U c6@sddlmZddlmZddlZddlmZmZmZm Z m Z m Z m Z ddl Z ddlZddlmZmZmZmZddlmZddlmZddlmZmZmZmZmZm Z dd l!m"Z"dd l#m$Z$dd l%m&Z&dd l'm(Z(dd l)m*Z*ddl+m,Z,m-Z-m.Z.m/Z/m0Z0m1Z1ddl2m3Z3m4Z4m5Z5m6Z6m7Z7m8Z8m9Z9m:Z:ddl;mZ>m?Z?ddl@mAZAmBZBmCZCmDZDddlEmFZFddlGmHZHmIZImJZJddlKmLmMZNddlOmPZPmQZQmRZRmSZSmTZTddlUmVZVddlWmXZXmYZYmZZZddl[m\Z\ddl]m^Z^m_Z_m`Z`maZambZbmcZcmdZdddlemfZfddlgmhZhddlimLmjZkddllmLmmmnZnddlompZpmqZqddlrmsZsddltmLmuZuerddlvmwZwmxZxddlymzZze{dZ|d d d!d"d#Z}Gd$d%d%ehZ~Gd&d'd'e~ZGd(d)d)ej~eZGd*d+d+eje~ZGd,d-d-eZGd.d/d/ejeZd0d1d2d3d4ZGd5d6d6eZGd7d8d8eZGd9d:d:eZGd;d<dd?d@ZdAdBdCdDZd=dEdFdGdHZdId%dJdKdLZdEdId1dMdNdOZdPdQdIdRdSdTdUZdfdVdWdXdYZdgd=dId=d[d\d]Zd^ddd_d`d=dad>dbdcZd=d=d>dddeZdS)h) annotations)wrapsN) TYPE_CHECKINGAnyCallableIterableSequencecastfinal) Timestamp internalslibwriters)BlockPlacement)IncompatibleFrequency) ArrayLikeDtypeObjF IgnoreRaiseShapenptAbstractMethodError)cache_readonly)find_stack_level)validate_bool_kwarg)astype_array_safe)LossySetitemErrorcan_hold_elementfind_result_typemaybe_downcast_to_dtypenp_can_hold_elementsoft_convert_objects)ensure_platform_intis_1d_only_ea_dtypeis_1d_only_ea_objis_dtype_equalis_interval_dtype is_list_like is_sparseis_string_dtype)CategoricalDtypeExtensionDtype PandasDtype PeriodDtype) ABCDataFrameABCIndexABCPandasArray ABCSeries)is_inferred_bool_dtype)is_valid_na_for_dtypeisnana_value_for_dtype)extract_bool_arrayputmask_inplaceputmask_without_repeatsetitem_datetimelike_compatvalidate_putmask)quantile_compat)compare_or_regex_search replace_regexshould_use_regex)shift) Categorical DatetimeArrayExtensionArray IntervalArray PandasArray PeriodArrayTimedeltaArray) SparseDtype) PandasObject)ensure_wrapped_if_datetimelike extract_array)check_setitem_lengths) Float64IndexIndex)NDArrayBackedExtensionArrayobjectr)methreturncs$tddfdd }tt|S)zp If we have a multi-column block, split and operate block-wise. Otherwise use the original method. list[Block]rRcs>|jdks|jddkr(|f||S|jf||SdS)Nr)ndimshapesplit_and_operate)selfargskwargsrQ@/tmp/pip-unpacked-wheel-g7fro6k3/pandas/core/internals/blocks.pynewfuncszmaybe_split..newfunc)rr r)rQr_r]r\r^ maybe_split{sr`c @sreZdZUdZded<ded<ded<dZd Zd Zd Zd Z d Z e e d d Z e e d dddZe e d dddZe ed dddZe ddZe e ddZe ddZeddddZejdddd dZe dddd"d#Ze dd$dd%d&d'Ze d(dd)d*Ze ddd+d,Ze d-dd.d/d0Ze d1ddd2d3d4Ze d5d d6d7d8Ze d9d d:d;d<Ze d=dd>d?Zdd d=d@dAdBZ e d9d=dCdDdEZ!e d=ddFdGZ"e d=ddHdIZ#e dddJdKZ$e dd=d=dLdMdNZ%e e&d=ddOdPZ'dd d d d d=dQdRdSZ(e dTddUdVZ)e ddTd dXddYdZd[Z*e dddd]d^Z+e dd dd_d`daZ,e dd dbd=dcdddeZ-e dd d d=dfdgdhZ.e ddidjd d d=dkdldmZ/e ddnd d d=dodpdqZ0drdrdsdtduZ1dvdwZ2edxddydzZ3d{drd|d}d~Z4dd9d.ddZ5dd9d ddddZ6d!e7j8fddd$ddddZ9ddndddZ:ddddZ;d=dddZdd(ddd dd(dddd=d ddZ?dddd=dddZ@dddd5d=dddZAe dddddddZBddddZCed dddZDeddddZEdddrdddZFdrdddZGd!S)Blockz Canonical n-dimensional unit of homogeneous