Detect non-missing values for an array-like object.
This function takes a scalar or array-like object and indicates whether values are valid (not missing, which is NaN in numeric arrays, None or NaN in object arrays, NaT in datetimelike).
Object to check for not null or non-missing values.
For scalar input, returns a scalar boolean. For array input, returns an array of boolean indicating whether each corresponding element is valid.
Examples
Scalar arguments (including strings) result in a scalar boolean.
>>> pd.notna(pd.NA) False
>>> pd.notna(np.nan) False
ndarrays result in an ndarray of booleans.
>>> array = np.array([[1, np.nan, 3], [4, 5, np.nan]])
>>> array
array([[ 1., nan, 3.],
[ 4., 5., nan]])
>>> pd.notna(array)
array([[ True, False, True],
[ True, True, False]])
For indexes, an ndarray of booleans is returned.
>>> index = pd.DatetimeIndex(["2017-07-05", "2017-07-06", None,
... "2017-07-08"])
>>> index
DatetimeIndex(['2017-07-05', '2017-07-06', 'NaT', '2017-07-08'],
dtype='datetime64[ns]', freq=None)
>>> pd.notna(index)
array([ True, True, False, True])
For Series and DataFrame, the same type is returned, containing booleans.
>>> df = pd.DataFrame([['ant', 'bee', 'cat'], ['dog', None, 'fly']])
>>> df
0 1 2
0 ant bee cat
1 dog None fly
>>> pd.notna(df)
0 1 2
0 True True True
1 True False True
>>> pd.notna(df[1]) 0 True 1 False Name: 1, dtype: bool
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