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keshe/.venv/Lib/site-packages/pandas/core/indexing.py

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from __future__ import annotations
from contextlib import suppress
import sys
from typing import (
TYPE_CHECKING,
Any,
TypeVar,
cast,
final,
)
import warnings
import numpy as np
from pandas._config import (
using_copy_on_write,
warn_copy_on_write,
)
from pandas._libs.indexing import NDFrameIndexerBase
from pandas._libs.lib import item_from_zerodim
from pandas.compat import PYPY
from pandas.errors import (
AbstractMethodError,
ChainedAssignmentError,
IndexingError,
InvalidIndexError,
LossySetitemError,
_chained_assignment_msg,
_chained_assignment_warning_msg,
_check_cacher,
)
from pandas.util._decorators import doc
from pandas.util._exceptions import find_stack_level
from pandas.core.dtypes.cast import (
can_hold_element,
maybe_promote,
)
from pandas.core.dtypes.common import (
is_array_like,
is_bool_dtype,
is_hashable,
is_integer,
is_iterator,
is_list_like,
is_numeric_dtype,
is_object_dtype,
is_scalar,
is_sequence,
)
from pandas.core.dtypes.concat import concat_compat
from pandas.core.dtypes.dtypes import ExtensionDtype
from pandas.core.dtypes.generic import (
ABCDataFrame,
ABCSeries,
)
from pandas.core.dtypes.missing import (
construct_1d_array_from_inferred_fill_value,
infer_fill_value,
is_valid_na_for_dtype,
isna,
na_value_for_dtype,
)
from pandas.core import algorithms as algos
import pandas.core.common as com
from pandas.core.construction import (
array as pd_array,
extract_array,
)
from pandas.core.indexers import (
check_array_indexer,
is_list_like_indexer,
is_scalar_indexer,
length_of_indexer,
)
from pandas.core.indexes.api import (
Index,
MultiIndex,
)
if TYPE_CHECKING:
from collections.abc import (
Hashable,
Sequence,
)
from pandas._typing import (
Axis,
AxisInt,
Self,
npt,
)
from pandas import (
DataFrame,
Series,
)
T = TypeVar("T")
# "null slice"
_NS = slice(None, None)
_one_ellipsis_message = "indexer may only contain one '...' entry"
# the public IndexSlicerMaker
class _IndexSlice:
"""
Create an object to more easily perform multi-index slicing.
See Also
--------
MultiIndex.remove_unused_levels : New MultiIndex with no unused levels.
Notes
-----
See :ref:`Defined Levels <advanced.shown_levels>`
for further info on slicing a MultiIndex.
Examples
--------
>>> midx = pd.MultiIndex.from_product([['A0','A1'], ['B0','B1','B2','B3']])
>>> columns = ['foo', 'bar']
>>> dfmi = pd.DataFrame(np.arange(16).reshape((len(midx), len(columns))),
... index=midx, columns=columns)
Using the default slice command:
>>> dfmi.loc[(slice(None), slice('B0', 'B1')), :]
foo bar
A0 B0 0 1
B1 2 3
A1 B0 8 9
B1 10 11
Using the IndexSlice class for a more intuitive command:
>>> idx = pd.IndexSlice
>>> dfmi.loc[idx[:, 'B0':'B1'], :]
foo bar
A0 B0 0 1
B1 2 3
A1 B0 8 9
B1 10 11
"""
def __getitem__(self, arg):
return arg
IndexSlice = _IndexSlice()
class IndexingMixin:
"""
Mixin for adding .loc/.iloc/.at/.iat to Dataframes and Series.
"""
@property
def iloc(self) -> _iLocIndexer:
"""
Purely integer-location based indexing for selection by position.
.. deprecated:: 2.2.0
Returning a tuple from a callable is deprecated.
``.iloc[]`` is primarily integer position based (from ``0`` to
``length-1`` of the axis), but may also be used with a boolean
array.
Allowed inputs are:
- An integer, e.g. ``5``.
- A list or array of integers, e.g. ``[4, 3, 0]``.
- A slice object with ints, e.g. ``1:7``.
- A boolean array.
- A ``callable`` function with one argument (the calling Series or
DataFrame) and that returns valid output for indexing (one of the above).
This is useful in method chains, when you don't have a reference to the
calling object, but would like to base your selection on
some value.
- A tuple of row and column indexes. The tuple elements consist of one of the
above inputs, e.g. ``(0, 1)``.
``.iloc`` will raise ``IndexError`` if a requested indexer is
out-of-bounds, except *slice* indexers which allow out-of-bounds
indexing (this conforms with python/numpy *slice* semantics).
See more at :ref:`Selection by Position <indexing.integer>`.
See Also
--------
DataFrame.iat : Fast integer location scalar accessor.
DataFrame.loc : Purely label-location based indexer for selection by label.
Series.iloc : Purely integer-location based indexing for
selection by position.
Examples
--------
>>> mydict = [{'a': 1, 'b': 2, 'c': 3, 'd': 4},
... {'a': 100, 'b': 200, 'c': 300, 'd': 400},
... {'a': 1000, 'b': 2000, 'c': 3000, 'd': 4000}]
>>> df = pd.DataFrame(mydict)
>>> df
a b c d
0 1 2 3 4
1 100 200 300 400
2 1000 2000 3000 4000
**Indexing just the rows**
With a scalar integer.
>>> type(df.iloc[0])
<class 'pandas.core.series.Series'>
>>> df.iloc[0]
a 1
b 2
c 3
d 4
Name: 0, dtype: int64
With a list of integers.
>>> df.iloc[[0]]
a b c d
0 1 2 3 4
>>> type(df.iloc[[0]])
<class 'pandas.core.frame.DataFrame'>
>>> df.iloc[[0, 1]]
a b c d
0 1 2 3 4
1 100 200 300 400
With a `slice` object.
>>> df.iloc[:3]
a b c d
0 1 2 3 4
1 100 200 300 400
2 1000 2000 3000 4000
With a boolean mask the same length as the index.
>>> df.iloc[[True, False, True]]
a b c d
0 1 2 3 4
2 1000 2000 3000 4000
With a callable, useful in method chains. The `x` passed
to the ``lambda`` is the DataFrame being sliced. This selects
the rows whose index label even.
>>> df.iloc[lambda x: x.index % 2 == 0]
a b c d
0 1 2 3 4
2 1000 2000 3000 4000
**Indexing both axes**
You can mix the indexer types for the index and columns. Use ``:`` to
select the entire axis.
With scalar integers.
>>> df.iloc[0, 1]
2
With lists of integers.
>>> df.iloc[[0, 2], [1, 3]]
b d
0 2 4
2 2000 4000
With `slice` objects.
>>> df.iloc[1:3, 0:3]
a b c
1 100 200 300
2 1000 2000 3000
With a boolean array whose length matches the columns.
>>> df.iloc[:, [True, False, True, False]]
a c
0 1 3
1 100 300
2 1000 3000
With a callable function that expects the Series or DataFrame.
>>> df.iloc[:, lambda df: [0, 2]]
a c
0 1 3
1 100 300
2 1000 3000
"""
return _iLocIndexer("iloc", self)
@property
def loc(self) -> _LocIndexer:
"""
Access a group of rows and columns by label(s) or a boolean array.
``.loc[]`` is primarily label based, but may also be used with a
boolean array.
Allowed inputs are:
- A single label, e.g. ``5`` or ``'a'``, (note that ``5`` is
interpreted as a *label* of the index, and **never** as an
integer position along the index).
- A list or array of labels, e.g. ``['a', 'b', 'c']``.
- A slice object with labels, e.g. ``'a':'f'``.
.. warning:: Note that contrary to usual python slices, **both** the
start and the stop are included
- A boolean array of the same length as the axis being sliced,
e.g. ``[True, False, True]``.
- An alignable boolean Series. The index of the key will be aligned before
masking.
- An alignable Index. The Index of the returned selection will be the input.
- A ``callable`` function with one argument (the calling Series or
DataFrame) and that returns valid output for indexing (one of the above)
See more at :ref:`Selection by Label <indexing.label>`.
Raises
------
KeyError
If any items are not found.
IndexingError
If an indexed key is passed and its index is unalignable to the frame index.
See Also
--------
DataFrame.at : Access a single value for a row/column label pair.
DataFrame.iloc : Access group of rows and columns by integer position(s).
DataFrame.xs : Returns a cross-section (row(s) or column(s)) from the
Series/DataFrame.
Series.loc : Access group of values using labels.
Examples
--------
**Getting values**
>>> df = pd.DataFrame([[1, 2], [4, 5], [7, 8]],
... index=['cobra', 'viper', 'sidewinder'],
... columns=['max_speed', 'shield'])
>>> df
max_speed shield
cobra 1 2
viper 4 5
sidewinder 7 8
Single label. Note this returns the row as a Series.
>>> df.loc['viper']
max_speed 4
shield 5
Name: viper, dtype: int64
List of labels. Note using ``[[]]`` returns a DataFrame.
>>> df.loc[['viper', 'sidewinder']]
max_speed shield
viper 4 5
sidewinder 7 8
Single label for row and column
>>> df.loc['cobra', 'shield']
2
Slice with labels for row and single label for column. As mentioned
above, note that both the start and stop of the slice are included.
