from __future__ import annotations from collections.abc import ( Hashable, Sequence, ) import itertools from typing import ( TYPE_CHECKING, Callable, Literal, cast, ) import warnings import numpy as np from pandas._config import ( using_copy_on_write, warn_copy_on_write, ) from pandas._libs import ( internals as libinternals, lib, ) from pandas._libs.internals import ( BlockPlacement, BlockValuesRefs, ) from pandas._libs.tslibs import Timestamp from pandas.errors import PerformanceWarning from pandas.util._decorators import cache_readonly from pandas.util._exceptions import find_stack_level from pandas.core.dtypes.cast import infer_dtype_from_scalar from pandas.core.dtypes.common import ( ensure_platform_int, is_1d_only_ea_dtype, is_list_like, ) from pandas.core.dtypes.dtypes import ( DatetimeTZDtype, ExtensionDtype, ) from pandas.core.dtypes.generic import ( ABCDataFrame, ABCSeries, ) from pandas.core.dtypes.missing import ( array_equals, isna, ) import pandas.core.algorithms as algos from pandas.core.arrays import ( ArrowExtensionArray, ArrowStringArray, DatetimeArray, ) from pandas.core.arrays._mixins import NDArrayBackedExtensionArray from pandas.core.construction import ( ensure_wrapped_if_datetimelike, extract_array, ) from pandas.core.indexers import maybe_convert_indices from pandas.core.indexes.api import ( Index, ensure_index, ) from pandas.core.internals.base import ( DataManager, SingleDataManager, ensure_np_dtype, interleaved_dtype, ) from pandas.core.internals.blocks import ( COW_WARNING_GENERAL_MSG, COW_WARNING_SETITEM_MSG, Block, NumpyBlock, ensure_block_shape, extend_blocks, get_block_type, maybe_coerce_values, new_block, new_block_2d, ) from pandas.core.internals.ops import ( blockwise_all, operate_blockwise, ) if TYPE_CHECKING: from pandas._typing import ( ArrayLike, AxisInt, DtypeObj, QuantileInterpolation, Self, Shape, npt, ) from pandas.api.extensions import ExtensionArray class BaseBlockManager(DataManager): """ Core internal data structure to implement DataFrame, Series, etc. Manage a bunch of labeled 2D mixed-type ndarrays. Essentially it's a lightweight blocked set of labeled data to be manipulated by the DataFrame public API class Attributes ---------- shape ndim axes values items Methods ------- set_axis(axis, new_labels) copy(deep=True) get_dtypes apply(func, axes, block_filter_fn) get_bool_data get_numeric_data get_slice(slice_like, axis) get(label) iget(loc) take(indexer, axis) reindex_axis(new_labels, axis) reindex_indexer(new_labels, indexer, axis) delete(label) insert(loc, label, value) set(label, value) Parameters ---------- blocks: Sequence of Block axes: Sequence of Index verify_integrity: bool, default True Notes ----- This is *not* a public API class """ __slots__ = () _blknos: npt.NDArray[np.intp] _blklocs: npt.NDArray[np.intp] blocks: tuple[Block, ...] axes: list[Index] @property def ndim(self) -> int: raise NotImplementedError _known_consolidated: bool _is_consolidated: bool def __init__(self, blocks, axes, verify_integrity: bool = True) -> None: raise NotImplementedError @classmethod def from_blocks(cls, blocks: list[Block], axes: list[Index]) -> Self: raise NotImplementedError @property def blknos(self) -> npt.NDArray[np.intp]: """ Suppose we want to find the array corresponding to our i'th column. blknos[i] identifies the block from self.blocks that contains this column. blklocs[i] identifies the column of interest within self.blocks[self.blknos[i]] """ if self._blknos is None: # Note: these can be altered by other BlockManager methods. self._rebuild_blknos_and_blklocs() return self._blknos @property def blklocs(self) -> npt.NDArray[np.intp]: """ See blknos.__doc__ """ if self._blklocs is None: # Note: these can be altered by other BlockManager methods. self._rebuild_blknos_and_blklocs() return self._blklocs def make_empty(self, axes=None) -> Self: """return an empty BlockManager with the items axis of len 0""" if axes is None: axes = [Index([])] + self.axes[1:] # preserve dtype if possible if self.ndim == 1: assert isinstance(self, SingleBlockManager) # for mypy blk = self.blocks[0] arr = blk.values[:0] bp = BlockPlacement(slice(0, 0)) nb = blk.make_block_same_class(arr, placement=bp) blocks = [nb] else: blocks = [] return type(self).from_blocks(blocks, axes) def __nonzero__(self) -> bool: return True # Python3 compat __bool__ = __nonzero__ def _normalize_axis(self, axis: AxisInt) -> int: # switch axis to follow BlockManager logic if self.ndim == 2: axis = 1 if axis == 0 else 0 return axis def set_axis(self, axis: AxisInt, new_labels: Index) -> None: # Caller is responsible for ensuring we have an Index object. self._validate_set_axis(axis, new_labels) self.axes[axis] = new_labels @property def is_single_block(self) -> bool: # Assumes we are 2D; overridden by SingleBlockManager return len(self.blocks) == 1 @property def items(self) -> Index: return self.axes[0] def _has_no_reference(self, i: int) -> bool: """ Check for column `i` if it has references. (whether it references another array or is itself being referenced) Returns True if the column has no references. """ blkno = self.blknos[i] return self._has_no_reference_block(blkno) def _has_no_reference_block(self, blkno: int) -> bool: """ Check for block `i` if it has references. (whether it references another array or is itself being referenced) Returns True if the block has no references. """ return not self.blocks[blkno].refs.has_reference() def add_references(self, mgr: BaseBlockManager) -> None: """ Adds the references from one manager to another. We assume that both managers have the same block structure. """ if len(self.blocks) != len(mgr.blocks): # If block structure changes, then we made a copy return for i, blk in enumerate(self.blocks): blk.refs = mgr.blocks[i].refs blk.refs.add_reference(blk) def references_same_values(self, mgr: BaseBlockManager, blkno: int) -> bool: """ Checks if two blocks from two different block managers reference the same underlying values. """ blk = self.blocks[blkno] return any(blk is ref() for ref in mgr.blocks[blkno].refs.referenced_blocks) def get_dtypes(self) -> npt.NDArray[np.object_]: dtypes = np.array([blk.dtype for blk in self.blocks], dtype=object) return dtypes.take(self.blknos) @property def arrays(self) -> list[ArrayLike]: """ Quick access to the backing arrays of the Blocks. Only for compatibility with ArrayManager for testing convenience. Not to be used in actual code, and return value is not the same as the ArrayManager method (list of 1D arrays vs iterator of 2D ndarrays / 1D EAs). Warning! The returned arrays don't handle Copy-on-Write, so this should be used with caution (only in read-mode). """ return [blk.values for blk in self.blocks] def __repr__(self) -> str: output = type(self).