JorgeVB/code_unlearning_dataset · Datasets at Hugging Face

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Concatenate list of single blocks of the same type. 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) ...
Delete given loc(-s) from block in-place. 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)
apply the function to my values; return a block if we are not one 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...
fillna on the block with the value. If we fail, then convert to ObjectBlock and try again 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_...
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 : callable accepting (1d-mask, 1d values, indexer) inplace : ...
try to downcast each item to the dict of dtypes if present 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 ...
Coerce to the new type Parameters ---------- dtype : str, dtype convertible copy : boolean, default False copy if indicated errors : str, {'raise', 'ignore'}, default 'ignore' - ``raise`` : allow exceptions to be raised - ``ignore`` : suppress...
require the same dtype as ourselves 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(elem...
try to cast the result to our original type, we may have roundtripped thru object in the mean-time 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: ...
provide coercion to our input arguments 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 con...
convert to our native types format, slicing if desired 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 = va...
copy constructor 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)
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 compatibility. def replace(self, to_replace, value, inplace=False, filter=None, regex=False, convert=True):...
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 Returns ------- Block Notes...
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 respect new : a ndarray/object align : boolean, perform alignment on other/cond, default is True ...
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 def coerce_to_target_dtype(self, other): """ coerce the current block to a dtype compat...
fillna but using the interpolate machinery 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 = validat...
interpolate using scipy wrappers 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 wrap...
Take values according to indexer and return them as a block.bb 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 nee...
return block for the diff of the values 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)]
shift the block by periods, possibly upcast 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(s...
evaluate the block; return result block(s) from the result Parameters ---------- other : a ndarray/object cond : the condition to respect align : boolean, perform alignment on other/cond errors : str, {'raise', 'ignore'}, default 'raise' - ``raise`` : allow ...
Return a list of unstacked blocks of self Parameters ---------- unstacker_func : callable Partially applied unstacker. new_columns : Index All columns of the unstacked BlockManager. n_rows : int Only used in ExtensionBlock.unstack fill...
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 Returns ------- Block def quantile(self, qs, interpolation='l...
Replace value corresponding to the given boolean array with another value. Parameters ---------- to_replace : object or pattern Scalar to replace or regular expression to match. value : object Replacement object. inplace : bool, default 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 respect new : a ndarray/object align : boolean, perform alignment on other/cond, default is True ...
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 to be used by a take later. Parameters ---------...
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 ------- ExtensionArray def _maybe...
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 subset of self.values to set value : object...
Take values according to indexer and return them as a block. 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_v...
return a slice of my values 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 AssertionErr...
Concatenate list of single blocks of the same type. 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...
Shift the block by `periods`. Dispatches to underlying ExtensionArray and re-boxes in an ExtensionBlock. def shift(self, periods: int, axis: libinternals.BlockPlacement = 0, fill_value: Any = None) -> List['ExtensionBlock']: """ Shift the block...
convert to our native types format, slicing if desired 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: ...
return object dtype as boxed values, such as Timestamps/Timedelta 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).ast...
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 ------- values : ndarray[datetime64ns] Overridden by...
these automatically copy, so copy=True has no effect raise on an except if raise == True 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 ...
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 ---------- values : ndarray-like other : ndarra...
reverse of try_coerce_args 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))...
convert to our native types format, slicing if desired 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') ...
Modify Block in-place with new item value Returns ------- None def set(self, locs, values): """ Modify Block in-place with new item value Returns ------- None """ values = conversion.ensure_datetime64ns(values, copy=False) self....
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 ------- values : DatetimeArray def _maybe_coerce_values(se...
Returns an ndarray of values. Parameters ---------- dtype : np.dtype Only `object`-like dtypes are respected here (not sure why). Returns ------- values : ndarray When ``dtype=object``, then and object-dtype ndarray of box...
return a slice of my values 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 se...
localize and return i8 for the values Parameters ---------- values : ndarray-like other : ndarray-like or scalar Returns ------- base-type values, base-type other def _try_coerce_args(self, values, other): """ localize and return i8 for the valu...
reverse of try_coerce_args 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))...
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 are mimicking shift so they are called...
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 ------- base-type values, base-type other def _try_c...
reverse of try_coerce_args / try_operate 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]') ...
convert to our native types format, slicing if desired 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[:, ...
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! def convert(self, args, *kwargs): """ attempt to coerce any object types to better types return a copy of the block (if ...
Modify Block in-place with new item value Returns ------- None def set(self, locs, values): """ Modify Block in-place with new item value Returns ------- None """ try: self.values[locs] = values except (ValueError): ...
provide coercion to our input arguments 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):...
Replace elements by the given value. Parameters ---------- to_replace : object or pattern Scalar to replace or regular expression to match. value : object Replacement object. inplace : bool, default False Perform inplace modification. ...
