CLN:Unused Variables

asv_bench/benchmarks/groupby.py

@@ -142,7 +142,7 @@ def time_frame_nth(self, dtype):
     def time_series_nth_any(self, dtype):
         self.df['values'].groupby(self.df['key']).nth(0, dropna='any')
 
-    def time_groupby_nth_all(self, dtype):
+    def time_series_nth_all(self, dtype):
         self.df['values'].groupby(self.df['key']).nth(0, dropna='all')
 
     def time_series_nth(self, dtype):

pandas/_libs/internals.pyx

@@ -390,7 +390,7 @@ def get_blkno_indexers(int64_t[:] blknos, bint group=True):
     start = 0
     cur_blkno = blknos[start]
 
-    if group == False:
+    if not group:
         for i in range(1, n):
             if blknos[i] != cur_blkno:
                 yield cur_blkno, slice(start, i)

pandas/_libs/ops.pyx

@@ -260,8 +260,8 @@ def maybe_convert_bool(ndarray[object] arr,
     result = np.empty(n, dtype=np.uint8)
 
     # the defaults
-    true_vals = set(('True', 'TRUE', 'true'))
-    false_vals = set(('False', 'FALSE', 'false'))
+    true_vals = {'True', 'TRUE', 'true'}
+    false_vals = {'False', 'FALSE', 'false'}
 
     if true_values is not None:
         true_vals = true_vals | set(true_values)

pandas/_libs/tslibs/frequencies.pyx

@@ -126,7 +126,7 @@ _lite_rule_alias = {
     'us': 'U',
     'ns': 'N'}
 
-_dont_uppercase = set(('MS', 'ms'))
+_dont_uppercase = {'MS', 'ms'}
 
 # ----------------------------------------------------------------------
 

pandas/_libs/tslibs/nattype.pyx

@@ -23,7 +23,7 @@ from util cimport (get_nat,
 
 # ----------------------------------------------------------------------
 # Constants
-nat_strings = set(['NaT', 'nat', 'NAT', 'nan', 'NaN', 'NAN'])
+nat_strings = {'NaT', 'nat', 'NAT', 'nan', 'NaN', 'NAN'}
 
 cdef int64_t NPY_NAT = get_nat()
 iNaT = NPY_NAT  # python-visible constant

pandas/_libs/tslibs/offsets.pyx

@@ -252,12 +252,12 @@ def _validate_business_time(t_input):
 # ---------------------------------------------------------------------
 # Constructor Helpers
 
-relativedelta_kwds = set([
+relativedelta_kwds = {
     'years', 'months', 'weeks', 'days',
     'year', 'month', 'week', 'day', 'weekday',
     'hour', 'minute', 'second', 'microsecond',
     'nanosecond', 'nanoseconds',
-    'hours', 'minutes', 'seconds', 'milliseconds', 'microseconds'])
+    'hours', 'minutes', 'seconds', 'milliseconds', 'microseconds'}
 
 
 def _determine_offset(kwds):

pandas/_libs/tslibs/period.pyx

@@ -1965,6 +1965,6 @@ def _validate_end_alias(how):
                 'START': 'S', 'FINISH': 'E',
                 'BEGIN': 'S', 'END': 'E'}
     how = how_dict.get(str(how).upper())
-    if how not in set(['S', 'E']):
+    if how not in {'S', 'E'}:
         raise ValueError('How must be one of S or E')
     return how

pandas/compat/pickle_compat.py

@@ -15,9 +15,6 @@ def load_reduce(self):
     args = stack.pop()
     func = stack[-1]
 
-    if len(args) and type(args[0]) is type:
-        n = args[0].__name__  # noqa
-
     try:
         stack[-1] = func(*args)
         return

pandas/core/arrays/categorical.py

@@ -347,7 +347,6 @@ def __init__(self, values, categories=None, ordered=None, dtype=None,
                                  " or `ordered`.")
 
             categories = dtype.categories
-            ordered = dtype.ordered
 
         elif is_categorical(values):
             # If no "dtype" was passed, use the one from "values", but honor

pandas/core/arrays/interval.py

@@ -25,7 +25,7 @@
 
 from . import ExtensionArray, Categorical
 
-_VALID_CLOSED = set(['left', 'right', 'both', 'neither'])
+_VALID_CLOSED = {'left', 'right', 'both', 'neither'}
 _interval_shared_docs = {}
 _shared_docs_kwargs = dict(
     klass='IntervalArray',
@@ -401,7 +401,6 @@ def from_tuples(cls, data, closed='right', copy=False, dtype=None):
                     msg = ('{name}.from_tuples received an invalid '
                            'item, {tpl}').format(name=name, tpl=d)
                     raise TypeError(msg)
-                lhs, rhs = d
             left.append(lhs)
             right.append(rhs)
 
