Fix deprecated np.unicode type (#2035)

Until numpy version 1.20.0, numpy.unicode was an alias for str in python3. In 1.20.0, it's fully deprecated and is an int. This is bad and breaks things. This commit drops the np.unicode alias and just uses str, as is advised here: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
chanzuckerberg · Feb 3, 2021 · 3c0b1d4 · 3c0b1d4
1 parent 90a4ff7
commit 3c0b1d4
Show file tree

Hide file tree

Showing 2 changed files with 4 additions and 4 deletions.
diff --git a/server/common/utils/type_conversion_utils.py b/server/common/utils/type_conversion_utils.py
@@ -44,7 +44,7 @@ def get_dtype_from_dtype(dtype, array_values=None):
     if dtype_name == "bool":
         return np.uint8
     if dtype_name == "object" and dtype_kind == "O":
-        return np.unicode
+        return str
     if dtype_name == "category":
         return get_dtype_from_dtype(dtype.categories.dtype, array_values)
 

diff --git a/server/test/unit/common/utils/test_type_conversion_utils.py b/server/test/unit/common/utils/test_type_conversion_utils.py
@@ -99,7 +99,7 @@ def test__can_cast_to_int32__int64_with_nans_is_false(self):
 
     def test__get_dtype_of_array__supported_dtypes_return_as_expected(self):
         types = [np.float32, np.int32, np.bool_, str]
-        expected_dtypes = [np.float32, np.int32, np.uint8, np.unicode]
+        expected_dtypes = [np.float32, np.int32, np.uint8, str]
 
         for test_type_index in range(len(types)):
             with self.subTest(
@@ -110,7 +110,7 @@ def test__get_dtype_of_array__supported_dtypes_return_as_expected(self):
 
     def test__get_dtype_of_array__categories_return_as_expected(self):
         array = Series(data=["a", "b", "c"], dtype="category")
-        expected_dtype = np.unicode
+        expected_dtype = str
 
         actual_dtype = get_dtype_of_array(array)
 
@@ -179,7 +179,7 @@ def test__get_dtypes_and_schemas_of_dataframe__dtype_and_schema_returns_as_expec
         category_array = Series(data=["a", "b", "b"], dtype="category")
         dataframe = DataFrame({"float_array": float_array, "category_array": category_array})
 
-        expected_data_types_dict = {"float_array": np.float32, "category_array": np.unicode}
+        expected_data_types_dict = {"float_array": np.float32, "category_array": str}
         expected_schema_type_hints_dict = {
             "float_array": {"type": "float32"},
             "category_array": {"type": "categorical", "categories": ["a", "b"]},