I have a Polars DataFrame, and I want to create a summarized view where aggregated values (e.g., unique IDs, total sends) are displayed in a format that makes comparison across months easier. Here’s an example of my dataset:

I have a Polars DataFrame, and I want to create a summarized view where aggregated values (e.g., unique IDs, total sends) are displayed in a format that makes comparison across months easier. Here’s an example of my dataset:

Suppose I have urls, a list of s3 Parquet urls (on S3).

Suppose I have urls, a list of s3 Parquet urls (on S3).

Suppose I have urls, a list of s3 Parquet urls (on S3).

I have a Polars dataframe with a lot of duplicate data I would like to consolidate.

I have 3 polars dataframes, one that contains 2 IDS, and the other ones contains an ID and a value. I would like to join the 3 dataframes if the ID of the main table exists on one of the other tables and bring a values from a desired column.

I have 3 polars dataframes, one that contains 2 IDS, and the other ones contains an ID and a value. I would like to join the 3 dataframes if the ID of the main table exists on one of the other tables and bring a values from a desired column.

In Pandas, after filtering/sorting a DataFrame, the row indices might become non-sequential (e.g., [0, 10, 4]). If I use .loc[10], I can retrieve the row corresponding to the original index 10 from the DataFrame, which is now the second row in the filtered/sorted DataFrame:

I need to compute cross-sectional rankings across a number of trading securities. Consider the following pl.DataFrame in long (tidy) format. It comprises three different symbols with respective prices, where each symbol also has a dedicated (i.e. local) trading calendar.