Merge 7b2e3dd into e4c2cfd

README.md

@@ -112,6 +112,9 @@ The detailed description can be found
 
 ## Changes
 
+#### 1.9.5
+- Fix calculating population counts for CAxCAT slices, that need to be treated as 0th cubes in tabbooks
+
 #### 1.9.4
 - Enable `CA_CAT` as a dimension type when calculating Pairwise Comparisons
 
@@ -121,16 +124,4 @@ The detailed description can be found
 #### 1.9.2
 - Fix `scale_means` for Categorical Array (as a 0th slice in Tabbooks) where categorical doesn't have any numerical values
 
-#### 1.9.1
-- Fix `scale_means` for Categorical Array (as a 0th slice in Tabbooks)
-
-#### 1.9.0
-- Implement pairwise comparisons
-
-#### 1.8.6
-- Fix pruning for single element MRs
-
-#### 1.8.5
-- Fix `index_table` for MR x MR where either dimension has a only single element
-
 For a complete list of changes see [history](https://github.com/Crunch-io/crunch-cube/blob/master/HISTORY.md).

src/cr/cube/__init__.py

@@ -2,4 +2,4 @@
 
 """Initialization module for crunch-cube package."""
 
-__version__ = "1.9.4"
+__version__ = "1.9.5"

src/cr/cube/crunch_cube.py

@@ -500,16 +500,20 @@ def population_counts(
                 [3000, 1800],
             ])
         """
-        return (
-            self.proportions(
+        population_counts = [
+            slice_.population_counts(
+                population_size,
                 weighted=weighted,
                 include_missing=include_missing,
                 include_transforms_for_dims=include_transforms_for_dims,
                 prune=prune,
             )
-            * population_size
-            * self.population_fraction
-        )
+            for slice_ in self.slices
+        ]
+
+        if len(population_counts) > 1:
+            return np.array(population_counts)
+        return population_counts[0]
 
     @lazyproperty
     def population_fraction(self):

src/cr/cube/cube_slice.py

@@ -338,6 +338,27 @@ def pairwise_pvals(self, axis=0):
             raise NotImplementedError("Pairwise comparison only implemented for colums")
         return PairwisePvalues(self, axis=axis).values
 
+    def population_counts(
+        self,
+        population_size,
+        weighted=True,
+        include_missing=False,
+        include_transforms_for_dims=None,
+        prune=False,
+    ):
+        axis = None if not self.ca_as_0th else 1
+        return (
+            self.proportions(
+                axis=axis,
+                weighted=weighted,
+                include_missing=include_missing,
+                include_transforms_for_dims=include_transforms_for_dims,
+                prune=prune,
+            )
+            * population_size
+            * self._cube.population_fraction
+        )
+
     def pvals(self, weighted=True, prune=False, hs_dims=None):
         """Return 2D ndarray with calculated P values