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BUG: groupby.cummin/cummax nans inf values for int64 #15205

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BUG: groupby.cummin/cummax nans inf values for int64 #15205

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2 changes: 1 addition & 1 deletion doc/source/whatsnew/v0.20.0.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -365,7 +365,7 @@ Performance Improvements
- Increased performance of ``pd.factorize()`` by releasing the GIL with ``object`` dtype when inferred as strings (:issue:`14859`)
- Improved performance of timeseries plotting with an irregular DatetimeIndex
(or with ``compat_x=True``) (:issue:`15073`).
- Improved performance of ``groupby().cummin()`` and ``groupby().cummax()`` (:issue:`15048`)
- Improved performance of ``groupby().cummin()`` and ``groupby().cummax()`` (:issue:`15048`, :issue:`15109`)

- When reading buffer object in ``read_sas()`` method without specified format, filepath string is inferred rather than buffer object.

Expand Down
35 changes: 21 additions & 14 deletions pandas/core/groupby.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,13 +18,13 @@
is_timedelta64_dtype, is_datetime64_dtype,
is_categorical_dtype,
is_datetimelike,
is_datetime_or_timedelta_dtype,
is_datetime64_any_dtype,
is_bool, is_integer_dtype,
is_complex_dtype,
is_bool_dtype,
is_scalar,
is_list_like,
needs_i8_conversion,
_ensure_float64,
_ensure_platform_int,
_ensure_int64,
Expand Down Expand Up @@ -1876,15 +1876,21 @@ def _cython_operation(self, kind, values, how, axis):
"supported for the 'how' argument")
out_shape = (self.ngroups,) + values.shape[1:]

is_datetimelike = needs_i8_conversion(values.dtype)
is_numeric = is_numeric_dtype(values.dtype)

if is_datetime_or_timedelta_dtype(values.dtype):
if is_datetimelike:
values = values.view('int64')
is_numeric = True
elif is_bool_dtype(values.dtype):
values = _ensure_float64(values)
elif is_integer_dtype(values):
values = values.astype('int64', copy=False)
# we use iNaT for the missing value on ints
# so pre-convert to guard this condition
if (values == tslib.iNaT).any():
values = _ensure_float64(values)
else:
values = values.astype('int64', copy=False)
elif is_numeric and not is_complex_dtype(values):
values = _ensure_float64(values)
else:
Expand Down Expand Up @@ -1913,20 +1919,20 @@ def _cython_operation(self, kind, values, how, axis):
fill_value=np.nan)
counts = np.zeros(self.ngroups, dtype=np.int64)
result = self._aggregate(
result, counts, values, labels, func, is_numeric)
result, counts, values, labels, func, is_numeric,
is_datetimelike)
elif kind == 'transform':
result = _maybe_fill(np.empty_like(values, dtype=out_dtype),
fill_value=np.nan)

# temporary storange for running-total type tranforms
accum = np.empty(out_shape, dtype=out_dtype)
result = self._transform(
result, accum, values, labels, func, is_numeric)
result, values, labels, func, is_numeric, is_datetimelike)

if is_integer_dtype(result):
if len(result[result == tslib.iNaT]) > 0:
mask = result == tslib.iNaT
if mask.any():
result = result.astype('float64')
result[result == tslib.iNaT] = np.nan
result[mask] = np.nan

if kind == 'aggregate' and \
self._filter_empty_groups and not counts.all():
Expand Down Expand Up @@ -1962,7 +1968,7 @@ def transform(self, values, how, axis=0):
return self._cython_operation('transform', values, how, axis)

def _aggregate(self, result, counts, values, comp_ids, agg_func,
is_numeric):
is_numeric, is_datetimelike):
if values.ndim > 3:
# punting for now
raise NotImplementedError("number of dimensions is currently "
Expand All @@ -1977,8 +1983,9 @@ def _aggregate(self, result, counts, values, comp_ids, agg_func,

