notna(obj)

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).

Parameters

obj : array-like or object value

Object to check for not null or non-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 valid.

Detect non-missing values for an array-like object.

See Also

DataFrame.notna

Detect valid values in a DataFrame.

Index.notna

Detect valid values in an Index.

Series.notna

Detect valid values in a Series.

isna

Boolean inverse of pandas.notna.

Examples

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

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

>>> pd.notna('dog')
True

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

>>> pd.notna(pd.NA)
False

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

>>> pd.notna(np.nan)
False

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

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.notna(index)
array([ True,  True, False,  True])

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

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

>>> pd.notna(df[1])
0     True
1    False
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.isna

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File: /pandas/core/dtypes/missing.py#287
type: <class 'function'>
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