Python pandas.compat.signature() Examples

def make_signature(func):
    """
    Returns a tuple containing the paramenter list with defaults
    and parameter list.

    Examples
    --------
    >>> def f(a, b, c=2):
    >>>     return a * b * c
    >>> print(make_signature(f))
    (['a', 'b', 'c=2'], ['a', 'b', 'c'])
    """

    spec = signature(func)
    if spec.defaults is None:
        n_wo_defaults = len(spec.args)
        defaults = ('',) * n_wo_defaults
    else:
        n_wo_defaults = len(spec.args) - len(spec.defaults)
        defaults = ('',) * n_wo_defaults + tuple(spec.defaults)
    args = []
    for var, default in zip(spec.args, defaults):
        args.append(var if default == '' else var + '=' + repr(default))
    if spec.varargs:
        args.append('*' + spec.varargs)
    if spec.keywords:
        args.append('**' + spec.keywords)
    return args, spec.args 

def _check_stat_op(self, name, alternative, obj=None, has_skipna=True,
                       skipna_alternative=None):
        if obj is None:
            obj = self.panel

            # # set some NAs
            # obj.loc[5:10] = np.nan
            # obj.loc[15:20, -2:] = np.nan

        f = getattr(obj, name)

        if has_skipna:

            skipna_wrapper = tm._make_skipna_wrapper(alternative,
                                                     skipna_alternative)

            def wrapper(x):
                return alternative(np.asarray(x))

            for i in range(obj.ndim):
                result = f(axis=i, skipna=False)
                assert_frame_equal(result, obj.apply(wrapper, axis=i))
        else:
            skipna_wrapper = alternative
            wrapper = alternative

        for i in range(obj.ndim):
            result = f(axis=i)
            if name in ['sum', 'prod']:
                assert_frame_equal(result, obj.apply(skipna_wrapper, axis=i))

        pytest.raises(Exception, f, axis=obj.ndim)

        # Unimplemented numeric_only parameter.
        if 'numeric_only' in signature(f).args:
            tm.assert_raises_regex(NotImplementedError, name, f,
                                   numeric_only=True) 

def _check_stat_op(self, name, alternative, obj=None, has_skipna=True,
                       skipna_alternative=None):
        if obj is None:
            obj = self.panel

            # # set some NAs
            # obj.loc[5:10] = np.nan
            # obj.loc[15:20, -2:] = np.nan

        f = getattr(obj, name)

        if has_skipna:

            skipna_wrapper = tm._make_skipna_wrapper(alternative,
                                                     skipna_alternative)

            def wrapper(x):
                return alternative(np.asarray(x))

            for i in range(obj.ndim):
                result = f(axis=i, skipna=False)
                assert_frame_equal(result, obj.apply(wrapper, axis=i))
        else:
            skipna_wrapper = alternative
            wrapper = alternative

        for i in range(obj.ndim):
            result = f(axis=i)
            if name in ['sum', 'prod']:
                assert_frame_equal(result, obj.apply(skipna_wrapper, axis=i))

        pytest.raises(Exception, f, axis=obj.ndim)

        # Unimplemented numeric_only parameter.
        if 'numeric_only' in signature(f).args:
            tm.assert_raises_regex(NotImplementedError, name, f,
                                   numeric_only=True) 

def _check_stat_op(self, name, alternative, obj=None, has_skipna=True,
                       skipna_alternative=None):
        if obj is None:
            obj = self.panel

            # # set some NAs
            # obj.loc[5:10] = np.nan
            # obj.loc[15:20, -2:] = np.nan

        f = getattr(obj, name)

        if has_skipna:

            skipna_wrapper = tm._make_skipna_wrapper(alternative,
                                                     skipna_alternative)

            def wrapper(x):
                return alternative(np.asarray(x))

            for i in range(obj.ndim):
                result = f(axis=i, skipna=False)
                assert_frame_equal(result, obj.apply(wrapper, axis=i))
        else:
            skipna_wrapper = alternative
            wrapper = alternative

        for i in range(obj.ndim):
            result = f(axis=i)
            if name in ['sum', 'prod']:
                assert_frame_equal(result, obj.apply(skipna_wrapper, axis=i))

        pytest.raises(Exception, f, axis=obj.ndim)

        # Unimplemented numeric_only parameter.
        if 'numeric_only' in signature(f).args:
            with pytest.raises(NotImplementedError, match=name):
                f(numeric_only=True) 

def make_signature(func):
    """
    Returns a string repr of the arg list of a func call, with any defaults.