return result

def _transform(self, result, accum, values, comp_ids, transform_func,
is_numeric):
def _transform(self, result, values, comp_ids, transform_func,
is_numeric, is_datetimelike):

comp_ids, _, ngroups = self.group_info
if values.ndim > 3:
# punting for now
Expand All @@ -1989,9 +1996,9 @@ def _transform(self, result, accum, values, comp_ids, transform_func,

chunk = chunk.squeeze()
transform_func(result[:, :, i], values,
comp_ids, accum)
comp_ids, is_datetimelike)
else:
transform_func(result, values, comp_ids, accum)
transform_func(result, values, comp_ids, is_datetimelike)

return result

Expand Down
22 changes: 14 additions & 8 deletions pandas/src/algos_groupby_helper.pxi.in
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Original file line number Diff line number Diff line change
Expand Up @@ -574,16 +574,18 @@ def group_min_{{name}}(ndarray[{{dest_type2}}, ndim=2] out,
def group_cummin_{{name}}(ndarray[{{dest_type2}}, ndim=2] out,
ndarray[{{dest_type2}}, ndim=2] values,
ndarray[int64_t] labels,
ndarray[{{dest_type2}}, ndim=2] accum):
bint is_datetimelike):
"""
Only transforms on axis=0
"""
cdef:
Py_ssize_t i, j, N, K, size
{{dest_type2}} val, min_val = 0
ndarray[{{dest_type2}}, ndim=2] accum
int64_t lab

N, K = (<object> values).shape
accum = np.empty_like(values)
accum.fill({{inf_val}})

with nogil:
Expand All @@ -600,7 +602,7 @@ def group_cummin_{{name}}(ndarray[{{dest_type2}}, ndim=2] out,
accum[lab, j] = min_val
out[i, j] = accum[lab, j]
# val = nan
else:
elif is_datetimelike:
out[i, j] = {{nan_val}}


Expand All @@ -609,16 +611,18 @@ def group_cummin_{{name}}(ndarray[{{dest_type2}}, ndim=2] out,
def group_cummax_{{name}}(ndarray[{{dest_type2}}, ndim=2] out,
ndarray[{{dest_type2}}, ndim=2] values,
ndarray[int64_t] labels,
ndarray[{{dest_type2}}, ndim=2] accum):
bint is_datetimelike):
"""
Only transforms on axis=0
"""
cdef:
Py_ssize_t i, j, N, K, size
{{dest_type2}} val, max_val = 0
ndarray[{{dest_type2}}, ndim=2] accum
int64_t lab

N, K = (<object> values).shape
accum = np.empty_like(values)
accum.fill(-{{inf_val}})

with nogil:
Expand All @@ -635,7 +639,7 @@ def group_cummax_{{name}}(ndarray[{{dest_type2}}, ndim=2] out,
accum[lab, j] = max_val
out[i, j] = accum[lab, j]
# val = nan
else:
elif is_datetimelike:
out[i, j] = {{nan_val}}

{{endfor}}
Expand Down Expand Up @@ -682,17 +686,18 @@ def group_median_float64(ndarray[float64_t, ndim=2] out,
def group_cumprod_float64(float64_t[:, :] out,
float64_t[:, :] values,
int64_t[:] labels,
float64_t[:, :] accum):
bint is_datetimelike):
"""
Only transforms on axis=0
"""
cdef:
Py_ssize_t i, j, N, K, size
float64_t val
float64_t[:, :] accum
int64_t lab

N, K = (<object> values).shape
accum = np.ones_like(accum)
accum = np.ones_like(values)

with nogil:
for i in range(N):
Expand All @@ -712,17 +717,18 @@ def group_cumprod_float64(float64_t[:, :] out,
def group_cumsum(numeric[:, :] out,
numeric[:, :] values,
int64_t[:] labels,
numeric[:, :] accum):
is_datetimelike):
"""
Only transforms on axis=0
"""
cdef:
Py_ssize_t i, j, N, K, size
numeric val
numeric[:, :] accum
int64_t lab

