BUG: pandas-dev#10228 resampling empty Series caused segfaults

Garrett-R · jreback · commit 25fc49d975d7 · 2015-06-26T19:19:18.000-04:00
diff --git a/doc/source/whatsnew/v0.17.0.txt b/doc/source/whatsnew/v0.17.0.txt
@@ -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`)
diff --git a/pandas/algos.pyx b/pandas/algos.pyx
@@ -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:
@@ -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:
diff --git a/pandas/src/generate_code.py b/pandas/src/generate_code.py
@@ -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:
@@ -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:
@@ -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:
@@ -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:
@@ -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:
@@ -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):
@@ -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:
@@ -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:
@@ -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:
@@ -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:
diff --git a/pandas/src/generated.pyx b/pandas/src/generated.pyx
@@ -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:
@@ -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:
@@ -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:
@@ -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:
@@ -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:
@@ -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:
@@ -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:
@@ -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:
@@ -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:
@@ -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:
@@ -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:
@@ -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:
@@ -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:
@@ -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:
@@ -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:
@@ -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:
@@ -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:
@@ -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:
@@ -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:
@@ -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:
@@ -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:
@@ -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:
@@ -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):
@@ -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):
@@ -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):
@@ -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):
@@ -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):
diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py
diff --git a/pandas/util/testing.py b/pandas/util/testing.py
