01_constructors.ipynb

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    "# Introduction to DataFrames\n",
    "**[Bogumił Kamiński](http://bogumilkaminski.pl/about/), February 13, 2023**\n",
    "\n",
    "Let's get started by loading the `DataFrames` package."
   ]
  },
  {
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   "source": [
    "using DataFrames, Random"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Constructors and conversion"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "### Constructors\n",
    "\n",
    "In this section, you'll see many ways to create a `DataFrame` using the `DataFrame()` constructor.\n",
    "\n",
    "First, we could create an empty DataFrame,"
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    "DataFrame()"
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   "source": [
    "Or we could call the constructor using keyword arguments to add columns to the `DataFrame`."
   ]
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   "source": [
    "DataFrame(A=1:3, B=rand(3), C=randstring.([3,3,3]), fixed=1)"
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   "source": [
    "note in column `:fixed` that scalars get automatically broadcasted."
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   "cell_type": "markdown",
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   "source": [
    "We can create a `DataFrame` from a dictionary, in which case keys from the dictionary will be sorted to create the `DataFrame` columns."
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   "cell_type": "code",
   "execution_count": 4,
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    "x = Dict(\"A\" => [1,2], \"B\" => [true, false], \"C\" => ['a', 'b'], \"fixed\" => Ref([1,1]))\n",
    "DataFrame(x)"
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   "cell_type": "markdown",
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   "source": [
    "This time we used `Ref` to protect a vector from being treated as a column and forcing broadcasting it into every row of `:fixed` column (note that the `[1,1]` vector is aliased in each row)."
   ]
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    "Rather than explicitly creating a dictionary first, as above, we could pass `DataFrame` arguments with the syntax of dictionary key-value pairs. \n",
    "\n",
    "Note that in this case, we use `Symbol`s to denote the column names and arguments are not sorted. For example, `:A`, the symbol, produces `A`, the name of the first column here:"
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    "Although, in general, using `Symbol`s rather than strings to denote column names is preferred (as it is faster) DataFrames.jl accepts passing strings as column names, so this also works:"
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   "source": [
    "You can also pass a vector of pairs, which is useful if it is constructed programatically:"
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       "<div><div style = \"float: left;\"><span>2×4 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;\">fixed</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 = \"Bool\" style = \"text-align: left;\">Bool</th><th title = \"Char\" style = \"text-align: left;\">Char</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;\">true</td><td style = \"text-align: left;\">a</td><td style = \"text-align: left;\">const</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;\">false</td><td style = \"text-align: left;\">b</td><td style = \"text-align: left;\">const</td></tr></tbody></table></div>"
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       "\u001b[1m Row \u001b[0m│\u001b[1m A     \u001b[0m\u001b[1m B     \u001b[0m\u001b[1m C    \u001b[0m\u001b[1m fixed  \u001b[0m\n",
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    "DataFrame([:A => [1,2], :B => [true, false], :C => ['a', 'b'], :fixed => \"const\"])"
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    "Here we create a `DataFrame` from a vector of vectors, and each vector becomes a column."
