Python | Pandas Series.mean()

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.mean() function return the mean of the underlying data in the given Series object.

Syntax: Series.mean(axis=None, skipna=None, level=None, numeric_only=None, **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.
**kwargs : Additional keyword arguments to be passed to the function.

Returns : mean : scalar or Series (if level specified)

Example #1: Use Series.mean() function to find the mean of the underlying data in the given series object.

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# importing pandas as pd
import pandas as pd
  
# Creating the Series
sr = pd.Series([10, 25, 3, 25, 24, 6])
  
# Create the Index
index_ = ['Coca Cola', 'Sprite', 'Coke', 'Fanta', 'Dew', 'ThumbsUp']
  
# set the index
sr.index = index_
  
# Print the series
print(sr)

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Output : 




Now we will use Series.mean() function to find the mean of the given series object.













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# return the mean 


result = sr.mean() 


  


# Print the result 


print(result) 



















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Output : 



As we can see in the output, the Series.mean() function has successfully returned the mean of the given series object.

 

Example #2:  Use Series.mean() function to find the mean of the underlying data in the given series object. The given series object also contains some missing values.











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# importing pandas as pd 


import pandas as pd 


  


# Creating the Series 


sr = pd.Series([19.5, 16.8, None, 22.78, 16.8, 20.124, None, 18.1002, 19.5]) 


  


# Print the series 


print(sr) 



















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Output : 




Now we will use Series.mean() function to find the mean of the given series object. we are going to skip all the missing values while calculating the mean.













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# return the mean 


# skip all the missing values 


result = sr.mean(skipna = True) 


  


# Print the result 


print(result) 



















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Output : 



As we can see in the output, the Series.mean() function has successfully returned the mean of the given series object.



          
          
          
          

            

    

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