04_loadsave.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": "markdown",
   "metadata": {},
   "source": [
    "## Load and save DataFrames\n",
    "We do not cover all features of the packages. Please refer to their documentation to learn them.\n",
    "\n",
    "Here we'll load CSV.jl to read and write CSV files and Arrow.jl, JLSO.jl, and serialization, which allow us to work with a binary format and JSONTables.jl for JSON interaction. Finally we consider a custom JDF.jl format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "using Arrow\n",
    "using CSV\n",
    "using Serialization\n",
    "using JLSO\n",
    "using JSONTables\n",
    "using CodecZlib\n",
    "using ZipFile\n",
    "using JDF\n",
    "using StatsPlots # for charts\n",
    "using Mmap # for compression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a simple `DataFrame` for testing purposes,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>3×4 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;\">C</th><th style = \"text-align: left;\">D</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 = \"Union{Missing, Int64}\" style = \"text-align: left;\">Int64?</th><th title = \"Union{Missing, String}\" style = \"text-align: left;\">String?</th><th title = \"Union{Missing, 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;\">true</td><td style = \"text-align: right;\">1</td><td style = \"font-style: italic; text-align: left;\">missing</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;\">false</td><td style = \"text-align: right;\">2</td><td style = \"text-align: left;\">b</td><td style = \"font-style: italic; text-align: left;\">missing</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">true</td><td style = \"font-style: italic; text-align: right;\">missing</td><td style = \"text-align: left;\">c</td><td style = \"text-align: left;\">c</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& A & B & C & D\\\\\n",
       "\t\\hline\n",
       "\t& Bool & Int64? & String? & Char?\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 1 & \\emph{missing} & a \\\\\n",
       "\t2 & 0 & 2 & b & \\emph{missing} \\\\\n",
       "\t3 & 1 & \\emph{missing} & c & c \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m3×4 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m A     \u001b[0m\u001b[1m B       \u001b[0m\u001b[1m C       \u001b[0m\u001b[1m D       \u001b[0m\n",
       "     │\u001b[90m Bool  \u001b[0m\u001b[90m Int64?  \u001b[0m\u001b[90m String? \u001b[0m\u001b[90m Char?   \u001b[0m\n",
       "─────┼──────────────────────────────────\n",
       "   1 │  true        1 \u001b[90m missing \u001b[0m a\n",
       "   2 │ false        2  b       \u001b[90m missing \u001b[0m\n",
       "   3 │  true \u001b[90m missing \u001b[0m c        c"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = DataFrame(A=[true, false, true], B=[1, 2, missing],\n",
    "              C=[missing, \"b\", \"c\"], D=['a', missing, 'c'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and use `eltypes` to look at the columnwise types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-element Vector{Type}:\n",
       " Bool\n",
       " Union{Missing, Int64}\n",
       " Union{Missing, String}\n",
       " Union{Missing, Char}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eltype.(eachcol(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CSV.jl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use `CSV` to save `x` to disk; make sure `x1.csv` does not conflict with some file in your working directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"x1.csv\""
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CSV.write(\"x1.csv\", x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can see how it was saved by reading `x.csv`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A,B,C,D\n",
      "true,1,,a\n",
      "false,2,b,\n",
      "true,,c,c\n"
     ]
    }
   ],
   "source": [
    "print(read(\"x1.csv\", String))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also load it back."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>3×4 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;\">C</th><th style = \"text-align: left;\">D</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 = \"Union{Missing, Int64}\" style = \"text-align: left;\">Int64?</th><th title = \"Union{Missing, String1}\" style = \"text-align: left;\">String1?</th><th title = \"Union{Missing, String1}\" style = \"text-align: left;\">String1?</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: right;\">true</td><td style = \"text-align: right;\">1</td><td style = \"font-style: italic; text-align: left;\">missing</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;\">false</td><td style = \"text-align: right;\">2</td><td style = \"text-align: left;\">b</td><td style = \"font-style: italic; text-align: left;\">missing</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">true</td><td style = \"font-style: italic; text-align: right;\">missing</td><td style = \"text-align: left;\">c</td><td style = \"text-align: left;\">c</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& A & B & C & D\\\\\n",
       "\t\\hline\n",
       "\t& Bool & Int64? & String1? & String1?\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 1 & \\emph{missing} & a \\\\\n",
       "\t2 & 0 & 2 & b & \\emph{missing} \\\\\n",
       "\t3 & 1 & \\emph{missing} & c & c \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m3×4 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m A     \u001b[0m\u001b[1m B       \u001b[0m\u001b[1m C        \u001b[0m\u001b[1m D        \u001b[0m\n",
       "     │\u001b[90m Bool  \u001b[0m\u001b[90m Int64?  \u001b[0m\u001b[90m String1? \u001b[0m\u001b[90m String1? \u001b[0m\n",
       "─────┼────────────────────────────────────\n",
       "   1 │  true        1 \u001b[90m missing  \u001b[0m a\n",
       "   2 │ false        2  b        \u001b[90m missing  \u001b[0m\n",
       "   3 │  true \u001b[90m missing \u001b[0m c         c"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = CSV.read(\"x1.csv\", DataFrame)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that when loading in a `DataFrame` from a `CSV` the column type for columns `:C` `:D` have changed to use special strings defined in the InlineStrings.jl package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-element Vector{Type}:\n",
       " Bool\n",
       " Union{Missing, Int64}\n",
       " Union{Missing, String1}\n",
       " Union{Missing, String1}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eltype.(eachcol(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Serialization, JDF.jl, and JLSO.jl\n",
    "\n",
    "#### Serialization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we use serialization to save `x`.\n",
    "\n",
    "There are two ways to perform serialization. The first way is to use the `Serialization.serialize` as below:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that in general, this process will not work if the reading and writing are done by different versions of Julia, or an instance of Julia with a different system image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "open(\"x.bin\", \"w\") do io\n",