>>> df.loc['cobra':'viper', 'max_speed']
cobra 1
viper 4
Name: max_speed, dtype: int64
Boolean list with the same length as the row axis
>>> df.loc[[False, False, True]]
max_speed shield
sidewinder 7 8
Alignable boolean Series:
>>> df.loc[pd.Series([False, True, False],
... index=['viper', 'sidewinder', 'cobra'])]
max_speed shield
sidewinder 7 8
Index (same behavior as ``df.reindex``)
>>> df.loc[pd.Index(["cobra", "viper"], name="foo")]
max_speed shield
foo
cobra 1 2
viper 4 5
Conditional that returns a boolean Series
>>> df.loc[df['shield'] > 6]
max_speed shield
sidewinder 7 8
Conditional that returns a boolean Series with column labels specified
>>> df.loc[df['shield'] > 6, ['max_speed']]
max_speed
sidewinder 7
Multiple conditional using ``&`` that returns a boolean Series
>>> df.loc[(df['max_speed'] > 1) & (df['shield'] < 8)]
max_speed shield
viper 4 5
Multiple conditional using ``|`` that returns a boolean Series
>>> df.loc[(df['max_speed'] > 4) | (df['shield'] < 5)]
max_speed shield
cobra 1 2
sidewinder 7 8
Please ensure that each condition is wrapped in parentheses ``()``.
See the :ref:`user guide<indexing.boolean>`
for more details and explanations of Boolean indexing.
.. note::
If you find yourself using 3 or more conditionals in ``.loc[]``,
consider using :ref:`advanced indexing<advanced.advanced_hierarchical>`.
See below for using ``.loc[]`` on MultiIndex DataFrames.
Callable that returns a boolean Series
>>> df.loc[lambda df: df['shield'] == 8]
max_speed shield
sidewinder 7 8
**Setting values**
Set value for all items matching the list of labels
>>> df.loc[['viper', 'sidewinder'], ['shield']] = 50
>>> df
max_speed shield
cobra 1 2
viper 4 50
sidewinder 7 50
Set value for an entire row
>>> df.loc['cobra'] = 10
>>> df
max_speed shield
cobra 10 10
viper 4 50
sidewinder 7 50
Set value for an entire column
>>> df.loc[:, 'max_speed'] = 30
>>> df
max_speed shield
cobra 30 10
viper 30 50
sidewinder 30 50
Set value for rows matching callable condition
>>> df.loc[df['shield'] > 35] = 0
>>> df
max_speed shield
cobra 30 10
viper 0 0
sidewinder 0 0
Add value matching location
>>> df.loc["viper", "shield"] += 5
>>> df
max_speed shield
cobra 30 10
viper 0 5
sidewinder 0 0
Setting using a ``Series`` or a ``DataFrame`` sets the values matching the
index labels, not the index positions.
>>> shuffled_df = df.loc[["viper", "cobra", "sidewinder"]]
>>> df.loc[:] += shuffled_df
>>> df
max_speed shield
cobra 60 20
viper 0 10
sidewinder 0 0
**Getting values on a DataFrame with an index that has integer labels**
Another example using integers for the index
>>> df = pd.DataFrame([[1, 2], [4, 5], [7, 8]],
... index=[7, 8, 9], columns=['max_speed', 'shield'])
>>> df
max_speed shield
7 1 2
8 4 5
9 7 8
Slice with integer labels for rows. As mentioned above, note that both
the start and stop of the slice are included.
>>> df.loc[7:9]
max_speed shield
7 1 2
8 4 5
9 7 8
**Getting values with a MultiIndex**
A number of examples using a DataFrame with a MultiIndex
>>> tuples = [
... ('cobra', 'mark i'), ('cobra', 'mark ii'),
... ('sidewinder', 'mark i'), ('sidewinder', 'mark ii'),
... ('viper', 'mark ii'), ('viper', 'mark iii')
... ]
>>> index = pd.MultiIndex.from_tuples(tuples)
>>> values = [[12, 2], [0, 4], [10, 20],
... [1, 4], [7, 1], [16, 36]]
>>> df = pd.DataFrame(values, columns=['max_speed', 'shield'], index=index)
>>> df
max_speed shield
cobra mark i 12 2
mark ii 0 4
sidewinder mark i 10 20
mark ii 1 4
viper mark ii 7 1
mark iii 16 36
Single label. Note this returns a DataFrame with a single index.
>>> df.loc['cobra']
max_speed shield
mark i 12 2
mark ii 0 4
Single index tuple. Note this returns a Series.
>>> df.loc[('cobra', 'mark ii')]
max_speed 0
shield 4
Name: (cobra, mark ii), dtype: int64
Single label for row and column. Similar to passing in a tuple, this
returns a Series.
>>> df.loc['cobra', 'mark i']
max_speed 12
shield 2
Name: (cobra, mark i), dtype: int64
Single tuple. Note using ``[[]]`` returns a DataFrame.
>>> df.loc[[('cobra', 'mark ii')]]
max_speed shield
cobra mark ii 0 4
Single tuple for the index with a single label for the column
>>> df.loc[('cobra', 'mark i'), 'shield']
2
Slice from index tuple to single label
>>> df.loc[('cobra', 'mark i'):'viper']
max_speed shield
cobra mark i 12 2
mark ii 0 4
sidewinder mark i 10 20
mark ii 1 4
viper mark ii 7 1
mark iii 16 36
Slice from index tuple to index tuple
>>> df.loc[('cobra', 'mark i'):('viper', 'mark ii')]
max_speed shield
cobra mark i 12 2
mark ii 0 4
sidewinder mark i 10 20
mark ii 1 4
viper mark ii 7 1
Please see the :ref:`user guide<advanced.advanced_hierarchical>`
for more details and explanations of advanced indexing.
"""
return _LocIndexer("loc", self)
@property
def at(self) -> _AtIndexer:
"""
Access a single value for a row/column label pair.
Similar to ``loc``, in that both provide label-based lookups. Use
``at`` if you only need to get or set a single value in a DataFrame
or Series.
Raises
------
KeyError
If getting a value and 'label' does not exist in a DataFrame or Series.
ValueError
If row/column label pair is not a tuple or if any label
from the pair is not a scalar for DataFrame.
If label is list-like (*excluding* NamedTuple) for Series.
See Also
--------
DataFrame.at : Access a single value for a row/column pair by label.
DataFrame.iat : Access a single value for a row/column pair by integer
position.
DataFrame.loc : Access a group of rows and columns by label(s).
DataFrame.iloc : Access a group of rows and columns by integer
position(s).
Series.at : Access a single value by label.
Series.iat : Access a single value by integer position.
Series.loc : Access a group of rows by label(s).
Series.iloc : Access a group of rows by integer position(s).
Notes
-----
See :ref:`Fast scalar value getting and setting <indexing.basics.get_value>`
for more details.
Examples
--------
>>> df = pd.DataFrame([[0, 2, 3], [0, 4, 1], [10, 20, 30]],
... index=[4, 5, 6], columns=['A', 'B', 'C'])
>>> df
A B C
4 0 2 3
5 0 4 1
6 10 20 30
Get value at specified row/column pair
>>> df.at[4, 'B']
2
Set value at specified row/column pair
>>> df.at[4, 'B'] = 10
>>> df.at[4, 'B']
10
Get value within a Series
>>> df.loc[5].at['B']
4
"""
return _AtIndexer("at", self)
@property
def iat(self) -> _iAtIndexer:
"""
Access a single value for a row/column pair by integer position.
Similar to ``iloc``, in that both provide integer-based lookups. Use
``iat`` if you only need to get or set a single value in a DataFrame
or Series.
Raises
------
IndexError
When integer position is out of bounds.
See Also
--------
DataFrame.at : Access a single value for a row/column label pair.
DataFrame.loc : Access a group of rows and columns by label(s).
DataFrame.iloc : Access a group of rows and columns by integer position(s).
Examples
--------
>>> df = pd.DataFrame([[0, 2, 3], [0, 4, 1], [10, 20, 30]],
... columns=['A', 'B', 'C'])
>>> df
A B C
0 0 2 3
1 0 4 1
2 10 20 30
Get value at specified row/column pair
>>> df.iat[1, 2]
1
Set value at specified row/column pair
>>> df.iat[1, 2] = 10
>>> df.iat[1, 2]
10
Get value within a series
>>> df.loc[0].iat[1]
2
"""
return _iAtIndexer("iat", self)
class _LocationIndexer(NDFrameIndexerBase):
_valid_types: str
axis: AxisInt | None = None
# sub-classes need to set _takeable
_takeable: bool
@final
def __call__(self, axis: Axis | None = None) -> Self:
# we need to return a copy of ourselves
new_self = type(self)(self.name, self.obj)
if axis is not None:
axis_int_none = self.obj._get_axis_number(axis)
else:
axis_int_none = axis
new_self.axis = axis_int_none
return new_self
def _get_setitem_indexer(self, key):
"""
Convert a potentially-label-based key into a positional indexer.
"""
if self.name == "loc":
# always holds here bc iloc overrides _get_setitem_indexer
self._ensure_listlike_indexer(key)
if isinstance(key, tuple):
for x in key:
check_dict_or_set_indexers(x)
if self.axis is not None:
key = _tupleize_axis_indexer(self.ndim, self.axis, key)
ax = self.obj._get_axis(0)
if (
isinstance(ax, MultiIndex)
and self.name != "iloc"
and is_hashable(key)
and not isinstance(key, slice)
):
with suppress(KeyError, InvalidIndexError):
# TypeError e.g. passed a bool
return ax.get_loc(key)
if isinstance(key, tuple):
with suppress(IndexingError):
# suppress "Too many indexers"
return self._convert_tuple(key)
if isinstance(key, range):
# GH#45479 test_loc_setitem_range_key
key = list(key)
return self._convert_to_indexer(key, axis=0)
@final
def _maybe_mask_setitem_value(self, indexer, value):
"""
If we have obj.iloc[mask] = series_or_frame and series_or_frame has the
same length as obj, we treat this as obj.iloc[mask] = series_or_frame[mask],
similar to Series.__setitem__.