N, K = (<object> values).shape
accum = np.zeros_like(accum)
accum = np.zeros_like(values)

with nogil:
for i in range(N):
Expand Down
30 changes: 11 additions & 19 deletions pandas/tests/groupby/test_groupby.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5507,39 +5507,38 @@ def test_cython_group_transform_algos(self):
ops = [(pd.algos.group_cumprod_float64, np.cumproduct, [np.float64]),
(pd.algos.group_cumsum, np.cumsum, dtypes)]

is_datetimelike = False
for pd_op, np_op, dtypes in ops:
for dtype in dtypes:
data = np.array([[1], [2], [3], [4]], dtype=dtype)
ans = np.zeros_like(data)
accum = np.array([[0]], dtype=dtype)
labels = np.array([0, 0, 0, 0], dtype=np.int64)
pd_op(ans, data, labels, accum)
pd_op(ans, data, labels, is_datetimelike)
self.assert_numpy_array_equal(np_op(data), ans[:, 0],
check_dtype=False)

# with nans
labels = np.array([0, 0, 0, 0, 0], dtype=np.int64)

data = np.array([[1], [2], [3], [np.nan], [4]], dtype='float64')
accum = np.array([[0.0]])
actual = np.zeros_like(data)
actual.fill(np.nan)
pd.algos.group_cumprod_float64(actual, data, labels, accum)
pd.algos.group_cumprod_float64(actual, data, labels, is_datetimelike)
expected = np.array([1, 2, 6, np.nan, 24], dtype='float64')
self.assert_numpy_array_equal(actual[:, 0], expected)

accum = np.array([[0.0]])
actual = np.zeros_like(data)
actual.fill(np.nan)
pd.algos.group_cumsum(actual, data, labels, accum)
pd.algos.group_cumsum(actual, data, labels, is_datetimelike)
expected = np.array([1, 3, 6, np.nan, 10], dtype='float64')
self.assert_numpy_array_equal(actual[:, 0], expected)

# timedelta
is_datetimelike = True
data = np.array([np.timedelta64(1, 'ns')] * 5, dtype='m8[ns]')[:, None]
accum = np.array([[0]], dtype='int64')
actual = np.zeros_like(data, dtype='int64')
pd.algos.group_cumsum(actual, data.view('int64'), labels, accum)
pd.algos.group_cumsum(actual, data.view('int64'), labels,
is_datetimelike)
expected = np.array([np.timedelta64(1, 'ns'), np.timedelta64(
2, 'ns'), np.timedelta64(3, 'ns'), np.timedelta64(4, 'ns'),
np.timedelta64(5, 'ns')])
Expand Down Expand Up @@ -5965,12 +5964,9 @@ def test_cummin_cummax(self):
df.loc[[2, 6], 'B'] = min_val
expected.loc[[2, 3, 6, 7], 'B'] = min_val
result = df.groupby('A').cummin()

# TODO: GH 15019
# overwriting NaNs
# tm.assert_frame_equal(result, expected)
tm.assert_frame_equal(result, expected)
expected = df.groupby('A').B.apply(lambda x: x.cummin()).to_frame()
# tm.assert_frame_equal(result, expected)
tm.assert_frame_equal(result, expected)

# cummax
expected = pd.DataFrame({'B': expected_maxs}).astype(dtype)
Expand All @@ -5983,13 +5979,9 @@ def test_cummin_cummax(self):
df.loc[[2, 6], 'B'] = max_val
expected.loc[[2, 3, 6, 7], 'B'] = max_val
result = df.groupby('A').cummax()

# TODO: GH 15019
# overwriting NaNs
# tm.assert_frame_equal(result, expected)

tm.assert_frame_equal(result, expected)
expected = df.groupby('A').B.apply(lambda x: x.cummax()).to_frame()
# tm.assert_frame_equal(result, expected)
tm.assert_frame_equal(result, expected)

# Test nan in some values
base_df.loc[[0, 2, 4, 6], 'B'] = np.nan
Expand Down