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/ core / indexing.py
import textwrap
from typing import Tuple
import warnings
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 (
ensure_platform_int,
is_float,
is_integer,
is_integer_dtype,
is_iterator,
is_list_like,
is_numeric_dtype,
is_scalar,
is_sequence,
is_sparse,
)
from pandas.core.dtypes.concat import _concat_compat
from pandas.core.dtypes.generic import ABCDataFrame, ABCSeries
from pandas.core.dtypes.missing import _infer_fill_value, isna
import pandas.core.common as com
from pandas.core.index import Index, InvalidIndexError, MultiIndex
from pandas.core.indexers import is_list_like_indexer, length_of_indexer
# the supported indexers
def get_indexers_list():
return [
("ix", _IXIndexer),
("iloc", _iLocIndexer),
("loc", _LocIndexer),
("at", _AtIndexer),
("iat", _iAtIndexer),
]
# "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 <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 IndexingError(Exception):
pass
class _NDFrameIndexer(_NDFrameIndexerBase):
_valid_types = None # type: str
_exception = Exception
axis = None
def __call__(self, axis=None):
# we need to return a copy of ourselves
new_self = self.__class__(self.name, self.obj)
if axis is not None:
axis = self.obj._get_axis_number(axis)
new_self.axis = axis
return new_self
def __iter__(self):
raise NotImplementedError("ix is not iterable")
def __getitem__(self, key):
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, is_setter=True)
ax = self.obj._get_axis(0)
if isinstance(ax, MultiIndex) and self.name != "iloc":
try:
return ax.get_loc(key)
except Exception:
pass
if isinstance(key, tuple):
try:
return self._convert_tuple(key, is_setter=True)
except IndexingError:
pass
if isinstance(key, range):
return self._convert_range(key, is_setter=True)
axis = self.axis or 0
try:
return self._convert_to_indexer(key, axis=axis, is_setter=True)
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
The 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):
""" check the key for valid keys across my indexer """
for i, k in enumerate(key):
if i >= self.obj.ndim:
raise IndexingError("Too many indexers")
try:
self._validate_key(k, i)
except ValueError:
raise ValueError(
"Location based indexing can only have "
"[{types}] types".format(types=self._valid_types)
)
def _is_nested_tuple_indexer(self, tup):
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
def _convert_tuple(self, key, is_setter: bool = False):
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, is_setter=is_setter)
)
else:
keyidx.append(slice(None))
else:
for i, k in enumerate(key):
if i >= self.obj.ndim:
raise IndexingError("Too many indexers")
idx = self._convert_to_indexer(k, axis=i, is_setter=is_setter)
keyidx.append(idx)
return tuple(keyidx)
def _convert_range(self, key, is_setter: bool = False):
""" convert a range argument """
return list(key)
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, 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):
return True
def _has_valid_positional_setitem_indexer(self, indexer):
""" validate that an positional indexer cannot enlarge its target
will raise if needed, does not modify the indexer externally
"""
if isinstance(indexer, dict):
raise IndexError("{0} cannot enlarge its target object".format(self.name))
else:
if not isinstance(indexer, tuple):
indexer = self._tuplify(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(
"{name} cannot enlarge its target "
"object".format(name=self.name)
)
elif isinstance(i, dict):
raise IndexError(
"{name} cannot enlarge its target object".format(name=self.name)
)
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, 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 == 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 = self._tuplify(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], MultiIndex):
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
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) and value.ndim > 1:
sub_indexer = list(indexer)
multiindex_indexer = isinstance(labels, MultiIndex)
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, multiindex_indexer=False):
"""
Parameters
----------
indexer : tuple, slice, scalar
The indexer used to get the locations that will be set to
`ser`
ser : pd.Series
The 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.obj.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):
is_frame = self.obj.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, 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)._values
return val
raise ValueError("Incompatible indexer with DataFrame")
def _getitem_tuple(self, tup):
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):
"""
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
-------
boolean: 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):
"""
Create the indexers for the passed tuple of keys, and execute 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):
# 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.obj.ndim:
raise ek
except Exception as e1:
if isinstance(tup[0], (slice, Index)):
raise IndexingError("Handle elsewhere")
# raise the error if we are not sorted
ax0 = self.obj._get_axis(0)
if not ax0.is_lexsorted_for_tuple(tup):
raise e1
return None
def _getitem_lowerdim(self, tup):
# 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":
result = self._handle_lowerdim_multi_index_axis0(tup)
if result is not None:
return result
if len(tup) > self.obj.ndim:
raise IndexingError("Too many indexers. handle elsewhere")
# to avoid wasted computation
# df.ix[d1:d2, 0] -> columns first (True)
# df.ix[0, ['C', 'B', A']] -> rows first (False)
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):
# 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
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, MultiIndex)
):
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, MultiIndex):
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
Target labels
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.
