Merge pull request #1070 from great-expectations/eugene/0.9.0_value_c…

…ount_fix

PandasDataset value_count fix for mixed types in object columns

great_expectations/dataset/pandas_dataset.py

@@ -369,7 +369,14 @@ def get_column_value_counts(self, column, sort="value", collate=None):
             )
         counts = self[column].value_counts()
         if sort == "value":
-            counts.sort_index(inplace=True)
+            try:
+                counts.sort_index(inplace=True)
+            except TypeError:
+                # Having values of multiple types in a object dtype column (e.g., strings and floats)
+                # raises a TypeError when the sorting method performs comparisons.
+                if self[column].dtype == object:
+                    counts.index = counts.index.astype(str)
+                    counts.sort_index(inplace=True)
         elif sort == "counts":
             counts.sort_values(inplace=True)
         counts.name = "count"

tests/dataset/test_pandas_dataset.py

@@ -577,3 +577,17 @@ def test_pandas_deepcopy():
     assert df2.expect_column_to_exist("a").success == True
     assert list(df["a"]) == [2, 3, 4]
     assert list(df2["a"]) == [1, 2, 3]
+
+def test_ge_value_count_of_object_dtype_column_with_mixed_types():
+    """
+    Having mixed type values in a object dtype column (e.g., strings and floats)
+    used to raise a TypeError when sorting value_counts. This test verifies
+    that the issue is fixed.
+    """
+    df = ge.dataset.PandasDataset({
+        'A': [1.5, 0.009, 0.5, "I am a string in an otherwise float column"],
+    })
+
+    value_counts = df.get_column_value_counts("A")
+    assert value_counts["I am a string in an otherwise float column"] == 1
+