Polars is a fast and efficient data manipulation library written in Rust. It’s designed to provide high-performance operations on large datasets, and handles them more quickly than pandas. It’s particularly more suitable when working with tabular data.
Note: For a detailed discussion on Polars vs. pandas, refer to this link.
In this Answer, we’ll use the min method to find the minimum value for each column from the DataFrame in Polars.
DataFrame.min() methodThe DataFrame.min() is a method used to compute the minimum value for each column in a DataFrame. It returns a new DataFrame with a single row that contains the minimum value. The minimum value is calculated independently for each column.
By default, the DataFrame.min() method ignores missing values (null or NaN) during the computation. If a column contains missing values, the minimum value will be computed, excluding those missing values.
Let’s first import the polars library using the following command:
import polars as pl
After importing the library, let’s examine in detail how the DataFrame.min() works.
import polars as pl# Create a DataFrame with mixed data typesdata = {'A': [1, 2, 3], 'B': [4, None, 6],'C': [7, 8, 9], 'D': ['foo', 'bar', 'baz']}df = pl.DataFrame(data)# Compute the minimum values for each columnmin_values = df.min()print(min_values)
Lines 4–6: We create the DataFrame df that contains a mix of numeric and non-numeric columns.
Line 9: We use the df.min() method that returns the DataFrame containing the minimum values for numeric columns A, B (excluding the missing value None), C, and the non-numeric column D.
Line 11: We print the min_values DataFrame that contains the minimum values [1, 4, 7, 'bar'] for the corresponding columns.
The min() function in Polars is a powerful method for performing minimum value calculations on specific columns, within a DataFrame. It simplifies the process of finding the minimum values, and enhances data analysis and manipulation capabilities in Polars.
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