fix: pass lint

Signed-off-by: OxalisCu <2127298698@qq.com>

pymilvus/bulk_writer/buffer.py

@@ -10,7 +10,6 @@
 # or implied. See the License for the specific language governing permissions and limitations under
 # the License.
 
-import csv
 import json
 import logging
 from pathlib import Path
@@ -290,27 +289,35 @@ def _persist_parquet(self, local_path: str, **kwargs):
 
     def _persist_csv(self, local_path: str, **kwargs):
         sep = self._config.get("sep", ",")
-        nullkey = self._config.get("nullkey", "")
-        
+        # nullkey is not supported in csv now
+
         header = list(self._buffer.keys())
-        df = pd.DataFrame(columns=header)
+        data = pd.DataFrame(columns=header)
         for k, v in self._buffer.items():
             field_schema = self._fields[k]
-            # When using df.to_csv(arr) to write non-scalar data, the repr function is used by default to convert the data to a string.
-            # if the value of arr is [1.0, 2.0], repr(arr) will change with the type of arr, making things complicated:
-            # when arr is a list, the output is '[1.0, 2.0]'
-            # when arr is a tuple, the output is '(1.0, 2.0)'
-            # when arr is a np.array, the output is '[1.0 2.0]'
+            # When using df.to_csv(arr) to write non-scalar data,
+            # the repr function is used to convert the data to a string.
+            # if the value of arr is [1.0, 2.0], repr(arr) will change with the type of arr:
+            #   when arr is a list, the output is '[1.0, 2.0]'
+            #   when arr is a tuple, the output is '(1.0, 2.0)'
+            #   when arr is a np.array, the output is '[1.0 2.0]'
             # we needs the output to be '[1.0, 2.0]', consistent with the array format in json
-            # so 1. whether make sure that arr of type (BINARY_VECTOR, FLOAT_VECTOR, FLOAT16_VECTOR, BFLOAT16_VECTOR) is a LIST,
+            # so 1. whether make sure that arr of type
+            #       (BINARY_VECTOR, FLOAT_VECTOR, FLOAT16_VECTOR, BFLOAT16_VECTOR) is a LIST,
             #    2. or convert arr into a string using json.dumps(arr) first and then add it to df
             # I choose method 2 here
-            if field_schema.dtype in {DataType.JSON, DataType.ARRAY, DataType.SPARSE_FLOAT_VECTOR, DataType.BINARY_VECTOR, DataType.FLOAT_VECTOR}:
+            if field_schema.dtype in {
+                DataType.JSON,
+                DataType.ARRAY,
+                DataType.SPARSE_FLOAT_VECTOR,
+                DataType.BINARY_VECTOR,
+                DataType.FLOAT_VECTOR,
+            }:
                 dt = np.dtype("str")
                 arr = []
                 for val in v:
                     arr.append(json.dumps(val))
-                df[k] = pd.Series(arr, dtype=dt)
+                data[k] = pd.Series(arr, dtype=dt)
             elif field_schema.dtype in {DataType.FLOAT16_VECTOR, DataType.BFLOAT16_VECTOR}:
                 # special process for float16 vector, the self._buffer stores bytes for
                 # float16 vector, convert the bytes to float list
@@ -321,24 +328,23 @@ def _persist_csv(self, local_path: str, **kwargs):
                 )
                 arr = []
                 for val in v:
-                    val = json.dumps(np.frombuffer(val, dtype=dt).tolist())
-                    arr.append(val)
-                df[k] = pd.Series(arr, dtype=np.dtype("str"))
+                    arr.append(json.dumps(np.frombuffer(val, dtype=dt).tolist()))
+                data[k] = pd.Series(arr, dtype=np.dtype("str"))
             elif field_schema.dtype in {DataType.BOOL}:
                 dt = np.dtype("str")
                 arr = ["true" if x else "false" for x in v]
-                df[k] = pd.Series(arr, dtype=dt)
+                data[k] = pd.Series(arr, dtype=dt)
             elif field_schema.dtype.name in NUMPY_TYPE_CREATOR:
                 dt = NUMPY_TYPE_CREATOR[field_schema.dtype.name]
-                df[k] = pd.Series(v, dtype=dt)
+                data[k] = pd.Series(v, dtype=dt)
             else:
-                df[k] = pd.Series(v)
+                data[k] = pd.Series(v)
 
         file_path = Path(local_path + ".csv")
         try:
-            df.to_csv(file_path, sep=sep, index=False)
+            data.to_csv(file_path, sep=sep, index=False)
         except Exception as e:
             self._throw(f"Failed to persist file {file_path}, error: {e}")
 
-        logger.info("Successfully persist file %s, row count: %s", file_path, len(df))
+        logger.info("Successfully persist file %s, row count: %s", file_path, len(data))
         return [str(file_path)]