Note this is only for loc, not iloc.
"""
if (
isinstance(indexer, tuple)
and len(indexer) == 2
and isinstance(value, (ABCSeries, ABCDataFrame))
):
pi, icols = indexer
ndim = value.ndim
if com.is_bool_indexer(pi) and len(value) == len(pi):
newkey = pi.nonzero()[0]
if is_scalar_indexer(icols, self.ndim - 1) and ndim == 1:
# e.g. test_loc_setitem_boolean_mask_allfalse
# test_loc_setitem_ndframe_values_alignment
value = self.obj.iloc._align_series(indexer, value)
indexer = (newkey, icols)
elif (
isinstance(icols, np.ndarray)
and icols.dtype.kind == "i"
and len(icols) == 1
):
if ndim == 1:
# We implicitly broadcast, though numpy does not, see
# github.com/pandas-dev/pandas/pull/45501#discussion_r789071825
# test_loc_setitem_ndframe_values_alignment
value = self.obj.iloc._align_series(indexer, value)
indexer = (newkey, icols)
elif ndim == 2 and value.shape[1] == 1:
# test_loc_setitem_ndframe_values_alignment
value = self.obj.iloc._align_frame(indexer, value)
indexer = (newkey, icols)
elif com.is_bool_indexer(indexer):
indexer = indexer.nonzero()[0]
return indexer, value
@final
def _ensure_listlike_indexer(self, key, axis=None, value=None) -> None:
"""
Ensure that a list-like of column labels are all present by adding them if
they do not already exist.
Parameters
----------
key : list-like of column labels
Target labels.
axis : key axis if known
"""
column_axis = 1
# column only exists in 2-dimensional DataFrame
if self.ndim != 2:
return
if isinstance(key, tuple) and len(key) > 1:
# key may be a tuple if we are .loc
# if length of key is > 1 set key to column part
key = key[column_axis]
axis = column_axis
if (
axis == column_axis
and not isinstance(self.obj.columns, MultiIndex)
and is_list_like_indexer(key)
and not com.is_bool_indexer(key)
and all(is_hashable(k) for k in key)
):
# GH#38148
keys = self.obj.columns.union(key, sort=False)
diff = Index(key).difference(self.obj.columns, sort=False)
if len(diff):
# e.g. if we are doing df.loc[:, ["A", "B"]] = 7 and "B"
# is a new column, add the new columns with dtype=np.void
# so that later when we go through setitem_single_column
# we will use isetitem. Without this, the reindex_axis
# below would create float64 columns in this example, which
# would successfully hold 7, so we would end up with the wrong
# dtype.
indexer = np.arange(len(keys), dtype=np.intp)
indexer[len(self.obj.columns) :] = -1
new_mgr = self.obj._mgr.reindex_indexer(
keys, indexer=indexer, axis=0, only_slice=True, use_na_proxy=True
)
self.obj._mgr = new_mgr
return
self.obj._mgr = self.obj._mgr.reindex_axis(keys, axis=0, only_slice=True)
@final
def __setitem__(self, key, value) -> None:
if not PYPY and using_copy_on_write():
if sys.getrefcount(self.obj) <= 2:
warnings.warn(
_chained_assignment_msg, ChainedAssignmentError, stacklevel=2
)
elif not PYPY and not using_copy_on_write():
ctr = sys.getrefcount(self.obj)
ref_count = 2
if not warn_copy_on_write() and _check_cacher(self.obj):
# see https://github.com/pandas-dev/pandas/pull/56060#discussion_r1399245221
ref_count += 1
if ctr <= ref_count:
warnings.warn(
_chained_assignment_warning_msg, FutureWarning, stacklevel=2
)
check_dict_or_set_indexers(key)
if isinstance(key, tuple):
key = tuple(list(x) if is_iterator(x) else x for x in key)
key = tuple(com.apply_if_callable(x, self.obj) for x in key)
else:
maybe_callable = com.apply_if_callable(key, self.obj)
key = self._check_deprecated_callable_usage(key, maybe_callable)
indexer = self._get_setitem_indexer(key)
self._has_valid_setitem_indexer(key)
iloc = self if self.name == "iloc" else self.obj.iloc
iloc._setitem_with_indexer(indexer, value, self.name)
def _validate_key(self, key, axis: AxisInt):
"""
Ensure that key is valid for current indexer.
Parameters
----------
key : scalar, slice or list-like
Key requested.
axis : int
Dimension on which the indexing is being made.
Raises
------
TypeError
If the key (or some element of it) has wrong type.
IndexError
If the key (or some element of it) is out of bounds.
KeyError
If the key was not found.
"""
raise AbstractMethodError(self)
@final
def _expand_ellipsis(self, tup: tuple) -> tuple:
"""
If a tuple key includes an Ellipsis, replace it with an appropriate
number of null slices.
"""
if any(x is Ellipsis for x in tup):
if tup.count(Ellipsis) > 1:
raise IndexingError(_one_ellipsis_message)
if len(tup) == self.ndim:
# It is unambiguous what axis this Ellipsis is indexing,
# treat as a single null slice.
i = tup.index(Ellipsis)
# FIXME: this assumes only one Ellipsis
new_key = tup[:i] + (_NS,) + tup[i + 1 :]
return new_key
# TODO: other cases? only one test gets here, and that is covered
# by _validate_key_length
return tup
@final
def _validate_tuple_indexer(self, key: tuple) -> tuple:
"""
Check the key for valid keys across my indexer.
"""
key = self._validate_key_length(key)
key = self._expand_ellipsis(key)
for i, k in enumerate(key):
try:
self._validate_key(k, i)
except ValueError as err:
raise ValueError(
"Location based indexing can only have "
f"[{self._valid_types}] types"
) from err
return key
@final
def _is_nested_tuple_indexer(self, tup: tuple) -> bool:
"""
Returns
-------
bool
"""
if any(isinstance(ax, MultiIndex) for ax in self.obj.axes):
return any(is_nested_tuple(tup, ax) for ax in self.obj.axes)
return False
@final
def _convert_tuple(self, key: tuple) -> tuple:
# Note: we assume _tupleize_axis_indexer has been called, if necessary.
self._validate_key_length(key)
keyidx = [self._convert_to_indexer(k, axis=i) for i, k in enumerate(key)]
return tuple(keyidx)
@final
def _validate_key_length(self, key: tuple) -> tuple:
if len(key) > self.ndim:
if key[0] is Ellipsis:
# e.g. Series.iloc[..., 3] reduces to just Series.iloc[3]
key = key[1:]
if Ellipsis in key:
raise IndexingError(_one_ellipsis_message)
return self._validate_key_length(key)
raise IndexingError("Too many indexers")
return key
@final
def _getitem_tuple_same_dim(self, tup: tuple):
"""
Index with indexers that should return an object of the same dimension
as self.obj.
This is only called after a failed call to _getitem_lowerdim.
"""
retval = self.obj
# Selecting columns before rows is significantly faster
start_val = (self.ndim - len(tup)) + 1
for i, key in enumerate(reversed(tup)):
i = self.ndim - i - start_val
if com.is_null_slice(key):
continue
retval = getattr(retval, self.name)._getitem_axis(key, axis=i)
# We should never have retval.ndim < self.ndim, as that should
# be handled by the _getitem_lowerdim call above.
assert retval.ndim == self.ndim
if retval is self.obj:
# if all axes were a null slice (`df.loc[:, :]`), ensure we still
# return a new object (https://github.com/pandas-dev/pandas/pull/49469)
retval = retval.copy(deep=False)
return retval
@final
def _getitem_lowerdim(self, tup: tuple):
# we can directly get the axis result since the axis is specified
if self.axis is not None:
axis = self.obj._get_axis_number(self.axis)
return self._getitem_axis(tup, axis=axis)
# we may have a nested tuples indexer here
if self._is_nested_tuple_indexer(tup):
return self._getitem_nested_tuple(tup)
# we maybe be using a tuple to represent multiple dimensions here
ax0 = self.obj._get_axis(0)
# ...but iloc should handle the tuple as simple integer-location
# instead of checking it as multiindex representation (GH 13797)
if (
isinstance(ax0, MultiIndex)
and self.name != "iloc"
and not any(isinstance(x, slice) for x in tup)
):
