BoghdadyJR/codesearch_python · Datasets at Hugging Face

text stringlengths 75 104k
def concat_same_type(self, to_concat, placement=None): """ Concatenate list of single blocks of the same type. """ values = self._concatenator([blk.values for blk in to_concat], axis=self.ndim - 1) return self.make_block_same_class( ...
def delete(self, loc): """ Delete given loc(-s) from block in-place. """ self.values = np.delete(self.values, loc, 0) self.mgr_locs = self.mgr_locs.delete(loc)
def apply(self, func, kwargs): """ apply the function to my values; return a block if we are not one """ with np.errstate(all='ignore'): result = func(self.values, kwargs) if not isinstance(result, Block): result = self.make_block(values=_block_shape(r...
def fillna(self, value, limit=None, inplace=False, downcast=None): """ fillna on the block with the value. If we fail, then convert to ObjectBlock and try again """ inplace = validate_bool_kwarg(inplace, 'inplace') if not self._can_hold_na: if inplace: ...
def split_and_operate(self, mask, f, inplace): """ split the block per-column, and apply the callable f per-column, return a new block for each. Handle masking which will not change a block unless needed. Parameters ---------- mask : 2-d boolean mask f : ...
def downcast(self, dtypes=None): """ try to downcast each item to the dict of dtypes if present """ # turn it off completely if dtypes is False: return self values = self.values # single block handling if self._is_single_block: # try to cast al...
def _astype(self, dtype, copy=False, errors='raise', values=None, **kwargs): """Coerce to the new type Parameters ---------- dtype : str, dtype convertible copy : boolean, default False copy if indicated errors : str, {'raise', 'ignore'}, defa...
def _can_hold_element(self, element): """ require the same dtype as ourselves """ dtype = self.values.dtype.type tipo = maybe_infer_dtype_type(element) if tipo is not None: return issubclass(tipo.type, dtype) return isinstance(element, dtype)
def _try_cast_result(self, result, dtype=None): """ try to cast the result to our original type, we may have roundtripped thru object in the mean-time """ if dtype is None: dtype = self.dtype if self.is_integer or self.is_bool or self.is_datetime: pass ...
def _try_coerce_args(self, values, other): """ provide coercion to our input arguments """ if np.any(notna(other)) and not self._can_hold_element(other): # coercion issues # let higher levels handle raise TypeError("cannot convert {} to an {}".format( ...
def to_native_types(self, slicer=None, na_rep='nan', quoting=None, **kwargs): """ convert to our native types format, slicing if desired """ values = self.get_values() if slicer is not None: values = values[:, slicer] mask = isna(values) if ...
def copy(self, deep=True): """ copy constructor """ values = self.values if deep: values = values.copy() return self.make_block_same_class(values, ndim=self.ndim)
def replace(self, to_replace, value, inplace=False, filter=None, regex=False, convert=True): """replace the to_replace value with value, possible to create new blocks here this is just a call to putmask. regex is not used here. It is used in ObjectBlocks. It is here for API comp...
def setitem(self, indexer, value): """Set the value inplace, returning a a maybe different typed block. Parameters ---------- indexer : tuple, list-like, array-like, slice The subset of self.values to set value : object The value being set Return...
def putmask(self, mask, new, align=True, inplace=False, axis=0, transpose=False): """ putmask the data to the block; it is possible that we may create a new dtype of block return the resulting block(s) Parameters ---------- mask : the condition to respe...
def coerce_to_target_dtype(self, other): """ coerce the current block to a dtype compat for other we will return a block, possibly object, and not raise we can also safely try to coerce to the same dtype and will receive the same block """ # if we cannot then co...
def _interpolate_with_fill(self, method='pad', axis=0, inplace=False, limit=None, fill_value=None, coerce=False, downcast=None): """ fillna but using the interpolate machinery """ inplace = validate_bool_kwarg(inplace, 'inplace') # ...
def _interpolate(self, method=None, index=None, values=None, fill_value=None, axis=0, limit=None, limit_direction='forward', limit_area=None, inplace=False, downcast=None, **kwargs): """ interpolate using scipy wrappers """ inplace = valida...
def take_nd(self, indexer, axis, new_mgr_locs=None, fill_tuple=None): """ Take values according to indexer and return them as a block.bb """ # algos.take_nd dispatches for DatetimeTZBlock, CategoricalBlock # so need to preserve types # sparse is treated like an ndarray,...
def diff(self, n, axis=1): """ return block for the diff of the values """ new_values = algos.diff(self.values, n, axis=axis) return [self.make_block(values=new_values)]
def shift(self, periods, axis=0, fill_value=None): """ shift the block by periods, possibly upcast """ # convert integer to float if necessary. need to do a lot more than # that, handle boolean etc also new_values, fill_value = maybe_upcast(self.values, fill_value) # make sure ...
def where(self, other, cond, align=True, errors='raise', try_cast=False, axis=0, transpose=False): """ evaluate the block; return result block(s) from the result Parameters ---------- other : a ndarray/object cond : the condition to respect align :...
def _unstack(self, unstacker_func, new_columns, n_rows, fill_value): """Return a list of unstacked blocks of self Parameters ---------- unstacker_func : callable Partially applied unstacker. new_columns : Index All columns of the unstacked BlockManager. ...
def quantile(self, qs, interpolation='linear', axis=0): """ compute the quantiles of the Parameters ---------- qs: a scalar or list of the quantiles to be computed interpolation: type of interpolation, default 'linear' axis: axis to compute, default 0 Re...
def _replace_coerce(self, to_replace, value, inplace=True, regex=False, convert=False, mask=None): """ Replace value corresponding to the given boolean array with another value. Parameters ---------- to_replace : object or pattern Scal...
