fix: use is_string_dtype to check for object/string types

Author: xmatthias

freqtrade/freqai/data_drawer.py

@@ -361,7 +361,7 @@ def append_model_predictions(
             label_loc = df.columns.get_loc(label)
             pred_label_loc = predictions.columns.get_loc(label)
             df.iloc[-1, label_loc] = predictions.iloc[-1, pred_label_loc]
-            if df[label].dtype == object:
+            if pd.api.types.is_string_dtype(df[label].dtype):
                 continue
             label_mean_loc = df.columns.get_loc(f"{label}_mean")
             label_std_loc = df.columns.get_loc(f"{label}_std")

freqtrade/freqai/data_kitchen.py

@@ -435,7 +435,7 @@ def get_predictions_to_append(
 
         for label in predictions.columns:
             append_dict[label] = predictions[label]
-            if predictions[label].dtype == object:
+            if pd.api.types.is_string_dtype(predictions[label].dtype):
                 continue
             if "labels_mean" in self.data and label in self.data["labels_mean"]:
                 append_dict[f"{label}_mean"] = self.data["labels_mean"][label]
@@ -879,7 +879,7 @@ def fit_labels(self) -> None:
 
         self.data["labels_mean"], self.data["labels_std"] = {}, {}
         for label in self.data_dictionary["train_labels"].columns:
-            if self.data_dictionary["train_labels"][label].dtype == object:
+            if pd.api.types.is_string_dtype(self.data_dictionary["train_labels"][label].dtype):
                 continue
             f = spy.stats.norm.fit(self.data_dictionary["train_labels"][label])
             self.data["labels_mean"][label], self.data["labels_std"][label] = f[0], f[1]
@@ -905,7 +905,7 @@ def get_unique_classes_from_labels(self, dataframe: DataFrame) -> None:
         self.find_labels(dataframe)
 
         for key in self.label_list:
-            if dataframe[key].dtype == object:
+            if pd.api.types.is_string_dtype(dataframe[key].dtype):
                 self.unique_classes[key] = dataframe[key].dropna().unique()
 
         if self.unique_classes:

freqtrade/freqai/freqai_interface.py

@@ -676,7 +676,7 @@ def set_initial_historic_predictions(
         self.set_start_dry_live_date(strat_df)
 
         for label in hist_preds_df.columns:
-            if hist_preds_df[label].dtype == object:
+            if pd.api.types.is_string_dtype(hist_preds_df[label].dtype):
                 continue
             hist_preds_df[f"{label}_mean"] = 0
             hist_preds_df[f"{label}_std"] = 0
@@ -706,7 +706,7 @@ def fit_live_predictions(self, dk: FreqaiDataKitchen, pair: str) -> None:
         num_candles = self.freqai_info.get("fit_live_predictions_candles", 100)
         dk.data["labels_mean"], dk.data["labels_std"] = {}, {}
         for label in full_labels:
-            if self.dd.historic_predictions[dk.pair][label].dtype == object:
+            if pd.api.types.is_string_dtype(self.dd.historic_predictions[dk.pair][label].dtype):
                 continue
             f = spy.stats.norm.fit(self.dd.historic_predictions[dk.pair][label].tail(num_candles))
             dk.data["labels_mean"][label], dk.data["labels_std"][label] = f[0], f[1]
@@ -896,7 +896,7 @@ def backtesting_fit_live_predictions(self, dk: FreqaiDataKitchen):
                     ]
                     self.fit_live_predictions(self.dk, self.dk.pair)
                     for label in label_columns:
-                        if dk.full_df[label].dtype == object:
+                        if pd.api.types.is_string_dtype(dk.full_df[label].dtype):
                             continue
                         if "labels_mean" in self.dk.data:
                             dk.full_df.at[index, f"{label}_mean"] = self.dk.data["labels_mean"][