__name__ for i, ax in enumerate(self.axes): if i == 0: output += f"\nItems: {ax}" else: output += f"\nAxis {i}: {ax}" for block in self.blocks: output += f"\n{block}" return output def apply( self, f, align_keys: list[str] | None = None, **kwargs, ) -> Self: """ Iterate over the blocks, collect and create a new BlockManager. Parameters ---------- f : str or callable Name of the Block method to apply. align_keys: List[str] or None, default None **kwargs Keywords to pass to `f` Returns ------- BlockManager """ assert "filter" not in kwargs align_keys = align_keys or [] result_blocks: list[Block] = [] # fillna: Series/DataFrame is responsible for making sure value is aligned aligned_args = {k: kwargs[k] for k in align_keys} for b in self.blocks: if aligned_args: for k, obj in aligned_args.items(): if isinstance(obj, (ABCSeries, ABCDataFrame)): # The caller is responsible for ensuring that # obj.axes[-1].equals(self.items) if obj.ndim == 1: kwargs[k] = obj.iloc[b.mgr_locs.indexer]._values else: kwargs[k] = obj.iloc[:, b.mgr_locs.indexer]._values else: # otherwise we have an ndarray kwargs[k] = obj[b.mgr_locs.indexer] if callable(f): applied = b.apply(f, **kwargs) else: applied = getattr(b, f)(**kwargs) result_blocks = extend_blocks(applied, result_blocks) out = type(self).from_blocks(result_blocks, self.axes) return out # Alias so we can share code with ArrayManager apply_with_block = apply def setitem(self, indexer, value, warn: bool = True) -> Self: """ Set values with indexer. For SingleBlockManager, this backs s[indexer] = value """ if isinstance(indexer, np.ndarray) and indexer.ndim > self.ndim: raise ValueError(f"Cannot set values with ndim > {self.ndim}") if warn and warn_copy_on_write() and not self._has_no_reference(0): warnings.warn( COW_WARNING_GENERAL_MSG, FutureWarning, stacklevel=find_stack_level(), ) elif using_copy_on_write() and not self._has_no_reference(0): # this method is only called if there is a single block -> hardcoded 0 # Split blocks to only copy the columns we want to modify if self.ndim == 2 and isinstance(indexer, tuple): blk_loc = self.blklocs[indexer[1]] if is_list_like(blk_loc) and blk_loc.ndim == 2: blk_loc = np.squeeze(blk_loc, axis=0) elif not is_list_like(blk_loc): # Keep dimension and copy data later blk_loc = [blk_loc] # type: ignore[assignment] if len(blk_loc) == 0: return self.copy(deep=False) values = self.blocks[0].values if values.ndim == 2: values = values[blk_loc] # "T" has no attribute "_iset_split_block" self._iset_split_block( # type: ignore[attr-defined] 0, blk_loc, values ) # first block equals values self.blocks[0].setitem((indexer[0], np.arange(len(blk_loc))), value) return self # No need to split if we either set all columns or on a single block # manager self = self.copy() return self.apply("setitem", indexer=indexer, value=value) def diff(self, n: int) -> Self: # only reached with self.ndim == 2 return self.apply("diff", n=n) def astype(self, dtype, copy: bool | None = False, errors: str = "raise") -> Self: if copy is None: if using_copy_on_write(): copy = False else: copy = True elif using_copy_on_write(): copy = False return self.apply( "astype", dtype=dtype, copy=copy, errors=errors, using_cow=using_copy_on_write(), ) def convert(self, copy: bool | None) -> Self: if copy is None: if using_copy_on_write(): copy = False else: copy = True elif using_copy_on_write(): copy = False return self.apply("convert", copy=copy, using_cow=using_copy_on_write()) def convert_dtypes(self, **kwargs): if using_copy_on_write(): copy = False else: copy = True return self.apply( "convert_dtypes", copy=copy, using_cow=using_copy_on_write(), **kwargs ) def get_values_for_csv( self, *, float_format, date_format, decimal, na_rep: str = "nan", quoting=None ) -> Self: """ Convert values to native types (strings / python objects) that are used in formatting (repr / csv). """ return self.apply( "get_values_for_csv", na_rep=na_rep, quoting=quoting, float_format=float_format, date_format=date_format, decimal=decimal, ) @property def any_extension_types(self) -> bool: """Whether any of the blocks in this manager are extension blocks""" return any(block.is_extension for block in self.blocks) @property def is_view(self) -> bool: """return a boolean if we are a single block and are a view""" if len(self.blocks) == 1: return self.blocks[0].is_view # It is technically possible to figure out which blocks are views # e.g. [ b.values.base is not None for b in self.blocks ] # but then we have the case of possibly some blocks being a view # and some blocks not. setting in theory is possible on the non-view # blocks w/o causing a SettingWithCopy raise/warn. But this is a bit # complicated return False def _get_data_subset(self, predicate: Callable) -> Self: blocks = [blk for blk in self.blocks if predicate(blk.values)] return self._combine(blocks) def get_bool_data(self) -> Self: """ Select blocks that are bool-dtype and columns from object-dtype blocks that are all-bool. """ new_blocks = [] for blk in self.blocks: if blk.dtype == bool: new_blocks.append(blk) elif blk.is_object: nbs = blk._split() new_blocks.extend(nb for nb in nbs if nb.is_bool) return self._combine(new_blocks) def get_numeric_data(self) -> Self: numeric_blocks = [blk for blk in self.blocks if blk.is_numeric] if len(numeric_blocks) == len(self.blocks): # Avoid somewhat expensive _combine return self return self._combine(numeric_blocks) def _combine(self, blocks: list[Block], index: Index | None = None) -> Self: """return a new manager with the blocks""" if len(blocks) == 0: if self.ndim == 2: # retain our own Index dtype if index is not None: axes = [self.items[:0], index] else: axes = [self.items[:0]] + self.axes[1:] return self.make_empty(axes) return self.make_empty() # FIXME: optimization potential indexer = np.sort(np.concatenate([b.mgr_locs.as_array for b in blocks])) inv_indexer = lib.get_reverse_indexer(indexer, self.shape[0]) new_blocks: list[Block] = [] for b in blocks: nb = b.copy(deep=False) nb.mgr_locs = BlockPlacement(inv_indexer[nb.mgr_locs.indexer]) new_blocks.append(nb) axes = list(self.axes) if index is not None: axes[-1] = index axes[0] = self.items.take(indexer) return type(self).from_blocks(new_blocks, axes) @property def nblocks(self) -> int: return len(self.blocks) def copy(self, deep: bool | None | Literal["all"] = True) -> Self: """ Make deep or shallow copy of BlockManager Parameters ---------- deep : bool, string or None, default True If False or None, return a shallow copy (do not copy data) If 'all', copy data and a deep copy of the index Returns ------- BlockManager """ if deep is None: if using_copy_on_write(): # use shallow copy deep = False else: # preserve deep copy for BlockManager with copy=None deep = True # this preserves the notion of view copying of axes if deep: # hit in e.g. tests.io.json.test_pandas def copy_func(ax): return ax.copy(deep=True) if deep == "all" else ax.view() new_axes = [copy_func(ax) for ax in self.axes] else: if using_copy_on_write(): new_axes = [ax.view() for ax in self.axes] else: new_axes = list(self.axes) res = self.apply("copy", deep=deep) res.axes = new_axes if self.ndim > 1: # Avoid needing to re-compute these blknos = self._blknos if blknos is not None: res._blknos = blknos.copy() res._blklocs = self._blklocs.copy() if deep: res._consolidate_inplace() return res def consolidate(self) -> Self: """ Join together blocks having same dtype Returns ------- y : BlockManager """ if self.is_consolidated(): return self bm = type(self)(self.blocks, self.axes, verify_integrity=False) bm._is_consolidated = False bm._consolidate_inplace() return bm def reindex_indexer( self, new_axis: Index, indexer: npt.NDArray[np.intp] | None, axis: AxisInt, fill_value=None, allow_dups: bool = False, copy: bool | None = True, only_slice: bool = False, *, use_na_proxy: bool = False, ) -> Self: """ Parameters ---------- new_axis : Index indexer : ndarray[intp] or None axis : int fill_value : object, default None allow_dups : bool, default False copy : bool or None, default True If None, regard as False to get shallow copy. only_slice : bool, default False Whether to take views, not copies, along columns. use_na_proxy : bool, default False Whether to use a np.void ndarray for newly introduced columns. pandas-indexer with -1's only. """ if copy is None: if using_copy_on_write(): # use shallow copy copy = False else: # preserve deep copy for BlockManager with copy=None copy = True if indexer is None: if new_axis is self.axes[axis] and not copy: return self result = self.copy(deep=copy) result.axes = list(self.axes) result.axes[axis] = new_axis return result # Should be intp, but in some cases we get int64 on 32bit builds assert isinstance(indexer, np.ndarray) # some axes don't allow reindexing with dups if