def nanprod(values, axis=None, skipna=True):
mask = isnull(values)
if skipna and not is_any_int_dtype(values):
values = values.copy()
values[mask] = 1
result = values.prod(axis)
return _maybe_null_out(result, axis, mask)
def nanprod(values, axis=None, skipna=True):
mask = isnull(values)
if skipna and not is_any_int_dtype(values):
values = values.copy()
values[mask] = 1
result = values.prod(axis)
return _maybe_null_out(result, axis, mask)
def _nanvar(values, axis=None, skipna=True, ddof=1):
# private nanvar calculator
mask = isnull(values)
if is_any_int_dtype(values):
values = values.astype('f8')
count, d = _get_counts_nanvar(mask, axis, ddof)
if skipna:
values = values.copy()
np.putmask(values, mask, 0)
X = _ensure_numeric(values.sum(axis))
XX = _ensure_numeric((values**2).sum(axis))
return np.fabs((XX - X**2 / count) / d)
def _nanvar(values, axis=None, skipna=True, ddof=1):
# private nanvar calculator
mask = isnull(values)
if is_any_int_dtype(values):
values = values.astype('f8')
count, d = _get_counts_nanvar(mask, axis, ddof)
if skipna:
values = values.copy()
np.putmask(values, mask, 0)
X = _ensure_numeric(values.sum(axis))
XX = _ensure_numeric((values ** 2).sum(axis))
return np.fabs((XX - X ** 2 / count) / d)
def nanvar(values, axis=None, skipna=True, ddof=1):
dtype = values.dtype
mask = isnull(values)
if is_any_int_dtype(values):
values = values.astype('f8')
values[mask] = np.nan
if is_float_dtype(values):
count, d = _get_counts_nanvar(mask, axis, ddof, values.dtype)
else:
count, d = _get_counts_nanvar(mask, axis, ddof)
if skipna:
values = values.copy()
np.putmask(values, mask, 0)
# xref GH10242
# Compute variance via two-pass algorithm, which is stable against
# cancellation errors and relatively accurate for small numbers of
# observations.
#
# See https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
avg = _ensure_numeric(values.sum(axis=axis, dtype=np.float64)) / count
if axis is not None:
avg = np.expand_dims(avg, axis)
sqr = _ensure_numeric((avg - values) ** 2)
np.putmask(sqr, mask, 0)
result = sqr.sum(axis=axis, dtype=np.float64) / d
# Return variance as np.float64 (the datatype used in the accumulator),
# unless we were dealing with a float array, in which case use the same
# precision as the original values array.
if is_float_dtype(dtype):
result = result.astype(dtype)
return _wrap_results(result, values.dtype)
def nanvar(values, axis=None, skipna=True, ddof=1):
dtype = values.dtype
mask = isnull(values)
if is_any_int_dtype(values):
values = values.astype('f8')
values[mask] = np.nan
if is_float_dtype(values):
count, d = _get_counts_nanvar(mask, axis, ddof, values.dtype)
else:
count, d = _get_counts_nanvar(mask, axis, ddof)
if skipna:
values = values.copy()
np.putmask(values, mask, 0)
# xref GH10242
# Compute variance via two-pass algorithm, which is stable against
# cancellation errors and relatively accurate for small numbers of
# observations.
#
# See https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
avg = _ensure_numeric(values.sum(axis=axis, dtype=np.float64)) / count
if axis is not None:
avg = np.expand_dims(avg, axis)
sqr = _ensure_numeric((avg - values)**2)
np.putmask(sqr, mask, 0)
result = sqr.sum(axis=axis, dtype=np.float64) / d
# Return variance as np.float64 (the datatype used in the accumulator),
# unless we were dealing with a float array, in which case use the same
# precision as the original values array.
if is_float_dtype(dtype):
result = result.astype(dtype)
return _wrap_results(result, values.dtype)
