from typing import Hashable, List, Tuple, Union import numpy as np from pandas._libs.indexing import _NDFrameIndexerBase from pandas._libs.lib import item_from_zerodim from pandas.errors import AbstractMethodError from pandas.util._decorators import Appender from pandas.core.dtypes.common import ( is_float, 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.generic import ABCDataFrame, ABCMultiIndex, ABCSeries from pandas.core.dtypes.missing import _infer_fill_value, isna import pandas.core.common as com from pandas.core.indexers import ( check_array_indexer, is_list_like_indexer, length_of_indexer, ) from pandas.core.indexes.api import Index, InvalidIndexError # "null slice" _NS = slice(None, None) # 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 ` 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 IndexingError(Exception): pass class IndexingMixin: """Mixin for adding .loc/.iloc/.at/.iat to Datafames and Series. """ @property def iloc(self) -> "_iLocIndexer": """ Purely integer-location based indexing for selection by position. ``.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. ``.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 `. 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]) >>> 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]]) >>> 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]``. - 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 ` Raises ------ KeyError If any items are not found. 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 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 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 **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 """ 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 'label' does not exist in DataFrame. See Also -------- 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). Series.at : Access a single value using a label. 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 _NDFrameIndexer(_NDFrameIndexerBase): _valid_types: str axis = None def __call__(self, axis=None): # we need to return a copy of ourselves new_self = type(self)(self.name, self.obj) if axis is not None: axis = self.obj._get_axis_number(axis) new_self.axis = axis return new_self # TODO: remove once geopandas no longer needs this def __getitem__(self, key): # Used in ix and downstream in geopandas _CoordinateIndexer if type(key) is tuple: # Note: we check the type exactly instead of with isinstance # because NamedTuple is checked separately. key = tuple(com.apply_if_callable(x, self.obj) for x in key) try: values = self.obj._get_value(*key) except (KeyError, TypeError, InvalidIndexError, AttributeError): # TypeError occurs here if the key has non-hashable entries, # generally slice or list. # TODO(ix): most/all of the TypeError cases here are for ix, # so this check can be removed once ix is removed. # The InvalidIndexError is only catched for compatibility # with geopandas, see # https://github.com/pandas-dev/pandas/issues/27258 # TODO: The AttributeError is for IntervalIndex which # incorrectly implements get_value, see # https://github.com/pandas-dev/pandas/issues/27865 pass else: if is_scalar(values): return values return self._getitem_tuple(key) else: # we by definition only have the 0th axis axis = self.axis or 0 key = com.apply_if_callable(key, self.obj) return self._getitem_axis(key, axis=axis) def _get_label(self, label, axis: int): if self.ndim == 1: # for perf reasons we want to try _xs first # as its basically direct indexing # but will fail when the index is not present # see GH5667 return self.obj._xs(label, axis=axis) elif isinstance(label, tuple) and isinstance(label[axis], slice): raise IndexingError("no slices here, handle elsewhere") return self.obj._xs(label, axis=axis) def _get_loc(self, key: int, axis: int): return self.obj._ixs(key, axis=axis) def _slice(self, obj, axis: int, kind=None): return self.obj._slice(obj, axis=axis, kind=kind) def _get_setitem_indexer(self, key): if self.axis is not None: return self._convert_tuple(key) ax = self.obj._get_axis(0) if isinstance(ax, ABCMultiIndex) and self.name != "iloc": try: return ax.get_loc(key) except (TypeError, KeyError, InvalidIndexError): # TypeError e.g. passed a bool pass if isinstance(key, tuple): try: return self._convert_tuple(key) except IndexingError: pass if isinstance(key, range): return list(key) axis = self.axis or 0 try: return