    Examples
    --------
    >>> def f(a,b,c=2) :
    >>>     return a*b*c
    >>> print(_make_signature(f))
    a,b,c=2
    """

    spec = signature(func)
    if spec.defaults is None:
        n_wo_defaults = len(spec.args)
        defaults = ('',) * n_wo_defaults
    else:
        n_wo_defaults = len(spec.args) - len(spec.defaults)
        defaults = ('',) * n_wo_defaults + tuple(spec.defaults)
    args = []
    for i, (var, default) in enumerate(zip(spec.args, defaults)):
        args.append(var if default == '' else var + '=' + repr(default))
    if spec.varargs:
        args.append('*' + spec.varargs)
    if spec.keywords:
        args.append('**' + spec.keywords)
    return args, spec.args 

def make_signature(func):
    """
    Returns a string repr of the arg list of a func call, with any defaults.

    Examples
    --------
    >>> def f(a,b,c=2) :
    >>>     return a*b*c
    >>> print(_make_signature(f))
    a,b,c=2
    """

    spec = signature(func)
    if spec.defaults is None:
        n_wo_defaults = len(spec.args)
        defaults = ('',) * n_wo_defaults
    else:
        n_wo_defaults = len(spec.args) - len(spec.defaults)
        defaults = ('',) * n_wo_defaults + tuple(spec.defaults)
    args = []
    for i, (var, default) in enumerate(zip(spec.args, defaults)):
        args.append(var if default == '' else var + '=' + repr(default))
    if spec.varargs:
        args.append('*' + spec.varargs)
    if spec.keywords:
        args.append('**' + spec.keywords)
    return args, spec.args 

def get_result(self):
        """ compute the results """

        # all empty
        if len(self.columns) == 0 and len(self.index) == 0:
            return self.apply_empty_result()

        # string dispatch
        if isinstance(self.f, compat.string_types):
            # Support for `frame.transform('method')`
            # Some methods (shift, etc.) require the axis argument, others
            # don't, so inspect and insert if nescessary.
            func = getattr(self.obj, self.f)
            sig = compat.signature(func)
            if 'axis' in sig.args:
                self.kwds['axis'] = self.axis
            return func(*self.args, **self.kwds)

        # ufunc
        elif isinstance(self.f, np.ufunc):
            with np.errstate(all='ignore'):
                results = self.f(self.values)
            return self.obj._constructor(data=results, index=self.index,
                                         columns=self.columns, copy=False)

        # broadcasting
        if self.result_type == 'broadcast':
            return self.apply_broadcast()

        # one axis empty
        elif not all(self.obj.shape):
            return self.apply_empty_result()

        # raw
        elif self.raw and not self.obj._is_mixed_type:
            return self.apply_raw()

        return self.apply_standard() 

def _check_stat_op(self, name, alternative, obj=None, has_skipna=True,
                       skipna_alternative=None):
        if obj is None:
            obj = self.panel

            # # set some NAs
            # obj.loc[5:10] = np.nan
            # obj.loc[15:20, -2:] = np.nan

        f = getattr(obj, name)

        if has_skipna:

            skipna_wrapper = tm._make_skipna_wrapper(alternative,
                                                     skipna_alternative)

            def wrapper(x):
                return alternative(np.asarray(x))

            for i in range(obj.ndim):
                result = f(axis=i, skipna=False)
                assert_frame_equal(result, obj.apply(wrapper, axis=i))
        else:
            skipna_wrapper = alternative
            wrapper = alternative

        for i in range(obj.ndim):
            result = f(axis=i)
            if name in ['sum', 'prod']:
                assert_frame_equal(result, obj.apply(skipna_wrapper, axis=i))

        pytest.raises(Exception, f, axis=obj.ndim)

        # Unimplemented numeric_only parameter.
        if 'numeric_only' in signature(f).args:
            with pytest.raises(NotImplementedError, match=name):
                f(numeric_only=True) 

def _check_stat_op(self, name, alternative, obj=None, has_skipna=True,
                       skipna_alternative=None):
        if obj is None:
            obj = self.panel

