How to Use sum() in pandas: Syntax, Examples, and Tips

Use the sum() function in pandas to add values across rows or columns in a DataFrame or Series. You can specify the axis to sum over with axis=0 for columns or axis=1 for rows. It works on numeric data and can skip missing values by default.

Syntax

The basic syntax of sum() in pandas is:

• DataFrame.sum(axis=None, skipna=True, numeric_only=None)
• Series.sum(skipna=True, numeric_only=None)

Explanation:

• axis: Choose 0 to sum each column, 1 to sum each row.
• skipna: If True, ignore missing values (NaN).
• numeric_only: If True, sum only numeric data.

python

DataFrame.sum(axis=None, skipna=True, numeric_only=None)
Series.sum(skipna=True, numeric_only=None)

Example

This example shows how to sum columns and rows in a pandas DataFrame.

python

import pandas as pd

data = {
    'A': [1, 2, 3],
    'B': [4, 5, None],
    'C': [7, 8, 9]
}
df = pd.DataFrame(data)

# Sum each column (axis=0)
column_sum = df.sum(axis=0)

# Sum each row (axis=1)
row_sum = df.sum(axis=1)

print('Sum of each column:')
print(column_sum)
print('\nSum of each row:')
print(row_sum)

Output

Sum of each column:
A     6.0
B     9.0
C    24.0
dtype: float64

Sum of each row:
0    12.0
1    15.0
2    12.0
dtype: float64

Common Pitfalls

Common mistakes when using sum() in pandas include:

• Not specifying axis and getting unexpected results.
• Summing columns with non-numeric data causing errors or unexpected output.
• Not handling NaN values properly, which can affect sums.

Example of a common mistake and fix:

python

# Wrong: Summing without axis on DataFrame returns sum of each column as a Series
import pandas as pd

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

# This sums each column in the DataFrame and returns a Series
total_sum_wrong = df.sum()

# Correct: Specify axis=0 to sum columns or axis=1 to sum rows
sum_columns = df.sum(axis=0)
sum_rows = df.sum(axis=1)

print('Sum without axis (wrong):')
print(total_sum_wrong)
print('\nSum of columns (correct):')
print(sum_columns)
print('\nSum of rows (correct):')
print(sum_rows)

Output

Sum without axis (wrong):
A    3
B    7
dtype: int64

Sum of columns (correct):
A    3
B    7
dtype: int64

Sum of rows (correct):
0    4
1    6
dtype: int64

Quick Reference

Summary tips for using sum() in pandas:

• Use axis=0 to sum down columns.
• Use axis=1 to sum across rows.
• Missing values (NaN) are ignored by default.
• Works best with numeric data; use numeric_only=True if needed.

Key Takeaways

Use pandas sum() to add values across rows or columns in DataFrames or Series.

Specify axis=0 to sum columns and axis=1 to sum rows.

Missing values are ignored by default, so sums skip NaN automatically.

Ensure data is numeric or use numeric_only=True to avoid errors.

Always check the axis parameter to avoid unexpected sum results.