Pandas Merge DataFrames on Index

To merge 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 on indices. pandas.concat() method to concatenate two DataFrames by setting axis=1. merge() is considered most efficient to combine on columns

In this article, I will explain how to merge two pandas DataFrames by index using merge(), concat() and join() methods with examples.

1. Quick Examples of Pandas Merge DataFrames by Index

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


# Below are some quick examples

# 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 results. First DataFrame contains column names Courses, Fee and 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(df)
print(df1)

Yields below output.


    Courses    Fee
r1    Spark  20000
r2  PySpark  25000
r3   Python  22000
r4   pandas  24000

   Duration  Discount
r1    30day      1000
r2   40days      2300
r3   35days      2500
r5   60days      2000
r6   55days      3000

3. Merge DataFrames by Index Using pandas.merge()

You can use pandas.merge() 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. and by default, the pd.merge() is a column-wise inner join. Let’s see with an example.


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

Yields below output.


    Courses    Fee Duration  Discount
r1    Spark  20000    30day      1000
r2  PySpark  25000   40days      2300
r3   Python  22000   35days      2500

This merges two DataFrames only when indexes are matching.

2. Use DataFrame.join() to Merge DataFrames by Index

DataFrame.join() method is also used 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,s index, but you can mention the key for left DataFrame. You can specify the join types for join() function same as we mention for merge(). You can use this syntax, DataFrame.join(DataFrame1).


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


    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

4. 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(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)


    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

5. 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)

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!!

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