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bugfix for #48757 #48876

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bugfix for #48757 #48876

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37 changes: 19 additions & 18 deletions pandas/core/nanops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -692,20 +692,20 @@ def nanmean(
values, mask, dtype, dtype_max, _ = _get_values(
values, skipna, fill_value=0, mask=mask
)
dtype_sum = dtype_max
# dtype_sum = dtype_max
dtype_count = np.dtype(np.float64)

# not using needs_i8_conversion because that includes period
if dtype.kind in ["m", "M"]:
dtype_sum = np.dtype(np.float64)
elif is_integer_dtype(dtype):
dtype_sum = np.dtype(np.float64)
elif is_float_dtype(dtype):
dtype_sum = dtype
dtype_count = dtype
# if dtype.kind in ["m", "M"]:
# dtype_sum = np.dtype(np.float64)
# elif is_integer_dtype(dtype):
# dtype_sum = np.dtype(np.float64)
# elif is_float_dtype(dtype):
# dtype_sum = dtype
# dtype_count = dtype

count = _get_counts(values.shape, mask, axis, dtype=dtype_count)
the_sum = _ensure_numeric(values.sum(axis, dtype=dtype_sum))
the_sum = _ensure_numeric(values.sum(axis, dtype=np.float64))
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We don't want to cast in integer situations

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Oh, Do would like to complete this pull or fix this bug? I have no idea how to fit it in a right way. Thank you for your reply!


if axis is not None and getattr(the_sum, "ndim", False):
count = cast(np.ndarray, count)
Expand Down Expand Up @@ -911,9 +911,10 @@ def nanstd(

orig_dtype = values.dtype
values, mask, _, _, _ = _get_values(values, skipna, mask=mask)

result = np.sqrt(nanvar(values, axis=axis, skipna=skipna, ddof=ddof, mask=mask))
return _wrap_results(result, orig_dtype)
# return _wrap_results(result, np.orig_dtype)
return result



@disallow("M8", "m8")
Expand Down Expand Up @@ -961,10 +962,10 @@ def nanvar(
if mask is not None:
values[mask] = np.nan

if is_float_dtype(values.dtype):
count, d = _get_counts_nanvar(values.shape, mask, axis, ddof, values.dtype)
else:
count, d = _get_counts_nanvar(values.shape, mask, axis, ddof)
# if is_float_dtype(values.dtype):
# count, d = _get_counts_nanvar(values.shape, mask, axis, ddof, values.dtype)
# else:
count, d = _get_counts_nanvar(values.shape, mask, axis, ddof)

if skipna and mask is not None:
values = values.copy()
Expand All @@ -979,16 +980,16 @@ def nanvar(
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)
sqr = _ensure_numeric((np.subtract(avg , values,dtype=np.float64)) ** 2)
if mask is not None:
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, copy=False)
# if is_float_dtype(dtype):
# result = result.astype(dtype, copy=False)
return result


Expand Down