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Python | Pandas Series.count()

  • Last Updated : 15 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.count() function return the count of non-NA/null observations in the given Series object.

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Syntax: Series.count(level=None)



Parameter :
level : If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a smaller Series

Returns : nobs : int or Series (if level specified)

Example #1: Use Series.count() function to find the count of non-missing values in the given series object.




# importing pandas as pd
import pandas as pd
  
# Creating the Series
sr = pd.Series([80, 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.count() function to find the count of non-missing values in the given series object.




# find the count of non-missing values
# in the given series object
result = sr.count()
  
# Print the result
print(result)

Output :

As we can see in the output, the Series.count() function has successfully returned the count of non-missing values in the given series object.
 
Example #2 : Use Series.count() function to find the count of non-missing values 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([100, None, None, 18, 65, None, 32, 10, 5, 24, None])
  
# Create the Index
index_ = pd.date_range('2010-10-09', periods = 11, freq ='M')
  
# set the index
sr.index = index_
  
# Print the series
print(sr)

Output :

Now we will use Series.count() function to find the count of non-missing values in the given series object.




# find the count of non-missing values
# in the given series object
result = sr.count()
  
# Print the result
print(result)

Output :

As we can see in the output, the Series.count() function has successfully returned the count of non-missing values in the given series object.




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