undo unwanted changes

Author: JustinZhengBC

pandas/__init__.py

@@ -18,15 +18,6 @@
     )
 del hard_dependencies, dependency, missing_dependencies
 
-from pandas._config import (
-    describe_option,
-    get_option,
-    option_context,
-    options,
-    reset_option,
-    set_option,
-)
-
 # numpy compat
 from pandas.compat.numpy import (
     _np_version_under1p14,
@@ -35,144 +26,161 @@
     _np_version_under1p17,
     _np_version_under1p18,
 )
-from pandas.util._print_versions import show_versions
-from pandas.util._tester import test
+
+try:
+    from pandas._libs import hashtable as _hashtable, lib as _lib, tslib as _tslib
+except ImportError as e:  # pragma: no cover
+    # hack but overkill to use re
+    module = str(e).replace("cannot import name ", "")
+    raise ImportError(
+        f"C extension: {module} not built. If you want to import "
+        "pandas from the source directory, you may need to run "
+        "'python setup.py build_ext --inplace --force' to build the C extensions first."
+    )
+
+from pandas._config import (
+    get_option,
+    set_option,
+    reset_option,
+    describe_option,
+    option_context,
+    options,
+)
 
 # let init-time option registration happen
-import pandas.api
-import pandas.arrays
-from pandas.core.api import (  # dtype; missing; indexes; tseries; conversion; misc
-    NA,
-    BooleanDtype,
-    Categorical,
-    CategoricalDtype,
-    CategoricalIndex,
-    DataFrame,
-    DateOffset,
-    DatetimeIndex,
-    DatetimeTZDtype,
-    Float64Index,
-    Grouper,
-    Index,
-    IndexSlice,
+import pandas.core.config_init
+
+from pandas.core.api import (
+    # dtype
     Int8Dtype,
     Int16Dtype,
     Int32Dtype,
     Int64Dtype,
-    Int64Index,
-    Interval,
-    IntervalDtype,
-    IntervalIndex,
-    MultiIndex,
-    NamedAgg,
-    NaT,
-    Period,
-    PeriodDtype,
-    PeriodIndex,
-    RangeIndex,
-    Series,
-    StringDtype,
-    Timedelta,
-    TimedeltaIndex,
-    Timestamp,
     UInt8Dtype,
     UInt16Dtype,
     UInt32Dtype,
     UInt64Dtype,
-    UInt64Index,
-    array,
-    bdate_range,
-    date_range,
-    factorize,
-    interval_range,
+    CategoricalDtype,
+    PeriodDtype,
+    IntervalDtype,
+    DatetimeTZDtype,
+    StringDtype,
+    BooleanDtype,
+    # missing
+    NA,
     isna,
     isnull,
     notna,
     notnull,
+    # indexes
+    Index,
+    CategoricalIndex,
+    Int64Index,
+    UInt64Index,
+    RangeIndex,
+    Float64Index,
+    MultiIndex,
+    IntervalIndex,
+    TimedeltaIndex,
+    DatetimeIndex,
+    PeriodIndex,
+    IndexSlice,
+    # tseries
+    NaT,
+    Period,
     period_range,
-    set_eng_float_format,
+    Timedelta,
     timedelta_range,
-    to_datetime,
+    Timestamp,
+    date_range,
+    bdate_range,
+    Interval,
+    interval_range,
+    DateOffset,
+    # conversion
     to_numeric,
+    to_datetime,
     to_timedelta,
+    # misc
+    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
-import pandas.core.config_init
+
 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,
 )
-import pandas.testing
 
-from pandas.io.api import (  # excel; parsers; pickle; pytables; sql; misc
+import pandas.api
+from pandas.util._print_versions import show_versions
+
+from pandas.io.api import (
+    # excel
     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_csv,
-    read_excel,
+    read_parquet,
+    read_orc,
     read_feather,
-    read_fwf,
     read_gbq,
-    read_hdf,
     read_html,
     read_json,
-    read_orc,
-    read_parquet,
-    read_pickle,
+    read_stata,
     read_sas,
     read_spss,
-    read_sql,
-    read_sql_query,
-    read_sql_table,
-    read_stata,
-    read_table,
-    to_pickle,
 )
+
 from pandas.io.json import _json_normalize as json_normalize
-from pandas.tseries import offsets
-from pandas.tseries.api import infer_freq
+
+from pandas.util._tester import test
+import pandas.testing
+import pandas.arrays
 
 # use the closest tagged version if possible
 from ._version import get_versions
 
-try:
-    from pandas._libs import hashtable as _hashtable, lib as _lib, tslib as _tslib
-except ImportError as e:  # pragma: no cover
-    # hack but overkill to use re
-    module = str(e).replace("cannot import name ", "")
-    raise ImportError(
-        f"C extension: {module} not built. If you want to import "
-        "pandas from the source directory, you may need to run "
-        "'python setup.py build_ext --inplace --force' to build the C extensions first."
-    )
-
-
-
-
-
-
-
-
-
-
-
-
-
 v = get_versions()
 __version__ = v.get("closest-tag", v["version"])
 __git_version__ = v.get("full-revisionid")

pandas/core/arrays/categorical.py

@@ -2412,6 +2412,9 @@ def isin(self, values):
         code_values = code_values[null_mask | (code_values >= 0)]
         return algorithms.isin(self.codes, code_values)
 
+    def replace_list(self, to_replace, value, inplace: bool = False):
+        return self.replace(to_replace, value, inplace)
+
     def replace(self, to_replace, value, inplace: bool = False):
         """
         Replaces all instances of one value with another
@@ -2442,8 +2445,12 @@ def replace(self, to_replace, value, inplace: bool = False):
         inplace = validate_bool_kwarg(inplace, "inplace")
         cat = self if inplace else self.copy()
         if is_list_like(to_replace):
-            # if value was also list-like, it would've been handled in generic.py
-            replace_dict = {replace_value: value for replace_value in to_replace}
+            if is_list_like(value):
+                if len(to_replace) != len(value):
+                    raise ValueError("to_replace and value must be same length!")
+                replace_dict = dict(zip(to_replace, value))
+            else:
+                replace_dict = {replace_value: value for replace_value in to_replace}
         else:
             replace_dict = {to_replace: value}
         for replace_value, new_value in replace_dict.items():