Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index.
Pandas Series.product() function returns the product of the underlying data in the given Series object.
Syntax: Series.product(axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs)
Parameter :
axis : Axis for the function to be applied on.
skipna : Exclude NA/null values when computing the result.
level : If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar.
numeric_only : Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.
min_count : The required number of valid values to perform the operation.
**kwargs : Additional keyword arguments to be passed to the function.Returns : prod : scalar or Series (if level specified)
Example #1: Use Series.product() function to find the product of the underlying data in the given Series object.
# importing pandas as pd import pandas as pd   # Creating the Series sr = pd.Series([10, 25, 3, 11, 24, 6])   # Create the Index index_ = ['Coca Cola', 'Sprite', 'Coke', 'Fanta', 'Dew', 'ThumbsUp']   # set the index sr.index = index_   # Print the series print(sr) |
Output :
Now we will use Series.product() function to find the product of the elements in the given series object.
# return the product of all elements result = sr.product() Â Â # Print the result print(result) |
Output :
As we can see in the output, the Series.product() function has successfully returned the product of the underlying data in the given series object.
Example #2 : Use Series.product() function to find the product of the underlying data in the given Series object. The given series object contains some missing values in it.
# importing pandas as pd import pandas as pd   # Creating the Series sr = pd.Series([19.5, 16.8, None, 22.78, None, 20.124, None, 18.1002, None])   # Print the series print(sr) |
Output :
Now we will use Series.product() function to find the product of the elements in the given series object. We are going to skip the missing values.
# return the product of all elements result = sr.product(skipna = True) Â Â # Print the result print(result) |
Output :
As we can see in the output, the Series.product() function has successfully returned the product of the underlying data in the given series object.
