x\c@ sdZddlmZddlZddlZddlZddlZddlZddl m Z m Z m ZddljZddlmZddlmZddlmZddlmZmZmZdd lmZmZmZmZmZm Z m!Z!m"Z"m#Z#m$Z$m%Z%m&Z&m'Z'm(Z(dd l)m*Z*m+Z+m,Z,m-Z-m.Z.m/Z/m0Z0dd l1m2Z2m3Z3ddl4Z5ddl6j7j8Z9ddl:j7j;Z;d Z<d Z=dZ>dZ?dZ@dZAdZBdZCdZDdZEdZFdZGdZHdZIdZJdZKdZLdZMdZNdZOd ZPiid!d"6d#d$6d%d&6d'6id(d"6d)d$6d*d&6d+6id,d"6d-d$6d.d&6dd/6d06id1d"6d2d$6d3d&6d46id5d"6d6d$6d7d&6dd/6d86id9d"6d:d$6d;d&6dd/6d<6id=d"6d>d$6d?d&6dd/6d@6idAd"6dBd$6dCd&6dd/6dA6idDd"6dEd$6dd&6dF6idGd"6dHd$6dd&6dI6idJd"6dKd$6dd&6dL6idMd"6dNd$6dd&6dO6idPd"6dQd$6dd&6dR6idSd"6dTd$6dd&6dU6ZReSeRjTZUxieUD]aZVeWeReVdVt|tjr6tdjdt|jn||S(Ns&{typ} cannot perform the operation modttyp(R!tcompatt string_typest TypeErrortformatR/t__name__(R&R'((s.lib/python2.7/site-packages/pandas/core/ops.pytrmods cC s t||S(N(tdivmod(R&R'((s.lib/python2.7/site-packages/pandas/core/ops.pytrdivmodscC s||S(N((R&R'((s.lib/python2.7/site-packages/pandas/core/ops.pytrpowscC stj||S(N(toperatortand_(R&R'((s.lib/python2.7/site-packages/pandas/core/ops.pytrand_scC stj||S(N(RItor_(R&R'((s.lib/python2.7/site-packages/pandas/core/ops.pytror_scC stj||S(N(RItxor(R&R'((s.lib/python2.7/site-packages/pandas/core/ops.pytrxorsc sdfd}|_|S(s Return a binary method that always raises a TypeError. Parameters ---------- name : str Returns ------- invalid_op : function c s+tdjddt|jdS(Ns1cannot perform {name} with this index type: {typ}R%R?(RBRCR/RD(tselftother(R%(s.lib/python2.7/site-packages/pandas/core/ops.pyt invalid_ops N(R*RD(R%RR((R%s.lib/python2.7/site-packages/pandas/core/ops.pytmake_invalid_ops  cC syi}|jdd}|jdrC|d krCt|d>> _gen_eval_kwargs("__add__") {} >>> _gen_eval_kwargs("rtruediv") {'reversed': True, 'truediv': True} t__ttrR9trandtrorROtreversedttruedivR=tnetmasker(R9RWRXRO(RZR=(R[(treplacet startswithtTrue(R%tkwargs((s.lib/python2.7/site-packages/pandas/core/ops.pyt_gen_eval_kwargss     cC sI|jd}d|kr'tj}nd|kr?tj}nd}|S(s# Find the appropriate fill value to use when filling in undefined values in the results of the given operation caused by operating on (generally dividing by) zero. Parameters ---------- name : str Returns ------- fill_value : {None, np.nan, np.inf} RTtdivtmodN(tstripR3tinftnanR*(R%t fill_value((s.lib/python2.7/site-packages/pandas/core/ops.pyt_gen_fill_zeross    cC s7|jdddkrdS|jdr/dSdSdS( s Only DataFrame cares about default_axis, specifically: special methods have default_axis=None and flex methods have default_axis='columns'. Parameters ---------- name : str Returns ------- default_axis: str or None t__rRTt__and__t__or__t__xor__tcolumnsN(RjRkRl(R]R^R*(R%((s.lib/python2.7/site-packages/pandas/core/ops.pyt_get_frame_op_default_axiss cC s$t|dd}d|k}|s(dSidtj6dt6dtj6dt6dtj6dt6dtj 6dt 6dtj 6dt 6dtj 6d t6d tj6d t6d tj6d tj6d tj6dtj6dtj6dtj6dtj6dt6dtj6dt6dtj6dt6dt6dt6|S(s Find the operation string, if any, to pass to numexpr for this operation. Parameters ---------- op : binary operator cls : class Returns ------- op_str : string or None t_subtypRUtsparset+t*t-t/s//t%s**s==s!