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….
Cleaning Text in Polars: The .str Expression Namespace
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…
PyScript Project: Load and Analyze a User’s CSV File in the Browser
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….
Time-Series in Polars: group_by_dynamic Explained
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.”…
Handling Missing Data in Polars (null, fill_null, drop_nulls)
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…
Master Polars: A Guide to groupby and Aggregations
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…
Joining DataFrames in Polars: The Blazing Fast join() Method
In Pandas, you use pd.merge() to combine datasets. In Polars, you use the join() method, which is one of the fastest in any library. If…
PyScript and APIs: How to fetch Data from the Web
A PyScript app is great, but it’s isolated. To build a real dashboard (like a weather app or a crypto tracker), you need to get…
Full-Stack Python: A PyScript Dashboard with Hugging Face & Polars
This is the future. Our dashboard will showcase how you can combine PyScript, Hugging Face, and Polars to create advanced data apps. We are going…
Master Polars: A Guide to the Expression API (select, filter, with_columns)
In our Polars vs. Pandas article, we showed that Polars is faster. The reason it’s faster is its Expression API. In this article, we’ll take…
