Convert Pandas Series to DataFrame (3 Methods with Examples)

The Pandas library is an essential tool when working with data analysis and manipulation in Python. Pandas provide data storage in 2 dimensional and 1 dimensional forms. The one-dimensional form of data is called a Series and the two-dimensional form consisting of rows and columns is called a DataFrame. One common task is converting Pandas Series to DataFrame, which we will learn in this article.

Revisiting Pandas Series & DataFrame

Pandas DataFrames are a two-dimensional array with labelled data structures having different column types. It is a convenient way to work with structured data in Python. DataFrames are a standard and convenient way to store data in a tabular format, with rows to store the information and columns to name the information.

Let us see an example of a DataFrame:

import pandas as pd

data = {
    "Name": ["John", "Emma", "Michael"],
    "Age": [45, 30, 55],
    "City": ["New York", "London", "Paris"]
}
df = pd.DataFrame(data)

# Display the DataFrame
print('DataFrame:\n', df)

Output:

DataFrame:
       Name  Age      City
0     John   45  New York
1     Emma   30    London
2  Michael   55     Paris

A Pandas Series is one-dimensional, similar to an array, that can hold any data type. It consists of a sequence of values that have associated labels, known as the index. The index can be of any data type, such as integers, strings, or dates.

Let’s see how we can create a series from list in Python.

import pandas as pd

# Creating a series from a list
data = [10, 20, 30, 40, 50]
series = pd.Series(data)

# print the series
print(series)

Output:

Now that we have a basic understanding of a Series and a DataFrame, let us do the conversion

3 Methods to Convert Pandas Series to DataFrame

Here are 3 methods to convert a Pandas Series to DataFrame with implementation in Python:

1) Using the reset_index Method

One of the easiest ways to convert a Pandas Series to a DataFrame is by using the reset_index method. This method promotes the index of the Series to a column in the resulting DataFrame.

Simply put, this method adds an extra column as the index of the series and converts it into a DataFrame. Let us learn how to do it with an example:

import pandas as pd

# Create a Series
s = pd.Series([1, 2, 3], index=['a', 'b', 'c']).rename_axis('A')

# Convert the Series to a DataFrame
df = s.reset_index(name='B')
print(df)

Output:

Please note: by default, the name is set to ‘0’. However, we can use the ‘name’ parameter to specify the name of the newly created column.

2) Using the to_frame Method

We can also use the to_frame method provided in the Pandas library of Python.

This method simply creates a DataFrame with the Series values as a single column. Unlike the reset_index method, the index does not get promoted to a column in this case.

Let us take an example in Python:

import pandas as pd

# Create a Series
s = pd.Series([1, 2, 3, 4], name='A')

# Convert the Series to a DataFrame
df = s.to_frame()

# Display the DataFrame
print(df)

Output:

3) Using the pd.DataFrame Constructor

Pandas library in Python also provides a default constructor to make DataFrames. We use pd.DataFrame() to create DataFrames from lists, dictionaries or series. This method allows us to specify both the values and column names explicitly.

By passing the Series object as the first argument and providing the column names as a list, we can create a DataFrame with the Series values assigned to the specified column.

Let us try an example:

import pandas as pd

# Create a Series
s = pd.Series([1, 2, 3, 4], name='A')

# Convert the Series to a DataFrame
df = pd.DataFrame(s, columns=['A'])

# Display the DataFrame
print(df)

Output:

You can also check out the ways to convert list to dataframe.

Conclusion

In this article, we discussed the various techniques we can use to convert Series to DataFrame. The ability to convert between Series and DataFrames is very helpful in data manipulation and analysis tasks. Whether you’re working with small datasets or large-scale projects, mastering these conversion techniques can greatly improve your data-handling capabilities in Python.