The Series.product()
function in pandas computes the product of the elements in the series.
Series.product(axis=None, skipna=True, level=None, numeric_only=None, min_count=0)
axis
: This indicates the axis in which the operation is to be performed. The value 0
and 1
indicates rows and columns, respectively. The default is 0
.skipna
: This is used to eliminate NAN or null values when calculating the result. The default is True
.level
(optional): The default value is None
. It represents the level to be broadcasted(in the case of multilevel).numeric_only
: Only float, int, and boolean columns are allowed. If there is None, it will try to use everything. Otherwise, it will just use numerical data. Not used during the Series.min_count
: This represents the minimum number of valid values needed to complete the operation. The default is 0
.The following code will demonstrate how to use the Series.product()
function in pandas:
import pandas as pd import numpy as np # create Series my_series = pd.Series([17, np.nan, 28, 15, 23, 7, np.nan]) # compute the product of the series # using product() print(my_series.product(skipna=True))
In the code above:
Line 5: We create the Series, my_series
.
Line 9: We use the product()
function to return the product of the Series elements.
Note: The NaN values are dropped by setting the
skipna
argument toTrue
.
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