def na_op(x, y): try: result = expressions.evaluate( op, str_rep, x, y, raise_on_error=True, **eval_kwargs) except TypeError: xrav = x.ravel() if isinstance(y, (np.ndarray, pd.Series)): dtype = np.find_common_type([x.dtype, y.dtype], []) result = np.empty(x.size, dtype=dtype) yrav = y.ravel() mask = notnull(xrav) & notnull(yrav) xrav = xrav[mask] yrav = yrav[mask] if np.prod(xrav.shape) and np.prod(yrav.shape): result[mask] = op(xrav, yrav) elif hasattr(x,'size'): result = np.empty(x.size, dtype=x.dtype) mask = notnull(xrav) xrav = xrav[mask] if np.prod(xrav.shape): result[mask] = op(xrav, y) else: raise TypeError("cannot perform operation {op} between objects " "of type {x} and {y}".format(op=name,x=type(x),y=type(y))) result, changed = com._maybe_upcast_putmask(result, ~mask, np.nan) result = result.reshape(x.shape) result = com._fill_zeros(result, x, y, name, fill_zeros) return result
def na_op(x, y): try: result = expressions.evaluate( op, str_rep, x, y, raise_on_error=True, **eval_kwargs) except TypeError: xrav = x.ravel() if isinstance(y, (np.ndarray, pd.Series)): dtype = np.find_common_type([x.dtype, y.dtype], []) result = np.empty(x.size, dtype=dtype) yrav = y.ravel() mask = notnull(xrav) & notnull(yrav) xrav = xrav[mask] yrav = yrav[mask] if np.prod(xrav.shape) and np.prod(yrav.shape): result[mask] = op(xrav, yrav) else: result = np.empty(x.size, dtype=x.dtype) mask = notnull(xrav) xrav = xrav[mask] if np.prod(xrav.shape): result[mask] = op(xrav, y) result, changed = com._maybe_upcast_putmask(result, ~mask, np.nan) result = result.reshape(x.shape) result = com._fill_zeros(result, x, y, name, fill_zeros) return result
def _get_values(values, skipna, fill_value=None, fill_value_typ=None, isfinite=False, copy=True): """ utility to get the values view, mask, dtype if necessary copy and mask using the specified fill_value copy = True will force the copy """ if isfinite: mask = _isfinite(values) else: mask = isnull(values) dtype = values.dtype dtype_ok = _na_ok_dtype(dtype) # get our fill value (in case we need to provide an alternative dtype for it) fill_value = _get_fill_value(dtype, fill_value=fill_value, fill_value_typ=fill_value_typ) if skipna: if copy: values = values.copy() if dtype_ok: np.putmask(values, mask, fill_value) # promote if needed else: values, changed = com._maybe_upcast_putmask(values, mask, fill_value) elif copy: values = values.copy() values = _view_if_needed(values) return values, mask, dtype
def na_op(x, y): try: result = expressions.evaluate(op, str_rep, x, y, raise_on_error=True, **eval_kwargs) except TypeError: if isinstance(y, (np.ndarray, pd.Series, pd.Index)): dtype = np.find_common_type([x.dtype, y.dtype], []) result = np.empty(x.size, dtype=dtype) mask = notnull(x) & notnull(y) result[mask] = op(x[mask], _values_from_object(y[mask])) elif isinstance(x, np.ndarray): result = np.empty(len(x), dtype=x.dtype) mask = notnull(x) result[mask] = op(x[mask], y) else: raise TypeError( "{typ} cannot perform the operation {op}".format( typ=type(x).__name__, op=str_rep)) result, changed = com._maybe_upcast_putmask(result, ~mask, np.nan) result = com._fill_zeros(result, x, y, name, fill_zeros) return result
def _get_values(values, skipna, fill_value=None, fill_value_typ=None, isfinite=False, copy=True): """ utility to get the values view, mask, dtype if necessary copy and mask using the specified fill_value copy = True will force the copy """ values = _values_from_object(values) if isfinite: mask = _isfinite(values) else: mask = isnull(values) dtype = values.dtype dtype_ok = _na_ok_dtype(dtype) # get our fill value (in case we need to provide an alternative dtype for it) fill_value = _get_fill_value(dtype, fill_value=fill_value, fill_value_typ=fill_value_typ) if skipna: if copy: values = values.copy() if dtype_ok: np.putmask(values, mask, fill_value) # promote if needed else: values, changed = com._maybe_upcast_putmask(values, mask, fill_value) elif copy: values = values.copy() values = _view_if_needed(values) return values, mask, dtype