    "    serialize(io, x)\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we load back the saved file to `y` variable. Again `y` is identical to `x`. However, please beware that if you session does not have DataFrames.jl loaded, then it may not recognise the content as DataFrames.jl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>3×4 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;\">C</th><th style = \"text-align: left;\">D</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 = \"Union{Missing, Int64}\" style = \"text-align: left;\">Int64?</th><th title = \"Union{Missing, String}\" style = \"text-align: left;\">String?</th><th title = \"Union{Missing, 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;\">true</td><td style = \"text-align: right;\">1</td><td style = \"font-style: italic; text-align: left;\">missing</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;\">false</td><td style = \"text-align: right;\">2</td><td style = \"text-align: left;\">b</td><td style = \"font-style: italic; text-align: left;\">missing</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">true</td><td style = \"font-style: italic; text-align: right;\">missing</td><td style = \"text-align: left;\">c</td><td style = \"text-align: left;\">c</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& A & B & C & D\\\\\n",
       "\t\\hline\n",
       "\t& Bool & Int64? & String? & Char?\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 1 & \\emph{missing} & a \\\\\n",
       "\t2 & 0 & 2 & b & \\emph{missing} \\\\\n",
       "\t3 & 1 & \\emph{missing} & c & c \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m3×4 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m A     \u001b[0m\u001b[1m B       \u001b[0m\u001b[1m C       \u001b[0m\u001b[1m D       \u001b[0m\n",
       "     │\u001b[90m Bool  \u001b[0m\u001b[90m Int64?  \u001b[0m\u001b[90m String? \u001b[0m\u001b[90m Char?   \u001b[0m\n",
       "─────┼──────────────────────────────────\n",
       "   1 │  true        1 \u001b[90m missing \u001b[0m a\n",
       "   2 │ false        2  b       \u001b[90m missing \u001b[0m\n",
       "   3 │  true \u001b[90m missing \u001b[0m c        c"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = open(deserialize, \"x.bin\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-element Vector{Type}:\n",
       " Bool\n",
       " Union{Missing, Int64}\n",
       " Union{Missing, String}\n",
       " Union{Missing, Char}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eltype.(eachcol(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### JDF.jl\n",
    "\n",
    "[JDF.jl](https://github.com/xiaodaigh/JDF) is a relatively new package designed to serialize DataFrames. You can save a DataFrame with the `savejdf` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "JDF.save(\"x.jdf\", x);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To load the saved JDF file, one can use the `loadjdf` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>3×4 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;\">C</th><th style = \"text-align: left;\">D</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 = \"Union{Missing, Int64}\" style = \"text-align: left;\">Int64?</th><th title = \"Union{Missing, String}\" style = \"text-align: left;\">String?</th><th title = \"Union{Missing, 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;\">true</td><td style = \"text-align: right;\">1</td><td style = \"font-style: italic; text-align: left;\">missing</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;\">false</td><td style = \"text-align: right;\">2</td><td style = \"text-align: left;\">b</td><td style = \"font-style: italic; text-align: left;\">missing</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">true</td><td style = \"font-style: italic; text-align: right;\">missing</td><td style = \"text-align: left;\">c</td><td style = \"text-align: left;\">c</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& A & B & C & D\\\\\n",
       "\t\\hline\n",
       "\t& Bool & Int64? & String? & Char?\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 1 & \\emph{missing} & a \\\\\n",
       "\t2 & 0 & 2 & b & \\emph{missing} \\\\\n",
       "\t3 & 1 & \\emph{missing} & c & c \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m3×4 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m A     \u001b[0m\u001b[1m B       \u001b[0m\u001b[1m C       \u001b[0m\u001b[1m D       \u001b[0m\n",
       "     │\u001b[90m Bool  \u001b[0m\u001b[90m Int64?  \u001b[0m\u001b[90m String? \u001b[0m\u001b[90m Char?   \u001b[0m\n",
       "─────┼──────────────────────────────────\n",
       "   1 │  true        1 \u001b[90m missing \u001b[0m a\n",
       "   2 │ false        2  b       \u001b[90m missing \u001b[0m\n",
       "   3 │  true \u001b[90m missing \u001b[0m c        c"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_loaded = JDF.load(\"x.jdf\") |> DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that they are the same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isequal(x_loaded, x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "JDF.jl offers the ability to load only certain columns from disk to help with working with large files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "JDFFile{String}(\"x.jdf\")"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set up a JDFFile which is a on disk representation of `x` backed by JDF.jl\n",
    "x_ondisk = jdf\"x.jdf\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see all the names of `x` without loading it into memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-element Vector{Symbol}:\n",
       " :A\n",
       " :B\n",
       " :C\n",
       " :D"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names(x_ondisk)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The below is an example of how to load only columns `:A` and `:D` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>3×2 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;\">D</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 = \"Union{Missing, 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;\">true</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;\">false</td><td style = \"font-style: italic; text-align: left;\">missing</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: left;\">c</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& A & D\\\\\n",
       "\t\\hline\n",
       "\t& Bool & Char?\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & a \\\\\n",
       "\t2 & 0 & \\emph{missing} \\\\\n",
       "\t3 & 1 & c \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m3×2 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m A     \u001b[0m\u001b[1m D       \u001b[0m\n",
       "     │\u001b[90m Bool  \u001b[0m\u001b[90m Char?   \u001b[0m\n",
       "─────┼────────────────\n",
       "   1 │  true  a\n",
       "   2 │ false \u001b[90m missing \u001b[0m\n",
       "   3 │  true  c"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xd = JDF.load(x_ondisk; cols = [\"A\", \"D\"]) |> DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### JDF.jl vs others\n",
    "\n",
    "For more details about design assumptions and limitations of JDF.jl check out https://github.com/xiaodaigh/JDF.jl."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### JLSO.jl\n",
    "\n",
