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.
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.
import pandas as pd
sr = pd.Series([ 80 , 25 , 3 , 25 , 24 , 6 ])
index_ = [ 'Coca Cola' , 'Sprite' , 'Coke' , 'Fanta' , 'Dew' , 'ThumbsUp' ]
sr.index = index_
print (sr)
|
Output :
Now we will use Series.count()
function to find the count of non-missing values in the given series object.
result = sr.count()
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.
import pandas as pd
sr = pd.Series([ 100 , None , None , 18 , 65 , None , 32 , 10 , 5 , 24 , None ])
index_ = pd.date_range( '2010-10-09' , periods = 11 , freq = 'M' )
sr.index = index_
print (sr)
|
Output :
Now we will use Series.count()
function to find the count of non-missing values in the given series object.
result = sr.count()
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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