diff --git a/dune_client/api/extensions.py b/dune_client/api/extensions.py
index c3e6424..918f183 100644
--- a/dune_client/api/extensions.py
+++ b/dune_client/api/extensions.py
@@ -77,7 +77,7 @@ def run_query_dataframe(
         This is a convenience method that uses run_query_csv() + pandas.read_csv() underneath
         """
         try:
-            import pandas  # type: ignore # pylint: disable=import-outside-toplevel
+            import pandas  # pylint: disable=import-outside-toplevel
         except ImportError as exc:
             raise ImportError(
                 "dependency failure, pandas is required but missing"
diff --git a/dune_client/client_async.py b/dune_client/client_async.py
index 0b042e4..7a44c8b 100644
--- a/dune_client/client_async.py
+++ b/dune_client/client_async.py
@@ -271,7 +271,7 @@ async def refresh_into_dataframe(
         This is a convenience method that uses refresh_csv underneath
         """
         try:
-            import pandas  # type: ignore # pylint: disable=import-outside-toplevel
+            import pandas  # pylint: disable=import-outside-toplevel
         except ImportError as exc:
             raise ImportError(
                 "dependency failure, pandas is required but missing"
diff --git a/dune_client/viz/__init__.py b/dune_client/viz/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/dune_client/viz/graphs.py b/dune_client/viz/graphs.py
new file mode 100644
index 0000000..d7a8eb6
--- /dev/null
+++ b/dune_client/viz/graphs.py
@@ -0,0 +1,90 @@
+"""
+Functions you can call to make different graphs
+"""
+
+from typing import Dict, Union
+
+# https://github.com/plotly/colorlover/issues/35
+import colorlover as cl  # type: ignore[import]
+import pandas as pd
+import plotly.graph_objects as go  # type: ignore[import]
+from plotly.graph_objs import Figure  # type: ignore[import]
+
+
+# function to create Sankey diagram
+def create_sankey(
+    query_result: pd.DataFrame,
+    predefined_colors: Dict[str, str],
+    columns: Dict[str, str],
+    viz_config: Dict[str, Union[int, float]],
+    title: str = "unnamed",
+) -> Figure:
+    """
+    Creates a Sankey diagram based on input query_result,
+    which must contain source, target, value columns.
+    Column names don't have to be exact same but there must be
+    these three columns conceptually and value column must be numeric.
+    """
+    # Check if the dataframe contains required columns
+    required_columns = [columns["source"], columns["target"], columns["value"]]
+    for col in required_columns:
+        if col not in query_result.columns:
+            raise ValueError(f"Error: The dataframe is missing the '{col}' column")
+
+    # Check if 'value' column is numeric
+    if not pd.api.types.is_numeric_dtype(query_result[columns["value"]]):
+        raise ValueError("Error: The 'value' column must be numeric")
+
+    # preprocess query result dataframe
+    all_nodes = list(
+        pd.concat(
+            [query_result[columns["source"]], query_result[columns["target"]]]
+        ).unique()
+    )
+    # In Sankey, 'source' and 'target' must be indices. Thus, you need to map projects to indices.
+    query_result["source_idx"] = query_result[columns["source"]].map(all_nodes.index)
+    query_result["target_idx"] = query_result[columns["target"]].map(all_nodes.index)
+
+    # create color map for Sankey
+    colors = cl.scales["12"]["qual"]["Set3"]  # default color
+    color_map = {}
+    for node in all_nodes:
+        for name, color in predefined_colors.items():
+            if name.lower() in node.lower():  # check if name exists in the node name
+                color_map[node] = color
+                break
+        else:
+            color_map[node] = colors[
+                len(color_map) % len(colors)
+            ]  # default color assignment
+
+    fig = go.Figure(
+        go.Sankey(
+            node={
+                "pad": viz_config["node_pad"],
+                "thickness": viz_config["node_thickness"],
+                "line": {"color": "black", "width": viz_config["node_line_width"]},
+                "label": all_nodes,
+                "color": [
+                    color_map.get(node, "blue") for node in all_nodes
+                ],  # customize node color
+            },
+            link={
+                "source": query_result["source_idx"],
+                "target": query_result["target_idx"],
+                "value": query_result[columns["value"]],
+                "color": [
+                    color_map.get(query_result[columns["source"]].iloc[i], "black")
+                    for i in range(len(query_result))
+                ],  # customize link color
+            },
+        )
+    )
+    fig.update_layout(
+        title_text=title,
+        font_size=viz_config["font_size"],
+        height=viz_config["figure_height"],
+        width=viz_config["figure_width"],
+    )
+
+    return fig
diff --git a/requirements/dev.txt b/requirements/dev.txt
index a3583c5..ede131a 100644
--- a/requirements/dev.txt
+++ b/requirements/dev.txt
@@ -1,8 +1,11 @@
 -r prod.txt
 black>=23.7.0
 pandas>=1.0.0
+pandas-stubs>=1.0.0
 pylint>=2.17.5
 pytest>=7.4.1
 python-dotenv>=1.0.0
 mypy>=1.5.1
 aiounittest>=1.4.2
+colorlover>=0.3.0
+plotly>=5.9.0
diff --git a/tests/unit/test_viz_sankey.py b/tests/unit/test_viz_sankey.py
new file mode 100644
index 0000000..b5d78dc
--- /dev/null
+++ b/tests/unit/test_viz_sankey.py
@@ -0,0 +1,69 @@
+import unittest
+from unittest.mock import patch
+import pandas as pd
+from dune_client.viz.graphs import create_sankey
+
+
+class TestCreateSankey(unittest.TestCase):
+    # Setting up a dataframe for testing
+    def setUp(self):
+        self.df = pd.DataFrame(
+            {
+                "source_col": ["WBTC", "USDC"],
+                "target_col": ["USDC", "WBTC"],
+                "value_col": [2184, 2076],
+            }
+        )
+
+        self.predefined_colors = {
+            "USDC": "rgb(38, 112, 196)",
+            "WBTC": "rgb(247, 150, 38)",
+        }
+
+        self.columns = {
+            "source": "source_col",
+            "target": "target_col",
+            "value": "value_col",
+        }
+        self.viz_config: dict = {
+            "node_pad": 15,
+            "node_thickness": 20,
+            "node_line_width": 0.5,
+            "font_size": 10,
+            "figure_height": 1000,
+            "figure_width": 1500,
+        }
+
+    def test_missing_column(self):
+        # Remove a required column from dataframe
+        df_without_target = self.df.drop(columns=["target_col"])
+        with self.assertRaises(ValueError):
+            create_sankey(
+                df_without_target, self.predefined_colors, self.columns, self.viz_config
+            )
+
+    def test_value_column_not_numeric(self):
+        # Change the 'value' column to a non-numeric type
+        df_with_str_values = self.df.copy()
+        df_with_str_values["value_col"] = ["10", "11"]
+        with self.assertRaises(ValueError):
+            create_sankey(
+                df_with_str_values,
+                self.predefined_colors,
+                self.columns,
+                self.viz_config,
+            )
+
+    # Mocking the visualization creation and just testing the processing logic
+    @patch("plotly.graph_objects.Figure")
+    def test_mocked_visualization(self, MockFigure):
+        result = create_sankey(
+            self.df, self.predefined_colors, self.columns, self.viz_config, "test"
+        )
+
+        # Ensuring our mocked Figure was called with the correct parameters
+        MockFigure.assert_called_once()
+
+
+if __name__ == "__main__":
+    unittest.main()