Skip to content

Instantly share code, notes, and snippets.

@vaclavdekanovsky

vaclavdekanovsky/GIST_Julia_Headers.ipynb

Last active December 19, 2020 20:22
Show Gist options
  • Save vaclavdekanovsky/d2f6cebfd661565222b24ee043634b05 to your computer and use it in GitHub Desktop.
Save vaclavdekanovsky/d2f6cebfd661565222b24ee043634b05 to your computer and use it in GitHub Desktop.
Download ZIP
Header parameter of Julia's CSV reader
Display the source blob
Display the rendered blob
Raw
GIST_Julia_Headers.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Header parameter of the Julia CSV parser\n",
"Written in [Julia](https://julialang.org/). See [CSV.jl](https://csv.juliadata.org/stable/) and [DataFrames.jl](https://dataframes.juliadata.org/stable/) for more details"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"using CSV, DataFrames"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will parse the following data using the IOBuffer to pass them to the CSV reader. "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"c|c|c|d\\n1|2|3|4\\n\\\"1\\\"|2|c|1.5\\n\\\"C|D\\\"|16|x|2.33\\n\""
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = \"\"\"\n",
"c|c|c|d\n",
"1|2|3|4\n",
"\"1\"|2|c|1.5\n",
"\"C|D\"|16|x|2.33\n",
"\"\"\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Default header on the first row"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>c</th><th>c_1</th><th>c_2</th><th>d</th></tr><tr><th></th><th>String</th><th>Int64</th><th>String</th><th>Float64</th></tr></thead><tbody><p>3 rows × 4 columns</p><tr><th>1</th><td>1</td><td>2</td><td>3</td><td>4.0</td></tr><tr><th>2</th><td>1</td><td>2</td><td>c</td><td>1.5</td></tr><tr><th>3</th><td>C|D</td><td>16</td><td>x</td><td>2.33</td></tr></tbody></table>"
],
"text/latex": [
"\\begin{tabular}{r|cccc}\n",
"\t& c & c\\_1 & c\\_2 & d\\\\\n",
"\t\\hline\n",
"\t& String & Int64 & String & Float64\\\\\n",
"\t\\hline\n",
"\t1 & 1 & 2 & 3 & 4.0 \\\\\n",
"\t2 & 1 & 2 & c & 1.5 \\\\\n",
"\t3 & C|D & 16 & x & 2.33 \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"3×4 DataFrame\n",
"│ Row │ c │ c_1 │ c_2 │ d │\n",
"│ │ \u001b[90mString\u001b[39m │ \u001b[90mInt64\u001b[39m │ \u001b[90mString\u001b[39m │ \u001b[90mFloat64\u001b[39m │\n",
"├─────┼────────┼───────┼────────┼─────────┤\n",
"│ 1 │ 1 │ 2 │ 3 │ 4.0 │\n",
"│ 2 │ 1 │ 2 │ c │ 1.5 │\n",
"│ 3 │ C|D │ 16 │ x │ 2.33 │"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# default header is the first row, duplicated column names are postfixed\n",
"df = CSV.read(IOBuffer(data), DataFrame)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## No header"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>Column1</th><th>Column2</th><th>Column3</th><th>Column4</th></tr><tr><th></th><th>String</th><th>String</th><th>String</th><th>String</th></tr></thead><tbody><p>4 rows × 4 columns</p><tr><th>1</th><td>c</td><td>c</td><td>c</td><td>d</td></tr><tr><th>2</th><td>1</td><td>2</td><td>3</td><td>4</td></tr><tr><th>3</th><td>1</td><td>2</td><td>c</td><td>1.5</td></tr><tr><th>4</th><td>C|D</td><td>16</td><td>x</td><td>2.33</td></tr></tbody></table>"
],
"text/latex": [
"\\begin{tabular}{r|cccc}\n",
"\t& Column1 & Column2 & Column3 & Column4\\\\\n",
"\t\\hline\n",
"\t& String & String & String & String\\\\\n",
"\t\\hline\n",
"\t1 & c & c & c & d \\\\\n",
"\t2 & 1 & 2 & 3 & 4 \\\\\n",
