updating polars version and bumping to 0.13.3

datacompy/__init__.py

@@ -18,7 +18,7 @@
 Then extended to carry that functionality over to Spark Dataframes.
 """
 
-__version__ = "0.13.2"
+__version__ = "0.13.3"
 
 import platform
 from warnings import warn

pyproject.toml

@@ -11,7 +11,7 @@ maintainers = [
   { name="Faisal Dosani", email="[email protected]" }
 ]
 license = {text = "Apache Software License"}
-dependencies = ["pandas<=2.2.2,>=0.25.0", "numpy<=1.26.4,>=1.22.0", "ordered-set<=4.1.0,>=4.0.2", "fugue<=0.9.1,>=0.8.7", "polars<=1.1.0,>=0.20.4"]
+dependencies = ["pandas<=2.2.2,>=0.25.0", "numpy<=1.26.4,>=1.22.0", "ordered-set<=4.1.0,>=4.0.2", "fugue<=0.9.1,>=0.8.7", "polars<=1.7.0,>=0.20.4"]
 requires-python = ">=3.9.0"
 classifiers = [
     "Intended Audience :: Developers",

tests/test_polars.py

@@ -386,7 +386,9 @@ def test_compare_df_setter_bad():
         PolarsCompare("a", "a", ["a"])
     with raises(ValueError, match="df1 must have all columns from join_columns"):
         PolarsCompare(df, df.clone(), ["b"])
-    with raises(DuplicateError, match="duplicate column names found"):
+    with raises(
+        DuplicateError, match="column with name 'a' has more than one occurrences"
+    ):
         PolarsCompare(df_same_col_names, df_same_col_names.clone(), ["a"])
     assert PolarsCompare(df_dupe, df_dupe.clone(), ["a", "b"]).df1.equals(df_dupe)
 
@@ -416,7 +418,9 @@ def test_compare_df_setter_bad_index():
     df = pl.DataFrame([{"a": 1, "A": 2}, {"a": 2, "A": 2}])
     with raises(TypeError, match="df1 must be a Polars DataFrame"):
         PolarsCompare("a", "a", join_columns="a")
-    with raises(DuplicateError, match="duplicate column names found"):
+    with raises(
+        DuplicateError, match="column with name 'a' has more than one occurrences"
+    ):
         PolarsCompare(df, df.clone(), join_columns="a")