Pandas Series: cumsum() function
Cumulative sum over a Pandas DataFrame or Series axis
The cumsum() function is used to get cumulative sum over a DataFrame or Series axis.
Returns a DataFrame or Series of the same size containing the cumulative sum.
Syntax:
Series.cumsum(self, axis=None, skipna=True, *args, **kwargs)
Parameters:
| Name | Description | Type/Default Value | Required / Optional |
|---|---|---|---|
| axis | The index or the name of the axis. 0 is equivalent to None or ‘index’. | {0 or ‘index’, 1 or ‘columns’} Default Value: 0 |
Required |
| skipna | Exclude NA/null values. If an entire row/column is NA, the result will be NA. | boolean Default Value: True |
Required |
| *args, **kwargs | Additional keywords have no effect but might be accepted for compatibility with NumPy. | Required |
Returns: scalar or Series
Example - Series:
Python-Pandas Code:
import numpy as np
import pandas as pd
s = pd.Series([3, np.nan, 4, -5, 0])
s
Output:
0 3.0 1 NaN 2 4.0 3 -5.0 4 0.0 dtype: float64
Example - By default, NA values are ignored:
Python-Pandas Code:
import numpy as np
import pandas as pd
s = pd.Series([3, np.nan, 4, -5, 0])
s.cumsum()
Output:
0 3.0 1 NaN 2 7.0 3 2.0 4 2.0 dtype: float64
Example - To include NA values in the operation, use skipna=False:
Python-Pandas Code:
import numpy as np
import pandas as pd
s = pd.Series([3, np.nan, 4, -5, 0])
s.cumsum(skipna=False)
Output:
0 3.0 1 NaN 2 NaN 3 NaN 4 NaN dtype: float64
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