def putmask(self, mask, new, align=True, inplace=False, axis=0, transpose=False): """ putmask the data to the block; we must be a single block and not generate other blocks return the resulting block Parameters ---------- mask : the condition to...
def _get_unstack_items(self, unstacker, new_columns): """ Get the placement, values, and mask for a Block unstack. This is shared between ObjectBlock and ExtensionBlock. They differ in that ObjectBlock passes the values, while ExtensionBlock passes the dummy ndarray of positions...
def _maybe_coerce_values(self, values): """Unbox to an extension array. This will unbox an ExtensionArray stored in an Index or Series. ExtensionArrays pass through. No dtype coercion is done. Parameters ---------- values : Index, Series, ExtensionArray Returns...
def setitem(self, indexer, value): """Set the value inplace, returning a same-typed block. This differs from Block.setitem by not allowing setitem to change the dtype of the Block. Parameters ---------- indexer : tuple, list-like, array-like, slice The subse...
def take_nd(self, indexer, axis=0, new_mgr_locs=None, fill_tuple=None): """ Take values according to indexer and return them as a block. """ if fill_tuple is None: fill_value = None else: fill_value = fill_tuple[0] # axis doesn't matter; we are re...
def _slice(self, slicer): """ return a slice of my values """ # slice the category # return same dims as we currently have if isinstance(slicer, tuple) and len(slicer) == 2: if not com.is_null_slice(slicer[0]): raise AssertionError("invalid slicing for a 1-n...
def concat_same_type(self, to_concat, placement=None): """ Concatenate list of single blocks of the same type. """ values = self._holder._concat_same_type( [blk.values for blk in to_concat]) placement = placement or slice(0, len(values), 1) return self.make_bl...
def shift(self, periods: int, axis: libinternals.BlockPlacement = 0, fill_value: Any = None) -> List['ExtensionBlock']: """ Shift the block by `periods`. Dispatches to underlying ExtensionArray and re-boxes in an ExtensionBlock. """ ...
def to_native_types(self, slicer=None, na_rep='', float_format=None, decimal='.', quoting=None, **kwargs): """ convert to our native types format, slicing if desired """ values = self.values if slicer is not None: values = values[:, slicer] # see gh-...
def get_values(self, dtype=None): """ return object dtype as boxed values, such as Timestamps/Timedelta """ if is_object_dtype(dtype): values = self.values.ravel() result = self._holder(values).astype(object) return result.reshape(self.values.shape) ...
def _maybe_coerce_values(self, values): """Input validation for values passed to __init__. Ensure that we have datetime64ns, coercing if necessary. Parameters ---------- values : array-like Must be convertible to datetime64 Returns ------- va...
def _astype(self, dtype, **kwargs): """ these automatically copy, so copy=True has no effect raise on an except if raise == True """ dtype = pandas_dtype(dtype) # if we are passed a datetime64[ns, tz] if is_datetime64tz_dtype(dtype): values = self.val...
def _try_coerce_args(self, values, other): """ Coerce values and other to dtype 'i8'. NaN and NaT convert to the smallest i8, and will correctly round-trip to NaT if converted back in _try_coerce_result. values is always ndarray-like, other may not be Parameters ...
def _try_coerce_result(self, result): """ reverse of try_coerce_args """ if isinstance(result, np.ndarray): if result.dtype.kind in ['i', 'f']: result = result.astype('M8[ns]') elif isinstance(result, (np.integer, np.float, np.datetime64)): result = self....
def to_native_types(self, slicer=None, na_rep=None, date_format=None, quoting=None, **kwargs): """ convert to our native types format, slicing if desired """ values = self.values i8values = self.values.view('i8') if slicer is not None: values = value...
def set(self, locs, values): """ Modify Block in-place with new item value Returns ------- None """ values = conversion.ensure_datetime64ns(values, copy=False) self.values[locs] = values
def _maybe_coerce_values(self, values): """Input validation for values passed to __init__. Ensure that we have datetime64TZ, coercing if necessary. Parametetrs ----------- values : array-like Must be convertible to datetime64 Returns ------- ...
def get_values(self, dtype=None): """ Returns an ndarray of values. Parameters ---------- dtype : np.dtype Only `object`-like dtypes are respected here (not sure why). Returns ------- values : ndarray When ``dtype=obje...
def _slice(self, slicer): """ return a slice of my values """ if isinstance(slicer, tuple): col, loc = slicer if not com.is_null_slice(col) and col != 0: raise IndexError("{0} only contains one item".format(self)) return self.values[loc] return...
def _try_coerce_args(self, values, other): """ localize and return i8 for the values Parameters ---------- values : ndarray-like other : ndarray-like or scalar Returns ------- base-type values, base-type other """ # asi8 is a view...
def _try_coerce_result(self, result): """ reverse of try_coerce_args """ if isinstance(result, np.ndarray): if result.dtype.kind in ['i', 'f']: result = result.astype('M8[ns]') elif isinstance(result, (np.integer, np.float, np.datetime64)): result = self....
def diff(self, n, axis=0): """1st discrete difference Parameters ---------- n : int, number of periods to diff axis : int, axis to diff upon. default 0 Return ------ A list with a new TimeDeltaBlock. Note ---- The arguments here ...
def _try_coerce_args(self, values, other): """ Coerce values and other to int64, with null values converted to iNaT. values is always ndarray-like, other may not be Parameters ---------- values : ndarray-like other : ndarray-like or scalar Returns ...
def _try_coerce_result(self, result): """ reverse of try_coerce_args / try_operate """ if isinstance(result, np.ndarray): mask = isna(result) if result.dtype.kind in ['i', 'f']: result = result.astype('m8[ns]') result[mask] = tslibs.iNaT elif ...
def to_native_types(self, slicer=None, na_rep=None, quoting=None, **kwargs): """ convert to our native types format, slicing if desired """ values = self.values if slicer is not None: values = values[:, slicer] mask = isna(values) rvalues = n...
