-align: right;\">5</td><td style = \"text-align: left;\">a</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">6</td><td style = \"text-align: right;\">6</td><td style = \"text-align: left;\">b</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">7</td><td style = \"text-align: right;\">7</td><td style = \"text-align: left;\">c</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">8</td><td style = \"text-align: right;\">8</td><td style = \"text-align: left;\">d</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">9</td><td style = \"text-align: right;\">9</td><td style = \"text-align: left;\">a</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">10</td><td style = \"text-align: right;\">10</td><td style = \"text-align: left;\">b</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">11</td><td style = \"text-align: right;\">11</td><td style = \"text-align: left;\">c</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">12</td><td style = \"text-align: right;\">12</td><td style = \"text-align: left;\">d</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& a & b\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Char\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & a \\\\\n",
       "\t2 & 2 & b \\\\\n",
       "\t3 & 3 & c \\\\\n",
       "\t4 & 4 & d \\\\\n",
       "\t5 & 5 & a \\\\\n",
       "\t6 & 6 & b \\\\\n",
       "\t7 & 7 & c \\\\\n",
       "\t8 & 8 & d \\\\\n",
       "\t9 & 9 & a \\\\\n",
       "\t10 & 10 & b \\\\\n",
       "\t11 & 11 & c \\\\\n",
       "\t12 & 12 & d \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m12×2 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m a     \u001b[0m\u001b[1m b    \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Char \u001b[0m\n",
       "─────┼─────────────\n",
       "   1 │     1  a\n",
       "   2 │     2  b\n",
       "   3 │     3  c\n",
       "   4 │     4  d\n",
       "   5 │     5  a\n",
       "   6 │     6  b\n",
       "   7 │     7  c\n",
       "   8 │     8  d\n",
       "   9 │     9  a\n",
       "  10 │    10  b\n",
       "  11 │    11  c\n",
       "  12 │    12  d"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@transform(g) # actually this is not the same as DataFrame() as it perserves the original row order"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataFramesMeta - rowwise operations on `DataFrame`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>12×2 DataFrame</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">a</th><th style = \"text-align: left;\">b</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">1</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">2</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">3</td><td style = \"text-align: right;\">3</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">4</td><td style = \"text-align: right;\">4</td><td style = \"text-align: right;\">4</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">5</td><td style = \"text-align: right;\">5</td><td style = \"text-align: right;\">1</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">6</td><td style = \"text-align: right;\">6</td><td style = \"text-align: right;\">2</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">7</td><td style = \"text-align: right;\">7</td><td style = \"text-align: right;\">3</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">8</td><td style = \"text-align: right;\">8</td><td style = \"text-align: right;\">4</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">9</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">1</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">10</td><td style = \"text-align: right;\">10</td><td style = \"text-align: right;\">2</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">11</td><td style = \"text-align: right;\">11</td><td style = \"text-align: right;\">3</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">12</td><td style = \"text-align: right;\">12</td><td style = \"text-align: right;\">4</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& a & b\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Int64\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 1 \\\\\n",