=s<=t=t>t&t|t^N(tgetattrR*RItaddR9tmulR;tsubR:RZR=tfloordivR>RcREtpowRHteqR[tletlttgetgtRJRKRLRMRNRORFRG(toptclstsubtypt use_numexpr((s.lib/python2.7/site-packages/pandas/core/ops.pyt _get_opstr8s@                 cC s1|jjd}|r-djd|}n|S(s Find the name to attach to this method according to conventions for special and non-special methods. Parameters ---------- op : binary operator special : bool Returns ------- op_name : str t_s __{opname}__topname(RDRdRC(RtspecialR((s.lib/python2.7/site-packages/pandas/core/ops.pyt _get_op_namelsRqRtAdditiontdescR9treverseR|Rst SubtractionR:R~RrtMultiplicationR;t df_examplesR}RutModuloRERcs**sExponential powerRHRRtsFloating divisionR=RZs//sInteger divisionR>RRFsInteger division and moduloRGs==sEqual toRs!=s Not equal toR[Rvs Less thanRs<=sLess than or equal toRRws Greater thanRs>=sGreater than or equal toRRYs {desc} of series and other, element-wise (binary operator `{op_name}`). Equivalent to ``{equiv}``, but with support to substitute a fill_value for missing data in one of the inputs. Parameters ---------- other : Series or scalar value fill_value : None or float value, default None (NaN) Fill existing missing (NaN) values, and any new element needed for successful Series alignment, with this value before computation. If data in both corresponding Series locations is missing the result will be missing level : int or name Broadcast across a level, matching Index values on the passed MultiIndex level Returns ------- result : Series See Also -------- Series.{reverse} Examples -------- >>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd']) >>> a a 1.0 b 1.0 c 1.0 d NaN dtype: float64 >>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e']) >>> b a 1.0 b NaN d 1.0 e NaN dtype: float64 >>> a.add(b, fill_value=0) a 2.0 b 1.0 c 1.0 d 1.0 e NaN dtype: float64 s Binary operator %s with support to substitute a fill_value for missing data in one of the inputs Parameters ---------- other : Series, DataFrame, or constant axis : {0, 1, 'index', 'columns'} For Series input, axis to match Series index on fill_value : None or float value, default None Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing level : int or name Broadcast across a level, matching Index values on the passed MultiIndex level Returns ------- result : DataFrame Notes ----- Mismatched indices will be unioned together sM {desc} of dataframe and other, element-wise (binary operator `{op_name}`). Equivalent to ``{equiv}``, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, `{reverse}`. Among flexible wrappers (`add`, `sub`, `mul`, `div`, `mod`, `pow`) to arithmetic operators: `+`, `-`, `*`, `/`, `//`, `%`, `**`. Parameters ---------- other : scalar, sequence, Series, or DataFrame Any single or multiple element data structure, or list-like object. axis : {{0 or 'index', 1 or 'columns'}} Whether to compare by the index (0 or 'index') or columns (1 or 'columns'). For Series input, axis to match Series index on. level : int or label Broadcast across a level, matching Index values on the passed MultiIndex level. fill_value : float or None, default None Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing. Returns ------- DataFrame Result of the arithmetic operation. See Also -------- DataFrame.add : Add DataFrames. DataFrame.sub : Subtract DataFrames. DataFrame.mul : Multiply DataFrames. DataFrame.div : Divide DataFrames (float division). DataFrame.truediv : Divide DataFrames (float division). DataFrame.floordiv : Divide DataFrames (integer division). DataFrame.mod : Calculate modulo (remainder after division). DataFrame.pow : Calculate exponential power. Notes ----- Mismatched indices will be unioned together. Examples -------- >>> df = pd.DataFrame({{'angles': [0, 3, 4], ... 