Why combining DataFrames matters in Pandas

Introduction

Combining DataFrames helps you join different pieces of data together. This lets you see the full picture and find useful insights.

You have sales data from different months and want to see the whole year.

You want to add customer details to their purchase records.

You need to merge survey answers with demographic info.

You want to stack daily logs into one big file for analysis.

You want to compare two sets of data side by side.

Syntax

Pandas

pd.concat([df1, df2], axis=0)
pd.merge(df1, df2, on='key')

pd.concat stacks DataFrames either vertically (rows) or horizontally (columns).

pd.merge joins DataFrames based on matching column values, like a database join.

Examples

This stacks two DataFrames vertically, adding rows from df2 below df1.

Pandas

import pandas as pd

df1 = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df2 = pd.DataFrame({'A': [5, 6], 'B': [7, 8]})

result = pd.concat([df1, df2], axis=0)
print(result)

This merges two DataFrames side by side where the 'key' column matches.

Pandas

import pandas as pd

df1 = pd.DataFrame({'key': ['K0', 'K1'], 'A': [1, 2]})
df2 = pd.DataFrame({'key': ['K0', 'K1'], 'B': [3, 4]})

result = pd.merge(df1, df2, on='key')
print(result)

Sample Program

This example stacks January and February sales data into one DataFrame to see total sales over two months.

Pandas

import pandas as pd

# Sales data for January
jan_sales = pd.DataFrame({
    'Product': ['Apple', 'Banana'],
    'Sales': [100, 150]
})

# Sales data for February
feb_sales = pd.DataFrame({
    'Product': ['Apple', 'Banana'],
    'Sales': [120, 130]
})

# Combine sales data for both months
all_sales = pd.concat([jan_sales, feb_sales], axis=0, ignore_index=True)

print(all_sales)

Important Notes

Use ignore_index=True in pd.concat to reset row numbers after combining.

When merging, make sure the key columns have matching names and data types.

Combining data helps avoid working with many small files separately.

Summary

Combining DataFrames lets you join or stack data to get a complete view.

Use pd.concat to stack rows or columns.

Use pd.merge to join DataFrames based on matching columns.