How to Concatenate Rows in pandas: Simple Guide

To concatenate rows in pandas, use pd.concat() with a list of DataFrames and set axis=0. This stacks the rows vertically, combining data from multiple DataFrames into one.

Syntax

The basic syntax to concatenate rows in pandas is:

• pd.concat([df1, df2, ...], axis=0): Concatenates DataFrames vertically (row-wise).
• ignore_index=True: Resets the row index in the result.
• join='outer': Includes all columns from all DataFrames.

python

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

Example

This example shows how to concatenate two DataFrames row-wise using pd.concat(). The rows from both DataFrames are stacked, and the index is reset for a clean result.

python

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, ignore_index=True)
print(result)

Output

   A  B
0  1  3
1  2  4
2  5  7
3  6  8

Common Pitfalls

Common mistakes when concatenating rows include:

• Forgetting to set axis=0 (default is 0, but explicit is clearer).
• Not using ignore_index=True, which keeps original indexes and can cause duplicate index values.
• Concatenating DataFrames with different columns without specifying join, which may result in missing values.

python

import pandas as pd

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

# Wrong: missing axis and ignore_index
wrong = pd.concat([df1, df2])

# Right: specify axis and ignore_index
right = pd.concat([df1, df2], axis=0, ignore_index=True, join='outer')

print('Wrong concatenation:\n', wrong)
print('\nRight concatenation:\n', right)

Output

Wrong concatenation:
      A    B
0  1.0  NaN
1  2.0  NaN
0  NaN  3.0
1  NaN  4.0

Right concatenation:
      A    B
0  1.0  NaN
1  2.0  NaN
2  NaN  3.0
3  NaN  4.0

Quick Reference

Summary tips for concatenating rows in pandas:

• Use pd.concat() with axis=0 to stack rows.
• Set ignore_index=True to reset row indexes.
• Use join='outer' to keep all columns when DataFrames differ.
• Check for missing values if columns don't match.

Key Takeaways

Use pd.concat with axis=0 to concatenate rows vertically.

Set ignore_index=True to reset the row index after concatenation.

Use join='outer' to include all columns from all DataFrames.

Be careful with DataFrames having different columns to avoid missing data.

Always check the result index and columns after concatenation.