• Post author:
  • Post category:Pandas
  • Post last modified:March 27, 2024
  • Reading time:12 mins read
You are currently viewing How to Merge Series into Pandas DataFrame

Let’s say you already have a pandas DataFrame with few columns and you would like to add/merge Series as columns into the existing DataFrame, this is certainly possible using pandas.Dataframe.merge() method.


Key Points –

  • Ensure that the indices or columns of the Series and DataFrame align correctly for a successful merge operation.
  • Select the appropriate merge method based on the merging scenario, such as merge or concat. Consider factors like index or column matching requirements.
  • Address potential conflicts arising from duplicate index or column names by using parameters like left_on, right_on, or suffixes in the merge function.
  • Familiarize yourself with different merging strategies, such as inner, outer, left, and right joins, using the how parameter in the merge function to control how the merge is performed.
  • Determine how missing values (NaN) should be handled in the merged result, using options like how='outer' in the merge function or appropriate handling mechanisms.

I will explain with the examples in this article. first, create a sample DataFrame and a few Series. You can also try by combining Multiple Series to create DataFrame.

import pandas as pd

technologies = ({
    'Fee' :[22000,25000,23000]
df = pd.DataFrame(technologies)

# Create Series
discount  = pd.Series([1000,2300,1000], name='Discount')

1. Merge Series into pandas DataFrame

To merge a Series into a Pandas DataFrame, you can use the concat() or merge() function. Now let’s say you wanted to merge by adding Series object discount to DataFrame df.

# Merge Series into DataFrame
df2=df.merge(discount,left_index=True, right_index=True)

# Using concat along axis=1
merged_df_concat = pd.concat([df, discount], axis=1)

You can choose the method that best suits your needs. If the merge is based on indices, as in this example, using merge() with left_index=True and right_index=True is common. Otherwise, concat() can be used along the appropriate axis. Yields below output. It merges the Series with DataFrame on index.

# Output:
   Courses    Fee  Discount
0    Spark  22000      1000
1  PySpark  25000      2300
2   Hadoop  23000      1000

This also works if your rows are in different order, but in this case you should have custom indexes. I will leave this to you to explore.

# Rename Series before Merge
             left_index=True, right_index=True)

This program renames the Series discount to Course_Discount using the rename method and then merges it into the DataFrame df based on their indices. The resulting DataFrame df2 will have the columns from both df and the renamed Series. This example yields the below output.

# Output:
   Courses    Fee  Course_Discount
0    Spark  22000             1000
1  PySpark  25000             2300
2   Hadoop  23000             1000

2. Using Series.to_frame() & DataFrame.merge() Methods

You can also create a DataFrame from Series using Series.to_frame() and use it with DataFrame to merge. Here’s an example of merging a Series into a DataFrame using the to_frame() method and merge() method.

# Merge by creating DataFrame from Series
df2=df.merge(discount.to_frame(), left_index=True, right_index=True)

In the below example, first converts the Series discount to a DataFrame using to_frame(), specifying the name of the new column as ‘Discount’. Then, it merges the original DataFrame df with the new DataFrame created from the Series using the merge() method based on their indices.

# Convert the Series to a DataFrame using to_frame()
discount_df = discount.to_frame(name='Discount')

# Merge the DataFrame with the Series DataFrame using merge()
df2 = df.merge(discount_df, left_index=True, right_index=True)

Yields the same output as in the first example.

Frequently Asked Questions on Merge Series into Pandas DataFrame

How do I merge a Series into a Pandas DataFrame?

To merge a Series into a Pandas DataFrame, you can use the merge() function or the concat() function. Ensure that the indices or columns align correctly for a successful merge.

What is the difference between merge() and concat() for merging a Series into a DataFrame?

merge() is more versatile and allows for more complex merging operations, while concat() is simpler and suitable for straightforward concatenation along a specified axis. Choose the method based on your specific requirements.

Can I merge on columns instead of indices?

You can merge on columns instead of indices using the merge() function in pandas. When merging on columns, you specify the common columns on which the merge operation should be based.

How do I handle duplicate index or column names during a merge?

Use parameters such as left_on, right_on, or suffixes in the merge() function to handle potential conflicts arising from duplicate index or column names.

How can I rename a Series before merging it into a DataFrame?

Use the rename() method on the Series before merging. For example, you can do series.rename('NewName') to change the name of the Series.


In this article, I have explained how to merge/add series objects to existing pandas DataFrame as columns by using merge() method. also covered by creating a DataFrame from series using to_frame() and using on merge() method.

Happy Learning


Leave a Reply