Handle min_count=0 (#238)

* Hande min_count=0

* fix

flox/aggregations.py

@@ -557,12 +557,15 @@ def _initialize_aggregation(
         assert isinstance(finalize_kwargs, dict)
         agg.finalize_kwargs = finalize_kwargs
 
+    if min_count is None:
+        min_count = 0
+
     # This is needed for the dask pathway.
     # Because we use intermediate fill_value since a group could be
     # absent in one block, but present in another block
     # We set it for numpy to get nansum, nanprod tests to pass
     # where the identity element is 0, 1
-    if min_count is not None:
+    if min_count > 0:
         agg.min_count = min_count
         agg.chunk += ("nanlen",)
         agg.numpy += ("nanlen",)
@@ -571,5 +574,7 @@ def _initialize_aggregation(
         agg.fill_value["numpy"] += (0,)
         agg.dtype["intermediate"] += (np.intp,)
         agg.dtype["numpy"] += (np.intp,)
+    else:
+        agg.min_count = 0
 
     return agg

flox/core.py

@@ -849,7 +849,7 @@ def _finalize_results(
     """
     squeezed = _squeeze_results(results, axis)
 
-    if agg.min_count is not None:
+    if agg.min_count > 0:
         counts = squeezed["intermediates"][-1]
         squeezed["intermediates"] = squeezed["intermediates"][:-1]
 
@@ -860,7 +860,7 @@ def _finalize_results(
     else:
         finalized[agg.name] = agg.finalize(*squeezed["intermediates"], **agg.finalize_kwargs)
 
-    if agg.min_count is not None:
+    if agg.min_count > 0:
         count_mask = counts < agg.min_count
         if count_mask.any():
             # For one count_mask.any() prevents promoting bool to dtype(fill_value) unless
@@ -1917,7 +1917,12 @@ def groupby_reduce(
             min_count = 1
 
     # TODO: set in xarray?
-    if min_count is not None and func in ["nansum", "nanprod"] and fill_value is None:
+    if (
+        min_count is not None
+        and min_count > 0
+        and func in ["nansum", "nanprod"]
+        and fill_value is None
+    ):
         # nansum, nanprod have fill_value=0, 1
         # overwrite than when min_count is set
         fill_value = np.nan