Pandas DataFrame Cummin Method

Introduction

In this lab, we will learn how to use the cummin() method in the Python pandas library. The cummin() method calculates the cumulative minimum over a DataFrame or Series axis. It returns a DataFrame or Series of the same size containing the cumulative minimum.

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Skills Graph

Importing the pandas library

First, we need to import the pandas library to use the cummin() method. We can do this by adding the following line of code at the beginning of our program:

import pandas as pd

Creating the DataFrame

Next, let's create a DataFrame that we can use for our examples. We can do this by using the pd.DataFrame() constructor and passing a dictionary of values. Here's an example:

df = pd.DataFrame({"A": [1, 2, 8, 4], "B": [9, 10, 7, 8], "C": [9, 10, 11, 12], "D": [13, 16, 15, 16]})

This will create a DataFrame with four columns (A, B, C, D) and four rows.

Finding the cumulative minimum over the index axis

To find the cumulative minimum over the index axis, we can use the cummin() method on our DataFrame. We need to specify the axis parameter as 0 or 'index'. Here's an example:

df_cummin = df.cummin(axis=0)

This will calculate the cumulative minimum of each column over the index axis and store the result in the df_cummin DataFrame.

Finding the cumulative minimum over the column axis

To find the cumulative minimum over the column axis, we can use the cummin() method on our DataFrame. We need to specify the axis parameter as 1 or 'columns'. Here's an example:

df_cummin = df.cummin(axis=1)

This will calculate the cumulative minimum of each row over the column axis and store the result in the df_cummin DataFrame.

Handling null values

By default, the cummin() method excludes NA/null values. If an entire row/column is NA, the result will be NA. We can change this behavior by setting the skipna parameter to False. Here's an example:

df_cummin = df.cummin(skipna=False)

This will calculate the cumulative minimum of each column over the index axis, including any null values, and store the result in the df_cummin DataFrame.

Summary

In this lab, we learned how to use the cummin() method in the pandas library. We learned how to find the cumulative minimum over the index or column axis of a DataFrame. We also learned how to handle null values during the calculation. The cummin() method is useful for analyzing data and finding trends over time.