    "Another way to perform serialization is by using the [JLSO.jl](https://github.com/invenia/JLSO.jl) library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "JLSO.save(\"x.jlso\", :data => x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can laod back the file to `y`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>3×4 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;\">C</th><th style = \"text-align: left;\">D</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 = \"Union{Missing, Int64}\" style = \"text-align: left;\">Int64?</th><th title = \"Union{Missing, String}\" style = \"text-align: left;\">String?</th><th title = \"Union{Missing, 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;\">true</td><td style = \"text-align: right;\">1</td><td style = \"font-style: italic; text-align: left;\">missing</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;\">false</td><td style = \"text-align: right;\">2</td><td style = \"text-align: left;\">b</td><td style = \"font-style: italic; text-align: left;\">missing</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">true</td><td style = \"font-style: italic; text-align: right;\">missing</td><td style = \"text-align: left;\">c</td><td style = \"text-align: left;\">c</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& A & B & C & D\\\\\n",
       "\t\\hline\n",
       "\t& Bool & Int64? & String? & Char?\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 1 & \\emph{missing} & a \\\\\n",
       "\t2 & 0 & 2 & b & \\emph{missing} \\\\\n",
       "\t3 & 1 & \\emph{missing} & c & c \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m3×4 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m A     \u001b[0m\u001b[1m B       \u001b[0m\u001b[1m C       \u001b[0m\u001b[1m D       \u001b[0m\n",
       "     │\u001b[90m Bool  \u001b[0m\u001b[90m Int64?  \u001b[0m\u001b[90m String? \u001b[0m\u001b[90m Char?   \u001b[0m\n",
       "─────┼──────────────────────────────────\n",
       "   1 │  true        1 \u001b[90m missing \u001b[0m a\n",
       "   2 │ false        2  b       \u001b[90m missing \u001b[0m\n",
       "   3 │  true \u001b[90m missing \u001b[0m c        c"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = JLSO.load(\"x.jlso\")[:data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-element Vector{Type}:\n",
       " Bool\n",
       " Union{Missing, Int64}\n",
       " Union{Missing, String}\n",
       " Union{Missing, Char}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eltype.(eachcol(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### JSONTables.jl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Often you might need to read and write data stored in JSON format. JSONTables.jl provides a way to process them in row-oriented or column-oriented layout. We present both options below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "106"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open(io -> arraytable(io, x), \"x1.json\", \"w\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open(io -> objecttable(io, x), \"x2.json\", \"w\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{\"A\":true,\"B\":1,\"C\":null,\"D\":\"a\"},{\"A\":false,\"B\":2,\"C\":\"b\",\"D\":null},{\"A\":true,\"B\":null,\"C\":\"c\",\"D\":\"c\"}]"
     ]
    }
   ],
   "source": [
    "print(read(\"x1.json\", String))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"A\":[true,false,true],\"B\":[1,2,null],\"C\":[null,\"b\",\"c\"],\"D\":[\"a\",null,\"c\"]}"
     ]
    }
   ],
   "source": [
    "print(read(\"x2.json\", String))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>3×4 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;\">C</th><th style = \"text-align: left;\">D</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 = \"Union{Missing, Int64}\" style = \"text-align: left;\">Int64?</th><th title = \"Union{Missing, String}\" style = \"text-align: left;\">String?</th><th title = \"Union{Missing, 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;\">true</td><td style = \"text-align: right;\">1</td><td style = \"font-style: italic; text-align: left;\">missing</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;\">false</td><td style = \"text-align: right;\">2</td><td style = \"text-align: left;\">b</td><td style = \"font-style: italic; text-align: left;\">missing</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">true</td><td style = \"font-style: italic; text-align: right;\">missing</td><td style = \"text-align: left;\">c</td><td style = \"text-align: left;\">c</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& A & B & C & D\\\\\n",
       "\t\\hline\n",
       "\t& Bool & Int64? & String? & String?\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 1 & \\emph{missing} & a \\\\\n",
       "\t2 & 0 & 2 & b & \\emph{missing} \\\\\n",
       "\t3 & 1 & \\emph{missing} & c & c \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m3×4 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m A     \u001b[0m\u001b[1m B       \u001b[0m\u001b[1m C       \u001b[0m\u001b[1m D       \u001b[0m\n",
       "     │\u001b[90m Bool  \u001b[0m\u001b[90m Int64?  \u001b[0m\u001b[90m String? \u001b[0m\u001b[90m String? \u001b[0m\n",
       "─────┼──────────────────────────────────\n",
       "   1 │  true        1 \u001b[90m missing \u001b[0m a\n",
       "   2 │ false        2  b       \u001b[90m missing \u001b[0m\n",
       "   3 │  true \u001b[90m missing \u001b[0m c        c"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y1 = open(jsontable, \"x1.json\") |> DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-element Vector{Type}:\n",
       " Bool\n",
       " Union{Missing, Int64}\n",
       " Union{Missing, String}\n",
       " Union{Missing, String}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eltype.(eachcol(y1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>3×4 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;\">C</th><th style = \"text-align: left;\">D</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 = \"Union{Missing, Int64}\" style = \"text-align: left;\">Int64?</th><th title = \"Union{Missing, String}\" style = \"text-align: left;\">String?</th><th title = \"Union{Missing, 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;\">true</td><td style = \"text-align: right;\">1</td><td style = \"font-style: italic; text-align: left;\">missing</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;\">false</td><td style = \"text-align: right;\">2</td><td style = \"text-align: left;\">b</td><td style = \"font-style: italic; text-align: left;\">missing</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">true</td><td style = \"font-style: italic; text-align: right;\">missing</td><td style = \"text-align: left;\">c</td><td style = \"text-align: left;\">c</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& A & B & C & D\\\\\n",
       "\t\\hline\n",
       "\t& Bool & Int64? & String? & String?\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 1 & \\emph{missing} & a \\\\\n",
       "\t2 & 0 & 2 & b & \\emph{missing} \\\\\n",
       "\t3 & 1 & \\emph{missing} & c & c \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m3×4 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m A     \u001b[0m\u001b[1m B       \u001b[0m\u001b[1m C       \u001b[0m\u001b[1m D       \u001b[0m\n",
       "     │\u001b[90m Bool  \u001b[0m\u001b[90m Int64?  \u001b[0m\u001b[90m String? \u001b[0m\u001b[90m String? \u001b[0m\n",
       "─────┼──────────────────────────────────\n",
       "   1 │  true        1 \u001b[90m missing \u001b[0m a\n",
       "   2 │ false        2  b       \u001b[90m missing \u001b[0m\n",
       "   3 │  true \u001b[90m missing \u001b[0m c        c"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2 = open(jsontable, \"x2.json\") |> DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-element Vector{Type}:\n",