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   "execution_count": 8,
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       "\t& x1 & x2 & x3\\\\\n",
       "\t\\hline\n",
       "\t& Float64 & Float64 & Float64\\\\\n",
       "\t\\hline\n",
       "\t1 & 0.0415832 & 0.555336 & 0.806788 \\\\\n",
       "\t2 & 0.593325 & 0.25966 & 0.613159 \\\\\n",
       "\t3 & 0.591768 & 0.728759 & 0.517681 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m3×3 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m x1        \u001b[0m\u001b[1m x2       \u001b[0m\u001b[1m x3       \u001b[0m\n",
       "     │\u001b[90m Float64   \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m Float64  \u001b[0m\n",
       "─────┼───────────────────────────────\n",
       "   1 │ 0.0415832  0.555336  0.806788\n",
       "   2 │ 0.593325   0.25966   0.613159\n",
       "   3 │ 0.591768   0.728759  0.517681"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrame([rand(3) for i in 1:3], [:x1, :x2, :x3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>3×3 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;\">x1</th><th style = \"text-align: left;\">x2</th><th style = \"text-align: left;\">x3</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"Float64\" style = \"text-align: left;\">Float64</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;\">0.628041</td><td style = \"text-align: right;\">0.651544</td><td style = \"text-align: right;\">0.354929</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: right;\">0.175792</td><td style = \"text-align: right;\">0.562795</td><td style = \"text-align: right;\">0.631323</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">0.243712</td><td style = \"text-align: right;\">0.856293</td><td style = \"text-align: right;\">0.722517</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ccc}\n",
       "\t& x1 & x2 & x3\\\\\n",
       "\t\\hline\n",
       "\t& Float64 & Float64 & Float64\\\\\n",
       "\t\\hline\n",
       "\t1 & 0.628041 & 0.651544 & 0.354929 \\\\\n",
       "\t2 & 0.175792 & 0.562795 & 0.631323 \\\\\n",
       "\t3 & 0.243712 & 0.856293 & 0.722517 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m3×3 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m x1       \u001b[0m\u001b[1m x2       \u001b[0m\u001b[1m x3       \u001b[0m\n",
       "     │\u001b[90m Float64  \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m Float64  \u001b[0m\n",
       "─────┼──────────────────────────────\n",
       "   1 │ 0.628041  0.651544  0.354929\n",
       "   2 │ 0.175792  0.562795  0.631323\n",
       "   3 │ 0.243712  0.856293  0.722517"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrame([rand(3) for i in 1:3], [\"x1\", \"x2\", \"x3\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see you either pass a vector of column names as a second argument or `:auto` in which case column names are generated automatically."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In particular it is not allowed to pass a vector of scalars to `DataFrame` constructor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "LoadError",
     "evalue": "ArgumentError: 'Vector{Int64}' iterates 'Int64' values, which doesn't satisfy the Tables.jl `AbstractRow` interface",
     "output_type": "error",
     "traceback": [
      "ArgumentError: 'Vector{Int64}' iterates 'Int64' values, which doesn't satisfy the Tables.jl `AbstractRow` interface",
      "",
      "Stacktrace:",
      " [1] invalidtable(#unused#::Vector{Int64}, #unused#::Int64)",
      "   @ Tables C:\\Users\\bogum\\.julia\\packages\\Tables\\T7rHm\\src\\tofromdatavalues.jl:42",
      " [2] iterate",
      "   @ C:\\Users\\bogum\\.julia\\packages\\Tables\\T7rHm\\src\\tofromdatavalues.jl:48 [inlined]",
      " [3] buildcolumns",
      "   @ C:\\Users\\bogum\\.julia\\packages\\Tables\\T7rHm\\src\\fallbacks.jl:202 [inlined]",
      " [4] columns",
      "   @ C:\\Users\\bogum\\.julia\\packages\\Tables\\T7rHm\\src\\fallbacks.jl:265 [inlined]",
      " [5] DataFrame(x::Vector{Int64}; copycols::Nothing)",
      "   @ DataFrames C:\\Users\\bogum\\.julia\\packages\\DataFrames\\LteEl\\src\\other\\tables.jl:57",
      " [6] DataFrame(x::Vector{Int64})",
      "   @ DataFrames C:\\Users\\bogum\\.julia\\packages\\DataFrames\\LteEl\\src\\other\\tables.jl:48",
      " [7] top-level scope",
      "   @ In[11]:1"
     ]
    }
   ],
   "source": [
    "DataFrame([1, 2, 3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead use a transposed vector if you have a vector of single values (in this way you effectively pass a two dimensional array to the constructor which is supported the same way as in vector of vectors case)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>1×3 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;\">x1</th><th style = \"text-align: left;\">x2</th><th style = \"text-align: left;\">x3</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></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;\">3</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ccc}\n",
       "\t& x1 & x2 & x3\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Int64 & Int64\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 2 & 3 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m1×3 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m x1    \u001b[0m\u001b[1m x2    \u001b[0m\u001b[1m x3    \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\n",
       "─────┼─────────────────────\n",
       "   1 │     1      2      3"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrame(permutedims([1, 2, 3]), :auto)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also pass a vector of `NamedTuple`s to construct a `DataFrame`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>2×2 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;\">2</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: 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 & 2 \\\\\n",
       "\t2 & 3 & 4 \\\\\n",
       "\\end{tabular}\n"
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      "text/plain": [
       "\u001b[1m2×2 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      2\n",
       "   2 │     3      4"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v = [(a=1, b=2), (a=3, b=4)]\n",