# Note: in all extant test cases, replacing the slice condition with
# `all(is_hashable(x) or com.is_null_slice(x) for x in tup)`
# is equivalent.
# (see the other place where we call _handle_lowerdim_multi_index_axis0)
with suppress(IndexingError):
return cast(_LocIndexer, self)._handle_lowerdim_multi_index_axis0(tup)
tup = self._validate_key_length(tup)
for i, key in enumerate(tup):
if is_label_like(key):
# We don't need to check for tuples here because those are
# caught by the _is_nested_tuple_indexer check above.
section = self._getitem_axis(key, axis=i)
# We should never have a scalar section here, because
# _getitem_lowerdim is only called after a check for
# is_scalar_access, which that would be.
if section.ndim == self.ndim:
# we're in the middle of slicing through a MultiIndex
# revise the key wrt to `section` by inserting an _NS
new_key = tup[:i] + (_NS,) + tup[i + 1 :]
else:
# Note: the section.ndim == self.ndim check above
# rules out having DataFrame here, so we dont need to worry
# about transposing.
new_key = tup[:i] + tup[i + 1 :]
if len(new_key) == 1:
new_key = new_key[0]
# Slices should return views, but calling iloc/loc with a null
# slice returns a new object.
if com.is_null_slice(new_key):
return section
# This is an elided recursive call to iloc/loc
return getattr(section, self.name)[new_key]
raise IndexingError("not applicable")
@final
def _getitem_nested_tuple(self, tup: tuple):
# we have a nested tuple so have at least 1 multi-index level
# we should be able to match up the dimensionality here
def _contains_slice(x: object) -> bool:
# Check if object is a slice or a tuple containing a slice
if isinstance(x, tuple):
return any(isinstance(v, slice) for v in x)
elif isinstance(x, slice):
return True
return False
for key in tup:
check_dict_or_set_indexers(key)
# we have too many indexers for our dim, but have at least 1
# multi-index dimension, try to see if we have something like
# a tuple passed to a series with a multi-index
if len(tup) > self.ndim:
if self.name != "loc":
# This should never be reached, but let's be explicit about it
raise ValueError("Too many indices") # pragma: no cover
if all(
(is_hashable(x) and not _contains_slice(x)) or com.is_null_slice(x)
for x in tup
):
# GH#10521 Series should reduce MultiIndex dimensions instead of
# DataFrame, IndexingError is not raised when slice(None,None,None)
# with one row.
with suppress(IndexingError):
return cast(_LocIndexer, self)._handle_lowerdim_multi_index_axis0(
tup
)
elif isinstance(self.obj, ABCSeries) and any(
isinstance(k, tuple) for k in tup
):
# GH#35349 Raise if tuple in tuple for series
# Do this after the all-hashable-or-null-slice check so that
# we are only getting non-hashable tuples, in particular ones
# that themselves contain a slice entry
# See test_loc_series_getitem_too_many_dimensions
raise IndexingError("Too many indexers")
# this is a series with a multi-index specified a tuple of
# selectors
axis = self.axis or 0
return self._getitem_axis(tup, axis=axis)
# handle the multi-axis by taking sections and reducing
# this is iterative
obj = self.obj
# GH#41369 Loop in reverse order ensures indexing along columns before rows
# which selects only necessary blocks which avoids dtype conversion if possible
axis = len(tup) - 1
for key in tup[::-1]:
if com.is_null_slice(key):
axis -= 1
continue
obj = getattr(obj, self.name)._getitem_axis(key, axis=axis)
axis -= 1
# if we have a scalar, we are done
if is_scalar(obj) or not hasattr(obj, "ndim"):
break
return obj
def _convert_to_indexer(self, key, axis: AxisInt):
raise AbstractMethodError(self)
def _check_deprecated_callable_usage(self, key: Any, maybe_callable: T) -> T:
# GH53533
if self.name == "iloc" and callable(key) and isinstance(maybe_callable, tuple):
warnings.warn(
"Returning a tuple from a callable with iloc "
"is deprecated and will be removed in a future version",
FutureWarning,
stacklevel=find_stack_level(),
)
return maybe_callable
@final
def __getitem__(self, key):
check_dict_or_set_indexers(key)
if type(key) is tuple:
key = tuple(list(x) if is_iterator(x) else x for x in key)
key = tuple(com.apply_if_callable(x, self.obj) for x in key)
if self._is_scalar_access(key):
return self.obj._get_value(*key, takeable=self._takeable)
return self._getitem_tuple(key)
else:
# we by definition only have the 0th axis
axis = self.axis or 0
maybe_callable = com.apply_if_callable(key, self.obj)
maybe_callable = self._check_deprecated_callable_usage(key, maybe_callable)
return self._getitem_axis(maybe_callable, axis=axis)
def _is_scalar_access(self, key: tuple):
raise NotImplementedError()
def _getitem_tuple(self, tup: tuple):
raise AbstractMethodError(self)
def _getitem_axis(self, key, axis: AxisInt):
raise NotImplementedError()
def _has_valid_setitem_indexer(self, indexer) -> bool:
raise AbstractMethodError(self)
@final
def _getbool_axis(self, key, axis: AxisInt):
# caller is responsible for ensuring non-None axis
labels = self.obj._get_axis(axis)
key = check_bool_indexer(labels, key)
inds = key.nonzero()[0]
return self.obj._take_with_is_copy(inds, axis=axis)
@doc(IndexingMixin.loc)
class _LocIndexer(_LocationIndexer):
_takeable: bool = False
_valid_types = (
"labels (MUST BE IN THE INDEX), slices of labels (BOTH "
"endpoints included! Can be slices of integers if the "
"index is integers), listlike of labels, boolean"
)
# -------------------------------------------------------------------
# Key Checks
@doc(_LocationIndexer._validate_key)
def _validate_key(self, key, axis: Axis):
# valid for a collection of labels (we check their presence later)
# slice of labels (where start-end in labels)
# slice of integers (only if in the labels)
# boolean not in slice and with boolean index
ax = self.obj._get_axis(axis)
if isinstance(key, bool) and not (
is_bool_dtype(ax.dtype)
or ax.dtype.name == "boolean"
or isinstance(ax, MultiIndex)
and is_bool_dtype(ax.get_level_values(0).dtype)
):
raise KeyError(
f"{key}: boolean label can not be used without a boolean index"
)
if isinstance(key, slice) and (
isinstance(key.start, bool) or isinstance(key.stop, bool)
):
raise TypeError(f"{key}: boolean values can not be used in a slice")
def _has_valid_setitem_indexer(self, indexer) -> bool:
return True
def _is_scalar_access(self, key: tuple) -> bool:
"""
Returns
-------
bool
"""
# this is a shortcut accessor to both .loc and .iloc
# that provide the equivalent access of .at and .iat
# a) avoid getting things via sections and (to minimize dtype changes)
# b) provide a performant path
if len(key) != self.ndim:
return False
for i, k in enumerate(key):
if not is_scalar(k):
return False
ax = self.obj.axes[i]
if isinstance(ax, MultiIndex):
return False
if isinstance(k, str) and ax._supports_partial_string_indexing:
# partial string indexing, df.loc['2000', 'A']
# should not be considered scalar
return False
if not ax._index_as_unique:
return False
return True
# -------------------------------------------------------------------
# MultiIndex Handling
def _multi_take_opportunity(self, tup: tuple) -> bool:
"""
Check whether there is the possibility to use ``_multi_take``.
Currently the limit is that all axes being indexed, must be indexed with
list-likes.
Parameters
----------
tup : tuple
Tuple of indexers, one per axis.
Returns
-------
bool
Whether the current indexing,
can be passed through `_multi_take`.
"""
if not all(is_list_like_indexer(x) for x in tup):
return False
# just too complicated
return not any(com.is_bool_indexer(x) for x in tup)
def _multi_take(self, tup: tuple):
"""
Create the indexers for the passed tuple of keys, and
executes the take operation. This allows the take operation to be
executed all at once, rather than once for each dimension.
Improving efficiency.
Parameters
----------
tup : tuple
Tuple of indexers, one per axis.
Returns
-------
values: same type as the object being indexed
"""
# GH 836
d = {
axis: self._get_listlike_indexer(key, axis)
for (key, axis) in zip(tup, self.obj._AXIS_ORDERS)
}
return self.obj._reindex_with_indexers(d, copy=True, allow_dups=True)
# -------------------------------------------------------------------
def _getitem_iterable(self, key, axis: AxisInt):
"""
Index current object with an iterable collection of keys.
Parameters
----------
key : iterable
Targeted labels.
axis : int
Dimension on which the indexing is being made.
Raises
------
KeyError
If no key was found. Will change in the future to raise if not all
keys were found.
Returns
-------
scalar, DataFrame, or Series: indexed value(s).
"""
# we assume that not com.is_bool_indexer(key), as that is
# handled before we get here.
self._validate_key(key, axis)
# A collection of keys
keyarr, indexer = self._get_listlike_indexer(key, axis)
return self.obj._reindex_with_indexers(
{axis: [keyarr, indexer]}, copy=True, allow_dups=True
)
def _getitem_tuple(self, tup: tuple):
with suppress(IndexingError):
tup = self._expand_ellipsis(tup)
return self._getitem_lowerdim(tup)
# no multi-index, so validate all of the indexers
tup = self._validate_tuple_indexer(tup)
# ugly hack for GH #836
if self._multi_take_opportunity(tup):
return self._multi_take(tup)
return self._getitem_tuple_same_dim(tup)
def _get_label(self, label, axis: AxisInt):
# GH#5567 this will fail if the label is not present in the axis.
return self.obj.xs(label, axis=axis)
def _handle_lowerdim_multi_index_axis0(self, tup: tuple):
# we have an axis0 multi-index, handle or raise
axis = self.axis or 0
try:
# fast path for series or for tup devoid of slices
return self._get_label(tup, axis=axis)
except KeyError as ek:
# raise KeyError if number of indexers match
# else IndexingError will be raised
if self.ndim < len(tup) <= self.obj.index.nlevels:
raise ek
raise IndexingError("No label returned") from ek
def _getitem_axis(self, key, axis: AxisInt):
key = item_from_zerodim(key)
if is_iterator(key):
key = list(key)
if key is Ellipsis:
key = slice(None)
labels = self.obj._get_axis(axis)
if isinstance(key, tuple) and isinstance(labels, MultiIndex):
key = tuple(key)
if isinstance(key, slice):
self._validate_key(key, axis)
return self._get_slice_axis(key, axis=axis)
elif com.is_bool_indexer(key):
return self._getbool_axis(key, axis=axis)
elif is_list_like_indexer(key):
# an iterable multi-selection
if not (isinstance(key, tuple) and isinstance(labels, MultiIndex)):
if hasattr(key, "ndim") and key.ndim > 1:
raise ValueError("Cannot index with multidimensional key")
return self._getitem_iterable(key, axis=axis)
# nested tuple slicing
if is_nested_tuple(key, labels):
locs = labels.get_locs(key)
indexer: list[slice | npt.NDArray[np.intp]] = [slice(None)] * self.ndim
indexer[axis] = locs
return self.obj.iloc[tuple(indexer)]
# fall thru to straight lookup
self._validate_key(key, axis)
return self._get_label(key, axis=axis)
def _get_slice_axis(self, slice_obj: slice, axis: AxisInt):
"""
This is pretty simple as we just have to deal with labels.
