MhJSrSSKJr SSKrSSKrSSKJr SSjrS Sjr Sr g) aP Missing data handling for arithmetic operations. In particular, pandas conventions regarding division by zero differ from numpy in the following ways: 1) np.array([-1, 0, 1], dtype=dtype1) // np.array([0, 0, 0], dtype=dtype2) gives [nan, nan, nan] for most dtype combinations, and [0, 0, 0] for the remaining pairs (the remaining being dtype1==dtype2==intN and dtype==dtype2==uintN). pandas convention is to return [-inf, nan, inf] for all dtype combinations. Note: the numpy behavior described here is py3-specific. 2) np.array([-1, 0, 1], dtype=dtype1) % np.array([0, 0, 0], dtype=dtype2) gives precisely the same results as the // operation. pandas convention is to return [nan, nan, nan] for all dtype combinations. 3) divmod behavior consistent with 1) and 2). ) annotationsN) roperatorcURRS:XaU$[US5n[U[R 5(+nU(d U(dU$U(a[R "U5nURRS;aiUS:HnUR5(aOU[R"U5)-nURSSS9n[R"X[R5 U$)z If this is a reversed op, then flip x,y If we have an integer value (or array in y) and we have 0's, fill them with np.nan, return the result. Mask the nan's from x. fdtypeiurfloat64Fcopy) rkindhasattr isinstancenpndarrayarrayanyisnanastypeputmasknan)resultxyis_variable_typeis_scalar_typeymaskmasks I/var/www/html/env/lib/python3.13/site-packages/pandas/core/ops/missing.py _fill_zerosr!s||C q'*#Arzz22N N  HHQKww||tQ 99;;BHHV,,,D]]95]9F JJvRVV , Mcd[US5(d[R"U5n[US5(d[R"U5nUS:HnUR5(aU[R"U5-nX4)-nUS:nUS:nX0S:H-nXV-XG--n XW-XF--n UR5(d*U R5(dU R5(aGUR SSS9n[R X('[RX*'[R*X)'U$)a Set results of 0 // 0 to np.nan, regardless of the dtypes of the numerator or the denominator. Parameters ---------- x : ndarray y : ndarray result : ndarray Returns ------- ndarray The filled result. Examples -------- >>> x = np.array([1, 0, -1], dtype=np.int64) >>> x array([ 1, 0, -1]) >>> y = 0 # int 0; numpy behavior is different with float >>> result = x // y >>> result # raw numpy result does not fill division by zero array([0, 0, 0]) >>> mask_zero_div_zero(x, y, result) array([ inf, nan, -inf]) rrr Fr )r rrrsignbitrrinf) rrrzmask zneg_mask zpos_maskx_lt0x_gt0nan_mask neginf_mask posinf_masks rmask_zero_div_zeror,Gs: 1g   HHQK 1g   HHQK FE yy{{BJJqM) J& AAF# (Y->?  (Y->? <<>>[__..+//2C2C]]95]9F!vvF "$&&F #%66'F  Mr cU[La [XUS5[USX54nU$U[RLa [X!US5[USX!54nU$U[ R La[XU5nU$U[RLa[X!U5nU$U[ RLa[X1U5nU$U[RLa [X2U5nU$)a Call _fill_zeros with the appropriate fill value depending on the operation, with special logic for divmod and rdivmod. Parameters ---------- op : function (operator.add, operator.div, ...) left : object (np.ndarray for non-reversed ops) We have excluded ExtensionArrays here right : object (np.ndarray for reversed ops) We have excluded ExtensionArrays here result : ndarray Returns ------- result : np.ndarray Notes ----- For divmod and rdivmod, the `result` parameter and returned `result` is a 2-tuple of ndarray objects. r) divmodr,rrrdivmodoperatorfloordiv rfloordivmodrmod)opleftrightrs rdispatch_fill_zerosr9s. V| tF1I 6 q 4 / * M# y  uF1I 6 q 5 /  M x  $D8 M y"" "$E8 M x|| V51 M y~~ VD1 Mr )r np.ndarray)rr:returnr:) __doc__ __future__rr1numpyr pandas.corerrr,r9r rrAs).#!#L9x-r