    "DataFrame(v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively you can pass a `NamedTuple` of vectors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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;\">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;\">11</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;\">12</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;\">13</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 & 11 \\\\\n",
       "\t2 & 2 & 12 \\\\\n",
       "\t3 & 3 & 13 \\\\\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 b     \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\n",
       "─────┼──────────────\n",
       "   1 │     1     11\n",
       "   2 │     2     12\n",
       "   3 │     3     13"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = (a=1:3, b=11:13)\n",
    "DataFrame(n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we create a `DataFrame` from a matrix,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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 = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</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;\">0.411745</td><td style = \"text-align: right;\">0.108472</td><td style = \"text-align: right;\">0.446003</td><td style = \"text-align: right;\">0.340551</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: right;\">0.158675</td><td style = \"text-align: right;\">0.211968</td><td style = \"text-align: right;\">0.570003</td><td style = \"text-align: right;\">0.246417</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">0.471452</td><td style = \"text-align: right;\">0.466808</td><td style = \"text-align: right;\">0.0745958</td><td style = \"text-align: right;\">0.219074</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& x1 & x2 & x3 & x4\\\\\n",
       "\t\\hline\n",
       "\t& Float64 & Float64 & Float64 & Float64\\\\\n",
       "\t\\hline\n",
       "\t1 & 0.411745 & 0.108472 & 0.446003 & 0.340551 \\\\\n",
       "\t2 & 0.158675 & 0.211968 & 0.570003 & 0.246417 \\\\\n",
       "\t3 & 0.471452 & 0.466808 & 0.0745958 & 0.219074 \\\\\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 Float64  \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m Float64   \u001b[0m\u001b[90m Float64  \u001b[0m\n",
       "─────┼─────────────────────────────────────────\n",
       "   1 │ 0.411745  0.108472  0.446003   0.340551\n",
       "   2 │ 0.158675  0.211968  0.570003   0.246417\n",
       "   3 │ 0.471452  0.466808  0.0745958  0.219074"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrame(rand(3, 4), :auto)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and here we do the same but also pass column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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 = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</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;\">0.30943</td><td style = \"text-align: right;\">0.620714</td><td style = \"text-align: right;\">0.580377</td><td style = \"text-align: right;\">0.649454</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: right;\">0.695982</td><td style = \"text-align: right;\">0.366982</td><td style = \"text-align: right;\">0.0774122</td><td style = \"text-align: right;\">0.324185</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">0.58183</td><td style = \"text-align: right;\">0.427705</td><td style = \"text-align: right;\">0.599087</td><td style = \"text-align: right;\">0.0207122</td></tr></tbody></table></div>"
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       "\\begin{tabular}{r|cccc}\n",
       "\t& a & b & c & d\\\\\n",
       "\t\\hline\n",
       "\t& Float64 & Float64 & Float64 & Float64\\\\\n",
       "\t\\hline\n",
       "\t1 & 0.30943 & 0.620714 & 0.580377 & 0.649454 \\\\\n",
       "\t2 & 0.695982 & 0.366982 & 0.0774122 & 0.324185 \\\\\n",
       "\t3 & 0.58183 & 0.427705 & 0.599087 & 0.0207122 \\\\\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 Float64  \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m Float64   \u001b[0m\u001b[90m Float64   \u001b[0m\n",
       "─────┼──────────────────────────────────────────\n",
       "   1 │ 0.30943   0.620714  0.580377   0.649454\n",
       "   2 │ 0.695982  0.366982  0.0774122  0.324185\n",
       "   3 │ 0.58183   0.427705  0.599087   0.0207122"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrame(rand(3, 4), Symbol.('a':'d'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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 = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</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;\">0.643073</td><td style = \"text-align: right;\">0.221819</td><td style = \"text-align: right;\">0.658396</td><td style = \"text-align: right;\">0.0976103</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: right;\">0.638833</td><td style = \"text-align: right;\">0.599563</td><td style = \"text-align: right;\">0.376978</td><td style = \"text-align: right;\">0.159511</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">0.295603</td><td style = \"text-align: right;\">0.227987</td><td style = \"text-align: right;\">0.621661</td><td style = \"text-align: right;\">0.287113</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& a & b & c & d\\\\\n",
       "\t\\hline\n",
       "\t& Float64 & Float64 & Float64 & Float64\\\\\n",
       "\t\\hline\n",
       "\t1 & 0.643073 & 0.221819 & 0.658396 & 0.0976103 \\\\\n",
       "\t2 & 0.638833 & 0.599563 & 0.376978 & 0.159511 \\\\\n",
       "\t3 & 0.295603 & 0.227987 & 0.621661 & 0.287113 \\\\\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 Float64  \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m Float64   \u001b[0m\n",
       "─────┼─────────────────────────────────────────\n",
       "   1 │ 0.643073  0.221819  0.658396  0.0976103\n",
       "   2 │ 0.638833  0.599563  0.376978  0.159511\n",
