Polars - Fast DataFrames Library for Python

Polars is a high-performance DataFrame library for Python that is designed for speed and efficiency, especially with large datasets. It provides a familiar API similar to pandas but uses parallel execution and optimized memory layouts for faster data processing. Polars supports lazy evaluation, eager execution, and CSV/Parquet reading and writing. It’s ideal for data analysis, machine learning preprocessing, and handling big data without compromising performance.

Installation:

pip install polars

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Example usage:

import polars as pl

df = pl.DataFrame({
    "name": ["Alice", "Bob", "Charlie"],
    "age": [25, 30, 35]
})

print(df.filter(pl.col("age") > 28))

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PyPI page: https://pypi.org/project/polars/
GitHub page: https://github.com/pola-rs/polars

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3 Project Ideas:

1. Analyze large CSV datasets for business insights.
2. Preprocess data efficiently for machine learning pipelines.
3. Build a dashboard that performs real-time analytics on streaming data.