pandas.notna() ⚬ Entries

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pandas.notna() ⚬｜Documentation｜1st｜20251021154832-00-⌔

pandas.notna — pandas 2.3.3 documentation#pandas.notna

pandas.notna(obj)

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:

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.

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[us]', 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  NaN  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

Printed 2026-06-28.

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