       "   3 │ 0.295603  0.227987  0.621661  0.287113"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrame(rand(3, 4), string.('a':'d'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is how you can create a data frame with no rows, but with predefined columns and their types:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>0×3 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;\">C</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 = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"String\" style = \"text-align: left;\">String</th></tr></thead><tbody></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ccc}\n",
       "\t& A & B & C\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Float64 & String\\\\\n",
       "\t\\hline\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m0×3 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m A     \u001b[0m\u001b[1m B       \u001b[0m\u001b[1m C      \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m String \u001b[0m\n",
       "─────┴────────────────────────"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrame(A=Int[], B=Float64[], C=String[])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can create a `DataFrame` by copying an existing `DataFrame`.\n",
    "\n",
    "Note that `copy` also copies the vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(false, true, true, false)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = DataFrame(a=1:2, b='a':'b')\n",
    "y = copy(x)\n",
    "(x === y), isequal(x, y), (x.a == y.a), (x.a === y.a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calling `DataFrame` on a `DataFrame` object works like `copy`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(false, true, true, false)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = DataFrame(a=1:2, b='a':'b')\n",
    "y = DataFrame(x)\n",
    "(x === y), isequal(x, y), (x.a == y.a), (x.a === y.a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can avoid copying of columns of a data frame (if it is possible) by passing `copycols=false` keyword argument:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(false, true, true, true)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = DataFrame(a=1:2, b='a':'b')\n",
    "y = DataFrame(x, copycols=false)\n",
    "(x === y), isequal(x, y), (x.a == y.a), (x.a === y.a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same rule applies to other constructors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(false, true)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [1, 2, 3]\n",
    "df1 = DataFrame(a=a)\n",
    "df2 = DataFrame(a=a, copycols=false)\n",
    "df1.a === a, df2.a === a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can create a similar uninitialized `DataFrame` based on an original one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>1×2 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 = \"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: right;\">1.0</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& a & b\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Float64\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 1.0 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m1×2 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 Float64 \u001b[0m\n",
       "─────┼────────────────\n",
       "   1 │     1      1.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = DataFrame(a=1, b=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>1×2 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 = \"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;\">140709703053424</td><td style = \"text-align: right;\">7.00259e-313</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& a & b\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Float64\\\\\n",
       "\t\\hline\n",
       "\t1 & 140709703053424 & 7.00259e-313 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m1×2 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 Float64      \u001b[0m\n",
       "─────┼───────────────────────────────\n",
       "   1 │ 140709703053424  7.00259e-313"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similar(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "number of rows in a new DataFrame can be passed as a second argument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>0×2 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 = \"Float64\" style = \"text-align: left;\">Float64</th></tr></thead><tbody></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& a & b\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Float64\\\\\n",
       "\t\\hline\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m0×2 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 Float64 \u001b[0m\n",
       "─────┴────────────────"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similar(x, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>2×2 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 = \"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;\">140709782238256</td><td style = \"text-align: right;\">6.95198e-310</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: right;\">140709782224464</td><td style = \"text-align: right;\">9.2876e-312</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& a & b\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Float64\\\\\n",
       "\t\\hline\n",
       "\t1 & 140709782238256 & 6.95198e-310 \\\\\n",
       "\t2 & 140709782224464 & 9.2876e-312 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m2×2 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 Float64      \u001b[0m\n",
       "─────┼───────────────────────────────\n",
       "   1 │ 140709782238256  6.95198e-310\n",
       "   2 │ 140709782224464  9.2876e-312"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similar(x, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also create a new `DataFrame` from `SubDataFrame` or `DataFrameRow` (discussed in detail later in the tutorial; in particular although `DataFrameRow` is considered a 1-dimensional object similar to a `NamedTuple` it gets converted to a 1-row `DataFrame` for convenience)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>2×2 SubDataFrame</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 = \"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: right;\">1.0</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">1.0</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& a & b\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Float64\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 1.0 \\\\\n",
