pandas.notnull¶

pandas.notnull(obj)[source]¶

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).

Parameters

objarray-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.

See also

isna: Boolean inverse of pandas.notna.
Series.notna: Detect valid values in a Series.
DataFrame.notna: Detect valid values in a DataFrame.
Index.notna: Detect valid values in an Index.

Examples

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

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

>>> 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

pandas.notna pandas.to_numeric