lint

src/visions/backends/shared/nan_handling.py

@@ -45,10 +45,8 @@ def is_missing(x):
         else:
             return False
 
-
     nb.extending.overload(is_missing)(is_missing)
 
-
     @nb.jit
     def hasna(x: np.ndarray) -> bool:
         for item in x:

src/visions/dtypes/boolean.py

@@ -197,7 +197,7 @@ def coerce_to_array(values, dtype, mask=None, copy=False):
         values = np.array(values, dtype=int, copy=copy)
 
     elif not (
-            is_integer_dtype(values) or is_float_dtype(values) or is_bool_dtype(values)
+        is_integer_dtype(values) or is_float_dtype(values) or is_bool_dtype(values)
     ):
         raise TypeError(f"{values.dtype} cannot be converted to an IntegerDtype")
 
@@ -302,9 +302,9 @@ def dtype(self):
 
     def __init__(self, values, mask, copy=False):
         if not (
-                isinstance(values, np.ndarray)
-                and is_integer_dtype(values.dtype)
-                or is_bool_dtype(values.dtype)
+            isinstance(values, np.ndarray)
+            and is_integer_dtype(values.dtype)
+            or is_bool_dtype(values.dtype)
         ):
             raise TypeError(
                 "values should be integer numpy array. Use "
@@ -677,7 +677,7 @@ def _maybe_mask_result(self, result, mask, other, op_name):
         # a float result
         # or our op is a divide
         if (is_float_dtype(other) or is_float(other)) or (
-                op_name in ["rtruediv", "truediv"]
+            op_name in ["rtruediv", "truediv"]
         ):
             result[mask] = np.nan
             return result

src/visions/test/series_sparse.py

@@ -6,7 +6,7 @@
 from visions.backends.pandas.test_utils import pandas_version
 
 not_pandas_1_0_5 = not (
-        (pandas_version[0] == 1) and (pandas_version[1] == 0) and (pandas_version[2] == 5)
+    (pandas_version[0] == 1) and (pandas_version[1] == 0) and (pandas_version[2] == 5)
 )