not allow_dups: self.axes[axis]._validate_can_reindex(indexer) if axis >= self.ndim: raise IndexError("Requested axis not found in manager") if axis == 0: new_blocks = self._slice_take_blocks_ax0( indexer, fill_value=fill_value, only_slice=only_slice, use_na_proxy=use_na_proxy, ) else: new_blocks = [ blk.take_nd( indexer, axis=1, fill_value=( fill_value if fill_value is not None else blk.fill_value ), ) for blk in self.blocks ] new_axes = list(self.axes) new_axes[axis] = new_axis new_mgr = type(self).from_blocks(new_blocks, new_axes) if axis == 1: # We can avoid the need to rebuild these new_mgr._blknos = self.blknos.copy() new_mgr._blklocs = self.blklocs.copy() return new_mgr def _slice_take_blocks_ax0( self, slice_or_indexer: slice | np.ndarray, fill_value=lib.no_default, only_slice: bool = False, *, use_na_proxy: bool = False, ref_inplace_op: bool = False, ) -> list[Block]: """ Slice/take blocks along axis=0. Overloaded for SingleBlock Parameters ---------- slice_or_indexer : slice or np.ndarray[int64] fill_value : scalar, default lib.no_default only_slice : bool, default False If True, we always return views on existing arrays, never copies. This is used when called from ops.blockwise.operate_blockwise. use_na_proxy : bool, default False Whether to use a np.void ndarray for newly introduced columns. ref_inplace_op: bool, default False Don't track refs if True because we operate inplace Returns ------- new_blocks : list of Block """ allow_fill = fill_value is not lib.no_default sl_type, slobj, sllen = _preprocess_slice_or_indexer( slice_or_indexer, self.shape[0], allow_fill=allow_fill ) if self.is_single_block: blk = self.blocks[0] if sl_type == "slice": # GH#32959 EABlock would fail since we can't make 0-width # TODO(EA2D): special casing unnecessary with 2D EAs if sllen == 0: return [] bp = BlockPlacement(slice(0, sllen)) return [blk.getitem_block_columns(slobj, new_mgr_locs=bp)] elif not allow_fill or self.ndim == 1: if allow_fill and fill_value is None: fill_value = blk.fill_value if not allow_fill and only_slice: # GH#33597 slice instead of take, so we get # views instead of copies blocks = [ blk.getitem_block_columns( slice(ml, ml + 1), new_mgr_locs=BlockPlacement(i), ref_inplace_op=ref_inplace_op, ) for i, ml in enumerate(slobj) ] return blocks else: bp = BlockPlacement(slice(0, sllen)) return [ blk.take_nd( slobj, axis=0, new_mgr_locs=bp, fill_value=fill_value, ) ] if sl_type == "slice": blknos = self.blknos[slobj] blklocs = self.blklocs[slobj] else: blknos = algos.take_nd( self.blknos, slobj, fill_value=-1, allow_fill=allow_fill ) blklocs = algos.take_nd( self.blklocs, slobj, fill_value=-1, allow_fill=allow_fill ) # When filling blknos, make sure blknos is updated before appending to # blocks list, that way new blkno is exactly len(blocks). blocks = [] group = not only_slice for blkno, mgr_locs in libinternals.get_blkno_placements(blknos, group=group): if blkno == -1: # If we've got here, fill_value was not lib.no_default blocks.append( self._make_na_block( placement=mgr_locs, fill_value=fill_value, use_na_proxy=use_na_proxy, ) ) else: blk = self.blocks[blkno] # Otherwise, slicing along items axis is necessary. if not blk._can_consolidate and not blk._validate_ndim: # i.e. we dont go through here for DatetimeTZBlock # A non-consolidatable block, it's easy, because there's # only one item and each mgr loc is a copy of that single # item. deep = not (only_slice or using_copy_on_write()) for mgr_loc in mgr_locs: newblk = blk.copy(deep=deep) newblk.mgr_locs = BlockPlacement(slice(mgr_loc, mgr_loc + 1)) blocks.append(newblk) else: # GH#32779 to avoid the performance penalty of copying, # we may try to only slice taker = blklocs[mgr_locs.indexer] max_len = max(len(mgr_locs), taker.max() + 1) if only_slice or using_copy_on_write(): taker = lib.maybe_indices_to_slice(taker, max_len) if isinstance(taker, slice): nb = blk.getitem_block_columns(taker, new_mgr_locs=mgr_locs) blocks.append(nb) elif only_slice: # GH#33597 slice instead of take, so we get # views instead of copies for i, ml in zip(taker, mgr_locs): slc = slice(i, i + 1) bp = BlockPlacement(ml) nb = blk.getitem_block_columns(slc, new_mgr_locs=bp) # We have np.shares_memory(nb.values, blk.values) blocks.append(nb) else: nb = blk.take_nd(taker, axis=0, new_mgr_locs=mgr_locs) blocks.append(nb) return blocks def _make_na_block( self, placement: BlockPlacement, fill_value=None, use_na_proxy: bool = False ) -> Block: # Note: we only get here with self.ndim == 2 if use_na_proxy: assert fill_value is None shape = (len(placement), self.shape[1]) vals = np.empty(shape, dtype=np.void) nb = NumpyBlock(vals, placement, ndim=2) return nb if fill_value is None: fill_value = np.nan shape = (len(placement), self.shape[1]) dtype, fill_value = infer_dtype_from_scalar(fill_value) block_values = make_na_array(dtype, shape, fill_value) return new_block_2d(block_values, placement=placement) def take( self, indexer: npt.NDArray[np.intp], axis: AxisInt = 1, verify: bool = True, ) -> Self: """ Take items along any axis. indexer : np.ndarray[np.intp] axis : int, default 1 verify : bool, default True Check that all entries are between 0 and len(self) - 1, inclusive. Pass verify=False if this check has been done by the caller. Returns ------- BlockManager """ # Caller is responsible for ensuring indexer annotation is accurate n = self.shape[axis] indexer = maybe_convert_indices(indexer, n, verify=verify) new_labels = self.axes[axis].take(indexer) return self.reindex_indexer( new_axis=new_labels, indexer=indexer, axis=axis, allow_dups=True, copy=None, ) class BlockManager(libinternals.BlockManager, BaseBlockManager): """ BaseBlockManager that holds 2D blocks. """ ndim = 2 # ---------------------------------------------------------------- # Constructors def __init__( self, blocks: Sequence[Block], axes: Sequence[Index], verify_integrity: bool = True, ) -> None: if verify_integrity: # Assertion disabled for performance # assert all(isinstance(x, Index) for x in axes) for block in blocks: if self.ndim != block.ndim: raise AssertionError( f"Number of Block dimensions ({block.ndim}) must equal " f"number of axes ({self.ndim})" ) # As of 2.0, the caller is responsible for ensuring that # DatetimeTZBlock with block.ndim == 2 has block.values.ndim ==2; # previously there was a special check for fastparquet compat. self._verify_integrity() def _verify_integrity(self) -> None: mgr_shape = self.shape tot_items = sum(len(x.mgr_locs) for x in self.blocks) for block in self.blocks: if block.shape[1:] != mgr_shape[1:]: raise_construction_error(tot_items, block.shape[1:], self.axes) if len(self.items) != tot_items: raise AssertionError( "Number of manager items must equal union of " f"block items\n# manager items: {len(self.items)}, # " f"tot_items: {tot_items}" ) @classmethod def from_blocks(cls, blocks: list[Block], axes: list[Index]) -> Self: """ Constructor for BlockManager and SingleBlockManager with same signature. """ return cls(blocks, axes, verify_integrity=False) # ---------------------------------------------------------------- # Indexing def fast_xs(self, loc: int) -> SingleBlockManager: """ Return the array corresponding to `frame.iloc[loc]`. Parameters ---------- loc : int Returns ------- np.ndarray or ExtensionArray """ if len(self.blocks) == 1: # TODO: this could be wrong if blk.mgr_locs is not slice(None)-like; # is this ruled out in the general case? result = self.blocks[0].iget((slice(None), loc)) # in the case of a single block, the new block is a view bp = BlockPlacement(slice(0, len(result))) block = new_block( result, placement=bp, ndim=1, refs=self.blocks[0].refs, ) return SingleBlockManager(block, self.axes[0]) dtype = interleaved_dtype([blk.dtype for blk in self.blocks]) n = len(self) if isinstance(dtype, ExtensionDtype): # TODO: use object dtype as workaround for non-performant # EA.