"""
# caller is responsible for ensuring non-None axis
obj = self.obj
if not need_slice(slice_obj):
return obj.copy(deep=False)
labels = obj._get_axis(axis)
indexer = labels.slice_indexer(slice_obj.start, slice_obj.stop, slice_obj.step)
if isinstance(indexer, slice):
return self.obj._slice(indexer, axis=axis)
else:
# DatetimeIndex overrides Index.slice_indexer and may
# return a DatetimeIndex instead of a slice object.
return self.obj.take(indexer, axis=axis)
def _convert_to_indexer(self, key, axis: AxisInt):
"""
Convert indexing key into something we can use to do actual fancy
indexing on a ndarray.
Examples
ix[:5] -> slice(0, 5)
ix[[1,2,3]] -> [1,2,3]
ix[['foo', 'bar', 'baz']] -> [i, j, k] (indices of foo, bar, baz)
Going by Zen of Python?
'In the face of ambiguity, refuse the temptation to guess.'
raise AmbiguousIndexError with integer labels?
- No, prefer label-based indexing
"""
labels = self.obj._get_axis(axis)
if isinstance(key, slice):
return labels._convert_slice_indexer(key, kind="loc")
if (
isinstance(key, tuple)
and not isinstance(labels, MultiIndex)
and self.ndim < 2
and len(key) > 1
):
raise IndexingError("Too many indexers")
# Slices are not valid keys passed in by the user,
# even though they are hashable in Python 3.12
contains_slice = False
if isinstance(key, tuple):
contains_slice = any(isinstance(v, slice) for v in key)
if is_scalar(key) or (
isinstance(labels, MultiIndex) and is_hashable(key) and not contains_slice
):
# Otherwise get_loc will raise InvalidIndexError
# if we are a label return me
try:
return labels.get_loc(key)
except LookupError:
if isinstance(key, tuple) and isinstance(labels, MultiIndex):
if len(key) == labels.nlevels:
return {"key": key}
raise
except InvalidIndexError:
# GH35015, using datetime as column indices raises exception
if not isinstance(labels, MultiIndex):
raise
except ValueError:
if not is_integer(key):
raise
return {"key": key}
if is_nested_tuple(key, labels):
if self.ndim == 1 and any(isinstance(k, tuple) for k in key):
# GH#35349 Raise if tuple in tuple for series
raise IndexingError("Too many indexers")
return labels.get_locs(key)
elif is_list_like_indexer(key):
if is_iterator(key):
key = list(key)
if com.is_bool_indexer(key):
key = check_bool_indexer(labels, key)
return key
else:
return self._get_listlike_indexer(key, axis)[1]
else:
try:
return labels.get_loc(key)
except LookupError:
# allow a not found key only if we are a setter
if not is_list_like_indexer(key):
return {"key": key}
raise
def _get_listlike_indexer(self, key, axis: AxisInt):
"""
Transform a list-like of keys into a new index and an indexer.
Parameters
----------
key : list-like
Targeted labels.
axis: int
Dimension on which the indexing is being made.
Raises
------
KeyError
If at least one key was requested but none was found.
Returns
-------
keyarr: Index
New index (coinciding with 'key' if the axis is unique).
values : array-like
Indexer for the return object, -1 denotes keys not found.
"""
ax = self.obj._get_axis(axis)
axis_name = self.obj._get_axis_name(axis)
keyarr, indexer = ax._get_indexer_strict(key, axis_name)
return keyarr, indexer
@doc(IndexingMixin.iloc)
class _iLocIndexer(_LocationIndexer):
_valid_types = (
"integer, integer slice (START point is INCLUDED, END "
"point is EXCLUDED), listlike of integers, boolean array"
)
_takeable = True
# -------------------------------------------------------------------
# Key Checks
def _validate_key(self, key, axis: AxisInt):
if com.is_bool_indexer(key):
if hasattr(key, "index") and isinstance(key.index, Index):
if key.index.inferred_type == "integer":
raise NotImplementedError(
"iLocation based boolean "
"indexing on an integer type "
"is not available"
)
raise ValueError(
"iLocation based boolean indexing cannot use "
"an indexable as a mask"
)
return
if isinstance(key, slice):
return
elif is_integer(key):
self._validate_integer(key, axis)
elif isinstance(key, tuple):
# a tuple should already have been caught by this point
# so don't treat a tuple as a valid indexer
raise IndexingError("Too many indexers")
elif is_list_like_indexer(key):
if isinstance(key, ABCSeries):
arr = key._values
elif is_array_like(key):
arr = key
else:
arr = np.array(key)
len_axis = len(self.obj._get_axis(axis))
# check that the key has a numeric dtype
if not is_numeric_dtype(arr.dtype):
raise IndexError(f".iloc requires numeric indexers, got {arr}")
# check that the key does not exceed the maximum size of the index
if len(arr) and (arr.max() >= len_axis or arr.min() < -len_axis):
raise IndexError("positional indexers are out-of-bounds")
else:
raise ValueError(f"Can only index by location with a [{self._valid_types}]")
def _has_valid_setitem_indexer(self, indexer) -> bool:
"""
Validate that a positional indexer cannot enlarge its target
will raise if needed, does not modify the indexer externally.
Returns
-------
bool
"""
if isinstance(indexer, dict):
raise IndexError("iloc cannot enlarge its target object")
if isinstance(indexer, ABCDataFrame):
raise TypeError(
"DataFrame indexer for .iloc is not supported. "
"Consider using .loc with a DataFrame indexer for automatic alignment.",
)
if not isinstance(indexer, tuple):
indexer = _tuplify(self.ndim, indexer)
for ax, i in zip(self.obj.axes, indexer):
if isinstance(i, slice):
# should check the stop slice?
pass
elif is_list_like_indexer(i):
# should check the elements?
pass
elif is_integer(i):
if i >= len(ax):
raise IndexError("iloc cannot enlarge its target object")
elif isinstance(i, dict):
raise IndexError("iloc cannot enlarge its target object")
return True
def _is_scalar_access(self, key: tuple) -> bool:
"""
Returns
-------
bool
"""
# this is a shortcut accessor to both .loc and .iloc
# that provide the equivalent access of .at and .iat
# a) avoid getting things via sections and (to minimize dtype changes)
# b) provide a performant path
if len(key) != self.ndim:
return False
return all(is_integer(k) for k in key)
def _validate_integer(self, key: int | np.integer, axis: AxisInt) -> None:
"""
Check that 'key' is a valid position in the desired axis.
Parameters
----------
key : int
Requested position.
axis : int
Desired axis.
Raises
------
IndexError
If 'key' is not a valid position in axis 'axis'.
"""
len_axis = len(self.obj._get_axis(axis))
if key >= len_axis or key < -len_axis:
raise IndexError("single positional indexer is out-of-bounds")
# -------------------------------------------------------------------
def _getitem_tuple(self, tup: tuple):
tup = self._validate_tuple_indexer(tup)
with suppress(IndexingError):
return self._getitem_lowerdim(tup)
return self._getitem_tuple_same_dim(tup)
def _get_list_axis(self, key, axis: AxisInt):
"""
Return Series values by list or array of integers.
Parameters
----------
key : list-like positional indexer
axis : int
Returns
-------
Series object
Notes
-----
`axis` can only be zero.
"""
try:
return self.obj._take_with_is_copy(key, axis=axis)
except IndexError as err:
# re-raise with different error message, e.g. test_getitem_ndarray_3d
raise IndexError("positional indexers are out-of-bounds") from err
def _getitem_axis(self, key, axis: AxisInt):
if key is Ellipsis:
key = slice(None)
elif isinstance(key, ABCDataFrame):
raise IndexError(
"DataFrame indexer is not allowed for .iloc\n"
"Consider using .loc for automatic alignment."
)
if isinstance(key, slice):
return self._get_slice_axis(key, axis=axis)
if is_iterator(key):
key = list(key)
if isinstance(key, list):
key = np.asarray(key)
if com.is_bool_indexer(key):
self._validate_key(key, axis)
return self._getbool_axis(key, axis=axis)
# a list of integers
elif is_list_like_indexer(key):
return self._get_list_axis(key, axis=axis)
# a single integer
else:
key = item_from_zerodim(key)
if not is_integer(key):
raise TypeError("Cannot index by location index with a non-integer key")
# validate the location
self._validate_integer(key, axis)
return self.obj._ixs(key, axis=axis)
def _get_slice_axis(self, slice_obj: slice, axis: AxisInt):
# caller is responsible for ensuring non-None axis
obj = self.obj
if not need_slice(slice_obj):
return obj.copy(deep=False)
labels = obj._get_axis(axis)
labels._validate_positional_slice(slice_obj)
return self.obj._slice(slice_obj, axis=axis)
def _convert_to_indexer(self, key, axis: AxisInt):
"""
Much simpler as we only have to deal with our valid types.
"""
return key
def _get_setitem_indexer(self, key):
# GH#32257 Fall through to let numpy do validation
if is_iterator(key):
key = list(key)
if self.axis is not None:
key = _tupleize_axis_indexer(self.ndim, self.axis, key)
return key
# -------------------------------------------------------------------
def _setitem_with_indexer(self, indexer, value, name: str = "iloc"):
"""
_setitem_with_indexer is for setting values on a Series/DataFrame
using positional indexers.
