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    <title>DEV Community: Maruti Jhawar</title>
    <description>The latest articles on DEV Community by Maruti Jhawar (@maruti_jhawar).</description>
    <link>https://dev.to/maruti_jhawar</link>
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      <title>DEV Community: Maruti Jhawar</title>
      <link>https://dev.to/maruti_jhawar</link>
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      <title>Financial Pipeline</title>
      <dc:creator>Maruti Jhawar</dc:creator>
      <pubDate>Sun, 04 Jan 2026 05:05:35 +0000</pubDate>
      <link>https://dev.to/maruti_jhawar/financial-pipeline-1ha6</link>
      <guid>https://dev.to/maruti_jhawar/financial-pipeline-1ha6</guid>
      <description>&lt;p&gt;Built an automated financial screening system that analyzed 226 real companies in 2 seconds 📊&lt;/p&gt;

&lt;p&gt;To see how I automated PE-grade due diligence →&lt;/p&gt;

&lt;p&gt;What I discovered:&lt;br&gt;
• eBay: 107% profit margin (highest performer)&lt;br&gt;
• Moderna: 3,065% revenue growth (COVID vaccine impact)&lt;br&gt;
• Only 2 companies have truly strong balance sheets&lt;br&gt;
• Tech &amp;amp; semiconductors dominate profitability&lt;/p&gt;

&lt;p&gt;The system calculates 20+ financial ratios automatically:&lt;br&gt;
✅ Profitability: ROE, ROA, margins&lt;br&gt;
✅ Liquidity: Current ratio, quick ratio, cash ratio&lt;br&gt;
✅ Leverage: Debt-to-equity, interest coverage&lt;br&gt;
✅ Efficiency: Asset turnover, DSO&lt;br&gt;
✅ Investment quality scoring (0-100)&lt;/p&gt;

&lt;p&gt;Built for PE/IB workflows with investor-grade standards:&lt;br&gt;
• Complete audit trails for compliance&lt;br&gt;
• Data quality scoring on every dataset&lt;br&gt;
• Handles CSV, Excel, and PDF statements&lt;br&gt;
• Defensive error handling throughout&lt;br&gt;
• Metadata tracking at every pipeline stage&lt;/p&gt;

&lt;p&gt;Tech stack: Python, pandas, numpy, matplotlib&lt;br&gt;
Dataset: 226 companies, 40 metrics each&lt;br&gt;
Processing time: ~2 seconds&lt;br&gt;
Code: 1,050 lines production + 2,500 lines docs&lt;/p&gt;

&lt;p&gt;The difference between a script and production code? Complete audit trails, stable interfaces, and handling real-world data messiness.&lt;/p&gt;

&lt;p&gt;What financial metrics would you add to this analysis?&lt;/p&gt;

&lt;p&gt;🔗 Code on GitHub: &lt;a href="https://github.com/marutijhawar/Financial-Pipeline-" rel="noopener noreferrer"&gt;https://github.com/marutijhawar/Financial-Pipeline-&lt;/a&gt; &lt;/p&gt;

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      <category>automation</category>
      <category>datascience</category>
      <category>performance</category>
      <category>showdev</category>
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    <item>
      <title>I wanna know about the best place to learn and practice my Python</title>
      <dc:creator>Maruti Jhawar</dc:creator>
      <pubDate>Sun, 02 Mar 2025 09:56:58 +0000</pubDate>
      <link>https://dev.to/maruti_jhawar/i-wanna-know-about-the-best-place-to-learn-and-practice-my-python-7p2</link>
      <guid>https://dev.to/maruti_jhawar/i-wanna-know-about-the-best-place-to-learn-and-practice-my-python-7p2</guid>
      <description></description>
      <category>python</category>
      <category>learning</category>
      <category>beginners</category>
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