       "\t2 & 1 & 1.0 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m2×2 SubDataFrame\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 Float64 \u001b[0m\n",
       "─────┼────────────────\n",
       "   1 │     1      1.0\n",
       "   2 │     1      1.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdf = view(x, [1,1], :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SubDataFrame{DataFrame, DataFrames.Index, Vector{Int64}}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "typeof(sdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>2×2 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 = \"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: right;\">1.0</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">1.0</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& a & b\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Float64\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 1.0 \\\\\n",
       "\t2 & 1 & 1.0 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m2×2 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 Float64 \u001b[0m\n",
       "─────┼────────────────\n",
       "   1 │     1      1.0\n",
       "   2 │     1      1.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrame(sdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>DataFrameRow (2 columns)</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 = \"rowLabel\" 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 = \"rowLabel\" style = \"font-weight: bold; text-align: right;\"></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 = \"rowLabel\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: right;\">1</td><td style = \"text-align: right;\">1.0</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& a & b\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Float64\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 1.0 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1mDataFrameRow\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 Float64 \u001b[0m\n",
       "─────┼────────────────\n",
       "   1 │     1      1.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfr = x[1, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>1×2 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 = \"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: right;\">1.0</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& a & b\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Float64\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 1.0 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m1×2 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 Float64 \u001b[0m\n",
       "─────┼────────────────\n",
       "   1 │     1      1.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrame(dfr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conversion to a matrix\n",
    "\n",
    "Let's start by creating a `DataFrame` with two rows and two columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>2×2 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;\">x</th><th style = \"text-align: left;\">y</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 = \"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: 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></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& x & y\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & String\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & A \\\\\n",
       "\t2 & 2 & B \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m2×2 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m x     \u001b[0m\u001b[1m y      \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\n",
       "─────┼───────────────\n",
       "   1 │     1  A\n",
       "   2 │     2  B"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = DataFrame(x=1:2, y=[\"A\", \"B\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create a matrix by passing this `DataFrame` to `Matrix` or `Array`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2×2 Matrix{Any}:\n",
       " 1  \"A\"\n",
       " 2  \"B\""
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Matrix(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2×2 Matrix{Any}:\n",
       " 1  \"A\"\n",
       " 2  \"B\""
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Array(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This would work even if the `DataFrame` had some `missing`s:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>2×2 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;\">x</th><th style = \"text-align: left;\">y</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 = \"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;\">1</td><td style = \"font-style: italic; text-align: left;\">missing</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></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& x & y\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & String?\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & \\emph{missing} \\\\\n",
       "\t2 & 2 & B \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m2×2 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m x     \u001b[0m\u001b[1m y       \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m String? \u001b[0m\n",
       "─────┼────────────────\n",
       "   1 │     1 \u001b[90m missing \u001b[0m\n",
       "   2 │     2  B"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = DataFrame(x=1:2, y=[missing,\"B\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2×2 Matrix{Any}:\n",
       " 1  missing\n",
       " 2  \"B\""
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Matrix(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the two previous matrix examples, Julia created matrices with elements of type `Any`. We can see more clearly that the type of matrix is inferred when we pass, for example, a `DataFrame` of integers to `Matrix`, creating a 2D `Array` of `Int64`s:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>2×2 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;\">x</th><th style = \"text-align: left;\">y</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;\">3</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></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& x & y\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Int64\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 3 \\\\\n",