self._convert_to_indexer(key, axis=axis) except TypeError as e: # invalid indexer type vs 'other' indexing errors if "cannot do" in str(e): raise raise IndexingError(key) def __setitem__(self, key, value): if isinstance(key, tuple): key = tuple(com.apply_if_callable(x, self.obj) for x in key) else: key = com.apply_if_callable(key, self.obj) indexer = self._get_setitem_indexer(key) self._setitem_with_indexer(indexer, value) def _validate_key(self, key, axis: int): """ 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) def _has_valid_tuple(self, key: Tuple): """ Check the key for valid keys across my indexer. """ for i, k in enumerate(key): if i >= self.ndim: raise IndexingError("Too many indexers") try: self._validate_key(k, i) except ValueError: raise ValueError( "Location based indexing can only have " f"[{self._valid_types}] types" ) def _is_nested_tuple_indexer(self, tup: Tuple) -> bool: """ Returns ------- bool """ if any(isinstance(ax, ABCMultiIndex) for ax in self.obj.axes): return any(is_nested_tuple(tup, ax) for ax in self.obj.axes) return False def _convert_tuple(self, key): keyidx = [] if self.axis is not None: axis = self.obj._get_axis_number(self.axis) for i in range(self.ndim): if i == axis: keyidx.append(self._convert_to_indexer(key, axis=axis)) else: keyidx.append(slice(None)) else: for i, k in enumerate(key): if i >= self.ndim: raise IndexingError("Too many indexers") idx = self._convert_to_indexer(k, axis=i) keyidx.append(idx) return tuple(keyidx) def _convert_scalar_indexer(self, key, axis: int): # if we are accessing via lowered dim, use the last dim ax = self.obj._get_axis(min(axis, self.ndim - 1)) # a scalar return ax._convert_scalar_indexer(key, kind=self.name) def _convert_slice_indexer(self, key: slice, axis: int): # if we are accessing via lowered dim, use the last dim ax = self.obj._get_axis(min(axis, self.ndim - 1)) return ax._convert_slice_indexer(key, kind=self.name) def _has_valid_setitem_indexer(self, indexer) -> bool: return True def _has_valid_positional_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(f"{self.name} cannot enlarge its target object") else: 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( f"{self.name} cannot enlarge its target object" ) elif isinstance(i, dict): raise IndexError(f"{self.name} cannot enlarge its target object") return True def _setitem_with_indexer(self, indexer, value): self._has_valid_setitem_indexer(indexer) # also has the side effect of consolidating in-place from pandas import Series info_axis = self.obj._info_axis_number # maybe partial set take_split_path = self.obj._is_mixed_type # 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 self.obj._data.blocks: (blk,) = self.obj._data.blocks if 1 < blk.ndim: # in case of dict, keys are indices val = list(value.values()) if isinstance(value, dict) else value take_split_path = not blk._can_hold_element(val) # 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, ABCMultiIndex) 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 == self.obj._info_axis_number: # 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 len_non_info_axes = ( len(_ax) for _i, _ax in enumerate(self.obj.axes) if _i != i ) if any(not l for l in len_non_info_axes): 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 self.obj # add a new item with the dtype setup 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) return self.obj # 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) labels = index.insert(len(index), key) self.obj._data = self.obj.reindex(labels, axis=i)._data 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: return self._setitem_with_indexer_missing(indexer, value) # set item_labels = self.obj._get_axis(info_axis) # align and set the values if take_split_path: # Above we only set take_split_path to True for 2D cases assert self.ndim == 2 assert info_axis == 1 if not isinstance(indexer, tuple): indexer = _tuplify(self.ndim, indexer) if isinstance(value, ABCSeries): value = self._align_series(indexer, value) info_idx = indexer[info_axis] if is_integer(info_idx): info_idx = [info_idx] labels = item_labels[info_idx] # if we have a partial multiindex, then need to adjust the plane # indexer here if len(labels) == 1 and isinstance( self.obj[labels[0]].axes[0], ABCMultiIndex ): item = labels[0] obj = self.obj[item] index = obj.index idx = indexer[:info_axis][0] plane_indexer = tuple([idx]) + indexer[info_axis + 1 :] lplane_indexer = length_of_indexer(plane_indexer[0], index) # require that we are setting the right number of values that # we are indexing if ( is_list_like_indexer(value) and np.iterable(value) and lplane_indexer != len(value) ): if len(obj[idx]) != len(value): raise ValueError( "cannot set using a multi-index " "selection indexer with a different " "length than the value" ) # make sure we have an ndarray value = getattr(value, "values", value).ravel() # we can directly set the series here # as we select a slice indexer on the mi if isinstance(idx, slice): idx = index._convert_slice_indexer(idx) obj._consolidate_inplace() obj = obj.copy() obj._data = obj._data.setitem(indexer=tuple([idx]), value=value) self.obj[item] = obj return # non-mi else: plane_indexer = indexer[:info_axis] + indexer[info_axis + 1 :] plane_axis = self.obj.axes[:info_axis][0] lplane_indexer = length_of_indexer(plane_indexer[0], plane_axis) def setter(item, v): s = self.obj[item] pi = plane_indexer[0] if lplane_indexer == 1 else plane_indexer # perform the equivalent of a setitem on the info axis # as we have a null slice or a slice with full bounds # which means essentially reassign to the columns of a # multi-dim object # GH6149 (null slice), GH10408 (full bounds) if isinstance(pi, tuple) and all( com.is_null_slice(idx) or com.is_full_slice(idx, len(self.obj)) for idx in pi ): s = v else: # set the item, possibly having a dtype change s._consolidate_inplace() s = s.copy() s._data = s._data.setitem(indexer=pi, value=v) s._maybe_update_cacher(clear=True) # reset the sliced object if unique self.obj[item] = s # 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: # we have an equal len Frame if isinstance(value, ABCDataFrame): sub_indexer = list(indexer) multiindex_indexer = isinstance(labels, ABCMultiIndex) for item in labels: if item in value: sub_indexer[info_axis] = item v = self._align_series( tuple(sub_indexer), value[item], multiindex_indexer ) else: v = np.nan setter(item, v) # we have an equal len ndarray/convertible to our labels # hasattr first, to avoid coercing to ndarray without reason. # But we may be relying on the ndarray coercion to check ndim. # Why not just convert to an ndarray earlier on if needed? elif np.ndim(value) == 2: # note that this coerces the dtype if we are mixed # GH 7551 value = np.array(value, dtype=object) if len(labels) != value.shape[1]: raise ValueError( "Must have equal len keys and value " "when setting with an ndarray" ) for i, item in enumerate(labels): # setting with a list, recoerces setter(item, value[:, i].tolist()) # we have an equal len list/ndarray elif _can_do_equal_len( labels, value, plane_indexer, lplane_indexer, self.obj ): setter(labels[0], value) # per label values else: if len(labels) != len(value): raise ValueError( "Must have equal len keys and value " "when setting with an iterable" ) for item, v in zip(labels, value): setter(item, v) else: # scalar for item in labels: setter(item, value) else: if isinstance(indexer, tuple): indexer = maybe_convert_ix(*indexer) # if we are setting on the info axis ONLY # set using those methods to avoid block-splitting # logic here if ( len(indexer) > info_axis and is_integer(indexer[info_axis]) and all( com.is_null_slice(idx) for i, idx in enumerate(indexer) if i != info_axis ) and item_labels.is_unique ): self.obj[item_labels[indexer[info_axis]]] = value return if isinstance(value, (ABCSeries, 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)) elif isinstance(value, ABCDataFrame): value = self._align_frame(indexer, value) # check for chained assignment self.obj._check_is_chained_assignment_possible() # actually do the set self.obj._consolidate_inplace() self.obj._data = self.obj._data.setitem(indexer=indexer, value=value) self.obj._maybe_update_cacher(clear=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 new_index = index.insert(len(index), indexer) # we have a coerced indexer, e.g. a float # that matches in an Int64Index, so # we will not create a duplicate index, rather # index to that element # e.g. 0.0 -> 0 # GH#12246 if index.is_unique: new_indexer = index.get_indexer([new_index[-1]]) if (new_indexer != -1).any(): return self._setitem_with_indexer(new_indexer, value) # this preserves dtype of the value new_values = Series([value])._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._data = self.obj._constructor( new_values, index=new_index, name=self.obj.name )._data self.obj._maybe_update_cacher(clear=True) return self.obj elif self.ndim == 2: if not len(self.obj.columns): # no columns and scalar raise ValueError("cannot set a frame with no defined columns") if isinstance(value, ABCSeries): # append a Series value = value.reindex(index=self.obj.columns, copy=True) value.name = indexer 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) self.obj._data = self.obj.append(value)._data self.obj._maybe_update_cacher(clear=True) return self.obj def _align_series(self, indexer, ser: ABCSeries, multiindex_indexer: 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 : boolean, 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 = tuple([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 = 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 = np.tile(ser, len_indexer).reshape(len_indexer, -1).T return ser 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) or not len(new_ix): 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_scalar(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: ABCDataFrame): 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()._values else: val = df.reindex(idx, columns=cols)._values 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()._values else: # we have a multi-index and are trying to align # with a particular, level GH3738 if ( isinstance(ax, ABCMultiIndex) and isinstance(df.index, ABCMultiIndex) 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)._values return val raise ValueError("Incompatible indexer with DataFrame") def _getitem_tuple(self, tup: Tuple): try: return self._getitem_lowerdim(tup) except IndexingError: pass # no multi-index, so validate all of the indexers self._has_valid_tuple(tup) # ugly hack for GH #836 if self._multi_take_opportunity(tup): return self._multi_take(tup) # no shortcut needed retval = self.obj for i, key in enumerate(tup): if com.is_null_slice(key): continue retval = getattr(retval, self.name)._getitem_axis(key, axis=i) return retval 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 if any(com.is_bool_indexer(x) for x in tup): return False return True 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 o = self.obj d = { axis: self._get_listlike_indexer(key, axis) for (key, axis) in zip(tup, o._AXIS_ORDERS) } return o._reindex_with_indexers(d, copy=True, allow_dups=True) def _convert_for_reindex(self, key, axis: int): return key 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 TypeError: # slices are unhashable pass except KeyError as ek: # raise KeyError if number of indexers match # else IndexingError will be raised if len(tup) <= self.obj.index.nlevels and len(tup) > self.ndim: raise ek return None 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, ABCMultiIndex) and self.name != "iloc": result = self._handle_lowerdim_multi_index_axis0(tup) if result is not None: return result if len(tup) > self.ndim: raise IndexingError("Too many indexers. handle elsewhere") for i, key in enumerate(tup): if is_label_like(key) or isinstance(key, tuple): section = self._getitem_axis(key, axis=i) # we have yielded a scalar ? if not is_list_like_indexer(section): return section elif 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: new_key = tup[:i] + tup[i + 1 :] # unfortunately need an odious kludge here because of # DataFrame transposing convention if ( isinstance(section, ABCDataFrame) and i > 0 and len(new_key) == 2 ): a, b = new_key new_key = b, a 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/etc' return getattr(section, self.name)[new_key] raise IndexingError("not applicable") 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 # 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: result = self._handle_lowerdim_multi_index_axis0(tup) if result is not None: return result # 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 axis = 0 for i, key in enumerate(tup): if com.is_null_slice(key): axis += 1 continue current_ndim = obj.ndim 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 # has the dim of the obj changed? # GH 7199 if obj.ndim < current_ndim: axis -= 1 return obj # TODO: remove once geopandas no longer needs __getitem__ def _getitem_axis(self, key, axis: int): if is_iterator(key): key = list(key) self._validate_key(key, axis) labels = self.obj._get_axis(axis) if isinstance(key, slice): return self._get_slice_axis(key, axis=axis) elif