Format code with black, yapf, autopep8 and isort

pandas/__init__.py

@@ -13,16 +13,19 @@
         missing_dependencies.append(f"{dependency}: {e}")
 
 if missing_dependencies:
-    raise ImportError(
-        "Unable to import required dependencies:\n" + "\n".join(missing_dependencies)
-    )
+    raise ImportError("Unable to import required dependencies:\n" +
+                      "\n".join(missing_dependencies))
 del hard_dependencies, dependency, missing_dependencies
 
 # numpy compat
 from pandas.compat import is_numpy_dev as _is_numpy_dev
 
 try:
-    from pandas._libs import hashtable as _hashtable, lib as _lib, tslib as _tslib
+    from pandas._libs import (
+        hashtable as _hashtable,
+        lib as _lib,
+        tslib as _tslib,
+    )
 except ImportError as err:  # pragma: no cover
     module = err.name
     raise ImportError(
@@ -34,143 +37,133 @@
     del _tslib, _lib, _hashtable
 
 from pandas._config import (
-    get_option,
-    set_option,
-    reset_option,
     describe_option,
+    get_option,
     option_context,
     options,
+    reset_option,
+    set_option,
 )
 
-from pandas.core.api import (
-    # dtype
+from pandas.util._print_versions import show_versions
+from pandas.util._tester import test
+
+from pandas import (
+    api,
+    arrays,
+    errors,
+    io,
+    plotting,
+    testing,
+    tseries,
+)
+
+# use the closest tagged version if possible
+from pandas._version import get_versions
+from pandas.core.api import (  # dtype; missing; indexes; tseries; conversion; misc
+    NA,
+    BooleanDtype,
+    Categorical,
+    CategoricalDtype,
+    CategoricalIndex,
+    DataFrame,
+    DateOffset,
+    DatetimeIndex,
+    DatetimeTZDtype,
+    Flags,
+    Float32Dtype,
+    Float64Dtype,
+    Grouper,
+    Index,
+    IndexSlice,
     Int8Dtype,
     Int16Dtype,
     Int32Dtype,
     Int64Dtype,
+    Interval,
+    IntervalDtype,
+    IntervalIndex,
+    MultiIndex,
+    NamedAgg,
+    NaT,
+    Period,
+    PeriodDtype,
+    PeriodIndex,
+    RangeIndex,
+    Series,
+    StringDtype,
+    Timedelta,
+    TimedeltaIndex,
+    Timestamp,
     UInt8Dtype,
     UInt16Dtype,
     UInt32Dtype,
     UInt64Dtype,
-    Float32Dtype,
-    Float64Dtype,
-    CategoricalDtype,
-    PeriodDtype,
-    IntervalDtype,
-    DatetimeTZDtype,
-    StringDtype,
-    BooleanDtype,
-    # missing
-    NA,
+    array,
+    bdate_range,
+    date_range,
+    factorize,
+    interval_range,
     isna,
     isnull,
     notna,
     notnull,
-    # indexes
-    Index,
-    CategoricalIndex,
-    RangeIndex,
-    MultiIndex,
-    IntervalIndex,
-    TimedeltaIndex,
-    DatetimeIndex,
-    PeriodIndex,
-    IndexSlice,
-    # tseries
-    NaT,
-    Period,
     period_range,
-    Timedelta,
+    set_eng_float_format,
     timedelta_range,
-    Timestamp,
-    date_range,
-    bdate_range,
-    Interval,
-    interval_range,
-    DateOffset,
-    # conversion
-    to_numeric,
     to_datetime,
+    to_numeric,
     to_timedelta,
-    # misc
-    Flags,
-    Grouper,
-    factorize,
     unique,
     value_counts,
-    NamedAgg,
-    array,
-    Categorical,
-    set_eng_float_format,
-    Series,
-    DataFrame,
 )
-
 from pandas.core.arrays.sparse import SparseDtype
-
-from pandas.tseries.api import infer_freq
-from pandas.tseries import offsets
-
 from pandas.core.computation.api import eval
-
 from pandas.core.reshape.api import (
     concat,
+    crosstab,
+    cut,
+    get_dummies,
     lreshape,
     melt,
-    wide_to_long,
     merge,
     merge_asof,
     merge_ordered,
-    crosstab,
     pivot,
     pivot_table,
-    get_dummies,
-    cut,
     qcut,
+    wide_to_long,
 )
 
-from pandas import api, arrays, errors, io, plotting, testing, tseries
-from pandas.util._print_versions import show_versions
-
-from pandas.io.api import (
-    # excel
+from pandas.io.api import (  # excel; parsers; pickle; pytables; sql; misc
     ExcelFile,
     ExcelWriter,
-    read_excel,
-    # parsers
-    read_csv,
-    read_fwf,
-    read_table,
-    # pickle
-    read_pickle,
-    to_pickle,
-    # pytables
     HDFStore,
-    read_hdf,
-    # sql
-    read_sql,
-    read_sql_query,
-    read_sql_table,
-    # misc
     read_clipboard,
-    read_parquet,
-    read_orc,
+    read_csv,
+    read_excel,
     read_feather,
+    read_fwf,
     read_gbq,
+    read_hdf,
     read_html,
-    read_xml,
     read_json,
-    read_stata,
+    read_orc,
+    read_parquet,
+    read_pickle,
     read_sas,
     read_spss,
+    read_sql,
+    read_sql_query,
+    read_sql_table,
+    read_stata,
+    read_table,
+    read_xml,
+    to_pickle,
 )
-
 from pandas.io.json import _json_normalize as json_normalize
-
-from pandas.util._tester import test
-
-# use the closest tagged version if possible
-from pandas._version import get_versions
+from pandas.tseries import offsets
+from pandas.tseries.api import infer_freq
 
 v = get_versions()
 __version__ = v.get("closest-tag", v["version"])
@@ -199,7 +192,11 @@ def __getattr__(name):
             FutureWarning,
             stacklevel=2,
         )
-        from pandas.core.api import Float64Index, Int64Index, UInt64Index
+        from pandas.core.api import (
+            Float64Index,
+            Int64Index,
+            UInt64Index,
+        )
 
         return {
             "Float64Index": Float64Index,