Fix unit tests that got broken by Pandas 1.3.0 release

Signed-off-by: Tsotne Tabidze <tsotne@tecton.ai>

sdk/python/feast/driver_test_data.py

@@ -140,8 +140,8 @@ def create_driver_hourly_stats_df(drivers, start_date, end_date) -> pd.DataFrame
     # TODO: These duplicate rows area indirectly being filtered out by the point in time join already. We need to
     #  inject a bad row at a timestamp where we know it will get joined to the entity dataframe, and then test that
     #  we are actually filtering it with the created timestamp
-    late_row = df_all_drivers.iloc[int(rows / 2)]
-    df_all_drivers = df_all_drivers.append(late_row).append(late_row)
+    late_row = df_all_drivers[rows // 2 : rows // 2 + 1]
+    df_all_drivers = pd.concat([df_all_drivers, late_row, late_row], ignore_index=True)
 
     return df_all_drivers
 

sdk/python/tests/test_historical_retrieval.py

@@ -195,13 +195,14 @@ def get_expected_training_df(
     expected_df = expected_df[[event_timestamp] + current_cols]
 
     # Cast some columns to expected types, since we lose information when converting pandas DFs into Python objects.
-    expected_df["order_is_success"] = expected_df["order_is_success"].astype("int32")
-    expected_df["customer_profile__current_balance"] = expected_df[
-        "customer_profile__current_balance"
-    ].astype("float32")
-    expected_df["customer_profile__avg_passenger_count"] = expected_df[
-        "customer_profile__avg_passenger_count"
-    ].astype("float32")
+    expected_column_types = {
+        "order_is_success": "int32",
+        "driver_stats__conv_rate": "float32",
+        "customer_profile__current_balance": "float32",
+        "customer_profile__avg_passenger_count": "float32",
+    }
+    for col, typ in expected_column_types.items():
+        expected_df[col] = expected_df[col].astype(typ)
 
     return expected_df
 

sdk/python/tests/test_offline_online_store_consistency.py

@@ -1,4 +1,5 @@
 import contextlib
+import math
 import tempfile
 import time
 import uuid
@@ -212,8 +213,7 @@ def check_offline_and_online_features(
     if expected_value:
         assert abs(df.to_dict()[f"{fv.name}__value"][0] - expected_value) < 1e-6
     else:
-        df = df.where(pd.notnull(df), None)
-        assert df.to_dict()[f"{fv.name}__value"][0] is None
+        assert math.isnan(df.to_dict()[f"{fv.name}__value"][0])
 
 
 def run_offline_online_store_consistency_test(