diff --git a/CHANGELOG.md b/CHANGELOG.md
index fa3bc0e..c5088b6 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -11,6 +11,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added `performance` in `toad.utils` for test code performance
 - Added `pickletracer` in `toad.utils` for infer requirements in pickle object
 
+### Fixed
+- Fixed `Value Error` in `select` and `drop_corr` method when using `pandas >= 2.0.x`
+
 ## [0.1.2] - 2023-04-09
 
 ### Add
diff --git a/toad/__init__.py b/toad/__init__.py
index 500dfa0..572e326 100644
--- a/toad/__init__.py
+++ b/toad/__init__.py
@@ -10,6 +10,7 @@
 from .detector import detect
 from .metrics import KS, KS_bucket, F1
 from .stats import quality, IV, VIF, WOE, entropy, entropy_cond, gini, gini_cond
+from .transform import Combiner, WOETransformer
 from .selection import select
 from .scorecard import ScoreCard
 from .utils import Progress, performance
diff --git a/toad/selection.py b/toad/selection.py
index 2253406..8e707f6 100644
--- a/toad/selection.py
+++ b/toad/selection.py
@@ -326,7 +326,7 @@ def drop_corr(frame, target = None, threshold = 0.7, by = 'IV',
 
     f, t = split_target(frame[cols], target)
 
-    corr = f.corr().abs()
+    corr = f.corr(numeric_only = True).abs()
 
     drops = []
 
diff --git a/toad/selection_test.py b/toad/selection_test.py
index 90cd323..8c1a245 100644
--- a/toad/selection_test.py
+++ b/toad/selection_test.py
@@ -48,7 +48,14 @@ def test_drop_var_exclude():
 
 def test_drop_corr():
     df = drop_corr(frame, target = 'target')
-    assert ['D', 'E', 'F', 'target'] == df.columns.tolist()
+    assert set(['D', 'E', 'F', 'target']) == set(df.columns.tolist())
+
+def test_drop_corr_with_string():
+    ab = np.array(list('ABCDEFG'))
+    str_feat = pd.Series(ab[np.random.choice(7, 500)])
+
+    df = drop_corr(pd.concat((frame, str_feat.rename('str_f')), axis = 1), target = 'target')
+    assert set(['D', 'E', 'F', 'target', 'str_f']) == set(df.columns.tolist())
 
 def test_drop_iv():
     df = drop_iv(frame, target = 'target', threshold = 0.25)