diff --git a/datafusion/tests/test_dataframe.py b/datafusion/tests/test_dataframe.py
index 9944abee..c8c74fa2 100644
--- a/datafusion/tests/test_dataframe.py
+++ b/datafusion/tests/test_dataframe.py
@@ -623,6 +623,36 @@ def test_to_arrow_table(df):
     assert set(pyarrow_table.column_names) == {"a", "b", "c"}
 
 
+def test_execute_stream(df):
+    stream = df.execute_stream()
+    assert all(batch is not None for batch in stream)
+    assert not list(stream)  # after one iteration the generator must be exhausted
+
+
+@pytest.mark.parametrize("schema", [True, False])
+def test_execute_stream_to_arrow_table(df, schema):
+    stream = df.execute_stream()
+
+    if schema:
+        pyarrow_table = pa.Table.from_batches(
+            (batch.to_pyarrow() for batch in stream), schema=df.schema()
+        )
+    else:
+        pyarrow_table = pa.Table.from_batches((batch.to_pyarrow() for batch in stream))
+
+    assert isinstance(pyarrow_table, pa.Table)
+    assert pyarrow_table.shape == (3, 3)
+    assert set(pyarrow_table.column_names) == {"a", "b", "c"}
+
+
+def test_execute_stream_partitioned(df):
+    streams = df.execute_stream_partitioned()
+    assert all(batch is not None for stream in streams for batch in stream)
+    assert all(
+        not list(stream) for stream in streams
+    )  # after one iteration all generators must be exhausted
+
+
 def test_empty_to_arrow_table(df):
     # Convert empty datafusion dataframe to pyarrow Table
     pyarrow_table = df.limit(0).to_arrow_table()
diff --git a/src/dataframe.rs b/src/dataframe.rs
index 1e879099..92a25188 100644
--- a/src/dataframe.rs
+++ b/src/dataframe.rs
@@ -15,21 +15,27 @@
 // specific language governing permissions and limitations
 // under the License.
 
-use crate::physical_plan::PyExecutionPlan;
-use crate::sql::logical::PyLogicalPlan;
-use crate::utils::wait_for_future;
-use crate::{errors::DataFusionError, expr::PyExpr};
+use std::sync::Arc;
+
 use datafusion::arrow::datatypes::Schema;
 use datafusion::arrow::pyarrow::{PyArrowType, ToPyArrow};
 use datafusion::arrow::util::pretty;
 use datafusion::dataframe::{DataFrame, DataFrameWriteOptions};
+use datafusion::execution::SendableRecordBatchStream;
 use datafusion::parquet::basic::{BrotliLevel, Compression, GzipLevel, ZstdLevel};
 use datafusion::parquet::file::properties::WriterProperties;
 use datafusion::prelude::*;
 use pyo3::exceptions::{PyTypeError, PyValueError};
 use pyo3::prelude::*;
 use pyo3::types::PyTuple;
-use std::sync::Arc;
+use tokio::task::JoinHandle;
+
+use crate::errors::py_datafusion_err;
+use crate::physical_plan::PyExecutionPlan;
+use crate::record_batch::PyRecordBatchStream;
+use crate::sql::logical::PyLogicalPlan;
+use crate::utils::{get_tokio_runtime, wait_for_future};
+use crate::{errors::DataFusionError, expr::PyExpr};
 
 /// A PyDataFrame is a representation of a logical plan and an API to compose statements.
 /// Use it to build a plan and `.collect()` to execute the plan and collect the result.
@@ -399,6 +405,35 @@ impl PyDataFrame {
         })
     }
 
+    fn execute_stream(&self, py: Python) -> PyResult<PyRecordBatchStream> {
+        // create a Tokio runtime to run the async code
+        let rt = &get_tokio_runtime(py).0;
+        let df = self.df.as_ref().clone();
+        let fut: JoinHandle<datafusion_common::Result<SendableRecordBatchStream>> =
+            rt.spawn(async move { df.execute_stream().await });
+        let stream = wait_for_future(py, fut).map_err(py_datafusion_err)?;
+        Ok(PyRecordBatchStream::new(stream?))
+    }
+
+    fn execute_stream_partitioned(&self, py: Python) -> PyResult<Vec<PyRecordBatchStream>> {
+        // create a Tokio runtime to run the async code
+        let rt = &get_tokio_runtime(py).0;
+        let df = self.df.as_ref().clone();
+        let fut: JoinHandle<datafusion_common::Result<Vec<SendableRecordBatchStream>>> =
+            rt.spawn(async move { df.execute_stream_partitioned().await });
+        let stream = wait_for_future(py, fut).map_err(py_datafusion_err)?;
+
+        match stream {
+            Ok(batches) => Ok(batches
+                .into_iter()
+                .map(|batch_stream| PyRecordBatchStream::new(batch_stream))
+                .collect()),
+            _ => Err(PyValueError::new_err(
+                "Unable to execute stream partitioned",
+            )),
+        }
+    }
+
     /// Convert to pandas dataframe with pyarrow
     /// Collect the batches, pass to Arrow Table & then convert to Pandas DataFrame
     fn to_pandas(&self, py: Python) -> PyResult<PyObject> {
diff --git a/src/record_batch.rs b/src/record_batch.rs
index aa3a392d..427807f2 100644
--- a/src/record_batch.rs
+++ b/src/record_batch.rs
@@ -20,6 +20,7 @@ use datafusion::arrow::pyarrow::ToPyArrow;
 use datafusion::arrow::record_batch::RecordBatch;
 use datafusion::physical_plan::SendableRecordBatchStream;
 use futures::StreamExt;
+use pyo3::prelude::*;
 use pyo3::{pyclass, pymethods, PyObject, PyResult, Python};
 
 #[pyclass(name = "RecordBatch", module = "datafusion", subclass)]
@@ -61,4 +62,12 @@ impl PyRecordBatchStream {
             Some(Err(e)) => Err(e.into()),
         }
     }
+
+    fn __next__(&mut self, py: Python) -> PyResult<Option<PyRecordBatch>> {
+        self.next(py)
+    }
+
+    fn __iter__(slf: PyRef<'_, Self>) -> PyRef<'_, Self> {
+        slf
+    }
 }