CLN: Unused varables pt2

pandas/core/arrays/interval.py

@@ -814,7 +814,6 @@ def _format_data(self):
                 summary = '[{head} ... {tail}]'.format(
                     head=', '.join(head), tail=', '.join(tail))
             else:
-                head = []
                 tail = [formatter(x) for x in self]
                 summary = '[{tail}]'.format(tail=', '.join(tail))
 

pandas/core/dtypes/dtypes.py

@@ -305,7 +305,6 @@ def _hash_categories(categories, ordered=True):
                     # everything to a str first, which means we treat
                     # {'1', '2'} the same as {'1', 2}
                     # find a better solution
-                    cat_array = np.array([hash(x) for x in categories])
                     hashed = hash((tuple(categories), ordered))
                     return hashed
             cat_array = hash_array(np.asarray(categories), categorize=False)

pandas/core/groupby/generic.py

@@ -134,7 +134,6 @@ def _cython_agg_blocks(self, how, alt=None, numeric_only=True,
                 obj = self.obj[data.items[locs]]
                 s = groupby(obj, self.grouper)
                 result = s.aggregate(lambda x: alt(x, axis=self.axis))
-                newb = result._data.blocks[0]
 
             finally:
 

pandas/core/indexes/base.py

@@ -1647,11 +1647,11 @@ def is_int(v):
         # if we are mixed and have integers
         try:
             if is_positional and self.is_mixed():
-                # TODO: i, j are not used anywhere
+                # Validate start & stop
                 if start is not None:
-                    i = self.get_loc(start)  # noqa
+                    self.get_loc(start)
                 if stop is not None:
-                    j = self.get_loc(stop)  # noqa
+                    self.get_loc(stop)
                 is_positional = False
         except KeyError:
             if self.inferred_type == 'mixed-integer-float':

pandas/core/indexing.py

@@ -788,11 +788,6 @@ def _align_frame(self, indexer, df):
 
         if isinstance(indexer, tuple):
 
-            aligners = [not com.is_null_slice(idx) for idx in indexer]
-            sum_aligners = sum(aligners)
-            # TODO: single_aligner is not used
-            single_aligner = sum_aligners == 1  # noqa
-
             idx, cols = None, None
             sindexers = []
             for i, ix in enumerate(indexer):
@@ -865,9 +860,6 @@ def _align_frame(self, indexer, df):
         raise ValueError('Incompatible indexer with DataFrame')
 
     def _align_panel(self, indexer, df):
-        # TODO: is_frame, is_panel are unused
-        is_frame = self.obj.ndim == 2  # noqa
-        is_panel = self.obj.ndim >= 3  # noqa
         raise NotImplementedError("cannot set using an indexer with a Panel "
                                   "yet!")
 

pandas/core/ops.py

@@ -1789,7 +1789,7 @@ def na_op(x, y):
     def f(self, other, axis=None):
         # Validate the axis parameter
         if axis is not None:
-            axis = self._get_axis_number(axis)
+            self._get_axis_number(axis)
 
         if isinstance(other, self._constructor):
             return self._compare_constructor(other, na_op, try_cast=False)

pandas/core/panel.py

@@ -296,7 +296,7 @@ def _getitem_multilevel(self, key):
         if isinstance(loc, (slice, np.ndarray)):
             new_index = info[loc]
             result_index = maybe_droplevels(new_index, key)
-            slices = [loc] + [slice(None) for x in range(self._AXIS_LEN - 1)]
+            slices = [loc] + [slice(None)] * (self._AXIS_LEN - 1)
             new_values = self.values[slices]
 
             d = self._construct_axes_dict(self._AXIS_ORDERS[1:])

pandas/core/sparse/series.py

@@ -624,8 +624,9 @@ def cumsum(self, axis=0, *args, **kwargs):
         cumsum : SparseSeries
         """
         nv.validate_cumsum(args, kwargs)
+        # Validate axis
         if axis is not None:
-            axis = self._get_axis_number(axis)
+            self._get_axis_number(axis)
 
         new_array = self.values.cumsum()
 
@@ -654,7 +655,8 @@ def dropna(self, axis=0, inplace=False, **kwargs):
         Analogous to Series.dropna. If fill_value=NaN, returns a dense Series
         """
         # TODO: make more efficient
-        axis = self._get_axis_number(axis or 0)
+        # Validate axis
+        self._get_axis_number(axis or 0)
         dense_valid = self.to_dense().dropna()
         if inplace:
             raise NotImplementedError("Cannot perform inplace dropna"

pandas/io/excel.py

@@ -647,7 +647,7 @@ def _parse_cell(cell_contents, cell_typ):
             if header is not None:
                 if is_list_like(header):
                     header_names = []
-                    control_row = [True for x in data[0]]
+                    control_row = [True] * len(data[0])
                     for row in header:
                         if is_integer(skiprows):
                             row += skiprows

pandas/io/formats/format.py

@@ -1567,7 +1567,7 @@ def get_level_lengths(levels, sentinel=''):
     if len(levels) == 0:
         return []
 
-    control = [True for x in levels[0]]
+    control = [True] * len(levels[0])
 
     result = []
     for level in levels:

pandas/io/formats/html.py

@@ -369,7 +369,7 @@ def _write_regular_rows(self, fmt_values, indent):
         for i in range(nrows):
 
             if truncate_v and i == (self.fmt.tr_row_num):
-                str_sep_row = ['...' for ele in row]
+                str_sep_row = ['...'] * len(row)
                 self.write_tr(str_sep_row, indent, self.indent_delta,
                               tags=None, nindex_levels=1)