fix: Inconsistent schemas when converting to pyarrow

python/tests/test_dataframe.py

@@ -1867,6 +1867,53 @@ def test_to_arrow_table(df):
     assert set(pyarrow_table.column_names) == {"a", "b", "c"}
 
 
+def test_parquet_non_null_column_to_pyarrow(ctx, tmp_path):
+    path = tmp_path.joinpath("t.parquet")
+
+    ctx.sql("create table t_(a int not null)").collect()
+    ctx.sql("insert into t_ values (1), (2), (3)").collect()
+    ctx.sql(f"copy (select * from t_) to '{path}'").collect()
+
+    ctx.register_parquet("t", path)
+    pyarrow_table = ctx.sql("select max(a) as m from t").to_arrow_table()
+    assert pyarrow_table.to_pydict() == {"m": [3]}
+
+
+def test_parquet_empty_batch_to_pyarrow(ctx, tmp_path):
+    path = tmp_path.joinpath("t.parquet")
+
+    ctx.sql("create table t_(a int not null)").collect()
+    ctx.sql("insert into t_ values (1), (2), (3)").collect()
+    ctx.sql(f"copy (select * from t_) to '{path}'").collect()
+
+    ctx.register_parquet("t", path)
+    pyarrow_table = ctx.sql("select * from t limit 0").to_arrow_table()
+    assert pyarrow_table.schema == pa.schema(
+        [
+            pa.field("a", pa.int32(), nullable=False),
+        ]
+    )
+
+
+def test_parquet_null_aggregation_to_pyarrow(ctx, tmp_path):
+    path = tmp_path.joinpath("t.parquet")
+
+    ctx.sql("create table t_(a int not null)").collect()
+    ctx.sql("insert into t_ values (1), (2), (3)").collect()
+    ctx.sql(f"copy (select * from t_) to '{path}'").collect()
+
+    ctx.register_parquet("t", path)
+    pyarrow_table = ctx.sql(
+        "select max(a) as m from (select * from t where a < 0)"
+    ).to_arrow_table()
+    assert pyarrow_table.to_pydict() == {"m": [None]}
+    assert pyarrow_table.schema == pa.schema(
+        [
+            pa.field("m", pa.int32(), nullable=True),
+        ]
+    )
+
+
 def test_execute_stream(df):
     stream = df.execute_stream()
     assert all(batch is not None for batch in stream)

src/dataframe.rs

@@ -1024,11 +1024,18 @@ impl PyDataFrame {
     /// Collect the batches and pass to Arrow Table
     fn to_arrow_table(&self, py: Python<'_>) -> PyResult<PyObject> {
         let batches = self.collect(py)?.into_pyobject(py)?;
-        let schema = self.schema().into_pyobject(py)?;
+
+        // only use the DataFrame's schema if there are no batches, otherwise let the schema be
+        // determined from the batches (avoids some inconsistencies with nullable columns)
+        let args = if batches.len()? == 0 {
+            let schema = self.schema().into_pyobject(py)?;
+            PyTuple::new(py, &[batches, schema])?
+        } else {
+            PyTuple::new(py, &[batches])?
+        };
 
         // Instantiate pyarrow Table object and use its from_batches method
         let table_class = py.import("pyarrow")?.getattr("Table")?;
-        let args = PyTuple::new(py, &[batches, schema])?;
         let table: PyObject = table_class.call_method1("from_batches", args)?.into();
         Ok(table)
     }