Pandas: Series - prod() function
Product of the values for the requested Pandas axis
The prod() function is used to get the product of the values for the requested axis.
Syntax:
Series.prod(self, axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs)
Parameters:
| Name | Description | Type/Default Value | Required / Optional |
|---|---|---|---|
| axis | Axis for the function to be applied on. | {index (0)} | Required |
| skipna | Exclude NA/null values when computing the result. | bool Default Value: True |
Required |
| level | If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar. | int or level name Default Value: None |
Required |
| numeric_only | Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series. | bool Default Value: None |
Required |
| min_count | The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. New in version 0.22.0: Added with the default being 0. This means the sum of an all-NA or empty Series is 0, and the product of an all-NA or empty Series is 1. |
int Default Value: 0 |
Required |
| **kwargs | Additional keyword arguments to be passed to the function. | Required |
Returns: scalar or Series (if level specified)
Example - By default, the product of an empty or all-NA Series is 1:
Python-Pandas Code:
import numpy as np
import pandas as pd
pd.Series([]).prod()
Output:
1.0
Example - This can be controlled with the min_count parameter:
Python-Pandas Code:
import numpy as np
import pandas as pd
pd.Series([]).prod(min_count=1)
Output:
nan
Example - skipna parameter, min_count handles all-NA and empty series identically:
Python-Pandas Code:
import numpy as np
import pandas as pd
pd.Series([np.nan]).prod()
Output:
1.0
Python-Pandas Code:
import numpy as np
import pandas as pd
pd.Series([np.nan]).prod(min_count=1)
Output:
nan
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