       " Bool\n",
       " Union{Missing, Int64}\n",
       " Union{Missing, String}\n",
       " Union{Missing, String}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eltype.(eachcol(y2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Arrow.jl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we use Apache Arrow format that allows, in particular, for data interchange with R or Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"x.arrow\""
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Arrow.write(\"x.arrow\", x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>3×4 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;\">C</th><th style = \"text-align: left;\">D</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 = \"Union{Missing, Int64}\" style = \"text-align: left;\">Int64?</th><th title = \"Union{Missing, String}\" style = \"text-align: left;\">String?</th><th title = \"Union{Missing, 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;\">true</td><td style = \"text-align: right;\">1</td><td style = \"font-style: italic; text-align: left;\">missing</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;\">false</td><td style = \"text-align: right;\">2</td><td style = \"text-align: left;\">b</td><td style = \"font-style: italic; text-align: left;\">missing</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">true</td><td style = \"font-style: italic; text-align: right;\">missing</td><td style = \"text-align: left;\">c</td><td style = \"text-align: left;\">c</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& A & B & C & D\\\\\n",
       "\t\\hline\n",
       "\t& Bool & Int64? & String? & Char?\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 1 & \\emph{missing} & a \\\\\n",
       "\t2 & 0 & 2 & b & \\emph{missing} \\\\\n",
       "\t3 & 1 & \\emph{missing} & c & c \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m3×4 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m A     \u001b[0m\u001b[1m B       \u001b[0m\u001b[1m C       \u001b[0m\u001b[1m D       \u001b[0m\n",
       "     │\u001b[90m Bool  \u001b[0m\u001b[90m Int64?  \u001b[0m\u001b[90m String? \u001b[0m\u001b[90m Char?   \u001b[0m\n",
       "─────┼──────────────────────────────────\n",
       "   1 │  true        1 \u001b[90m missing \u001b[0m a\n",
       "   2 │ false        2  b       \u001b[90m missing \u001b[0m\n",
       "   3 │  true \u001b[90m missing \u001b[0m c        c"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = Arrow.Table(\"x.arrow\") |> DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-element Vector{Type}:\n",
       " Bool\n",
       " Union{Missing, Int64}\n",
       " Union{Missing, String}\n",
       " Union{Missing, Char}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eltype.(eachcol(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that columns of `y` are immutable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "ename": "LoadError",
     "evalue": "ReadOnlyMemoryError()",
     "output_type": "error",
     "traceback": [
      "ReadOnlyMemoryError()",
      "",
      "Stacktrace:",
      " [1] setindex!",
      "   @ .\\array.jl:969 [inlined]",
      " [2] setindex!(p::Arrow.BoolVector{Bool}, v::Bool, i::Int64)",
      "   @ Arrow C:\\Users\\bogum\\.julia\\packages\\Arrow\\MJoOf\\src\\arraytypes\\bool.jl:49",
      " [3] top-level scope",
      "   @ In[32]:1"
     ]
    }
   ],
   "source": [
    "y.A[1] = false"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is because `Arrow.Table` uses memory mapping and thus uses a custom vector types:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3-element Arrow.BoolVector{Bool}:\n",
       " 1\n",
       " 0\n",
       " 1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3-element Arrow.Primitive{Union{Missing, Int64}, Vector{Int64}}:\n",
       " 1\n",
       " 2\n",
       "  missing"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can get standard Julia Base vectors by copying a data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\\begin{tabular}{r|cccc}\n",
       "\t& A & B & C & D\\\\\n",
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       "\t& Bool & Int64? & String? & Char?\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 1 & \\emph{missing} & a \\\\\n",
       "\t2 & 0 & 2 & b & \\emph{missing} \\\\\n",
       "\t3 & 1 & \\emph{missing} & c & c \\\\\n",
       "\\end{tabular}\n"
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       "\u001b[1m3×4 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m A     \u001b[0m\u001b[1m B       \u001b[0m\u001b[1m C       \u001b[0m\u001b[1m D       \u001b[0m\n",
       "     │\u001b[90m Bool  \u001b[0m\u001b[90m Int64?  \u001b[0m\u001b[90m String? \u001b[0m\u001b[90m Char?   \u001b[0m\n",
       "─────┼──────────────────────────────────\n",
       "   1 │  true        1 \u001b[90m missing \u001b[0m a\n",
       "   2 │ false        2  b       \u001b[90m missing \u001b[0m\n",
       "   3 │  true \u001b[90m missing \u001b[0m c        c"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2 = copy(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3-element Vector{Bool}:\n",
       " 1\n",
       " 0\n",
       " 1"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2.A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3-element Vector{Union{Missing, Int64}}:\n",
       " 1\n",
       " 2\n",
       "  missing"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2.B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic bechmarking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we'll create some files, so be careful that you don't already have these files in your working directory!\n",
    "\n",
    "In particular, we'll time how long it takes us to write a `DataFrame` with 20,000 rows and 500 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First run\n",
      "CSV.jl\n",
      "  3.283824 seconds (40.03 M allocations: 1.060 GiB, 8.51% gc time, 37.86% compilation time)\n",
      "Serialization\n",
      "  0.262582 seconds (187.80 k allocations: 11.432 MiB, 27.28% compilation time)\n",
      "JDF.jl\n",
      "  0.710423 seconds (42.50 k allocations: 85.206 MiB, 5.51% gc time, 11.57% compilation time)\n",
      "JLSO.jl\n",
      "  1.176828 seconds (206.74 k allocations: 16.556 MiB, 1.51% gc time, 10.60% compilation time)\n",
      "Arrow.jl\n",
      "  3.938905 seconds (7.83 M allocations: 537.319 MiB, 9.41% gc time, 97.58% compilation time)\n",
      "JSONTables.jl arraytable\n",
      " 13.672869 seconds (229.99 M allocations: 5.430 GiB, 6.73% gc time, 0.07% compilation time)\n",
      "JSONTables.jl objecttable\n",
      "  0.444853 seconds (53.29 k allocations: 302.214 MiB, 21.97% gc time, 21.43% compilation time)\n",
      "Second run\n",
      "CSV.jl\n",
      "  2.408964 seconds (39.78 M allocations: 1.044 GiB, 9.72% gc time)\n",
      "Serialization\n",
      "  0.183141 seconds (22.93 k allocations: 596.169 KiB, 5.57% compilation time)\n",
      "JDF.jl\n",
      "  0.473582 seconds (21.64 k allocations: 83.814 MiB)\n",
      "JLSO.jl\n",
      "  1.018204 seconds (40.89 k allocations: 4.999 MiB)\n",
      "Arrow.jl\n",
      "  0.098347 seconds (347.02 k allocations: 10.075 MiB)\n",
      "JSONTables.jl arraytable\n",
      " 13.582579 seconds (229.99 M allocations: 5.430 GiB, 6.26% gc time, 0.07% compilation time)\n",
      "JSONTables.jl objecttable\n",