"\t3 & 1 & 2 & c & 1.5 \\\\\n",
"\t4 & C|D & 16 & x & 2.33 \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"4×4 DataFrame\n",
"│ Row │ Column1 │ Column2 │ Column3 │ Column4 │\n",
"│ │ \u001b[90mString\u001b[39m │ \u001b[90mString\u001b[39m │ \u001b[90mString\u001b[39m │ \u001b[90mString\u001b[39m │\n",
"├─────┼─────────┼─────────┼─────────┼─────────┤\n",
"│ 1 │ c │ c │ c │ d │\n",
"│ 2 │ 1 │ 2 │ 3 │ 4 │\n",
"│ 3 │ 1 │ 2 │ c │ 1.5 │\n",
"│ 4 │ C|D │ 16 │ x │ 2.33 │"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Julia convention is to separate the key arguments (kwargs) by semicolon (;). Comma though works as well\n",
"CSV.read(IOBuffer(data), DataFrame; header=0)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>Column1</th><th>Column2</th><th>Column3</th><th>Column4</th></tr><tr><th></th><th>String</th><th>String</th><th>String</th><th>String</th></tr></thead><tbody><p>4 rows × 4 columns</p><tr><th>1</th><td>c</td><td>c</td><td>c</td><td>d</td></tr><tr><th>2</th><td>1</td><td>2</td><td>3</td><td>4</td></tr><tr><th>3</th><td>1</td><td>2</td><td>c</td><td>1.5</td></tr><tr><th>4</th><td>C|D</td><td>16</td><td>x</td><td>2.33</td></tr></tbody></table>"
],
"text/latex": [
"\\begin{tabular}{r|cccc}\n",
"\t& Column1 & Column2 & Column3 & Column4\\\\\n",
"\t\\hline\n",
"\t& String & String & String & String\\\\\n",
"\t\\hline\n",
"\t1 & c & c & c & d \\\\\n",
"\t2 & 1 & 2 & 3 & 4 \\\\\n",
"\t3 & 1 & 2 & c & 1.5 \\\\\n",
"\t4 & C|D & 16 & x & 2.33 \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"4×4 DataFrame\n",
"│ Row │ Column1 │ Column2 │ Column3 │ Column4 │\n",
"│ │ \u001b[90mString\u001b[39m │ \u001b[90mString\u001b[39m │ \u001b[90mString\u001b[39m │ \u001b[90mString\u001b[39m │\n",
"├─────┼─────────┼─────────┼─────────┼─────────┤\n",
"│ 1 │ c │ c │ c │ d │\n",
"│ 2 │ 1 │ 2 │ 3 │ 4 │\n",
"│ 3 │ 1 │ 2 │ c │ 1.5 │\n",
"│ 4 │ C|D │ 16 │ x │ 2.33 │"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"CSV.read(IOBuffer(data), DataFrame; header=false)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Specify own column names using headers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can pass the vector of column names (as strings or symbols) to specify the headers"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>first</th><th>second</th><th>third</th><th>fourth</th></tr><tr><th></th><th>String</th><th>String</th><th>String</th><th>String</th></tr></thead><tbody><p>4 rows × 4 columns</p><tr><th>1</th><td>c</td><td>c</td><td>c</td><td>d</td></tr><tr><th>2</th><td>1</td><td>2</td><td>3</td><td>4</td></tr><tr><th>3</th><td>1</td><td>2</td><td>c</td><td>1.5</td></tr><tr><th>4</th><td>C|D</td><td>16</td><td>x</td><td>2.33</td></tr></tbody></table>"
],
"text/latex": [
"\\begin{tabular}{r|cccc}\n",
"\t& first & second & third & fourth\\\\\n",
"\t\\hline\n",
"\t& String & String & String & String\\\\\n",
"\t\\hline\n",
"\t1 & c & c & c & d \\\\\n",
"\t2 & 1 & 2 & 3 & 4 \\\\\n",
"\t3 & 1 & 2 & c & 1.5 \\\\\n",
"\t4 & C|D & 16 & x & 2.33 \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"4×4 DataFrame\n",
"│ Row │ first │ second │ third │ fourth │\n",
"│ │ \u001b[90mString\u001b[39m │ \u001b[90mString\u001b[39m │ \u001b[90mString\u001b[39m │ \u001b[90mString\u001b[39m │\n",
"├─────┼────────┼────────┼────────┼────────┤\n",
"│ 1 │ c │ c │ c │ d │\n",
"│ 2 │ 1 │ 2 │ 3 │ 4 │\n",