__setitem__ methods. (primarily ArrowExtensionArray.__setitem__ # when iteratively setting individual values) # https://github.com/pandas-dev/pandas/pull/54508#issuecomment-1675827918 result = np.empty(n, dtype=object) else: result = np.empty(n, dtype=dtype) result = ensure_wrapped_if_datetimelike(result) for blk in self.blocks: # Such assignment may incorrectly coerce NaT to None # result[blk.mgr_locs] = blk._slice((slice(None), loc)) for i, rl in enumerate(blk.mgr_locs): result[rl] = blk.iget((i, loc)) if isinstance(dtype, ExtensionDtype): cls = dtype.construct_array_type() result = cls._from_sequence(result, dtype=dtype) bp = BlockPlacement(slice(0, len(result))) block = new_block(result, placement=bp, ndim=1) return SingleBlockManager(block, self.axes[0]) def iget(self, i: int, track_ref: bool = True) -> SingleBlockManager: """ Return the data as a SingleBlockManager. """ block = self.blocks[self.blknos[i]] values = block.iget(self.blklocs[i]) # shortcut for select a single-dim from a 2-dim BM bp = BlockPlacement(slice(0, len(values))) nb = type(block)( values, placement=bp, ndim=1, refs=block.refs if track_ref else None ) return SingleBlockManager(nb, self.axes[1]) def iget_values(self, i: int) -> ArrayLike: """ Return the data for column i as the values (ndarray or ExtensionArray). Warning! The returned array is a view but doesn't handle Copy-on-Write, so this should be used with caution. """ # TODO(CoW) making the arrays read-only might make this safer to use? block = self.blocks[self.blknos[i]] values = block.iget(self.blklocs[i]) return values @property def column_arrays(self) -> list[np.ndarray]: """ Used in the JSON C code to access column arrays. This optimizes compared to using `iget_values` by converting each Warning! This doesn't handle Copy-on-Write, so should be used with caution (current use case of consuming this in the JSON code is fine). """ # This is an optimized equivalent to # result = [self.iget_values(i) for i in range(len(self.items))] result: list[np.ndarray | None] = [None] * len(self.items) for blk in self.blocks: mgr_locs = blk._mgr_locs values = blk.array_values._values_for_json() if values.ndim == 1: # TODO(EA2D): special casing not needed with 2D EAs result[mgr_locs[0]] = values else: for i, loc in enumerate(mgr_locs): result[loc] = values[i] # error: Incompatible return value type (got "List[None]", # expected "List[ndarray[Any, Any]]") return result # type: ignore[return-value] def iset( self, loc: int | slice | np.ndarray, value: ArrayLike, inplace: bool = False, refs: BlockValuesRefs | None = None, ) -> None: """ Set new item in-place. Does not consolidate. Adds new Block if not contained in the current set of items """ # FIXME: refactor, clearly separate broadcasting & zip-like assignment # can prob also fix the various if tests for sparse/categorical if self._blklocs is None and self.ndim > 1: self._rebuild_blknos_and_blklocs() # Note: we exclude DTA/TDA here value_is_extension_type = is_1d_only_ea_dtype(value.dtype) if not value_is_extension_type: if value.ndim == 2: value = value.T else: value = ensure_block_shape(value, ndim=2) if value.shape[1:] != self.shape[1:]: raise AssertionError( "Shape of new values must be compatible with manager shape" ) if lib.is_integer(loc): # We have 6 tests where loc is _not_ an int. # In this case, get_blkno_placements will yield only one tuple, # containing (self._blknos[loc], BlockPlacement(slice(0, 1, 1))) # Check if we can use _iset_single fastpath loc = cast(int, loc) blkno = self.blknos[loc] blk = self.blocks[blkno] if len(blk._mgr_locs) == 1: # TODO: fastest way to check this? return self._iset_single( loc, value, inplace=inplace, blkno=blkno, blk=blk, refs=refs, ) # error: Incompatible types in assignment (expression has type # "List[Union[int, slice, ndarray]]", variable has type "Union[int, # slice, ndarray]") loc = [loc] # type: ignore[assignment] # categorical/sparse/datetimetz if value_is_extension_type: def value_getitem(placement): return value else: def value_getitem(placement): return value[placement.indexer] # Accessing public blknos ensures the public versions are initialized blknos = self.blknos[loc] blklocs = self.blklocs[loc].copy() unfit_mgr_locs = [] unfit_val_locs = [] removed_blknos = [] for blkno_l, val_locs in libinternals.get_blkno_placements(blknos, group=True): blk = self.blocks[blkno_l] blk_locs = blklocs[val_locs.indexer] if inplace and blk.should_store(value): # Updating inplace -> check if we need to do Copy-on-Write if using_copy_on_write() and not self._has_no_reference_block(blkno_l): self._iset_split_block( blkno_l, blk_locs, value_getitem(val_locs), refs=refs ) else: blk.set_inplace(blk_locs, value_getitem(val_locs)) continue else: unfit_mgr_locs.append(blk.mgr_locs.as_array[blk_locs]) unfit_val_locs.append(val_locs) # If all block items are unfit, schedule the block for removal. if len(val_locs) == len(blk.mgr_locs): removed_blknos.append(blkno_l) continue else: # Defer setting the new values to enable consolidation self._iset_split_block(blkno_l, blk_locs, refs=refs) if len(removed_blknos): # Remove blocks & update blknos accordingly is_deleted = np.zeros(self.nblocks, dtype=np.bool_) is_deleted[removed_blknos] = True new_blknos = np.empty(self.nblocks, dtype=np.intp) new_blknos.fill(-1) new_blknos[~is_deleted] = np.arange(self.nblocks - len(removed_blknos)) self._blknos = new_blknos[self._blknos] self.blocks = tuple( blk for i, blk in enumerate(self.blocks) if i not in set(removed_blknos) ) if unfit_val_locs: unfit_idxr = np.concatenate(unfit_mgr_locs) unfit_count = len(unfit_idxr) new_blocks: list[Block] = [] if value_is_extension_type: # This code (ab-)uses the fact that EA blocks contain only # one item. # TODO(EA2D): special casing unnecessary with 2D EAs new_blocks.extend( new_block_2d( values=value, placement=BlockPlacement(slice(mgr_loc, mgr_loc + 1)), refs=refs, ) for mgr_loc in unfit_idxr ) self._blknos[unfit_idxr] = np.arange(unfit_count) + len(self.blocks) self._blklocs[unfit_idxr] = 0 else: # unfit_val_locs contains BlockPlacement objects unfit_val_items = unfit_val_locs[0].append(unfit_val_locs[1:]) new_blocks.append( new_block_2d( values=value_getitem(unfit_val_items), placement=BlockPlacement(unfit_idxr), refs=refs, ) ) self._blknos[unfit_idxr] = len(self.blocks) self._blklocs[unfit_idxr] = np.arange(unfit_count) self.blocks += tuple(new_blocks) # Newly created block's dtype may already be present. self._known_consolidated = False def _iset_split_block( self, blkno_l: int, blk_locs: np.ndarray | list[int], value: ArrayLike | None = None, refs: BlockValuesRefs | None = None, ) -> None: """Removes columns from a block by splitting the block. Avoids copying the whole block through slicing and updates the manager after determinint the new block structure. Optionally adds a new block, otherwise has to be done by the caller. Parameters ---------- blkno_l: The block number to operate on, relevant for updating the manager blk_locs: The locations of our block that should be deleted. value: The value to set as a replacement. refs: The reference tracking object of the value to set. """ blk = self.blocks[blkno_l] if self._blklocs is None: self._rebuild_blknos_and_blklocs() nbs_tup = tuple(blk.delete(blk_locs)) if value is not None: locs = blk.mgr_locs.as_array[blk_locs] first_nb = new_block_2d(value, BlockPlacement(locs), refs=refs) else: first_nb = nbs_tup[0] nbs_tup = tuple(nbs_tup[1:]) nr_blocks = len(self.blocks) blocks_tup = ( self.blocks[:blkno_l] + (first_nb,) + self.blocks[blkno_l + 1 :] + nbs_tup ) self.blocks = blocks_tup if not nbs_tup and value is not None: # No need to update anything if split did