CLN: remove compat.itervalues

pandas/compat/__init__.py

@@ -6,8 +6,6 @@
 
 Key items to import for compatible code:
 * lists: lrange(), lmap(), lzip(), lfilter()
-* iterable method compatibility: itervalues
-  * Uses the original method if available, otherwise uses items, keys, values.
 * add_metaclass(metaclass) - class decorator that recreates class with with the
   given metaclass instead (and avoids intermediary class creation)
 
@@ -41,9 +39,6 @@ def lfilter(*args, **kwargs):
     return list(filter(*args, **kwargs))
 
 
-def itervalues(obj, **kw):
-    return iter(obj.values(**kw))
-
 # ----------------------------------------------------------------------------
 # functions largely based / taken from the six module
 

pandas/core/base.py

@@ -9,7 +9,6 @@
 import numpy as np
 
 import pandas._libs.lib as lib
-import pandas.compat as compat
 from pandas.compat import PYPY
 from pandas.compat.numpy import function as nv
 from pandas.errors import AbstractMethodError
@@ -362,7 +361,7 @@ def nested_renaming_depr(level=4):
             # if we have a dict of any non-scalars
             # eg. {'A' : ['mean']}, normalize all to
             # be list-likes
-            if any(is_aggregator(x) for x in compat.itervalues(arg)):
+            if any(is_aggregator(x) for x in arg.values()):
                 new_arg = OrderedDict()
                 for k, v in arg.items():
                     if not isinstance(v, (tuple, list, dict)):
@@ -493,13 +492,12 @@ def _agg(arg, func):
 
             def is_any_series():
                 # return a boolean if we have *any* nested series
-                return any(isinstance(r, ABCSeries)
-                           for r in compat.itervalues(result))
+                return any(isinstance(r, ABCSeries) for r in result.values())
 
             def is_any_frame():
                 # return a boolean if we have *any* nested series
                 return any(isinstance(r, ABCDataFrame)
-                           for r in compat.itervalues(result))
+                           for r in result.values())
 
             if isinstance(result, list):
                 return concat(result, keys=keys, axis=1, sort=True), True

pandas/core/dtypes/concat.py

@@ -14,8 +14,6 @@
     ABCDatetimeArray, ABCDatetimeIndex, ABCIndexClass, ABCPeriodIndex,
     ABCRangeIndex, ABCSparseDataFrame, ABCTimedeltaIndex)
 
-from pandas import compat
-
 
 def get_dtype_kinds(l):
     """
@@ -69,7 +67,7 @@ def _get_series_result_type(result, objs=None):
     if isinstance(result, dict):
         # concat Series with axis 1
         if all(isinstance(c, (SparseSeries, SparseDataFrame))
-               for c in compat.itervalues(result)):
+               for c in result.values()):
             return SparseDataFrame
         else:
             return DataFrame

pandas/core/groupby/generic.py

@@ -15,7 +15,6 @@
 import numpy as np
 
 from pandas._libs import Timestamp, lib
-import pandas.compat as compat
 from pandas.compat import lzip
 from pandas.errors import AbstractMethodError
 from pandas.util._decorators import Appender, Substitution
@@ -850,7 +849,7 @@ def _aggregate_multiple_funcs(self, arg, _level):
                 obj._selection = name
             results[name] = obj.aggregate(func)
 
-        if any(isinstance(x, DataFrame) for x in compat.itervalues(results)):
+        if any(isinstance(x, DataFrame) for x in results.values()):
             # let higher level handle
             if _level:
                 return results

