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‎__site/assets/literate/D0-loading.md

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typeof(boston)
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For a short introduction to DataFrame objects, see [this tutorial](/data/).
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For a short introduction to DataFrame objects, see [this tutorial](/data/dataframe).
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## Using CSV
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‎__site/data/loading/index.html

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<!doctype html> <html lang=en > <meta charset=UTF-8 > <meta name=viewport content="width=device-width, initial-scale=1"> <link rel=stylesheet href="/MLJTutorials/libs/highlight/github.min.css"> <link rel=stylesheet href="/MLJTutorials/css/franklin.css"> <link rel=stylesheet href="/MLJTutorials/css/pure.css"> <link rel=stylesheet href="/MLJTutorials/css/side-menu.css"> <link rel=stylesheet href="/MLJTutorials/css/extra.css"> <title>Loading and elementary processing of data</title> <script src="/MLJTutorials/libs/lunr/lunr.min.js"></script> <script src="/MLJTutorials/libs/lunr/lunr_index.js"></script> <script src="/MLJTutorials/libs/lunr/lunrclient.min.js"></script> <div id=layout > <a href="#menu" id=menuLink class=menu-link ><span></span></a> <div id=menu > <div class=pure-menu > <a href="/MLJTutorials/" id=menu-logo-link > <div class=menu-logo > <img id=menu-logo alt="MLJ Logo" src="/MLJTutorials/assets/infra/MLJLogo2.svg" /> <p><strong>MLJ Tutorials</strong></p> </div> </a> <form id=lunrSearchForm name=lunrSearchForm > <input class=search-input name=q placeholder="Enter search term" type=text > <input type=submit value=Search formaction="/MLJTutorials/search/index.html" style="visibility:hidden"> </form> <ul class=pure-menu-list > <li class="pure-menu-item pure-menu-top-item "><a href="/MLJTutorials/" class=pure-menu-link ><strong>Home</strong></a> <li class=pure-menu-sublist-title ><strong>Data basics</strong> <ul class=pure-menu-sublist > <li class="pure-menu-item "><a href="/MLJTutorials/data/loading/" class=pure-menu-link ><span style="padding-right:0.5rem;">•</span> Loading data</a> <li class="pure-menu-item "><a href="/MLJTutorials/data/dataframe/" class=pure-menu-link ><span style="padding-right:0.5rem;">•</span> Data Frames</a> <li class="pure-menu-item "><a href="/MLJTutorials/data/categorical/" class=pure-menu-link ><span style="padding-right:0.5rem;">•</span> Categorical Arrays</a> <li class="pure-menu-item "><a href="/MLJTutorials/data/scitype/" class=pure-menu-link ><span style="padding-right:0.5rem;">•</span> Scientific Type</a> </ul> <li class=pure-menu-sublist-title ><strong>Getting started</strong> <ul class=pure-menu-sublist > <li class="pure-menu-item "><a href="/MLJTutorials/getting-started/choosing-a-model/" class=pure-menu-link ><span style="padding-right:0.5rem;">•</span> Choosing a model</a> <li class="pure-menu-item "><a href="/MLJTutorials/getting-started/fit-and-predict/" class=pure-menu-link ><span style="padding-right:0.5rem;">•</span> Fit, predict, transform</a> <li class="pure-menu-item "><a href="/MLJTutorials/getting-started/model-tuning/" class=pure-menu-link ><span style="padding-right:0.5rem;">•</span> Model tuning</a> <li class="pure-menu-item "><a href="/MLJTutorials/getting-started/ensembles/" class=pure-menu-link ><span style="padding-right:0.5rem;">•</span> Ensembles</a> <li class="pure-menu-item "><a href="/MLJTutorials/getting-started/ensembles-2/" class=pure-menu-link ><span style="padding-right:0.5rem;">•</span> Ensembles (2)</a> <li class="pure-menu-item "><a href="/MLJTutorials/getting-started/ensembles-3/" class=pure-menu-link ><span style="padding-right:0.5rem;">•</span> Ensembles (3)</a> <li class="pure-menu-item "><a href="/MLJTutorials/getting-started/composing-models/" class=pure-menu-link ><span style="padding-right:0.5rem;">•</span> Composing models</a> <li class="pure-menu-item "><a href="/MLJTutorials/getting-started/learning-networks/" class=pure-menu-link ><span style="padding-right:0.5rem;">•</span> Learning networks</a> <li class="pure-menu-item "><a href="/MLJTutorials/getting-started/learning-networks-2/" class=pure-menu-link ><span style="padding-right:0.5rem;">•</span> Learning networks (2)</a> <li class="pure-menu-item "><a href="/MLJTutorials/getting-started/stacking/" class=pure-menu-link ><span style="padding-right:0.5rem;">•</span> Stacking</a> </ul> <li class=pure-menu-sublist-title ><strong>Intro to Stats Learning</strong> <ul class=pure-menu-sublist id=isl> <li class="pure-menu-item "><a href="/MLJTutorials/isl/lab-2/" class=pure-menu-link ><span style="padding-right:0.5rem;">•</span> Lab 2</a> <li class="pure-menu-item "><a href="/MLJTutorials/isl/lab-3/" class=pure-menu-link ><span style="padding-right:0.5rem;">•</span> Lab 3</a> <li class="pure-menu-item "><a href="/MLJTutorials/isl/lab-4/" class=pure-menu-link ><span style="padding-right:0.5rem;">•</span> Lab 4</a> <li class="pure-menu-item "><a href="/MLJTutorials/isl/lab-5/" class=pure-menu-link ><span style="padding-right:0.5rem;">•</span> Lab 