            # # set some NAs
            # obj.loc[5:10] = np.nan
            # obj.loc[15:20, -2:] = np.nan

        f = getattr(obj, name)

        if has_skipna:

            skipna_wrapper = tm._make_skipna_wrapper(alternative,
                                                     skipna_alternative)

            def wrapper(x):
                return alternative(np.asarray(x))

            for i in range(obj.ndim):
                result = f(axis=i, skipna=False)
                assert_frame_equal(result, obj.apply(wrapper, axis=i))
        else:
            skipna_wrapper = alternative
            wrapper = alternative

        for i in range(obj.ndim):
            result = f(axis=i)
            if name in ['sum', 'prod']:
                assert_frame_equal(result, obj.apply(skipna_wrapper, axis=i))

        pytest.raises(Exception, f, axis=obj.ndim)

        # Unimplemented numeric_only parameter.
        if 'numeric_only' in signature(f).args:
            with pytest.raises(NotImplementedError, match=name):
                f(numeric_only=True) 

def make_signature(func):
    """
    Returns a tuple containing the paramenter list with defaults
    and parameter list.

    Examples
    --------
    >>> def f(a, b, c=2):
    >>>     return a * b * c
    >>> print(make_signature(f))
    (['a', 'b', 'c=2'], ['a', 'b', 'c'])
    """

    spec = signature(func)
    if spec.defaults is None:
        n_wo_defaults = len(spec.args)
        defaults = ('',) * n_wo_defaults
    else:
        n_wo_defaults = len(spec.args) - len(spec.defaults)
        defaults = ('',) * n_wo_defaults + tuple(spec.defaults)
    args = []
    for var, default in zip(spec.args, defaults):
        args.append(var if default == '' else var + '=' + repr(default))
    if spec.varargs:
        args.append('*' + spec.varargs)
    if spec.keywords:
        args.append('**' + spec.keywords)
    return args, spec.args 

def make_signature(func):
    """
    Returns a string repr of the arg list of a func call, with any defaults.

    Examples
    --------
    >>> def f(a,b,c=2) :
    >>>     return a*b*c
    >>> print(_make_signature(f))
    a,b,c=2
    """

    spec = signature(func)
    if spec.defaults is None:
        n_wo_defaults = len(spec.args)
        defaults = ('',) * n_wo_defaults
    else:
        n_wo_defaults = len(spec.args) - len(spec.defaults)
        defaults = ('',) * n_wo_defaults + tuple(spec.defaults)
    args = []
    for i, (var, default) in enumerate(zip(spec.args, defaults)):
        args.append(var if default == '' else var + '=' + repr(default))
    if spec.varargs:
        args.append('*' + spec.varargs)
    if spec.keywords:
        args.append('**' + spec.keywords)
    return args, spec.args 

def _check_stat_op(self, name, alternative, obj=None, has_skipna=True):
        if obj is None:
            obj = self.panel

            # # set some NAs
            # obj.loc[5:10] = np.nan
            # obj.loc[15:20, -2:] = np.nan

        f = getattr(obj, name)

        if has_skipna:

            def skipna_wrapper(x):
                nona = remove_na_arraylike(x)
                if len(nona) == 0:
                    return np.nan
                return alternative(nona)

            def wrapper(x):
                return alternative(np.asarray(x))

            for i in range(obj.ndim):
                result = f(axis=i, skipna=False)
                assert_frame_equal(result, obj.apply(wrapper, axis=i))
        else:
            skipna_wrapper = alternative
            wrapper = alternative

        for i in range(obj.ndim):
            result = f(axis=i)
            if name in ['sum', 'prod']:
                assert_frame_equal(result, obj.apply(skipna_wrapper, axis=i))

        pytest.raises(Exception, f, axis=obj.ndim)

        # Unimplemented numeric_only parameter.
        if 'numeric_only' in signature(f).args:
            tm.assert_raises_regex(NotImplementedError, name, f,
                                   numeric_only=True) 

def _check_stat_op(self, name, alternate, string_series_,
                       check_objects=False, check_allna=False):

        with pd.option_context('use_bottleneck', False):
            f = getattr(Series, name)

            # add some NaNs
            string_series_[5:15] = np.NaN

            # mean, idxmax, idxmin, min, and max are valid for dates
            if name not in ['max', 'min', 'mean']:
                ds = Series(pd.date_range('1/1/2001', periods=10))
                with pytest.raises(TypeError):
                    f(ds)

