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.
import pandas as pd
sr = pd.Series([ 10 , 25 , 3 , 25 , 24 , 6 ])
index_ = [ 'Coca Cola' , 'Sprite' , 'Coke' , 'Fanta' , 'Dew' , 'ThumbsUp' ]
sr.index = index_
print (sr)
|
Output :
Now we will use Series.mean()
function to find the mean of the given series object.
result = sr.mean()
print (result)
|
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.
import pandas as pd
sr = pd.Series([ 19.5 , 16.8 , None , 22.78 , 16.8 , 20.124 , None , 18.1002 , 19.5 ])
print (sr)
|
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.
result = sr.mean(skipna = True )
print (result)
|
Output :
As we can see in the output, the Series.mean()
function has successfully returned the mean of the given series object.
Last Updated :
11 Feb, 2019
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