def convert(self, args, *kwargs): """ attempt to coerce any object types to better types return a copy of the block (if copy = True) by definition we ARE an ObjectBlock!!!!! can return multiple blocks! """ if args: raise NotImplementedError by_item = kwarg...
def set(self, locs, values): """ Modify Block in-place with new item value Returns ------- None """ try: self.values[locs] = values except (ValueError): # broadcasting error # see GH6171 new_shape = list(va...
def _try_coerce_args(self, values, other): """ provide coercion to our input arguments """ if isinstance(other, ABCDatetimeIndex): # May get a DatetimeIndex here. Unbox it. other = other.array if isinstance(other, DatetimeArray): # hit in pandas/tests/indexi...
def _replace_single(self, to_replace, value, inplace=False, filter=None, regex=False, convert=True, mask=None): """ Replace elements by the given value. Parameters ---------- to_replace : object or pattern Scalar to replace or regular expressi...
def _replace_coerce(self, to_replace, value, inplace=True, regex=False, convert=False, mask=None): """ Replace value corresponding to the given boolean array with another value. Parameters ---------- to_replace : object or pattern Scal...
def _try_coerce_result(self, result): """ reverse of try_coerce_args """ # GH12564: CategoricalBlock is 1-dim only # while returned results could be any dim if ((not is_categorical_dtype(result)) and isinstance(result, np.ndarray)): result = _block_shape(resu...
def to_native_types(self, slicer=None, na_rep='', quoting=None, **kwargs): """ convert to our native types format, slicing if desired """ values = self.values if slicer is not None: # Categorical is always one dimension values = values[slicer] mask = isna(values)...
def _style_to_xlwt(cls, item, firstlevel=True, field_sep=',', line_sep=';'): """helper which recursively generate an xlwt easy style string for example: hstyle = {"font": {"bold": True}, "border": {"top": "thin", "right": "thin", ...
def _convert_to_style(cls, style_dict, num_format_str=None): """ converts a style_dict to an xlwt style object Parameters ---------- style_dict : style dictionary to convert num_format_str : optional number format string """ import xlwt if style_d...
def convert(cls, style_dict, num_format_str=None): """ converts a style_dict to an xlsxwriter format dict Parameters ---------- style_dict : style dictionary to convert num_format_str : optional number format string """ # Create a XlsxWriter format objec...
def _unstack_extension_series(series, level, fill_value): """ Unstack an ExtensionArray-backed Series. The ExtensionDtype is preserved. Parameters ---------- series : Series A Series with an ExtensionArray for values level : Any The level name or number. fill_value : An...
def stack(frame, level=-1, dropna=True): """ Convert DataFrame to Series with multi-level Index. Columns become the second level of the resulting hierarchical index Returns ------- stacked : Series """ def factorize(index): if index.is_unique: return index, np.arange...
def get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False, columns=None, sparse=False, drop_first=False, dtype=None): """ Convert categorical variable into dummy/indicator variables. Parameters ---------- data : array-like, Series, or DataFrame Data of which to get d...
def make_axis_dummies(frame, axis='minor', transform=None): """ Construct 1-0 dummy variables corresponding to designated axis labels Parameters ---------- frame : DataFrame axis : {'major', 'minor'}, default 'minor' transform : function, default None Function to apply to axis l...
def _reorder_for_extension_array_stack(arr, n_rows, n_columns): """ Re-orders the values when stacking multiple extension-arrays. The indirect stacking method used for EAs requires a followup take to get the order correct. Parameters ---------- arr : ExtensionArray n_rows, n_columns : ...
def _split_line(s, parts): """ Parameters ---------- s: string Fixed-length string to split parts: list of (name, length) pairs Used to break up string, name '_' will be filtered from output. Returns ------- Dict of name:contents of string at given location. """ ...
def _parse_float_vec(vec): """ Parse a vector of float values representing IBM 8 byte floats into native 8 byte floats. """ dtype = np.dtype('>u4,>u4') vec1 = vec.view(dtype=dtype) xport1 = vec1['f0'] xport2 = vec1['f1'] # Start by setting first half of ieee number to first half of...
def _record_count(self): """ Get number of records in file. This is maybe suboptimal because we have to seek to the end of the file. Side effect: returns file position to record_start. """ self.filepath_or_buffer.seek(0, 2) total_records_length = (self....
def get_chunk(self, size=None): """ Reads lines from Xport file and returns as dataframe Parameters ---------- size : int, defaults to None Number of lines to read. If None, reads whole file. Returns ------- DataFrame """ if ...
def construction_error(tot_items, block_shape, axes, e=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] ...
def _simple_blockify(tuples, dtype): """ return a single array of a block that has a single dtype; if dtype is not None, coerce to this dtype """ values, placement = _stack_arrays(tuples, dtype) # CHECK DTYPE? if dtype is not None and values.dtype != dtype: # pragma: no cover values = ...
def _multi_blockify(tuples, dtype=None): """ return an array of blocks that potentially have different dtypes """ # group by dtype grouper = itertools.groupby(tuples, lambda x: x[2].dtype) new_blocks = [] for dtype, tup_block in grouper: values, placement = _stack_arrays(list(tup_block), ...
def _sparse_blockify(tuples, dtype=None): """ return an array of blocks that potentially have different dtypes (and are sparse) """ new_blocks = [] for i, names, array in tuples: array = _maybe_to_sparse(array) block = make_block(array, placement=[i]) new_blocks.append(block...
def _interleaved_dtype( blocks: List[Block] ) -> Optional[Union[np.dtype, ExtensionDtype]]: """Find the common dtype for `blocks`. Parameters ---------- blocks : List[Block] Returns ------- dtype : Optional[Union[np.dtype, ExtensionDtype]] None is returned when `blocks` is ...