5</a> <li class="pure-menu-item "><a href="/MLJTutorials/isl/lab-6b/" class=pure-menu-link ><span style="padding-right:0.5rem;">•</span> Lab 6b</a> <li class="pure-menu-item "><a href="/MLJTutorials/isl/lab-8/" class=pure-menu-link ><span style="padding-right:0.5rem;">•</span> Lab 8</a> <li class="pure-menu-item "><a href="/MLJTutorials/isl/lab-9/" class=pure-menu-link ><span style="padding-right:0.5rem;">•</span> Lab 9</a> <li class="pure-menu-item "><a href="/MLJTutorials/isl/lab-10/" class=pure-menu-link ><span style="padding-right:0.5rem;">•</span> Lab 10</a> </ul> <li class=pure-menu-sublist-title ><strong>End to end examples</strong> <ul class=pure-menu-sublist id=e2e> <li class="pure-menu-item "><a href="/MLJTutorials/end-to-end/AMES/" class=pure-menu-link ><span style="padding-right:0.5rem;">•</span> AMES</a> <li class="pure-menu-item "><a href="/MLJTutorials/end-to-end/wine/" class=pure-menu-link ><span style="padding-right:0.5rem;">•</span> Wine</a> <li class="pure-menu-item "><a href="/MLJTutorials/end-to-end/crabs-xgb/" class=pure-menu-link ><span style="padding-right:0.5rem;">•</span> Crabs (XGB)</a> <li class="pure-menu-item "><a href="/MLJTutorials/end-to-end/horse/" class=pure-menu-link ><span style="padding-right:0.5rem;">•</span> Horse</a> <li class="pure-menu-item "><a href="/MLJTutorials/end-to-end/HouseKingCounty/" class=pure-menu-link ><span style="padding-right:0.5rem;">•</span> King County Houses</a> </ul> </ul> </div> </div> <div id=main > <div class=franklin-content > <h1 id=loading_and_elementary_processing_of_data ><a href="#loading_and_elementary_processing_of_data">Loading and elementary processing of data</a></h1> <em>Download the</em> <a href="https://raw.githubusercontent.com/alan-turing-institute/MLJTutorials/gh-pages/generated/notebooks/D0-loading.ipynb" target=_blank ><em>notebook</em></a>, <em>the</em> <a href="https://raw.githubusercontent.com/alan-turing-institute/MLJTutorials/gh-pages/generated/scripts/D0-loading-raw.jl" target=_blank ><em>raw script</em></a>, <em>or the</em> <a href="https://raw.githubusercontent.com/alan-turing-institute/MLJTutorials/gh-pages/generated/scripts/D0-loading.jl" target=_blank ><em>annotated script</em></a> <em>for this tutorial &#40;right-click on the link and save&#41;.</em> <div class=franklin-toc ><ol><li><a href="#using_rdatasets">Using RDatasets</a><li><a href="#using_csv">Using CSV</a><ol><li><a href="#basic_usage">Basic usage</a><li><a href="#example_1">Example 1</a><li><a href="#example_2">Example 2</a></ol></ol></div>In this short tutorial we discuss two ways to easily load data in Julia:</p> <ol> <li><p>loading a standard dataset via <code>RDatasets.jl</code>,</p> <li><p>loading a local file with <code>CSV.jl</code>,</p> </ol> <h2 id=using_rdatasets ><a href="#using_rdatasets">Using RDatasets</a></h2> <p>The package <a href="https://github.com/JuliaStats/RDatasets.jl">RDatasets.jl</a> provides access to most of the many datasets listed on <a href="http://vincentarelbundock.github.io/Rdatasets/datasets.html">this page</a>. These are well known, standard datasets that can be used to get started with data processing and classical machine learning such as for instance <code>iris</code>, <code>crabs</code>, <code>Boston</code>, etc.</p> <p>To load such a dataset, you will need to specify which R package it belongs to as well as its name; for instance <code>Boston</code> is part of <code>MASS</code>.</p> <pre><code class="julia hljs"><span class=hljs-keyword >using</span> RDatasets
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boston = dataset(<span class=hljs-string >"MASS"</span>, <span class=hljs-string >"Boston"</span>);</code></pre> <p>The fact that <code>Boston</code> is part of <code>MASS</code> is clearly indicated on the <a href="http://vincentarelbundock.github.io/Rdatasets/datasets.html">list</a> linked to earlier. While it can be a bit slow, loading a dataset via RDatasets is very simple and convenient as you don&#39;t have to worry about setting the names of columns etc.</p> <p>The <code>dataset</code> function returns a <code>DataFrame</code> object from the <a href="https://github.com/JuliaData/DataFrames.jl">DataFrames.jl</a> package.</p> <pre><code class="julia hljs">typeof(boston)</code></pre><pre><code class="plaintext hljs">DataFrames.DataFrame</code></pre>
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<p>For a short introduction to DataFrame objects, see <a href="/MLJTutorials/data/">this tutorial</a>.</p>
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<p>For a short introduction to DataFrame objects, see <a href="/MLJTutorials/data/dataframe">this tutorial</a>.</p>
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<h2 id=using_csv ><a href="#using_csv">Using CSV</a></h2>
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<p>The package <a href="https://github.com/JuliaData/CSV.jl">CSV.jl</a> offers a powerful way to read arbitrary CSV files efficiently. In particular the <code>CSV.read</code> function allows to read a file and return a DataFrame.</p>
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<h3 id=basic_usage ><a href="#basic_usage">Basic usage</a></h3>