dtype contained in a pandas data structure Index-ignorant; let the container take care of that np.ndarray | ExtensionArrayvaluesintrVr__init__r]FTcCs|j|jjfSN)_can_consolidatedtypenamerYr]r]r^_consolidate_keyszBlock._consolidate_keyboolrTcCs"|j}t|tjr|jdkS|jS)z7 Can we store NA values in this Block? )biu)rh isinstancenpkind _can_hold_narYrhr]r]r^rss  zBlock._can_hold_nacCstjdttdt|jtS)NzBlock.is_categorical is deprecated and will be removed in a future version. 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Only supports slices that preserve dimensionality. z Only same dim slicing is allowed)r_slicerVrc ValueErrorrrYrrrr]r]r^ getitem_blocks  zBlock.getitem_blockslice)rrrRcCs2||}|j|jjkr tdt||||jSr)rrVrcrrrr]r]r^getitem_block_columns-s zBlock.getitem_block_columnsr)elementrRcCst|dd}t|j|S)z#require the same dtype as ourselvesTZ extract_numpy)rKrrc)rYrr]r]r^_can_hold_element=s zBlock._can_hold_elementr)rrRcCs*z|j|jkWStk r$YdSXdS)z Should we set self.values[indexer] = value inplace or do we need to cast? Parameters ---------- value : np.ndarray or ExtensionArray Returns ------- bool FN)rh TypeErrorrr]r]r^ should_storeCszBlock.should_storerScKs||jf|}||S)z[ apply the function to my values; return a block if we are not one )rc_split_op_result)rYfuncr[rr]r]r^applyYsz Block.applyignore_failuresrRc Cs||jdkstz||j}Wn&ttfk rB|r.csg|]}|qSr]) _downcast_2drrmdowncastr]r^rs)rhr extend_blocks)rYrrr]rr^_maybe_downcasts  zBlock._maybe_downcastcCst|j|d}||gS)zs downcast specialized to 2D case post-validation. Refactored to allow use of maybe_split. rh)r rcr)rYrhrr]r]r^rszBlock._downcast_2drrr timedeltarRcCs|r|gS|gS)z attempt to coerce any object types to better types return a copy of the block (if copy = True) by definition we are not an ObjectBlock here! r)rYrrrrr]r]r^rs z Block.convertrcCs|jjSrfrcrhrjr]r]r^rhsz Block.dtyperaiser)rhrerrorsrRc Csl|j}t||||d}t|}||}|j|jkrhtd|d|jjd|jd|jjd|jd |S)a Coerce to the new dtype. Parameters ---------- dtype : np.dtype or ExtensionDtype copy : bool, default False copy if indicated errors : str, {'raise', 'ignore'}, default 'raise' - ``raise`` : allow exceptions to be raised - ``ignore`` : suppress exceptions. On error return original object Returns ------- Block )rrzcannot set astype for copy = [z ] for dtype (z [z]) to different shape (z]))rcrmaybe_coerce_valuesrrWrrhri)rYrhrrrcrZnewbr]r]r^rs  .z Block.astypenancKs"t|jf||d|}||S)"convert to our native types format)na_repquoting)to_native_typesrcr)rYrrr[rr]r]r^rszBlock.to_native_types)deeprRcCs(|j}|r|}t|||j|jdS)zcopy constructorr)rcrrrrV)rYrrcr]r]r^r sz Block.copyznpt.NDArray[np.bool_] | None)inplacemaskrRc Csv|j}t|tr8|r|n|}|jj||dd|gS||sV|rL|gS|gS|dkrjt||}|s|r||gS|gS||r|r|n|}t |j|||j r|dks|j ddd}n|g}|S|j dks|j ddkr(|dkr |tt}n ||}|j||d|dSg}t|D]4\}} |t|j| ||d|||ddq8|SdS) z replace the to_replace value with