Python | Pandas Index.dropna()
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 Index.dropna()
function return Index without NA/NaN values. All the missing values are removed and a new object is returned which does not have any NaN
values present in it.
Syntax: Index.dropna(how=’any’)
Parameters :
how : {‘any’, ‘all’}, default ‘any’
If the Index is a MultiIndex, drop the value when any or all levels are NaN.Returns : valid : Index
Example #1: Use Index.dropna()
function to remove all missing values from the given Index containing datetime data.
Python3
# importing pandas as pd import pandas as pd # Creating the Index idx = pd.Index([ '2015-10-31' , '2015-12-02' , None , '2016-01-03' , '2016-02-08' , '2017-05-05' , None , '2014-02-11' ]) # Print the Index idx |
Output :
Let’s drop all the NaN
values from the Index.
Python3
# drop all missing values. idx.dropna(how = 'all' ) |
Output :
As we can see in the output, the Index.dropna()
function has removed all the missing values.
Example #2: Use Index.dropna()
function to delete all the missing values in the Index. Index contains string type data.
Python3
# importing pandas as pd import pandas as pd # Creating the Index idx = pd.Index([ 'Jan' , 'Feb' , 'Mar' , None , 'May' , 'Jun' , None , 'Aug' , 'Sep' , 'Oct' , 'Nov' , 'Dec' ]) # Print the Index idx |
Output :
Let’s drop all the missing values.
Python3
# drop the missing values idx.dropna(how = 'any' ) |
Output :
As we can see in the output all missing values of months has been removed.
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