CLN: remove compat.iteritems (#26079)

Author: topper-123

pandas/compat/__init__.py

@@ -6,7 +6,7 @@
 
 Key items to import for compatible code:
 * lists: lrange(), lmap(), lzip(), lfilter()
-* iterable method compatibility: iteritems, iterkeys, itervalues
+* iterable method compatibility: iterkeys, 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)
@@ -42,10 +42,6 @@ def lfilter(*args, **kwargs):
     return list(filter(*args, **kwargs))
 
 
-def iteritems(obj, **kw):
-    return iter(obj.items(**kw))
-
-
 def iterkeys(obj, **kw):
     return iter(obj.keys(**kw))
 

pandas/core/arrays/categorical.py

@@ -9,7 +9,6 @@
 from pandas._config import get_option
 
 from pandas._libs import algos as libalgos, lib
-import pandas.compat as compat
 from pandas.compat import lzip
 from pandas.compat.numpy import function as nv
 from pandas.util._decorators import (
@@ -1317,7 +1316,7 @@ def __setstate__(self, state):
             state['_dtype'] = CategoricalDtype(state['_categories'],
                                                state['_ordered'])
 
-        for k, v in compat.iteritems(state):
+        for k, v in state.items():
             setattr(self, k, v)
 
     @property

pandas/core/base.py

@@ -364,7 +364,7 @@ def nested_renaming_depr(level=4):
             # be list-likes
             if any(is_aggregator(x) for x in compat.itervalues(arg)):
                 new_arg = OrderedDict()
-                for k, v in compat.iteritems(arg):
+                for k, v in arg.items():
                     if not isinstance(v, (tuple, list, dict)):
                         new_arg[k] = [v]
                     else:
@@ -432,7 +432,7 @@ def _agg(arg, func):
                 return an OrderedDict
                 """
                 result = OrderedDict()
-                for fname, agg_how in compat.iteritems(arg):
+                for fname, agg_how in arg.items():
                     result[fname] = func(fname, agg_how)
                 return result
 

pandas/core/common.py

@@ -14,7 +14,7 @@
 import numpy as np
 
 from pandas._libs import lib, tslibs
-from pandas.compat import PY36, iteritems
+from pandas.compat import PY36
 
 from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
 from pandas.core.dtypes.common import (
@@ -362,7 +362,7 @@ def dict_compat(d):
     dict
 
     """
-    return {maybe_box_datetimelike(key): value for key, value in iteritems(d)}
+    return {maybe_box_datetimelike(key): value for key, value in d.items()}
 
 
 def standardize_mapping(into):

pandas/core/computation/align.py

@@ -9,7 +9,6 @@
 from pandas.errors import PerformanceWarning
 
 import pandas as pd
-from pandas import compat
 import pandas.core.common as com
 from pandas.core.computation.common import _result_type_many
 
@@ -30,7 +29,7 @@ def _align_core_single_unary_op(term):
 
 def _zip_axes_from_type(typ, new_axes):
     axes = {ax_name: new_axes[ax_ind]
-            for ax_ind, ax_name in compat.iteritems(typ._AXIS_NAMES)}
+            for ax_ind, ax_name in typ._AXIS_NAMES.items()}
     return axes
 
 
@@ -84,7 +83,7 @@ def _align_core(terms):
             if not axes[ax].is_(itm):
                 axes[ax] = axes[ax].join(itm, how='outer')
 
-    for i, ndim in compat.iteritems(ndims):
+    for i, ndim in ndims.items():
         for axis, items in zip(range(ndim), axes):
             ti = terms[i].value
 

pandas/core/computation/expr.py

@@ -10,7 +10,7 @@
 
 import numpy as np
 
-from pandas.compat import iteritems, lmap
+from pandas.compat import lmap
 
 import pandas as pd
 from pandas.core import common as com
@@ -300,7 +300,7 @@ def f(self, node, *args, **kwargs):
 def add_ops(op_classes):
     """Decorator to add default implementation of ops."""
     def f(cls):
-        for op_attr_name, op_class in iteritems(op_classes):
+        for op_attr_name, op_class in op_classes.items():
             ops = getattr(cls, '{name}_ops'.format(name=op_attr_name))
             ops_map = getattr(cls, '{name}_op_nodes_map'.format(
                 name=op_attr_name))

pandas/core/frame.py

@@ -33,7 +33,6 @@
 from pandas.util._validators import (validate_bool_kwarg,
                                      validate_axis_style_args)
 