def na_op(x, y): try: result = expressions.evaluate(op, str_rep, x, y, raise_on_error=True, **eval_kwargs) except TypeError: result = pa.empty(len(x), dtype=x.dtype) mask = notnull(x) result[mask] = op(x[mask], y) result, changed = com._maybe_upcast_putmask(result, -mask, pa.NA) result = com._fill_zeros(result, y, fill_zeros) return result
def where(ser, cond, other=np.nan, inplace=False): """ Analagous to Series.where, DataFrame.where. Also works with np.ndarray """ if hasattr(ser, "where"): return getattr(ser, "where")(cond, other) ser = ser if inplace else ser.copy() if np.isscalar(other): other = np.array([other]) if len(other) != len(ser): if len(other) == 1: other = np.array(other[0] * len(ser)) change = ser com._maybe_upcast_putmask(ser, ~cond, other, change=change) return None if inplace else ser
def where(ser, cond, other=np.nan, inplace=False): """ Analagous to Series.where, DataFrame.where. Also works with np.ndarray """ if hasattr(ser, 'where'): return getattr(ser, 'where')(cond, other) ser = ser if inplace else ser.copy() if np.isscalar(other): other = np.array([other]) if len(other) != len(ser): if len(other) == 1: other = np.array(other[0] * len(ser)) change = ser com._maybe_upcast_putmask(ser, ~cond, other, change=change) return None if inplace else ser
def na_op(x, y): try: result = expressions.evaluate(op, str_rep, x, y, raise_on_error=True, **eval_kwargs) except TypeError: # TODO: might need to find_common_type here? result = np.empty(len(x), dtype=x.dtype) mask = notnull(x) result[mask] = op(x[mask], y) result, changed = com._maybe_upcast_putmask(result, ~mask, np.nan) result = missing.fill_zeros(result, x, y, name, fill_zeros) return result
def _get_values(values, skipna, fill_value=None, fill_value_typ=None, isfinite=False, copy=True): """ utility to get the values view, mask, dtype if necessary copy and mask using the specified fill_value copy = True will force the copy """ values = _values_from_object(values) if isfinite: mask = _isfinite(values) else: mask = isnull(values) dtype = values.dtype dtype_ok = _na_ok_dtype(dtype) # get our fill value (in case we need to provide an alternative # dtype for it) fill_value = _get_fill_value(dtype, fill_value=fill_value, fill_value_typ=fill_value_typ) if skipna: if copy: values = values.copy() if dtype_ok: np.putmask(values, mask, fill_value) # promote if needed else: values, changed = com._maybe_upcast_putmask( values, mask, fill_value) elif copy: values = values.copy() values = _view_if_needed(values) # return a platform independent precision dtype dtype_max = dtype if dtype.kind == 'i' and not issubclass( dtype.type, (np.bool, np.datetime64, np.timedelta64)): dtype_max = np.int64 elif dtype.kind in ['b'] or issubclass(dtype.type, np.bool): dtype_max = np.int64 elif dtype.kind in ['f']: dtype_max = np.float64 return values, mask, dtype, dtype_max
def na_op(x, y): try: result = expressions.evaluate(op, str_rep, x, y, raise_on_error=True, **eval_kwargs) except TypeError: # TODO: might need to find_common_type here? result = pa.empty(len(x), dtype=x.dtype) mask = notnull(x) result[mask] = op(x[mask], y) result, changed = com._maybe_upcast_putmask(result, ~mask, pa.NA) result = com._fill_zeros(result, x, y, name, fill_zeros) return result
