CLN: Cleanup use of super() in instance methods.

Author: topper-123

asv_bench/benchmarks/io/stata.py

@@ -38,7 +38,7 @@ def time_write_stata(self, convert_dates):
 
 class StataMissing(Stata):
     def setup(self, convert_dates):
-        super(StataMissing, self).setup(convert_dates)
+        super().setup(convert_dates)
         for i in range(10):
             missing_data = np.random.randn(self.N)
             missing_data[missing_data < 0] = np.nan

pandas/_libs/tslibs/strptime.pyx

@@ -528,7 +528,7 @@ class TimeRE(dict):
             self.locale_time = locale_time
         else:
             self.locale_time = LocaleTime()
-        base = super(TimeRE, self)
+        base = super()
         base.__init__({
             # The " \d" part of the regex is to make %c from ANSI C work
             'd': r"(?P<d>3[0-1]|[1-2]\d|0[1-9]|[1-9]| [1-9])",

pandas/compat/pickle_compat.py

@@ -144,7 +144,7 @@ def find_class(self, module, name):
         # override superclass
         key = (module, name)
         module, name = _class_locations_map.get(key, key)
-        return super(Unpickler, self).find_class(module, name)
+        return super().find_class(module, name)
 
 
 Unpickler.dispatch = copy.copy(Unpickler.dispatch)

pandas/core/algorithms.py

@@ -1155,7 +1155,7 @@ class SelectNFrame(SelectN):
     """
 
     def __init__(self, obj, n, keep, columns):
-        super(SelectNFrame, self).__init__(obj, n, keep)
+        super().__init__(obj, n, keep)
         if not is_list_like(columns) or isinstance(columns, tuple):
             columns = [columns]
         columns = list(columns)

pandas/core/apply.py

@@ -317,7 +317,7 @@ class FrameRowApply(FrameApply):
     axis = 0
 
     def apply_broadcast(self):
-        return super(FrameRowApply, self).apply_broadcast(self.obj)
+        return super().apply_broadcast(self.obj)
 
     @property
     def series_generator(self):
@@ -356,7 +356,7 @@ class FrameColumnApply(FrameApply):
     axis = 1
 
     def apply_broadcast(self):
-        result = super(FrameColumnApply, self).apply_broadcast(self.obj.T)
+        result = super().apply_broadcast(self.obj.T)
         return result.T
 
     @property

pandas/core/arrays/categorical.py

@@ -1549,7 +1549,7 @@ def argsort(self, *args, **kwargs):
         array([3, 0, 1, 2])
         """
         # Keep the implementation here just for the docstring.
-        return super(Categorical, self).argsort(*args, **kwargs)
+        return super().argsort(*args, **kwargs)
 
     def sort_values(self, inplace=False, ascending=True, na_position='last'):
         """

pandas/core/arrays/datetimelike.py

@@ -1381,9 +1381,7 @@ def _reduce(self, name, axis=0, skipna=True, **kwargs):
         if op:
             return op(axis=axis, skipna=skipna, **kwargs)
         else:
-            return super(DatetimeLikeArrayMixin, self)._reduce(
-                name, skipna, **kwargs
-            )
+            return super()._reduce(name, skipna, **kwargs)
 
     def min(self, axis=None, skipna=True, *args, **kwargs):
         """

pandas/core/arrays/datetimes.py

@@ -580,7 +580,7 @@ def __array__(self, dtype=None):
             # The default for tz-aware is object, to preserve tz info
             dtype = object
 
-        return super(DatetimeArray, self).__array__(dtype=dtype)
+        return super().__array__(dtype=dtype)
 
     def __iter__(self):
         """
@@ -771,7 +771,7 @@ def _add_delta(self, delta):
         -------
         result : DatetimeArray
         """
-        new_values = super(DatetimeArray, self)._add_delta(delta)
+        new_values = super()._add_delta(delta)
         return type(self)._from_sequence(new_values, tz=self.tz, freq='infer')
 
     # -----------------------------------------------------------------

pandas/core/arrays/period.py

@@ -505,7 +505,7 @@ def astype(self, dtype, copy=True):
 
         if is_period_dtype(dtype):
             return self.asfreq(dtype.freq)
-        return super(PeriodArray, self).astype(dtype, copy=copy)
+        return super().astype(dtype, copy=copy)
 
     @property
     def flags(self):
@@ -560,7 +560,7 @@ def _add_offset(self, other):
         # Note: when calling parent class's _add_timedeltalike_scalar,
         #  it will call delta_to_nanoseconds(delta).  Because delta here
         #  is an integer, delta_to_nanoseconds will return it unchanged.
-        result = super(PeriodArray, self)._add_timedeltalike_scalar(other.n)
+        result = super()._add_timedeltalike_scalar(other.n)
         return type(self)(result, freq=self.freq)
 
     def _add_timedeltalike_scalar(self, other):
@@ -584,7 +584,7 @@ def _add_timedeltalike_scalar(self, other):
         # Note: when calling parent class's _add_timedeltalike_scalar,
         #  it will call delta_to_nanoseconds(delta).  Because delta here
         #  is an integer, delta_to_nanoseconds will return it unchanged.
-        ordinals = super(PeriodArray, self)._add_timedeltalike_scalar(other)
+        ordinals = super()._add_timedeltalike_scalar(other)
         return ordinals
 
     def _add_delta_tdi(self, other):
@@ -620,7 +620,7 @@ def _add_delta(self, other):
             # We cannot add timedelta-like to non-tick PeriodArray
             _raise_on_incompatible(self, other)
 
-        new_ordinals = super(PeriodArray, self)._add_delta(other)
+        new_ordinals = super()._add_delta(other)
         return type(self)(new_ordinals, freq=self.freq)
 
     def _check_timedeltalike_freq_compat(self, other):

pandas/core/arrays/sparse.py

@@ -118,7 +118,7 @@ def __init__(
     def __hash__(self):
         # Python3 doesn't inherit __hash__ when a base class overrides
         # __eq__, so we explicitly do it here.
-        return super(SparseDtype, self).__hash__()
+        return super().__hash__()
 
     def __eq__(self, other):
         # We have to override __eq__ to handle NA values in _metadata.