ENH: Implement _from_sequence_of_strings for BooleanArray

doc/source/whatsnew/v1.0.0.rst

@@ -144,7 +144,7 @@ type dedicated to boolean data that can hold missing values. The default
 ``bool`` data type based on a bool-dtype NumPy array, the column can only hold
 ``True`` or ``False``, and not missing values. This new :class:`~arrays.BooleanArray`
 can store missing values as well by keeping track of this in a separate mask.
-(:issue:`29555`, :issue:`30095`)
+(:issue:`29555`, :issue:`30095`, :issue:`31131`)
 
 .. ipython:: python
 

pandas/core/arrays/boolean.py

@@ -286,6 +286,19 @@ def _from_sequence(cls, scalars, dtype=None, copy: bool = False):
         values, mask = coerce_to_array(scalars, copy=copy)
         return BooleanArray(values, mask)
 
+    @classmethod
+    def _from_sequence_of_strings(cls, strings, dtype=None, copy=False):
+        def map_string(s):
+            if s in ["True", "true", "1"]:
+                return True
+            elif s in ["False", "false", "0"]:
+                return False
+            else:
+                return s
+
+        scalars = [map_string(x) for x in strings]
+        return cls._from_sequence(scalars, dtype, copy)
+
     def _values_for_factorize(self) -> Tuple[np.ndarray, Any]:
         data = self._data.astype("int8")
         data[self._mask] = -1

pandas/tests/arrays/test_boolean.py

@@ -1,3 +1,4 @@
+import io
 import operator
 
 import numpy as np
@@ -251,6 +252,22 @@ def test_coerce_to_numpy_array():
         np.array(arr, dtype="bool")
 
 
+def test_to_boolean_array_from_strings():
+    result = BooleanArray._from_sequence_of_strings(["True", "False"])
+    expected = BooleanArray(np.array([True, False]), np.array([False, False]))
+
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_boolean_from_csv():
+    input_string = "a\nTrue\nFalse\nNA\n"
+
+    result = pd.read_csv(io.StringIO(input_string), dtype="boolean")
+    expected = pd.DataFrame({"a": pd.array([True, False, None], dtype="boolean")})
+
+    tm.assert_frame_equal(result, expected)
+
+
 def test_repr():
     df = pd.DataFrame({"A": pd.array([True, False, None], dtype="boolean")})
     expected = "       A\n0   True\n1  False\n2   <NA>"