If the relevant keys are not present, the Series/DataFrame may be
expanded.
This method is currently broken when dealing with non-unique Indexes,
since it goes from positional indexers back to labels when calling
BlockManager methods, see GH#12991, GH#22046, GH#15686.
"""
info_axis = self.obj._info_axis_number
# maybe partial set
take_split_path = not self.obj._mgr.is_single_block
if not take_split_path and isinstance(value, ABCDataFrame):
# Avoid cast of values
take_split_path = not value._mgr.is_single_block
# if there is only one block/type, still have to take split path
# unless the block is one-dimensional or it can hold the value
if not take_split_path and len(self.obj._mgr.arrays) and self.ndim > 1:
# in case of dict, keys are indices
val = list(value.values()) if isinstance(value, dict) else value
arr = self.obj._mgr.arrays[0]
take_split_path = not can_hold_element(
arr, extract_array(val, extract_numpy=True)
)
# if we have any multi-indexes that have non-trivial slices
# (not null slices) then we must take the split path, xref
# GH 10360, GH 27841
if isinstance(indexer, tuple) and len(indexer) == len(self.obj.axes):
for i, ax in zip(indexer, self.obj.axes):
if isinstance(ax, MultiIndex) and not (
is_integer(i) or com.is_null_slice(i)
):
take_split_path = True
break
if isinstance(indexer, tuple):
nindexer = []
for i, idx in enumerate(indexer):
if isinstance(idx, dict):
# reindex the axis to the new value
# and set inplace
key, _ = convert_missing_indexer(idx)
# if this is the items axes, then take the main missing
# path first
# this correctly sets the dtype and avoids cache issues
# essentially this separates out the block that is needed
# to possibly be modified
if self.ndim > 1 and i == info_axis:
# add the new item, and set the value
# must have all defined axes if we have a scalar
# or a list-like on the non-info axes if we have a
# list-like
if not len(self.obj):
if not is_list_like_indexer(value):
raise ValueError(
"cannot set a frame with no "
"defined index and a scalar"
)
self.obj[key] = value
return
# add a new item with the dtype setup
if com.is_null_slice(indexer[0]):
# We are setting an entire column
self.obj[key] = value
return
elif is_array_like(value):
# GH#42099
arr = extract_array(value, extract_numpy=True)
taker = -1 * np.ones(len(self.obj), dtype=np.intp)
empty_value = algos.take_nd(arr, taker)
if not isinstance(value, ABCSeries):
# if not Series (in which case we need to align),
# we can short-circuit
if (
isinstance(arr, np.ndarray)
and arr.ndim == 1
and len(arr) == 1
):
# NumPy 1.25 deprecation: https://github.com/numpy/numpy/pull/10615
arr = arr[0, ...]
empty_value[indexer[0]] = arr
self.obj[key] = empty_value
return
self.obj[key] = empty_value
elif not is_list_like(value):
self.obj[key] = construct_1d_array_from_inferred_fill_value(
value, len(self.obj)
)
else:
# FIXME: GH#42099#issuecomment-864326014
self.obj[key] = infer_fill_value(value)
new_indexer = convert_from_missing_indexer_tuple(
indexer, self.obj.axes
)
self._setitem_with_indexer(new_indexer, value, name)
return
# reindex the axis
# make sure to clear the cache because we are
# just replacing the block manager here
# so the object is the same
index = self.obj._get_axis(i)
with warnings.catch_warnings():
# TODO: re-issue this with setitem-specific message?
warnings.filterwarnings(
"ignore",
"The behavior of Index.insert with object-dtype "
"is deprecated",
category=FutureWarning,
)
labels = index.insert(len(index), key)
# We are expanding the Series/DataFrame values to match
# the length of thenew index `labels`. GH#40096 ensure
# this is valid even if the index has duplicates.
taker = np.arange(len(index) + 1, dtype=np.intp)
taker[-1] = -1
reindexers = {i: (labels, taker)}
new_obj = self.obj._reindex_with_indexers(
reindexers, allow_dups=True
)
self.obj._mgr = new_obj._mgr
self.obj._maybe_update_cacher(clear=True)
self.obj._is_copy = None
nindexer.append(labels.get_loc(key))
else:
nindexer.append(idx)
indexer = tuple(nindexer)
else:
indexer, missing = convert_missing_indexer(indexer)
if missing:
self._setitem_with_indexer_missing(indexer, value)
return
if name == "loc":
# must come after setting of missing
indexer, value = self._maybe_mask_setitem_value(indexer, value)
# align and set the values
if take_split_path:
# We have to operate column-wise
self._setitem_with_indexer_split_path(indexer, value, name)
else:
self._setitem_single_block(indexer, value, name)
def _setitem_with_indexer_split_path(self, indexer, value, name: str):
"""
Setitem column-wise.
"""
# Above we only set take_split_path to True for 2D cases
assert self.ndim == 2
if not isinstance(indexer, tuple):
indexer = _tuplify(self.ndim, indexer)
if len(indexer) > self.ndim:
raise IndexError("too many indices for array")
if isinstance(indexer[0], np.ndarray) and indexer[0].ndim > 2:
raise ValueError(r"Cannot set values with ndim > 2")
if (isinstance(value, ABCSeries) and name != "iloc") or isinstance(value, dict):
from pandas import Series
value = self._align_series(indexer, Series(value))
# Ensure we have something we can iterate over
info_axis = indexer[1]
ilocs = self._ensure_iterable_column_indexer(info_axis)
pi = indexer[0]
lplane_indexer = length_of_indexer(pi, self.obj.index)
# lplane_indexer gives the expected length of obj[indexer[0]]
# we need an iterable, with a ndim of at least 1
# eg. don't pass through np.array(0)
if is_list_like_indexer(value) and getattr(value, "ndim", 1) > 0:
if isinstance(value, ABCDataFrame):
self._setitem_with_indexer_frame_value(indexer, value, name)
elif np.ndim(value) == 2:
# TODO: avoid np.ndim call in case it isn't an ndarray, since
# that will construct an ndarray, which will be wasteful
self._setitem_with_indexer_2d_value(indexer, value)
elif len(ilocs) == 1 and lplane_indexer == len(value) and not is_scalar(pi):
# We are setting multiple rows in a single column.
self._setitem_single_column(ilocs[0], value, pi)
elif len(ilocs) == 1 and 0 != lplane_indexer != len(value):
# We are trying to set N values into M entries of a single
# column, which is invalid for N != M
# Exclude zero-len for e.g. boolean masking that is all-false
if len(value) == 1 and not is_integer(info_axis):
# This is a case like df.iloc[:3, [1]] = [0]
# where we treat as df.iloc[:3, 1] = 0
return self._setitem_with_indexer((pi, info_axis[0]), value[0])
raise ValueError(
"Must have equal len keys and value "
"when setting with an iterable"
)
elif lplane_indexer == 0 and len(value) == len(self.obj.index):
# We get here in one case via .loc with a all-False mask
pass
elif self._is_scalar_access(indexer) and is_object_dtype(
self.obj.dtypes._values[ilocs[0]]
):
# We are setting nested data, only possible for object dtype data
self._setitem_single_column(indexer[1], value, pi)
elif len(ilocs) == len(value):
# We are setting multiple columns in a single row.
for loc, v in zip(ilocs, value):
self._setitem_single_column(loc, v, pi)
elif len(ilocs) == 1 and com.is_null_slice(pi) and len(self.obj) == 0:
# This is a setitem-with-expansion, see
# test_loc_setitem_empty_append_expands_rows_mixed_dtype
# e.g. df = DataFrame(columns=["x", "y"])
# df["x"] = df["x"].astype(np.int64)
# df.loc[:, "x"] = [1, 2, 3]
self._setitem_single_column(ilocs[0], value, pi)
else:
raise ValueError(
"Must have equal len keys and value "
"when setting with an iterable"
)
else:
# scalar value
for loc in ilocs:
self._setitem_single_column(loc, value, pi)
def _setitem_with_indexer_2d_value(self, indexer, value):
# We get here with np.ndim(value) == 2, excluding DataFrame,
# which goes through _setitem_with_indexer_frame_value
pi = indexer[0]
ilocs = self._ensure_iterable_column_indexer(indexer[1])
if not is_array_like(value):
# cast lists to array
value = np.array(value, dtype=object)
if len(ilocs) != value.shape[1]:
raise ValueError(
"Must have equal len keys and value when setting with an ndarray"
)
for i, loc in enumerate(ilocs):
value_col = value[:, i]
if is_object_dtype(value_col.dtype):
# casting to list so that we do type inference in setitem_single_column
value_col = value_col.tolist()
self._setitem_single_column(loc, value_col, pi)
def _setitem_with_indexer_frame_value(self, indexer, value: DataFrame, name: str):
ilocs = self._ensure_iterable_column_indexer(indexer[1])
sub_indexer = list(indexer)
pi = indexer[0]
multiindex_indexer = isinstance(self.obj.columns, MultiIndex)
unique_cols = value.columns.is_unique
# We do not want to align the value in case of iloc GH#37728
if name == "iloc":
for i, loc in enumerate(ilocs):
val = value.iloc[:, i]
self._setitem_single_column(loc, val, pi)
elif not unique_cols and value.columns.equals(self.obj.columns):
# We assume we are already aligned, see
# test_iloc_setitem_frame_duplicate_columns_multiple_blocks
for loc in ilocs:
item = self.obj.columns[loc]
if item in value:
sub_indexer[1] = item
val = self._align_series(
tuple(sub_indexer),
value.iloc[:, loc],
multiindex_indexer,
)
else:
val = np.nan
self._setitem_single_column(loc, val, pi)
elif not unique_cols:
raise ValueError("Setting with non-unique columns is not allowed.")
else:
for loc in ilocs:
item = self.obj.columns[loc]
if item in value:
sub_indexer[1] = item
val = self._align_series(
tuple(sub_indexer),
value[item],
multiindex_indexer,
using_cow=using_copy_on_write(),
)
else:
val = np.nan
self._setitem_single_column(loc, val, pi)
def _setitem_single_column(self, loc: int, value, plane_indexer) -> None:
"""
Parameters
----------
loc : int
Indexer for column position
plane_indexer : int, slice, listlike[int]
The indexer we use for setitem along axis=0.