value, possible to create new blocks here this is just a call to putmask. T) to_replacerrNFrrrUrrrrr)rcrprAr_replacermissing mask_missinganyr8 is_objectrrVrWrrqrhrPrreplacerrrr) rYrrrrrcrrrnrr]r]r^r+sX        z Block.replace)rrrRc Csb||s|r|gS|gSt|}|r2|jn|j}t||||||}|jdddS)aP 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. convert : bool, default True If true, try to coerce any object types to better types. mask : array-like of bool, optional True indicate corresponding element is ignored. Returns ------- List[Block] Fr)rrrecompilercr>rr) rYrrrrrrxrrr]r]r^_replace_regex|s   zBlock._replace_regexz Iterable[Any]z Sequence[Any])src_list dest_listrregexrRc spjfddt||D}t|s:|r0gSgSt|d}tjrrtfdd|D}nfdd|D}dd|D}|rng}t|D]\} \} } | |k} g} t|D]\}}t|dkr|| }n(|| }t|t rt |||d}|j | | ||d}| rZ|j rZt dd |DsZtd d|D}| |q| }q|S) z: See BlockManager.replace_list docstring. cs"g|]\}}|r||fqSr])r)rxyrjr]r^rs z&Block.replace_list..rUcs g|]}t|ddqS)r)rr)r=r)rrrcr]r^rscsg|]}t|dqS)r)rrrrcr]r^rscSsg|] }t|qSr])r7rrr]r]r^rs)rrrrrcss|]}|dkVqdSrfr]rr]r]r^ sz%Block.replace_list..cSsg|]}|jdddqS)FTrrrr]r]r^rs)rcziprrr*rhr5rrprlr_replace_coercerallrr)rYrrrrpairsZsrc_lenmasksrbrnsrcdestrZnew_rbZblk_numrrZmibrr])rrrYrcr^ replace_listsL       "  zBlock.replace_listnpt.NDArray[np.bool_])rrrrRcCst||r|j|||d|dS|dkr~|rj|jttdd}||krV|sV|}t|j |||gS|rt|gS|gS|j ||||dSdS)ai 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. mask : np.ndarray[bool] True indicate corresponding element is ignored. inplace : bool, default True Perform inplace modification. regex : bool, default False If true, perform regular expression substitution. Returns ------- List[Block] F)rrrNrr) r?rrrrqrhrPrr8rcr)rYrrrrrrr]r]r^rs,  zBlock._replace_coerce np.ndarray)argrRcCs|SzA For compatibility with 1D-only ExtensionArrays. r]rYr r]r]r^_maybe_squeeze_arg)szBlock._maybe_squeeze_argcCs|Sr r])rYrr]r]r^_unwrap_setitem_indexer/szBlock._unwrap_setitem_indexerrcCs|jjSrf)rcrWrjr]r]r^rW7sz Block.shape)int | tuple[int, int] | tuple[slice, int])rnrRcCs |j|Srfr)rYrnr]r]r^iget;sz Block.iget4slice | npt.NDArray[np.bool_] | npt.NDArray[np.intp]cCs |j|S)zreturn a slice of my valuesr)rYrr]r]r^rDsz Block._slicercrrRcCs|r|j|_||j|<dS)a Modify block values in-place with new item value. If copy=True, first copy the underlying values in place before modifying (for Copy-on-Write). Notes ----- `set_inplace` never creates a new array or new Block, whereas `setitem` _may_ create a new array and always creates a new Block. Caller is responsible for checking values.dtype == self.dtype. NrcrrYZlocsrcrr]r]r^ set_inplaceKs zBlock.set_inplacenpt.NDArray[np.intp]raxisrrRcCs|j}|tjkr|j}d}nd}tj|||||d}|dkrH|dkrHt|dkrV|j}t|j |j sp| ||S| ||SdS)N Take values according to indexer and return them as a block. FT)r allow_fillr~rN) rcr no_defaultr~algostake_ndrrr&rhrr)rYrrrr~rcrrr]r]r^r]s$   