def _consolidate(blocks): """ 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 = [] for (_can_consolidate, dtype), group_bloc...
def _compare_or_regex_search(a, b, regex=False): """ Compare two array_like inputs of the same shape or two scalar values Calls operator.eq or re.search, depending on regex argument. If regex is True, perform an element-wise regex matching. Parameters ---------- a : array_like or scalar ...
def items_overlap_with_suffix(left, lsuffix, right, rsuffix): """ If two indices overlap, add suffixes to overlapping entries. If corresponding suffix is empty, the entry is simply converted to string. """ to_rename = left.intersection(right) if len(to_rename) == 0: return left, right ...
def _transform_index(index, func, level=None): """ Apply function to all values found in index. This includes transforming multiindex entries separately. Only apply function to one level of the MultiIndex if level is specified. """ if isinstance(index, MultiIndex): if level is not None...
def _fast_count_smallints(arr): """Faster version of set(arr) for sequences of small numbers.""" counts = np.bincount(arr.astype(np.int_)) nz = counts.nonzero()[0] return np.c_[nz, counts[nz]]
def concatenate_block_managers(mgrs_indexers, axes, concat_axis, copy): """ Concatenate block managers into one. Parameters ---------- mgrs_indexers : list of (BlockManager, {axis: indexer,...}) tuples axes : list of Index concat_axis : int copy : bool """ concat_plans = [get_m...
def make_empty(self, axes=None): """ return an empty BlockManager with the items axis of len 0 """ if axes is None: axes = [ensure_index([])] + [ensure_index(a) for a in self.axes[1:]] # preserve dtype if possible if self.ndim == 1: ...
def rename_axis(self, mapper, axis, copy=True, level=None): """ Rename one of axes. Parameters ---------- mapper : unary callable axis : int copy : boolean, default True level : int, default None """ obj = self.copy(deep=copy) obj....
def _rebuild_blknos_and_blklocs(self): """ Update mgr._blknos / mgr._blklocs. """ new_blknos = np.empty(self.shape[0], dtype=np.int64) new_blklocs = np.empty(self.shape[0], dtype=np.int64) new_blknos.fill(-1) new_blklocs.fill(-1) for blkno, blk in enumera...
def _get_counts(self, f): """ return a dict of the counts of the function in BlockManager """ self._consolidate_inplace() counts = dict() for b in self.blocks: v = f(b) counts[v] = counts.get(v, 0) + b.shape[0] return counts
def apply(self, f, axes=None, filter=None, do_integrity_check=False, consolidate=True, **kwargs): """ iterate over the blocks, collect and create a new block manager Parameters ---------- f : the callable or function name to operate on at the block level ax...
def quantile(self, axis=0, consolidate=True, transposed=False, interpolation='linear', qs=None, numeric_only=None): """ Iterate over blocks applying quantile reduction. This routine is intended for reduction type operations and will do inference on the generated blocks. ...
def replace_list(self, src_list, dest_list, inplace=False, regex=False): """ do a list replace """ inplace = validate_bool_kwarg(inplace, 'inplace') # figure out our mask a-priori to avoid repeated replacements values = self.as_array() def comp(s, regex=False): """...
def get_bool_data(self, copy=False): """ Parameters ---------- copy : boolean, default False Whether to copy the blocks """ self._consolidate_inplace() return self.combine([b for b in self.blocks if b.is_bool], copy)
def get_numeric_data(self, copy=False): """ Parameters ---------- copy : boolean, default False Whether to copy the blocks """ self._consolidate_inplace() return self.combine([b for b in self.blocks if b.is_numeric], copy)
def combine(self, blocks, copy=True): """ return a new manager with the blocks """ if len(blocks) == 0: return self.make_empty() # FIXME: optimization potential indexer = np.sort(np.concatenate([b.mgr_locs.as_array for b in blocks]))...
def copy(self, deep=True): """ Make deep or shallow copy of BlockManager Parameters ---------- deep : boolean o rstring, default True If False, return shallow copy (do not copy data) If 'all', copy data and a deep copy of the index Returns ...
def as_array(self, transpose=False, items=None): """Convert the blockmanager data into an numpy array. Parameters ---------- transpose : boolean, default False If True, transpose the return array items : list of strings or None Names of block items that w...
def _interleave(self): """ Return ndarray from blocks with specified item order Items must be contained in the blocks """ from pandas.core.dtypes.common import is_sparse dtype = _interleaved_dtype(self.blocks) # TODO: https://github.com/pandas-dev/pandas/issues/2...
def to_dict(self, copy=True): """ Return a dict of str(dtype) -> BlockManager Parameters ---------- copy : boolean, default True Returns ------- values : a dict of dtype -> BlockManager Notes ----- This consolidates based on str(...
def fast_xs(self, loc): """ get a cross sectional for a given location in the items ; handle dups return the result, is could be a view in the case of a single block """ if len(self.blocks) == 1: return self.blocks[0].iget((slice(None), loc)) ...
def consolidate(self): """ Join together blocks having same dtype Returns ------- y : BlockManager """ if self.is_consolidated(): return self bm = self.__class__(self.blocks, self.axes) bm._is_consolidated = False bm._consolid...
def get(self, item, fastpath=True): """ Return values for selected item (ndarray or BlockManager). """ if self.items.is_unique: if not isna(item): loc = self.items.get_loc(item) else: indexer = np.arange(len(self.items))[isna(self....
def iget(self, i, fastpath=True): """ Return the data as a SingleBlockManager if fastpath=True and possible Otherwise return as a ndarray """ block = self.blocks[self._blknos[i]] values = block.iget(self._blklocs[i]) if not fastpath or not block._box_to_block_val...
def delete(self, item): """ Delete selected item (items if non-unique) in-place. """ indexer = self.items.get_loc(item) is_deleted = np.zeros(self.shape[0], dtype=np.bool_) is_deleted[indexer] = True ref_loc_offset = -is_deleted.cumsum() is_blk_deleted =...
def set(self, item, value): """ 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/c...