'degrees': [360, 180, 360]}}, ... index=['circle', 'triangle', 'rectangle']) >>> df angles degrees circle 0 360 triangle 3 180 rectangle 4 360 Add a scalar with operator version which return the same results. >>> df + 1 angles degrees circle 1 361 triangle 4 181 rectangle 5 361 >>> df.add(1) angles degrees circle 1 361 triangle 4 181 rectangle 5 361 Divide by constant with reverse version. >>> df.div(10) angles degrees circle 0.0 36.0 triangle 0.3 18.0 rectangle 0.4 36.0 >>> df.rdiv(10) angles degrees circle inf 0.027778 triangle 3.333333 0.055556 rectangle 2.500000 0.027778 Subtract a list and Series by axis with operator version. >>> df - [1, 2] angles degrees circle -1 358 triangle 2 178 rectangle 3 358 >>> df.sub([1, 2], axis='columns') angles degrees circle -1 358 triangle 2 178 rectangle 3 358 >>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']), ... axis='index') angles degrees circle -1 359 triangle 2 179 rectangle 3 359 Multiply a DataFrame of different shape with operator version. >>> other = pd.DataFrame({{'angles': [0, 3, 4]}}, ... index=['circle', 'triangle', 'rectangle']) >>> other angles circle 0 triangle 3 rectangle 4 >>> df * other angles degrees circle 0 NaN triangle 9 NaN rectangle 16 NaN >>> df.mul(other, fill_value=0) angles degrees circle 0 0.0 triangle 9 0.0 rectangle 16 0.0 Divide by a MultiIndex by level. >>> df_multindex = pd.DataFrame({{'angles': [0, 3, 4, 4, 5, 6], ... 'degrees': [360, 180, 360, 360, 540, 720]}}, ... index=[['A', 'A', 'A', 'B', 'B', 'B'], ... ['circle', 'triangle', 'rectangle', ... 'square', 'pentagon', 'hexagon']]) >>> df_multindex angles degrees A circle 0 360 triangle 3 180 rectangle 4 360 B square 4 360 pentagon 5 540 hexagon 6 720 >>> df.div(df_multindex, level=1, fill_value=0) angles degrees A circle NaN 1.0 triangle 1.0 1.0 rectangle 1.0 1.0 B square 0.0 0.0 pentagon 0.0 0.0 hexagon 0.0 0.0 s< {desc} of dataframe and other, element-wise (binary operator `{op_name}`). Among flexible wrappers (`eq`, `ne`, `le`, `lt`, `ge`, `gt`) to comparison operators. Equivalent to `==`, `=!`, `<=`, `<`, `>=`, `>` with support to choose axis (rows or columns) and level for comparison. Parameters ---------- other : scalar, sequence, Series, or DataFrame Any single or multiple element data structure, or list-like object. axis : {{0 or 'index', 1 or 'columns'}}, default 'columns' Whether to compare by the index (0 or 'index') or columns (1 or 'columns'). level : int or label Broadcast across a level, matching Index values on the passed MultiIndex level. Returns ------- DataFrame of bool Result of the comparison. See Also -------- DataFrame.eq : Compare DataFrames for equality