In this tutorial, we will look at how to convert a pandas series to a dataframe.
How to convert a pandas series to a dataframe?
There are a number of ways to get a dataframe from a series. You can use the
to_frame() function, the
reset_index() function, or simply create a new dataframe using the values and index of the pandas series. The following is the syntax to use the above functions:
# using to_frame() df = s.to_frame() # using reset_index() df = s.reset_index()
Here, s is the pandas series you want to convert. Both
reset_index() return different dataframes. The
to_frame() function retains the index of the series as the index of the dataframe whereas the
reset_index() function creates a dataframe with the index of the series as a separate column. (See the examples below)
Let’s look at some examples of using the above methods to create a dataframe from a series. First, we’ll create a sample pandas series which we will be using throughout this tutorial.
import pandas as pd # pandas series Wimbledon winners from 2015 to 2019 wimbledon_winners = pd.Series(index=[2015, 2016, 2017, 2018, 2019], data=['Novak Djokovic', 'Andy Murray', 'Roger Federer', 'Novak Djokovic', 'Novak Djokovic'], name='Name') # display the series print(wimbledon_winners)
2015 Novak Djokovic 2016 Andy Murray 2017 Roger Federer 2018 Novak Djokovic 2019 Novak Djokovic Name: Name, dtype: object
You can see the contents of the series object above. Let’s confirm the type of the object.
# check the type print(type(wimbledon_winners))
We now have a pandas series containing the name of Wimbledon Winners from 2015 to 2019 with the year as its index.
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1. Series to DataFrame using
First, let’s see the usage of the
to_frame() function to get a pandas dataframe from a series.
df = wimbledon_winners.to_frame() # check the type print(type(df))
Let’s check how the dataframe looks.
Notice that when using the
to_frame() function, the resulting dataframe retains the index as was present in the series.
For more on the
to_frame() function, refer to its documentation.
2. Series to DataFrame using
reset_index() function is generally used to reset the index of a dataframe. But, you can also use it to get a dataframe from a pandas series.
df = wimbledon_winners.reset_index() # check the type print(type(df))
Let’s check how the dataframe looks.
You can see that the resulting dataframe has two columns, one for the series values and the other for the original index of the series. You can also separately set any column as your dataframe index using the
df = wimbledon_winners.reset_index().set_index('index') print(type(df))
Let’s see how the dataframe looks.
With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having pandas version 1.0.5
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