       "\t2 & 2 & 2 \\\\\n",
       "\t3 & 3 & 3 \\\\\n",
       "\t4 & 4 & 4 \\\\\n",
       "\t5 & 5 & 1 \\\\\n",
       "\t6 & 6 & 2 \\\\\n",
       "\t7 & 7 & 3 \\\\\n",
       "\t8 & 8 & 4 \\\\\n",
       "\t9 & 9 & 1 \\\\\n",
       "\t10 & 10 & 2 \\\\\n",
       "\t11 & 11 & 3 \\\\\n",
       "\t12 & 12 & 4 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m12×2 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m a     \u001b[0m\u001b[1m b     \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\n",
       "─────┼──────────────\n",
       "   1 │     1      1\n",
       "   2 │     2      2\n",
       "   3 │     3      3\n",
       "   4 │     4      4\n",
       "   5 │     5      1\n",
       "   6 │     6      2\n",
       "   7 │     7      3\n",
       "   8 │     8      4\n",
       "   9 │     9      1\n",
       "  10 │    10      2\n",
       "  11 │    11      3\n",
       "  12 │    12      4"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = DataFrame(a = 1:12, b = repeat(1:4, outer=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>12×3 DataFrame</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">a</th><th style = \"text-align: left;\">b</th><th style = \"text-align: left;\">x</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"String\" style = \"text-align: left;\">String</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">1</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">2</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">3</td><td style = \"text-align: right;\">3</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">4</td><td style = \"text-align: right;\">4</td><td style = \"text-align: right;\">4</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">5</td><td style = \"text-align: right;\">5</td><td style = \"text-align: right;\">1</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">6</td><td style = \"text-align: right;\">6</td><td style = \"text-align: right;\">2</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">7</td><td style = \"text-align: right;\">7</td><td style = \"text-align: right;\">3</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">8</td><td style = \"text-align: right;\">8</td><td style = \"text-align: right;\">4</td><td style = \"text-align: left;\">yes</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">9</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">1</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">10</td><td style = \"text-align: right;\">10</td><td style = \"text-align: right;\">2</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">11</td><td style = \"text-align: right;\">11</td><td style = \"text-align: right;\">3</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">12</td><td style = \"text-align: right;\">12</td><td style = \"text-align: right;\">4</td><td style = \"text-align: left;\">yes</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ccc}\n",
       "\t& a & b & x\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Int64 & String\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 1 & no \\\\\n",
       "\t2 & 2 & 2 & no \\\\\n",
       "\t3 & 3 & 3 & no \\\\\n",
       "\t4 & 4 & 4 & no \\\\\n",
       "\t5 & 5 & 1 & no \\\\\n",
       "\t6 & 6 & 2 & no \\\\\n",
       "\t7 & 7 & 3 & no \\\\\n",
       "\t8 & 8 & 4 & yes \\\\\n",
       "\t9 & 9 & 1 & no \\\\\n",
       "\t10 & 10 & 2 & no \\\\\n",
       "\t11 & 11 & 3 & no \\\\\n",
       "\t12 & 12 & 4 & yes \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m12×3 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m a     \u001b[0m\u001b[1m b     \u001b[0m\u001b[1m x      \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\n",
       "─────┼──────────────────────\n",
       "   1 │     1      1  no\n",
       "   2 │     2      2  no\n",
       "   3 │     3      3  no\n",
       "   4 │     4      4  no\n",
       "   5 │     5      1  no\n",
       "   6 │     6      2  no\n",
       "   7 │     7      3  no\n",
       "   8 │     8      4  yes\n",
