Built site for gh-pages

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@@ -1 +1 @@
-d0449cbc
+255ed74a

index.html

@@ -249,13 +249,14 @@ <h2 class="anchored" data-anchor-id="running-the-code-yourself">Running the code
 <p>You can install the exact packages that the book uses with the <a href="https://github.com/kevinheavey/modern-polars/blob/master/env.yml">env.yml</a> file:</p>
 <pre class="shell"><code>mamba env create -f env.yml</code></pre>
 <p>If you’re not using mamba/conda you can install the following package versions and it should work:</p>
-<pre><code>polars: 0.15.13
+<pre><code>polars: 0.19.9
 pyarrow: 10.0.1
-pandas: 1.5.2
+pandas: 2.1.1
 numpy: 1.23.5
 fsspec: 2022.11.0
-matplotlib: 3.6.2
-seaborn: 0.12.1
+matplotlib: 3.8.0
+seaborn: 0.13.0
+statsmodels: 0.14.0
 filprofiler: 2022.11.0</code></pre>
 <section id="data" class="level3">
 <h3 class="anchored" data-anchor-id="data">Data</h3>

summary.html

@@ -178,7 +178,7 @@ <h2 id="toc-title">Table of contents</h2>
   <li><a href="#reasons-to-use-polars" id="toc-reasons-to-use-polars" class="nav-link active" data-scroll-target="#reasons-to-use-polars">Reasons to use Polars</a></li>
   <li><a href="#reasons-not-to-use-polars" id="toc-reasons-not-to-use-polars" class="nav-link" data-scroll-target="#reasons-not-to-use-polars">Reasons not to use Polars</a>
   <ul class="collapse">
-  <li><a href="#bugs" id="toc-bugs" class="nav-link" data-scroll-target="#bugs">Bugs</a></li>
+  <li><a href="#bugs-maybe" id="toc-bugs-maybe" class="nav-link" data-scroll-target="#bugs-maybe">Bugs (maybe)</a></li>
   <li><a href="#if-it-aint-broke-dont-fix-it" id="toc-if-it-aint-broke-dont-fix-it" class="nav-link" data-scroll-target="#if-it-aint-broke-dont-fix-it">If it ain’t broke, don’t fix it</a></li>
   <li><a href="#api-stability" id="toc-api-stability" class="nav-link" data-scroll-target="#api-stability">API stability</a></li>
   </ul></li>
@@ -219,9 +219,10 @@ <h2 class="anchored" data-anchor-id="reasons-to-use-polars">Reasons to use Polar
 </section>
 <section id="reasons-not-to-use-polars" class="level2">
 <h2 class="anchored" data-anchor-id="reasons-not-to-use-polars">Reasons not to use Polars</h2>
-<section id="bugs" class="level3">
-<h3 class="anchored" data-anchor-id="bugs">Bugs</h3>
-<p>By my count I ran into <a href="https://github.com/pola-rs/polars/issues?q=is%3Aissue+author%3Akevinheavey">11 bugs</a> while writing this book. They were fixed quickly and tests were added to make sure they don’t happen again, but it all makes me feel somewhat uneasy. It’s not like Pandas hasn’t gone through a whole insectarium of bugs, but Polars probably has more right now due to being newer.</p>
+<section id="bugs-maybe" class="level3">
+<h3 class="anchored" data-anchor-id="bugs-maybe">Bugs (maybe)</h3>
+<p>By my count I ran into <a href="https://github.com/pola-rs/polars/issues?q=is%3Aissue+author%3Akevinheavey">11 bugs</a> while writing this book. They were fixed quickly and tests were added to make sure they don’t happen again, but it made me feel somewhat uneasy.</p>
+<p>However that was some time ago, and Pandas also has plenty of bugs, so at this point it’s quite difficult to measure which library is buggier.</p>
 </section>
 <section id="if-it-aint-broke-dont-fix-it" class="level3">
 <h3 class="anchored" data-anchor-id="if-it-aint-broke-dont-fix-it">If it ain’t broke, don’t fix it</h3>