is_list_like_indexer(key) and not ( isinstance(key, tuple) and isinstance(labels, ABCMultiIndex) ): if hasattr(key, "ndim") and key.ndim > 1: raise ValueError("Cannot index with multidimensional key") return self._getitem_iterable(key, axis=axis) else: # maybe coerce a float scalar to integer key = labels._maybe_cast_indexer(key) if is_integer(key): if axis == 0 and isinstance(labels, ABCMultiIndex): try: return self._get_label(key, axis=axis) except (KeyError, TypeError): if self.obj.index.levels[0].is_integer(): raise # this is the fallback! (for a non-float, non-integer index) if not labels.is_floating() and not labels.is_integer(): return self._get_loc(key, axis=axis) return self._get_label(key, axis=axis) def _get_listlike_indexer(self, key, axis: int, raise_missing: bool = False): """ 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. raise_missing: bool, default False Whether to raise a KeyError if some labels were not found. Will be removed in the future, and then this method will always behave as if ``raise_missing=True``. Raises ------ KeyError If at least one key was requested but none was found, and raise_missing=True. 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. """ o = self.obj ax = o._get_axis(axis) # Have the index compute an indexer or return None # if it cannot handle: indexer, keyarr = ax._convert_listlike_indexer(key, kind=self.name) # We only act on all found values: if indexer is not None and (indexer != -1).all(): self._validate_read_indexer(key, indexer, axis, raise_missing=raise_missing) return ax[indexer], indexer if ax.is_unique and not getattr(ax, "is_overlapping", False): # If we are trying to get actual keys from empty Series, we # patiently wait for a KeyError later on - otherwise, convert if len(ax) or not len(key): key = self._convert_for_reindex(key, axis) indexer = ax.get_indexer_for(key) keyarr = ax.reindex(keyarr)[0] else: keyarr, indexer, new_indexer = ax._reindex_non_unique(keyarr) self._validate_read_indexer( keyarr, indexer, o._get_axis_number(axis), raise_missing=raise_missing ) return keyarr, indexer def _getitem_iterable(self, key, axis: int): """ Index current object with an an iterable key. The iterable key can be a boolean indexer or a collection of keys. Parameters ---------- key : iterable Targeted labels or boolean indexer. 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. IndexingError If the boolean indexer is unalignable with the object being indexed. Returns ------- scalar, DataFrame, or Series: indexed value(s). """ # caller is responsible for ensuring non-None axis self._validate_key(key, axis) labels = self.obj._get_axis(axis) if com.is_bool_indexer(key): # A boolean indexer key = check_bool_indexer(labels, key) (inds,) = key.nonzero() return self.obj._take_with_is_copy(inds, axis=axis) else: # A collection of keys keyarr, indexer = self._get_listlike_indexer(key, axis, raise_missing=False) return self.obj._reindex_with_indexers( {axis: [keyarr, indexer]}, copy=True, allow_dups=True ) def _validate_read_indexer( self, key, indexer, axis: int, raise_missing: bool = False ): """ Check that indexer can be used to return a result. e.g. at least one element was found, unless the list of keys was actually empty. Parameters ---------- key : list-like Targeted labels (only used to show correct error message). indexer: array-like of booleans Indices corresponding to the key, (with -1 indicating not found). axis: int Dimension on which the indexing is being made. raise_missing: bool Whether to raise a KeyError if some labels are not found. Will be removed in the future, and then this method will always behave as if raise_missing=True. Raises ------ KeyError If at least one key was requested but none was found, and raise_missing=True. """ ax = self.obj._get_axis(axis) if len(key) == 0: return # Count missing values: missing = (indexer < 0).sum() if missing: if missing == len(indexer): axis_name = self.obj._get_axis_name(axis) raise KeyError(f"None of [{key}] are in the [{axis_name}]") # We (temporarily) allow for some missing keys with .loc, except in # some cases (e.g. setting) in which "raise_missing" will be False if not (self.name == "loc" and not raise_missing): not_found = list(set(key) - set(ax)) raise KeyError(f"{not_found} not in index") # we skip the warning on Categorical/Interval # as this check is actually done (check for # non-missing