Replace value corresponding to the given boolean array with another value. Parameters ---------- to_replace : object or pattern Scalar to replace or regular expression to match. value : object Replacement object. inplace : bool, default False ...
reverse of try_coerce_args 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)): ...
convert to our native types format, slicing if desired 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 ...
helper which recursively generate an xlwt easy style string for example: hstyle = {"font": {"bold": True}, "border": {"top": "thin", "right": "thin", "bottom": "thin", "left": "thin"}, "align": {"horiz": "center"}} ...
converts a style_dict to an xlwt style object Parameters ---------- style_dict : style dictionary to convert num_format_str : optional number format string def _convert_to_style(cls, style_dict, num_format_str=None): """ converts a style_dict to an xlwt style object ...
converts a style_dict to an xlsxwriter format dict Parameters ---------- style_dict : style dictionary to convert num_format_str : optional number format string def convert(cls, style_dict, num_format_str=None): """ converts a style_dict to an xlsxwriter format dict ...
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 : Any The user-level (not physical storage) fill value to use for ...
Convert DataFrame to Series with multi-level Index. Columns become the second level of the resulting hierarchical index Returns ------- stacked : Series def stack(frame, level=-1, dropna=True): """ Convert DataFrame to Series with multi-level Index. Columns become the second level of the r...
Convert categorical variable into dummy/indicator variables. Parameters ---------- data : array-like, Series, or DataFrame Data of which to get dummy indicators. prefix : str, list of str, or dict of str, default None String to append DataFrame column names. Pass a list with len...
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 labels first. For example, to get "day of week" dummies in a time...
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 : int The number of rows and columns in the original DataFrame. R...
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 _split_line(s, parts): """ Parameters ...
Parse a vector of float values representing IBM 8 byte floats into native 8 byte floats. 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['f...
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. def _record_count(self): """ Get number of records in file. This is maybe suboptimal because we have to seek to ...
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 def get_chunk(self, size=None): """ Reads lines from Xport...
raise a helpful message about our construction 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(p...
return a single array of a block that has a single dtype; if dtype is not None, coerce to this dtype 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) ...
return an array of blocks that potentially have different dtypes 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 gro...
return an array of blocks that potentially have different dtypes (and are sparse) 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(...
Find the common dtype for `blocks`. Parameters ---------- blocks : List[Block] Returns ------- dtype : Optional[Union[np.dtype, ExtensionDtype]] None is returned when `blocks` is empty. def _interleaved_dtype( blocks: List[Block] ) -> Optional[Union[np.dtype, ExtensionDtype]]:...
Merge blocks having same dtype, exclude non-consolidating blocks 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) ...
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 b : array_like or scalar regex : bool, default False ...
If two indices overlap, add suffixes to overlapping entries. If corresponding suffix is empty, the entry is simply converted to string. def items_overlap_with_suffix(left, lsuffix, right, rsuffix): """ If two indices overlap, add suffixes to overlapping entries. If corresponding suffix is empty, the ...
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. def _transform_index(index, func, level=None): """ Apply function to all values found in index. This includes transformi...
Faster version of set(arr) for sequences of small numbers. 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]]
Concatenate block managers into one. Parameters ---------- mgrs_indexers : list of (BlockManager, {axis: indexer,...}) tuples axes : list of Index concat_axis : int copy : bool def concatenate_block_managers(mgrs_indexers, axes, concat_axis, copy): """ Concatenate block managers into o...
return an empty BlockManager with the items axis of len 0 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:]] ...
Rename one of axes. Parameters ---------- mapper : unary callable axis : int copy : boolean, default True level : int, default None def rename_axis(self, mapper, axis, copy=True, level=None): """ Rename one of axes. Parameters ----------...
Update mgr._blknos / mgr._blklocs. 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...
return a dict of the counts of the function in BlockManager 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.s...
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 axes : optional (if not supplied, use self.axes) filter : list, if supplied, only call the block if the filter is in ...
Iterate over blocks applying quantile reduction. This routine is intended for reduction type operations and will do inference on the generated blocks. Parameters ---------- axis: reduction axis, default 0 consolidate: boolean, default True. Join together blocks having sa...
do a list replace 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=Fals...
Parameters ---------- copy : boolean, default False Whether to copy the blocks def get_bool_data(self, copy=False): """ Parameters ---------- copy : boolean, default False Whether to copy the blocks """ self._consolidate_inplace() ...
Parameters ---------- copy : boolean, default False Whether to copy the blocks def get_numeric_data(self, copy=False): """ Parameters ---------- copy : boolean, default False Whether to copy the blocks """ self._consolidate_inplace...
return a new manager with the blocks 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 ...
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 ------- copy : BlockManager def copy...
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 will be included in the returned array. ``None`` ...
Return ndarray from blocks with specified item order Items must be contained in the blocks 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 dt...
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(dtype) def to_dict(self, copy=True): """ ...
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 def fast_xs(self, loc): """ get a cross sectional for a given location in the items ; handle dups return the result, is *...
Join together blocks having same dtype Returns ------- y : BlockManager def consolidate(self): """ Join together blocks having same dtype Returns ------- y : BlockManager """ if self.is_consolidated(): return self bm...
Return values for selected item (ndarray or BlockManager). 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: ...
Return the data as a SingleBlockManager if fastpath=True and possible Otherwise return as a ndarray 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._b...
Delete selected item (items if non-unique) in-place. 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_o...
Set new item in-place. Does not consolidate. Adds new Block if not contained in the current set of items 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, clearl...