Returns
-------
keyarr: Index
New index (coinciding with 'key' if the axis is unique)
values : array-like
An 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 (which can be a boolean
indexer, or a collection of keys).
Parameters
----------
key : iterable
Target 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(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
Target 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):
raise KeyError(
"None of [{key}] are in the [{axis}]".format(
key=key, axis=self.obj._get_axis_name(axis)
)
)
# 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("{} not in index".format(not_found))
# 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
_missing_key_warning = textwrap.dedent(
"""
Passing list-likes to .loc or [] with any missing label will raise
KeyError in the future, you can use .reindex() as an alternative.
See the documentation here:
https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike""" # noqa: E501
)
if not (ax.is_categorical() or ax.is_interval()):
warnings.warn(_missing_key_warning, FutureWarning, stacklevel=6)
def _convert_to_indexer(
self, obj, axis: int, is_setter: bool = False, raise_missing: bool = False
):
"""
Convert indexing key into something we can use to do actual fancy
indexing on an 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
if is_setter:
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, MultiIndex):
if is_setter and 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 is_setter:
# always valid
if self.name == "loc":
return {"key": obj}
# a positional
if obj >= self.obj.shape[axis] and not isinstance(labels, MultiIndex):
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:
kwargs = {"raise_missing": True if is_setter else raise_missing}
return self._get_listlike_indexer(obj, axis, **kwargs)[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) and is_setter:
return {"key": obj}
raise
def _tuplify(self, loc):
tup = [slice(None, None) for _ in range(self.ndim)]
tup[0] = loc
return tuple(tup)
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 _IXIndexer(_NDFrameIndexer):
"""
A primarily label-location based indexer, with integer position
fallback.
Warning: Starting in 0.20.0, the .ix indexer is deprecated, in
favor of the more strict .iloc and .loc indexers.
``.ix[]`` supports mixed integer and label based access. It is
primarily label based, but will fall back to integer positional
access unless the corresponding axis is of integer type.
``.ix`` is the most general indexer and will support any of the
inputs in ``.loc`` and ``.iloc``. ``.ix`` also supports floating
point label schemes. ``.ix`` is exceptionally useful when dealing
with mixed positional and label based hierarchical indexes.
However, when an axis is integer based, ONLY label based access
and not positional access is supported. Thus, in such cases, it's
usually better to be explicit and use ``.iloc`` or ``.loc``.
See more at :ref:`Advanced Indexing <advanced>`.
"""
_ix_deprecation_warning = textwrap.dedent(
"""
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexing
See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated""" # noqa: E501
)
def __init__(self, name, obj):
warnings.warn(self._ix_deprecation_warning, FutureWarning, stacklevel=2)
super().__init__(name, obj)
@Appender(_NDFrameIndexer._validate_key.__doc__)
def _validate_key(self, key, axis: int):
if isinstance(key, slice):
return True
elif com.is_bool_indexer(key):
return True
elif is_list_like_indexer(key):
return True
else:
self._convert_scalar_indexer(key, axis)
return True
def _convert_for_reindex(self, key, axis: int):
"""
Transform a list of keys into a new array ready to be used as axis of
the object we return (e.g. including NaNs).
