isna(obj)

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

obj : scalar 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.

Detect missing values for an array-like object.

See Also

DataFrame.isna

Detect missing values in a DataFrame.

Index.isna

Detect missing values in an Index.

Series.isna

Detect missing values in a Series.

notna

Boolean inverse of pandas.isna.

Examples

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

This example is valid syntax, but we were not able to check execution

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

This example is valid syntax, but we were not able to check execution

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

This example is valid syntax, but we were not able to check execution

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

ndarrays result in an ndarray of booleans.

This example is valid syntax, but we were not able to check execution

>>> array = np.array([[1, np.nan, 3], [4, 5, np.nan]])
... array
array([[ 1., nan,  3.],
       [ 4.,  5., nan]])

This example is valid syntax, but we were not able to check execution

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

For indexes, an ndarray of booleans is returned.

This example is valid syntax, but we were not able to check execution

>>> 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)

This example is valid syntax, but we were not able to check execution

>>> pd.isna(index)
array([False, False,  True, False])

For Series and DataFrame, the same type is returned, containing booleans.

This example is valid syntax, but we were not able to check execution

>>> df = pd.DataFrame([['ant', 'bee', 'cat'], ['dog', None, 'fly']])
... df
     0     1    2
0  ant   bee  cat
1  dog  None  fly

This example is valid syntax, but we were not able to check execution

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

This example is valid syntax, but we were not able to check execution

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

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

pandas.core.dtypes.missing.notna

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them

---

File: /pandas/core/dtypes/missing.py#66
type: <class 'function'>
Commit: