How to Use cumsum in pandas for Cumulative Sums

Use the cumsum() method in pandas on a Series or DataFrame to get the cumulative sum of values. It adds each value to the sum of all previous values along the specified axis.

Syntax

The cumsum() method calculates the cumulative sum of values in a pandas Series or DataFrame.

• axis=0: Sum down each column (default for DataFrame).
• axis=1: Sum across each row.
• skipna=True: Ignore missing values when summing.

python

Series.cumsum(axis=None, skipna=True, *args, **kwargs)
DataFrame.cumsum(axis=0, skipna=True, *args, **kwargs)

Example

This example shows how to use cumsum() on a pandas Series and DataFrame to get cumulative sums down columns and across rows.

python

import pandas as pd

# Series example
s = pd.Series([1, 2, 3, 4])
print('Series cumulative sum:')
print(s.cumsum())

# DataFrame example
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})
print('\nDataFrame cumulative sum down columns (axis=0):')
print(df.cumsum())

print('\nDataFrame cumulative sum across rows (axis=1):')
print(df.cumsum(axis=1))

Output

Series cumulative sum:
0     1
1     3
2     6
3    10
dtype: int64

DataFrame cumulative sum down columns (axis=0):
   A   B
0  1   4
1  3   9
2  6  15

DataFrame cumulative sum across rows (axis=1):
   A   B
0  1   5
1  2   7
2  3   9

Common Pitfalls

One common mistake is forgetting the axis parameter when using cumsum() on DataFrames, which defaults to summing down columns. Another is not handling missing values properly, which can affect the cumulative sum if skipna=False.

Also, cumsum() works element-wise and does not sort data first, so ensure your data is ordered if order matters.

python

import pandas as pd

# DataFrame with missing values

df = pd.DataFrame({
    'A': [1, None, 3],
    'B': [4, 5, None]
})

# Wrong: skipna=False includes NaN and results in NaN after first missing
print('Cumsum with skipna=False:')
print(df.cumsum(skipna=False))

# Right: skipna=True ignores NaN and continues sum
print('\nCumsum with skipna=True:')
print(df.cumsum(skipna=True))

Output

Cumsum with skipna=False:
     A    B
0  1.0  4.0
1  NaN  9.0
2  NaN  NaN

Cumsum with skipna=True:
     A    B
0  1.0  4.0
1  1.0  9.0
2  4.0  9.0

Quick Reference

Parameter	Description	Default
axis	Axis to sum along: 0 for columns, 1 for rows	0 (DataFrame), None (Series)
skipna	Whether to ignore NaN values during sum	True
*args, **kwargs	Additional arguments (rarely used)	None

Key Takeaways

Use pandas cumsum() to get cumulative sums of Series or DataFrame values.

Specify axis=0 to sum down columns and axis=1 to sum across rows in DataFrames.

Handle missing values with skipna=True to avoid NaNs breaking the cumulative sum.

Ensure your data is ordered correctly before applying cumsum() if order matters.