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    <title>Oop on theplaybook</title>
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      <title>OOP for Data Analysts: When and Why It Actually Matters</title>
      <link>https://stannomarjones.com/posts/object-oriented-programming/</link>
      <pubDate>Sat, 15 Feb 2025 12:18:35 -0400</pubDate>
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      <description>&lt;h2 id=&#34;background&#34;&gt;Background&lt;/h2&gt;
&lt;p&gt;Most data analysts learn Python in a very procedural way: write a script, transform some data, export a result, repeat.&lt;/p&gt;
&lt;p&gt;That works. Until it doesn&amp;rsquo;t.&lt;/p&gt;
&lt;p&gt;As projects grow, logic gets duplicated, scripts become harder to maintain, and simple changes start breaking multiple parts of your workflow. That&amp;rsquo;s usually the point where OOP (Object-Oriented Programming) starts to matter.&lt;/p&gt;
&lt;p&gt;My mentor once told me to use Python like a developer—not just as an analyst. That stuck with me. Understanding data structures is important, but learning how to structure code is what actually makes your work scalable.&lt;/p&gt;</description>
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