Concatenate list of single blocks of the same type. 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) ...

Delete given loc(-s) from block in-place. 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)

apply the function to my values; return a block if we are not one 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...

fillna on the block with the value. If we fail, then convert to ObjectBlock and try again 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_...

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 : callable accepting (1d-mask, 1d values, indexer) inplace : ...

try to downcast each item to the dict of dtypes if present 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 ...

Coerce to the new type Parameters ---------- dtype : str, dtype convertible copy : boolean, default False copy if indicated errors : str, {'raise', 'ignore'}, default 'ignore' - ``raise`` : allow exceptions to be raised - ``ignore`` : suppress...

require the same dtype as ourselves 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(elem...

try to cast the result to our original type, we may have roundtripped thru object in the mean-time 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: ...

provide coercion to our input arguments 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 con...

convert to our native types format, slicing if desired 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 = va...

copy constructor 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)

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 compatibility. def replace(self, to_replace, value, inplace=False, filter=None, regex=False, convert=True):...

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 Returns ------- Block Notes...

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 respect new : a ndarray/object align : boolean, perform alignment on other/cond, default is True ...

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 def coerce_to_target_dtype(self, other): """ coerce the current block to a dtype compat...

fillna but using the interpolate machinery 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 = validat...

interpolate using scipy wrappers 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 wrap...

Take values according to indexer and return them as a block.bb 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 nee...

return block for the diff of the values 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)]

shift the block by periods, possibly upcast 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(s...

evaluate the block; return result block(s) from the result Parameters ---------- other : a ndarray/object cond : the condition to respect align : boolean, perform alignment on other/cond errors : str, {'raise', 'ignore'}, default 'raise' - ``raise`` : allow ...

Return a list of unstacked blocks of self Parameters ---------- unstacker_func : callable Partially applied unstacker. new_columns : Index All columns of the unstacked BlockManager. n_rows : int Only used in ExtensionBlock.unstack fill...

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 Returns ------- Block def quantile(self, qs, interpolation='l...

Replace value corresponding to the given boolean array with another value. Parameters ---------- to_replace : object or pattern Scalar to replace or regular expression to match. value : object Replacement object. inplace : bool, default 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 respect new : a ndarray/object align : boolean, perform alignment on other/cond, default is True ...

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 to be used by a take later. Parameters ---------...

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 ------- ExtensionArray def _maybe...

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 subset of self.values to set value : object...

Take values according to indexer and return them as a block. 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_v...

return a slice of my values 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 AssertionErr...

Concatenate list of single blocks of the same type. 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...

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

convert to our native types format, slicing if desired 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: ...

return object dtype as boxed values, such as Timestamps/Timedelta 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).ast...

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 ------- values : ndarray[datetime64ns] Overridden by...

these automatically copy, so copy=True has no effect raise on an except if raise == True 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 ...

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 ---------- values : ndarray-like other : ndarra...

reverse of try_coerce_args 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))...

convert to our native types format, slicing if desired 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') ...

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

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 ------- values : DatetimeArray def _maybe_coerce_values(se...

Returns an ndarray of values. Parameters ---------- dtype : np.dtype Only `object`-like dtypes are respected here (not sure why). Returns ------- values : ndarray When ``dtype=object``, then and object-dtype ndarray of box...

return a slice of my values 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 se...