            # skipna or no
            assert pd.notna(f(string_series_))
            assert pd.isna(f(string_series_, skipna=False))

            # check the result is correct
            nona = string_series_.dropna()
            tm.assert_almost_equal(f(nona), alternate(nona.values))
            tm.assert_almost_equal(f(string_series_), alternate(nona.values))

            allna = string_series_ * np.nan

            if check_allna:
                assert np.isnan(f(allna))

            # dtype=object with None, it works!
            s = Series([1, 2, 3, None, 5])
            f(s)

            # GH#2888
            items = [0]
            items.extend(lrange(2 ** 40, 2 ** 40 + 1000))
            s = Series(items, dtype='int64')
            tm.assert_almost_equal(float(f(s)), float(alternate(s.values)))

            # check date range
            if check_objects:
                s = Series(pd.bdate_range('1/1/2000', periods=10))
                res = f(s)
                exp = alternate(s)
                assert res == exp

            # check on string data
            if name not in ['sum', 'min', 'max']:
                with pytest.raises(TypeError):
                    f(Series(list('abc')))

            # Invalid axis.
            with pytest.raises(ValueError):
                f(string_series_, axis=1)

            # Unimplemented numeric_only parameter.
            if 'numeric_only' in compat.signature(f).args:
                with pytest.raises(NotImplementedError, match=name):
                    f(string_series_, numeric_only=True) 

def _check_stat_op(self, name, alternate, check_objects=False,
                       check_allna=False):

        with pd.option_context('use_bottleneck', False):
            f = getattr(Series, name)

            # add some NaNs
            self.series[5:15] = np.NaN

            # idxmax, idxmin, min, and max are valid for dates
            if name not in ['max', 'min']:
                ds = Series(date_range('1/1/2001', periods=10))
                pytest.raises(TypeError, f, ds)

            # skipna or no
            assert notna(f(self.series))
            assert isna(f(self.series, skipna=False))

            # check the result is correct
            nona = self.series.dropna()
            assert_almost_equal(f(nona), alternate(nona.values))
            assert_almost_equal(f(self.series), alternate(nona.values))

            allna = self.series * nan

            if check_allna:
                assert np.isnan(f(allna))

            # dtype=object with None, it works!
            s = Series([1, 2, 3, None, 5])
            f(s)

            # 2888
            l = [0]
            l.extend(lrange(2 ** 40, 2 ** 40 + 1000))
            s = Series(l, dtype='int64')
            assert_almost_equal(float(f(s)), float(alternate(s.values)))

            # check date range
            if check_objects:
                s = Series(bdate_range('1/1/2000', periods=10))
                res = f(s)
                exp = alternate(s)
                assert res == exp

            # check on string data
            if name not in ['sum', 'min', 'max']:
                pytest.raises(TypeError, f, Series(list('abc')))

            # Invalid axis.
            pytest.raises(ValueError, f, self.series, axis=1)

            # Unimplemented numeric_only parameter.
            if 'numeric_only' in compat.signature(f).args:
                tm.assert_raises_regex(NotImplementedError, name, f,
                                       self.series, numeric_only=True) 

def _check_stat_op(self, name, alternate, check_objects=False,
                       check_allna=False):

        with pd.option_context('use_bottleneck', False):
            f = getattr(Series, name)

            # add some NaNs
            self.series[5:15] = np.NaN

            # idxmax, idxmin, min, and max are valid for dates
            if name not in ['max', 'min']:
                ds = Series(date_range('1/1/2001', periods=10))
                pytest.raises(TypeError, f, ds)

            # skipna or no
            assert notna(f(self.series))
            assert isna(f(self.series, skipna=False))

            # check the result is correct
            nona = self.series.dropna()
            assert_almost_equal(f(nona), alternate(nona.values))
            assert_almost_equal(f(self.series), alternate(nona.values))

            allna = self.series * nan

            if check_allna:
                assert np.isnan(f(allna))

            # dtype=object with None, it works!
            s = Series([1, 2, 3, None, 5])
            f(s)

            # 2888
            l = [0]
            l.extend(lrange(2 ** 40, 2 ** 40 + 1000))
            s = Series(l, dtype='int64')
            assert_almost_equal(float(f(s)), float(alternate(s.values)))