-from pandas import compat
 from pandas.compat import PY36, lmap, lzip, raise_with_traceback
 from pandas.compat.numpy import function as nv
 from pandas.core.dtypes.cast import (
@@ -1275,9 +1274,9 @@ def to_dict(self, orient='dict', into=dict):
         into_c = com.standardize_mapping(into)
         if orient.lower().startswith('d'):
             return into_c(
-                (k, v.to_dict(into)) for k, v in compat.iteritems(self))
+                (k, v.to_dict(into)) for k, v in self.items())
         elif orient.lower().startswith('l'):
-            return into_c((k, v.tolist()) for k, v in compat.iteritems(self))
+            return into_c((k, v.tolist()) for k, v in self.items())
         elif orient.lower().startswith('sp'):
             return into_c((('index', self.index.tolist()),
                            ('columns', self.columns.tolist()),
@@ -1287,14 +1286,14 @@ def to_dict(self, orient='dict', into=dict):
                            ])))
         elif orient.lower().startswith('s'):
             return into_c((k, com.maybe_box_datetimelike(v))
-                          for k, v in compat.iteritems(self))
+                          for k, v in self.items())
         elif orient.lower().startswith('r'):
             columns = self.columns.tolist()
             rows = (dict(zip(columns, row))
                     for row in self.itertuples(index=False, name=None))
             return [
                 into_c((k, com.maybe_box_datetimelike(v))
-                       for k, v in compat.iteritems(row))
+                       for k, v in row.items())
                 for row in rows]
         elif orient.lower().startswith('i'):
             if not self.index.is_unique:
@@ -1480,7 +1479,7 @@ def from_records(cls, data, index=None, exclude=None, columns=None,
             else:
                 arrays = []
                 arr_columns = []
-                for k, v in compat.iteritems(data):
+                for k, v in data.items():
                     if k in columns:
                         arr_columns.append(k)
                         arrays.append(v)
@@ -2430,7 +2429,7 @@ def _sizeof_fmt(num, size_qualifier):
 
         counts = self.get_dtype_counts()
         dtypes = ['{k}({kk:d})'.format(k=k[0], kk=k[1]) for k
-                  in sorted(compat.iteritems(counts))]
+                  in sorted(counts.items())]
         lines.append('dtypes: {types}'.format(types=', '.join(dtypes)))
 
         if memory_usage is None:
@@ -8051,8 +8050,8 @@ def isin(self, values):
 def _from_nested_dict(data):
     # TODO: this should be seriously cythonized
     new_data = OrderedDict()
-    for index, s in compat.iteritems(data):
-        for col, v in compat.iteritems(s):
+    for index, s in data.items():
+        for col, v in s.items():
             new_data[col] = new_data.get(col, OrderedDict())
             new_data[col][index] = v
     return new_data

pandas/core/generic.py

@@ -15,7 +15,6 @@
 from pandas._config import config
 
 from pandas._libs import Timestamp, iNaT, properties
-import pandas.compat as compat
 from pandas.compat import lrange, lzip, set_function_name, to_str
 from pandas.compat.numpy import function as nv
 from pandas.errors import AbstractMethodError
@@ -6154,7 +6153,7 @@ def fillna(self, value=None, method=None, axis=None, inplace=False,
                                               'by column')
 
                 result = self if inplace else self.copy()
-                for k, v in compat.iteritems(value):
+                for k, v in value.items():
                     if k not in result:
                         continue
                     obj = result[k]
@@ -6512,7 +6511,7 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None,
                 to_replace = regex
                 regex = True
 
-            items = list(compat.iteritems(to_replace))
+            items = list(to_replace.items())
             keys, values = lzip(*items) or ([], [])
 
             are_mappings = [is_dict_like(v) for v in values]
@@ -6551,7 +6550,7 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None,
             if is_dict_like(to_replace):
                 if is_dict_like(value):  # {'A' : NA} -> {'A' : 0}
                     res = self if inplace else self.copy()
-                    for c, src in compat.iteritems(to_replace):
+                    for c, src in to_replace.items():
                         if c in value and c in self:
                             # object conversion is handled in
                             # series.replace which is called recursivelly
@@ -6563,7 +6562,7 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None,
 
                 # {'A': NA} -> 0
                 elif not is_list_like(value):
-                    keys = [(k, src) for k, src in compat.iteritems(to_replace)
+                    keys = [(k, src) for k, src in to_replace.items()
                             if k in self]
                     keys_len = len(keys) - 1
                     for i, (k, src) in enumerate(keys):
@@ -6610,7 +6609,7 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None,
                 if is_dict_like(value):  # NA -> {'A' : 0, 'B' : -1}
                     new_data = self._data
 