z Block.take_nd new_placement needs_maskingc CsP|j|jj|d\}}|d}|j|}||}t|}t||dg}||fS)a  Return a list of unstacked blocks of self Parameters ---------- unstacker : reshape._Unstacker fill_value : int Only used in ExtensionBlock._unstack new_placement : np.ndarray[np.intp] allow_fill : bool needs_masking : np.ndarray[bool] Returns ------- blocks : list of Block New blocks of unstacked values. mask : array-like of bool The mask of columns of `blocks` we should keep. r~rr)Zget_new_valuesrcTrr new_block_2d) rY unstackerr~rr rrrrr]r]r^_unstacks   zBlock._unstackcCs||}ttj|j}|jdkr(|j}t|||t|dd}zt |j |}Wn*t k rz| |}| ||YSX|j tkr||}t|rt|t||}|||<|S)a Attempt self.values[indexer] = value, possibly creating a new array. Parameters ---------- indexer : tuple, list-like, array-like, slice, int The subset of self.values to set value : object The value being set Returns ------- Block Notes ----- `indexer` is a direct slice/positional indexer. `value` must be a compatible shape. rTr)rr rqndarrayrcrVr#rLrKr!rhrrsetitemrr r(r:r)rYrrrccastedrvir]r]r^r(s"       z Block.setitemc Cs|}ttj|j}t|j|\}}t|ttt fr6t |t j krF|j }||}t|dd}|rf|gSz"t|j|}t|j|||gWStk r|jdks|jddkrt|s||||YS|d}||||}|gYSnt|tj} g} |} t| D]Z\} }|} | rB|dd| | df} |dd| | df}||| }| |q| YSYnXdS)aV 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 : np.ndarray[bool], SparseArray[bool], or BooleanArray new : a ndarray/object Returns ------- List[Block] TrrUrN)r rqr'rcr;r#rpr0r2r/rr rr~rrKr!rhr9rrVrWr(rputmaskZnonzeror(rrr)rYrnew orig_maskrcnoopr)rris_arrayrrrnnsubmaskrr]r]r^r+s@        z Block.putmaskinferc CsT|j|jkstt|tttfr$t|jdk}t|}ttj |j }|}|rR|j }t ||\}}|rp| gS|tjkr|j}||}zt|j|} Wntttfk r|jdks|jddkr||} | ||} |j| |dYSt|tj tf} g} |}t|D]^\}}|}| r@|dd||df}|dd||df}|j|||d}| |q| YSYnX| }t|| |}||k rt!|rt"|t"|krt|||t#d| }t$|||nNt!|r,t|tj s,t"||jdkr,t%|&|j}t'|||}|rH|j }|(|gS) a evaluate the block; return result block(s) from the result Parameters ---------- other : a ndarray/object cond : np.ndarray[bool], SparseArray[bool], or BooleanArray _downcast : str or None, default "infer" Private because we only specify it when calling from fillna. Returns ------- List[Block] rrUrrN _downcastzThis should not be reached; call to np.where above is expected to raise ValueError. Please report a bug at github.com/pandas-dev/pandasr))rVrrpr0r2r/r7r rqr'rcr#r;rr rr~rr!rhrrrrWrwhererrCrrrr:sumr(rrr+rr expressionsr)rYrcondr4Z transposerc orig_othericondr.r)rrr/rrrnrZothr1rZaltrr]r]r^r5sj        z Block.where int | NonelimitrrRc st|d}|jsd}nt|j}t|j|\}}|rZ|r>|gS|}|j|gd}|S|dk rzd|||jd|k<|r| |j |}n|j ||j dd}t fdd |DS) zs fillna on the block with the value. If we fail, then convert to ObjectBlock and try again rTrNFrUr3csg|]}|j|gdqS)r)rrrr]r^rsz Block.fillna..) rrsr5rcr;rrZcumsumrVr+r#r5r) rYrr=rrr.rrrr]rr^fillnas&  z Block.fillnapadrforwardz Index | Nonez str | Nonez Any | None) methodrindexrr=limit_direction limit_arear~rrRc  Kst|d}|js$|r|gS|gSzt|} Wntk rJd} YnX| dkrt|jjdkrt|rj|gS|gS|jr|j dkr|j ddkr|dkr|j t |j ||||||||| f | S|r|jn|j} ttj| } tj| f|||||||d| || } | | g| S)NrfrrrU)rArrBr=rCrDr~)rrsrrZclean_fill_methodrrhrrrrVrWrXr interpolatercr rqr'Zinterpolate_array_2drr)rYrArrBrr=rCrDr~rr[rdatarr]r]r^rFsR  &    zBlock.interpolaterUr0rrRcCs tj|j||d}|j|dgS)z'return block for the diff of the valuesrr)rdiffrcr)rYr0rrr]r]r^rJsz Block.diffperiodsrr~rRcCst|s|jtkrtd||}zt|j|}Wn.tk rd||}|j |||dYSXt t j |j }t ||||}||gSdS)z+shift the block by periods, possibly upcastzfill_value must be a scalar)rr~N)r Z is_scalarrhrrrr!rrr@r rqr'rcr)rYrLrr~r)rrcrr]r]r^r@s   z Block.shiftlinearrM)qsrrRcCsV|jdkst|dkstt|s&tt|jt|j|}t|dd}t ||j dS)a\ compute the quantiles of the Parameters ---------- qs : Float64Index List of the quantiles to be computed. interpolation : str, default 'linear' Type of interpolation. axis : int, default 0 Axis to compute. Returns ------- Block rrUrr") rVrr(r<rcrqasarray_valuesrr$r)rYrN interpolationrrr]r]r^quantiles    zBlock.quantilecCs t|dS)zC Return a new Block with the given loc(s) deleted. Nr)rYrr]r]r^delete@sz Block.deletecCs t|dSz(return a boolean if I am possibly a viewNrrjr]r]r^is_viewFsz Block.is_viewrCcCs t|dS)zP The array that Series.array returns. Always an ExtensionArray. Nrrjr]r]r^ array_valuesKszBlock.array_valuesDtypeObj | NonerhrRcCs t|dS)z return an internal format, currently just the ndarray this is often overridden to handle to_dense like operations Nrrtr]r]r^ get_valuesRszBlock.get_valuescCs t|dSrfrrjr]r]r^values_for_jsonYszBlock.values_for_json)N)N)F)N)TTTT)Fr)rN)T)FN)FTN)FF)TF)F)r2)NFN) r?rNFNr@NNN)rU)rN)rMr)N)Hr __module__ __qualname____doc____annotations__ __slots__ is_numericrrrg_validate_ndimr rrkrsrzpropertyr{r|r~rrsetterrrrrrrrrrrrrrXrrr`rrrhrrrrrrrr rrWrrrr rrr&r(r+r5r>rFrJr@rRrSrUrVrYrZr]r]r]r^ras           ! P)J7 ".0?p0"@ !racseZdZUdZded<ddZd$ddd d Zddd d Zd%ddddfdd ZddddZ e ddddZ d&dddddZ ddddZ d'd"d#ZZS)( EABackedBlockz> Mixin for Block subclasses backed by ExtensionArray. rCrcc Cs|}|}||}||}|j}|jdkr2|j}t|||z |||<Wnttfk r}z^t|t |j r| |}| ||WY0St |tr| |}| ||WYSW5d}~XYnX|SdS)a7 Attempt self.values[indexer] = value, possibly creating a new array. This differs from Block.setitem by not allowing setitem to change the dtype of the Block. Parameters ---------- indexer : tuple, list-like, array-like, slice, int The subset of self.values to set value : object The value being set Returns ------- Block Notes ----- `indexer` is a direct slice/positional indexer. `value` must be a compatible shape. rN)rr rcrVr#rLrr_catch_deprecated_value_errorr'rhrr(rpNDArrayBackedExtensionBlock)rYrrZ orig_indexerZ orig_valuercerrrr]r]r^r(ds(         zEABackedBlock.setitemr2rSrTc Cs|jj}t|}|}|}||}||}|tjkr<|j}t||\}}|rZ|gSz| ||j} WnJt