"""
pi = plane_indexer
is_full_setter = com.is_null_slice(pi) or com.is_full_slice(pi, len(self.obj))
is_null_setter = com.is_empty_slice(pi) or is_array_like(pi) and len(pi) == 0
if is_null_setter:
# no-op, don't cast dtype later
return
elif is_full_setter:
try:
self.obj._mgr.column_setitem(
loc, plane_indexer, value, inplace_only=True
)
except (ValueError, TypeError, LossySetitemError):
# If we're setting an entire column and we can't do it inplace,
# then we can use value's dtype (or inferred dtype)
# instead of object
dtype = self.obj.dtypes.iloc[loc]
if dtype not in (np.void, object) and not self.obj.empty:
# - Exclude np.void, as that is a special case for expansion.
# We want to warn for
# df = pd.DataFrame({'a': [1, 2]})
# df.loc[:, 'a'] = .3
# but not for
# df = pd.DataFrame({'a': [1, 2]})
# df.loc[:, 'b'] = .3
# - Exclude `object`, as then no upcasting happens.
# - Exclude empty initial object with enlargement,
# as then there's nothing to be inconsistent with.
warnings.warn(
f"Setting an item of incompatible dtype is deprecated "
"and will raise in a future error of pandas. "
f"Value '{value}' has dtype incompatible with {dtype}, "
"please explicitly cast to a compatible dtype first.",
FutureWarning,
stacklevel=find_stack_level(),
)
self.obj.isetitem(loc, value)
else:
# set value into the column (first attempting to operate inplace, then
# falling back to casting if necessary)
dtype = self.obj.dtypes.iloc[loc]
if dtype == np.void:
# This means we're expanding, with multiple columns, e.g.
# df = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6]})
# df.loc[df.index <= 2, ['F', 'G']] = (1, 'abc')
# Columns F and G will initially be set to np.void.
# Here, we replace those temporary `np.void` columns with
# columns of the appropriate dtype, based on `value`.
self.obj.iloc[:, loc] = construct_1d_array_from_inferred_fill_value(
value, len(self.obj)
)
self.obj._mgr.column_setitem(loc, plane_indexer, value)
self.obj._clear_item_cache()
def _setitem_single_block(self, indexer, value, name: str) -> None:
"""
_setitem_with_indexer for the case when we have a single Block.
"""
from pandas import Series
if (isinstance(value, ABCSeries) and name != "iloc") or isinstance(value, dict):
# TODO(EA): ExtensionBlock.setitem this causes issues with
# setting for extensionarrays that store dicts. Need to decide
# if it's worth supporting that.
value = self._align_series(indexer, Series(value))
info_axis = self.obj._info_axis_number
item_labels = self.obj._get_axis(info_axis)
if isinstance(indexer, tuple):
# if we are setting on the info axis ONLY
# set using those methods to avoid block-splitting
# logic here
if (
self.ndim == len(indexer) == 2
and is_integer(indexer[1])
and com.is_null_slice(indexer[0])
):
col = item_labels[indexer[info_axis]]
if len(item_labels.get_indexer_for([col])) == 1:
# e.g. test_loc_setitem_empty_append_expands_rows
loc = item_labels.get_loc(col)
self._setitem_single_column(loc, value, indexer[0])
return
indexer = maybe_convert_ix(*indexer) # e.g. test_setitem_frame_align
if isinstance(value, ABCDataFrame) and name != "iloc":
value = self._align_frame(indexer, value)._values
# check for chained assignment
self.obj._check_is_chained_assignment_possible()
# actually do the set
self.obj._mgr = self.obj._mgr.setitem(indexer=indexer, value=value)
self.obj._maybe_update_cacher(clear=True, inplace=True)
def _setitem_with_indexer_missing(self, indexer, value):
"""
Insert new row(s) or column(s) into the Series or DataFrame.
"""
from pandas import Series
# reindex the axis to the new value
# and set inplace
if self.ndim == 1:
index = self.obj.index
with warnings.catch_warnings():
# TODO: re-issue this with setitem-specific message?
warnings.filterwarnings(
"ignore",
"The behavior of Index.insert with object-dtype is deprecated",
category=FutureWarning,
)
new_index = index.insert(len(index), indexer)
# we have a coerced indexer, e.g. a float
# that matches in an int64 Index, so
# we will not create a duplicate index, rather
# index to that element
# e.g. 0.0 -> 0
# GH#12246
if index.is_unique:
# pass new_index[-1:] instead if [new_index[-1]]
# so that we retain dtype
new_indexer = index.get_indexer(new_index[-1:])
if (new_indexer != -1).any():
# We get only here with loc, so can hard code
return self._setitem_with_indexer(new_indexer, value, "loc")
# this preserves dtype of the value and of the object
if not is_scalar(value):
new_dtype = None
elif is_valid_na_for_dtype(value, self.obj.dtype):
if not is_object_dtype(self.obj.dtype):
# Every NA value is suitable for object, no conversion needed
value = na_value_for_dtype(self.obj.dtype, compat=False)
new_dtype = maybe_promote(self.obj.dtype, value)[0]
elif isna(value):
new_dtype = None
elif not self.obj.empty and not is_object_dtype(self.obj.dtype):
# We should not cast, if we have object dtype because we can
# set timedeltas into object series
curr_dtype = self.obj.dtype
curr_dtype = getattr(curr_dtype, "numpy_dtype", curr_dtype)
new_dtype = maybe_promote(curr_dtype, value)[0]
else:
new_dtype = None
new_values = Series([value], dtype=new_dtype)._values
if len(self.obj._values):
# GH#22717 handle casting compatibility that np.concatenate
# does incorrectly
new_values = concat_compat([self.obj._values, new_values])
self.obj._mgr = self.obj._constructor(
new_values, index=new_index, name=self.obj.name
)._mgr
self.obj._maybe_update_cacher(clear=True)
elif self.ndim == 2:
if not len(self.obj.columns):
# no columns and scalar
raise ValueError("cannot set a frame with no defined columns")
has_dtype = hasattr(value, "dtype")
if isinstance(value, ABCSeries):
# append a Series
value = value.reindex(index=self.obj.columns, copy=True)
value.name = indexer
elif isinstance(value, dict):
value = Series(
value, index=self.obj.columns, name=indexer, dtype=object
)
else:
# a list-list
if is_list_like_indexer(value):
# must have conforming columns
if len(value) != len(self.obj.columns):
raise ValueError("cannot set a row with mismatched columns")
value = Series(value, index=self.obj.columns, name=indexer)
if not len(self.obj):
# We will ignore the existing dtypes instead of using
# internals.concat logic
df = value.to_frame().T
idx = self.obj.index
if isinstance(idx, MultiIndex):
name = idx.names
else:
name = idx.name
df.index = Index([indexer], name=name)
if not has_dtype:
# i.e. if we already had a Series or ndarray, keep that
# dtype. But if we had a list or dict, then do inference
df = df.infer_objects(copy=False)
self.obj._mgr = df._mgr
else:
self.obj._mgr = self.obj._append(value)._mgr
self.obj._maybe_update_cacher(clear=True)
def _ensure_iterable_column_indexer(self, column_indexer):
"""
Ensure that our column indexer is something that can be iterated over.
"""
ilocs: Sequence[int | np.integer] | np.ndarray
if is_integer(column_indexer):
ilocs = [column_indexer]
elif isinstance(column_indexer, slice):
ilocs = np.arange(len(self.obj.columns))[column_indexer]
elif (
isinstance(column_indexer, np.ndarray) and column_indexer.dtype.kind == "b"
):
ilocs = np.arange(len(column_indexer))[column_indexer]
else:
ilocs = column_indexer
return ilocs
def _align_series(
self,
indexer,
ser: Series,
multiindex_indexer: bool = False,
using_cow: bool = False,
):
"""
Parameters
----------
indexer : tuple, slice, scalar
Indexer used to get the locations that will be set to `ser`.
ser : pd.Series
Values to assign to the locations specified by `indexer`.
multiindex_indexer : bool, optional
Defaults to False. Should be set to True if `indexer` was from
a `pd.MultiIndex`, to avoid unnecessary broadcasting.