       "   9 │     9      1  no\n",
       "  10 │    10      2  no\n",
       "  11 │    11      3  no\n",
       "  12 │    12      4  yes"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# such conditions are often needed but are complex to write\n",
    "@transform(df, :x = ifelse.((:a .> 6) .& (:b .== 4), \"yes\", \"no\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>12×3 DataFrame</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">a</th><th style = \"text-align: left;\">b</th><th style = \"text-align: left;\">x</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"String\" style = \"text-align: left;\">String</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">1</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">2</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">3</td><td style = \"text-align: right;\">3</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">4</td><td style = \"text-align: right;\">4</td><td style = \"text-align: right;\">4</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">5</td><td style = \"text-align: right;\">5</td><td style = \"text-align: right;\">1</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">6</td><td style = \"text-align: right;\">6</td><td style = \"text-align: right;\">2</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">7</td><td style = \"text-align: right;\">7</td><td style = \"text-align: right;\">3</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">8</td><td style = \"text-align: right;\">8</td><td style = \"text-align: right;\">4</td><td style = \"text-align: left;\">yes</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">9</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">1</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">10</td><td style = \"text-align: right;\">10</td><td style = \"text-align: right;\">2</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">11</td><td style = \"text-align: right;\">11</td><td style = \"text-align: right;\">3</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">12</td><td style = \"text-align: right;\">12</td><td style = \"text-align: right;\">4</td><td style = \"text-align: left;\">yes</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ccc}\n",
       "\t& a & b & x\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Int64 & String\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 1 & no \\\\\n",
       "\t2 & 2 & 2 & no \\\\\n",
       "\t3 & 3 & 3 & no \\\\\n",
       "\t4 & 4 & 4 & no \\\\\n",
       "\t5 & 5 & 1 & no \\\\\n",
       "\t6 & 6 & 2 & no \\\\\n",
       "\t7 & 7 & 3 & no \\\\\n",
       "\t8 & 8 & 4 & yes \\\\\n",
       "\t9 & 9 & 1 & no \\\\\n",
       "\t10 & 10 & 2 & no \\\\\n",
       "\t11 & 11 & 3 & no \\\\\n",
       "\t12 & 12 & 4 & yes \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m12×3 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m a     \u001b[0m\u001b[1m b     \u001b[0m\u001b[1m x      \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\n",
       "─────┼──────────────────────\n",
       "   1 │     1      1  no\n",
       "   2 │     2      2  no\n",
       "   3 │     3      3  no\n",
       "   4 │     4      4  no\n",
       "   5 │     5      1  no\n",
       "   6 │     6      2  no\n",
       "   7 │     7      3  no\n",
       "   8 │     8      4  yes\n",
       "   9 │     9      1  no\n",
       "  10 │    10      2  no\n",
       "  11 │    11      3  no\n",
       "  12 │    12      4  yes"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one option is to use a function that works on a single observation and broadcast it\n",
    "myfun(a, b) = a > 6 && b == 4 ? \"yes\" : \"no\"\n",
