Reformat with black

Author: jiangyue12392

pandas/core/frame.py

@@ -390,7 +390,6 @@ def _constructor_expanddim(self):
     # ----------------------------------------------------------------------
     # Constructors
 
-
     def __init__(
         self,
         data=None,
@@ -452,8 +451,9 @@ def __init__(
                 if is_list_like(data[0]) and getattr(data[0], "ndim", 1) == 1:
                     if is_named_tuple(data[0]) and columns is None:
                         columns = data[0]._fields
-                    arrays, columns = to_arrays(data, columns, dtype=dtype,
-                                                to_integer_array=to_integer_array)
+                    arrays, columns = to_arrays(
+                        data, columns, dtype=dtype, to_integer_array=to_integer_array
+                    )
                     columns = ensure_index(columns)
 
                     # set the index

pandas/core/internals/construction.py

@@ -426,8 +426,7 @@ def _get_axes(N, K, index, columns):
 # Conversion of Inputs to Arrays
 
 
-def to_arrays(data, columns, coerce_float=False, dtype=None,
-              to_integer_array=False):
+def to_arrays(data, columns, coerce_float=False, dtype=None, to_integer_array=False):
     """
     Return list of arrays, columns.
     """
@@ -454,8 +453,11 @@ def to_arrays(data, columns, coerce_float=False, dtype=None,
         return _list_to_arrays(data, columns, coerce_float=coerce_float, dtype=dtype)
     elif isinstance(data[0], abc.Mapping):
         return _list_of_dict_to_arrays(
-            data, columns, coerce_float=coerce_float, dtype=dtype,
-            to_integer_array=to_integer_array
+            data,
+            columns,
+            coerce_float=coerce_float,
+            dtype=dtype,
+            to_integer_array=to_integer_array,
         )
     elif isinstance(data[0], ABCSeries):
         return _list_of_series_to_arrays(
@@ -526,8 +528,9 @@ def _list_of_series_to_arrays(data, columns, coerce_float=False, dtype=None):
         return values.T, columns
 
 
-def _list_of_dict_to_arrays(data, columns, coerce_float=False, dtype=None,
-                            to_integer_array=False):
+def _list_of_dict_to_arrays(
+    data, columns, coerce_float=False, dtype=None, to_integer_array=False
+):
     """Convert list of dicts to numpy arrays
 
     if `columns` is not passed, column names are inferred from the records
@@ -560,13 +563,17 @@ def _list_of_dict_to_arrays(data, columns, coerce_float=False, dtype=None,
 
     content = list(lib.dicts_to_array(data, list(columns)).T)
     return _convert_object_array(
-        content, columns, dtype=dtype, coerce_float=coerce_float,
-        to_integer_array=to_integer_array
+        content,
+        columns,
+        dtype=dtype,
+        coerce_float=coerce_float,
+        to_integer_array=to_integer_array,
     )
 
 
-def _convert_object_array(content, columns, coerce_float=False, dtype=None,
-                          to_integer_array=False):
+def _convert_object_array(
+    content, columns, coerce_float=False, dtype=None, to_integer_array=False
+):
     if columns is None:
         columns = ibase.default_index(len(content))
     else:
@@ -580,8 +587,9 @@ def _convert_object_array(content, columns, coerce_float=False, dtype=None,
     # provide soft conversion of object dtypes
     def convert(arr):
         if dtype != object and dtype != np.object:
-            arr = lib.maybe_convert_objects(arr, try_float=coerce_float,
-                                            to_integer_array=to_integer_array)
+            arr = lib.maybe_convert_objects(
+                arr, try_float=coerce_float, to_integer_array=to_integer_array
+            )
             arr = maybe_cast_to_datetime(arr, dtype)
         return arr
 

pandas/io/json/_normalize.py

@@ -120,7 +120,7 @@ def json_normalize(
     errors: Optional[str] = "raise",
     sep: str = ".",
     max_level: Optional[int] = None,
-    to_integer_array: Optional[bool] = False
+    to_integer_array: Optional[bool] = False,
 ):
     """
     Normalize semi-structured JSON data into a flat table.