      "  0.325636 seconds (9.05 k allocations: 299.300 MiB, 28.60% gc time, 3.45% compilation time)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.3260445"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigdf = DataFrame(rand(Bool, 20_000, 500), :auto)\n",
    "bigdf[!, 1] = Int.(bigdf[!, 1])\n",
    "bigdf[!, 2] = bigdf[!, 2] .+ 0.5\n",
    "bigdf[!, 3] = string.(bigdf[!, 3], \", as string\")\n",
    "println(\"First run\")\n",
    "println(\"CSV.jl\")\n",
    "csvwrite1 = @elapsed @time CSV.write(\"bigdf1.csv\", bigdf)\n",
    "println(\"Serialization\")\n",
    "serializewrite1 = @elapsed @time open(io -> serialize(io, bigdf), \"bigdf.bin\", \"w\")\n",
    "println(\"JDF.jl\")\n",
    "jdfwrite1 = @elapsed @time JDF.save(\"bigdf.jdf\", bigdf)\n",
    "println(\"JLSO.jl\")\n",
    "jlsowrite1 = @elapsed @time JLSO.save(\"bigdf.jlso\", :data => bigdf)\n",
    "println(\"Arrow.jl\")\n",
    "arrowwrite1 = @elapsed @time Arrow.write(\"bigdf.arrow\", bigdf)\n",
    "println(\"JSONTables.jl arraytable\")\n",
    "jsontablesawrite1 = @elapsed @time open(io -> arraytable(io, bigdf), \"bigdf1.json\", \"w\")\n",
    "println(\"JSONTables.jl objecttable\")\n",
    "jsontablesowrite1 = @elapsed @time open(io -> objecttable(io, bigdf), \"bigdf2.json\", \"w\")\n",
    "println(\"Second run\")\n",
    "println(\"CSV.jl\")\n",
    "csvwrite2 = @elapsed @time CSV.write(\"bigdf1.csv\", bigdf)\n",
    "println(\"Serialization\")\n",
    "serializewrite2 = @elapsed @time open(io -> serialize(io, bigdf), \"bigdf.bin\", \"w\")\n",
    "println(\"JDF.jl\")\n",
    "jdfwrite2 = @elapsed @time JDF.save(\"bigdf.jdf\", bigdf)\n",
    "println(\"JLSO.jl\")\n",
    "jlsowrite2 = @elapsed @time JLSO.save(\"bigdf.jlso\", :data => bigdf)\n",
    "println(\"Arrow.jl\")\n",
    "arrowwrite2 = @elapsed @time Arrow.write(\"bigdf.arrow\", bigdf)\n",
    "println(\"JSONTables.jl arraytable\")\n",
    "jsontablesawrite2 = @elapsed @time open(io -> arraytable(io, bigdf), \"bigdf1.json\", \"w\")\n",
    "println(\"JSONTables.jl objecttable\")\n",
    "jsontablesowrite2 = @elapsed @time open(io -> objecttable(io, bigdf), \"bigdf2.json\", \"w\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
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      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupedbar(\n",
    "    # Exclude JSONTables.jl arraytable due to timing\n",
    "    repeat([\"CSV.jl\", \"Serialization\", \"JDF.jl\", \"JLSO.jl\", \"Arrow.jl\", \"JSONTables.jl\\nobjecttable\"],\n",
    "            inner = 2),\n",
    "    [csvwrite1, csvwrite2, serializewrite1, serializewrite1, jdfwrite1, jdfwrite2,\n",
    "     jlsowrite1, jlsowrite2, arrowwrite1, arrowwrite2, jsontablesowrite2, jsontablesowrite2],\n",
    "    group = repeat([\"1st\", \"2nd\"], outer = 6),\n",
    "    ylab = \"Second\",\n",
    "    title = \"Write Performance\\nDataFrame: bigdf\\nSize: $(size(bigdf))\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>6×2 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;\">file</th><th style = \"text-align: left;\">size</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"String\" style = \"text-align: left;\">String</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: left;\">bigdf.arrow</td><td style = \"text-align: right;\">2022202</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: left;\">bigdf.jdf</td><td style = \"text-align: right;\">5270589</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: left;\">bigdf.bin</td><td style = \"text-align: right;\">5363897</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">4</td><td style = \"text-align: left;\">bigdf1.csv</td><td style = \"text-align: right;\">55159820</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">5</td><td style = \"text-align: left;\">bigdf2.json</td><td style = \"text-align: right;\">55161821</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">6</td><td style = \"text-align: left;\">bigdf1.json</td><td style = \"text-align: right;\">123037429</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& file & size\\\\\n",
       "\t\\hline\n",
       "\t& String & Int64\\\\\n",
       "\t\\hline\n",
       "\t1 & bigdf.arrow & 2022202 \\\\\n",
       "\t2 & bigdf.jdf & 5270589 \\\\\n",
       "\t3 & bigdf.bin & 5363897 \\\\\n",
       "\t4 & bigdf1.csv & 55159820 \\\\\n",
       "\t5 & bigdf2.json & 55161821 \\\\\n",
       "\t6 & bigdf1.json & 123037429 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m6×2 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m file        \u001b[0m\u001b[1m size      \u001b[0m\n",
       "     │\u001b[90m String      \u001b[0m\u001b[90m Int64     \u001b[0m\n",
       "─────┼────────────────────────\n",
       "   1 │ bigdf.arrow    2022202\n",
       "   2 │ bigdf.jdf      5270589\n",
       "   3 │ bigdf.bin      5363897\n",
       "   4 │ bigdf1.csv    55159820\n",
       "   5 │ bigdf2.json   55161821\n",
       "   6 │ bigdf1.json  123037429"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_files = [\"bigdf1.csv\", \"bigdf.bin\", \"bigdf.arrow\", \"bigdf1.json\", \"bigdf2.json\"]\n",
    "df = DataFrame(file = data_files, size = getfield.(stat.(data_files), :size))\n",
    "append!(df, DataFrame(file = \"bigdf.jdf\", size=reduce((x,y)->x+y.size,\n",
    "                                                      stat.(joinpath.(\"bigdf.jdf\", readdir(\"bigdf.jdf\"))),\n",
    "                                                      init=0)))\n",
    "sort!(df, :size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@df df plot(:file, :size/1024^2, seriestype=:bar, title = \"Format File Size (MB)\", label=\"Size\", ylab=\"MB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First run\n",
      "CSV.jl\n",
      "  1.284889 seconds (558.31 k allocations: 66.884 MiB, 29.36% compilation time)\n",
      "Serialization\n",
      "  0.484369 seconds (9.72 M allocations: 158.767 MiB, 4.73% compilation time)\n",
      "JDF.jl\n",
      "  0.771087 seconds (129.46 k allocations: 92.848 MiB, 81.06% compilation time)\n",
      "JLSO.jl\n",
      "  0.539299 seconds (9.74 M allocations: 161.459 MiB, 17.68% gc time, 4.61% compilation time)\n",
      "Arrow.jl\n",
      "  0.473688 seconds (367.48 k allocations: 24.175 MiB, 96.43% compilation time)\n",
      "  0.104741 seconds (21.51 k allocations: 10.696 MiB)\n",
      "JSONTables.jl arraytable\n",
      " 16.186853 seconds (40.38 M allocations: 2.550 GiB, 2.28% gc time)\n",
      "JSONTables.jl objecttable\n",
      "  0.642286 seconds (57.11 k allocations: 285.899 MiB, 10.93% gc time, 4.49% compilation time)\n",
      "Second run\n",
      "  1.124207 seconds (67.20 k allocations: 33.199 MiB)\n",
      "Serialization\n",
      "  0.464462 seconds (9.71 M allocations: 158.273 MiB)\n",
      "JDF.jl\n",
      "  0.132816 seconds (38.82 k allocations: 86.973 MiB)\n",
      "JLSO.jl\n",
      "  0.434510 seconds (9.74 M allocations: 161.084 MiB)\n",
      "Arrow.jl\n",
      "  0.020483 seconds (36.63 k allocations: 1.863 MiB)\n",
      "  0.089482 seconds (21.51 k allocations: 10.696 MiB)\n",
      "JSONTables.jl arraytable\n",
      " 16.576991 seconds (40.38 M allocations: 2.550 GiB, 3.12% gc time)\n",
      "JSONTables.jl objecttable\n",
      "  0.616051 seconds (34.07 k allocations: 284.348 MiB)\n"
     ]
    }
   ],
   "source": [
    "println(\"First run\")\n",
    "println(\"CSV.jl\")\n",
    "csvread1 = @elapsed @time CSV.read(\"bigdf1.csv\", DataFrame)\n",
    "println(\"Serialization\")\n",