elementwise. DataFrame.ne : Compare DataFrames for inequality elementwise. DataFrame.le : Compare DataFrames for less than inequality or equality elementwise. DataFrame.lt : Compare DataFrames for strictly less than inequality elementwise. DataFrame.ge : Compare DataFrames for greater than inequality or equality elementwise. DataFrame.gt : Compare DataFrames for strictly greater than inequality elementwise. Notes -------- Mismatched indices will be unioned together. `NaN` values are considered different (i.e. `NaN` != `NaN`). Examples -------- >>> df = pd.DataFrame({{'cost': [250, 150, 100], ... 'revenue': [100, 250, 300]}}, ... index=['A', 'B', 'C']) >>> df cost revenue A 250 100 B 150 250 C 100 300 Comparison with a scalar, using either the operator or method: >>> df == 100 cost revenue A False True B False False C True False >>> df.eq(100) cost revenue A False True B False False C True False When `other` is a :class:`Series`, the columns of a DataFrame are aligned with the index of `other` and broadcast: >>> df != pd.Series([100, 250], index=["cost", "revenue"]) cost revenue A True True B True False C False True Use the method to control the broadcast axis: >>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index') cost revenue A True False B True True C True True D True True When comparing to an arbitrary sequence, the number of columns must match the number elements in `other`: >>> df == [250, 100] cost revenue A True True B False False C False False Use the method to control the axis: >>> df.eq([250, 250, 100], axis='index') cost revenue A True False B False True C True False Compare to a DataFrame of different shape. >>> other = pd.DataFrame({{'revenue': [300, 250, 100, 150]}}, ... index=['A', 'B', 'C', 'D']) >>> other revenue A 300 B 250 C 100 D 150 >>> df.gt(other) cost revenue A False False B False False C False True D False False Compare to a MultiIndex by level. >>> df_multindex = pd.DataFrame({{'cost': [250, 150, 100, 150, 300, 220], ... 'revenue': [100, 250, 300, 200, 175, 225]}}, ... index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'], ... ['A', 'B', 'C', 'A', 'B', 'C']]) >>> df_multindex cost revenue Q1 A 250 100 B 150 250 C 100 300 Q2 A 150 200 B 300 175 C 220 225 >>> df.le(df_multindex, level=1) cost revenue Q1 A True True B True True C True True Q2 A False True B True False C True False s {desc} of series and other, element-wise (binary operator `{op_name}`). Equivalent to ``{equiv}``. Parameters ---------- other : DataFrame or Panel axis : {{items, major_axis, minor_axis}} Axis to broadcast over Returns ------- Panel See Also -------- Panel.{reverse} s Wrapper method for {op_name} Parameters ---------- other : DataFrame or Panel axis : {{items, major_axis, minor_axis}} Axis to broadcast over Returns ------- Panel c C s(|jdd}t|}|dr?d|dd|}n|d|dd}|dkrt}|jd |d d |d |d |d }n|d krt}|jd |d d |d |d |d }nM|dkrt}|jd |d d |d |d |d }n td|S(st Make the appropriate substitutions for the given operation and class-typ into either _flex_doc_SERIES or _flex_doc_FRAME to return the docstring to attach to a generated method. Parameters ---------- op_name : str {'__add__', '__sub__', ... '__eq__', '__ne__', ...