def concat_same_type(self, to_concat, placement=None): """ Concatenate list of single blocks of the same type. """ values = self._concatenator([blk.values for blk in to_concat], axis=self.ndim - 1) return self.make_block_same_class( ...

def delete(self, loc): """ Delete given loc(-s) from block in-place. """ self.values = np.delete(self.values, loc, 0) self.mgr_locs = self.mgr_locs.delete(loc)

def apply(self, func, **kwargs): """ apply the function to my values; return a block if we are not one """ with np.errstate(all='ignore'): result = func(self.values, **kwargs) if not isinstance(result, Block): result = self.make_block(values=_block_shape(r...

def fillna(self, value, limit=None, inplace=False, downcast=None): """ fillna on the block with the value. If we fail, then convert to ObjectBlock and try again """ inplace = validate_bool_kwarg(inplace, 'inplace') if not self._can_hold_na: if inplace: ...

def split_and_operate(self, mask, f, inplace): """ split the block per-column, and apply the callable f per-column, return a new block for each. Handle masking which will not change a block unless needed. Parameters ---------- mask : 2-d boolean mask f : ...

def downcast(self, dtypes=None): """ try to downcast each item to the dict of dtypes if present """ # turn it off completely if dtypes is False: return self values = self.values # single block handling if self._is_single_block: # try to cast al...

def _astype(self, dtype, copy=False, errors='raise', values=None, **kwargs): """Coerce to the new type Parameters ---------- dtype : str, dtype convertible copy : boolean, default False copy if indicated errors : str, {'raise', 'ignore'}, defa...

def _can_hold_element(self, element): """ require the same dtype as ourselves """ dtype = self.values.dtype.type tipo = maybe_infer_dtype_type(element) if tipo is not None: return issubclass(tipo.type, dtype) return isinstance(element, dtype)

def _try_cast_result(self, result, dtype=None): """ try to cast the result to our original type, we may have roundtripped thru object in the mean-time """ if dtype is None: dtype = self.dtype if self.is_integer or self.is_bool or self.is_datetime: pass ...

def _try_coerce_args(self, values, other): """ provide coercion to our input arguments """ if np.any(notna(other)) and not self._can_hold_element(other): # coercion issues # let higher levels handle raise TypeError("cannot convert {} to an {}".format( ...

def to_native_types(self, slicer=None, na_rep='nan', quoting=None, **kwargs): """ convert to our native types format, slicing if desired """ values = self.get_values() if slicer is not None: values = values[:, slicer] mask = isna(values) if ...

def copy(self, deep=True): """ copy constructor """ values = self.values if deep: values = values.copy() return self.make_block_same_class(values, ndim=self.ndim)

def replace(self, to_replace, value, inplace=False, filter=None, regex=False, convert=True): """replace the to_replace value with value, possible to create new blocks here this is just a call to putmask. regex is not used here. It is used in ObjectBlocks. It is here for API comp...

def setitem(self, indexer, value): """Set the value inplace, returning a a maybe different typed block. Parameters ---------- indexer : tuple, list-like, array-like, slice The subset of self.values to set value : object The value being set Return...

def putmask(self, mask, new, align=True, inplace=False, axis=0, transpose=False): """ putmask the data to the block; it is possible that we may create a new dtype of block return the resulting block(s) Parameters ---------- mask : the condition to respe...

def coerce_to_target_dtype(self, other): """ coerce the current block to a dtype compat for other we will return a block, possibly object, and not raise we can also safely try to coerce to the same dtype and will receive the same block """ # if we cannot then co...

def _interpolate_with_fill(self, method='pad', axis=0, inplace=False, limit=None, fill_value=None, coerce=False, downcast=None): """ fillna but using the interpolate machinery """ inplace = validate_bool_kwarg(inplace, 'inplace') # ...

def _interpolate(self, method=None, index=None, values=None, fill_value=None, axis=0, limit=None, limit_direction='forward', limit_area=None, inplace=False, downcast=None, **kwargs): """ interpolate using scipy wrappers """ inplace = valida...

def take_nd(self, indexer, axis, new_mgr_locs=None, fill_tuple=None): """ Take values according to indexer and return them as a block.bb """ # algos.take_nd dispatches for DatetimeTZBlock, CategoricalBlock # so need to preserve types # sparse is treated like an ndarray,...

def diff(self, n, axis=1): """ return block for the diff of the values """ new_values = algos.diff(self.values, n, axis=axis) return [self.make_block(values=new_values)]

def shift(self, periods, axis=0, fill_value=None): """ shift the block by periods, possibly upcast """ # convert integer to float if necessary. need to do a lot more than # that, handle boolean etc also new_values, fill_value = maybe_upcast(self.values, fill_value) # make sure ...

def where(self, other, cond, align=True, errors='raise', try_cast=False, axis=0, transpose=False): """ evaluate the block; return result block(s) from the result Parameters ---------- other : a ndarray/object cond : the condition to respect align :...

def _unstack(self, unstacker_func, new_columns, n_rows, fill_value): """Return a list of unstacked blocks of self Parameters ---------- unstacker_func : callable Partially applied unstacker. new_columns : Index All columns of the unstacked BlockManager. ...

def quantile(self, qs, interpolation='linear', axis=0): """ compute the quantiles of the Parameters ---------- qs: a scalar or list of the quantiles to be computed interpolation: type of interpolation, default 'linear' axis: axis to compute, default 0 Re...

def _replace_coerce(self, to_replace, value, inplace=True, regex=False, convert=False, mask=None): """ Replace value corresponding to the given boolean array with another value. Parameters ---------- to_replace : object or pattern Scal...

def putmask(self, mask, new, align=True, inplace=False, axis=0, transpose=False): """ putmask the data to the block; we must be a single block and not generate other blocks return the resulting block Parameters ---------- mask : the condition to...