not happen return self._blklocs[first_nb.mgr_locs.indexer] = np.arange(len(first_nb)) for i, nb in enumerate(nbs_tup): self._blklocs[nb.mgr_locs.indexer] = np.arange(len(nb)) self._blknos[nb.mgr_locs.indexer] = i + nr_blocks def _iset_single( self, loc: int, value: ArrayLike, inplace: bool, blkno: int, blk: Block, refs: BlockValuesRefs | None = None, ) -> None: """ Fastpath for iset when we are only setting a single position and the Block currently in that position is itself single-column. In this case we can swap out the entire Block and blklocs and blknos are unaffected. """ # Caller is responsible for verifying value.shape if inplace and blk.should_store(value): copy = False if using_copy_on_write() and not self._has_no_reference_block(blkno): # perform Copy-on-Write and clear the reference copy = True iloc = self.blklocs[loc] blk.set_inplace(slice(iloc, iloc + 1), value, copy=copy) return nb = new_block_2d(value, placement=blk._mgr_locs, refs=refs) old_blocks = self.blocks new_blocks = old_blocks[:blkno] + (nb,) + old_blocks[blkno + 1 :] self.blocks = new_blocks return def column_setitem( self, loc: int, idx: int | slice | np.ndarray, value, inplace_only: bool = False ) -> None: """ Set values ("setitem") into a single column (not setting the full column). This is a method on the BlockManager level, to avoid creating an intermediate Series at the DataFrame level (`s = df[loc]; s[idx] = value`) """ needs_to_warn = False if warn_copy_on_write() and not self._has_no_reference(loc): if not isinstance( self.blocks[self.blknos[loc]].values, (ArrowExtensionArray, ArrowStringArray), ): # We might raise if we are in an expansion case, so defer # warning till we actually updated needs_to_warn = True elif using_copy_on_write() and not self._has_no_reference(loc): blkno = self.blknos[loc] # Split blocks to only copy the column we want to modify blk_loc = self.blklocs[loc] # Copy our values values = self.blocks[blkno].values if values.ndim == 1: values = values.copy() else: # Use [blk_loc] as indexer to keep ndim=2, this already results in a # copy values = values[[blk_loc]] self._iset_split_block(blkno, [blk_loc], values) # this manager is only created temporarily to mutate the values in place # so don't track references, otherwise the `setitem` would perform CoW again col_mgr = self.iget(loc, track_ref=False) if inplace_only: col_mgr.setitem_inplace(idx, value) else: new_mgr = col_mgr.setitem((idx,), value) self.iset(loc, new_mgr._block.values, inplace=True) if needs_to_warn: warnings.warn( COW_WARNING_GENERAL_MSG, FutureWarning, stacklevel=find_stack_level(), ) def insert(self, loc: int, item: Hashable, value: ArrayLike, refs=None) -> None: """ Insert item at selected position. Parameters ---------- loc : int item : hashable value : np.ndarray or ExtensionArray refs : The reference tracking object of the value to set. """ with warnings.catch_warnings(): # TODO: re-issue this with setitem-specific message? warnings.filterwarnings( "ignore", "The behavior of Index.insert with object-dtype is deprecated", category=FutureWarning, ) new_axis = self.items.insert(loc, item) if value.ndim == 2: value = value.T if len(value) > 1: raise ValueError( f"Expected a 1D array, got an array with shape {value.T.shape}" ) else: value = ensure_block_shape(value, ndim=self.ndim) bp = BlockPlacement(slice(loc, loc + 1)) block = new_block_2d(values=value, placement=bp, refs=refs) if not len(self.blocks): # Fastpath self._blklocs = np.array([0], dtype=np.intp) self._blknos = np.array([0], dtype=np.intp) else: self._insert_update_mgr_locs(loc) self._insert_update_blklocs_and_blknos(loc) self.axes[0] = new_axis self.blocks += (block,) self._known_consolidated = False if sum(not block.is_extension for block in self.blocks) > 100: warnings.warn( "DataFrame is highly fragmented. This is usually the result " "of calling `frame.insert` many times, which has poor performance. " "Consider joining all columns at once using pd.concat(axis=1) " "instead. To get a de-fragmented frame, use `newframe = frame.copy()`", PerformanceWarning, stacklevel=find_stack_level(), ) def _insert_update_mgr_locs(self, loc) -> None: """ When inserting a new Block at location 'loc', we increment all of the mgr_locs of blocks above that by one. """ for blkno, count in _fast_count_smallints(self.blknos[loc:]): # .620 this way, .326 of which is in increment_above blk = self.blocks[blkno] blk._mgr_locs = blk._mgr_locs.increment_above(loc) def _insert_update_blklocs_and_blknos(self, loc) -> None: """ When inserting a new Block at location 'loc', we update our _blklocs and _blknos. """ # Accessing public blklocs ensures the public versions are initialized if loc == self.blklocs.shape[0]: # np.append is a lot faster, let's use it if we can. self._blklocs = np.append(self._blklocs, 0) self._blknos = np.append(self._blknos, len(self.blocks)) elif loc == 0: # np.append is a lot faster, let's use it if we can. self._blklocs = np.append(self._blklocs[::-1], 0)[::-1] self._blknos = np.append(self._blknos[::-1], len(self.blocks))[::-1] else: new_blklocs, new_blknos = libinternals.update_blklocs_and_blknos( self.blklocs, self.blknos, loc, len(self.blocks) ) self._blklocs = new_blklocs self._blknos = new_blknos def idelete(self, indexer) -> BlockManager: """ Delete selected locations, returning a new BlockManager. """ is_deleted = np.zeros(self.shape[0], dtype=np.bool_) is_deleted[indexer] = True taker = (~is_deleted).nonzero()[0] nbs = self._slice_take_blocks_ax0(taker, only_slice=True, ref_inplace_op=True) new_columns = self.items[~is_deleted] axes = [new_columns, self.axes[1]] return type(self)(tuple(nbs), axes, verify_integrity=False) # ---------------------------------------------------------------- # Block-wise Operation def grouped_reduce(self, func: Callable) -> Self: """ Apply grouped reduction function blockwise, returning a new BlockManager. Parameters ---------- func : grouped reduction function Returns ------- BlockManager """ result_blocks: list[Block] = [] for blk in self.blocks: if blk.is_object: # split on object-dtype blocks bc some columns may raise # while others do not. for sb in blk._split(): applied = sb.apply(func) result_blocks = extend_blocks(applied, result_blocks) else: applied = blk.apply(func) result_blocks = extend_blocks(applied, result_blocks) if len(result_blocks) == 0: nrows = 0 else: nrows = result_blocks[0].values.shape[-1] index = Index(range(nrows)) return type(self).from_blocks(result_blocks, [self.axes[0], index]) def reduce(self, func: Callable) -> Self: """ Apply reduction function blockwise, returning a single-row BlockManager. Parameters ---------- func : reduction function Returns ------- BlockManager """ # If 2D, we assume that we're operating column-wise assert self.ndim == 2 res_blocks: list[Block] = [] for blk in self.blocks: nbs = blk.reduce(func) res_blocks.extend(nbs) index = Index([None]) # placeholder new_mgr = type(self).from_blocks(res_blocks, [self.items, index]) return new_mgr def operate_blockwise(self, other: BlockManager, array_op) -> BlockManager: """ Apply array_op blockwise with another (aligned) BlockManager. """ return operate_blockwise(self, other, array_op) def _equal_values(self: BlockManager, other: BlockManager) -> bool: """ Used in .equals defined in base class. Only check the column values assuming shape and indexes have already been checked. """ return blockwise_all(self, other, array_equals) def quantile( self, *, qs: Index, # with dtype float 64 interpolation: QuantileInterpolation = "linear", ) -> Self: """ Iterate over blocks applying quantile reduction. This routine is intended for reduction type operations and will do inference on the generated blocks. Parameters ---------- interpolation : type of interpolation, default 