This method is used to detect missing values for an array-like object. This function takes a scalar or array-like object and indicates whether values are missing (“NaN“ in numeric arrays, “None“ or “NaN“ in object arrays, “NaT“ in datetimelike).
Syntax : pandas.isna(obj)
Argument :
- obj : scalar or array-like, Object to check for null or missing values.
Below is the implementation of the above method with some examples :
Example 1 :
Python3
# importing package import numpy import pandas # string "deep" is not nan value print(pandas.isna("deep")) # numpy.nan represents a nan value print(pandas.isna(numpy.nan)) |
Output :
False True
Example 2 :
Python3
# importing package import numpy import pandas # create and view data array = numpy.array([[1, numpy.nan, 3], [4, 5, numpy.nan]]) print(array) # numpy.nan represents a nan value print(pandas.isna(array)) |
Output :
[[ 1. nan 3.] [ 4. 5. nan]] [[False True False] [False False True]]
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