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

reverse of try_coerce_args 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))...

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 are mimicking shift so they are called...

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 ------- base-type values, base-type other def _try_c...

reverse of try_coerce_args / try_operate 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]') ...

convert to our native types format, slicing if desired 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[:, ...

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! def convert(self, *args, **kwargs): """ attempt to coerce any object types to better types return a copy of the block (if ...

Modify Block in-place with new item value Returns ------- None def set(self, locs, values): """ Modify Block in-place with new item value Returns ------- None """ try: self.values[locs] = values except (ValueError): ...

provide coercion to our input arguments 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):...

Replace elements by the given value. Parameters ---------- to_replace : object or pattern Scalar to replace or regular expression to match. value : object Replacement object. inplace : bool, default False Perform inplace modification. ...

Replace value corresponding to the given boolean array with another value. Parameters ---------- to_replace : object or pattern Scalar to replace or regular expression to match. value : object Replacement object. inplace : bool, default False ...

reverse of try_coerce_args 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)): ...

convert to our native types format, slicing if desired 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 ...

helper which recursively generate an xlwt easy style string for example: hstyle = {"font": {"bold": True}, "border": {"top": "thin", "right": "thin", "bottom": "thin", "left": "thin"}, "align": {"horiz": "center"}} ...

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

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

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 : Any The user-level (not physical storage) fill value to use for ...

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

Convert categorical variable into dummy/indicator variables. Parameters ---------- data : array-like, Series, or DataFrame Data of which to get dummy indicators. prefix : str, list of str, or dict of str, default None String to append DataFrame column names. Pass a list with len...

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 labels first. For example, to get "day of week" dummies in a time...

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 : int The number of rows and columns in the original DataFrame. R...

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 _split_line(s, parts): """ Parameters ...

Parse a vector of float values representing IBM 8 byte floats into native 8 byte floats. 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['f...

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. def _record_count(self): """ Get number of records in file. This is maybe suboptimal because we have to seek to ...

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 def get_chunk(self, size=None): """ Reads lines from Xport...

raise a helpful message about our construction 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(p...

return a single array of a block that has a single dtype; if dtype is not None, coerce to this dtype 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) ...

return an array of blocks that potentially have different dtypes 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 gro...

return an array of blocks that potentially have different dtypes (and are sparse) 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(...

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

Merge blocks having same dtype, exclude non-consolidating blocks 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) ...

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 b : array_like or scalar regex : bool, default False ...

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

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. def _transform_index(index, func, level=None): """ Apply function to all values found in index. This includes transformi...

Faster version of set(arr) for sequences of small numbers. 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]]

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

return an empty BlockManager with the items axis of len 0 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:]] ...

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

Update mgr._blknos / mgr._blklocs. 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...

return a dict of the counts of the function in BlockManager 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.s...

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 axes : optional (if not supplied, use self.axes) filter : list, if supplied, only call the block if the filter is in ...

Iterate over blocks applying quantile reduction. This routine is intended for reduction type operations and will do inference on the generated blocks. Parameters ---------- axis: reduction axis, default 0 consolidate: boolean, default True. Join together blocks having sa...

do a list replace 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=Fals...

Parameters ---------- copy : boolean, default False Whether to copy the blocks def get_bool_data(self, copy=False): """ Parameters ---------- copy : boolean, default False Whether to copy the blocks """ self._consolidate_inplace() ...

Parameters ---------- copy : boolean, default False Whether to copy the blocks def get_numeric_data(self, copy=False): """ Parameters ---------- copy : boolean, default False Whether to copy the blocks """ self._consolidate_inplace...

return a new manager with the blocks 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 ...

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 ------- copy : BlockManager def copy...

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 will be included in the returned array. ``None`` ...

Return ndarray from blocks with specified item order Items must be contained in the blocks 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 dt...

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(dtype) def to_dict(self, copy=True): """ ...

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 def fast_xs(self, loc): """ get a cross sectional for a given location in the items ; handle dups return the result, is *...

Join together blocks having same dtype Returns ------- y : BlockManager def consolidate(self): """ Join together blocks having same dtype Returns ------- y : BlockManager """ if self.is_consolidated(): return self bm...

Return values for selected item (ndarray or BlockManager). 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: ...

Return the data as a SingleBlockManager if fastpath=True and possible Otherwise return as a ndarray 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._b...

Delete selected item (items if non-unique) in-place. 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_o...

Set new item in-place. Does not consolidate. Adds new Block if not contained in the current set of items 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, clearl...