pandas.concat() function in Python (original) (raw)

Last Updated : 24 Jun, 2025

**pandas.concat() function concatenate two or more pandas objects like DataFrames or Series along a particular axis. It is especially useful when combining datasets either vertically (row-wise) or horizontally (column-wise). **Example:

Python `

import pandas as pd df1 = pd.DataFrame({'A': ['A0', 'A1'], 'B': ['B0', 'B1']}) df2 = pd.DataFrame({'A': ['A2', 'A3'], 'B': ['B2', 'B3']})

res = pd.concat([df1, df2]) print(res)

Output

A   B

0 A0 B0 1 A1 B1 0 A2 B2 1 A3 B3

**Explanation: pd.concat([df1, df2]) stacks DataFrames row-wise and keeps their original indices, causing duplicates.

Syntax

pandas.concat(objs, axis=0, join='outer', ignore_index=False, keys=None, ...)

**Parameters:

Parameter	Description
objs	A list or tuple of pandas objects (DataFrames or Series) to concatenate
axis	The axis along which to concatenate: 0 for rows (default), 1 for columns
join	How to handle indexes: 'outer' (default) or 'inner'
ignore_index	If True, the index will be reset in the result
keys	Create a hierarchical index using passed keys

**Returns: A new pandas object (typically a DataFrame) that is the result of concatenation.

Examples

**Example 1: In this, we are concatenating two DataFrames side by side (column-wise) using **axis=1.

Python `

import pandas as pd df1 = pd.DataFrame({'A': ['A0', 'A1']}) df2 = pd.DataFrame({'B': ['B0', 'B1']})

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

Output

A   B

0 A0 B0 1 A1 B1

**Explanation: pd.concat([df1, df2], axis=1) joins DataFrames column-wise, aligning rows by index. It places the columns of **df2 to the right of **df1, forming a wider DataFrame.

**Example 2: In this, we are concatenating two DataFrames vertically (row-wise) and resetting the index using **ignore_index=True.

Python `

import pandas as pd df1 = pd.DataFrame({'A': ['A0', 'A1']}) df2 = pd.DataFrame({'B': ['B0', 'B1']})

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

Output

 A    B

0 A0 NaN 1 A1 NaN 2 NaN B0 3 NaN B1

**Explanation: pd.concat([df1, df2], ignore_index=True) stacks the DataFrames row-wise and resets the index in the result. Since the columns don’t match, pandas fills missing values with **NaN.

**Example 3: In this, we are concatenating two DataFrames vertically and labeling each with a group name using keys.

Python `

import pandas as pd df1 = pd.DataFrame({'A': ['A0', 'A1']}) df2 = pd.DataFrame({'B': ['B0', 'B1']})

res = pd.concat([df1, df2], keys=['group1', 'group2']) print(res)

Output

        A    B

group1 0 A0 NaN 1 A1 NaN group2 0 NaN B0 1 NaN B1

**Explanation: pd.concat([df1, df2], keys=['group1', 'group2']) stacks DataFrames vertically and adds a hierarchical index. The keys label each block, making it easier to identify which DataFrame each row came from.

**Example 4: In this, we are concatenating two DataFrames but keeping only the common columns using **join='inner' and resetting index.

Python `

import pandas as pd df1 = pd.DataFrame({'A': ['A0'], 'B': ['B0']}) df2 = pd.DataFrame({'B': ['B1'], 'C': ['C1']})

res = pd.concat([df1, df2], join='inner', ignore_index=True) print(res)

**Explanation: pd.concat([df1, df2], join='inner', ignore_index=True) combines DataFrames row-wise, keeping only the common columns ('B' here). ignore_index=True resets the index in the final result.