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 pdimport pandas as pd # Creating the Seriessr = pd.Series(['New York', 'Chicago', None, 'Toronto', 'Lisbon', 'Rio', 'Chicago', 'Lisbon']) # Print the seriesprint(sr) |
Output :
Now we will use Series.valid() function to remove the null values from the given series object.
# return valid valuessr.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 pdimport pandas as pd # Creating the Seriessr = pd.Series([100, 214, 325, 88, None, 325, None, 325, 100]) # Print the seriesprint(sr) |
Output :
Now we will use Series.valid() function to remove the null values from the given series object.
# return valid valuessr.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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