t fk r} z$t | |j dks|jddkrt|jr||} | ||} |j| |dWYSt|tr||} | ||} |j| |dWYSnt|tjtf} g}|} t| D]Z\}}|}| rj|dd||df}|dd||df}|||}||q@|WYSW5d} ~ XYnX|| }|gS)NrUrr)rcr#r7r r rr~r;rZ_whererrrerVrWr'rhrr5rrprfrqr'rCrrrr)rYrr8r4Zarrr9Z orig_condr:r.rrgrrr/rrnrr0r1rr]r]r^r5sL            zEABackedBlock.wherec Cst|}|j}|jdkr|j}|}|}||}||}|sH|gSz|||Wn*ttfk r}zt ||jdks|j ddkrt |j r| |}|||WYSt|tr| |}|||WYSnt|tjtf}g} |} t| D]Z\} } |} |r6|dd| | df} |dd| | df}| || }| |q | WYSW5d}~XYnX|gS)z+ See Block.putmask.__doc__ rrUrN)r7rcrVr#r rZ_putmaskrrrerWr'rhrr+rprfrqr'rCrrr)rYrr,rcZorig_newr-rgrr/rrrnrr0r1rr]r]r^r+sD        zEABackedBlock.putmaskNFr;rlr<c s~|jjdkrjz|jj||d}Wn8ttfk rXtjdt|dt t dYnX| |}|gSt j||||dS)Nr)r=zMThe behavior of fillna with timedelta64[ns] dtype and an incompatible value (z) is deprecated. In a future version, this will cast to a common dtype (usually object) instead of raising, matching the behavior of other dtypes.ru)r=rr) rhrrrcr>rrrwrxr FutureWarningrrsuper)rYrr=rrrZres_blk __class__r]r^r>s   zEABackedBlock.fillnaracCs,|j|}|j|}t||||jdS)Nr)rcrSrrrVrYrrcrr]r]r^rS4s  zEABackedBlock.deletecCs|jSrfrrjr]r]r^rV:szEABackedBlock.array_valuesrWr rXcCs*|j}|tkr|t}t||jS)zS return object dtype as boxed values, such as Timestamps/Timedelta )rcrrrPrqrOrrW)rYrhrcr]r]r^rY>s zEABackedBlock.get_valuescCs t|jSrf)rqrOrcrjr]r]r^rZHszEABackedBlock.values_for_jsonr?rc KsH|j}|jdkr.|dkr.|jj|||dj}n|j|||d}||S)Nrr)rrAr=)rcrVr#r>r) rYrArrr=r~r[rcrr]r]r^rFKs zEABackedBlock.interpolate)r2)NFN)N)r?rFNN)rr[r\r]r^r(r5r+r>rSrrVrYrZrF __classcell__r]r]rjr^rd]s( 8?= rdcs eZdZUdZdZdZdZded<edddd Z d d d d Z d9ddddddZ ddZ ddZ eddddZeddZddejfddd d!d"d#d$Zd%dd&d'd(Zed)dd&d*d+Zd:ddd-d.fd/d0 Zd;ddd1d-d2d3d4Zdd5d6d7d8ZZS)<ExtensionBlocka Block for holding extension types. Notes ----- This holds all 3rd-party extension array types. It's also the immediate parent class for our internal extension types' blocks, CategoricalBlock. ExtensionArrays are limited to 1-D. FTrCrcrrTcCs*|jdkrt|jfSt|jt|jfS)NrU)rVrrcrrjr]r]r^rWis  zExtensionBlock.shaper)rncCst|trp|\}}t|s4|dkr4t|dn2t|trf|dkrT|t|j7}|j||dS|j|S|dkrt|d|jSdS)Nrz only contains one itemrU)rptuplecom is_null_slice IndexErrorrrrc)rYrncolrr]r]r^rps   zExtensionBlock.igetrrlrrcCs"|r|j|_||jdd<dSrfrrr]r]r^rs zExtensionBlock.set_inplacecCsvt|tjtfrF|j|jjdkrF|jddks4t|dddf}n,t|trr|jddksbt|j dddj }|S)z@ If necessary, squeeze a (N, 1) ndarray to (N,) rUNrrI) rprqr'rCrVrcrWrr/Z_ixsrPr r]r]r^r s z!ExtensionBlock._maybe_squeeze_argcCst|trt|dkrtdd|Drn|\}}|jdkrT|dkrT|jddks\td|dddf}njt|dr|ddkr|d}nFt |dr|d}n.t |dr|dddkr|d}ntd|S)zw Adapt a 2D-indexer to our 1D values. This is intended for 'setitem', not 'iget' or '_slice'. rcss$|]}t|tjo|jdkVqdS)rN)rprqr'rVrr]r]r^rsz9ExtensionBlock._unwrap_setitem_indexer..rUrzPThis should not be reached. Please report a bug at github.com/pandas-dev/pandas/N) rprorrsizerWrr