values), but a bit later in the # code, so we want to avoid warning & then # just raising if not (ax.is_categorical() or ax.is_interval()): raise KeyError( "Passing list-likes to .loc or [] with any missing labels " "is no longer supported, see " "https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike" # noqa:E501 ) def _convert_to_indexer(self, obj, axis: int, raise_missing: bool = False): """ 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(obj, slice): return self._convert_slice_indexer(obj, axis) # try to find out correct indexer, if not type correct raise try: obj = self._convert_scalar_indexer(obj, axis) except TypeError: # but we will allow setting pass # see if we are positional in nature is_int_index = labels.is_integer() is_int_positional = is_integer(obj) and not is_int_index # if we are a label return me try: return labels.get_loc(obj) except LookupError: if isinstance(obj, tuple) and isinstance(labels, ABCMultiIndex): if len(obj) == labels.nlevels: return {"key": obj} raise except TypeError: pass except ValueError: if not is_int_positional: raise # a positional if is_int_positional: # if we are setting and its not a valid location # its an insert which fails by definition if self.name == "loc": # always valid return {"key": obj} if obj >= self.obj.shape[axis] and not isinstance(labels, ABCMultiIndex): # a positional raise ValueError("cannot set by positional indexing with enlargement") return obj if is_nested_tuple(obj, labels): return labels.get_locs(obj) elif is_list_like_indexer(obj): if com.is_bool_indexer(obj): obj = check_bool_indexer(labels, obj) (inds,) = obj.nonzero() return inds else: # When setting, missing keys are not allowed, even with .loc: return self._get_listlike_indexer(obj, axis, raise_missing=True)[1] else: try: return labels.get_loc(obj) except LookupError: # allow a not found key only if we are a setter if not is_list_like_indexer(obj): return {"key": obj} raise def _get_slice_axis(self, slice_obj: slice, axis: int): # caller is responsible for ensuring non-None axis obj = self.obj if not need_slice(slice_obj): return obj.copy(deep=False) indexer = self._convert_slice_indexer(slice_obj, axis) return self._slice(indexer, axis=axis, kind="iloc") class _LocationIndexer(_NDFrameIndexer): def __getitem__(self, key): if type(key) is tuple: key = tuple(com.apply_if_callable(x, self.obj) for x in key) if self._is_scalar_access(key): try: return self._getitem_scalar(key) except (KeyError, IndexError, AttributeError): pass 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) return self._getitem_axis(maybe_callable, axis=axis) def _is_scalar_access(self, key: Tuple): raise NotImplementedError() def _getitem_scalar(self, key): raise NotImplementedError() def _getitem_axis(self, key, axis: int): raise NotImplementedError() def _getbool_axis(self, key, axis: int): # 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) def _get_slice_axis(self, slice_obj: slice, axis: int): """ 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, kind=self.name ) if isinstance(indexer, slice): return self._slice(indexer, axis=axis, kind="iloc") else: # DatetimeIndex overrides Index.slice_indexer and may # return a DatetimeIndex instead of a slice object. return self.obj._take_with_is_copy(indexer, axis=axis) @Appender(IndexingMixin.loc.__doc__) class _LocIndexer(_LocationIndexer): _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" ) @Appender(_NDFrameIndexer._validate_key.__doc__) def _validate_key(self, key, axis: int): # 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 if isinstance(key, slice): return if com.is_bool_indexer(key): return if not is_list_like_indexer(key): self._convert_scalar_indexer(key, axis) 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, ABCMultiIndex): 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.is_unique: return False return True def _getitem_scalar(self, key): # a fast-path to scalar access # if not, raise values = self.obj._get_value(*key) return values def _get_partial_string_timestamp_match_key(self, key, labels): """ Translate any partial string timestamp matches in key, returning the new key. (GH 10331) """ if isinstance(labels, ABCMultiIndex): if ( isinstance(key, str) and labels.levels[0]._supports_partial_string_indexing ): # Convert key '2016-01-01' to # ('2016-01-01'[, slice(None, None, None)]+) key = tuple([key] + [slice(None)] * (len(labels.levels) - 