       "\t2 & 2 & 4 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m2×2 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m x     \u001b[0m\u001b[1m y     \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\n",
       "─────┼──────────────\n",
       "   1 │     1      3\n",
       "   2 │     2      4"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = DataFrame(x=1:2, y=3:4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2×2 Matrix{Int64}:\n",
       " 1  3\n",
       " 2  4"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Matrix(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this next example, Julia correctly identifies that `Union` is needed to express the type of the resulting `Matrix` (which contains `missing`s)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>2×2 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;\">x</th><th style = \"text-align: left;\">y</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 = \"Union{Missing, 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 = \"font-style: italic; text-align: right;\">missing</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></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& x & y\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Int64?\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & \\emph{missing} \\\\\n",
       "\t2 & 2 & 4 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m2×2 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m x     \u001b[0m\u001b[1m y       \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Int64?  \u001b[0m\n",
       "─────┼────────────────\n",
       "   1 │     1 \u001b[90m missing \u001b[0m\n",
       "   2 │     2        4"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = DataFrame(x=1:2, y=[missing,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2×2 Matrix{Union{Missing, Int64}}:\n",
       " 1   missing\n",
       " 2  4"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Matrix(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we can't force a conversion of `missing` values to `Int`s!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "ename": "LoadError",
     "evalue": "ArgumentError: cannot convert a DataFrame containing missing values to Matrix{Int64} (found for column y)",
     "output_type": "error",
     "traceback": [
      "ArgumentError: cannot convert a DataFrame containing missing values to Matrix{Int64} (found for column y)",
      "",
      "Stacktrace:",
      " [1] Matrix{Int64}(df::DataFrame)",
      "   @ DataFrames C:\\Users\\bogum\\.julia\\packages\\DataFrames\\LteEl\\src\\abstractdataframe\\abstractdataframe.jl:1358",
      " [2] top-level scope",
      "   @ In[41]:1"
     ]
    }
   ],
   "source": [
    "Matrix{Int}(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conversion to `NamedTuple` related tabular structures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First define some data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>2×2 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;\">x</th><th style = \"text-align: left;\">y</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 = \"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: 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></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& x & y\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & String\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & A \\\\\n",
       "\t2 & 2 & B \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m2×2 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m x     \u001b[0m\u001b[1m y      \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\n",
       "─────┼───────────────\n",
       "   1 │     1  A\n",
       "   2 │     2  B"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = DataFrame(x=1:2, y=[\"A\", \"B\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we convert a `DataFrame` into a `NamedTuple` of vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(x = [1, 2], y = [\"A\", \"B\"])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct = Tables.columntable(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we convert it into a vector of `NamedTuples`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2-element Vector{NamedTuple{(:x, :y), Tuple{Int64, String}}}:\n",
       " (x = 1, y = \"A\")\n",
       " (x = 2, y = \"B\")"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rt = Tables.rowtable(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can perform the conversions back to a `DataFrame` using a standard constructor call:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>2×2 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;\">x</th><th style = \"text-align: left;\">y</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 = \"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: 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></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& x & y\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & String\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & A \\\\\n",
       "\t2 & 2 & B \\\\\n",
       "\\end{tabular}\n"
      ],
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       "\u001b[1m2×2 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m x     \u001b[0m\u001b[1m y      \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\n",
       "─────┼───────────────\n",
       "   1 │     1  A\n",
       "   2 │     2  B"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrame(ct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>2×2 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;\">x</th><th style = \"text-align: left;\">y</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 = \"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: 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></tbody></table></div>"
      ],