pandas/core/nanops.py

@@ -9,7 +9,6 @@
 from pandas._config import get_option
 
 from pandas._libs import iNaT, lib, tslibs
-import pandas.compat as compat
 
 from pandas.core.dtypes.cast import _int64_max, maybe_upcast_putmask
 from pandas.core.dtypes.common import (
@@ -68,7 +67,7 @@ def check(self, obj):
     def __call__(self, f):
         @functools.wraps(f)
         def _f(*args, **kwargs):
-            obj_iter = itertools.chain(args, compat.itervalues(kwargs))
+            obj_iter = itertools.chain(args, kwargs.values())
             if any(self.check(obj) for obj in obj_iter):
                 msg = 'reduction operation {name!r} not allowed for this dtype'
                 raise TypeError(msg.format(name=f.__name__.replace('nan', '')))

pandas/core/panel.py

@@ -6,7 +6,6 @@
 
 import numpy as np
 
-import pandas.compat as compat
 from pandas.compat.numpy import function as nv
 from pandas.util._decorators import Appender, Substitution, deprecate_kwarg
 from pandas.util._validators import validate_axis_style_args
@@ -1180,7 +1179,7 @@ def _construct_return_type(self, result, axes=None):
         # need to assume they are the same
         if ndim is None:
             if isinstance(result, dict):
-                ndim = getattr(list(compat.itervalues(result))[0], 'ndim', 0)
+                ndim = getattr(list(result.values())[0], 'ndim', 0)
 
                 # have a dict, so top-level is +1 dim
                 if ndim != 0:

pandas/io/json/normalize.py

@@ -8,7 +8,7 @@
 
 from pandas._libs.writers import convert_json_to_lines
 
-from pandas import DataFrame, compat
+from pandas import DataFrame
 
 
 def _convert_to_line_delimits(s):
@@ -198,8 +198,7 @@ def _pull_field(js, spec):
         data = [data]
 
     if record_path is None:
-        if any([isinstance(x, dict)
-                for x in compat.itervalues(y)] for y in data):
+        if any([isinstance(x, dict) for x in y.values()] for y in data):
             # naive normalization, this is idempotent for flat records
             # and potentially will inflate the data considerably for
             # deeply nested structures:

pandas/io/parsers.py

@@ -1154,7 +1154,7 @@ def read(self, nrows=None):
         if index is None:
             if col_dict:
                 # Any column is actually fine:
-                new_rows = len(next(compat.itervalues(col_dict)))
+                new_rows = len(next(iter(col_dict.values())))
                 index = RangeIndex(self._currow, self._currow + new_rows)
             else:
                 new_rows = 0

pandas/tests/io/test_packers.py

@@ -14,7 +14,7 @@
 import pandas
 from pandas import (
     Categorical, DataFrame, Index, Interval, MultiIndex, NaT, Period, Series,
-    Timestamp, bdate_range, compat, date_range, period_range)
+    Timestamp, bdate_range, date_range, period_range)
 import pandas.util.testing as tm
 from pandas.util.testing import (
     assert_categorical_equal, assert_frame_equal, assert_index_equal,
@@ -818,12 +818,12 @@ def setup_method(self, method):
     def test_utf(self):
         # GH10581
         for encoding in self.utf_encodings:
-            for frame in compat.itervalues(self.frame):
+            for frame in self.frame.values():
                 result = self.encode_decode(frame, encoding=encoding)
                 assert_frame_equal(result, frame)
 
     def test_default_encoding(self):
-        for frame in compat.itervalues(self.frame):
+        for frame in self.frame.values():
             result = frame.to_msgpack()
             expected = frame.to_msgpack(encoding='utf8')
             assert result == expected

pandas/tests/test_compat.py

@@ -5,7 +5,7 @@
 import builtins
 import re
 
-from pandas.compat import itervalues, lfilter, lmap, lrange, lzip, re_type
+from pandas.compat import lfilter, lmap, lrange, lzip, re_type
 
 
 class TestBuiltinIterators(object):
@@ -50,9 +50,6 @@ def test_lzip(self):
         lengths = 10,
         self.check_results(results, expecteds, lengths)
 
-    def test_dict_iterators(self):
-        assert next(itervalues({1: 2})) == 2
-
 
 def test_re_type():
     assert isinstance(re.compile(''), re_type)