release gil in groupby

Author: CloseChoice

pandas/_libs/groupby.pyx

@@ -279,6 +279,7 @@ def group_cumsum(numeric_t[:, ::1] out,
                 elif numeric_t == float64_t:
                     if (val == MAXfloat64) or (val == MINfloat64):
                         if (t == MAXfloat64) or (t == MINfloat64):
+                            val = t
                         out[i, j] = val
                         accum[lab, j] = val
                         break
@@ -579,34 +580,34 @@ def group_add(add_t[:, ::1] out,
                 else:
                     out[i, j] = sumx[i, j]
     else:
-        for i in range(N):
-            lab = labels[i]
-            if lab < 0:
-                continue
+        with nogil:
+            for i in range(N):
+                lab = labels[i]
+                if lab < 0:
+                    continue
 
-            counts[lab] += 1
-            for j in range(K):
-                val = values[i, j]
+                counts[lab] += 1
+                for j in range(K):
+                    val = values[i, j]
 
-                # todo: using np.isinf not possible without gil, find other
-                #  function to release gil for the whole block
-                if np.isinf(val):
-                    sumx[lab, j] = val
-                    break
-                # not nan
-                if val == val:
-                    nobs[lab, j] += 1
-                    y = val - compensation[lab, j]
-                    t = sumx[lab, j] + y
-                    compensation[lab, j] = t - sumx[lab, j] - y
-                    sumx[lab, j] = t
+                    #  function to release gil for the whole block
+                    if (val == MAXfloat64) or (val == MINfloat64):
+                        sumx[lab, j] = val
+                        break
+                    # not nan
+                    if val == val:
+                        nobs[lab, j] += 1
+                        y = val - compensation[lab, j]
+                        t = sumx[lab, j] + y
+                        compensation[lab, j] = t - sumx[lab, j] - y
+                        sumx[lab, j] = t
 
-        for i in range(ncounts):
-            for j in range(K):
-                if nobs[i, j] < min_count:
-                    out[i, j] = NAN
-                else:
-                    out[i, j] = sumx[i, j]
+            for i in range(ncounts):
+                for j in range(K):
+                    if nobs[i, j] < min_count:
+                        out[i, j] = NAN
+                    else:
+                        out[i, j] = sumx[i, j]
 
 
 @cython.wraparound(False)