180 lines
5.0 KiB
Python
180 lines
5.0 KiB
Python
"""
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Missing data handling for arithmetic operations.
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In particular, pandas conventions regarding division by zero differ
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from numpy in the following ways:
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1) np.array([-1, 0, 1], dtype=dtype1) // np.array([0, 0, 0], dtype=dtype2)
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gives [nan, nan, nan] for most dtype combinations, and [0, 0, 0] for
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the remaining pairs
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(the remaining being dtype1==dtype2==intN and dtype==dtype2==uintN).
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pandas convention is to return [-inf, nan, inf] for all dtype
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combinations.
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Note: the numpy behavior described here is py3-specific.
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2) np.array([-1, 0, 1], dtype=dtype1) % np.array([0, 0, 0], dtype=dtype2)
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gives precisely the same results as the // operation.
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pandas convention is to return [nan, nan, nan] for all dtype
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combinations.
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3) divmod behavior consistent with 1) and 2).
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"""
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import operator
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import numpy as np
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from pandas.core.dtypes.common import is_float_dtype, is_integer_dtype, is_scalar
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from pandas.core.ops.roperator import rdivmod, rfloordiv, rmod
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def fill_zeros(result, x, y):
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"""
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If this is a reversed op, then flip x,y
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If we have an integer value (or array in y)
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and we have 0's, fill them with np.nan,
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return the result.
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Mask the nan's from x.
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"""
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if is_float_dtype(result.dtype):
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return result
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is_variable_type = hasattr(y, "dtype") or hasattr(y, "type")
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is_scalar_type = is_scalar(y)
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if not is_variable_type and not is_scalar_type:
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return result
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if is_scalar_type:
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y = np.array(y)
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if is_integer_dtype(y.dtype):
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if (y == 0).any():
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# GH#7325, mask and nans must be broadcastable (also: GH#9308)
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# Raveling and then reshaping makes np.putmask faster
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mask = ((y == 0) & ~np.isnan(result)).ravel()
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shape = result.shape
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result = result.astype("float64", copy=False).ravel()
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np.putmask(result, mask, np.nan)
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result = result.reshape(shape)
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return result
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def mask_zero_div_zero(x, y, result):
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"""
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Set results of 0 // 0 to np.nan, regardless of the dtypes
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of the numerator or the denominator.
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Parameters
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----------
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x : ndarray
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y : ndarray
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result : ndarray
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Returns
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-------
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ndarray
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The filled result.
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Examples
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--------
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>>> x = np.array([1, 0, -1], dtype=np.int64)
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>>> x
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array([ 1, 0, -1])
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>>> y = 0 # int 0; numpy behavior is different with float
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>>> result = x // y
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>>> result # raw numpy result does not fill division by zero
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array([0, 0, 0])
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>>> mask_zero_div_zero(x, y, result)
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array([ inf, nan, -inf])
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"""
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if not isinstance(result, np.ndarray):
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# FIXME: SparseArray would raise TypeError with np.putmask
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return result
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if is_scalar(y):
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y = np.array(y)
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zmask = y == 0
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if isinstance(zmask, bool):
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# FIXME: numpy did not evaluate pointwise, seen in docs build
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return result
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if zmask.any():
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# Flip sign if necessary for -0.0
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zneg_mask = zmask & np.signbit(y)
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zpos_mask = zmask & ~zneg_mask
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nan_mask = zmask & (x == 0)
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with np.errstate(invalid="ignore"):
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neginf_mask = (zpos_mask & (x < 0)) | (zneg_mask & (x > 0))
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posinf_mask = (zpos_mask & (x > 0)) | (zneg_mask & (x < 0))
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if nan_mask.any() or neginf_mask.any() or posinf_mask.any():
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# Fill negative/0 with -inf, positive/0 with +inf, 0/0 with NaN
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result = result.astype("float64", copy=False)
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result[nan_mask] = np.nan
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result[posinf_mask] = np.inf
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result[neginf_mask] = -np.inf
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return result
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def dispatch_fill_zeros(op, left, right, result):
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"""
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Call fill_zeros with the appropriate fill value depending on the operation,
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with special logic for divmod and rdivmod.
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Parameters
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----------
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op : function (operator.add, operator.div, ...)
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left : object (np.ndarray for non-reversed ops)
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right : object (np.ndarray for reversed ops)
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result : ndarray
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Returns
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-------
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result : np.ndarray
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Notes
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-----
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For divmod and rdivmod, the `result` parameter and returned `result`
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is a 2-tuple of ndarray objects.
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"""
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if op is divmod:
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result = (
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mask_zero_div_zero(left, right, result[0]),
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fill_zeros(result[1], left, right),
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)
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elif op is rdivmod:
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result = (
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mask_zero_div_zero(right, left, result[0]),
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fill_zeros(result[1], right, left),
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)
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elif op is operator.floordiv:
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# Note: no need to do this for truediv; in py3 numpy behaves the way
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# we want.
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result = mask_zero_div_zero(left, right, result)
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elif op is rfloordiv:
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# Note: no need to do this for rtruediv; in py3 numpy behaves the way
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# we want.
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result = mask_zero_div_zero(right, left, result)
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elif op is operator.mod:
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result = fill_zeros(result, left, right)
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elif op is rmod:
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result = fill_zeros(result, right, left)
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return result
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