Python | Pandas Series.dropna()
Last Updated :
13 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.dropna()
function return a new Series with missing values in the given series object removed.
Syntax: Series.dropna(axis=0, inplace=False, **kwargs)
Parameter :
axis : There is only one axis to drop values from.
inplace : If True, do operation inplace and return None.
Returns : Series
Example #1: Use Series.dropna()
function to drop the missing values in the given series object.
import pandas as pd
sr = pd.Series([ 'New York' , 'Chicago' , 'Toronto' , None , 'Rio' ])
index_ = [ 'City 1' , 'City 2' , 'City 3' , 'City 4' , 'City 5' ]
sr.index = index_
print (sr)
|
Output :
Now we will use Series.dropna()
function to drop all the missing values in the given series object.
result = sr.dropna()
print (result)
|
Output :
As we can see in the output, the Series.dropna()
function has successfully dropped all the missing values in the given series object.
Example #2 : Use Series.dropna()
function to drop the missing values in the given series object.
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.dropna()
function to drop all the missing values in the given series object.
result = sr.dropna()
print (result)
|
Output :
As we can see in the output, the Series.dropna()
function has successfully dropped all the missing values in the given series object.
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