            # check date range
            if check_objects:
                s = Series(bdate_range('1/1/2000', periods=10))
                res = f(s)
                exp = alternate(s)
                assert res == exp

            # check on string data
            if name not in ['sum', 'min', 'max']:
                pytest.raises(TypeError, f, Series(list('abc')))

            # Invalid axis.
            pytest.raises(ValueError, f, self.series, axis=1)

            # Unimplemented numeric_only parameter.
            if 'numeric_only' in compat.signature(f).args:
                tm.assert_raises_regex(NotImplementedError, name, f,
                                       self.series, numeric_only=True) 

def get_result(self):
        """ compute the results """

        # dispatch to agg
        if is_list_like(self.f) or is_dict_like(self.f):
            return self.obj.aggregate(self.f, axis=self.axis,
                                      *self.args, **self.kwds)

        # all empty
        if len(self.columns) == 0 and len(self.index) == 0:
            return self.apply_empty_result()

        # string dispatch
        if isinstance(self.f, compat.string_types):
            # Support for `frame.transform('method')`
            # Some methods (shift, etc.) require the axis argument, others
            # don't, so inspect and insert if necessary.
            func = getattr(self.obj, self.f)
            sig = compat.signature(func)
            if 'axis' in sig.args:
                self.kwds['axis'] = self.axis
            return func(*self.args, **self.kwds)

        # ufunc
        elif isinstance(self.f, np.ufunc):
            with np.errstate(all='ignore'):
                results = self.obj._data.apply('apply', func=self.f)
            return self.obj._constructor(data=results, index=self.index,
                                         columns=self.columns, copy=False)

        # broadcasting
        if self.result_type == 'broadcast':
            return self.apply_broadcast()

        # one axis empty
        elif not all(self.obj.shape):
            return self.apply_empty_result()

        # raw
        elif self.raw and not self.obj._is_mixed_type:
            return self.apply_raw()

        return self.apply_standard() 

def deprecate(name, alternative, version, alt_name=None,
              klass=None, stacklevel=2, msg=None):
    """Return a new function that emits a deprecation warning on use.

    To use this method for a deprecated function, another function
    `alternative` with the same signature must exist. The deprecated
    function will emit a deprecation warning, and in the docstring
    it will contain the deprecation directive with the provided version
    so it can be detected for future removal.

    Parameters
    ----------
    name : str
        Name of function to deprecate.
    alternative : func
        Function to use instead.
    version : str
        Version of pandas in which the method has been deprecated.
    alt_name : str, optional
        Name to use in preference of alternative.__name__.
    klass : Warning, default FutureWarning
    stacklevel : int, default 2
    msg : str
        The message to display in the warning.
        Default is '{name} is deprecated. Use {alt_name} instead.'
    """

    alt_name = alt_name or alternative.__name__
    klass = klass or FutureWarning
    warning_msg = msg or '{} is deprecated, use {} instead'.format(name,
                                                                   alt_name)

    # adding deprecated directive to the docstring
    msg = msg or 'Use `{alt_name}` instead.'.format(alt_name=alt_name)
    msg = '\n    '.join(wrap(msg, 70))

    @Substitution(version=version, msg=msg)
    @Appender(alternative.__doc__)
    def wrapper(*args, **kwargs):
        """
        .. deprecated:: %(version)s

           %(msg)s

        """
        warnings.warn(warning_msg, klass, stacklevel=stacklevel)
        return alternative(*args, **kwargs)

    # Since we are using Substitution to create the required docstring,
    # remove that from the attributes that should be assigned to the wrapper
    assignments = tuple(x for x in WRAPPER_ASSIGNMENTS if x != '__doc__')
    update_wrapper(wrapper, alternative, assigned=assignments)

    return wrapper 

def _check_stat_op(self, name, alternate, check_objects=False,
                       check_allna=False):

        with pd.option_context('use_bottleneck', False):
            f = getattr(Series, name)

            # add some NaNs
            self.series[5:15] = np.NaN

            # idxmax, idxmin, min, and max are valid for dates
            if name not in ['max', 'min']:
                ds = Series(date_range('1/1/2001', periods=10))
                pytest.raises(TypeError, f, ds)