    "@transform(df, :x = myfun.(:a, :b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>12×3 DataFrame</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">a</th><th style = \"text-align: left;\">b</th><th style = \"text-align: left;\">x</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"String\" style = \"text-align: left;\">String</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">1</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">2</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">3</td><td style = \"text-align: right;\">3</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">4</td><td style = \"text-align: right;\">4</td><td style = \"text-align: right;\">4</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">5</td><td style = \"text-align: right;\">5</td><td style = \"text-align: right;\">1</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">6</td><td style = \"text-align: right;\">6</td><td style = \"text-align: right;\">2</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">7</td><td style = \"text-align: right;\">7</td><td style = \"text-align: right;\">3</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">8</td><td style = \"text-align: right;\">8</td><td style = \"text-align: right;\">4</td><td style = \"text-align: left;\">yes</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">9</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">1</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">10</td><td style = \"text-align: right;\">10</td><td style = \"text-align: right;\">2</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">11</td><td style = \"text-align: right;\">11</td><td style = \"text-align: right;\">3</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">12</td><td style = \"text-align: right;\">12</td><td style = \"text-align: right;\">4</td><td style = \"text-align: left;\">yes</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ccc}\n",
       "\t& a & b & x\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Int64 & String\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 1 & no \\\\\n",
       "\t2 & 2 & 2 & no \\\\\n",
       "\t3 & 3 & 3 & no \\\\\n",
       "\t4 & 4 & 4 & no \\\\\n",
       "\t5 & 5 & 1 & no \\\\\n",
       "\t6 & 6 & 2 & no \\\\\n",
       "\t7 & 7 & 3 & no \\\\\n",
       "\t8 & 8 & 4 & yes \\\\\n",
       "\t9 & 9 & 1 & no \\\\\n",
       "\t10 & 10 & 2 & no \\\\\n",
       "\t11 & 11 & 3 & no \\\\\n",
       "\t12 & 12 & 4 & yes \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m12×3 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m a     \u001b[0m\u001b[1m b     \u001b[0m\u001b[1m x      \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\n",
       "─────┼──────────────────────\n",
       "   1 │     1      1  no\n",
       "   2 │     2      2  no\n",
       "   3 │     3      3  no\n",
       "   4 │     4      4  no\n",
       "   5 │     5      1  no\n",
       "   6 │     6      2  no\n",
       "   7 │     7      3  no\n",
       "   8 │     8      4  yes\n",
       "   9 │     9      1  no\n",
       "  10 │    10      2  no\n",
       "  11 │    11      3  no\n",
       "  12 │    12      4  yes"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# or you can use @eachrow macro that allows you to process DataFrame rowwise\n",
    "@eachrow df begin\n",
    "   @newcol :x::Vector{String}\n",
    "    :x = :a > 6 && :b == 4 ? \"yes\" : \"no\"\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In DataFramses.jl you would write this as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>12×3 DataFrame</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">a</th><th style = \"text-align: left;\">b</th><th style = \"text-align: left;\">x</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"String\" style = \"text-align: left;\">String</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">1</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">2</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">3</td><td style = \"text-align: right;\">3</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">4</td><td style = \"text-align: right;\">4</td><td style = \"text-align: right;\">4</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">5</td><td style = \"text-align: right;\">5</td><td style = \"text-align: right;\">1</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">6</td><td style = \"text-align: right;\">6</td><td style = \"text-align: right;\">2</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">7</td><td style = \"text-align: right;\">7</td><td style = \"text-align: right;\">3</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">8</td><td style = \"text-align: right;\">8</td><td style = \"text-align: right;\">4</td><td style = \"text-align: left;\">yes</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">9</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">1</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">10</td><td style = \"text-align: right;\">10</td><td style = \"text-align: right;\">2</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">11</td><td style = \"text-align: right;\">11</td><td style = \"text-align: right;\">3</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">12</td><td style = \"text-align: right;\">12</td><td style = \"text-align: right;\">4</td><td style = \"text-align: left;\">yes</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ccc}\n",
       "\t& a & b & x\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Int64 & String\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 1 & no \\\\\n",
       "\t2 & 2 & 2 & no \\\\\n",
       "\t3 & 3 & 3 & no \\\\\n",
       "\t4 & 4 & 4 & no \\\\\n",
       "\t5 & 5 & 1 & no \\\\\n",
       "\t6 & 6 & 2 & no \\\\\n",
       "\t7 & 7 & 3 & no \\\\\n",
       "\t8 & 8 & 4 & yes \\\\\n",
       "\t9 & 9 & 1 & no \\\\\n",
       "\t10 & 10 & 2 & no \\\\\n",
       "\t11 & 11 & 3 & no \\\\\n",
       "\t12 & 12 & 4 & yes \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m12×3 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m a     \u001b[0m\u001b[1m b     \u001b[0m\u001b[1m x      \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\n",
       "─────┼──────────────────────\n",
       "   1 │     1      1  no\n",
       "   2 │     2      2  no\n",
       "   3 │     3      3  no\n",
       "   4 │     4      4  no\n",
       "   5 │     5      1  no\n",
       "   6 │     6      2  no\n",
       "   7 │     7      3  no\n",
       "   8 │     8      4  yes\n",
       "   9 │     9      1  no\n",
       "  10 │    10      2  no\n",
       "  11 │    11      3  no\n",
       "  12 │    12      4  yes"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transform(df, [:a, :b] => ByRow((a,b) -> ifelse(a > 6 && b == 4, \"yes\", \"no\")) => :x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also use `eachrow` from DataFrames to perform the same transformation. However `@eachrow` will be faster than the operation below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>12×3 DataFrame</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">a</th><th style = \"text-align: left;\">b</th><th style = \"text-align: left;\">x</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"String\" style = \"text-align: left;\">String</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">1</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">2</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">3</td><td style = \"text-align: right;\">3</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">4</td><td style = \"text-align: right;\">4</td><td style = \"text-align: right;\">4</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">5</td><td style = \"text-align: right;\">5</td><td style = \"text-align: right;\">1</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">6</td><td style = \"text-align: right;\">6</td><td style = \"text-align: right;\">2</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">7</td><td style = \"text-align: right;\">7</td><td style = \"text-align: right;\">3</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">8</td><td style = \"text-align: right;\">8</td><td style = \"text-align: right;\">4</td><td style = \"text-align: left;\">yes</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">9</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">1</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">10</td><td style = \"text-align: right;\">10</td><td style = \"text-align: right;\">2</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">11</td><td style = \"text-align: right;\">11</td><td style = \"text-align: right;\">3</td><td style = \"text-align: left;\">no</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">12</td><td style = \"text-align: right;\">12</td><td style = \"text-align: right;\">4</td><td style = \"text-align: left;\">yes</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ccc}\n",
