polars

Polars

Polars is an alternative to pandas that is designed to have better performance.

What is it?

1. patient_data.ipynb: Jupyter notebook that explores functional differences between pandas and polars. It replicates the notebook in the pandas directory with the same name.
2. polars_large_data_benchmark.ipynb: Jupyter notebook that compares the performance of polars and pandas on large data sets.
3. create_csv_data.py: Python script to generate one or more large CSV files for benchmarking.
4. create_csv_data.slurm: Slurm script to run create_csv_data.py on a cluster.
5. polars_performance.ipynb: Jupyter notebook that compares the performance of polars and pandas on a variety of operations.
6. data: Directory containing the data used in the notebook.
7. polars_execution_modes_benchmark.py: Python script that compares the performance of polars in eager and lazy execution modes.
8. polars_query_plan.py: Python script that demonstrates how to view the query plan of a polars lazy frame.
9. polars_datatypes.ipynb: Jupyter notebook illustrating the use of schema information when reading data.
10. polars_missing_values.ipynb: Jupyter notebook illustrating how polars handles missing values.

Exercises

Some exercises to practice your skills:

1. EXERCISES.md: list of exercises.
2. EXERCISES_INSTRUCTOR.md: some hints and sketch of solutions.
3. EXERCISES_SOLUTIONS.ipynb: Jupyter notebook with solutions to the exercises.