"│ 3 │ 1 │ 2 │ c │ 1.5 │\n",
"│ 4 │ C|D │ 16 │ x │ 2.33 │"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"CSV.read(IOBuffer(data), DataFrame; header=[\"first\",\"second\",\"third\",\"fourth\"])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>a</th><th>b</th><th>c</th><th>d</th></tr><tr><th></th><th>String</th><th>String</th><th>String</th><th>String</th></tr></thead><tbody><p>4 rows × 4 columns</p><tr><th>1</th><td>c</td><td>c</td><td>c</td><td>d</td></tr><tr><th>2</th><td>1</td><td>2</td><td>3</td><td>4</td></tr><tr><th>3</th><td>1</td><td>2</td><td>c</td><td>1.5</td></tr><tr><th>4</th><td>C|D</td><td>16</td><td>x</td><td>2.33</td></tr></tbody></table>"
],
"text/latex": [
"\\begin{tabular}{r|cccc}\n",
"\t& a & b & c & d\\\\\n",
"\t\\hline\n",
"\t& String & String & String & String\\\\\n",
"\t\\hline\n",
"\t1 & c & c & c & d \\\\\n",
"\t2 & 1 & 2 & 3 & 4 \\\\\n",
"\t3 & 1 & 2 & c & 1.5 \\\\\n",
"\t4 & C|D & 16 & x & 2.33 \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"4×4 DataFrame\n",
"│ Row │ a │ b │ c │ d │\n",
"│ │ \u001b[90mString\u001b[39m │ \u001b[90mString\u001b[39m │ \u001b[90mString\u001b[39m │ \u001b[90mString\u001b[39m │\n",
"├─────┼────────┼────────┼────────┼────────┤\n",
"│ 1 │ c │ c │ c │ d │\n",
"│ 2 │ 1 │ 2 │ 3 │ 4 │\n",
"│ 3 │ 1 │ 2 │ c │ 1.5 │\n",
"│ 4 │ C|D │ 16 │ x │ 2.33 │"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"CSV.read(IOBuffer(data), DataFrame; header=[:a,:b,:c,:d])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>z</th><th>zy</th><th>zyx</th><th>zyxw</th></tr><tr><th></th><th>String</th><th>String</th><th>String</th><th>String</th></tr></thead><tbody><p>4 rows × 4 columns</p><tr><th>1</th><td>c</td><td>c</td><td>c</td><td>d</td></tr><tr><th>2</th><td>1</td><td>2</td><td>3</td><td>4</td></tr><tr><th>3</th><td>1</td><td>2</td><td>c</td><td>1.5</td></tr><tr><th>4</th><td>C|D</td><td>16</td><td>x</td><td>2.33</td></tr></tbody></table>"
],
"text/latex": [
"\\begin{tabular}{r|cccc}\n",
"\t& z & zy & zyx & zyxw\\\\\n",
"\t\\hline\n",
"\t& String & String & String & String\\\\\n",
"\t\\hline\n",
"\t1 & c & c & c & d \\\\\n",
"\t2 & 1 & 2 & 3 & 4 \\\\\n",
"\t3 & 1 & 2 & c & 1.5 \\\\\n",
"\t4 & C|D & 16 & x & 2.33 \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"4×4 DataFrame\n",
"│ Row │ z │ zy │ zyx │ zyxw │\n",
"│ │ \u001b[90mString\u001b[39m │ \u001b[90mString\u001b[39m │ \u001b[90mString\u001b[39m │ \u001b[90mString\u001b[39m │\n",
"├─────┼────────┼────────┼────────┼────────┤\n",
"│ 1 │ c │ c │ c │ d │\n",
"│ 2 │ 1 │ 2 │ 3 │ 4 │\n",
"│ 3 │ 1 │ 2 │ c │ 1.5 │\n",
"│ 4 │ C|D │ 16 │ x │ 2.33 │"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"CSV.read(IOBuffer(data), DataFrame; header=[Symbol(\"z\"),Symbol(\"zy\"),Symbol(\"zyx\"),Symbol(\"zyxw\")])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Header on the first row"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>c</th><th>c_1</th><th>c_2</th><th>d</th></tr><tr><th></th><th>String</th><th>Int64</th><th>String</th><th>Float64</th></tr></thead><tbody><p>3 rows × 4 columns</p><tr><th>1</th><td>1</td><td>2</td><td>3</td><td>4.0</td></tr><tr><th>2</th><td>1</td><td>2</td><td>c</td><td>1.5</td></tr><tr><th>3</th><td>C|D</td><td>16</td><td>x</td><td>2.33</td></tr></tbody></table>"
],
"text/latex": [
"\\begin{tabular}{r|cccc}\n",
"\t& c & c\\_1 & c\\_2 & d\\\\\n",
"\t\\hline\n",
