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      <title>Exploratory Data Analysis Core Guidelines</title>
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      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
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      <description>&lt;h2 id=&#34;jupyter-notebooks&#34;&gt;Jupyter Notebooks&lt;/h2&gt;
&lt;h3 id=&#34;jupy0-good-notebooks-use-data-to-tell-a-story-and-comments-need-to&#34;&gt;Jupy.0. Good notebooks use data to tell a story and comments need to&lt;/h3&gt;
&lt;p&gt;concisely and tastefully facilitate the storytelling.&lt;/p&gt;
&lt;h3 id=&#34;jupy1-persist-notebooks-in-a-format-that-is-amenable-to-version-control&#34;&gt;Jupy.1. Persist notebooks in a format that is amenable to version-control,&lt;/h3&gt;
&lt;p&gt;executable, and well-documented. Export to HTML or PDF as desired. As of 2026,
&lt;code&gt;jupytext&lt;/code&gt; works well for this.&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Reason:&lt;/strong&gt; Version control and well-documented formats enable reproducibility
and repeatability of analysis. With jupytext, some guidelines enable all
analytical notebooks to be executable and immediately reproducible.&lt;/p&gt;</description>
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      <title>Python Core Guidelines</title>
      <link>/python_core_guidelines/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
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      <description>Write production-grade Python programs that prioritize reliability and resilience</description>
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