Transitioning from Pandas to Polars the straightforward way — by taking a pit stop at SQL.The key’s out! Polars is the most popular thing on the block, and everybody wants a slice 😎I recently...
Basic recommenders which are easy to grasp and implement, in addition to fast to coachWe start with the unique dataframe.We then group by customer_id and article_id and aggregate via a count.We then aggregate again...
Learn easy methods to use SQL to question your Polars DataFramesSay, you now want to search out all phones from Apple which has sales of greater than 80. You should use the filter() function...
I got here for the speed, but I stayed for the syntaxAnd that brings us to .scan_parquet() and .sink_parquet().Through the use of .scan_parquet() as your data input function, LazyFrame as your dataframe, and .sink_parquet()...
Though the brand new PyArrow backend for Pandas is bringing exciting features, it still looks disappointing when it comes to speed.Things are changingFor years now, Pandas have stood on the shoulders of NumPy because...
Though the brand new PyArrow backend for Pandas is bringing exciting features, it still looks disappointing when it comes to speed.Things are changingFor years now, Pandas have stood on the shoulders of NumPy because...
Web scraping made easy and fast with Polars in Python.Polars is a dataframe library for Python that is quicker than pandas.Identical to Pandas, we are able to use polars to simply scrape web sites....
Here’s why you need to select polars over pandas (and the right way to learn polars very quickly!).How can polars outperform pandas?Unlike pandas, polars is lazy and semi-lazy. In lazy Polars, we are able...