def _get_values(values, skipna, fill_value=None, fill_value_typ=None, isfinite=False, copy=True): """ utility to get the values view, mask, dtype if necessary copy and mask using the specified fill_value copy = True will force the copy """ values = _values_from_object(values) if isfinite: mask = _isfinite(values) else: mask = isnull(values) dtype = values.dtype dtype_ok = _na_ok_dtype(dtype) # get our fill value (in case we need to provide an alternative # dtype for it) fill_value = _get_fill_value(dtype, fill_value=fill_value, fill_value_typ=fill_value_typ) if skipna: if copy: values = values.copy() if dtype_ok: np.putmask(values, mask, fill_value) # promote if needed else: values, changed = com._maybe_upcast_putmask(values, mask, fill_value) elif copy: values = values.copy() values = _view_if_needed(values) # return a platform independent precision dtype dtype_max = dtype if dtype.kind == 'i' and not issubclass(dtype.type, (np.bool, np.datetime64, np.timedelta64)): dtype_max = np.int64 elif dtype.kind in ['b'] or issubclass(dtype.type, np.bool): dtype_max = np.int64 elif dtype.kind in ['f']: dtype_max = np.float64 return values, mask, dtype, dtype_max
def na_op(x, y): try: result = expressions.evaluate(op, str_rep, x, y, raise_on_error=True, **eval_kwargs) except TypeError: if isinstance(y, (pa.Array, pd.Series)): dtype = np.find_common_type([x.dtype, y.dtype], []) result = np.empty(x.size, dtype=dtype) mask = notnull(x) & notnull(y) result[mask] = op(x[mask], y[mask]) else: result = pa.empty(len(x), dtype=x.dtype) mask = notnull(x) result[mask] = op(x[mask], y) result, changed = com._maybe_upcast_putmask(result, -mask, pa.NA) result = com._fill_zeros(result, y, fill_zeros) return result
def na_op(x, y): try: result = expressions.evaluate(op, str_rep, x, y, raise_on_error=True, **eval_kwargs) except TypeError: if isinstance(y, (pa.Array, pd.Series)): dtype = np.find_common_type([x.dtype, y.dtype], []) result = np.empty(x.size, dtype=dtype) mask = notnull(x) & notnull(y) result[mask] = op(x[mask], y[mask]) else: result = pa.empty(len(x), dtype=x.dtype) mask = notnull(x) result[mask] = op(x[mask], y) result, changed = com._maybe_upcast_putmask(result, ~mask, pa.NA) result = com._fill_zeros(result, x, y, name, fill_zeros) return result
def na_op(x, y): try: result = expressions.evaluate(op, str_rep, x, y, raise_on_error=True, **eval_kwargs) except TypeError: if isinstance(y, (np.ndarray, ABCSeries, pd.Index)): dtype = np.find_common_type([x.dtype, y.dtype], []) result = np.empty(x.size, dtype=dtype) mask = notnull(x) & notnull(y) result[mask] = op(x[mask], _values_from_object(y[mask])) elif isinstance(x, np.ndarray): result = np.empty(len(x), dtype=x.dtype) mask = notnull(x) result[mask] = op(x[mask], y) else: raise TypeError("{typ} cannot perform the operation " "{op}".format(typ=type(x).__name__, op=str_rep)) result, changed = com._maybe_upcast_putmask(result, ~mask, np.nan) result = missing.fill_zeros(result, x, y, name, fill_zeros) return result
def na_op(x, y): try: result = expressions.evaluate( op, str_rep, x, y, raise_on_error=True, **eval_kwargs) except TypeError: xrav = x.ravel() result = np.empty(x.size, dtype=x.dtype) if isinstance(y, (np.ndarray, pd.Series)): yrav = y.ravel() mask = notnull(xrav) & notnull(yrav) result[mask] = op(xrav[mask], yrav[mask]) else: mask = notnull(xrav) result[mask] = op(xrav[mask], y) result, changed = com._maybe_upcast_putmask(result, -mask, np.nan) result = result.reshape(x.shape) result = com._fill_zeros(result, y, fill_zeros) return result