def _get_unstack_items(self, unstacker, new_columns): """ Get the placement, values, and mask for a Block unstack. This is shared between ObjectBlock and ExtensionBlock. They differ in that ObjectBlock passes the values, while ExtensionBlock passes the dummy ndarray of positions...

def _maybe_coerce_values(self, values): """Unbox to an extension array. This will unbox an ExtensionArray stored in an Index or Series. ExtensionArrays pass through. No dtype coercion is done. Parameters ---------- values : Index, Series, ExtensionArray Returns...

def setitem(self, indexer, value): """Set the value inplace, returning a same-typed block. This differs from Block.setitem by not allowing setitem to change the dtype of the Block. Parameters ---------- indexer : tuple, list-like, array-like, slice The subse...

def take_nd(self, indexer, axis=0, new_mgr_locs=None, fill_tuple=None): """ Take values according to indexer and return them as a block. """ if fill_tuple is None: fill_value = None else: fill_value = fill_tuple[0] # axis doesn't matter; we are re...

def _slice(self, slicer): """ return a slice of my values """ # slice the category # return same dims as we currently have if isinstance(slicer, tuple) and len(slicer) == 2: if not com.is_null_slice(slicer[0]): raise AssertionError("invalid slicing for a 1-n...

def concat_same_type(self, to_concat, placement=None): """ Concatenate list of single blocks of the same type. """ values = self._holder._concat_same_type( [blk.values for blk in to_concat]) placement = placement or slice(0, len(values), 1) return self.make_bl...

def shift(self, periods: int, axis: libinternals.BlockPlacement = 0, fill_value: Any = None) -> List['ExtensionBlock']: """ Shift the block by `periods`. Dispatches to underlying ExtensionArray and re-boxes in an ExtensionBlock. """ ...

def to_native_types(self, slicer=None, na_rep='', float_format=None, decimal='.', quoting=None, **kwargs): """ convert to our native types format, slicing if desired """ values = self.values if slicer is not None: values = values[:, slicer] # see gh-...

def get_values(self, dtype=None): """ return object dtype as boxed values, such as Timestamps/Timedelta """ if is_object_dtype(dtype): values = self.values.ravel() result = self._holder(values).astype(object) return result.reshape(self.values.shape) ...

def _maybe_coerce_values(self, values): """Input validation for values passed to __init__. Ensure that we have datetime64ns, coercing if necessary. Parameters ---------- values : array-like Must be convertible to datetime64 Returns ------- va...

def _astype(self, dtype, **kwargs): """ these automatically copy, so copy=True has no effect raise on an except if raise == True """ dtype = pandas_dtype(dtype) # if we are passed a datetime64[ns, tz] if is_datetime64tz_dtype(dtype): values = self.val...

def _try_coerce_args(self, values, other): """ Coerce values and other to dtype 'i8'. NaN and NaT convert to the smallest i8, and will correctly round-trip to NaT if converted back in _try_coerce_result. values is always ndarray-like, other may not be Parameters ...

def _try_coerce_result(self, result): """ reverse of try_coerce_args """ if isinstance(result, np.ndarray): if result.dtype.kind in ['i', 'f']: result = result.astype('M8[ns]') elif isinstance(result, (np.integer, np.float, np.datetime64)): result = self....

def to_native_types(self, slicer=None, na_rep=None, date_format=None, quoting=None, **kwargs): """ convert to our native types format, slicing if desired """ values = self.values i8values = self.values.view('i8') if slicer is not None: values = value...

def set(self, locs, values): """ Modify Block in-place with new item value Returns ------- None """ values = conversion.ensure_datetime64ns(values, copy=False) self.values[locs] = values

def _maybe_coerce_values(self, values): """Input validation for values passed to __init__. Ensure that we have datetime64TZ, coercing if necessary. Parametetrs ----------- values : array-like Must be convertible to datetime64 Returns ------- ...

def get_values(self, dtype=None): """ Returns an ndarray of values. Parameters ---------- dtype : np.dtype Only `object`-like dtypes are respected here (not sure why). Returns ------- values : ndarray When ``dtype=obje...

def _slice(self, slicer): """ return a slice of my values """ if isinstance(slicer, tuple): col, loc = slicer if not com.is_null_slice(col) and col != 0: raise IndexError("{0} only contains one item".format(self)) return self.values[loc] return...

def _try_coerce_args(self, values, other): """ localize and return i8 for the values Parameters ---------- values : ndarray-like other : ndarray-like or scalar Returns ------- base-type values, base-type other """ # asi8 is a view...

def _try_coerce_result(self, result): """ reverse of try_coerce_args """ if isinstance(result, np.ndarray): if result.dtype.kind in ['i', 'f']: result = result.astype('M8[ns]') elif isinstance(result, (np.integer, np.float, np.datetime64)): result = self....

def diff(self, n, axis=0): """1st discrete difference Parameters ---------- n : int, number of periods to diff axis : int, axis to diff upon. default 0 Return ------ A list with a new TimeDeltaBlock. Note ---- The arguments here ...

def _try_coerce_args(self, values, other): """ Coerce values and other to int64, with null values converted to iNaT. values is always ndarray-like, other may not be Parameters ---------- values : ndarray-like other : ndarray-like or scalar Returns ...

def _try_coerce_result(self, result): """ reverse of try_coerce_args / try_operate """ if isinstance(result, np.ndarray): mask = isna(result) if result.dtype.kind in ['i', 'f']: result = result.astype('m8[ns]') result[mask] = tslibs.iNaT elif ...

def to_native_types(self, slicer=None, na_rep=None, quoting=None, **kwargs): """ convert to our native types format, slicing if desired """ values = self.values if slicer is not None: values = values[:, slicer] mask = isna(values) rvalues = n...