@@ -815,7 +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/computation/expressions.py

@@ -24,8 +24,8 @@
 
 # the set of dtypes that we will allow pass to numexpr
 _ALLOWED_DTYPES = {
-    'evaluate': set(['int64', 'int32', 'float64', 'float32', 'bool']),
-    'where': set(['int64', 'float64', 'bool'])
+    'evaluate': {'int64', 'int32', 'float64', 'float32', 'bool'},
+    'where': {'int64', 'float64', 'bool'}
 }
 
 # the minimum prod shape that we will use numexpr
@@ -81,7 +81,7 @@ def _can_use_numexpr(op, op_str, a, b, dtype_check):
                         return False
                     dtypes |= set(s.index)
                 elif isinstance(o, np.ndarray):
-                    dtypes |= set([o.dtype.name])
+                    dtypes |= {o.dtype.name}
 
             # allowed are a superset
             if not len(dtypes) or _ALLOWED_DTYPES[dtype_check] >= dtypes:

pandas/core/dtypes/concat.py

@@ -188,8 +188,8 @@ def is_nonempty(x):
         typs = get_dtype_kinds(to_concat)
         if len(typs) != 1:
 
-            if (not len(typs - set(['i', 'u', 'f'])) or
-                    not len(typs - set(['bool', 'i', 'u']))):
+            if (not len(typs - {'i', 'u', 'f'}) or
+                    not len(typs - {'bool', 'i', 'u'})):
                 # let numpy coerce
                 pass
             else:
@@ -599,7 +599,7 @@ def convert_sparse(x, axis):
     to_concat = [convert_sparse(x, axis) for x in to_concat]
     result = np.concatenate(to_concat, axis=axis)
 
-    if not len(typs - set(['sparse', 'f', 'i'])):
+    if not len(typs - {'sparse', 'f', 'i'}):
         # sparsify if inputs are sparse and dense numerics
         # first sparse input's fill_value and SparseIndex is used
         result = SparseArray(result.ravel(), fill_value=fill_values[0],

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/generic.py

@@ -1022,9 +1022,6 @@ def rename(self, *args, **kwargs):
         copy = kwargs.pop('copy', True)
         inplace = kwargs.pop('inplace', False)
         level = kwargs.pop('level', None)
-        axis = kwargs.pop('axis', None)
-        if axis is not None:
-            axis = self._get_axis_number(axis)
 
         if kwargs:
             raise TypeError('rename() got an unexpected keyword '
@@ -5206,8 +5203,6 @@ def __copy__(self, deep=True):
         return self.copy(deep=deep)
 
     def __deepcopy__(self, memo=None):
-        if memo is None:
-            memo = {}
         return self.copy(deep=True)
 
     def _convert(self, datetime=False, numeric=False, timedelta=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/groupby/grouper.py

@@ -481,7 +481,7 @@ def _get_grouper(obj, key=None, axis=0, level=None, sort=True,
         if key.key is None:
             return grouper, [], obj
         else:
-            return grouper, set([key.key]), obj
+            return grouper, {key.key}, obj
 
     # already have a BaseGrouper, just return it
     elif isinstance(key, BaseGrouper):

pandas/core/groupby/ops.py

@@ -387,6 +387,7 @@ def get_func(fname):
 
             # otherwise find dtype-specific version, falling back to object
             for dt in [dtype_str, 'object']:
+                # TODO: Should dtype_str below be replaced with dt?
                 f = getattr(libgroupby, "%s_%s" % (fname, dtype_str), None)
                 if f is not None:
                     return f
@@ -582,7 +583,6 @@ def _transform(self, result, values, comp_ids, transform_func,
         elif values.ndim > 2:
             for i, chunk in enumerate(values.transpose(2, 0, 1)):
 
-                chunk = chunk.squeeze()
                 transform_func(result[:, :, i], values,
                                comp_ids, is_datetimelike, **kwargs)
         else:

pandas/core/indexes/base.py

@@ -237,7 +237,7 @@ class Index(IndexOpsMixin, PandasObject):
 
     _engine_type = libindex.ObjectEngine
 
-    _accessors = set(['str'])
+    _accessors = {'str'}
 
     str = CachedAccessor("str", StringMethods)
 