       "\t& a & b & x\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Int64 & String\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 1 & no \\\\\n",
       "\t2 & 2 & 2 & no \\\\\n",
       "\t3 & 3 & 3 & no \\\\\n",
       "\t4 & 4 & 4 & no \\\\\n",
       "\t5 & 5 & 1 & no \\\\\n",
       "\t6 & 6 & 2 & no \\\\\n",
       "\t7 & 7 & 3 & no \\\\\n",
       "\t8 & 8 & 4 & yes \\\\\n",
       "\t9 & 9 & 1 & no \\\\\n",
       "\t10 & 10 & 2 & no \\\\\n",
       "\t11 & 11 & 3 & no \\\\\n",
       "\t12 & 12 & 4 & yes \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m12×3 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m a     \u001b[0m\u001b[1m b     \u001b[0m\u001b[1m x      \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\n",
       "─────┼──────────────────────\n",
       "   1 │     1      1  no\n",
       "   2 │     2      2  no\n",
       "   3 │     3      3  no\n",
       "   4 │     4      4  no\n",
       "   5 │     5      1  no\n",
       "   6 │     6      2  no\n",
       "   7 │     7      3  no\n",
       "   8 │     8      4  yes\n",
       "   9 │     9      1  no\n",
       "  10 │    10      2  no\n",
       "  11 │    11      3  no\n",
       "  12 │    12      4  yes"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = copy(df)\n",
    "df2.x = Vector{String}(undef, nrow(df2))\n",
    "for row in eachrow(df2)\n",
    "   row[:x] = row[:a] > 6 && row[:b] == 4 ? \"yes\" : \"no\"\n",
    "end\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing data with StatsPlots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "using StatsPlots # you might need to setup Plots package and some plotting backend first\n",
    "default(fmt=:png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we present only a minimal functionality of the package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "using Random\n",
    "Random.seed!(1)\n",
    "df = DataFrame(x = sort(randn(1000)), y=randn(1000), z = [fill(\"b\", 500); fill(\"a\", 500)]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@df df plot(:x, :y, legend=:topleft, label=\"y(x)\") # a most basic plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@df df density(:x, label=\"\") # density plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@df df histogram(:y, label=\"y\") # and a histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DhhAuWbJk1KhRzZo1i4+Pf+KJJ5YsWRL6mgAAhIcNIdy3b1/Hjh2rXnfs2DEvL+9SR1qW5fF4zvxLRUVF6GcHACAUNjwjLC4ubtCgQdXruLi4oqIiy7I07SJPiXfs2LF27dqFCxdWbTocjo8//rhNmzahz4AaqXpGWF/egAfUOz6fz+fzlZeXyx4Ewu12OxzVXPLZEMKmTZuWlJRUvS4uLm7WrNlFKyiE6Ny587XXXpuZyQNkyUzTjIqK4s0yQC1xOp1OZ+DcFQLqOBtujaampm7fvr3q9fbt21NTU0NfEwCA8LAhhCNHjpwzZ86uXbv2798/a9askSNHhr4mAADhYcOt0UGDBu3du3fw4MGGYYwYMWLYsGGhrwkAQHjY85tlxo0bN27cOFuWAgAgnOrH7ysCAKCWEEIAgNIIIQBAaYQQAKA0QggAUBohBAAojRACAJRGCAEASiOEAACl2fObZQDUR6PH/depMo/sKSLQnl07Ksyoob8aK3uQCHSFU58/c4rT6bRxTUIIKMowjFfmz7fuf0H2IJGoRXNh+Pd7msmeIwI535k6fdL4xMREG9ckhIDKNNH7l7JnAGrA8dEs+9e0fUUAAOoRQggAUBohBAAojRACAJRGCAEASiOEAAClEUIAgNIIIQBAaYQQAKA0QggAUBohBAAojRACAJRGCAEASiOEAAClEUIAgNIIIQBAaYQQAKA0QggAUBohBAAoLUr2AACksWIaindflD0FUAM+K8owDHvXJISAurTKYmvvJtlTADWgnT1t+5qEEFCY5hD/uVb2EEANRP+xk67r9q7JM0IAgNIIIQBAaYQQAKA0QggAUBohBAAojRACAJRGCAEASiOEAAClEUIAgNIIIQBAaYQQAKA0QggAUBohBAAozYZvn/B6vRs2bMjJyTlx4sT06dPj4+NDXxMAgPCw4Yrw5MmTL730UlFR0fz588+ePRv6ggAAhI0NV4StW7fetGlTaWnp3LlzQ18NAIBw4hkhAEBpQV0RBgKBPXv2XLg/OTk5Li4u+JPt3r37ww8/zM7Ortp0uVyLFy9u3bp18CvAFqZper1ewzBkDwKZ+AuAesmyysvL3W53kIe73e5qv9E+qBAWFRWlpaVduH/WrFl9+/YNchohxFVXXXXPPfeMHj26alPTtGuvvdbh4Ko03EzTjI6ODv5vEiISIUS9pGkNGjSo0TVYtYIKYfPmzbdv3x76yVwuV8uWLbt16xb6UgAA2IKrMQCA0mx416gQolWrVhUVFUKIjh07OhyOEydOREXZszIAALXKnlzt3LnTsqx/L0oFAQD1hD3F4rfJAADqKZ4RAgCURggBAErjYR6gMkt8vFD2DEANmN5K29ckhICidF0f85vfnDizV/YgEeibXd9UWFHdOqbIHiQCJfwivXHjxvauSQgBdf152vOyR4hMU6dOzS+u+Mv0TNmDICg8IwQAKI0QAgCURggBAEojhAAApRFCAIDSCCEAQGmEEACgNEIIAFAaIQQAKI0QAgCURggBAEojhAAApRFCAIDSCCEAQGmEEACgNEIIAFAaIQQAKI0QAgCURggBAEojhAAApRFCAIDSCCEAQGmEEACgNEIIAFAaIQQAKI0QAgCURggBAEojhAAApRFCAIDSCCEAQGmEEACgNEIIAFAaIQQAKI0QAgCURggBAEojhAAApRFCAIDSCCEAQGmEEACgNEIIAFAaIQQAKC0q9CUsy8rJydm6datlWX369OnUqVPoawIAEB42XBHOnz//kUce2bFjx86dO3v16rVgwYLQ1wQAIDxsuCIcMmTIyJEjHQ6HEOKWW2557rnnRo4cGfqyAACEgQ0hbNKkybnXbrdb1/XQ1wQAIDxsCOE5Ho8nMzPziSeeuNQBBQUFX331lcfjqdp0Op1jxoxJSEiwcQYEwzRNr9fLjyxALTEMwzAMr9crexAIp9OpadrljwkqhIZhJCcnX7h/+vTp6enpVa/9fv9DDz10zTXXjBkz5lLraJoWGxsbHx9/brPa+QAAqFVBhVDX9W3btl24v2HDhlUvAoHAI488YlnWG2+8cZnrjMTExPj4+IkTJ/64WWEX0zQty3K5XLIHASKTruu6rvNPrL4I9tZoYmLipf4jwzAyMjJKSkrWrFnjdDptGgwAgHCw4RnhvHnz3nzzzXvvvfexxx6r2rNs2TKePwEA6gUbQti/f//ly5efv6fqoxQAANR9NoQwNTU1NTU19HUAAAg/Lt0AAEojhAAApRFCAIDSCCEAQGmEEACgNEIIAFAaIQQAKI0QAgCURggBAEojhAAApRFCAIDSCCEAQGmEEACgNEIIAFAaIQQAKI0QAgCURggBAEojhAAApRFCAIDSCCEAQGlRsgcAgOp9++23D6T9ImDKniM4BQUFAd25efOnsgcJikOzXpv3l06dOskeRBpCCKAeSEpKeuXPMyzLkj1IUAKBgGmaTqdT9iDBuuaaa2SPIBMhBFAP6LretWtX2VMEy+/3m6bpcrlkD4KgEEIAsNPatdlrP/zYMIy7+/Qc8uADmqbJngjV4M0yAGCPs2fP/uSOex6bs26B3u9V14BfLvm0y639zpw5I3suVIMrQgCwx9PPvfB163t9vX5ZtVma2nfXtr/9+slnVix+Re5guDyuCAHAHmvefd/Xc/j5e4yuD/7j0y8kjYNgEUIAsIdhCeH44W02KzomEAhImQdBIoQAYA+3K1pUln5vV8AXZXijongIVacRQgCwR8eUdtrbY4VpfLdtWWLVhORWSVKHQvX4OQUA7LFjz36r3UAxrY+4rr9wRIlvPhQpvY4cOS57LlSDEAKAPTz+gLh3oujzK3Hka2EERJ+RolGLwEubAoEAd0frMv5sAMAe0Q5NBHwirpnoeNd3uyzL4a+kgnUczwgBwB5pQ+6L/fufz9/j+nTRvQPulDUPgsTPKQBgj8xnf5+bPuKLxQ8XXXefpUc13r3u+isqZs99Q/ZcqAZXhABgj+jo6FVLF6bf0r7tltnJn8x48LqEd//6ptvtlj0XqkEIAcAep06d6nhznwXHGh98eOmRR99eWnZ1h+63HT16VPZcqAa3RgHAHmN+/9zBW8cZNwyu2vT0efxIy07/MWb8++8skzsYLo8rQgCwx8effWF0+dn5e6xrem/f9Y2seRAkQggA9jCFJi789sEoF79rtI4jhABgj/i4BqK08Hu7vOUxmsnnCOs4QggA9njhv8Y2XDZaeMq+2/ZXxr39+IQnfyN1KFSPn1MAwB4dO6SKgt1iSi+RfIPQo8TBHMvh79blKdlzoRqEEADsMXr8pNJfLBXJXcSJ/cI0RGJK+enDI8f+9uuPN8oeDZdDCAHAHrv35YnB3YQQosW13+1qfvWxE0WWZWkXvokGdQbPCAHAHpa4SO0sIUzTDP8wCJ49ISwtLc3Nzc3LyzMMo/qjASASaZYpju/+3q7ifH9Fua7rkiZCUGwI4euvv962bdv09PQ777wzNTU1Nzc39DUBoN4x/V6xeIQ4tvO77RP7xSuPODQuB+s6G54RDh48OC0treqDMo8//viECROysrJCXxYA6heHyy0emidWTRSlhUJzCHe8+Pn0qNVP8sW8dZwNfzaNGjU697p9+/YHDx4MfU0AqHdindGiWTsxZrWwLCEsoTmEGYgyfFSwjrPnj6egoGDWrFmnTp3Kzc2dP3/+pQ7zeDyHDh364IMPqjajo6N79erF3XMAkWH0f6RnrvtD2f3/IzRNCE0I4X5v6qM/f0D2XKhGUCHcvXv39OnTL9z/pz/9KSkpSQih63pCQkIgECgsLNy7d2/nzp0vus7hw4c3b96cn59/bs+cOXNat279oybHj2eaptfr5Z1NgL1+nfFY3sHMVbP7nb3up6amx+15746u1z4zdkpZWVn1/2XUDrfbXe3llmZZVrULFRYWbtx4kQ+EDho06Pz7okKINWvWjBo16vjx4xdd54UXXvB6vZmZmdWeEbXKNE2Px8P3hQK14ciRI5999lkgEOjZs2e7du1kj4PqBXVFmJiYmJ6eHsyR8fHxPp8vtJFQ6/bt27dly5bHHntM9iBABEpOTj5y5EhZWRkVrC9seEb44osvJiQkJCcn5+fnT5kyJSMjI/Q1Uav++c9/ZmVlEUKglnzyySenT58eOHCg7EEQFBtC2KlTp+XLlx87dqxp06YTJ05MS0sLfU0AAMLDhhAOGDBgwIABoa8DAED48btGAQBKC+pdo3Z59NFH161bl5CQELYz4qIqKirKy8ubN28uexAgMpWUlBiG0bhxY9mDQKSlpVX7UYWwhrCysvLo0aN8gl46y7L8fr/T6ZQ9CBCZDMOwLItfKFMXtGzZMjY29vLHhDWEAADUNTwjBAAojRACAJRGCAEASiOEAAClEUIAsM3999+/c+fO6o9DXUIIAcA2ubm5Z8+elT0FaoaPuShn7969X375pWVZPXv2bN++vexxgEhjWda6devOnDnTr1+/qm9sRR3HFaFajh8/npGRsWPHju3bt/fu3XvdunWyJwIizW9/+9usrKzPP/+8a9euubm5ssdB9fhAvbqys7NnzJixadMm2YMAkSMlJeXBBx+cNm2aEOL555/fsWPHypUrZQ+FanBrVC2maU6fPn316tVlZWUOh6O0tFT2RECkueuuu6pe3HHHHUuWLJE6C4LCrVG1vPbaa9nZ2VlZWbt27Xr11VcDgYDsiYBIYxhG1QvLsjRNkzsMgkEI1bJv374ePXokJiYKIdauXSt7HCACvfvuu+de9OjRQ+4wCAa3RtVy3333DRgwwOv1njp1qqioSPY4QATKyclJS0vTdf2jjz766KOPZI+D6vFmGeUcOnRoy5YtrVq16tat2759+zp37ix7IiBy5ObmXnXVVTk5OUVFRb17927SpInsiVA9QggAUBrPCAEASiOEAAClEUIAgNIIIQBAaYQQAKA0QggAUBohBAAojRACAJRGCAEASiOEAAClEUIAgNL+H7mC0GfxzadtAAAAAElFTkSuQmCC"
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@df df boxplot(:z, :x, label=\"x\") # the warning is likely to be removed in future releases of plotting packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@df df violin(:z, :y, label=\"y\") "
   ]
  }
 ],
 "metadata": {
  "@webio": {
   "lastCommId": null,
   "lastKernelId": null
  },
  "kernelspec": {
   "display_name": "Julia 1.9.0",
   "language": "julia",
   "name": "julia-1.9"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "1.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}