pandas.isna() function in Python
Last Updated :
14 Aug, 2020
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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 :
# 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 :
# 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]]