"\t& String & Int64 & String & Float64\\\\\n",
"\t\\hline\n",
"\t1 & 1 & 2 & 3 & 4.0 \\\\\n",
"\t2 & 1 & 2 & c & 1.5 \\\\\n",
"\t3 & C|D & 16 & x & 2.33 \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"3×4 DataFrame\n",
"│ Row │ c │ c_1 │ c_2 │ d │\n",
"│ │ \u001b[90mString\u001b[39m │ \u001b[90mInt64\u001b[39m │ \u001b[90mString\u001b[39m │ \u001b[90mFloat64\u001b[39m │\n",
"├─────┼────────┼───────┼────────┼─────────┤\n",
"│ 1 │ 1 │ 2 │ 3 │ 4.0 │\n",
"│ 2 │ 1 │ 2 │ c │ 1.5 │\n",
"│ 3 │ C|D │ 16 │ x │ 2.33 │"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Remember that Julia stasts indexing at 1\n",
"CSV.read(IOBuffer(data), DataFrame; header=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Header on the x-th row\n",
"Everything above the header is ignored"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>1</th><th>2</th><th>3</th><th>4</th></tr><tr><th></th><th>String</th><th>Int64</th><th>String</th><th>Float64</th></tr></thead><tbody><p>2 rows × 4 columns</p><tr><th>1</th><td>1</td><td>2</td><td>c</td><td>1.5</td></tr><tr><th>2</th><td>C|D</td><td>16</td><td>x</td><td>2.33</td></tr></tbody></table>"
],
"text/latex": [
"\\begin{tabular}{r|cccc}\n",
"\t& 1 & 2 & 3 & 4\\\\\n",
"\t\\hline\n",
"\t& String & Int64 & String & Float64\\\\\n",
"\t\\hline\n",
"\t1 & 1 & 2 & c & 1.5 \\\\\n",
"\t2 & C|D & 16 & x & 2.33 \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"2×4 DataFrame\n",
"│ Row │ 1 │ 2 │ 3 │ 4 │\n",
"│ │ \u001b[90mString\u001b[39m │ \u001b[90mInt64\u001b[39m │ \u001b[90mString\u001b[39m │ \u001b[90mFloat64\u001b[39m │\n",
"├─────┼────────┼───────┼────────┼─────────┤\n",
"│ 1 │ 1 │ 2 │ c │ 1.5 │\n",
"│ 2 │ C|D │ 16 │ x │ 2.33 │"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"CSV.read(IOBuffer(data), DataFrame; header=2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Multirow header\n",
"Can be specified using range, for example `1:2` or list `[1,2]`. Some rows can even be skipped, e.g. `[1,3]`. The columns names are concatenation of the values on these rows."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>Column1</th><th>Column2</th><th>Column3</th><th>Column4</th></tr><tr><th></th><th>String</th><th>String</th><th>String</th><th>String</th></tr></thead><tbody><p>4 rows × 4 columns</p><tr><th>1</th><td>c</td><td>c</td><td>c</td><td>d</td></tr><tr><th>2</th><td>1</td><td>2</td><td>3</td><td>4</td></tr><tr><th>3</th><td>1</td><td>2</td><td>c</td><td>1.5</td></tr><tr><th>4</th><td>C|D</td><td>16</td><td>x</td><td>2.33</td></tr></tbody></table>"
],
"text/latex": [
"\\begin{tabular}{r|cccc}\n",
"\t& Column1 & Column2 & Column3 & Column4\\\\\n",
"\t\\hline\n",
"\t& String & String & String & String\\\\\n",
"\t\\hline\n",
"\t1 & c & c & c & d \\\\\n",
"\t2 & 1 & 2 & 3 & 4 \\\\\n",
"\t3 & 1 & 2 & c & 1.5 \\\\\n",
"\t4 & C|D & 16 & x & 2.33 \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"4×4 DataFrame\n",
"│ Row │ Column1 │ Column2 │ Column3 │ Column4 │\n",
"│ │ \u001b[90mString\u001b[39m │ \u001b[90mString\u001b[39m │ \u001b[90mString\u001b[39m │ \u001b[90mString\u001b[39m │\n",
"├─────┼─────────┼─────────┼─────────┼─────────┤\n",
"│ 1 │ c │ c │ c │ d │\n",
"│ 2 │ 1 │ 2 │ 3 │ 4 │\n",
"│ 3 │ 1 │ 2 │ c │ 1.5 │\n",
"│ 4 │ C|D │ 16 │ x │ 2.33 │"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# no headers, to remind you the data\n",