is_integerrprqr()rYrfirstsecondr]r]r^rs0       z&ExtensionBlock._unwrap_setitem_indexercCsdS)z,Extension arrays are never treated as views.Fr]rjr]r]r^rUszExtensionBlock.is_viewcCs |jjjSrf)rcrhZ _is_numericrjr]r]r^r`szExtensionBlock.is_numericrNrrdrrarcCsP|tjkrd}|jj||dd}|jdkr6|dkr6t|dkrD|j}|||S)rNT)r~rrU)r rrctakerVrrr)rYrrrr~rr]r]r^rs zExtensionBlock.take_ndrrcCsN|jdkrDt|tstd|td|}t|s.)Z arange_resultr#rr)rYr%r~rr rrrr]r}r^r&Bs    zExtensionBlock._unstack)F)rU)rN)rr[r\r]rgrarr^rrWrrr rrbrUr`r rrrr rzrJr@r&rmr]r]rjr^rnWs2  , (   rnc@sheZdZUded<eddddZedddd Zdd dd d dZddddZddddZ d S) NumpyBlockr rcrlrTcCs |jjdk SrT)rcbaserjr]r]r^rUoszNumpyBlock.is_viewrCcCs t|jSrf)rErcrjr]r]r^rVtszNumpyBlock.array_valuesNrWrXcCs|tkr|jtS|jSrf)rrcrrtr]r]r^rYxs zNumpyBlock.get_valuescCs|jSrfrrjr]r]r^rZ}szNumpyBlock.values_for_jsonracCs0t|j|d}|j|}t||||jdS)Nrr)rqrSrcrrrVrlr]r]r^rSs zNumpyBlock.delete)N) rr[r\r^rbrUrVrYrZrSr]r]r]r^r~ls r~c@seZdZdZdZdS) NumericBlockr]TN)rr[r\r_r`r]r]r]r^rsrc@sheZdZUdZded<eddddZedddd Zdd d d d ddZ dd d dd dddZ dS)rfz8 Block backed by an NDArrayBackedExtensionArray rOrcrlrTcCst|jtj Srf)rprhrqrjr]r]r^rsz(NDArrayBackedExtensionBlock.is_extensioncCs|jjjdk SrT)rc_ndarrayrrjr]r]r^rUsz#NDArrayBackedExtensionBlock.is_viewrrdrSrHcCs$|j}||j||d}||gS)a 1st discrete difference. Parameters ---------- n : int Number of periods to diff. axis : int, default 0 Axis to diff upon. Returns ------- A list with a new Block. Notes ----- The arguments here are mimicking shift so they are called correctly by apply. rI)rcr@r)rYr0rrcrr]r]r^rJsz NDArrayBackedExtensionBlock.diffNrrKcCs"|j}|j|||d}||gS)N)r~rr{)rYrLrr~rcrr]r]r^r@sz!NDArrayBackedExtensionBlock.shift)r)rN) rr[r\r]r^rrrbrUrJr@r]r]r]r^rfs rf Exceptionr)rgrRcCs6t|tr2t|trndt|kr$ndt|kr2dS)z We catch ValueError for now, but only a specific one raised by DatetimeArray which will no longer be raised in version.2.0. z'value.closed' iszTimezones don't matchN)rprrr)rgr]r]r^res    rec@s0eZdZUdZdZdZded<dddd Zd S) DatetimeLikeBlockz*Block for datetime64[ns], timedelta64[ns].r]FzDatetimeArray | TimedeltaArrayrcr rTcCs|jjSrf)rcrrjr]r]r^rZsz!DatetimeLikeBlock.values_for_jsonN)rr[r\r]r_r`r^rZr]r]r]r^rs rc@s0eZdZUdZded<dZdZdZdZe j Z dS)DatetimeTZBlockz0implement a datetime64 block with a tz attributerBrcr]TFN) rr[r\r]r^r_rrargrfrZr]r]r]r^rs rc@sFeZdZdZdZed dddddZeddddddd d d Zd S) ObjectBlockr]TFrlrSrcCsr|jdkstz||j}Wntk r:|s2gYSXt|tjsLt|jdksZt|dd}||gS)z; For object-dtype, we operate column-wise. rrUr) rVrrcrrprqr'rr)rYrrresr]r]r^rs  zObjectBlock.reducercCsB|j}|jdkr|d}t|||||d}t||j}||gS)z attempt to cast any object types to better types return a copy of the block (if copy = True) by definition we ARE an ObjectBlock!!!!! rr)rrrr)rcrVr"rr)rYrrrrrcrr]r]r^rs   