Pandas - Merge two dataframes with different columns

Last Updated : 23 Jul, 2025

Let's understand how to merge two dataframes with different columns. In Pandas, you can merge two DataFrames with different columns using concat(), merge() and join().

Merging Two DataFrames with Different Columns - using concat()

concat() method is ideal for combining multiple DataFrames vertically (adding rows) or horizontally (adding columns) without requiring a key column or index.

Row-wise Concatenation

If two DataFrames have different columns, missing values will be filled with NaN.

Python 
# DataFrame 1
import pandas as pd

details = {
    'Name': ['Sravan', 'Sai', 'Mohan', 'Ishitha'],
    'College': ['Vignan', 'Vignan', 'Vignan', 'Vignan'],
    'Physics': [99, 76, 71, 93],
    'Chemistry': [97, 67, 65, 89],
    'Data Science': [93, 65, 65, 85]
}

df = pd.DataFrame(details)
print("DataFrame1:")
display(df)

# DataFrame 2
details1 = {
    'Name': ['Harsha', 'Saiteja', 'Abhilash', 'Harini'],
    'College': ['VVIT', 'VVIT', 'VVIT', 'VVIT'],
    'Physics': [69, 76, 51, 43],
    'Chemistry': [67, 67, 55, 89],
    'Maths': [73, 65, 61, 85]
}

df1 = pd.DataFrame(details1)
print("DataFrame2:")
display(df1)

# Column-wise concatenation
result = pd.concat([df, df1], axis=1, ignore_index=False)
print('Merged Table using concat()')

Output:

Dataframe
DataFrame 1
Dataframe
DataFrame 2
Row-wise-concatenation
Row-wise Concatenation using concat()

Column-wise Concatenation

This aligns rows by index, filling missing values with NaN.

Python 
# Column-wise concatenation
result = pd.concat([df, df1], axis=1, ignore_index=False)
display(result)

Output:

Column-wise-concatenation-using-concat
Column-wise concatenation using concat()

Join two DataFrames with Different Columns Using merge()

The merge() method is useful when you want to combine DataFrames based on a key column or custom matching rules.

Outer Merge on Name

Combines all rows from both DataFrames, filling missing values with NaN.

Python 
# Merging based on 'Name' with an outer join
result = pd.merge(df, df1, on='Name', how='outer')
print(result)

Output:

Outer-Merge
Merging Pandas Column using merge()

Inner Merge on Name

Returns rows where both DataFrames have matching values in the key column.

Python 
# Merging based on 'Name' with an inner join
result = pd.merge(df, df1, on='Name', how='inner')
print(result)

Output: 

Empty DataFrame
Columns: [Name, College_x, Physics_x, Chemistry_x, Data Science, College_y, Physics_y, Chemistry_y, Maths]
Index: []

Merge Pandas DataFrame with different columns using join()

The join() method aligns DataFrames based on their index and is useful for side-by-side alignment.

Python 
# DataFrame 1
import pandas as pd
details = {
    'Name': ['Sravan', 'Sai', 'Mohan', 'Ishitha'],
    'College': ['Vignan', 'Vignan', 'Vignan', 'Vignan'],
    'Physics': [99, 76, 71, 93],
    'Chemistry': [97, 67, 65, 89],
    'Data Science': [93, 65, 65, 85]
}
df = pd.DataFrame(details)
# DataFrame 2
details1 = {
    'Name': ['Harsha', 'Saiteja', 'Abhilash', 'Harini'],
    'College': ['VVIT', 'VVIT', 'VVIT', 'VVIT'],
    'Physics': [69, 76, 51, 43],
    'Chemistry': [67, 67, 55, 89],
    'Maths': [73, 65, 61, 85]
}

df1 = pd.DataFrame(details1)

# Setting the index for join
df.set_index('Name', inplace=True)
df1.set_index('Name', inplace=True)

# Joining DataFrames with suffixes for overlapping columns
result = df.join(df1, how='outer', lsuffix='_df1', rsuffix='_df2')
print(result)

Output:

Joined-DataFrame

To choose the method that fits your requirement, you can refer to this table:

MethodUse Case
concat()Combine DataFrames vertically or horizontally without a key column.
merge()Combine DataFrames based on common key columns, supporting SQL-like join operations.
join()Align DataFrames side-by-side based on their index.
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