clean PySessionContext metehods from_polars, from_pandas, from_arrow_…

…table

src/context.rs

@@ -432,14 +432,13 @@ impl PySessionContext {
         data: Bound<'_, PyList>,
         name: Option<&str>,
     ) -> PyResult<PyDataFrame> {
-
         // Acquire GIL Token
         let py = data.py();
-        
+
         // Instantiate pyarrow Table object & convert to Arrow Table
         let table_class = py.import_bound("pyarrow")?.getattr("Table")?;
         let args = PyTuple::new_bound(py, &[data]);
-        let table = table_class.call_method1("from_pylist", args)?.into();
+        let table = table_class.call_method1("from_pylist", args)?;
 
         // Convert Arrow Table to datafusion DataFrame
         let df = self.from_arrow_table(table, name, py)?;
@@ -452,80 +451,72 @@ impl PySessionContext {
         data: Bound<'_, PyDict>,
         name: Option<&str>,
     ) -> PyResult<PyDataFrame> {
-
         // Acquire GIL Token
         let py = data.py();
 
         // Instantiate pyarrow Table object & convert to Arrow Table
         let table_class = py.import_bound("pyarrow")?.getattr("Table")?;
         let args = PyTuple::new_bound(py, &[data]);
-        let table = table_class.call_method1("from_pydict", args)?.into();
+        let table = table_class.call_method1("from_pydict", args)?;
 
         // Convert Arrow Table to datafusion DataFrame
         let df = self.from_arrow_table(table, name, py)?;
         Ok(df)
     }
 
     /// Construct datafusion dataframe from Arrow Table
-    #[allow(clippy::wrong_self_convention)]
     pub fn from_arrow_table(
         &mut self,
-        data: PyObject,
+        data: Bound<'_, PyAny>,
         name: Option<&str>,
-        _py: Python,
+        py: Python,
     ) -> PyResult<PyDataFrame> {
-        Python::with_gil(|py| {
-            // Instantiate pyarrow Table object & convert to batches
-            let table = data.call_method0(py, "to_batches")?;
-
-            let schema = data.getattr(py, "schema")?;
-            let schema = schema.extract::<PyArrowType<Schema>>(py)?;
-
-            // Cast PyObject to RecordBatch type
-            // Because create_dataframe() expects a vector of vectors of record batches
-            // here we need to wrap the vector of record batches in an additional vector
-            let batches = table.extract::<PyArrowType<Vec<RecordBatch>>>(py)?;
-            let list_of_batches = PyArrowType::from(vec![batches.0]);
-            self.create_dataframe(list_of_batches, name, Some(schema), py)
-        })
+        // Instantiate pyarrow Table object & convert to batches
+        let table = data.call_method0("to_batches")?;
+
+        let schema = data.getattr("schema")?;
+        let schema = schema.extract::<PyArrowType<Schema>>()?;
+
+        // Cast PyAny to RecordBatch type
+        // Because create_dataframe() expects a vector of vectors of record batches
+        // here we need to wrap the vector of record batches in an additional vector
+        let batches = table.extract::<PyArrowType<Vec<RecordBatch>>>()?;
+        let list_of_batches = PyArrowType::from(vec![batches.0]);
+        self.create_dataframe(list_of_batches, name, Some(schema), py)
     }
 
     /// Construct datafusion dataframe from pandas
     #[allow(clippy::wrong_self_convention)]
     pub fn from_pandas(
         &mut self,
-        data: PyObject,
+        data: Bound<'_, PyAny>,
         name: Option<&str>,
-        _py: Python,
     ) -> PyResult<PyDataFrame> {
-        Python::with_gil(|py| {
-            // Instantiate pyarrow Table object & convert to Arrow Table
-            let table_class = py.import_bound("pyarrow")?.getattr("Table")?;
-            let args = PyTuple::new_bound(py, &[data]);
-            let table = table_class.call_method1("from_pandas", args)?.into();
-
-            // Convert Arrow Table to datafusion DataFrame
-            let df = self.from_arrow_table(table, name, py)?;
-            Ok(df)
-        })
+        // Obtain GIL token
+        let py = data.py();
+
+        // Instantiate pyarrow Table object & convert to Arrow Table
+        let table_class = py.import_bound("pyarrow")?.getattr("Table")?;
+        let args = PyTuple::new_bound(py, &[data]);
+        let table = table_class.call_method1("from_pandas", args)?;
+
+        // Convert Arrow Table to datafusion DataFrame
+        let df = self.from_arrow_table(table, name, py)?;
+        Ok(df)
     }
 
     /// Construct datafusion dataframe from polars
-    #[allow(clippy::wrong_self_convention)]
     pub fn from_polars(
         &mut self,
-        data: PyObject,
+        data: Bound<'_, PyAny>,
         name: Option<&str>,
-        _py: Python,
     ) -> PyResult<PyDataFrame> {
-        Python::with_gil(|py| {
-            // Convert Polars dataframe to Arrow Table
-            let table = data.call_method0(py, "to_arrow")?;
+        // Convert Polars dataframe to Arrow Table
+        let table = data.call_method0("to_arrow")?;
 
-            // Convert Arrow Table to datafusion DataFrame
-            let df = self.from_arrow_table(table, name, py)?;
-            Ok(df)
-        })
+        // Convert Arrow Table to datafusion DataFrame
+        let df = self.from_arrow_table(table, name, data.py())?;
+        Ok(df)
     }
 
     pub fn register_table(&mut self, name: &str, table: &PyTable) -> PyResult<()> {