@@ -979,8 +979,6 @@ def __copy__(self, **kwargs):
         return self.copy(**kwargs)
 
     def __deepcopy__(self, memo=None):
-        if memo is None:
-            memo = {}
         return self.copy(deep=True)
 
     def _validate_names(self, name=None, names=None, deep=False):
@@ -1622,11 +1620,6 @@ 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
-                if start is not None:
-                    i = self.get_loc(start)  # noqa
-                if stop is not None:
-                    j = self.get_loc(stop)  # noqa
                 is_positional = False
         except KeyError:
             if self.inferred_type == 'mixed-integer-float':

pandas/core/indexes/category.py

@@ -132,6 +132,7 @@ def _create_from_codes(self, codes, categories=None, ordered=None,
             ordered = self.ordered
         if name is None:
             name = self.name
+        # TODO: ordered above is unused, should ordered be passed here?
         cat = Categorical.from_codes(codes, categories=categories,
                                      ordered=self.ordered)
         return CategoricalIndex(cat, name=name)

pandas/core/indexes/interval.py

@@ -44,7 +44,7 @@
 from pandas.core.arrays.interval import (IntervalArray,
                                          _interval_shared_docs)
 
-_VALID_CLOSED = set(['left', 'right', 'both', 'neither'])
+_VALID_CLOSED = {'left', 'right', 'both', 'neither'}
 _index_doc_kwargs = dict(ibase._index_doc_kwargs)
 _index_doc_kwargs.update(
     dict(klass='IntervalIndex',
@@ -939,7 +939,6 @@ def _format_data(self, name=None):
                 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/indexes/period.py

@@ -315,7 +315,6 @@ def __contains__(self, key):
                 return True
             except Exception:
                 return False
-            return False
 
     contains = __contains__
 

pandas/core/indexing.py

@@ -789,9 +789,6 @@ 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 = []
@@ -865,9 +862,6 @@ 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/internals.py

@@ -1255,7 +1255,6 @@ def take_nd(self, indexer, axis, new_mgr_locs=None, fill_tuple=None):
             values = self.get_values()
 
         if fill_tuple is None:
-            fill_value = self.fill_value
             new_values = algos.take_nd(values, indexer, axis=axis,
                                        allow_fill=False)
         else:
@@ -2708,7 +2707,6 @@ def _try_coerce_args(self, values, other):
 
         values_mask = isna(values)
         values = values.view('i8')
-        other_mask = False
 
         if isinstance(other, bool):
             raise TypeError
@@ -2881,11 +2879,9 @@ def _try_coerce_args(self, values, other):
         values_mask = _block_shape(isna(values), ndim=self.ndim)
         # asi8 is a view, needs copy
         values = _block_shape(values.asi8, ndim=self.ndim)
-        other_mask = False
 
         if isinstance(other, ABCSeries):
             other = self._holder(other)
-            other_mask = isna(other)
 
         if isinstance(other, bool):
             raise TypeError

pandas/core/nanops.py

@@ -479,7 +479,6 @@ def nanvar(values, axis=None, skipna=True, ddof=1):
 
 @disallow('M8', 'm8')
 def nansem(values, axis=None, skipna=True, ddof=1):
-    var = nanvar(values, axis, skipna, ddof=ddof)
 
     mask = isna(values)
     if not is_float_dtype(values.dtype):
@@ -635,7 +634,6 @@ def nankurt(values, axis=None, skipna=True):
         adj = 3 * (count - 1) ** 2 / ((count - 2) * (count - 3))
         numer = count * (count + 1) * (count - 1) * m4
         denom = (count - 2) * (count - 3) * m2**2
-        result = numer / denom - adj
 
     # floating point error
     #

pandas/core/ops.py

@@ -1743,9 +1743,6 @@ def na_op(x, y):
 
     @Appender('Wrapper for comparison method {name}'.format(name=op_name))
     def f(self, other, axis=None):
-        # Validate the axis parameter
-        if axis is not None:
-            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

@@ -716,7 +716,7 @@ def dropna(self, axis=0, how='any', inplace=False):
         values = self.values
         mask = notna(values)
 
-        for ax in reversed(sorted(set(range(self._AXIS_LEN)) - set([axis]))):
+        for ax in reversed(sorted(set(range(self._AXIS_LEN)) - {axis})):
             mask = mask.sum(ax)
 
         per_slice = np.prod(values.shape[:axis] + values.shape[axis + 1:])

pandas/core/resample.py

@@ -1199,7 +1199,7 @@ def __init__(self, freq='Min', closed=None, label=None, how='mean',
 
         freq = to_offset(freq)
 
-        end_types = set(['M', 'A', 'Q', 'BM', 'BA', 'BQ', 'W'])
+        end_types = {'M', 'A', 'Q', 'BM', 'BA', 'BQ', 'W'}
         rule = freq.rule_code
         if (rule in end_types or
                 ('-' in rule and rule[:rule.find('-')] in end_types)):