timeseries.html

@@ -358,6 +358,7 @@ <h2 data-number="5.2" class="anchored" data-anchor-id="filtering"><span class="h
 .dataframe > thead > tr > th,
 .dataframe > tbody > tr > td {
   text-align: right;
+  white-space: pre-wrap;
 }
 </style>
 <small>shape: (28, 6)</small><table class="dataframe table table-sm table-striped"><thead><tr><th>time</th><th>open</th><th>high</th><th>low</th><th>close</th><th>volume</th></tr><tr><td>date</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2021-02-01</td><td>33092.97</td><td>34717.27</td><td>32296.16</td><td>33526.37</td><td>82718.276882</td></tr><tr><td>2021-02-02</td><td>33517.09</td><td>35984.33</td><td>33418.0</td><td>35466.24</td><td>78056.65988</td></tr><tr><td>…</td><td>…</td><td>…</td><td>…</td><td>…</td><td>…</td></tr><tr><td>2021-02-27</td><td>46276.88</td><td>48394.0</td><td>45000.0</td><td>46106.43</td><td>66060.834292</td></tr><tr><td>2021-02-28</td><td>46103.67</td><td>46638.46</td><td>43000.0</td><td>45135.66</td><td>83055.369042</td></tr></tbody></table></div>
@@ -456,7 +457,7 @@ <h3 data-number="5.3.1" class="anchored" data-anchor-id="downsampling"><span cla
 <div class="cell" data-execution_count="4">
 <div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>(</span>
 <span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>    ohlcv_pl</span>
-<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a>    .groupby_dynamic(<span class="st">"time"</span>, every<span class="op">=</span><span class="st">"5d"</span>)</span>
+<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a>    .group_by_dynamic(<span class="st">"time"</span>, every<span class="op">=</span><span class="st">"5d"</span>)</span>
 <span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a>    .agg(pl.col(pl.Float64).mean())</span>
 <span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="4">
@@ -465,9 +466,10 @@ <h3 data-number="5.3.1" class="anchored" data-anchor-id="downsampling"><span cla
 .dataframe > thead > tr > th,
 .dataframe > tbody > tr > td {
   text-align: right;
+  white-space: pre-wrap;
 }
 </style>
-<small>shape: (74, 6)</small><table class="dataframe table table-sm table-striped"><thead><tr><th>time</th><th>open</th><th>high</th><th>low</th><th>close</th><th>volume</th></tr><tr><td>date</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2021-01-01</td><td>31084.316</td><td>33127.622</td><td>29512.818</td><td>32089.662</td><td>112416.84957</td></tr><tr><td>2021-01-06</td><td>38165.31</td><td>40396.842</td><td>35983.822</td><td>39004.538</td><td>118750.076685</td></tr><tr><td>…</td><td>…</td><td>…</td><td>…</td><td>…</td><td>…</td></tr><tr><td>2021-12-27</td><td>48521.24</td><td>49475.878</td><td>47087.4</td><td>47609.528</td><td>35886.94371</td></tr><tr><td>2022-01-01</td><td>46216.93</td><td>47954.63</td><td>46208.37</td><td>47722.65</td><td>19604.46325</td></tr></tbody></table></div>
+<small>shape: (74, 6)</small><table class="dataframe table table-sm table-striped"><thead><tr><th>time</th><th>open</th><th>high</th><th>low</th><th>close</th><th>volume</th></tr><tr><td>date</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2020-12-29</td><td>29127.665</td><td>31450.0</td><td>28785.55</td><td>30755.01</td><td>92088.399186</td></tr><tr><td>2021-01-03</td><td>33577.028</td><td>36008.464</td><td>31916.198</td><td>35027.986</td><td>127574.470245</td></tr><tr><td>…</td><td>…</td><td>…</td><td>…</td><td>…</td><td>…</td></tr><tr><td>2021-12-24</td><td>50707.08</td><td>51407.656</td><td>49540.152</td><td>50048.066</td><td>29607.160572</td></tr><tr><td>2021-12-29</td><td>46836.5525</td><td>48135.4925</td><td>45970.84</td><td>46881.2775</td><td>31098.406725</td></tr></tbody></table></div>
 </div>
 </div>
 </div>
@@ -555,7 +557,7 @@ <h3 data-number="5.3.1" class="anchored" data-anchor-id="downsampling"><span cla
 <div class="cell" data-execution_count="6">
 <div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>(</span>
 <span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>    ohlcv_pl</span>
-<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>    .groupby_dynamic(<span class="st">"time"</span>, every<span class="op">=</span><span class="st">"1w"</span>, start_by<span class="op">=</span><span class="st">"friday"</span>)</span>
+<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>    .group_by_dynamic(<span class="st">"time"</span>, every<span class="op">=</span><span class="st">"1w"</span>, start_by<span class="op">=</span><span class="st">"friday"</span>)</span>
