Pandas Series.idxmax() and Series.idxmin() methods: A detailed guide

Updated: February 18, 2024 By: Guest Contributor Post a comment


In data analysis, finding the index of maximum and minimum values in a dataset can be crucial for understanding the distribution, trends, and outliers within your data. Pandas, a powerful Python library for data manipulation and analysis, offers two handy methods for achieving this: idxmax() and idxmin(). These methods return the index of the first occurrence of the maximum and minimum values respectively in a Series. This tutorial will provide a detailed guide on using these methods effectively with various code examples.


First, ensure that you have Pandas installed in your Python environment. You can install Pandas using pip:

pip install pandas

Once installed, you can import Pandas in your Python script:

import pandas as pd

Basic Usage

Let’s start with a basic example of a Pandas Series:

import pandas as pd

# Sample Series
data = pd.Series([10, 20, 15, 30, 25])

# Find index of maximum value
max_index = data.idxmax()
print("Index of max value:", max_index)

# Find index of minimum value
min_index = data.idxmin()
print("Index of min value:", min_index)


Index of max value: 3
Index of min value: 0

This code snippet creates a simple Series and uses idxmax() and idxmin() to find the indices of the maximum and minimum values respectively.

Handling NaN Values

When your Series contains NaN (Not a Number) values, idxmax() and idxmin() gracefully handle these by default, ignoring them:

import pandas as pd

# Series with NaN values
data = pd.Series([10, float('nan'), 15, float('nan'), 25])

# Index of max and min
max_index = data.idxmax()
min_index = data.idxmin()

print("Index of max value:", max_index)
print("Index of min value:", min_index)


Index of max value: 4
Index of min value: 0

This shows that the presence of NaN values does not affect the functionality of idxmax() and idxmin().

Working with Date Indices

Pandas is well-suited for time series data. When working with such data, the Series object might have date indices. Here’s how you can find max and min values in this context:

import pandas aspd

# Time series data
timestamps = pd.date_range('20210101', periods=5)
values = [10, 20, 15, 30, 25]
data = pd.Series(values, index=timestamps)

# Find index of max and min values
max_date = data.idxmax()
min_date = data.idxmin()

print("Date of max value:", max_date)
print("Date of min value:", min_date)


Date of max value: 2021-01-04 00:00:00
Date of min value: 2021-01-01 00:00:00

This demonstrates how idxmax() and idxmin() can be used to find the significant dates in time series data.

Advanced Techniques

For more complex analyses, such as when dealing with multi-level indices (MultiIndex), the idxmax() and idxmin() methods still prove useful. Consider a Series with a MultiIndex:

import pandas as pd

# MultiIndex Series
countries = ['USA', 'USA', 'Canada', 'Canada']
cities = ['New York', 'San Francisco', 'Toronto', 'Montreal']
index = pd.MultiIndex.from_tuples(list(zip(countries, cities)), names=['Country', 'City'])
values = [100, 200, 150, 250]
data = pd.Series(values, index=index)

# Max and min
max_info = data.idxmax()
min_info = data.idxmin()

print("Max value at:", max_info)
print("Min value at:", min_info)


Max value at: ('Canada', 'Montreal')
Min value at: ('USA', 'New York')

This example shows that idxmax() and idxmin() can efficiently handle Series with complex indices, providing insightful analyses.


This tutorial explored the idxmax() and idxmin() methods provided by Pandas for finding the indices of the maximum and minimum values in a Series. Starting from basic examples and moving to more advanced cases, we covered a wide array of scenarios to illustrate the versatility and efficiency of these methods. Understanding how to leverage idxmax() and idxmin() in your data analysis workflows can significantly enhance your ability to extract meaningful insights from your data.