def convert(self, *args, **kwargs): """ attempt to coerce any object types to better types return a copy of the block (if copy = True) by definition we ARE an ObjectBlock!!!!! can return multiple blocks! """ if args: raise NotImplementedError by_item = kwarg...

def set(self, locs, values): """ Modify Block in-place with new item value Returns ------- None """ try: self.values[locs] = values except (ValueError): # broadcasting error # see GH6171 new_shape = list(va...

def _try_coerce_args(self, values, other): """ provide coercion to our input arguments """ if isinstance(other, ABCDatetimeIndex): # May get a DatetimeIndex here. Unbox it. other = other.array if isinstance(other, DatetimeArray): # hit in pandas/tests/indexi...

def _replace_single(self, to_replace, value, inplace=False, filter=None, regex=False, convert=True, mask=None): """ Replace elements by the given value. Parameters ---------- to_replace : object or pattern Scalar to replace or regular expressi...

def _replace_coerce(self, to_replace, value, inplace=True, regex=False, convert=False, mask=None): """ Replace value corresponding to the given boolean array with another value. Parameters ---------- to_replace : object or pattern Scal...

def _try_coerce_result(self, result): """ reverse of try_coerce_args """ # GH12564: CategoricalBlock is 1-dim only # while returned results could be any dim if ((not is_categorical_dtype(result)) and isinstance(result, np.ndarray)): result = _block_shape(resu...

def to_native_types(self, slicer=None, na_rep='', quoting=None, **kwargs): """ convert to our native types format, slicing if desired """ values = self.values if slicer is not None: # Categorical is always one dimension values = values[slicer] mask = isna(values)...

def _style_to_xlwt(cls, item, firstlevel=True, field_sep=',', line_sep=';'): """helper which recursively generate an xlwt easy style string for example: hstyle = {"font": {"bold": True}, "border": {"top": "thin", "right": "thin", ...

def _convert_to_style(cls, style_dict, num_format_str=None): """ converts a style_dict to an xlwt style object Parameters ---------- style_dict : style dictionary to convert num_format_str : optional number format string """ import xlwt if style_d...

def convert(cls, style_dict, num_format_str=None): """ converts a style_dict to an xlsxwriter format dict Parameters ---------- style_dict : style dictionary to convert num_format_str : optional number format string """ # Create a XlsxWriter format objec...

def _unstack_extension_series(series, level, fill_value): """ Unstack an ExtensionArray-backed Series. The ExtensionDtype is preserved. Parameters ---------- series : Series A Series with an ExtensionArray for values level : Any The level name or number. fill_value : An...

def stack(frame, level=-1, dropna=True): """ Convert DataFrame to Series with multi-level Index. Columns become the second level of the resulting hierarchical index Returns ------- stacked : Series """ def factorize(index): if index.is_unique: return index, np.arange...

def get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False, columns=None, sparse=False, drop_first=False, dtype=None): """ Convert categorical variable into dummy/indicator variables. Parameters ---------- data : array-like, Series, or DataFrame Data of which to get d...

def make_axis_dummies(frame, axis='minor', transform=None): """ Construct 1-0 dummy variables corresponding to designated axis labels Parameters ---------- frame : DataFrame axis : {'major', 'minor'}, default 'minor' transform : function, default None Function to apply to axis l...

def _reorder_for_extension_array_stack(arr, n_rows, n_columns): """ Re-orders the values when stacking multiple extension-arrays. The indirect stacking method used for EAs requires a followup take to get the order correct. Parameters ---------- arr : ExtensionArray n_rows, n_columns : ...

def _split_line(s, parts): """ Parameters ---------- s: string Fixed-length string to split parts: list of (name, length) pairs Used to break up string, name '_' will be filtered from output. Returns ------- Dict of name:contents of string at given location. """ ...

def _parse_float_vec(vec): """ Parse a vector of float values representing IBM 8 byte floats into native 8 byte floats. """ dtype = np.dtype('>u4,>u4') vec1 = vec.view(dtype=dtype) xport1 = vec1['f0'] xport2 = vec1['f1'] # Start by setting first half of ieee number to first half of...

def _record_count(self): """ Get number of records in file. This is maybe suboptimal because we have to seek to the end of the file. Side effect: returns file position to record_start. """ self.filepath_or_buffer.seek(0, 2) total_records_length = (self....

def get_chunk(self, size=None): """ Reads lines from Xport file and returns as dataframe Parameters ---------- size : int, defaults to None Number of lines to read. If None, reads whole file. Returns ------- DataFrame """ if ...

def construction_error(tot_items, block_shape, axes, e=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] ...

def _simple_blockify(tuples, dtype): """ return a single array of a block that has a single dtype; if dtype is not None, coerce to this dtype """ values, placement = _stack_arrays(tuples, dtype) # CHECK DTYPE? if dtype is not None and values.dtype != dtype: # pragma: no cover values = ...

def _multi_blockify(tuples, dtype=None): """ return an array of blocks that potentially have different dtypes """ # group by dtype grouper = itertools.groupby(tuples, lambda x: x[2].dtype) new_blocks = [] for dtype, tup_block in grouper: values, placement = _stack_arrays(list(tup_block), ...

def _sparse_blockify(tuples, dtype=None): """ return an array of blocks that potentially have different dtypes (and are sparse) """ new_blocks = [] for i, names, array in tuples: array = _maybe_to_sparse(array) block = make_block(array, placement=[i]) new_blocks.append(block...

def _interleaved_dtype( blocks: List[Block] ) -> Optional[Union[np.dtype, ExtensionDtype]]: """Find the common dtype for `blocks`. Parameters ---------- blocks : List[Block] Returns ------- dtype : Optional[Union[np.dtype, ExtensionDtype]] None is returned when `blocks` is ...

def _consolidate(blocks): """ 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 = [] for (_can_consolidate, dtype), group_bloc...

