Pandas: Convert a single-row DataFrame to a Series (multiple ways)

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


Working with pandas, a popular data manipulation library in Python, often requires converting data between different formats for ease of processing and analysis. In this tutorial, we will walk through the step-by-step process of converting a single-row DataFrame into a Series object. This conversion is useful for simplifying data manipulation tasks, enabling direct access to values and improving performance for operations involving single data entities. We will cover basic to advanced conversion techniques, assisting you with easily applicable code examples.

Understanding Data Structures

Before diving into the conversion process, it’s crucial to understand the difference between a DataFrame and a Series in pandas. A DataFrame is a two-dimensional, size-mutable, and potentially heterogeneous tabular data structure with labeled axes (rows and columns). A Series, on the other hand, is a one-dimensional array-like object containing a sequence of values and an associated array of data labels, called its index. Turning a single-row DataFrame into a Series allows for more straightforward data access as a Series object will treat column names as index labels.

Basic Conversion

To start with, let’s convert a simple single-row DataFrame to a Series. This process can be achieved using the iloc method combined with the squeeze method.

import pandas as pd

# Creating a single-row DataFrame
df = pd.DataFrame({'A': [1], 'B': [2], 'C': [3]})

# Conversion to Series
series = df.iloc[0].squeeze()


   A  B  C
0  1  2  3
A    1
B    2
C    3
dtype: int64

The iloc[0] specifies that we are interested in the first (and only) row of the DataFrame, and squeeze converts the row into a Series.

Conversion with Custom Index

Next, let’s look at how to keep or change the index during the conversion. You might want to retain the original index of the DataFrame, especially if it holds meaningful data that you wish to preserve.

# Keeping the original index
df = pd.DataFrame({'A': [1], 'B': [2], 'C': [3]}, index=['X'])
series = df.iloc[0].squeeze()

Notice that in this instance, the original index label ‘X’ is not retained in the Series, because a Series index corresponds to column names in the context of this conversion. To include custom index values as part of the Series, you’ll need a different approach.

Advanced: Including DataFrame Index in Series

If retaining the DataFrame’s original index in the Series is crucial for your analysis, you can achieve this by manually constructing a Series and including the index as part of the data. Let’s explore this advanced technique.

df = pd.DataFrame({'Values': ['Data']}, index=['X'])
# Manual conversion to Series including index
data_with_index = {'index': df.index[0], 'value': df.iloc[0][0]}
series_with_index = pd.Series(data_with_index)


index     X
value     Data
dtype: object

This method manually integrates the index into the Series, allowing for highly customized Series objects that maintain critical DataFrame information.

Using to_frame() for Reversal

Conversely, it may also be beneficial to understand how to reverse the process, converting a Series back into a DataFrame. This is straightforward with the to_frame() method, providing flexibility in data structure manipulation.

series = pd.Series([1, 2, 3], index=['A', 'B', 'C'])
df = series.to_frame().T # Transpose to ensure a single-row DataFrame


   A  B  C
0  1  2  3

The T makes sure that the resultant DataFrame is in the desired single-row format.

Throughout this tutorial, we have seen different methods of converting a DataFrame to a Series. Choosing the right approach depends on your specific data manipulation goals, the importance of retaining indexes, and other factors unique to your dataset. Practice with these examples and incorporate them into your data analysis routines for more efficient coding.


Converting a single-row DataFrame to a Series in pandas effectively simplifies data handling, offering direct access to values while potentially enhancing performance for certain operations. Whether you’re keeping it simple or require a custom approach to retain index data, the techniques provided in this guide will help streamline your data analysis workflow.