append equivalent with concat in Pandas

Introduction

We use concat to join two or more tables (DataFrames) together. It helps combine data easily.

You want to add rows from one table to another.

You have multiple data files and want to combine them into one table.

You want to join data vertically or horizontally.

You want a faster or more flexible way than <code>append</code> to combine tables.

Syntax

Pandas

pd.concat([df1, df2], axis=0, ignore_index=False)

df1 and df2 are the tables (DataFrames) you want to join.

axis=0 means join rows (one below the other). Use axis=1 to join columns side by side.

Examples

Joins two DataFrames by rows (default axis=0).

Pandas

pd.concat([df1, df2])

Joins two DataFrames by columns (side by side).

Pandas

pd.concat([df1, df2], axis=1)

Joins rows and resets the row numbers to be continuous.

Pandas

pd.concat([df1, df2], ignore_index=True)

Sample Program

This code creates two tables with names and ages. Then it joins them using concat to get one bigger table with all rows.

Pandas

import pandas as pd

# Create two small DataFrames with same columns
df1 = pd.DataFrame({'Name': ['Alice', 'Bob'], 'Age': [25, 30]})
df2 = pd.DataFrame({'Name': ['Charlie', 'David'], 'Age': [35, 40]})

# Use concat to join them like append
combined = pd.concat([df1, df2], ignore_index=True)

print(combined)

Important Notes

concat is more powerful and flexible than append.

Use ignore_index=True to reset row numbers after joining.

Remember to pass a list of DataFrames inside concat.

Summary

concat joins tables by rows or columns.

It replaces append for adding rows.

Use ignore_index=True to reset row numbers.