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Expand Up @@ -6,11 +6,13 @@ authors:
orcid: "https://orcid.org/0000-0003-0637-6028"
repository-code: "https://github.com/gagolews/datawranglingpy"
title: "Minimalist Data Wrangling with Python"
doi: 10.5281/zenodo.6451068
preferred-citation:
type: book
year: 2022
url: "https://datawranglingpy.gagolewski.com/"
title: "Minimalist Data Wrangling with Python"
doi: 10.5281/zenodo.6451068
authors:
- family-names: Gagolewski
given-names: Marek
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# Minimalist Data Wrangling with Python
# (Open Access Textbook)

> **This is a draft version of a forthcoming book by [Marek Gagolewski][1].
> Any remarks and bug fixes are appreciated — please submit them via
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URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
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<a href="../index.html"> Minimalist Data Wrangling with Python [DRAFTv0.4.2]
<a href="../index.html"> Minimalist Data Wrangling with Python [DRAFTv0.5.1]



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Expand Down Expand Up @@ -580,7 +580,7 @@ <h2>Acknowledgements<a class="headerlink" href="#acknowledgements" title="Permal
<p>
&#169; Copyright 2022, Marek Gagolewski. Licensed under CC BY-NC-ND 4.0.
<span class="lastupdated">
Last updated on 2022-06-03T13:05:59+1000.
Last updated on 2022-06-13T15:25:17+1000.
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Expand Down Expand Up @@ -622,16 +622,16 @@ <h2><span class="section-number">1.3. </span>Best Note Taking App<a class="heade
</thead>
<tbody>
<tr class="row-even"><td><p><strong class="program">numpy</strong></p></td>
<td><p>1.22.2</p></td>
<td><p>1.22.4</p></td>
</tr>
<tr class="row-odd"><td><p><strong class="program">scipy</strong></p></td>
<td><p>1.7.3</p></td>
<td><p>1.8.1</p></td>
</tr>
<tr class="row-even"><td><p><strong class="program">pandas</strong></p></td>
<td><p>1.4.1</p></td>
<td><p>1.4.2</p></td>
</tr>
<tr class="row-odd"><td><p><strong class="program">matplotlib</strong></p></td>
<td><p>3.5.1</p></td>
<td><p>3.5.2</p></td>
</tr>
<tr class="row-even"><td><p><strong class="program">seaborn</strong></p></td>
<td><p>0.11.2</p></td>
Expand Down Expand Up @@ -721,7 +721,7 @@ <h2><span class="section-number">1.6. </span>Exercises<a class="headerlink" href
<p>
&#169; Copyright 2022, Marek Gagolewski. Licensed under CC BY-NC-ND 4.0.
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Last updated on 2022-06-03T13:05:59+1000.
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Expand Down Expand Up @@ -938,7 +938,7 @@ <h2><span class="section-number">2.5. </span>Exercises<a class="headerlink" href
<p>
&#169; Copyright 2022, Marek Gagolewski. Licensed under CC BY-NC-ND 4.0.
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Last updated on 2022-06-03T13:05:59+1000.
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Expand Down Expand Up @@ -988,7 +988,7 @@ <h3><span class="section-number">3.5.3. </span>Object Copies<a class="headerlink
<span class="kn">import</span> <span class="nn">random</span>
<span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="c1"># modifies x in-place</span>
<span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="c1">## [4, 1, 2, 3, 5]</span>
<span class="c1">## [3, 4, 2, 1, 5]</span>
</pre></div>
</div>
<p>Moreover, later we will learn about <code class="docutils literal notranslate"><span class="pre">Series</span></code> objects in <strong class="program">pandas</strong>,
Expand Down Expand Up @@ -1122,7 +1122,7 @@ <h2><span class="section-number">3.7. </span>Exercises<a class="headerlink" href
<p>
&#169; Copyright 2022, Marek Gagolewski. Licensed under CC BY-NC-ND 4.0.
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Last updated on 2022-06-03T13:05:59+1000.
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Expand Down Expand Up @@ -1215,7 +1215,7 @@ <h2><span class="section-number">4.4. </span>Exercises<a class="headerlink" href
<p>
&#169; Copyright 2022, Marek Gagolewski. Licensed under CC BY-NC-ND 4.0.
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Last updated on 2022-06-03T13:05:59+1000.
