Aircall: fix "calls" datasource

Replace null values with the label 'NO TEAMS' in the column "team" of the
"calls" dataset (in coherence with what we do for the "users" dataset).

tests/aircall/test_aircall.py

@@ -172,7 +172,7 @@ def test__retrieve_data_no_teams_case(con, mocker):
     # must have calls and still have a team column even if everything is NaN
     assert run_fetches_mock.call_count == 1
     assert df.shape == (10, 11)
-    assert df['team'].isna().any()
+    assert df['team'].isin(['NO TEAM']).all()
 
 
 def test__retrieve_tags_from_fetch(con, mocker):

tests/aircall/test_helpers.py

@@ -119,7 +119,7 @@ def test_build_calls_df():
     fake_list_of_data_3 = [empty_df, empty_var_df, calls_df]
     df_3 = build_df('calls', fake_list_of_data_3)
     assert df_3.shape == (10, 11)
-    assert df_3['team'].isna().all()
+    assert df_3['team'].isin(['NO TEAM']).all()
 
     # empty arrays
     fake_list_of_data_4 = [empty_df, empty_var_df, empty_var_df]

toucan_connectors/aircall/helpers.py

@@ -64,6 +64,7 @@ def build_df(dataset: str, list_of_data: List[dict]) -> pd.DataFrame:
             answered_at=lambda t: pd.to_datetime(t['answered_at'], unit='s'),
             ended_at=lambda t: pd.to_datetime(t['ended_at'], unit='s'),
             day=lambda t: t['ended_at'].astype(str).str[:10],
+            team=lambda x: x['team'].replace({np.NaN: 'NO TEAM'}),
         )
         return total_df[COLUMN_DICTIONARY[dataset]]