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       "\\begin{tabular}{r|cc}\n",
       "\t& x & y\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & String\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & A \\\\\n",
       "\t2 & 2 & B \\\\\n",
       "\\end{tabular}\n"
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      "text/plain": [
       "\u001b[1m2×2 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m x     \u001b[0m\u001b[1m y      \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\n",
       "─────┼───────────────\n",
       "   1 │     1  A\n",
       "   2 │     2  B"
      ]
     },
     "execution_count": 46,
     "metadata": {},
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    }
   ],
   "source": [
    "DataFrame(rt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Iterating data frame by rows or columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes it is useful to create a wrapper around a `DataFrame` that produces its rows or columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For iterating columns you can use the `eachcol` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>2×2 DataFrameColumns</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></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 = \"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: 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></tbody></table></div>"
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       "\\begin{tabular}{r|cc}\n",
       "\t& x & y\\\\\n",
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       "\t& Int64 & String\\\\\n",
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       "\t1 & 1 & A \\\\\n",
       "\t2 & 2 & B \\\\\n",
       "\\end{tabular}\n"
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       "\u001b[1m2×2 DataFrameColumns\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m x     \u001b[0m\u001b[1m y      \u001b[0m\n",
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       "─────┼───────────────\n",
       "   1 │     1  A\n",
       "   2 │     2  B"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ec = eachcol(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`DataFrameColumns` object behaves as a vector (note though it is not `AbstractVector`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "false"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "ec isa AbstractVector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2-element Vector{Int64}:\n",
       " 1\n",
       " 2"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ec[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "but you can also index into it using column names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2-element Vector{Int64}:\n",
       " 1\n",
       " 2"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ec[\"x\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "similarly `eachrow` creates a `DataFrameRows` object that is a vector of its rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>2×2 DataFrameRows</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></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 = \"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: 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></tbody></table></div>"
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       "\\begin{tabular}{r|cc}\n",
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       "\t1 & 1 & A \\\\\n",
       "\t2 & 2 & B \\\\\n",
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       "\u001b[1m2×2 DataFrameRows\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m x     \u001b[0m\u001b[1m y      \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\n",
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     },
     "execution_count": 51,
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   ],
   "source": [
    "er = eachrow(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`DataFrameRows` is an `AbstractVector`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
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    {
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    "er isa AbstractVector"
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   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\\begin{tabular}{r|cc}\n",
       "\t& x & y\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & String\\\\\n",
       "\t\\hline\n",
       "\t2 & 2 & B \\\\\n",
       "\\end{tabular}\n"
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      "text/plain": [
       "\u001b[1mDataFrameRow\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m x     \u001b[0m\u001b[1m y      \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\n",
       "─────┼───────────────\n",
       "   2 │     2  B"
      ]
     },
     "execution_count": 53,
     "metadata": {},
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   ],
   "source": [
    "er[end]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that both data frame and also `DataFrameColumns` and `DataFrameRows` objects are not type stable (they do not know the types of their columns). This is useful to avoid compilation cost if you have very wide data frames with heterogenous column types."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, often (especially if a data frame is narrows) it is useful to create a lazy iterator that produces `NamedTuple`s for each row of the `DataFrame`. Its key benefit is that it is type stable (so it is useful when you want to perform some operations in a fast way on a small subset of columns of a `DataFrame` - this strategy is often used internally by DataFrames.jl package):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tables.NamedTupleIterator{Tables.Schema{(:x, :y), Tuple{Int64, String}}, Tables.RowIterator{NamedTuple{(:x, :y), Tuple{Vector{Int64}, Vector{String}}}}}(Tables.RowIterator{NamedTuple{(:x, :y), Tuple{Vector{Int64}, Vector{String}}}}((x = [1, 2], y = [\"A\", \"B\"]), 2))"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nti = Tables.namedtupleiterator(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "row = (1, (x = 1, y = \"A\"))\n",
      "row = (2, (x = 2, y = \"B\"))\n"