} typ : str {series, 'dataframe']} Returns ------- doc : str RTRURYsother Rt s othertseriesRtop_nametequivRt dataframetpanelsInvalid typ argument.(R]t_op_descriptionst_flex_doc_SERIESRCt_flex_doc_FRAMEt_flex_doc_PANELtAssertionError(RR?top_descRtbase_doctdoc((s.lib/python2.7/site-packages/pandas/core/ops.pyt_make_flex_doc^s&      cC so|dk ret|}t|}|j}|j}||A}||||@<||||@gs (trangeRRm(R+R,(R(R+R,s.lib/python2.7/site-packages/pandas/core/ops.pyt column_opfsc s)fdttjDS(Nc sHi|]>}jdd|fjdd|f|qS(N(R(RR(R+R,R(s.lib/python2.7/site-packages/pandas/core/ops.pys ns (RRRm(R+R,(R(R+R,s.lib/python2.7/site-packages/pandas/core/ops.pyRmsRmc s)fdttjDS(Nc s<i|]2}jdd|fj||qS(N(R(RR(R+R,R(s.lib/python2.7/site-packages/pandas/core/ops.pys ws (RRRm(R+R,(R(R+R,s.lib/python2.7/site-packages/pandas/core/ops.pyRvsc s)fdttjDS(Nc s5i|]+}jdd|f|qS(N(R(RR(R+R,R(s.lib/python2.7/site-packages/pandas/core/ops.pys ~s (RRRm(R+R,(R(R+R,s.lib/python2.7/site-packages/pandas/core/ops.pyR}stindexR(t#pandas.core.computation.expressionstcoret computationt expressionsRtitem_from_zerodimRR3tndimR!Rt _indexed_sameRRRtequalsRmtNotImplementedErrortevaluatet _constructorR( R&R'Rtstr_reptaxisRRtnew_dataR((Rs.lib/python2.7/site-packages/pandas/core/ops.pytdispatch_to_seriesNs$$  cC s||}t|dddk r9|jdd}ny|||}Wn,tk rztdjd|jnX|S(s Wrap Series left in the given index_class to delegate the operation op to the index implementation. DatetimeIndex and TimedeltaIndex perform type checking, timezone handling, overflow checks, etc. Parameters ---------- op : binary operator (operator.add, operator.sub, ...) left : Series right : object index_class : DatetimeIndex or TimedeltaIndex Returns ------- result : object, usually DatetimeIndex, TimedeltaIndex, or Series tfreqs=incompatible type for a datetime/timedelta operation [{name}]R%N(R{R*t _shallow_copyRRBRCRD(RR&R't index_classtleft_idxR((s.lib/python2.7/site-packages/pandas/core/ops.pytdispatch_to_index_ops   cC st|ttfr!|j}n|}t|ttfrH|j}n|}|||}t||}|jdkrt|||j|St|||j|S(sr Assume that left or right is a Series backed by an ExtensionArray, apply the operator defined by op. RFRG(RFRG( R!RRt_valuesR(RDt_construct_divmod_resultRt_construct_result(RR&R'tnew_leftt new_rightRtres_name((s.lib/python2.7/site-packages/pandas/core/ops.pytdispatch_to_extension_ops   cC st|tr0t}t}t}t}t}nt|tr`t}t}t}t}t}nt|trd}d}t }t }t }n`t|t rt }t }t}t }t}n0t|trt}t}t}t}t}n|||||fS(s Find the appropriate operation-wrappers to use when defining flex/special arithmetic, boolean, and comparison operations with the given class. Parameters ---------- cls : class Returns ------- arith_flex : function or None comp_flex : function or None arith_special : function comp_special : function bool_special : function Notes ----- None is only returned for SparseArray N(t issubclassRt_flex_method_SERIESt_arith_method_SPARSE_SERIESt_bool_method_SERIESRt_arith_method_SERIESt_comp_method_SERIESRR*t_arith_method_SPARSE_ARRAYRt_flex_method_PANELt_comp_method_PANELt_arith_method_PANELRt_arith_method_FRAMEt_flex_comp_method_FRAMEt_comp_method_FRAME(Rt arith_flext comp_flext arith_specialt