Pandas – Using DataFrame idxmax() and idxmin() methods (4 examples)

Updated: February 25, 2024 By: Guest Contributor Post a comment


In data analysis, identifying the maximum and minimum values in your dataset is a common requirement. Pandas, a popular Python library for data manipulation and analysis, provides powerful tools for handling this task efficiently. Two such tools are the idxmax() and idxmin() methods. These methods help you find the index of the first occurrence of the maximum and minimum values in a DataFrame or Series, respectively. Throughout this tutorial, we’ll explore how to use these methods with four progressive examples.

The Fundamentals of idxmax() and idxmin()

Before diving into the examples, it’s crucial to understand what idxmax() and idxmin() do and when to use them. The idxmax() method returns the index of the first occurrence of the maximum value, while idxmin() returns the index of the first occurrence of the minimum value. You can apply these methods across rows or columns of a DataFrame or on a Series object.

Here’s a quick reference to the syntax:

  • DataFrame.idxmax(axis=0, skipna=True)
  • DataFrame.idxmin(axis=0, skipna=True)

axis=0 indicates column-wise operation, and axis=1 indicates row-wise operation. skipna=True tells Pandas to ignore NULL values during the calculation.

Basic Example: Single Column Operation

Let’s start with the simplest use case, where we have a DataFrame and want to find the index of the maximum and minimum values in a specific column.

import pandas as pd

# Sample DataFrame
data = {
    'Temperatures': [23, 28, 32, 21, 30, 25, 29]
df = pd.DataFrame(data)

# Find the index of the maximum temperature
max_temp_index = df['Temperatures'].idxmax()
print("Index of Maximum Temperature: ", max_temp_index)

# Find the index of the minimum temperature
min_temp_index = df['Temperatures'].idxmin()
print("Index of Minimum Temperature: ", min_temp_index)

In this example, the output will specify the index values corresponding to the maximum and minimum temperatures in the DataFrame.

Example 2: Column-Wise Operation

Now, let’s see how you can apply idxmax() and idxmin() methods column-wise on a DataFrame with multiple columns.

import pandas as pd

# Create a DataFrame with multiple columns
data = {
    'Product A': [230, 200, 210],
    'Product B': [180, 250, 220],
    'Product C': [240, 210, 230]
df = pd.DataFrame(data)

# Find the index of max value for each column

# Find the index of min value for each column

This will print the indices of the maximum and minimum values for each column individually.

Example 3: Row-Wise Operation

To understand the versatility of idxmax() and idxmin(), it’s beneficial to see how they can be applied row-wise. This approach is particularly useful when you want to compare values across multiple columns in the same row.

import pandas as pd

# DataFrame for demonstration

data = {
    'Q1': [23, 28, 32],
    'Q2': [21, 30, 25],
    'Q3': [29, 31, 33],
    'Q4': [24, 29, 27]
df = pd.DataFrame(data)

# Setting axis=1 for row-wise operation
print("Max value for each row:")
print("Min value for each row:")

This row-wise operation allows you to see which quarter (column) had the maximum or minimum value for each row.

Example 4: Handling Missing Values

In real-world datasets, handling missing values is a frequent challenge. Here’s how you can use idxmax() and idxmin() when your DataFrame contains missing (NaN) values.

import pandas as pd
import numpy as np

# DataFrame with missing values

data = {
    'Sales A': [200, np.nan, 250, 300],
    'Sales B': [220, 180, np.nan, 290]
df = pd.DataFrame(data)

# Demonstrating skipna parameter
print("Ignoring NaNs:")

# Including NaNs
print("Including NaNs:")
except ValueError as e:
    print("Error: ", str(e))

This demonstrates the behavior of the idxmax() and idxmin() methods in the presence of missing values and how the skipna parameter impacts the result.


Through these examples, we’ve seen the versatility and convenience of the idxmax() and idxmin() methods in Pandas for identifying the index of maximum and minimum values. Whether you’re analyzing single columns, multiple columns, or dealing with missing data, these methods offer succinct and powerful ways to extract meaningful insights from your data.