fix(LogSerialization): dataframe timestamp serialization (#751)

* fix(json_serializer): datetime convert to json serialization

* refactor(QueryTracker): add function to convert dataframe to dict

* chore(tests): remove leftover print statements

pandasai/helpers/query_exec_tracker.py

@@ -88,6 +88,10 @@ def start_new_track(self):
         self._query_info = {}
         self._func_exec_count: dict = defaultdict(int)
 
+    def convert_dataframe_to_dict(self, df):
+        json_data = json.loads(df.to_json(orient="split", date_format="iso"))
+        return {"headers": json_data["columns"], "rows": json_data["data"]}
+
     def add_dataframes(self, dfs: List) -> None:
         """
         Add used dataframes for the query to query exec tracker
@@ -96,9 +100,7 @@ def add_dataframes(self, dfs: List) -> None:
         """
         for df in dfs:
             head = df.head_df
-            self._dataframes.append(
-                {"headers": head.columns.tolist(), "rows": head.values.tolist()}
-            )
+            self._dataframes.append(self.convert_dataframe_to_dict(head))
 
     def add_step(self, step: dict) -> None:
         """
@@ -200,13 +202,9 @@ def _format_response(self, result: ResponseType) -> ResponseType:
             ResponseType: formatted response output
         """
         if result["type"] == "dataframe":
-            return {
-                "type": result["type"],
-                "value": {
-                    "headers": result["value"].columns.tolist(),
-                    "rows": result["value"].values.tolist(),
-                },
-            }
+            df_dict = self.convert_dataframe_to_dict(result["value"])
+            return {"type": result["type"], "value": df_dict}
+
         elif result["type"] == "plot":
             with open(result["value"], "rb") as image_file:
                 image_data = image_file.read()

tests/test_query_tracker.py

@@ -1,3 +1,4 @@
+import json
 import os
 import time
 from typing import Optional
@@ -9,6 +10,7 @@
 from pandasai.llm.fake import FakeLLM
 from pandasai.smart_dataframe import SmartDataframe
 from unittest import TestCase
+from datetime import datetime, timedelta
 
 
 assert_almost_equal = TestCase().assertAlmostEqual
@@ -120,6 +122,29 @@ def test_format_response_dataframe(
         assert len(formatted_response["value"]["headers"]) == 3
         assert len(formatted_response["value"]["rows"]) == 10
 
+    def test_format_response_dataframe_with_datetime_field(
+        self, tracker: QueryExecTracker, sample_df: pd.DataFrame
+    ):
+        # Add a date column with random dates for demonstration
+        start_date = datetime(2023, 1, 1)
+        date_range = [start_date + timedelta(days=x) for x in range(len(sample_df))]
+
+        sample_df["date"] = date_range
+
+        # Create a sample ResponseType for a dataframe
+        response = {"type": "dataframe", "value": sample_df}
+
+        # Format the response using _format_response
+        formatted_response = tracker._format_response(response)
+
+        # Validate dataframe json serialization
+        json.dumps(formatted_response)
+
+        # Check if the response is formatted correctly
+        assert formatted_response["type"] == "dataframe"
+        assert len(formatted_response["value"]["headers"]) == 4
+        assert len(formatted_response["value"]["rows"]) == 10
+
     def test_format_response_other_type(self, tracker: QueryExecTracker):
         # Create a sample ResponseType for a non-dataframe response
         response = {