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Python | Pandas Series.product()
  • Last Updated : 11 Feb, 2019

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()
  
# 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)
  
# 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.

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