'linear' qs : list of the quantiles to be computed Returns ------- BlockManager """ # Series dispatches to DataFrame for quantile, which allows us to # simplify some of the code here and in the blocks assert self.ndim >= 2 assert is_list_like(qs) # caller is responsible for this new_axes = list(self.axes) new_axes[1] = Index(qs, dtype=np.float64) blocks = [ blk.quantile(qs=qs, interpolation=interpolation) for blk in self.blocks ] return type(self)(blocks, new_axes) # ---------------------------------------------------------------- def unstack(self, unstacker, fill_value) -> BlockManager: """ Return a BlockManager with all blocks unstacked. Parameters ---------- unstacker : reshape._Unstacker fill_value : Any fill_value for newly introduced missing values. Returns ------- unstacked : BlockManager """ new_columns = unstacker.get_new_columns(self.items) new_index = unstacker.new_index allow_fill = not unstacker.mask_all if allow_fill: # calculating the full mask once and passing it to Block._unstack is # faster than letting calculating it in each repeated call new_mask2D = (~unstacker.mask).reshape(*unstacker.full_shape) needs_masking = new_mask2D.any(axis=0) else: needs_masking = np.zeros(unstacker.full_shape[1], dtype=bool) new_blocks: list[Block] = [] columns_mask: list[np.ndarray] = [] if len(self.items) == 0: factor = 1 else: fac = len(new_columns) / len(self.items) assert fac == int(fac) factor = int(fac) for blk in self.blocks: mgr_locs = blk.mgr_locs new_placement = mgr_locs.tile_for_unstack(factor) blocks, mask = blk._unstack( unstacker, fill_value, new_placement=new_placement, needs_masking=needs_masking, ) new_blocks.extend(blocks) columns_mask.extend(mask) # Block._unstack should ensure this holds, assert mask.sum() == sum(len(nb._mgr_locs) for nb in blocks) # In turn this ensures that in the BlockManager call below # we have len(new_columns) == sum(x.shape[0] for x in new_blocks) # which suffices to allow us to pass verify_inegrity=False new_columns = new_columns[columns_mask] bm = BlockManager(new_blocks, [new_columns, new_index], verify_integrity=False) return bm def to_dict(self) -> dict[str, Self]: """ Return a dict of str(dtype) -> BlockManager Returns ------- values : a dict of dtype -> BlockManager """ bd: dict[str, list[Block]] = {} for b in self.blocks: bd.setdefault(str(b.dtype), []).append(b) # TODO(EA2D): the combine will be unnecessary with 2D EAs return {dtype: self._combine(blocks) for dtype, blocks in bd.items()} def as_array( self, dtype: np.dtype | None = None, copy: bool = False, na_value: object = lib.no_default, ) -> np.ndarray: """ Convert the blockmanager data into an numpy array. Parameters ---------- dtype : np.dtype or None, default None Data type of the return array. copy : bool, default False If True then guarantee that a copy is returned. A value of False does not guarantee that the underlying data is not copied. na_value : object, default lib.no_default Value to be used as the missing value sentinel. Returns ------- arr : ndarray """ passed_nan = lib.is_float(na_value) and isna(na_value) if len(self.blocks) == 0: arr = np.empty(self.shape, dtype=float) return arr.transpose() if self.is_single_block: blk = self.blocks[0] if na_value is not lib.no_default: # We want to copy when na_value is provided to avoid # mutating the original object if lib.is_np_dtype(blk.dtype, "f") and passed_nan: # We are already numpy-float and na_value=np.nan pass else: copy = True if blk.is_extension: # Avoid implicit conversion of extension blocks to object # error: Item "ndarray" of "Union[ndarray, ExtensionArray]" has no # attribute "to_numpy" arr = blk.values.to_numpy( # type: ignore[union-attr] dtype=dtype, na_value=na_value, copy=copy, ).reshape(blk.shape) elif not copy: arr = np.asarray(blk.values, dtype=dtype) else: arr = np.array(blk.values, dtype=dtype, copy=copy) if using_copy_on_write() and not copy: arr = arr.view() arr.flags.writeable = False else: arr = self._interleave(dtype=dtype, na_value=na_value) # The underlying data was copied within _interleave, so no need # to further copy if copy=True or setting na_value if na_value is lib.no_default: pass elif arr.dtype.kind == "f" and passed_nan: pass else: arr[isna(arr)] = na_value return arr.transpose() def _interleave( self, dtype: np.dtype | None = None, na_value: object = lib.no_default, ) -> np.ndarray: """ Return ndarray from blocks with specified item order Items must be contained in the blocks """ if not dtype: # Incompatible types in assignment (expression has type # "Optional[Union[dtype[Any], ExtensionDtype]]", variable has # type "Optional[dtype[Any]]") dtype = interleaved_dtype( # type: ignore[assignment] [blk.dtype for blk in self.blocks] ) # error: Argument 1 to "ensure_np_dtype" has incompatible type # "Optional[dtype[Any]]"; expected "Union[dtype[Any], ExtensionDtype]" dtype = ensure_np_dtype(dtype) # type: ignore[arg-type] result = np.empty(self.shape, dtype=dtype) itemmask = np.zeros(self.shape[0]) if dtype == np.dtype("object") and na_value is lib.no_default: # much more performant than using to_numpy below for blk in self.blocks: rl = blk.mgr_locs arr = blk.get_values(dtype) result[rl.indexer] = arr itemmask[rl.indexer] = 1 return result for blk in self.blocks: rl = blk.mgr_locs if blk.is_extension: # Avoid implicit conversion of extension blocks to object # error: Item "ndarray" of "Union[ndarray, ExtensionArray]" has no # attribute "to_numpy" arr = blk.values.to_numpy( # type: ignore[union-attr] dtype=dtype, na_value=na_value, ) else: arr = blk.get_values(dtype) result[rl.indexer] = arr itemmask[rl.indexer] = 1 if not itemmask.all(): raise AssertionError("Some items were not contained in blocks") return result # ---------------------------------------------------------------- # Consolidation def is_consolidated(self) -> bool: """ Return True if more than one block with the same dtype """ if not self._known_consolidated: self._consolidate_check() return self._is_consolidated def _consolidate_check(self) -> None: if len(self.blocks) == 1: # fastpath self._is_consolidated = True self._known_consolidated = True return dtypes = [blk.dtype for blk in self.blocks if blk._can_consolidate] self._is_consolidated = len(dtypes) == len(set(dtypes)) self._known_consolidated = True def _consolidate_inplace(self) -> None: # In general, _consolidate_inplace should only be called via # DataFrame._consolidate_inplace, otherwise we will fail to invalidate # the DataFrame's _item_cache. The exception is for newly-created # BlockManager objects not yet attached to a DataFrame. if not self.is_consolidated(): self.blocks = _consolidate(self.blocks) self._is_consolidated = True self._known_consolidated = True self._rebuild_blknos_and_blklocs() # ---------------------------------------------------------------- # Concatenation @classmethod def concat_horizontal(cls, mgrs: list[Self], axes: list[Index]) -> Self: """ Concatenate uniformly-indexed BlockManagers horizontally. """ offset = 0 blocks: list[Block] = [] for mgr in mgrs: for blk in mgr.blocks: # We need to do getitem_block here otherwise we would be altering # blk.mgr_locs in place, which would render it invalid. This is only # relevant in the copy=False case. nb = blk.slice_block_columns(slice(None)) nb._mgr_locs = nb._mgr_locs.add(offset) blocks.append(nb) offset += len(mgr.items) new_mgr = cls(tuple(blocks), axes) return new_mgr @classmethod def concat_vertical(cls, mgrs: list[Self], axes: list[Index]) -> Self: """ Concatenate uniformly-indexed BlockManagers vertically. """ raise NotImplementedError("This logic lives (for now) in internals.concat") class SingleBlockManager(BaseBlockManager, SingleDataManager): """manage a single block with""" @property def ndim(self) -> Literal[1]: return 1 _is_consolidated = True _known_consolidated = True __slots__ = () is_single_block = True def __init__( self, block: Block, axis: Index, verify_integrity: bool = False, ) -> None: # Assertions disabled for performance # assert isinstance(block, Block), type(block) # assert isinstance(axis, Index), type(axis) self.axes = [axis] self.blocks = (block,) @classmethod def from_blocks( cls, blocks: list[Block], axes: list[Index], ) -> Self: """ Constructor for BlockManager and SingleBlockManager with same signature. """ assert len(blocks) == 1 assert len(axes) == 1 return cls(blocks[0], axes[0], verify_integrity=False) @classmethod def from_array( cls, array: ArrayLike, index: Index, refs: BlockValuesRefs | None = None ) -> SingleBlockManager: """ Constructor for if we have an array that is not yet a Block. """ array = maybe_coerce_values(array) bp = BlockPlacement(slice(0, len(index))) block = new_block(array, placement=bp, ndim=1, refs=refs) return cls(block, index) def to_2d_mgr(self, columns: Index) -> BlockManager: """ Manager analogue of Series.to_frame """ blk = self.blocks[0] arr = ensure_block_shape(blk.values, ndim=2) bp = BlockPlacement(0) new_blk = type(blk)(arr, placement=bp, ndim=2, refs=blk.refs) axes = [columns, self.axes[0]] return BlockManager([new_blk], axes=axes, verify_integrity=False) def _has_no_reference(self, i: int = 0) -> bool: """ Check for column `i` if it has references. (whether it references another array or is itself being referenced) Returns True if the column has no references. """ return not self.blocks[0].refs.has_reference() def __getstate__(self): block_values = [b.values for b in self.blocks] block_items = [self.items[b.mgr_locs.indexer] for b in self.blocks] axes_array = list(self.axes) extra_state = { "0.14.1": { "axes": axes_array, "blocks": [ {"values": b.values, "mgr_locs": b.mgr_locs.indexer} for b in self.blocks ], } } # First three elements of the state are to maintain forward # compatibility with 0.13.1. return axes_array, block_values, block_items, extra_state def __setstate__(self, state) -> None: def unpickle_block(values, mgr_locs, ndim: int) -> Block: # TODO(EA2D): ndim would be unnecessary with 2D EAs # older pickles may store e.g. DatetimeIndex instead of DatetimeArray values = extract_array(values, extract_numpy=True) if not isinstance(mgr_locs, BlockPlacement): mgr_locs = BlockPlacement(mgr_locs) values = maybe_coerce_values(values) return new_block(values, placement=mgr_locs, ndim=ndim) if isinstance(state, tuple) and len(state) >= 4 and "0.14.1" in state[3]: state = state[3]["0.14.1"] self.axes = [ensure_index(ax) for ax in state["axes"]] ndim = len(self.axes) self.blocks = tuple( unpickle_block(b["values"], b["mgr_locs"], ndim=ndim) for b in state["blocks"] ) else: raise NotImplementedError("pre-0.14.1 pickles are no longer supported") self._post_setstate() def _post_setstate(self) -> None: pass @cache_readonly def _block(self) -> Block: return self.blocks[0] @property def _blknos(self): """compat with BlockManager""" return None @property def _blklocs(self): """compat with BlockManager""" return None def get_rows_with_mask(self, indexer: npt.NDArray[np.bool_]) -> Self: # similar to get_slice, but not restricted to slice indexer blk = self._block if using_copy_on_write() and len(indexer) > 0 and indexer.all(): return type(self)(blk.copy(deep=False), self.index) array = blk.values[indexer] if isinstance(indexer, np.ndarray) and indexer.dtype.kind == "b": # boolean indexing always gives a copy with numpy refs = None else: # TODO(CoW) in theory only need to track reference if new_array is a view refs = blk.refs bp = BlockPlacement(slice(0, len(array))) block = type(blk)(array, placement=bp, ndim=1, refs=refs) new_idx = self.index[indexer] return type(self)(block, new_idx) def get_slice(self, slobj: slice, axis: AxisInt = 0) -> SingleBlockManager: # Assertion disabled for performance # assert isinstance(slobj, slice), type(slobj) if axis >= self.ndim: raise IndexError("Requested axis not found in manager") blk = self._block array = blk.values[slobj] bp = BlockPlacement(slice(0, len(array))) # TODO this method is only used in groupby SeriesSplitter at the moment, # so passing refs is not yet covered by the tests block = type(blk)(array, placement=bp, ndim=1, refs=blk.refs) new_index = self.index._getitem_slice(slobj) return type(self)(block, new_index) @property def index(self) -> Index: return self.axes[0] @property def dtype(self) -> DtypeObj: return self._block.dtype def get_dtypes(self) -> npt.NDArray[np.object_]: return np.array([self._block.dtype], dtype=object) def external_values(self): """The array that Series.values returns""" return self._block.external_values() def internal_values(self): """The array that Series._values returns""" return self._block.values def array_values(self) -> ExtensionArray: """The array that Series.array returns""" return self._block.array_values def get_numeric_data(self) -> Self: if self._block.is_numeric: return self.copy(deep=False) return self.make_empty() @property def _can_hold_na(self) -> bool: return self._block._can_hold_na def setitem_inplace(self, indexer, value, warn: bool = True) -> None: """ Set values with indexer. For Single[Block/Array]Manager, this backs s[indexer] = value This is an inplace version of `setitem()`, mutating the manager/values in place, not returning a new Manager (and Block), and thus never changing the dtype. """ using_cow = using_copy_on_write() warn_cow = warn_copy_on_write() if (using_cow or warn_cow) and not self._has_no_reference(0): if using_cow: self.blocks = (self._block.copy(),) self._cache.clear() elif warn_cow and warn: warnings.warn( COW_WARNING_SETITEM_MSG, FutureWarning, stacklevel=find_stack_level(), ) super().setitem_inplace(indexer, value) def idelete(self, indexer) -> SingleBlockManager: """ Delete single location from SingleBlockManager. Ensures that self.blocks doesn't become empty. """ nb = self._block.delete(indexer)[0] self.blocks = (nb,) self.axes[0] = self.axes[0].delete(indexer) self._cache.clear() return self def fast_xs(self, loc): """ fast path for getting a cross-section return a view of the data """ raise NotImplementedError("Use series._values[loc] instead") def set_values(self, values: ArrayLike) -> None: """ Set the values of the single block in place. Use at your own risk! This does not check if the passed values are valid for the current Block/SingleBlockManager (length, dtype, etc), and this does not properly keep track of references. """ # NOTE(CoW) Currently this is only used for FrameColumnApply.series_generator # which handles CoW by setting the refs manually if necessary self.blocks[0].values = values self.blocks[0]._mgr_locs = BlockPlacement(slice(len(values))) def _equal_values(self, other: Self) -> bool: """ Used in .equals defined in base class. Only check the column values assuming shape and indexes have already been checked. """ # For SingleBlockManager (i.e.Series) if other.ndim != 1: return False left = self.blocks[0].values right = other.blocks[0].values return array_equals(left, right) # -------------------------------------------------------------------- # Constructor Helpers def create_block_manager_from_blocks( blocks: list[Block], axes: list[Index], consolidate: bool = True, verify_integrity: bool = True, ) -> BlockManager: # If verify_integrity=False, then caller is responsible for checking # all(x.shape[-1] == len(axes[1]) for x in blocks) # sum(x.shape[0] for x in blocks) == len(axes[0]) # set(x for blk in blocks for x in blk.mgr_locs) == set(range(len(axes[0]))) # all(blk.ndim == 2 for blk in blocks) # This allows us to safely pass verify_integrity=False try: mgr = BlockManager(blocks, axes, verify_integrity=verify_integrity) except ValueError as err: arrays = [blk.values for blk in blocks] tot_items = sum(arr.shape[0] for arr in arrays) raise_construction_error(tot_items, arrays[0].shape[1:], axes, err) if consolidate: mgr._consolidate_inplace() return mgr def create_block_manager_from_column_arrays( arrays: list[ArrayLike], axes: list[Index], consolidate: bool, refs: list, ) -> BlockManager: # Assertions disabled for performance (caller is responsible for verifying) # assert isinstance(axes, list) # assert all(isinstance(x, Index) for x in axes) # assert all(isinstance(x, (np.ndarray, ExtensionArray)) for x in arrays) # assert all(type(x) is not NumpyExtensionArray for x in arrays) # assert all(x.ndim == 1 for x in arrays) # assert all(len(x) == len(axes[1]) for x in arrays) # assert len(arrays) == len(axes[0]) # These last three are sufficient to allow us to safely pass # verify_integrity=False below. try: blocks = _form_blocks(arrays, consolidate, refs) mgr = BlockManager(blocks, axes, verify_integrity=False) except ValueError as e: raise_construction_error(len(arrays), arrays[0].shape, axes, e) if consolidate: mgr._consolidate_inplace() return mgr def raise_construction_error( tot_items: int, block_shape: Shape, axes: list[Index], e: ValueError | None = None, ): """raise a helpful message about our construction""" passed = tuple(map(int, [tot_items] + list(block_shape))) # Correcting the user facing error message during dataframe construction if len(passed) <= 2: passed = passed[::-1] implied = tuple(len(ax) for ax in axes) # Correcting the user facing error message during dataframe construction if len(implied) <= 2: implied = implied[::-1] # We return the exception object instead of raising it so that we # can raise it in the caller; mypy plays better with that if passed == implied and e is not None: raise e if block_shape[0] == 0: raise ValueError("Empty data passed with indices specified.") raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") # ----------------------------------------------------------------------- def _grouping_func(tup: tuple[int, ArrayLike]) -> tuple[int, DtypeObj]: dtype = tup[1].dtype if is_1d_only_ea_dtype(dtype): # We know these won't be consolidated, so don't need to group these. # This avoids expensive comparisons of CategoricalDtype objects sep = id(dtype) else: sep = 0 return sep, dtype def _form_blocks(arrays: list[ArrayLike], consolidate: bool, refs: list) -> list[Block]: tuples = list(enumerate(arrays)) if not consolidate: return _tuples_to_blocks_no_consolidate(tuples, refs) # when consolidating, we can ignore refs (either stacking always copies, # or the EA is already copied in the calling dict_to_mgr) # group by dtype grouper = itertools.groupby(tuples, _grouping_func) nbs: list[Block] = [] for (_, dtype), tup_block in grouper: block_type = get_block_type(dtype) if isinstance(dtype, np.dtype): is_dtlike = dtype.kind in "mM" if issubclass(dtype.type, (str, bytes)): dtype = np.dtype(object) values, placement = _stack_arrays(list(tup_block), dtype) if is_dtlike: values = ensure_wrapped_if_datetimelike(values) blk = block_type(values, placement=BlockPlacement(placement), ndim=2) nbs.append(blk) elif is_1d_only_ea_dtype(dtype): dtype_blocks = [ block_type(x[1], placement=BlockPlacement(x[0]), ndim=2) for x in tup_block ] nbs.extend(dtype_blocks) else: dtype_blocks = [ block_type( ensure_block_shape(x[1], 2), placement=BlockPlacement(x[0]), ndim=2 ) for x in tup_block ] nbs.extend(dtype_blocks) return nbs def _tuples_to_blocks_no_consolidate(tuples, refs) -> list[Block]: # tuples produced within _form_blocks are of the form (placement, array) return [ new_block_2d( ensure_block_shape(arr, ndim=2), placement=BlockPlacement(i), refs=ref ) for ((i, arr), ref) in zip(tuples, refs) ] def _stack_arrays(tuples, dtype: np.dtype): placement, arrays = zip(*tuples) first = arrays[0] shape = (len(arrays),) + first.shape stacked = np.empty(shape, dtype=dtype) for i, arr in enumerate(arrays): stacked[i] = arr return stacked, placement def _consolidate(blocks: tuple[Block, ...]) -> tuple[Block, ...]: """ Merge blocks having same dtype, exclude non-consolidating blocks """ # sort by _can_consolidate, dtype gkey = lambda x: x._consolidate_key grouper = itertools.groupby(sorted(blocks, key=gkey), gkey) new_blocks: list[Block] = [] for (_can_consolidate, dtype), group_blocks in grouper: merged_blocks, _ = _merge_blocks( list(group_blocks), dtype=dtype, can_consolidate=_can_consolidate ) new_blocks = extend_blocks(merged_blocks, new_blocks) return tuple(new_blocks) def _merge_blocks( blocks: list[Block], dtype: DtypeObj, can_consolidate: bool ) -> tuple[list[Block], bool]: if len(blocks) == 1: return blocks, False if can_consolidate: # TODO: optimization potential in case all mgrs contain slices and # combination of those slices is a slice, too. new_mgr_locs = np.concatenate([b.mgr_locs.as_array for b in blocks]) new_values: ArrayLike if isinstance(blocks[0].dtype, np.dtype): # error: List comprehension has incompatible type List[Union[ndarray, # ExtensionArray]]; expected List[Union[complex, generic, # Sequence[Union[int, float, complex, str, bytes, generic]], # Sequence[Sequence[Any]], SupportsArray]] new_values = np.vstack([b.values for b in blocks]) # type: ignore[misc] else: bvals = [blk.values for blk in blocks] bvals2 = cast(Sequence[NDArrayBackedExtensionArray], bvals) new_values = bvals2[0]._concat_same_type(bvals2, axis=0) argsort = np.argsort(new_mgr_locs) new_values = new_values[argsort] new_mgr_locs = new_mgr_locs[argsort] bp = BlockPlacement(new_mgr_locs) return [new_block_2d(new_values, placement=bp)], True # can't consolidate --> no merge return blocks, False def _fast_count_smallints(arr: npt.NDArray[np.intp]): """Faster version of set(arr) for sequences of small numbers.""" counts = np.bincount(arr) nz = counts.nonzero()[0] # Note: list(zip(...) outperforms list(np.c_[nz, counts[nz]]) here, # in one benchmark by a factor of 11 return zip(nz, counts[nz]) def _preprocess_slice_or_indexer( slice_or_indexer: slice | np.ndarray, length: int, allow_fill: bool ): if isinstance(slice_or_indexer, slice): return ( "slice", slice_or_indexer, libinternals.slice_len(slice_or_indexer, length), ) else: if ( not isinstance(slice_or_indexer, np.ndarray) or slice_or_indexer.dtype.kind != "i" ): dtype = getattr(slice_or_indexer, "dtype", None) raise TypeError(type(slice_or_indexer), dtype) indexer = ensure_platform_int(slice_or_indexer) if not allow_fill: indexer = maybe_convert_indices(indexer, length) return "fancy", indexer, len(indexer) def make_na_array(dtype: DtypeObj, shape: Shape, fill_value) -> ArrayLike: if isinstance(dtype, DatetimeTZDtype): # NB: exclude e.g. pyarrow[dt64tz] dtypes ts = Timestamp(fill_value).as_unit(dtype.unit) i8values = np.full(shape, ts._value) dt64values = i8values.view(f"M8[{dtype.unit}]") return DatetimeArray._simple_new(dt64values, dtype=dtype) elif is_1d_only_ea_dtype(dtype): dtype = cast(ExtensionDtype, dtype) cls = dtype.construct_array_type() missing_arr = cls._from_sequence([], dtype=dtype) ncols, nrows = shape assert ncols == 1, ncols empty_arr = -1 * np.ones((nrows,), dtype=np.intp) return missing_arr.take(empty_arr, allow_fill=True, fill_value=fill_value) elif isinstance(dtype, ExtensionDtype): # TODO: no tests get here, a handful would if we disabled # the dt64tz special-case above (which is faster) cls = dtype.construct_array_type() missing_arr = cls._empty(shape=shape, dtype=dtype) missing_arr[:] = fill_value return missing_arr else: # NB: we should never get here with dtype integer or bool; # if we did, the missing_arr.fill would cast to gibberish missing_arr = np.empty(shape, dtype=dtype) missing_arr.fill(fill_value) if dtype.kind in "mM": missing_arr = ensure_wrapped_if_datetimelike(missing_arr) return missing_arr