Returns
-------
`np.array` of `ser` broadcast to the appropriate shape for assignment
to the locations selected by `indexer`
"""
if isinstance(indexer, (slice, np.ndarray, list, Index)):
indexer = (indexer,)
if isinstance(indexer, tuple):
# flatten np.ndarray indexers
def ravel(i):
return i.ravel() if isinstance(i, np.ndarray) else i
indexer = tuple(map(ravel, indexer))
aligners = [not com.is_null_slice(idx) for idx in indexer]
sum_aligners = sum(aligners)
single_aligner = sum_aligners == 1
is_frame = self.ndim == 2
obj = self.obj
# are we a single alignable value on a non-primary
# dim (e.g. panel: 1,2, or frame: 0) ?
# hence need to align to a single axis dimension
# rather that find all valid dims
# frame
if is_frame:
single_aligner = single_aligner and aligners[0]
# we have a frame, with multiple indexers on both axes; and a
# series, so need to broadcast (see GH5206)
if sum_aligners == self.ndim and all(is_sequence(_) for _ in indexer):
ser_values = ser.reindex(obj.axes[0][indexer[0]], copy=True)._values
# single indexer
if len(indexer) > 1 and not multiindex_indexer:
len_indexer = len(indexer[1])
ser_values = (
np.tile(ser_values, len_indexer).reshape(len_indexer, -1).T
)
return ser_values
for i, idx in enumerate(indexer):
ax = obj.axes[i]
# multiple aligners (or null slices)
if is_sequence(idx) or isinstance(idx, slice):
if single_aligner and com.is_null_slice(idx):
continue
new_ix = ax[idx]
if not is_list_like_indexer(new_ix):
new_ix = Index([new_ix])
else:
new_ix = Index(new_ix)
if ser.index.equals(new_ix):
if using_cow:
return ser
return ser._values.copy()
return ser.reindex(new_ix)._values
# 2 dims
elif single_aligner:
# reindex along index
ax = self.obj.axes[1]
if ser.index.equals(ax) or not len(ax):
return ser._values.copy()
return ser.reindex(ax)._values
elif is_integer(indexer) and self.ndim == 1:
if is_object_dtype(self.obj.dtype):
return ser
ax = self.obj._get_axis(0)
if ser.index.equals(ax):
return ser._values.copy()
return ser.reindex(ax)._values[indexer]
elif is_integer(indexer):
ax = self.obj._get_axis(1)
if ser.index.equals(ax):
return ser._values.copy()
return ser.reindex(ax)._values
raise ValueError("Incompatible indexer with Series")
def _align_frame(self, indexer, df: DataFrame) -> DataFrame:
is_frame = self.ndim == 2
if isinstance(indexer, tuple):
idx, cols = None, None
sindexers = []
for i, ix in enumerate(indexer):
ax = self.obj.axes[i]
if is_sequence(ix) or isinstance(ix, slice):
if isinstance(ix, np.ndarray):
ix = ix.ravel()
if idx is None:
idx = ax[ix]
elif cols is None:
cols = ax[ix]
else:
break
else:
sindexers.append(i)
if idx is not None and cols is not None:
if df.index.equals(idx) and df.columns.equals(cols):
val = df.copy()
else:
val = df.reindex(idx, columns=cols)
return val
elif (isinstance(indexer, slice) or is_list_like_indexer(indexer)) and is_frame:
ax = self.obj.index[indexer]
if df.index.equals(ax):
val = df.copy()
else:
# we have a multi-index and are trying to align
# with a particular, level GH3738
if (
isinstance(ax, MultiIndex)
and isinstance(df.index, MultiIndex)
and ax.nlevels != df.index.nlevels
):
raise TypeError(
"cannot align on a multi-index with out "
"specifying the join levels"
)
val = df.reindex(index=ax)
return val
raise ValueError("Incompatible indexer with DataFrame")
class _ScalarAccessIndexer(NDFrameIndexerBase):
"""
Access scalars quickly.
"""
# sub-classes need to set _takeable
_takeable: bool
def _convert_key(self, key):
raise AbstractMethodError(self)
def __getitem__(self, key):
if not isinstance(key, tuple):
# we could have a convertible item here (e.g. Timestamp)
if not is_list_like_indexer(key):
key = (key,)
else:
raise ValueError("Invalid call for scalar access (getting)!")
key = self._convert_key(key)
return self.obj._get_value(*key, takeable=self._takeable)
def __setitem__(self, key, value) -> None:
if isinstance(key, tuple):
key = tuple(com.apply_if_callable(x, self.obj) for x in key)
else:
# scalar callable may return tuple
key = com.apply_if_callable(key, self.obj)
if not isinstance(key, tuple):
key = _tuplify(self.ndim, key)
key = list(self._convert_key(key))
if len(key) != self.ndim:
raise ValueError("Not enough indexers for scalar access (setting)!")
self.obj._set_value(*key, value=value, takeable=self._takeable)
@doc(IndexingMixin.at)
class _AtIndexer(_ScalarAccessIndexer):
_takeable = False
def _convert_key(self, key):
"""
Require they keys to be the same type as the index. (so we don't
fallback)
"""
# GH 26989
# For series, unpacking key needs to result in the label.
# This is already the case for len(key) == 1; e.g. (1,)
if self.ndim == 1 and len(key) > 1:
key = (key,)
return key
@property
def _axes_are_unique(self) -> bool:
# Only relevant for self.ndim == 2
assert self.ndim == 2
return self.obj.index.is_unique and self.obj.columns.is_unique
def __getitem__(self, key):
if self.ndim == 2 and not self._axes_are_unique:
# GH#33041 fall back to .loc
if not isinstance(key, tuple) or not all(is_scalar(x) for x in key):
raise ValueError("Invalid call for scalar access (getting)!")
return self.obj.loc[key]
return super().__getitem__(key)
def __setitem__(self, key, value) -> None:
if self.ndim == 2 and not self._axes_are_unique:
# GH#33041 fall back to .loc
if not isinstance(key, tuple) or not all(is_scalar(x) for x in key):
raise ValueError("Invalid call for scalar access (setting)!")
self.obj.loc[key] = value
return
return super().__setitem__(key, value)
@doc(IndexingMixin.iat)
class _iAtIndexer(_ScalarAccessIndexer):
_takeable = True
def _convert_key(self, key):
"""
Require integer args. (and convert to label arguments)
"""
for i in key:
if not is_integer(i):
raise ValueError("iAt based indexing can only have integer indexers")
return key
def _tuplify(ndim: int, loc: Hashable) -> tuple[Hashable | slice, ...]:
"""
Given an indexer for the first dimension, create an equivalent tuple
for indexing over all dimensions.
Parameters
----------
ndim : int
loc : object
Returns
-------
tuple
"""
_tup: list[Hashable | slice]
_tup = [slice(None, None) for _ in range(ndim)]
_tup[0] = loc
return tuple(_tup)
def _tupleize_axis_indexer(ndim: int, axis: AxisInt, key) -> tuple:
"""
If we have an axis, adapt the given key to be axis-independent.
"""
new_key = [slice(None)] * ndim
new_key[axis] = key
return tuple(new_key)
def check_bool_indexer(index: Index, key) -> np.ndarray:
"""
Check if key is a valid boolean indexer for an object with such index and
perform reindexing or conversion if needed.
This function assumes that is_bool_indexer(key) == True.
Parameters
----------
index : Index
Index of the object on which the indexing is done.
key : list-like
Boolean indexer to check.
Returns
-------
np.array
Resulting key.
Raises
------
IndexError
If the key does not have the same length as index.
IndexingError
If the index of the key is unalignable to index.
"""
result = key
if isinstance(key, ABCSeries) and not key.index.equals(index):
indexer = result.index.get_indexer_for(index)
if -1 in indexer:
raise IndexingError(
"Unalignable boolean Series provided as "
"indexer (index of the boolean Series and of "
"the indexed object do not match)."
)
result = result.take(indexer)
# fall through for boolean
if not isinstance(result.dtype, ExtensionDtype):
return result.astype(bool)._values
if is_object_dtype(key):
# key might be object-dtype bool, check_array_indexer needs bool array
result = np.asarray(result, dtype=bool)
elif not is_array_like(result):
# GH 33924
# key may contain nan elements, check_array_indexer needs bool array
result = pd_array(result, dtype=bool)
return check_array_indexer(index, result)
def convert_missing_indexer(indexer):
"""
Reverse convert a missing indexer, which is a dict
return the scalar indexer and a boolean indicating if we converted
"""
if isinstance(indexer, dict):
# a missing key (but not a tuple indexer)
indexer = indexer["key"]
if isinstance(indexer, bool):
raise KeyError("cannot use a single bool to index into setitem")
return indexer, True
return indexer, False
def convert_from_missing_indexer_tuple(indexer, axes):
"""
Create a filtered indexer that doesn't have any missing indexers.
"""
def get_indexer(_i, _idx):
return axes[_i].get_loc(_idx["key"]) if isinstance(_idx, dict) else _idx
return tuple(get_indexer(_i, _idx) for _i, _idx in enumerate(indexer))
def maybe_convert_ix(*args):
"""
We likely want to take the cross-product.
"""
for arg in args:
if not isinstance(arg, (np.ndarray, list, ABCSeries, Index)):
return args
return np.ix_(*args)
def is_nested_tuple(tup, labels) -> bool:
"""
Returns
-------
bool
"""
# check for a compatible nested tuple and multiindexes among the axes
if not isinstance(tup, tuple):
return False
for k in tup:
if is_list_like(k) or isinstance(k, slice):
return isinstance(labels, MultiIndex)
return False
def is_label_like(key) -> bool:
"""
Returns
-------
bool
"""
# select a label or row
return (
not isinstance(key, slice)
and not is_list_like_indexer(key)
and key is not Ellipsis
)
def need_slice(obj: slice) -> bool:
"""
Returns
-------
bool
"""
return (
obj.start is not None
or obj.stop is not None
or (obj.step is not None and obj.step != 1)
)
def check_dict_or_set_indexers(key) -> None:
"""
Check if the indexer is or contains a dict or set, which is no longer allowed.
"""
if (
isinstance(key, set)
or isinstance(key, tuple)
and any(isinstance(x, set) for x in key)
):
raise TypeError(
"Passing a set as an indexer is not supported. Use a list instead."
)
if (
isinstance(key, dict)
or isinstance(key, tuple)
and any(isinstance(x, dict) for x in key)
):
raise TypeError(
"Passing a dict as an indexer is not supported. Use a list instead."
)