Merging, Joining, and Concatenating DataFrames

When working with large datasets in Python, we often need to combine multiple DataFrames. Pandas provides powerful functions for merging, joining, and concatenating datasets efficiently. In this tutorial, we will explore these techniques with practical examples.

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1. Concatenating DataFrames

Concatenation in Pandas is used to stack DataFrames either vertically (along rows) or horizontally (along columns).

Concatenating Along Rows (axis=0)

import pandas as pd

data1 = pd.DataFrame({'ID': [1, 2], 'Name': ['Amit', 'Priya']})
data2 = pd.DataFrame({'ID': [3, 4], 'Name': ['Rahul', 'Sneha']})

result = pd.concat([data1, data2], ignore_index=True)
print(result)

Concatenating Along Columns (axis=1)

data3 = pd.DataFrame({'Age': [25, 30]})
merged_data = pd.concat([data1, data3], axis=1)
print(merged_data)

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2. Merging DataFrames

Merging is similar to SQL joins and allows combining DataFrames based on common columns.

Inner Join (Default Merge)

dataA = pd.DataFrame({'ID': [1, 2, 3], 'Name': ['Amit', 'Priya', 'Rahul']})
dataB = pd.DataFrame({'ID': [2, 3, 4], 'Salary': [50000, 60000, 70000]})

merged_data = pd.merge(dataA, dataB, on='ID')
print(merged_data)

Left Join

left_join = pd.merge(dataA, dataB, on='ID', how='left')
print(left_join)

Right Join

right_join = pd.merge(dataA, dataB, on='ID', how='right')
print(right_join)

Outer Join (Full Join)

outer_join = pd.merge(dataA, dataB, on='ID', how='outer')
print(outer_join)

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3. Joining DataFrames

Pandas join() is used when combining DataFrames based on index.

df1 = pd.DataFrame({'Salary': [50000, 60000]}, index=['Amit', 'Priya'])
df2 = pd.DataFrame({'Age': [25, 30]}, index=['Amit', 'Priya'])

joined_df = df1.join(df2)
print(joined_df)

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Summary

• Concatenation: Used for stacking DataFrames vertically or horizontally.
• Merging: Similar to SQL joins, merging combines DataFrames based on a key column.
• Joining: Works on index-based merging.