code stringlengths 3 6.57k |
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self._get_axis_number(axis) |
rename_axis() |
list(kwargs.keys() |
validate_bool_kwarg(inplace, "inplace") |
is_scalar(mapper) |
is_list_like(mapper) |
is_dict_like(mapper) |
self._set_axis_name(mapper, axis=axis, inplace=inplace) |
ValueError("Use `.rename` to alter labels with a mapper.") |
self.copy(deep=copy) |
range(self._AXIS_LEN) |
axes.get(self._AXIS_NAMES[axis]) |
is_scalar(v) |
or (is_list_like(v) |
is_dict_like(v) |
com.get_rename_function(v) |
self._get_axis(axis) |
f(name) |
result._set_axis_name(newnames, axis=axis, inplace=True) |
_set_axis_name(self, name, axis=0, inplace=False) |
name(s) |
Name(s) |
df._set_axis_name("animal") |
df._set_axis_name(["type", "name"]) |
self._get_axis_number(axis) |
self._get_axis(axis) |
set_names(name) |
validate_bool_kwarg(inplace, "inplace") |
self.copy() |
renamed.set_axis(idx, axis=axis, inplace=True) |
_indexed_same(self, other) |
self._get_axis(a) |
equals(other._get_axis(a) |
equals(self, other) |
pd.DataFrame({1: [10], 2: [20]}) |
pd.DataFrame({1: [10], 2: [20]}) |
df.equals(exactly_equal) |
pd.DataFrame({1.0: [10], 2.0: [20]}) |
df.equals(different_column_type) |
pd.DataFrame({1: [10.0], 2: [20.0]}) |
df.equals(different_data_type) |
isinstance(other, self._constructor) |
self._data.equals(other._data) |
__neg__(self) |
is_bool_dtype(values) |
operator.inv(values) |
is_numeric_dtype(values) |
is_timedelta64_dtype(values) |
is_object_dtype(values) |
operator.neg(values) |
TypeError(f"Unary negative expects numeric dtype, not {values.dtype}") |
self.__array_wrap__(arr) |
__pos__(self) |
is_bool_dtype(values) |
is_numeric_dtype(values) |
is_timedelta64_dtype(values) |
is_object_dtype(values) |
operator.pos(values) |
TypeError(f"Unary plus expects numeric dtype, not {values.dtype}") |
self.__array_wrap__(arr) |
__invert__(self) |
self._data.apply(operator.invert) |
self._constructor(new_data) |
__finalize__(self) |
__nonzero__(self) |
type(self) |
a.bool() |
a.item() |
a.any() |
a.all() |
bool(self) |
self.squeeze() |
isinstance(v, (bool, np.bool_) |
bool(v) |
is_scalar(v) |
type(self) |
self.__nonzero__() |
__abs__(self: FrameOrSeries) |
self.abs() |
__round__(self: FrameOrSeries, decimals: int = 0) |
self.round(decimals) |
_is_level_reference(self, key, axis=0) |
with (0 for index, 1 for columns) |
self._get_axis_number(axis) |
is_hashable(key) |
self._is_label_reference(key, axis=axis) |
_is_label_reference(self, key, axis=0) |
with
(0 means search for column labels, 1 means search for index labels) |
self._get_axis_number(axis) |
range(self._AXIS_LEN) |
is_hashable(key) |
any(key in self.axes[ax] for ax in other_axes) |
_is_label_or_level_reference(self, key: str, axis: int = 0) |
with (0 for index, 1 for columns) |
self._is_level_reference(key, axis=axis) |
_check_label_or_level_ambiguity(self, key, axis: int = 0) |
with (0 for index, 1 for columns) |
self._get_axis_number(axis) |
range(self._AXIS_LEN) |
is_hashable(key) |
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