            # skipna or no
            assert notna(f(self.series))
            assert isna(f(self.series, skipna=False))

            # check the result is correct
            nona = self.series.dropna()
            assert_almost_equal(f(nona), alternate(nona.values))
            assert_almost_equal(f(self.series), alternate(nona.values))

            allna = self.series * nan

            if check_allna:
                assert np.isnan(f(allna))

            # dtype=object with None, it works!
            s = Series([1, 2, 3, None, 5])
            f(s)

            # 2888
            l = [0]
            l.extend(lrange(2 ** 40, 2 ** 40 + 1000))
            s = Series(l, dtype='int64')
            assert_almost_equal(float(f(s)), float(alternate(s.values)))

            # check date range
            if check_objects:
                s = Series(bdate_range('1/1/2000', periods=10))
                res = f(s)
                exp = alternate(s)
                assert res == exp

            # check on string data
            if name not in ['sum', 'min', 'max']:
                pytest.raises(TypeError, f, Series(list('abc')))

            # Invalid axis.
            pytest.raises(ValueError, f, self.series, axis=1)

            # Unimplemented numeric_only parameter.
            if 'numeric_only' in compat.signature(f).args:
                tm.assert_raises_regex(NotImplementedError, name, f,
                                       self.series, numeric_only=True) 

def get_result(self):
        """ compute the results """

        # dispatch to agg
        if is_list_like(self.f) or is_dict_like(self.f):
            return self.obj.aggregate(self.f, axis=self.axis,
                                      *self.args, **self.kwds)

        # all empty
        if len(self.columns) == 0 and len(self.index) == 0:
            return self.apply_empty_result()

        # string dispatch
        if isinstance(self.f, compat.string_types):
            # Support for `frame.transform('method')`
            # Some methods (shift, etc.) require the axis argument, others
            # don't, so inspect and insert if necessary.
            func = getattr(self.obj, self.f)
            sig = compat.signature(func)
            if 'axis' in sig.args:
                self.kwds['axis'] = self.axis
            return func(*self.args, **self.kwds)

        # ufunc
        elif isinstance(self.f, np.ufunc):
            with np.errstate(all='ignore'):
                results = self.obj._data.apply('apply', func=self.f)
            return self.obj._constructor(data=results, index=self.index,
                                         columns=self.columns, copy=False)

        # broadcasting
        if self.result_type == 'broadcast':
            return self.apply_broadcast()

        # one axis empty
        elif not all(self.obj.shape):
            return self.apply_empty_result()

        # raw
        elif self.raw and not self.obj._is_mixed_type:
            return self.apply_raw()

        return self.apply_standard() 

def _check_stat_op(self, name, alternate, string_series_,
                       check_objects=False, check_allna=False):

        with pd.option_context('use_bottleneck', False):
            f = getattr(Series, name)

            # add some NaNs
            string_series_[5:15] = np.NaN

            # mean, idxmax, idxmin, min, and max are valid for dates
            if name not in ['max', 'min', 'mean']:
                ds = Series(pd.date_range('1/1/2001', periods=10))
                with pytest.raises(TypeError):
                    f(ds)

            # skipna or no
            assert pd.notna(f(string_series_))
            assert pd.isna(f(string_series_, skipna=False))

            # check the result is correct
            nona = string_series_.dropna()
            tm.assert_almost_equal(f(nona), alternate(nona.values))
            tm.assert_almost_equal(f(string_series_), alternate(nona.values))

            allna = string_series_ * np.nan

            if check_allna:
                assert np.isnan(f(allna))

            # dtype=object with None, it works!
            s = Series([1, 2, 3, None, 5])
            f(s)

            # GH#2888
            items = [0]
            items.extend(lrange(2 ** 40, 2 ** 40 + 1000))
            s = Series(items, dtype='int64')
            tm.assert_almost_equal(float(f(s)), float(alternate(s.values)))

            # check date range
            if check_objects:
                s = Series(pd.bdate_range('1/1/2000', periods=10))
                res = f(s)
                exp = alternate(s)
                assert res == exp

            # check on string data
            if name not in ['sum', 'min', 'max']:
                with pytest.raises(TypeError):
                    f(Series(list('abc')))

            # Invalid axis.
            with pytest.raises(ValueError):
                f(string_series_, axis=1)

            # Unimplemented numeric_only parameter.
            if 'numeric_only' in compat.signature(f).args:
                with pytest.raises(NotImplementedError, match=name):
                    f(string_series_, numeric_only=True)