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Expand Down Expand Up @@ -814,7 +814,7 @@ <h3><span class="section-number">5.1.3. </span>Measures of Shape (*)<a class="he
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">sns</span><span class="o">.</span><span class="n">violinplot</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">income</span><span class="p">,</span> <span class="n">orient</span><span class="o">=</span><span class="s2">&quot;h&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;lightgray&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="s2">&quot;income&quot;</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="c1">## ([&lt;matplotlib.axis.YTick object at 0x7fe840983730&gt;], [Text(0, 0, &#39;income&#39;)])</span>
<span class="c1">## ([&lt;matplotlib.axis.YTick object at 0x7fa67faadc10&gt;], [Text(0, 0, &#39;income&#39;)])</span>
</pre></div>
</div>
<div class="figure align-default" id="id19">
Expand Down Expand Up @@ -1020,8 +1020,8 @@ <h3><span class="section-number">5.1.5. </span>Further Methods (*)<a class="head
<div class="admonition important">
<p class="admonition-title">Important</p>
<p>The classical handbook of mathematical functions
and the (in)equalities related to them is <span id="id8">[<a class="reference internal" href="999-bibliography.html#id127">AS72</a>]</span>,
see <span id="id9">[<a class="reference internal" href="999-bibliography.html#id128">O+22</a>]</span> for its updated version.</p>
and the (in)equalities related to them is <span id="id8">[<a class="reference internal" href="999-bibliography.html#id126">AS72</a>]</span>,
see <span id="id9">[<a class="reference internal" href="999-bibliography.html#id127">O+22</a>]</span> for its updated version.</p>
</div>
</div>
<div class="section" id="arithmetic-operators">
Expand Down Expand Up @@ -1793,7 +1793,7 @@ <h3><span class="section-number">5.5.3. </span>Cumulative Sums and Iterated Diff
</div>
</div>
<p>A good introductory reference to the topic of numerical
inaccuracies is <span id="id10">[<a class="reference internal" href="999-bibliography.html#id131">Gol91</a>]</span>, see also <span id="id11">[<a class="reference internal" href="999-bibliography.html#id130">Hig02</a>, <a class="reference internal" href="999-bibliography.html#id132">Knu97</a>]</span>
inaccuracies is <span id="id10">[<a class="reference internal" href="999-bibliography.html#id130">Gol91</a>]</span>, see also <span id="id11">[<a class="reference internal" href="999-bibliography.html#id129">Hig02</a>, <a class="reference internal" href="999-bibliography.html#id131">Knu97</a>]</span>
for a more comprehensive treatment of numerical analysis.</p>
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<div class="section" id="vectorising-scalar-operations-with-list-comprehensions">
Expand Down Expand Up @@ -2045,7 +2045,7 @@ <h2><span class="section-number">5.6. </span>Exercises<a class="headerlink" href
<p>
&#169; Copyright 2022, Marek Gagolewski. Licensed under CC BY-NC-ND 4.0.
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Expand Down Expand Up @@ -1526,7 +1526,7 @@ <h2><span class="section-number">6.5. </span>Exercises<a class="headerlink" href
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<p>
&#169; Copyright 2022, Marek Gagolewski. Licensed under CC BY-NC-ND 4.0.
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</div>
<div class="section" id="matrix-indexing">
<span id="sec-matrix-indexing"></span><h2><span class="section-number">8.2. </span>Matrix Indexing<a class="headerlink" href="#matrix-indexing" title="Permalink to this headline"></a></h2>
<p>Recall that for 1-dimensional arrays we have
4 possible choices of an indexer
(i.e., where performing filtering like <code class="docutils literal notranslate"><span class="pre">x[i]</span></code>):</p>
<p>Recall that for 1-dimensional arrays we have four possible choices
of indexers (i.e., where performing filtering like <code class="docutils literal notranslate"><span class="pre">x[i]</span></code>):</p>
<ul class="simple">
<li><p>scalar (extracts a single element),</p></li>
<li><p>slice (selects a regular subsequence, e.g., every 2nd element
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<span id="sec-matrix-multiply"></span><h2><span class="section-number">8.3. </span>Matrix Multiplication, Dot Products, and the Euclidean Norm<a class="headerlink" href="#matrix-multiplication-dot-products-and-the-euclidean-norm" title="Permalink to this headline"></a></h2>
<p>Matrix algebra is at the core of most methods used in data analysis
and matrix multiply is one of the most fundamental operations therein
(e.g., <span id="id1">[<a class="reference internal" href="999-bibliography.html#id70">DFO20</a>, <a class="reference internal" href="999-bibliography.html#id126">Gen17</a>]</span>).</p>
(e.g., <span id="id1">[<a class="reference internal" href="999-bibliography.html#id70">DFO20</a>, <a class="reference internal" href="999-bibliography.html#id125">Gen17</a>]</span>).</p>
<p>Given <span class="math notranslate nohighlight">\(\mathbf{A}\in\mathbb{R}^{n\times p}\)</span>
and <span class="math notranslate nohighlight">\(\mathbf{B}\in\mathbb{R}^{p\times m}\)</span>, their <em>multiply</em> is a matrix
<span class="math notranslate nohighlight">\(\mathbf{C}=\mathbf{A}\mathbf{B}\in\mathbb{R}^{n\times m}\)</span>
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</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>(**) A bag plot <span id="id5">[<a class="reference internal" href="999-bibliography.html#id129">RRT99</a>]</span>
<p>(**) A bag plot <span id="id5">[<a class="reference internal" href="999-bibliography.html#id128">RRT99</a>]</span>
is one of the possible multidimensional generalisations
of the box-and-whisker plot. Unfortunately, its use is quite limited
due its low popularity amongst practitioners.</p>
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<p>
&#169; Copyright 2022, Marek Gagolewski. Licensed under CC BY-NC-ND 4.0.
<span class="lastupdated">
Last updated on 2022-06-03T13:05:59+1000.
Last updated on 2022-06-13T15:25:17+1000.
</span>
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