comp_specialt bool_special((s.lib/python2.7/site-packages/pandas/core/ops.pyt_get_method_wrapperss>     c  st|t}td||tj|d||t|d||tj|d||tj|d||tj|d||tj |d||tj |d||tj |d ||t |d ||t |d ||t|d ||t|d ||t|d||t|}|d|d<|d |d<|rz||t||d<||t||dRHRERbR<RFRGRR[RRRRRJRLRNRKRMROcS sdjd|jdS(Ns __{name}__R%R(RCRd(R((s.lib/python2.7/site-packages/pandas/core/ops.pytCscS s|S(N((R((s.lib/python2.7/site-packages/pandas/core/ops.pyREsc s%i|]\}}||qS(((Rtktv(t dunderize(s.lib/python2.7/site-packages/pandas/core/ops.pys Fs ("RRtdictRIR|R9R~R}RZRRcRR;R:R=R>RHRERFRGtupdateRR[RRRRRJRLRNRKRMROtitems(Rt arith_methodt comp_methodt bool_methodRt have_divmodt new_methods((Rs.lib/python2.7/site-packages/pandas/core/ops.pyt_create_methods sR   cC shxa|jD]S\}}t|to4|jd }|sM||jkr t|||q q WdS(Nt__i(R RRR^t__dict__R(RRR%tmethodtforce((s.lib/python2.7/site-packages/pandas/core/ops.pyt add_methodsJs cC s3t|\}}}}}t||||dt}d}|jtd||dd||dd||dd ||d d ||d d ||dd||dtjs||d|dsRMRORKRN(RMRORK( RRR*RR R RRR(Rtflex_arith_methodtflex_comp_methodR((Rs.lib/python2.7/site-packages/pandas/core/ops.pytadd_flex_arithmetic_methodss     #cC svt|trl|jj|jsl|rK|jt}|jt}n|j|dt\}}qln||fS(s align lhs and rhs Series R(R!RRRtastypetobjecttalignR(R&R'talign_asobject((s.lib/python2.7/site-packages/pandas/core/ops.pyt_align_method_SERIESs!cC s(|j|d|d|}||_|S(s If the raw op result has a non-None name (e.g. it is an Index object) and the name argument is None, then passing name to the constructor will not be enough; we still need to override the name attribute. RR(RR%(R&RRR%Rtout((s.lib/python2.7/site-packages/pandas/core/ops.pyRs c C sK|j}||dd|d|d|||dd|d|d|fS(sNdivmod returns a tuple of like indexed series instead of a single series. iRR%Ri(R(R&RRR%Rt constructor((s.lib/python2.7/site-packages/pandas/core/ops.pyRs c st|t|ttttgkrNtntfdfdfd}|_|S(s[ Wrapper function for Series arithmetic operations, to avoid code duplication. c syddljjj}y|j||}Wn#tk rYt||}nXtj|||}|S(Ni( RRRRRRBRtmissingt fill_zeros(RRRR(t eval_kwargsRARRR(s.lib/python2.7/site-packages/pandas/core/ops.pytna_ops c smy*tjdd|SWdQXWn<tk rht|rbtj|fdSnXdS(s@ return the result of evaluating na_op on the passed in values try coercion to object type if the native types are not compatible Parameters ---------- lvalues : array-like rvalues : array-like Raises ------ TypeError: invalid operation RRNc s |S(N((R(Rtrvalues(s.lib/python2.7/site-packages/pandas/core/ops.pyRs(R3Rt ExceptionRtlibalgost arrmap_object(tlvaluesRD(RCR(RDs.lib/python2.7/site-packages/pandas/core/ops.pyt safe_na_ops   c  st|trtSt||\}}t||}t|}t|rytdjdt |j dnt |st |rt ||tj}||d|jd|d|jSt|st|rt| rt||St|rCt ||tj}||d|jd|St|rtj||}||d|jd|d|jS|j}|}t|tr|j}n||}||d|jd|ddS(Ns'{typ} cannot perform the operation {op}R?RRR%R(R!RtNotImplementedR=R(R8R RBRCR/RDR RRR"t DatetimeIndexRRRRRRR6R#tvaluesRR*(R&R'RRRHRD(tconstruct_resultRRIR(s.lib/python2.7/site-packages/pandas/core/ops.pytwrappers@              ( RRRaRhRFRGRRRD(RRRRN((RMRBRARCRRRIRs.lib/python2.7/site-packages/pandas/core/ops.pyRs   3 cC st|trt|}nt|tjttfrt|js]|j tj }nt|ttfr~|j }nt j |||}nt j|||}|S(N(R!RRR3R5RRRRR9tobject_RLtlibopst vec_comparetscalar_compare(RRRR((s.lib/python2.7/site-packages/pandas/core/ops.pyt_comp_method_OBJECT_ARRAY7s