# Buggy code
def _encode(values, uniques=None, encode=False, check_unknown=True):
    """Helper function to factorize (find uniques) and encode values.

    Uses pure python method for object dtype, and numpy method for
    all other dtypes.
    The numpy method has the limitation that the `uniques` need to
    be sorted. Importantly, this is not checked but assumed to already be
    the case. The calling method needs to ensure this for all non-object
    values.

    Parameters
    ----------
    values : array
        Values to factorize or encode.
    uniques : array, optional
        If passed, uniques are not determined from passed values (this
        can be because the user specified categories, or because they
        already have been determined in fit).
    encode : bool, default False
        If True, also encode the values into integer codes based on `uniques`.
    check_unknown : bool, default True
        If True, check for values in ``values`` that are not in ``unique``
        and raise an error. This is ignored for object dtype, and treated as
        True in this case. This parameter is useful for
        _BaseEncoder._transform() to avoid calling _encode_check_unknown()
        twice.

    Returns
    -------
    uniques
        If ``encode=False``. The unique values are sorted if the `uniques`
        parameter was None (and thus inferred from the data).
    (uniques, encoded)
        If ``encode=True``.

    """
    if values.dtype == object:
        try:
            res = _encode_python(values, uniques, encode)
        except TypeError:
            raise TypeError("argument must be a string or number")
        return res
    else:
        return _encode_numpy(values, uniques, encode, check_unknown=check_unknown)


# Traceback (TypeError)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~/code/scikit-learn/sklearn/preprocessing/_label.py in _encode(values, uniques, encode, check_unknown)
111         try:
--> 112             res = _encode_python(values, uniques, encode)
113         except TypeError:

~/code/scikit-learn/sklearn/preprocessing/_label.py in _encode_python(values, uniques, encode)
59     if uniques is None:
---> 60         uniques = sorted(set(values))
61         uniques = np.array(uniques, dtype=values.dtype)

TypeError: '<' not supported between instances of 'str' and 'NoneType'

During handling of the above exception, another exception occurred:

TypeError                                 Traceback (most recent call last)
<ipython-input-31-4b176a24c3a2> in <module>
4
5 df = pd.DataFrame({"cat_feature": ["a", None, "b", "a"]})
----> 6 OneHotEncoder().fit(df)

~/code/scikit-learn/sklearn/preprocessing/_encoders.py in fit(self, X, y)
375         """
376         self._validate_keywords()
--> 377         self._fit(X, handle_unknown=self.handle_unknown)
378         self.drop_idx_ = self._compute_drop_idx()
379         return self

~/code/scikit-learn/sklearn/preprocessing/_encoders.py in _fit(self, X, handle_unknown)
84             Xi = X_list[i]
85             if self.categories == 'auto':
---> 86                 cats = _encode(Xi)
87             else:
88                 cats = np.array(self.categories[i], dtype=Xi.dtype)

~/code/scikit-learn/sklearn/preprocessing/_label.py in _encode(values, uniques, encode, check_unknown)
112             res = _encode_python(values, uniques, encode)
113         except TypeError:
--> 114             raise TypeError("argument must be a string or number")
115         return res
116     else:

Sister issue for #16703 (`OrdinalEncoder`)

TypeError: argument must be a string or number

# Fixed code
def _encode(values, uniques=None, encode=False, check_unknown=True):
    """Helper function to factorize (find uniques) and encode values.

    Uses pure python method for object dtype, and numpy method for
    all other dtypes.
    The numpy method has the limitation that the `uniques` need to
    be sorted. Importantly, this is not checked but assumed to already be
    the case. The calling method needs to ensure this for all non-object
    values.

    Parameters
    ----------
    values : array
        Values to factorize or encode.
    uniques : array, optional
        If passed, uniques are not determined from passed values (this
        can be because the user specified categories, or because they
        already have been determined in fit).
    encode : bool, default False
        If True, also encode the values into integer codes based on `uniques`.
    check_unknown : bool, default True
        If True, check for values in ``values`` that are not in ``unique``
        and raise an error. This is ignored for object dtype, and treated as
        True in this case. This parameter is useful for
        _BaseEncoder._transform() to avoid calling _encode_check_unknown()
        twice.

    Returns
    -------
    uniques
        If ``encode=False``. The unique values are sorted if the `uniques`
        parameter was None (and thus inferred from the data).
    (uniques, encoded)
        If ``encode=True``.

    """
    if values.dtype == object:
        try:
            res = _encode_python(values, uniques, encode)
        except TypeError:
            types = sorted(t.__qualname__ for t in set(type(v) for v in values))
            raise TypeError(
                "Encoders require their input to be uniformly "
                f"strings or numbers. Got {types}"
            )
        return res
    else:
        return _encode_numpy(values, uniques, encode, check_unknown=check_unknown)