feature: support string column validation for pandas 2.1.3 (#1425)

* fix: always cast series to bool dtype Signed-off-by: karlma821 <[email protected]> * test: add test_pandas_data_type_check Signed-off-by: karlma821 <[email protected]> --------- Signed-off-by: karlma821 <[email protected]>
unionai-oss · Dec 4, 2023 · a318e48 · a318e48
1 parent d89ddaf
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diff --git a/pandera/engines/pandas_engine.py b/pandera/engines/pandas_engine.py
@@ -718,7 +718,7 @@ def check(
             )
         else:
             is_python_string = data_container.map(lambda x: isinstance(x, str))  # type: ignore[operator]
-        return is_python_string | data_container.isna()
+        return is_python_string.astype(bool) | data_container.isna()
 
 
 Engine.register_dtype(

diff --git a/tests/core/test_pandas_engine.py b/tests/core/test_pandas_engine.py
@@ -5,6 +5,7 @@
 import hypothesis
 import hypothesis.extra.pandas as pd_st
 import hypothesis.strategies as st
+import numpy as np
 import pandas as pd
 import pytest
 import pytz
@@ -58,6 +59,35 @@ def test_pandas_data_type_coerce(data_type_cls):
         assert exc.failure_cases.shape[0] > 0
 
 
+@pytest.mark.parametrize(
+    "data_type_cls", list(pandas_engine.Engine.get_registered_dtypes())
+)
+def test_pandas_data_type_check(data_type_cls):
+    """
+    Test that pandas data type check results can be reduced.
+    """
+    try:
+        data_type = data_type_cls()
+    except TypeError:
+        # don't test data types that require parameters
+        return
+
+    try:
+        data_container = pd.Series([], dtype=data_type.type)
+    except TypeError:
+        # don't test complex data types, e.g. PythonDict, PythonTuple, etc
+        return
+
+    check_result = data_type.check(
+        pandas_engine.Engine.dtype(data_container.dtype),
+        data_container,
+    )
+    assert isinstance(check_result, bool) or isinstance(
+        check_result.all(),
+        (bool, np.bool_),
+    )
+
+
 CATEGORIES = ["A", "B", "C"]