Parameters
----------
key : list-like
Target labels
axis: int
Where the indexing is being made
Returns
-------
list-like of labels
"""
labels = self.obj._get_axis(axis)
if com.is_bool_indexer(key):
key = check_bool_indexer(labels, key)
return labels[key]
if isinstance(key, Index):
keyarr = labels._convert_index_indexer(key)
else:
# asarray can be unsafe, NumPy strings are weird
keyarr = com.asarray_tuplesafe(key)
if is_integer_dtype(keyarr):
# Cast the indexer to uint64 if possible so
# that the values returned from indexing are
# also uint64.
keyarr = labels._convert_arr_indexer(keyarr)
if not labels.is_integer():
keyarr = ensure_platform_int(keyarr)
return labels.take(keyarr)
return keyarr
class _LocationIndexer(_NDFrameIndexer):
_exception = Exception
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()
try:
return self.obj.take(inds, axis=axis)
except Exception as detail:
raise self._exception(detail)
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(indexer, axis=axis)
class _LocIndexer(_LocationIndexer):
"""
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 <indexing.label>`
Raises
------
KeyError:
when 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
"""
_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"
)
_exception = KeyError
@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):
# 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.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, MultiIndex):
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, MultiIndex):
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, 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 = [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)
class _iLocIndexer(_LocationIndexer):
"""
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 <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
"""
_valid_types = (
"integer, integer slice (START point is INCLUDED, END "
"point is EXCLUDED), listlike of integers, boolean array"
)
_exception = IndexError
_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(
".iloc requires numeric indexers, got {arr}".format(arr=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(
"Can only index by location with "
"a [{types}]".format(types=self._valid_types)
)
def _has_valid_setitem_indexer(self, indexer):
self._has_valid_positional_setitem_indexer(indexer)
def _is_scalar_access(self, key: Tuple):
# 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, axis):
"""
Check that 'key' is a valid position in the desired axis.
Parameters
----------
key : int
Requested position
axis : int
Desired axis
Returns
-------
None
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):
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:
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 (can only be zero)
Returns
-------
Series object
"""
try:
return self.obj.take(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, is_setter: bool = False, 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(
"Can only index by location with "
"a [{types}]".format(types=self._valid_types)
)
class _ScalarAccessIndexer(_NDFrameIndexer):
""" access scalars quickly """
def _convert_key(self, key, is_setter: bool = False):
return list(key)
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 = self._tuplify(key)
if len(key) != self.obj.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)
class _AtIndexer(_ScalarAccessIndexer):
"""
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
When 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
"""
_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
class _iAtIndexer(_ScalarAccessIndexer):
"""
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
"""
_takeable = True
def _has_valid_setitem_indexer(self, indexer):
self._has_valid_positional_setitem_indexer(indexer)
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 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
-------
result: 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
else:
if is_sparse(result):
result = result.to_dense()
result = np.asarray(result, dtype=bool)
# GH26658
if len(result) != len(index):
raise IndexError(
"Item wrong length {} instead of {}.".format(len(result), len(index))
)
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):
# 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, MultiIndex)
return False
def is_label_like(key):
# select a label or row
return not isinstance(key, slice) and not is_list_like_indexer(key)
def need_slice(obj):
return (
obj.start is not None
or obj.stop is not None
or (obj.step is not None and obj.step != 1)
)
def maybe_droplevels(index, key):
# drop levels
original_index = index
if isinstance(key, tuple):
for _ in key:
try:
index = index.droplevel(0)
except ValueError:
# we have dropped too much, so back out
return original_index
else:
try:
index = index.droplevel(0)
except ValueError:
pass
return index
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):
# 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):
""" return 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