https://github.com/riju18/polars-vs-pandas-deepdive
This repository examines the conflict between Pandas and Polars, contrasting how well they perform in terms of execution time and efficiency. We demonstrate which library is the best in terms of speed, memory utilization, and scalability using thorough benchmarks, real-world use cases, and optimized code.
https://github.com/riju18/polars-vs-pandas-deepdive
dataframe jupyter-notebook pandas polars python
Last synced: 2 months ago
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This repository examines the conflict between Pandas and Polars, contrasting how well they perform in terms of execution time and efficiency. We demonstrate which library is the best in terms of speed, memory utilization, and scalability using thorough benchmarks, real-world use cases, and optimized code.
- Host: GitHub
- URL: https://github.com/riju18/polars-vs-pandas-deepdive
- Owner: riju18
- Created: 2025-03-01T11:36:29.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-03-04T16:26:46.000Z (3 months ago)
- Last Synced: 2025-03-04T17:33:05.104Z (3 months ago)
- Topics: dataframe, jupyter-notebook, pandas, polars, python
- Language: Jupyter Notebook
- Homepage:
- Size: 21.5 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# polars-vs-pandas-deepdive
# Get the data
- Go to this [website](https://www.stats.govt.nz/large-datasets/csv-files-for-download/)
- Navigate to section named `Census`
- Donwload the zip file named `Age and sex by ethnic group (grouped total responses), for census usually resident population counts, 2006, 2013, and 2018 Censuses (RC, TA, SA2, DHB),CSV zipped file, 103 MB`
# Run the code
- clone the repo
- install the `requirements.txt` file
- Extract the zip file and place the file named `data8277.csv` in root dir
- Run either `.ipynb` or `.py` file