Check for LF and run black with -l 220

packages/vaex-core/vaex/dataframe_protocol.py

@@ -251,7 +251,17 @@ class Device(enum.IntEnum):
         return (Device.CPU, None)
 
     def __repr__(self) -> str:
-        return "VaexBuffer(" + str({"bufsize": self.bufsize, "ptr": self.ptr, "device": self.__dlpack_device__()[0].name}) + ")"
+        return (
+            "VaexBuffer("
+            + str(
+                {
+                    "bufsize": self.bufsize,
+                    "ptr": self.ptr,
+                    "device": self.__dlpack_device__()[0].name,
+                }
+            )
+            + ")"
+        )
 
 
 class _VaexColumn:
@@ -341,7 +351,12 @@ def dtype(self) -> Tuple[enum.IntEnum, int, str, str]:
         # If it is internal, kind must be categorical (23)
         # If it is external (call from_dataframe), dtype must give type of the data
         if self._col.df.is_category(self._col):
-            return (_DtypeKind.CATEGORICAL, 64, "u", "=")  # what should be the default??
+            return (
+                _DtypeKind.CATEGORICAL,
+                64,
+                "u",
+                "=",
+            )  # what should be the default??
 
         return self._dtype_from_vaexdtype(dtype)
 
@@ -353,7 +368,15 @@ def _dtype_from_vaexdtype(self, dtype) -> Tuple[enum.IntEnum, int, str, str]:
         #       'b', 'B' (bytes), 'S', 'a', (old-style string) 'V' (void) not handled
         #       datetime, timedelta not implemented yet
         _k = _DtypeKind
-        _np_kinds = {"i": _k.INT, "u": _k.UINT, "f": _k.FLOAT, "b": _k.BOOL, "O": _k.STRING, "M": _k.DATETIME, "m": _k.DATETIME}
+        _np_kinds = {
+            "i": _k.INT,
+            "u": _k.UINT,
+            "f": _k.FLOAT,
+            "b": _k.BOOL,
+            "O": _k.STRING,
+            "M": _k.DATETIME,
+            "m": _k.DATETIME,
+        }
         kind = _np_kinds.get(dtype.kind, None)
 
         if kind is None:
@@ -448,7 +471,7 @@ def describe_null(self) -> Tuple[int, Any]:
             else:
                 # otherwise we have categorize() with a normal numpy array
                 null = 0
-                value = None                
+                value = None
         else:
             raise NotImplementedError(f"Data type {self.dtype} not yet supported")
 
@@ -537,7 +560,9 @@ def get_buffers(self) -> Dict[str, Any]:
 
         return buffers
 
-    def _get_data_buffer(self) -> Tuple[_VaexBuffer, Any]:  # Any is for self.dtype tuple
+    def _get_data_buffer(
+        self,
+    ) -> Tuple[_VaexBuffer, Any]:  # Any is for self.dtype tuple
         """
         Return the buffer containing the data and the buffer's associated dtype.
         """
@@ -581,7 +606,7 @@ def _get_validity_buffer(self) -> Tuple[_VaexBuffer, Any]:
         null, invalid = self.describe_null
 
         _k = _DtypeKind
-        if null == 3 or null == 4: #arrow
+        if null == 3 or null == 4:  # arrow
             mask = self._col.ismissing()
 
             # if arrow use .tolist and then turn it into np.array
@@ -622,7 +647,12 @@ class _VaexDataFrame:
     attributes defined on this class.
     """
 
-    def __init__(self, df: vaex.dataframe.DataFrame, nan_as_null: bool = False, allow_copy: bool = True) -> None:
+    def __init__(
+        self,
+        df: vaex.dataframe.DataFrame,
+        nan_as_null: bool = False,
+        allow_copy: bool = True,
+    ) -> None:
         """
         Constructor - an instance of this (private) class is returned from
         `vaex.dataframe.DataFrame.__dataframe__`.