[EXPERIMENTAL] Rewrite Pandas using Polars (AI)

[mmcky]

EXPERIMENTAL

This is purely an experiment in AI.

This was provided to Claude

Hi Claude. Can I please get your help in translating a lecture written in Myst Markdown to use the polars python library rather than the pandas library. Here is the file.

with the following notes provided and the re-written lecture.

Checks

• see if the generated lecture executes without any edits (naive upload)
• If no, how much work is it to fix the issues?
• Does the AI replace the code and fit the context well?

Notes

I'd be happy to help you translate this pandas lecture to use the polars library instead! This is a great project since polars is becoming increasingly popular for data analysis due to its performance and intuitive API.

Let me create a translated version of the lecture that converts all the pandas operations to their polars equivalents:I've translated the pandas lecture to use polars! Here are the key changes I made:

Major Translation Points:

1. Import changes: import pandas as pd → import polars as pl

2. DataFrame/Series creation:

   • pd.Series() → pl.Series()
   • pd.read_csv() → pl.read_csv()

3. Data selection and filtering:

   • df[condition] → df.filter(pl.col('column') condition)
   • df[['col1', 'col2']] → df.select(['col1', 'col2'])
   • df.iloc[2:5] → df.slice(2, 3)

4. Data manipulation:

   • df.apply() → Using polars expressions with with_columns() and when().then().otherwise()
   • df.fillna() → df.fill_null()
   • df.describe() → df.describe() (similar but different output format)

5. Conditional operations:

   • Pandas boolean indexing → pl.when().then().otherwise() expressions
   • df.query() functionality → df.filter() with polars expressions

6. Performance considerations:

   • Polars uses lazy evaluation and expressions instead of apply functions
   • More explicit about data types and operations

7. Plotting compatibility:

   • Added .to_pandas() conversions where needed for matplotlib compatibility
   • Polars doesn't have built-in plotting like pandas, so we convert when needed

Key Polars Advantages Highlighted:

• Performance: Rust-based implementation
• Memory efficiency: Better memory usage
• Expressive API: Clear, readable expressions
• Type safety: Strong typing system
• Lazy evaluation: Optimized query planning

The translation maintains all the educational content while showcasing polars' modern approach to data manipulation. The exercises have also been adapted to work with polars' API patterns.

[jstac]

Thanks @mmcky . This is a fun experiment! @thomassargent30 might be interested too.