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# 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[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