     ]
    }
   ],
   "source": [
    "for row in enumerate(nti)\n",
    "    @show row\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "similarly to the previous options you can easily convert `NamedTupleIterator` back to a `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>2×2 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;\">x</th><th style = \"text-align: left;\">y</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 = \"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: 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></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& x & y\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & String\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & A \\\\\n",
       "\t2 & 2 & B \\\\\n",
       "\\end{tabular}\n"
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       "\u001b[1m2×2 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m x     \u001b[0m\u001b[1m y      \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\n",
       "─────┼───────────────\n",
       "   1 │     1  A\n",
       "   2 │     2  B"
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     "metadata": {},
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   ],
   "source": [
    "DataFrame(nti)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling of duplicate column names\n",
    "\n",
    "We can pass the `makeunique` keyword argument to allow passing duplicate names (they get deduplicated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>1×3 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;\">a_2</th><th style = \"text-align: left;\">a_1</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></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;\">3</td></tr></tbody></table></div>"
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       "\\begin{tabular}{r|ccc}\n",
       "\t& a & a\\_2 & a\\_1\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Int64 & Int64\\\\\n",
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       "\t1 & 1 & 2 & 3 \\\\\n",
       "\\end{tabular}\n"
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       "\u001b[1m1×3 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m a     \u001b[0m\u001b[1m a_2   \u001b[0m\u001b[1m a_1   \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\n",
       "─────┼─────────────────────\n",
       "   1 │     1      2      3"
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     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = DataFrame(:a=>1, :a=>2, :a_1=>3; makeunique=true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Otherwise, duplicates are not allowed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "ename": "LoadError",
     "evalue": "ArgumentError: Duplicate variable names: :a. Pass makeunique=true to make them unique using a suffix automatically.",
     "output_type": "error",
     "traceback": [
      "ArgumentError: Duplicate variable names: :a. Pass makeunique=true to make them unique using a suffix automatically.",
      "",
      "Stacktrace:",
      " [1] make_unique!(names::Vector{Symbol}, src::Vector{Symbol}; makeunique::Bool)",
      "   @ DataFrames C:\\Users\\bogum\\.julia\\packages\\DataFrames\\LteEl\\src\\other\\utils.jl:99",
      " [2] #make_unique#4",
      "   @ C:\\Users\\bogum\\.julia\\packages\\DataFrames\\LteEl\\src\\other\\utils.jl:121 [inlined]",
      " [3] #Index#7",
      "   @ C:\\Users\\bogum\\.julia\\packages\\DataFrames\\LteEl\\src\\other\\index.jl:27 [inlined]",
      " [4] kwcall(::NamedTuple{(:makeunique,), Tuple{Bool}}, ::Type{DataFrames.Index}, names::Vector{Symbol})",
      "   @ DataFrames C:\\Users\\bogum\\.julia\\packages\\DataFrames\\LteEl\\src\\other\\index.jl:26",
      " [5] DataFrame(::Pair{Symbol, Int64}, ::Vararg{Pair{Symbol, Int64}}; makeunique::Bool, copycols::Bool)",
      "   @ DataFrames C:\\Users\\bogum\\.julia\\packages\\DataFrames\\LteEl\\src\\dataframe\\dataframe.jl:255",
      " [6] DataFrame(::Pair{Symbol, Int64}, ::Vararg{Pair{Symbol, Int64}})",
      "   @ DataFrames C:\\Users\\bogum\\.julia\\packages\\DataFrames\\LteEl\\src\\dataframe\\dataframe.jl:251",
      " [7] top-level scope",
      "   @ In[58]:1"
     ]
    }
   ],
   "source": [
    "df = DataFrame(:a=>1, :a=>2, :a_1=>3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observe that currently `nothing` is not printed when displaying a `DataFrame` in Jupyter Notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "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 = \"Union{Nothing, Int64}\" style = \"text-align: left;\">Union…</th><th title = \"Union{Nothing, String}\" style = \"text-align: left;\">Union…</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: left;\">1</td><td style = \"font-style: italic; text-align: left;\"></td><td style = \"font-style: italic; text-align: left;\">missing</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"font-style: italic; text-align: left;\"></td><td style = \"text-align: left;\">a</td><td style = \"text-align: left;\">c</td></tr></tbody></table></div>"
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       "     │\u001b[90m Union… \u001b[0m\u001b[90m Union… \u001b[0m\u001b[90m String? \u001b[0m\n",
       "─────┼─────────────────────────\n",
       "   1 │ 1      \u001b[90m        \u001b[0m\u001b[90m missing \u001b[0m\n",
       "   2 │\u001b[90m        \u001b[0m a       c"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = DataFrame(x=[1, nothing], y=[nothing, \"a\"], z=[missing, \"c\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally you can use `empty` and `empty!` functions to remove all rows from a data frame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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    "empty(df)"
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   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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    "df"
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  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
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  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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