    "serializeread1 = @elapsed @time open(deserialize, \"bigdf.bin\")\n",
    "println(\"JDF.jl\")\n",
    "jdfread1 = @elapsed @time JDF.load(\"bigdf.jdf\") |> DataFrame\n",
    "println(\"JLSO.jl\")\n",
    "jlsoread1 = @elapsed @time JLSO.load(\"bigdf.jlso\")\n",
    "println(\"Arrow.jl\")\n",
    "arrowread1 = @elapsed @time df_tmp = Arrow.Table(\"bigdf.arrow\") |> DataFrame\n",
    "arrowread1copy = @elapsed @time copy(df_tmp)\n",
    "println(\"JSONTables.jl arraytable\")\n",
    "jsontablesaread1 = @elapsed @time open(jsontable, \"bigdf1.json\")\n",
    "println(\"JSONTables.jl objecttable\")\n",
    "jsontablesoread1 = @elapsed @time open(jsontable, \"bigdf2.json\")\n",
    "println(\"Second run\")\n",
    "csvread2 = @elapsed @time CSV.read(\"bigdf1.csv\", DataFrame)\n",
    "println(\"Serialization\")\n",
    "serializeread2 = @elapsed @time open(deserialize, \"bigdf.bin\")\n",
    "println(\"JDF.jl\")\n",
    "jdfread2 = @elapsed @time JDF.load(\"bigdf.jdf\") |> DataFrame\n",
    "println(\"JLSO.jl\")\n",
    "jlsoread2 = @elapsed @time JLSO.load(\"bigdf.jlso\")\n",
    "println(\"Arrow.jl\")\n",
    "arrowread2 = @elapsed @time df_tmp = Arrow.Table(\"bigdf.arrow\") |> DataFrame\n",
    "arrowread2copy = @elapsed @time copy(df_tmp)\n",
    "println(\"JSONTables.jl arraytable\")\n",
    "jsontablesaread2 = @elapsed @time open(jsontable, \"bigdf1.json\")\n",
    "println(\"JSONTables.jl objecttable\")\n",
    "jsontablesoread2 = @elapsed @time open(jsontable, \"bigdf2.json\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
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      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exclude JSON\\narraytable arraytable due to much longer timing\n",
    "groupedbar(\n",
    "    repeat([\"CSV.jl\", \"Serialization\", \"JDF.jl\", \"JLSO.jl\", \"Arrow.jl\", \"Arrow.jl\\ncopy\", #\"JSON\\narraytable\",\n",
    "            \"JSON\\nobjecttable\"], inner = 2),\n",
    "    [csvread1, csvread2, serializeread1, serializeread2, jdfread1, jdfread2, jlsoread1, jlsoread2,\n",
    "     arrowread1, arrowread2, arrowread1+arrowread1copy, arrowread2+arrowread2copy,\n",
    "     # jsontablesaread1, jsontablesaread2,\n",
    "     jsontablesoread1, jsontablesoread2],    \n",
    "    group = repeat([\"1st\", \"2nd\"], outer = 7),\n",
    "    ylab = \"Second\",\n",
    "    title = \"Read Performance\\nDataFrame: bigdf\\nSize: $(size(bigdf))\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using gzip compression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A common user requirement is to be able to load and save CSV that are compressed using gzip.\n",
    "Below we show how this can be accomplished using CodecZlib.jl.\n",
    "The same pattern is applicable to JSONTables.jl compression/decompression.\n",
    "\n",
    "Again make sure that you do not have file named `df_compress_test.csv.gz` in your working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first generate a random data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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right;\">10</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">10</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">5</td><td style = \"text-align: right;\">7</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">3</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">4</td><td style = \"text-align: right;\">6</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">4</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">7</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">10</td><td style = \"text-align: right;\">8</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">4</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">8</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">3</td><td style = \"text-align: right;\">3</td><td style = \"text-align: right;\">3</td><td style = \"text-align: right;\">8</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">7</td><td style = \"text-align: right;\">8</td><td style = \"text-align: right;\">3</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">3</td><td style = \"text-align: right;\">10</td><td style = \"text-align: right;\">7</td><td style = \"text-align: right;\">7</td><td style = \"text-align: right;\">8</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">8</td><td style = \"text-align: right;\">7</td><td style = \"text-align: right;\">8</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">4</td><td style = \"text-align: right;\">&ctdot;</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ccccccccccccc}\n",
       "\t& x1 & x2 & x3 & x4 & x5 & x6 & x7 & x8 & x9 & x10 & x11 & x12 & \\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & \\\\\n",
       "\t\\hline\n",
       "\t1 & 6 & 8 & 5 & 9 & 10 & 9 & 5 & 4 & 8 & 9 & 6 & 4 & $\\dots$ \\\\\n",
       "\t2 & 1 & 10 & 7 & 9 & 8 & 8 & 5 & 10 & 2 & 10 & 8 & 4 & $\\dots$ \\\\\n",
       "\t3 & 5 & 7 & 7 & 1 & 6 & 2 & 6 & 1 & 9 & 9 & 5 & 1 & $\\dots$ \\\\\n",
       "\t4 & 1 & 7 & 4 & 10 & 8 & 5 & 1 & 3 & 8 & 1 & 5 & 1 & $\\dots$ \\\\\n",
       "\t5 & 5 & 4 & 1 & 9 & 3 & 5 & 10 & 10 & 5 & 5 & 6 & 10 & $\\dots$ \\\\\n",
       "\t6 & 2 & 1 & 4 & 8 & 5 & 5 & 6 & 2 & 4 & 2 & 2 & 1 & $\\dots$ \\\\\n",
       "\t7 & 7 & 8 & 6 & 10 & 6 & 8 & 1 & 9 & 9 & 7 & 3 & 2 & $\\dots$ \\\\\n",
       "\t8 & 9 & 4 & 3 & 6 & 1 & 7 & 9 & 3 & 4 & 8 & 7 & 6 & $\\dots$ \\\\\n",
       "\t9 & 1 & 4 & 6 & 4 & 10 & 2 & 1 & 5 & 6 & 9 & 4 & 9 & $\\dots$ \\\\\n",
       "\t10 & 9 & 5 & 5 & 3 & 2 & 5 & 3 & 1 & 4 & 8 & 6 & 5 & $\\dots$ \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m10×1000 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m x1    \u001b[0m\u001b[1m x2    \u001b[0m\u001b[1m x3    \u001b[0m\u001b[1m x4    \u001b[0m\u001b[1m x5    \u001b[0m\u001b[1m x6    \u001b[0m\u001b[1m x7    \u001b[0m\u001b[1m x8    \u001b[0m\u001b[1m x9    \u001b[0m\u001b[1m x10   \u001b[0m\u001b[1m x\u001b[0m ⋯\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m I\u001b[0m ⋯\n",
       "─────┼──────────────────────────────────────────────────────────────────────────\n",
       "   1 │     6      8      5      9     10      9      5      4      8      9    ⋯\n",
       "   2 │     1     10      7      9      8      8      5     10      2     10\n",
       "   3 │     5      7      7      1      6      2      6      1      9      9\n",
       "   4 │     1      7      4     10      8      5      1      3      8      1\n",
       "   5 │     5      4      1      9      3      5     10     10      5      5    ⋯\n",
       "   6 │     2      1      4      8      5      5      6      2      4      2\n",
       "   7 │     7      8      6     10      6      8      1      9      9      7\n",
       "   8 │     9      4      3      6      1      7      9      3      4      8\n",
       "   9 │     1      4      6      4     10      2      1      5      6      9    ⋯\n",
       "  10 │     9      5      5      3      2      5      3      1      4      8\n",
       "\u001b[36m                                                             990 columns omitted\u001b[0m"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = DataFrame(rand(1:10, 10, 1000), :auto)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GzipCompressorStream comes from CodecZlib\n",
    "\n",
    "open(\"df_compress_test.csv.gz\", \"w\") do io\n",
    "    stream = GzipCompressorStream(io)\n",
    "    CSV.write(stream, df)\n",
