Pandas DataFrame: How to get the nth row of each group

Updated: February 21, 2024 By: Guest Contributor Post a comment


Pandas is a powerful Python library for data manipulation and analysis, especially for tabular data. One of the common tasks in data analysis is grouping data based on one or more columns and then applying some operations to these groups. Often, there might be a need to retrieve the nth row from each group in a DataFrame. This article will explore various methods to achieve this, progressing from basic to advanced techniques.

First, you need to understand how grouping in Pandas works. Grouping enables you to split your data into sets and then apply a function to each subset. In practice, you may want to analyze or manipulate a specific row from each group based on its position – for example, the first row (such as for getting the earliest entry in time series data) or the last row (to find the most recent entry).

We’ll start with the simplest scenario – getting the first row from each group – and move towards retrieving any nth row from a DataFrame, using different examples and addressing potential challenges along the way.

Setting Up Your Environment

Before diving into the examples, ensure you have Pandas installed in your environment. If you haven’t, you can install Pandas using pip:

pip install pandas

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

import pandas as pd

Basic Example – Getting the First Row

For our initial example, let’s consider a simple DataFrame that groups data by a ‘Category’ column.

df = pd.DataFrame({
    'Category': ['A', 'A', 'B', 'B', 'C', 'C'],
    'Value': [1, 2, 3, 4, 5, 6]

grouped_df = df.groupby('Category')
first_row = grouped_df.first()

This code groups the DataFrame by the ‘Category’ column and uses the .first() method to obtain the first entry from each group. The output would be:

A              1
B              3
C              5

This method is straightforward for getting the first or last entry of each group using the .last() counterpart. But, what about getting the nth row?

Retrieving the Nth Row

To get the nth row from each group, you can use the .nth() method. For example, to get the second row from each group:

second_row = grouped_df.nth(1) # Indexing starts at 0


A              2
B              4
C              6

This method is very sleek and directly addresses our main goal. However, it assumes that each group has at least n rows, which might not always be the case.

Handling Groups with Less Than n Rows

In cases where some groups have fewer than n rows, .nth() will return NaN values for those groups. To address this, you might want to either filter these groups out or handle them differently. One approach to filtering such groups is to use the .filter() method to keep only the groups of a certain size:

filtered_df = grouped_df.filter(lambda x: len(x) >= 2)
updated_group = filtered_df.groupby('Category')
second_row_filtered = updated_group.nth(1)

This ensures every group has at least two rows, thus eliminating the possibility of NaN values when retrieving the second row of each group.

More Advanced Techniques

What if our requirements are more sophisticated? Say, we need the 2nd row from each group, but only if the ‘Value’ is above a certain number. This introduces conditional logic into our grouping and nth-row retrieval process, which necessitates a more manual approach.

def retrieve_conditional_nth_row(df, nth, condition_column, threshold):
    filtered_groups = []
    for name, group in df.groupby('Category'):
        if group[condition_column].iloc[nth] > threshold:
    return pd.concat(filtered_groups, axis=1).T

result = retrieve_conditional_nth_row(df, 1, 'Value', 3)

This method filters and retrieves the nth row based on a condition applied to a specific column. It provides flexibility but requires more code and manual handling.


Pandas provides several built-in methods, such as .first(), .last(), and .nth(), to retrieve specific rows from each group in a DataFrame. These tools can be utilized directly or customized for more complex scenarios, offering both convenience and flexibility in handling grouped data. Understanding and applying these techniques will enhance your data analysis and manipulation skills in Python.