pandas.isna#

pandas.isna(obj)[source] #

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).

Parameters:

objscalar or array-like

Object to check for null or missing values.

Returns:

bool or array-like of bool

For scalar input, returns a scalar boolean. For array input, returns an array of boolean indicating whether each corresponding element is missing.

See also

notna

Boolean inverse of pandas.isna.

Series.isna

Detect missing values in a Series.

DataFrame.isna

Detect missing values in a DataFrame.

Index.isna

Detect missing values in an Index.

Examples

Scalar arguments (including strings) result in a scalar boolean.

>>> pd.isna('dog')
False

>>> pd.isna(pd.NA)
True

>>> pd.isna(np.nan)
True

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.isna(array)
array([[False, True, False],
 [False, False, True]])

For indexes, an ndarray of booleans is returned.

>>> index = pd.DatetimeIndex(["2017年07月05日", "2017年07月06日", None,
...  "2017年07月08日"])
>>> index
DatetimeIndex(['2017年07月05日', '2017年07月06日', 'NaT', '2017年07月08日'],
 dtype='datetime64[ns]', freq=None)
>>> pd.isna(index)
array([False, False, True, False])

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.isna(df)
 0 1 2
0 False False False
1 False True False

>>> pd.isna(df[1])
0 False
1 True
Name: 1, dtype: bool