"df = CSV.read(IOBuffer(data), DataFrame; header=false)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>c_1</th><th>c_2</th><th>c_3</th><th>d_4</th></tr><tr><th></th><th>String</th><th>Int64</th><th>String</th><th>Float64</th></tr></thead><tbody><p>2 rows × 4 columns</p><tr><th>1</th><td>1</td><td>2</td><td>c</td><td>1.5</td></tr><tr><th>2</th><td>C|D</td><td>16</td><td>x</td><td>2.33</td></tr></tbody></table>"
],
"text/latex": [
"\\begin{tabular}{r|cccc}\n",
"\t& c\\_1 & c\\_2 & c\\_3 & d\\_4\\\\\n",
"\t\\hline\n",
"\t& String & Int64 & String & Float64\\\\\n",
"\t\\hline\n",
"\t1 & 1 & 2 & c & 1.5 \\\\\n",
"\t2 & C|D & 16 & x & 2.33 \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"2×4 DataFrame\n",
"│ Row │ c_1 │ c_2 │ c_3 │ d_4 │\n",
"│ │ \u001b[90mString\u001b[39m │ \u001b[90mInt64\u001b[39m │ \u001b[90mString\u001b[39m │ \u001b[90mFloat64\u001b[39m │\n",
"├─────┼────────┼───────┼────────┼─────────┤\n",
"│ 1 │ 1 │ 2 │ c │ 1.5 │\n",
"│ 2 │ C|D │ 16 │ x │ 2.33 │"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# headers are formed as concat of row 1 till row 2\n",
"CSV.read(IOBuffer(data), DataFrame; header=1:2)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>c_1</th><th>c_2</th><th>c_3</th><th>d_4</th></tr><tr><th></th><th>String</th><th>Int64</th><th>String</th><th>Float64</th></tr></thead><tbody><p>2 rows × 4 columns</p><tr><th>1</th><td>1</td><td>2</td><td>c</td><td>1.5</td></tr><tr><th>2</th><td>C|D</td><td>16</td><td>x</td><td>2.33</td></tr></tbody></table>"
],
"text/latex": [
"\\begin{tabular}{r|cccc}\n",
"\t& c\\_1 & c\\_2 & c\\_3 & d\\_4\\\\\n",
"\t\\hline\n",
"\t& String & Int64 & String & Float64\\\\\n",
"\t\\hline\n",
"\t1 & 1 & 2 & c & 1.5 \\\\\n",
"\t2 & C|D & 16 & x & 2.33 \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"2×4 DataFrame\n",
"│ Row │ c_1 │ c_2 │ c_3 │ d_4 │\n",
"│ │ \u001b[90mString\u001b[39m │ \u001b[90mInt64\u001b[39m │ \u001b[90mString\u001b[39m │ \u001b[90mFloat64\u001b[39m │\n",
"├─────┼────────┼───────┼────────┼─────────┤\n",
"│ 1 │ 1 │ 2 │ c │ 1.5 │\n",
"│ 2 │ C|D │ 16 │ x │ 2.33 │"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# the same using the list\n",
"CSV.read(IOBuffer(data), DataFrame; header=[1,2])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>c_1</th><th>c_2</th><th>c_c</th><th>d_1.5</th></tr><tr><th></th><th>String</th><th>Int64</th><th>String</th><th>Float64</th></tr></thead><tbody><p>1 rows × 4 columns</p><tr><th>1</th><td>C|D</td><td>16</td><td>x</td><td>2.33</td></tr></tbody></table>"
],
"text/latex": [
"\\begin{tabular}{r|cccc}\n",
"\t& c\\_1 & c\\_2 & c\\_c & d\\_1.5\\\\\n",
"\t\\hline\n",
"\t& String & Int64 & String & Float64\\\\\n",
"\t\\hline\n",
"\t1 & C|D & 16 & x & 2.33 \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"1×4 DataFrame\n",
"│ Row │ c_1 │ c_2 │ c_c │ d_1.5 │\n",
"│ │ \u001b[90mString\u001b[39m │ \u001b[90mInt64\u001b[39m │ \u001b[90mString\u001b[39m │ \u001b[90mFloat64\u001b[39m │\n",
"├─────┼────────┼───────┼────────┼─────────┤\n",
"│ 1 │ C|D │ 16 │ x │ 2.33 │"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# skipping row number 2\n",
"CSV.read(IOBuffer(data), DataFrame; header=[1,3])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 1.4.1",
"language": "julia",
"name": "julia-1.4"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.4.1"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment