Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
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.valid()
function return the same Series object but without the null values.
Syntax: Series.valid(inplace=False, **kwargs)
Parameter :
inplace: booleanReturns : Series
Example #1: Use Series.valid()
function to remove the null values from the given Series object.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([ 'New York' , 'Chicago' , None , 'Toronto' , 'Lisbon' , 'Rio' , 'Chicago' , 'Lisbon' ]) # Print the series print (sr) |
Output :
Now we will use Series.valid()
function to remove the null values from the given series object.
# return valid values sr.valid() |
Output :
As we can see in the output, the Series.valid()
function has returned a Series object containing all the valid value of the original series object on which it was called.
Example #2: Use Series.valid()
function to remove the null values from the given Series object.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([ 100 , 214 , 325 , 88 , None , 325 , None , 325 , 100 ]) # Print the series print (sr) |
Output :
Now we will use Series.valid()
function to remove the null values from the given series object.
# return valid values sr.valid() |
Output :
As we can see in the output, the Series.valid()
function has returned a Series object containing all the valid value of the original series object on which it was called.
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