zObjectBlock.convertN)F)TTTT)rr[r\r_rr`rrr]r]r]r^rsrc@s"eZdZdZeddddZdS)CategoricalBlockr]rrTcCs|jjSrfrrjr]r]r^rh"szCategoricalBlock.dtypeN)rr[r\r_rbrhr]r]r]r^rsrr)rcrRcCsVt|tjr0t|}t|jjtr0tj|t d}t|t t frR|j dk rR| d}|S)a: Input validation for values passed to __init__. Ensure that any datetime64/timedelta64 dtypes are in nanoseconds. Ensure that we do not have string dtypes. Parameters ---------- values : np.ndarray or ExtensionArray Returns ------- values : np.ndarray or ExtensionArray rN)rprqr'rJ issubclassrhrrrrPrBrGfreqZ _with_freqrr]r]r^r+s  rrrcCs~|j}|j}t|trt}n^t|tr,t}nN|tkr:t}n@t|t rJt }n0t|t rZt}n |dkrht }n|dkrvt }nt}|S)z Find the appropriate Block subclass to use for the given values and dtype. Parameters ---------- dtype : numpy or pandas dtype Returns ------- cls : class, subclass of Block )rr)rEcrnrorm)rrrrprHrnr+rr rr.rfr,rrr)rhvtyperrclsr]r]r^get_block_typeHs$    rrrcCs t|j}t|}||d|dS)NrrVr)rrhr)rcrklassr]r]r^r$qs r$rd)rVrRcCs>t|tst|}t|||t|j}t|}||||dS)Nr)rpr check_ndimrrhr)rcrrVrr]r]r^r|s    r)rrVrRcCs|j|kr$td|jd|dnvt|js~|j|krPtd|jd|dt|t|krtdt|dt|n|dkrt|d krtd d S) aV ndim inference and validation. Validates that values.ndim and ndim are consistent. Validates that len(values) and len(placement) are consistent. Parameters ---------- values : array-like placement : BlockPlacement ndim : int Raises ------ ValueError : the number of dimensions do not match z0Wrong number of dimensions. values.ndim > ndim [z > ]z1Wrong number of dimensions. values.ndim != ndim [z != zWrong number of items passed z, placement implies rrUz need to splitN)rVrr$rhr)rcrrVr]r]r^rs   rrbrWz3tuple[np.ndarray | ExtensionArray, DtypeObj | None])rcrhrVrRcCs@t|tr(|}|r(|dkr(t|}t|tr8|j}||fS)zL Ensure that we don't allow PandasArray / PandasDtype in internals. rU)rpr1Zto_numpyrqZ atleast_2dr-Z numpy_dtype)rcrhrVr]r]r^extract_pandas_arrays    rrSrTcCsf|dkr g}t|trB|D]$}t|tr4||q||qn t|tsXtt||||S)z.return a new extended blocks, given the resultN)rplistrrrarr)rrrr]r]r^rs    rrU)rcrVrRcCs.|j|kr*t|js*td|}|dd}|S)z: Reshape if possible to have values.ndim == ndim. z+np.ndarray | DatetimeArray | TimedeltaArrayrUr)rVr$rhr r)rcrVr]r]r^rs     rr.)rr float_formatdecimalr cKs0t|tr2|jjjdkr2tj|jjt|j |d}t |}t|t t fr|j dkrx|jfd|i|}|jtdd}|Sg}tt|D]8}||ddfjfd|i|}||jtddqt|S|jjdkrtt|st|dkr:|d kr:t|} |s|t}ntj|d d }||| <|jtdd}|Sd d lm} | |||||dd} | } | jtdd} | St|trt|} t|t} || | <| St|} t |}|jt!kr|s|r|t}|jj"tdj"|kr|d|}ntj|d d }||| <|jtdd}|SdS)rZMmr!rUrFrNrErrPrr)FloatArrayFormatter)rrrrZ fixed_widthZU1zr?Z"pandas.core.array_algos.transformsr@Zpandas.core.arraysrArBrCrDrErFrGZpandas.core.arrays.sparserHZpandas.core.baserIZpandas.core.commoncommonrpZ#pandas.core.computation.expressionsZ computationr7Zpandas.core.constructionrJrKZpandas.core.indexersrLZpandas.core.missingrZpandasrMrNZpandas.core.arrays._mixinsrOrhrr`rardrnr~rZNDArrayBackedBlockrfrerrrrrrr$rrrrrrr|r]r]r]r^s  $          (    $     Y{2 4) *X