def _compare_or_regex_search(a, b, regex=False): """ Compare two array_like inputs of the same shape or two scalar values Calls operator.eq or re.search, depending on regex argument. If regex is True, perform an element-wise regex matching. Parameters ---------- a : array_like or scalar ...

def items_overlap_with_suffix(left, lsuffix, right, rsuffix): """ If two indices overlap, add suffixes to overlapping entries. If corresponding suffix is empty, the entry is simply converted to string. """ to_rename = left.intersection(right) if len(to_rename) == 0: return left, right ...

def _transform_index(index, func, level=None): """ Apply function to all values found in index. This includes transforming multiindex entries separately. Only apply function to one level of the MultiIndex if level is specified. """ if isinstance(index, MultiIndex): if level is not None...

def _fast_count_smallints(arr): """Faster version of set(arr) for sequences of small numbers.""" counts = np.bincount(arr.astype(np.int_)) nz = counts.nonzero()[0] return np.c_[nz, counts[nz]]

def concatenate_block_managers(mgrs_indexers, axes, concat_axis, copy): """ Concatenate block managers into one. Parameters ---------- mgrs_indexers : list of (BlockManager, {axis: indexer,...}) tuples axes : list of Index concat_axis : int copy : bool """ concat_plans = [get_m...

def make_empty(self, axes=None): """ return an empty BlockManager with the items axis of len 0 """ if axes is None: axes = [ensure_index([])] + [ensure_index(a) for a in self.axes[1:]] # preserve dtype if possible if self.ndim == 1: ...

def rename_axis(self, mapper, axis, copy=True, level=None): """ Rename one of axes. Parameters ---------- mapper : unary callable axis : int copy : boolean, default True level : int, default None """ obj = self.copy(deep=copy) obj....

def _rebuild_blknos_and_blklocs(self): """ Update mgr._blknos / mgr._blklocs. """ new_blknos = np.empty(self.shape[0], dtype=np.int64) new_blklocs = np.empty(self.shape[0], dtype=np.int64) new_blknos.fill(-1) new_blklocs.fill(-1) for blkno, blk in enumera...

def _get_counts(self, f): """ return a dict of the counts of the function in BlockManager """ self._consolidate_inplace() counts = dict() for b in self.blocks: v = f(b) counts[v] = counts.get(v, 0) + b.shape[0] return counts

def apply(self, f, axes=None, filter=None, do_integrity_check=False, consolidate=True, **kwargs): """ iterate over the blocks, collect and create a new block manager Parameters ---------- f : the callable or function name to operate on at the block level ax...

def quantile(self, axis=0, consolidate=True, transposed=False, interpolation='linear', qs=None, numeric_only=None): """ Iterate over blocks applying quantile reduction. This routine is intended for reduction type operations and will do inference on the generated blocks. ...

def replace_list(self, src_list, dest_list, inplace=False, regex=False): """ do a list replace """ inplace = validate_bool_kwarg(inplace, 'inplace') # figure out our mask a-priori to avoid repeated replacements values = self.as_array() def comp(s, regex=False): """...

def get_bool_data(self, copy=False): """ Parameters ---------- copy : boolean, default False Whether to copy the blocks """ self._consolidate_inplace() return self.combine([b for b in self.blocks if b.is_bool], copy)

def get_numeric_data(self, copy=False): """ Parameters ---------- copy : boolean, default False Whether to copy the blocks """ self._consolidate_inplace() return self.combine([b for b in self.blocks if b.is_numeric], copy)

def combine(self, blocks, copy=True): """ return a new manager with the blocks """ if len(blocks) == 0: return self.make_empty() # FIXME: optimization potential indexer = np.sort(np.concatenate([b.mgr_locs.as_array for b in blocks]))...

def copy(self, deep=True): """ Make deep or shallow copy of BlockManager Parameters ---------- deep : boolean o rstring, default True If False, return shallow copy (do not copy data) If 'all', copy data and a deep copy of the index Returns ...

def as_array(self, transpose=False, items=None): """Convert the blockmanager data into an numpy array. Parameters ---------- transpose : boolean, default False If True, transpose the return array items : list of strings or None Names of block items that w...

def _interleave(self): """ Return ndarray from blocks with specified item order Items must be contained in the blocks """ from pandas.core.dtypes.common import is_sparse dtype = _interleaved_dtype(self.blocks) # TODO: https://github.com/pandas-dev/pandas/issues/2...

def to_dict(self, copy=True): """ Return a dict of str(dtype) -> BlockManager Parameters ---------- copy : boolean, default True Returns ------- values : a dict of dtype -> BlockManager Notes ----- This consolidates based on str(...

def fast_xs(self, loc): """ get a cross sectional for a given location in the items ; handle dups return the result, is *could* be a view in the case of a single block """ if len(self.blocks) == 1: return self.blocks[0].iget((slice(None), loc)) ...

def consolidate(self): """ Join together blocks having same dtype Returns ------- y : BlockManager """ if self.is_consolidated(): return self bm = self.__class__(self.blocks, self.axes) bm._is_consolidated = False bm._consolid...

def get(self, item, fastpath=True): """ Return values for selected item (ndarray or BlockManager). """ if self.items.is_unique: if not isna(item): loc = self.items.get_loc(item) else: indexer = np.arange(len(self.items))[isna(self....

def iget(self, i, fastpath=True): """ Return the data as a SingleBlockManager if fastpath=True and possible Otherwise return as a ndarray """ block = self.blocks[self._blknos[i]] values = block.iget(self._blklocs[i]) if not fastpath or not block._box_to_block_val...

def delete(self, item): """ Delete selected item (items if non-unique) in-place. """ indexer = self.items.get_loc(item) is_deleted = np.zeros(self.shape[0], dtype=np.bool_) is_deleted[indexer] = True ref_loc_offset = -is_deleted.cumsum() is_blk_deleted =...

def set(self, item, value): """ 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/c...