Polars is a Rust-based DataFrame library for Python, offering scalable data handling as an alternative to pandas. With features like parallel processing and memory optimization, it excels in speed. Its DataFrame and Series structures allow seamless, efficient, and chained data transformations.
Firstly, we import the polars library as follows:
import polars as pl
polarsLet’s create a Polars DataFrame for the Books such as English, Science, and Math. We will add their Prices as follows:
import polars as plpolars_df = pl.DataFrame({"Books": ["English", "Science", "Math"], "Prices": [200, 500, 600]})print(polars_df)
polars DataFrame to pandasDataFrameAfter creating the polars DataFrame, we will import the pandas library and use a single line of code to convert it to a pandas DataFrame. Let’s write the code below:
import polars as plimport pandas as pd# Create a Polars DataFramepolars_df = pl.DataFrame({"Books": ["English", "Science", "Math"], "Prices": [200, 500, 600]})# Convert polars DataFrame to pandas DataFramepandas_df = polars_df.to_pandas()# Display the pandas DataFrameprint(pandas_df)
Line 2: Import the pandas library.
Line 9: Convert the polars DataFrame to pandas DataFrame.
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