Handling Nested Data in Polars: explode() and unnest()
Real-world data from APIs often comes as nested JSON. Pandas struggles with this, but Polars has two powerful expressions built for it: explode and unnest….
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Real-world data from APIs often comes as nested JSON. Pandas struggles with this, but Polars has two powerful expressions built for it: explode and unnest….

You’ve built amazing AI models with Hugging Face, but they’re stuck in your script. Want to know how to deploy Hugging Face API so other…

Text data is almost always messy. One of the most efficient ways to tackle this is with Polars string manipulation. In Pandas, you use .str…

You’ve used the Hugging Face pipeline to understand text and images. Now, let’s teach it to understand audio using Hugging Face Speech to Text. We…

This is the ultimate goal of PyScript for Data Science: building a tool that lets your users analyze their own data, all inside their browser….

One of the most common tasks in data analysis is “resampling” time data. For example, turning a list of daily sales into “Total Monthly Sales.”…

We’ve done single-label classification (e.g., “POSITIVE” or “NEGATIVE”). But what if a text can be both? A news article could be about “POLITICS” and “FINANCE”….

Just like Pandas has NaN, Polars has null to represent missing or empty data. Before you can analyze a dataset, you must have a strategy…

The most common data analysis task is “Split-Apply-Combine.” When using Polars, the groupby operation is essential for this task. In Polars, this is done with…

We’ve used Hugging Face to understand text and generate text. Now, let’s use it to understand images with Hugging Face Image Classification. Image Classification is…
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