1)) if isinstance(key, tuple): # Convert (..., '2016-01-01', ...) in tuple to # (..., slice('2016-01-01', '2016-01-01', None), ...) new_key = [] for i, component in enumerate(key): if ( isinstance(component, str) and labels.levels[i]._supports_partial_string_indexing ): new_key.append(slice(component, component, None)) else: new_key.append(component) key = tuple(new_key) return key def _getitem_axis(self, key, axis: int): key = item_from_zerodim(key) if is_iterator(key): key = list(key) labels = self.obj._get_axis(axis) key = self._get_partial_string_timestamp_match_key(key, labels) 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): # convert various list-like indexers # to a list of keys # we will use the *values* of the object # and NOT the index if its a PandasObject if isinstance(labels, ABCMultiIndex): if isinstance(key, (ABCSeries, np.ndarray)) and key.ndim <= 1: # Series, or 0,1 ndim ndarray # GH 14730 key = list(key) elif isinstance(key, ABCDataFrame): # GH 15438 raise NotImplementedError( "Indexing a MultiIndex with a " "DataFrame key is not " "implemented" ) elif hasattr(key, "ndim") and key.ndim > 1: raise NotImplementedError( "Indexing a MultiIndex with a " "multidimensional key is not " "implemented" ) if ( not isinstance(key, tuple) and len(key) and not isinstance(key[0], tuple) ): key = tuple([key]) # an iterable multi-selection if not (isinstance(key, tuple) and isinstance(labels, ABCMultiIndex)): 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 = [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) @Appender(IndexingMixin.iloc.__doc__) class _iLocIndexer(_LocationIndexer): _valid_types = ( "integer, integer slice (START point is INCLUDED, END " "point is EXCLUDED), listlike of integers, boolean array" ) _get_slice_axis = _NDFrameIndexer._get_slice_axis def _validate_key(self, key, axis: int): 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): 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): self._has_valid_positional_setitem_indexer(indexer) 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_integer(k): return False ax = self.obj.axes[i] if not ax.is_unique: return False return True def _getitem_scalar(self, key): # a fast-path to scalar access # if not, raise values = self.obj._get_value(*key, takeable=True) return values def _validate_integer(self, key: int, axis: int) -> 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): self._has_valid_tuple(tup) try: return self._getitem_lowerdim(tup) except IndexingError: pass retval = self.obj axis = 0 for i, key in enumerate(tup): if com.is_null_slice(key): axis += 1 continue retval = getattr(retval, self.name)._getitem_axis(key, axis=axis) # if the dim was reduced, then pass a lower-dim the next time if retval.ndim < self.ndim: # TODO: this is never reached in tests; can we confirm that # it is impossible? axis -= 1 # try to get for the next axis axis += 1 return retval def _get_list_axis(self, key, axis: int): """ 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: # re-raise with different error message raise IndexError("positional indexers are out-of-bounds") def _getitem_axis(self, key, axis: int): if isinstance(key, slice): return self._get_slice_axis(key, axis=axis) 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._get_loc(key, axis=axis) # raise_missing is included for compat with the parent class signature def _convert_to_indexer(self, obj, axis: int, raise_missing: bool = False): """ Much simpler as we only have to deal with our valid types. """ # make need to convert a float key if isinstance(obj, slice): return self._convert_slice_indexer(obj, axis) elif is_float(obj): return self._convert_scalar_indexer(obj, axis) try: self._validate_key(obj, axis) return obj except ValueError: raise ValueError(f"Can only index by location with a [{self._valid_types}]") class _ScalarAccessIndexer(_NDFrameIndexerBase): """ Access scalars quickly. """ def _convert_key(self, key, is_setter: bool = False): 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 = tuple([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): 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) if len(key) != self.ndim: raise ValueError("Not enough indexers for scalar access (setting)!") key = list(self._convert_key(key, is_setter=True)) key.append(value) self.obj._set_value(*key, takeable=self._takeable) @Appender(IndexingMixin.at.