Pandas DataFrame vs Spark DataFrame: Choosing the Right Tool for the Job

If youโ€™ve spent time in Python for data analysis, you know the magic of Pandas. A few lines of code, and you can filter, aggregate, and transform data like a wizard. But when your dataset starts hitting millions of rows or you want to run computations across a cluster, Pandas starts to sweat โ€” thatโ€™s... Continue Reading →

Python Project Structures That Donโ€™t Collapse in Production

Thereโ€™s something oddly satisfying about writing a quick Python script that just works. You run it, see the output, maybe toss in a few print statements, and boomโ€”done. But the trouble starts when that โ€œquick scriptโ€ grows into a project with multiple files, dependencies, and people contributing to it. Suddenly, that neat little script feels... Continue Reading →

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