diff --git a/qlib/data/dataset/__init__.py b/qlib/data/dataset/__init__.py
index ecbeebc95..690436ba9 100644
--- a/qlib/data/dataset/__init__.py
+++ b/qlib/data/dataset/__init__.py
@@ -413,7 +413,7 @@ class TSDataSampler:
         # 1) for better performance, use the last nan line for padding the lost date
         # 2) In case of precision problems. We use np.float64. # TODO: I'm not sure if whether np.float64 will result in
         # precision problems. It will not cause any problems in my tests at least
-        indices = np.nan_to_num(indices.astype(np.float64), nan=self.nan_idx).astype(np.int)
+        indices = np.nan_to_num(indices.astype(np.float64), nan=self.nan_idx).astype(int)
 
         data = self.data_arr[indices]
         if isinstance(idx, mtit):
diff --git a/qlib/utils/__init__.py b/qlib/utils/__init__.py
index 6640dae2c..f550a0419 100644
--- a/qlib/utils/__init__.py
+++ b/qlib/utils/__init__.py
@@ -64,7 +64,7 @@ def np_ffill(arr: np.array):
     arr : np.array
         Input numpy 1D array
     """
-    mask = np.isnan(arr.astype(np.float))  # np.isnan only works on np.float
+    mask = np.isnan(arr.astype(float))  # np.isnan only works on np.float
     # get fill index
     idx = np.where(~mask, np.arange(mask.shape[0]), 0)
     np.maximum.accumulate(idx, out=idx)