Python | Pandas Series.product()

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. Python3

# 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. Python3

# 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. Python3

# 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. Python3

# 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.