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Python | Pandas Series.median()

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

Syntax: Series.median(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 : median : scalar or Series (if level specified)

Example #1: Use Series.median() function to find the median 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, 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)


Output :

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




# return the median
result = sr.median()
  
# Print the result
print(result)


Output :

As we can see in the output, the Series.median() function has successfully returned the median of the given series object.
 
Example #2: Use Series.median() function to find the median of the underlying data in the given series object. The given series object contains some missing values.




# 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)


Output :

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




# return the median
result = sr.median(skipna = True)
  
# Print the result
print(result)


Output :

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

Shaida Kate Naidoo
am passionate about learning the latest technologies available to developers in either a Front End or Back End capacity. I enjoy creating applications that are well designed and responsive, in addition to being user friendly. I thrive in fast paced environments. With a diverse educational and work experience background, I excel at collaborating with teams both local and international. A versatile developer with interests in Software Development and Software Engineering. I consider myself to be adaptable and a self motivated learner. I am interested in new programming technologies, and continuous self improvement.
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