 <span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a>    .agg([</span>
 <span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a>        pl.col(pl.Float64).mean().suffix(<span class="st">"_mean"</span>),</span>
 <span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a>        pl.col(pl.Float64).<span class="bu">sum</span>().suffix(<span class="st">"_sum"</span>)</span>
@@ -567,6 +569,7 @@ <h3 data-number="5.3.1" class="anchored" data-anchor-id="downsampling"><span cla
 .dataframe > thead > tr > th,
 .dataframe > tbody > tr > td {
   text-align: right;
+  white-space: pre-wrap;
 }
 </style>
 <small>shape: (53, 11)</small><table class="dataframe table table-sm table-striped"><thead><tr><th>time</th><th>open_mean</th><th>high_mean</th><th>low_mean</th><th>close_mean</th><th>volume_mean</th><th>open_sum</th><th>high_sum</th><th>low_sum</th><th>close_sum</th><th>volume_sum</th></tr><tr><td>date</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2021-01-01</td><td>32305.781429</td><td>34706.045714</td><td>31021.727143</td><td>33807.135714</td><td>117435.5928</td><td>226140.47</td><td>242942.32</td><td>217152.09</td><td>236649.95</td><td>822049.149598</td></tr><tr><td>2021-01-08</td><td>37869.797143</td><td>39646.105714</td><td>34623.334286</td><td>37827.52</td><td>135188.296617</td><td>265088.58</td><td>277522.74</td><td>242363.34</td><td>264792.64</td><td>946318.076319</td></tr><tr><td>…</td><td>…</td><td>…</td><td>…</td><td>…</td><td>…</td><td>…</td><td>…</td><td>…</td><td>…</td><td>…</td></tr><tr><td>2021-12-24</td><td>49649.114286</td><td>50439.622857</td><td>48528.25</td><td>49117.98</td><td>31126.709793</td><td>347543.8</td><td>353077.36</td><td>339697.75</td><td>343825.86</td><td>217886.96855</td></tr><tr><td>2021-12-31</td><td>46668.905</td><td>48251.445</td><td>45943.185</td><td>46969.79</td><td>27271.230605</td><td>93337.81</td><td>96502.89</td><td>91886.37</td><td>93939.58</td><td>54542.46121</td></tr></tbody></table></div>
@@ -708,6 +711,7 @@ <h3 data-number="5.3.2" class="anchored" data-anchor-id="upsampling"><span class
 .dataframe > thead > tr > th,
 .dataframe > tbody > tr > td {
   text-align: right;
+  white-space: pre-wrap;
 }
 </style>
 <small>shape: (1_461, 6)</small><table class="dataframe table table-sm table-striped"><thead><tr><th>time</th><th>open</th><th>high</th><th>low</th><th>close</th><th>volume</th></tr><tr><td>date</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2021-01-01</td><td>28923.63</td><td>29600.0</td><td>28624.57</td><td>29331.69</td><td>54182.925011</td></tr><tr><td>2021-01-01</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td></tr><tr><td>…</td><td>…</td><td>…</td><td>…</td><td>…</td><td>…</td></tr><tr><td>2021-12-31</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td></tr><tr><td>2022-01-01</td><td>46216.93</td><td>47954.63</td><td>46208.37</td><td>47722.65</td><td>19604.46325</td></tr></tbody></table></div>
@@ -914,6 +918,7 @@ <h3 data-number="5.5.1" class="anchored" data-anchor-id="offsets"><span class="h
 .dataframe > thead > tr > th,
 .dataframe > tbody > tr > td {
   text-align: right;
+  white-space: pre-wrap;
 }
 </style>
 <small>shape: (366, 1)</small><table class="dataframe table table-sm table-striped"><thead><tr><th>time</th></tr><tr><td>date</td></tr></thead><tbody><tr><td>2021-03-22</td></tr><tr><td>2021-03-23</td></tr><tr><td>…</td></tr><tr><td>2022-03-21</td></tr><tr><td>2022-03-22</td></tr></tbody></table></div>
@@ -980,6 +985,7 @@ <h3 data-number="5.5.3" class="anchored" data-anchor-id="timezones"><span class=
 .dataframe > thead > tr > th,
 .dataframe > tbody > tr > td {
   text-align: right;
+  white-space: pre-wrap;
 }
 </style>
 <small>shape: (366, 6)</small><table class="dataframe table table-sm table-striped"><thead><tr><th>time</th><th>open</th><th>high</th><th>low</th><th>close</th><th>volume</th></tr><tr><td>datetime[μs, US/Eastern]</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2020-12-31 19:00:00 EST</td><td>28923.63</td><td>29600.0</td><td>28624.57</td><td>29331.69</td><td>54182.925011</td></tr><tr><td>2021-01-01 19:00:00 EST</td><td>29331.7</td><td>33300.0</td><td>28946.53</td><td>32178.33</td><td>129993.873362</td></tr><tr><td>…</td><td>…</td><td>…</td><td>…</td><td>…</td><td>…</td></tr><tr><td>2021-12-30 19:00:00 EST</td><td>47120.88</td><td>48548.26</td><td>45678.0</td><td>46216.93</td><td>34937.99796</td></tr><tr><td>2021-12-31 19:00:00 EST</td><td>46216.93</td><td>47954.63</td><td>46208.37</td><td>47722.65</td><td>19604.46325</td></tr></tbody></table></div>