Here is how to use the polars library in Python.

pip install polars

import polars as pl

# Read a CSV file into a Polars DataFrame
df = pl.read_csv("iris.csv")

print(df.head())

print(df.shape)

print(df.columns)

print(df.dtypes)

selected_columns = ["column1", "column2"]
subset = df[selected_columns]

filtered_data = df[df["column1"] > 10]

sorted_data = df.sort("column_name", ascending=False)  # Descending order

grouped_data = df.groupby("category").agg(avg_value=("column3", pl.mean))

df.write_csv("output.csv")

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Both Polars and pandas are powerful Python libraries for data analysis, but they have distinct advantages and disadvantages. Here's a breakdown to help you decide which might be better for your specific needs:

Polars Advantages:

• Performance: Polars often shines in terms of speed. Its Rust backend offers significant performance gains,especially for large datasets. Operations like filtering, sorting, and aggregations can be considerably faster in Polars.
• Memory Efficiency: Polars utilizes column-oriented data storage, leading to efficient memory usage, particularly beneficial when dealing with extensive data.
• Lazy Evaluation: Polars employs lazy evaluation, delaying actual calculations until necessary. This can save processing time for complex workflows where not all operations are ultimately used.
• Expressive API: Polars provides a user-friendly and expressive API for building data manipulation pipelines.

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