pandas.concat() function in Python

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:

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)

    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:

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.

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)

    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.

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)

     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.

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)

            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.

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)

    B
0  B0
1  B1

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.