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.”…
AI Project: Multi-Label Text Classification with Hugging Face
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”….
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…
AI Project: Build an Image Classifier with Hugging Face (Vision)
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…
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…
AI Project: Text Classification with Hugging Face (The Manual Way)
The Hugging Face pipeline is great for simple tasks, and it’s particularly well-known for Hugging Face Text Classification. But what if you need the raw…
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…
AI Project: Text Generation with gpt-2 (Hugging Face)
We’ve used the Hugging Face pipeline to understand text (sentiment-analysis) and answer questions (question-answering). Now, let’s use it for its most famous task: Text Generation….