__doc__) class _AtIndexer(_ScalarAccessIndexer): _takeable = False def _convert_key(self, key, is_setter: bool = False): """ Require they keys to be the same type as the index. (so we don't fallback) """ # allow arbitrary setting if is_setter: return list(key) for ax, i in zip(self.obj.axes, key): if ax.is_integer(): if not is_integer(i): raise ValueError( "At based indexing on an integer index " "can only have integer indexers" ) else: if is_integer(i) and not ax.holds_integer(): raise ValueError( "At based indexing on an non-integer " "index can only have non-integer " "indexers" ) return key @Appender(IndexingMixin.iat.__doc__) class _iAtIndexer(_ScalarAccessIndexer): _takeable = True def _convert_key(self, key, is_setter: bool = False): """ Require integer args. (and convert to label arguments) """ for a, i in zip(self.obj.axes, 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[Union[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[Union[Hashable, slice]] _tup = [slice(None, None) for _ in range(ndim)] _tup[0] = loc return tuple(_tup) def convert_to_index_sliceable(obj, key): """ If we are index sliceable, then return my slicer, otherwise return None. """ idx = obj.index if isinstance(key, slice): return idx._convert_slice_indexer(key, kind="getitem") elif isinstance(key, str): # we are an actual column if key in obj._data.items: return None # We might have a datetimelike string that we can translate to a # slice here via partial string indexing if idx._supports_partial_string_indexing: try: return idx._get_string_slice(key) except (KeyError, ValueError, NotImplementedError): return None return None 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): result = result.reindex(index) mask = isna(result._values) if mask.any(): raise IndexingError( "Unalignable boolean Series provided as " "indexer (index of the boolean Series and of " "the indexed object do not match)." ) result = result.astype(bool)._values elif is_object_dtype(key): # key might be object-dtype bool, check_array_indexer needs bool array result = np.asarray(result, dtype=bool) result = check_array_indexer(index, result) else: result = check_array_indexer(index, result) return 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. """ ixify = True for arg in args: if not isinstance(arg, (np.ndarray, list, ABCSeries, Index)): ixify = False if ixify: return np.ix_(*args) else: return 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 i, k in enumerate(tup): if is_list_like(k) or isinstance(k, slice): return isinstance(labels, ABCMultiIndex) 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) def need_slice(obj) -> 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 _non_reducing_slice(slice_): """ Ensurse that a slice doesn't reduce to a Series or Scalar. Any user-paseed `subset` should have this called on it to make sure we're always working with DataFrames. """ # default to column slice, like DataFrame # ['A', 'B'] -> IndexSlices[:, ['A', 'B']] kinds = (ABCSeries, np.ndarray, Index, list, str) if isinstance(slice_, kinds): slice_ = IndexSlice[:, slice_] def pred(part) -> bool: """ Returns ------- bool True if slice does *not* reduce, False if `part` is a tuple. """ # true when slice does *not* reduce, False when part is a tuple, # i.e. MultiIndex slice return (isinstance(part, slice) or is_list_like(part)) and not isinstance( part, tuple ) if not is_list_like(slice_): if not isinstance(slice_, slice): # a 1-d slice, like df.loc[1] slice_ = [[slice_]] else: # slice(a, b, c) slice_ = [slice_] # to tuplize later else: slice_ = [part if pred(part) else [part] for part in slice_] return tuple(slice_) def _maybe_numeric_slice(df, slice_, include_bool=False): """ Want nice defaults for background_gradient that don't break with non-numeric data. But if slice_ is passed go with that. """ if slice_ is None: dtypes = [np.number] if include_bool: dtypes.append(bool) slice_ = IndexSlice[:, df.select_dtypes(include=dtypes).columns] return slice_ def _can_do_equal_len(labels, value, plane_indexer, lplane_indexer, obj) -> bool: """ Returns ------- bool True if we have an equal len settable. """ if not len(labels) == 1 or not np.iterable(value) or is_scalar(plane_indexer[0]): return False item = labels[0] index = obj[item].index values_len = len(value) # equal len list/ndarray if len(index) == values_len: return True elif lplane_indexer == values_len: return True return False