c s^t|tjdtfddfd}|_|S(s[ Wrapper function for Series arithmetic operations, to avoid code duplication. R\c sft|ot| s tt|jrDt||}nt||rct||St| svtd}t| rt|rt |t |B}|j d}|j d}nt |d}|dk r.t jdd||}WdQX|tkr=t||Sn||}|dk rb|jrb|||j|St||jttfrwtdj d|jn|j |SdS(Ns0input needs alignment for this object [{object}]R:( R*RXR!Rt_compare_constructorRRRRERCR(RPRQR(RC(s.lib/python2.7/site-packages/pandas/core/ops.pyRds   (RRRRCR*RD(RRRRR((RCRRs.lib/python2.7/site-packages/pandas/core/ops.pyRWs  c st|t|ttfdtkrotd}ntjd}t|dfd}|_ |S(Nc s|ddljjj}y%|j||dd}Wn tk r\||}nXtj|||}|S(Niterrorstraise(RRRRRRBR@RA(RRRR(RBRARRR(s.lib/python2.7/site-packages/pandas/core/ops.pyRC~s  RRic s|j|d|S(NR(R(RPRQR(RC(s.lib/python2.7/site-packages/pandas/core/ops.pyRs( RRRaRhRRt_agg_doc_PANELRCRRD(RRRRR((RBRARCRRRs.lib/python2.7/site-packages/pandas/core/ops.pyRxs     cC sddlm}|jd}t|rt|r|d kr|jdkjr|j|tj|j }|j|tj|j }q|d kr|jdkjr|j|tj|j }|j|tj|j }qn||fS( s For SparseSeries operation, coerce to float64 if the result is expected to have NaN or inf values Parameters ---------- left : SparseArray right : SparseArray opname : str Returns ------- left : SparseArray right : SparseArray i(t SparseDtypeRRRciR>RE(RRc(R>RE( tpandas.core.sparse.apiRRdRRLRR9R3tfloat64Rg(R&R'RR((s.lib/python2.7/site-packages/pandas/core/ops.pyt_cast_sparse_series_ops!!!$c s.t|fd}|_|S(s[ Wrapper function for Series arithmetic operations, to avoid code duplication. c st|trtSt|tr\t|tsI|jd|j}nt||St|rt j dd|j |}WdQX|j |d|j d|jStdjdt|dS(NRgRRRR%s$operation with {other} not supportedRQ(R!RRJRRt to_sparseRgt_sparse_series_opRR3RRLRRR%RBRCR/(RPRQt new_values(RR(s.lib/python2.7/site-packages/pandas/core/ops.pyRNs     (RRD(RRRRN((RRs.lib/python2.7/site-packages/pandas/core/ops.pyRs c C s|j|dddt\}}|j}t||}ddlm}t|j|j|\}}|||||} |j| d|d|S(NR\RRi(t_sparse_array_opRR%( R;RRR(tpandas.core.arrays.sparseRRRLR( R&R'RR%t new_indextnew_nameRRHRDR((s.lib/python2.7/site-packages/pandas/core/ops.pyRs! c s.t|fd}|_|S(s[ Wrapper function for Series arithmetic operations, to avoid code duplication. c  sXddlm}m}m}m}t|tjrt|t|kryt dj dt|dt|nt||st |dd}||d|j d|}n|||St|r6tjdd 5||tj|}|j|}WdQX|||j|Std j dt|dS( Ni(t SparseArrayRt _wrap_resultt _get_fills#length mismatch: {self} vs. {other}RPRQRRgRRs$operation with {other} not supported(tpandas.core.arrays.sparse.arrayRRRRR!R3R5RRRCR{R*RgRRRYt sp_valuestsp_indexRBR/( RPRQRRRRRR{R(RR(s.lib/python2.7/site-packages/pandas/core/ops.pyRNs"" !   (RRD(RRRRN((RRs.lib/python2.7/site-packages/pandas/core/ops.pyRs (t__doc__t __future__RR0RIR]R_tnumpyR3t pandas._libsRRFRRRPt pandas.compatR@Rt pandas.errorsRtpandas.util._decoratorsRtpandas.core.dtypes.castRRR tpandas.core.dtypes.commonR R R R RRRRRRRRRRtpandas.core.dtypes.genericRRRRRRRtpandas.core.dtypes.missingRR tpandasR"tpandas.core.commonRtcommonRtpandas.core.missingR@R(R$R8R9R:R;R<R=R>RERGRHRKRMRORSRaRhRnRRR*RRtkeyst _op_namestkeyRt reverse_opRR_RRRRRRRRRRRRRRRRRRR1R8R=RRRRSRRRRRRRRRRRRRRR(((s.lib/python2.7/site-packages/pandas/core/ops.pyts     ^4   # *             )   4                              3 + # # : %@ " " ; =  5   e  P  ) 9 5 ) #  ! ' !