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BUG: #10228 resampling empty Series caused segfaults
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@Garrett-R @jreback
Garrett-R authored and jreback committed Jun 26, 2015
1 parent 7e9026d commit 25fc49d
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18 changes: 18 additions & 0 deletions doc/source/whatsnew/v0.17.0.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -73,10 +73,28 @@ Bug Fixes
~~~~~~~~~
- Bug in ``DataFrame.apply`` when function returns categorical series. (:issue:`9573`)


- Bug in ``Index.drop_duplicates`` dropping name(s) (:issue:`10115`)


- Bug in ``pd.Series`` when setting a value on an empty ``Series`` whose index has a frequency. (:issue:`10193`)


- Bug in ``DataFrame.reset_index`` when index contains `NaT`. (:issue:`10388`)


- Bug in ``ExcelReader`` when worksheet is empty (:issue:`6403`)


- Bug in ``Table.select_column`` where name is not preserved (:issue:`10392`)


- Bug in ``DataFrame.interpolate`` with ``axis=1`` and ``inplace=True`` (:issue:`10395`)



- Bug in ``test_categorical`` on big-endian builds (:issue:`10425`)



- Bug that caused segfault when resampling an empty Series (:issue:`10228`)
4 changes: 4 additions & 0 deletions pandas/algos.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -2157,6 +2157,8 @@ def group_nth_bin_object(ndarray[object, ndim=2] out,
nobs = np.zeros((<object> out).shape, dtype=np.float64)
resx = np.empty((<object> out).shape, dtype=object)

if len(bins) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down Expand Up @@ -2247,6 +2249,8 @@ def group_last_bin_object(ndarray[object, ndim=2] out,
nobs = np.zeros((<object> out).shape, dtype=np.float64)
resx = np.empty((<object> out).shape, dtype=object)

if len(bins) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down
20 changes: 20 additions & 0 deletions pandas/src/generate_code.py
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Original file line number Diff line number Diff line change
Expand Up @@ -751,6 +751,8 @@ def group_last_bin_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
nobs = np.zeros_like(out)
resx = np.empty_like(out)
if len(bins) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down Expand Up @@ -797,6 +799,8 @@ def group_nth_bin_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
nobs = np.zeros_like(out)
resx = np.empty_like(out)
if len(bin) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down Expand Up @@ -948,6 +952,8 @@ def group_add_bin_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
nobs = np.zeros_like(out)
sumx = np.zeros_like(out)
if len(bins) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down Expand Up @@ -1064,6 +1070,8 @@ def group_prod_bin_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
nobs = np.zeros_like(out)
prodx = np.ones_like(out)
if len(bins) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down Expand Up @@ -1184,6 +1192,8 @@ def group_var_bin_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
sumx = np.zeros_like(out)
sumxx = np.zeros_like(out)
if len(bins) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down Expand Up @@ -1285,6 +1295,8 @@ def group_count_bin_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
ndarray[int64_t, ndim=2] nobs = np.zeros((out.shape[0], out.shape[1]),
dtype=np.int64)
if len(bins) == 0:
return
ngroups = len(bins) + (bins[len(bins) - 1] != N)
for i in range(N):
Expand Down Expand Up @@ -1329,6 +1341,8 @@ def group_min_bin_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
minx = np.empty_like(out)
minx.fill(%(inf_val)s)
if len(bins) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down Expand Up @@ -1453,6 +1467,8 @@ def group_max_bin_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
maxx = np.empty_like(out)
maxx.fill(-%(inf_val)s)
if len(bins) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down Expand Up @@ -1629,6 +1645,8 @@ def group_mean_bin_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
sumx = np.zeros_like(out)
N, K = (<object> values).shape
if len(bins) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down Expand Up @@ -1685,6 +1703,8 @@ def group_ohlc_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
%(dest_type2)s vopen, vhigh, vlow, vclose, NA
bint got_first = 0
if len(bins) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down
54 changes: 54 additions & 0 deletions pandas/src/generated.pyx
View file
Original file line number Diff line number Diff line change
Expand Up @@ -6725,6 +6725,8 @@ def group_add_bin_float64(ndarray[float64_t, ndim=2] out,
nobs = np.zeros_like(out)
sumx = np.zeros_like(out)

if len(bins) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down Expand Up @@ -6781,6 +6783,8 @@ def group_add_bin_float32(ndarray[float32_t, ndim=2] out,
nobs = np.zeros_like(out)
sumx = np.zeros_like(out)

if len(bins) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down Expand Up @@ -6951,6 +6955,8 @@ def group_prod_bin_float64(ndarray[float64_t, ndim=2] out,
nobs = np.zeros_like(out)
prodx = np.ones_like(out)

if len(bins) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down Expand Up @@ -7007,6 +7013,8 @@ def group_prod_bin_float32(ndarray[float32_t, ndim=2] out,
nobs = np.zeros_like(out)
prodx = np.ones_like(out)