‎__site/generated/notebooks/D0-loading.ipynb

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"For a short introduction to DataFrame objects, see [this tutorial](/data/)."
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"For a short introduction to DataFrame objects, see [this tutorial](/data/dataframe)."
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‎__site/generated/scripts/D0-loading.jl

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# The fact that `Boston` is part of `MASS` is clearly indicated on the [list](http://vincentarelbundock.github.io/Rdatasets/datasets.html) linked to earlier.# While it can be a bit slow, loading a dataset via RDatasets is very simple and convenient as you don't have to worry about setting the names of columns etc.## The `dataset` function returns a `DataFrame` object from the [DataFrames.jl](https://github.com/JuliaData/DataFrames.jl) package.
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# For a short introduction to DataFrame objects, see [this tutorial](/data/).
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# For a short introduction to DataFrame objects, see [this tutorial](/data/dataframe).
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# ## Using CSV## The package [CSV.jl](https://github.com/JuliaData/CSV.jl) offers a powerful way to read arbitrary CSV files efficiently.# In particular the `CSV.read` function allows to read a file and return a DataFrame.## ### Basic usage## Let's say you have a file `foo.csv` at some path `fpath=joinpath("data", "foo.csv")` with the content## ```# col1,col2,col3,col4,col5,col6,col7,col8# ,1,1.0,1,one,2019-01-01,2019-01-01T00:00:00,true# ,2,2.0,2,two,2019-01-02,2019-01-02T00:00:00,false# ,3,3.0,3.14,three,2019-01-03,2019-01-03T00:00:00,true# ```
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