    "    close(stream)\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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right;\">5</td><td style = \"text-align: right;\">3</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">5</td><td style = \"text-align: right;\">3</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">4</td><td style = \"text-align: right;\">8</td><td style = \"text-align: right;\">6</td><td style = \"text-align: right;\">5</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">3</td><td style = \"text-align: right;\">5</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">5</td><td style = \"text-align: right;\">8</td><td style = \"text-align: right;\">7</td><td style = \"text-align: right;\">10</td><td style = \"text-align: right;\">8</td><td style = \"text-align: right;\">6</td><td style = \"text-align: right;\">5</td><td style = \"text-align: right;\">6</td><td style = \"text-align: right;\">7</td><td style = \"text-align: right;\">10</td><td style = \"text-align: right;\">10</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">8</td><td style = \"text-align: right;\">5</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">6</td><td style = \"text-align: right;\">6</td><td style = \"text-align: right;\">3</td><td style = \"text-align: right;\">3</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">4</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">4</td><td style = \"text-align: right;\">7</td><td style = \"text-align: right;\">7</td><td style = \"text-align: right;\">7</td><td style = \"text-align: right;\">4</td><td style = \"text-align: right;\">6</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">8</td><td style = \"text-align: right;\">3</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">7</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">10</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">10</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">5</td><td style = \"text-align: right;\">7</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">3</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">4</td><td style = \"text-align: right;\">6</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">4</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">7</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">10</td><td style = \"text-align: right;\">8</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">4</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">8</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">3</td><td style = \"text-align: right;\">3</td><td style = \"text-align: right;\">3</td><td style = \"text-align: right;\">8</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">7</td><td style = \"text-align: right;\">8</td><td style = \"text-align: right;\">3</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">3</td><td style = \"text-align: right;\">10</td><td style = \"text-align: right;\">7</td><td style = \"text-align: right;\">7</td><td style = \"text-align: right;\">8</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">8</td><td style = \"text-align: right;\">7</td><td style = \"text-align: right;\">8</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">4</td><td style = \"text-align: right;\">&ctdot;</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ccccccccccccc}\n",
       "\t& x1 & x2 & x3 & x4 & x5 & x6 & x7 & x8 & x9 & x10 & x11 & x12 & \\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & \\\\\n",
       "\t\\hline\n",
       "\t1 & 6 & 8 & 5 & 9 & 10 & 9 & 5 & 4 & 8 & 9 & 6 & 4 & $\\dots$ \\\\\n",
       "\t2 & 1 & 10 & 7 & 9 & 8 & 8 & 5 & 10 & 2 & 10 & 8 & 4 & $\\dots$ \\\\\n",
       "\t3 & 5 & 7 & 7 & 1 & 6 & 2 & 6 & 1 & 9 & 9 & 5 & 1 & $\\dots$ \\\\\n",
       "\t4 & 1 & 7 & 4 & 10 & 8 & 5 & 1 & 3 & 8 & 1 & 5 & 1 & $\\dots$ \\\\\n",
       "\t5 & 5 & 4 & 1 & 9 & 3 & 5 & 10 & 10 & 5 & 5 & 6 & 10 & $\\dots$ \\\\\n",
       "\t6 & 2 & 1 & 4 & 8 & 5 & 5 & 6 & 2 & 4 & 2 & 2 & 1 & $\\dots$ \\\\\n",
       "\t7 & 7 & 8 & 6 & 10 & 6 & 8 & 1 & 9 & 9 & 7 & 3 & 2 & $\\dots$ \\\\\n",
       "\t8 & 9 & 4 & 3 & 6 & 1 & 7 & 9 & 3 & 4 & 8 & 7 & 6 & $\\dots$ \\\\\n",
       "\t9 & 1 & 4 & 6 & 4 & 10 & 2 & 1 & 5 & 6 & 9 & 4 & 9 & $\\dots$ \\\\\n",
       "\t10 & 9 & 5 & 5 & 3 & 2 & 5 & 3 & 1 & 4 & 8 & 6 & 5 & $\\dots$ \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m10×1000 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m x1    \u001b[0m\u001b[1m x2    \u001b[0m\u001b[1m x3    \u001b[0m\u001b[1m x4    \u001b[0m\u001b[1m x5    \u001b[0m\u001b[1m x6    \u001b[0m\u001b[1m x7    \u001b[0m\u001b[1m x8    \u001b[0m\u001b[1m x9    \u001b[0m\u001b[1m x10   \u001b[0m\u001b[1m x\u001b[0m ⋯\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m I\u001b[0m ⋯\n",
       "─────┼──────────────────────────────────────────────────────────────────────────\n",
       "   1 │     6      8      5      9     10      9      5      4      8      9    ⋯\n",
       "   2 │     1     10      7      9      8      8      5     10      2     10\n",
       "   3 │     5      7      7      1      6      2      6      1      9      9\n",
       "   4 │     1      7      4     10      8      5      1      3      8      1\n",
       "   5 │     5      4      1      9      3      5     10     10      5      5    ⋯\n",
       "   6 │     2      1      4      8      5      5      6      2      4      2\n",
       "   7 │     7      8      6     10      6      8      1      9      9      7\n",
       "   8 │     9      4      3      6      1      7      9      3      4      8\n",
       "   9 │     1      4      6      4     10      2      1      5      6      9    ⋯\n",
       "  10 │     9      5      5      3      2      5      3      1      4      8\n",
       "\u001b[36m                                                             990 columns omitted\u001b[0m"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = CSV.File(transcode(GzipDecompressor, Mmap.mmap(\"df_compress_test.csv.gz\"))) |> DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df == df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using zip files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes you may have files compressed inside a zip file.\n",
    "\n",
    "In such a situation you may use [ZipFile.jl](https://github.com/fhs/ZipFile.jl) in conjunction an an appropriate reader to read the files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we first create a ZIP file and then read back its contents into a `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>3×4 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;\">x1</th><th style = \"text-align: left;\">x2</th><th style = \"text-align: left;\">x3</th><th style = \"text-align: left;\">x4</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 = \"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;\">2</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">7</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: right;\">10</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">10</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: right;\">8</td><td style = \"text-align: right;\">6</td><td style = \"text-align: right;\">3</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& x1 & x2 & x3 & x4\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Int64 & Int64 & Int64\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 2 & 9 & 7 \\\\\n",
       "\t2 & 8 & 10 & 9 & 10 \\\\\n",
       "\t3 & 4 & 8 & 6 & 3 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m3×4 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m x1    \u001b[0m\u001b[1m x2    \u001b[0m\u001b[1m x3    \u001b[0m\u001b[1m x4    \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\n",
       "─────┼────────────────────────────\n",
       "   1 │     1      2      9      7\n",
       "   2 │     8     10      9     10\n",
       "   3 │     4      8      6      3"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = DataFrame(rand(1:10, 3, 4), :auto)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>3×4 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;\">x1</th><th style = \"text-align: left;\">x2</th><th style = \"text-align: left;\">x3</th><th style = \"text-align: left;\">x4</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 = \"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;\">5</td><td style = \"text-align: right;\">5</td><td style = \"text-align: right;\">5</td><td style = \"text-align: right;\">7</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;\">5</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">6</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">5</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">6</td><td style = \"text-align: right;\">2</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& x1 & x2 & x3 & x4\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Int64 & Int64 & Int64\\\\\n",
       "\t\\hline\n",
       "\t1 & 5 & 5 & 5 & 7 \\\\\n",
       "\t2 & 2 & 5 & 2 & 6 \\\\\n",
       "\t3 & 5 & 9 & 6 & 2 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m3×4 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m x1    \u001b[0m\u001b[1m x2    \u001b[0m\u001b[1m x3    \u001b[0m\u001b[1m x4    \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\n",
       "─────┼────────────────────────────\n",
       "   1 │     5      5      5      7\n",
       "   2 │     2      5      2      6\n",
       "   3 │     5      9      6      2"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = DataFrame(rand(1:10, 3, 4), :auto)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we show yet another way to write a `DataFrame` into a CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write a CSV file into the zip file\n",
    "w = ZipFile.Writer(\"x.zip\")\n",
    "\n",
    "f1 = ZipFile.addfile(w, \"x1.csv\")\n",
    "write(f1, sprint(show, \"text/csv\", df1))\n",
    "\n",
    "# write a second CSV file into zip file\n",
    "f2 = ZipFile.addfile(w, \"x2.csv\", method=ZipFile.Deflate)\n",
    "write(f2, sprint(show, \"text/csv\", df2))\n",
    "\n",
    "close(w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we read the CSV we have written:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = ZipFile.Reader(\"x.zip\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>3×4 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;\">x1</th><th style = \"text-align: left;\">x2</th><th style = \"text-align: left;\">x3</th><th style = \"text-align: left;\">x4</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 = \"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;\">2</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">7</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: right;\">10</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">10</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: right;\">8</td><td style = \"text-align: right;\">6</td><td style = \"text-align: right;\">3</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& x1 & x2 & x3 & x4\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Int64 & Int64 & Int64\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 2 & 9 & 7 \\\\\n",
       "\t2 & 8 & 10 & 9 & 10 \\\\\n",
       "\t3 & 4 & 8 & 6 & 3 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m3×4 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m x1    \u001b[0m\u001b[1m x2    \u001b[0m\u001b[1m x3    \u001b[0m\u001b[1m x4    \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\n",
       "─────┼────────────────────────────\n",
       "   1 │     1      2      9      7\n",
       "   2 │     8     10      9     10\n",
       "   3 │     4      8      6      3"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the index index of file called x1.csv\n",
    "index_xcsv = findfirst(x->x.name == \"x1.csv\", z.files)\n",
    "# to read the x1.csv file in the zip file\n",
    "df1_2 = CSV.read(read(z.files[index_xcsv]), DataFrame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_2 == df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>3×4 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;\">x1</th><th style = \"text-align: left;\">x2</th><th style = \"text-align: left;\">x3</th><th style = \"text-align: left;\">x4</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 = \"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;\">5</td><td style = \"text-align: right;\">5</td><td style = \"text-align: right;\">5</td><td style = \"text-align: right;\">7</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;\">5</td><td style = \"text-align: right;\">2</td><td style = \"text-align: right;\">6</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">5</td><td style = \"text-align: right;\">9</td><td style = \"text-align: right;\">6</td><td style = \"text-align: right;\">2</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& x1 & x2 & x3 & x4\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Int64 & Int64 & Int64\\\\\n",
       "\t\\hline\n",
       "\t1 & 5 & 5 & 5 & 7 \\\\\n",
       "\t2 & 2 & 5 & 2 & 6 \\\\\n",
       "\t3 & 5 & 9 & 6 & 2 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m3×4 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m x1    \u001b[0m\u001b[1m x2    \u001b[0m\u001b[1m x3    \u001b[0m\u001b[1m x4    \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\n",
       "─────┼────────────────────────────\n",
       "   1 │     5      5      5      7\n",
       "   2 │     2      5      2      6\n",
       "   3 │     5      9      6      2"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the index index of file called x2.csv\n",
    "index_xcsv = findfirst(x->x.name == \"x2.csv\", z.files)\n",
    "# to read the x2.csv file in the zip file\n",
    "df2_2 = CSV.read(read(z.files[index_xcsv]), DataFrame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_2 == df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that once you read a given file from `z` object its stream is all used-up (it is at its end). Therefore to read it again you need to close `z` and open it again.\n",
    "\n",
    "Also do not forget to close the zip file once done."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "close(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's clean up. Do not run the next cell unless you are sure that it will not erase your important files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "foreach(rm, [\"x1.csv\", \"x.bin\", \"x.jlso\", \"x1.json\", \"x2.json\",\n",
    "             \"bigdf1.csv\", \"bigdf.bin\", \"bigdf.jlso\", \"bigdf1.json\", \"bigdf2.json\", \n",
    "             \"x.zip\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "rm(\"bigdf.jdf\", recursive=true)\n",
    "rm(\"x.jdf\", recursive=true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we did not remove `x.arrow` and `bigdf.arrow` and `df_compress_test.csv.gz` - you have to do it manually, as these files are memory mapped."
   ]
  }
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