if len(bins) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down Expand Up @@ -7186,6 +7194,8 @@ def group_var_bin_float64(ndarray[float64_t, ndim=2] out,
sumx = np.zeros_like(out)
sumxx = np.zeros_like(out)

if len(bins) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down Expand Up @@ -7247,6 +7257,8 @@ def group_var_bin_float32(ndarray[float32_t, ndim=2] out,
sumx = np.zeros_like(out)
sumxx = np.zeros_like(out)

if len(bins) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down Expand Up @@ -7412,6 +7424,8 @@ def group_mean_bin_float64(ndarray[float64_t, ndim=2] out,
sumx = np.zeros_like(out)

N, K = (<object> values).shape
if len(bins) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down Expand Up @@ -7465,6 +7479,8 @@ def group_mean_bin_float32(ndarray[float32_t, ndim=2] out,
sumx = np.zeros_like(out)

N, K = (<object> values).shape
if len(bins) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down Expand Up @@ -7520,6 +7536,8 @@ def group_ohlc_float64(ndarray[float64_t, ndim=2] out,
float64_t vopen, vhigh, vlow, vclose, NA
bint got_first = 0

if len(bins) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down Expand Up @@ -7594,6 +7612,8 @@ def group_ohlc_float32(ndarray[float32_t, ndim=2] out,
float32_t vopen, vhigh, vlow, vclose, NA
bint got_first = 0

if len(bins) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down Expand Up @@ -7801,6 +7821,8 @@ def group_last_bin_float64(ndarray[float64_t, ndim=2] out,
nobs = np.zeros_like(out)
resx = np.empty_like(out)

if len(bins) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down Expand Up @@ -7845,6 +7867,8 @@ def group_last_bin_float32(ndarray[float32_t, ndim=2] out,
nobs = np.zeros_like(out)
resx = np.empty_like(out)

if len(bins) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down Expand Up @@ -7889,6 +7913,8 @@ def group_last_bin_int64(ndarray[int64_t, ndim=2] out,
nobs = np.zeros_like(out)
resx = np.empty_like(out)

if len(bins) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down Expand Up @@ -8067,6 +8093,8 @@ def group_nth_bin_float64(ndarray[float64_t, ndim=2] out,
nobs = np.zeros_like(out)
resx = np.empty_like(out)

if len(bin) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down Expand Up @@ -8112,6 +8140,8 @@ def group_nth_bin_float32(ndarray[float32_t, ndim=2] out,
nobs = np.zeros_like(out)
resx = np.empty_like(out)

if len(bin) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down Expand Up @@ -8157,6 +8187,8 @@ def group_nth_bin_int64(ndarray[int64_t, ndim=2] out,
nobs = np.zeros_like(out)
resx = np.empty_like(out)

if len(bin) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down Expand Up @@ -8386,6 +8418,8 @@ def group_min_bin_float64(ndarray[float64_t, ndim=2] out,
minx = np.empty_like(out)
minx.fill(np.inf)

if len(bins) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down Expand Up @@ -8447,6 +8481,8 @@ def group_min_bin_float32(ndarray[float32_t, ndim=2] out,
minx = np.empty_like(out)
minx.fill(np.inf)

if len(bins) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down Expand Up @@ -8508,6 +8544,8 @@ def group_min_bin_int64(ndarray[int64_t, ndim=2] out,
minx = np.empty_like(out)
minx.fill(9223372036854775807)

if len(bins) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down Expand Up @@ -8750,6 +8788,8 @@ def group_max_bin_float64(ndarray[float64_t, ndim=2] out,
maxx = np.empty_like(out)
maxx.fill(-np.inf)

if len(bins) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down Expand Up @@ -8810,6 +8850,8 @@ def group_max_bin_float32(ndarray[float32_t, ndim=2] out,
maxx = np.empty_like(out)
maxx.fill(-np.inf)

if len(bins) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down Expand Up @@ -8870,6 +8912,8 @@ def group_max_bin_int64(ndarray[int64_t, ndim=2] out,
maxx = np.empty_like(out)
maxx.fill(-9223372036854775807)

if len(bins) == 0:
return
if bins[len(bins) - 1] == len(values):
ngroups = len(bins)
else:
Expand Down Expand Up @@ -9110,6 +9154,8 @@ def group_count_bin_float64(ndarray[float64_t, ndim=2] out,
ndarray[int64_t, ndim=2] nobs = np.zeros((out.shape[0], out.shape[1]),
dtype=np.int64)

if len(bins) == 0:
return
ngroups = len(bins) + (bins[len(bins) - 1] != N)

for i in range(N):
Expand Down Expand Up @@ -9144,6 +9190,8 @@ def group_count_bin_float32(ndarray[float32_t, ndim=2] out,
ndarray[int64_t, ndim=2] nobs = np.zeros((out.shape[0], out.shape[1]),
dtype=np.int64)

if len(bins) == 0:
return
ngroups = len(bins) + (bins[len(bins) - 1] != N)

for i in range(N):
Expand Down Expand Up @@ -9178,6 +9226,8 @@ def group_count_bin_int64(ndarray[int64_t, ndim=2] out,
ndarray[int64_t, ndim=2] nobs = np.zeros((out.shape[0], out.shape[1]),
dtype=np.int64)

if len(bins) == 0:
return
ngroups = len(bins) + (bins[len(bins) - 1] != N)

for i in range(N):
Expand Down Expand Up @@ -9212,6 +9262,8 @@ def group_count_bin_object(ndarray[object, ndim=2] out,
ndarray[int64_t, ndim=2] nobs = np.zeros((out.shape[0], out.shape[1]),
dtype=np.int64)

if len(bins) == 0:
return
ngroups = len(bins) + (bins[len(bins) - 1] != N)

for i in range(N):
Expand Down Expand Up @@ -9246,6 +9298,8 @@ def group_count_bin_int64(ndarray[int64_t, ndim=2] out,
ndarray[int64_t, ndim=2] nobs = np.zeros((out.shape[0], out.shape[1]),
dtype=np.int64)

if len(bins) == 0:
return
ngroups = len(bins) + (bins[len(bins) - 1] != N)

for i in range(N):
Expand Down
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