How to convert series to dataframe in pandas : Various Methods

Pandas is a python package that allows you to perform data analysis on large datasets. You can easily manipulate any dataset after converting it into dataframe. In this entire tutorial, you will learn how to convert series to dataframe in pandas.

What is a Series?

A Series is a one-dimensional object that acts like an array. You can think of it as a simple list but it has an index or label for each element present in the series. The syntax for creating a Series is below.

pandas.Series(array_elemets or list)

How to convert Series to Dataframe in Pandas?

Once you have got Series then you can easily convert it into Dataframe using various methods. In this section, you will know all the methods to convert series to df in pandas.

Method 1: Using the to_frame() function

The first method to convert series to df is the to_frame() function. Here you will use the dot operator on the series to convert it into dataframe.

The syntax for the function is the below

series.to_frame()

Complete Code

import pandas as pd
s = pd.Series([10,20,30,40],name="numbers")
print(s.to_frame())

Here name inside the Series() constructor is the name of the series.

Output

Method 2: Convert series to dataframe in pandas using the DataFrame() constructor

The second and most widely used method to convert series to dataframe in python is the use of pandas.DataFrame() constructor. Here you have to just pass the series as the argument with the columns name. The python interpreter will convert the series to datframe.

The syntax of the DataFrame() is below.

pandas.DataFrame(data=None, index=None, columns=None, dtype=None, copy=None)

Here data is the series for our example.

Complete Code

import pandas as pd
s = pd.Series([10,20,30,40],name="numbers")
df = pd.DataFrame(s)
print(df)

Output

Conclusion

The pandas package is a powerful tool for performing data analysis on large datasets. This tutorial has shown how to convert series in pandas using several different methods. I hope you have easily understood it.

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