Pandas Merge DataFrames on Index

To merge Pandas DataFrames by index use pandas.merge(), pandas.concat(), and DataFrame.join() methods. All these methods are very similar but join() is considered a more efficient way to join indices. pandas.concat() method to concatenate two DataFrames by setting axis=1. merge() is considered the most efficient to combine DataFrames on multiple columns.

Quick Examples of Pandas Merging DataFrames by Index

If you are in a hurry, below are some quick examples of merging two pandas DataFrames by index.


# Quick examples of pandas merging DataFrames by index

# Merge two DataFrames by index 
# Using pandas.merge()
df2 = pd.merge(df, df1, left_index=True, right_index=True)

# Join two Dataframes
df2 = df.join(df1)

# Join two DataFrames with concat
df2 = pd.concat([df, df1], axis=1)

Now, let’s create a DataFrame with a few rows and columns, execute these examples, and validate the results. The first DataFrame contains column names Courses, Fee and the second DataFrame contains column names Duration, Discount. I will be merging these two DataFrames into a single one by combining columns from both.


import pandas as pd
df = pd.DataFrame({'Courses':["Spark","PySpark","Python","pandas"],
                      'Fee' :[20000,25000,22000,24000]},
                     index=['r1','r2','r3','r4'])
  
df1 = pd.DataFrame({'Duration':['30day','40days','35days','60days','55days'],
                      'Discount':[1000,2300,2500,2000,3000]}, 
                     index=['r1','r2','r3','r5','r6'])
 
print("First DataFrame:\n", df)
print("Second DataFrame:\n", df1)

Yields below output.

Merge DataFrames on Index Using pandas.merge()

You can use pandas.merge() function to merge DataFrames by matching their index. When merging two DataFrames on the index, the value of left_index and right_index parameters of merge() function should be True. By default, the pd.merge() is a column-wise inner join. Let’s see with an example.


# Merge two DataFrames on index using pandas.merge()
df2 = pd.merge(df, df1, left_index=True, right_index=True)
print("After merging the DataFrames:"df2)

Yields below output.

This merges two DataFrames only when indexes are matching.

Pandas Join DataFrames on Index

Alternatively, you can use DataFrame.join() method to join the two DataFrames based on indexes, and by default, the join is a column-wise left join. It always uses the right DataFrame index, but you can mention the key for the left DataFrame. You can specify the join types for join() function same as we mentioned for merge(). You can use this syntax, DataFrame.join(DataFrame1).


# Join two DataFrames
df2 = df.join(df1)
print("After merging the DataFrames:"df2)

Yields below output. Since by default it is left join, you get all rows from the left side and NaN for columns on the right side for non-matching indexes.


# Output:
After merging the DataFrames:
    Courses    Fee Duration  Discount
r1    Spark  20000    30day    1000.0
r2  PySpark  25000   40days    2300.0
r3   Python  22000   35days    2500.0
r4   pandas  24000      NaN       NaN

Use pandas.concat() to Merge Two DataFrames by Index

You can concatenate two DataFrames by using pandas.concat() method by setting axis=1, and by default, pd.concat is a row-wise outer join. For instance, you can use this syntax, pandas.concat([DataFrame,DataFrame1],axis=1).


# Join two DataFrames with concat
df2 = pd.concat([df, df1], axis=1)
print("After merging the DataFrames:"df2)

Yields below output. Since this is outer join by default, it returns all rows from both sides but contains Nan for columns on non-matching rows (index)


# Output:
    Courses      Fee Duration  Discount
r1    Spark  20000.0    30day    1000.0
r2  PySpark  25000.0   40days    2300.0
r3   Python  22000.0   35days    2500.0
r4   pandas  24000.0      NaN       NaN
r5      NaN      NaN   60days    2000.0
r6      NaN      NaN   55days    3000.0

Complete Example For Merge DataFrames by Index


import pandas as pd
df = pd.DataFrame({'Courses':["Spark","PySpark","Python","pandas"],
                      'Fee' :[20000,25000,22000,24000]},
                     index=['r1','r2','r3','r4'])
  
df1 = pd.DataFrame({'Duration':['30day','40days','35days','60days','55days'],
                      'Discount':[1000,2300,2500,2000,3000]}, 
                     index=['r1','r2','r3','r5','r6'])
 
print(df)
print(df1)

# Join two DataFrames
df2 = df.join(df1)
print(df2)

# Merge two DataFrames by index 
# Using pandas.merge()
df2 = pd.merge(df, df1, left_index=True, right_index=True)
print(df2)

# Join two DataFrames with concat
df2 = pd.concat([df, df1], axis=1)
print(df2)

Frequently Asked Questions on Pandas Merge DataFrames on Index

What does it mean to merge DataFrames on the index?

Merging on the index involves combining two or more DataFrames using their row labels (index) as the key for the operation. Instead of using columns for the join, the index acts as the matching reference.

How do you merge DataFrames on their index?

You can use the pd.merge() function or the join() method. Both allow merging on the index.

What happens when there are duplicate indices?

If the DataFrames have duplicate indices, the resulting merged DataFrame will have all possible combinations of the duplicate rows.

How can I merge on both the index and columns?

To merge on both the index and columns in Pandas, you can use the pd.merge() function with a combination of left_index=True, right_index=True, and the on parameter for columns.

Can I merge more than two DataFrames on their index?

You can merge more than two DataFrames on their index using the pd.concat() function. This function allows you to concatenate multiple DataFrames along a specified axis (rows or columns), using the index as the default key for alignment.

Conclusion

In this article, I have explained how to merge two pandas DataFrames by index by using Pandas.merge(), Pandas.concat() and DataFrame.join() methods with examples.

Happy Learning!!

References

https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html