-                    for k, v in compat.iteritems(value):
+                    for k, v in value.items():
                         if k in self:
                             new_data = new_data.replace(to_replace=to_replace,
                                                         value=v, filter=[k],

pandas/core/groupby/groupby.py

@@ -20,7 +20,6 @@ class providing the base-class of operations.
 from pandas._config.config import option_context
 
 from pandas._libs import Timestamp, groupby as libgroupby
-import pandas.compat as compat
 from pandas.compat import set_function_name
 from pandas.compat.numpy import function as nv
 from pandas.errors import AbstractMethodError
@@ -876,7 +875,7 @@ def _python_agg_general(self, func, *args, **kwargs):
         if self.grouper._filter_empty_groups:
 
             mask = counts.ravel() > 0
-            for name, result in compat.iteritems(output):
+            for name, result in output.items():
 
                 # since we are masking, make sure that we have a float object
                 values = result

pandas/core/indexes/base.py

@@ -11,7 +11,6 @@
 from pandas._libs.lib import is_datetime_array
 from pandas._libs.tslibs import OutOfBoundsDatetime, Timedelta, Timestamp
 from pandas._libs.tslibs.timezones import tz_compare
-import pandas.compat as compat
 from pandas.compat import set_function_name
 from pandas.compat.numpy import function as nv
 from pandas.util._decorators import Appender, Substitution, cache_readonly
@@ -535,7 +534,7 @@ def _simple_new(cls, values, name=None, dtype=None, **kwargs):
         # we actually set this value too.
         result._index_data = values
         result.name = name
-        for k, v in compat.iteritems(kwargs):
+        for k, v in kwargs.items():
             setattr(result, k, v)
         return result._reset_identity()
 
@@ -1754,7 +1753,7 @@ def __setstate__(self, state):
 
         if isinstance(state, dict):
             self._data = state.pop('data')
-            for k, v in compat.iteritems(state):
+            for k, v in state.items():
                 setattr(self, k, v)
 
         elif isinstance(state, tuple):
@@ -4486,7 +4485,7 @@ def groupby(self, values):
         result = values._reverse_indexer()
 
         # map to the label
-        result = {k: self.take(v) for k, v in compat.iteritems(result)}
+        result = {k: self.take(v) for k, v in result.items()}
 
         return result
 

pandas/core/indexes/category.py

@@ -238,7 +238,7 @@ def _simple_new(cls, values, name=None, dtype=None, **kwargs):
         values = cls._create_categorical(values, dtype=dtype)
         result._data = values
         result.name = name
-        for k, v in compat.iteritems(kwargs):
+        for k, v in kwargs.items():
             setattr(result, k, v)
 
         result._reset_identity()

pandas/core/indexes/range.py

@@ -156,7 +156,7 @@ def _simple_new(cls, start, stop=None, step=None, name=None,
         result._stop = stop or 0
         result._step = step or 1
         result.name = name
-        for k, v in compat.iteritems(kwargs):
+        for k, v in kwargs.items():
             setattr(result, k, v)
 
         result._reset_identity()

pandas/core/nanops.py

@@ -103,7 +103,7 @@ def __call__(self, alt):
         @functools.wraps(alt)
         def f(values, axis=None, skipna=True, **kwds):
             if len(self.kwargs) > 0:
-                for k, v in compat.iteritems(self.kwargs):
+                for k, v in self.kwargs.items():
                     if k not in kwds:
                         kwds[k] = v
             try:

pandas/core/panel.py

@@ -200,13 +200,13 @@ def _init_dict(self, data, axes, dtype=None):
         if haxis is not None:
             haxis = ensure_index(haxis)
             data = OrderedDict((k, v)
-                               for k, v in compat.iteritems(data)
+                               for k, v in data.items()
                                if k in haxis)
         else:
             keys = com.dict_keys_to_ordered_list(data)
             haxis = Index(keys)
 
-        for k, v in compat.iteritems(data):
+        for k, v in data.items():
             if isinstance(v, dict):
                 data[k] = self._constructor_sliced(v)
 
@@ -266,8 +266,8 @@ def from_dict(cls, data, intersect=False, orient='items', dtype=None):
         orient = orient.lower()
         if orient == 'minor':
             new_data = defaultdict(OrderedDict)
-            for col, df in compat.iteritems(data):
-                for item, s in compat.iteritems(df):
+            for col, df in data.items():
+                for item, s in df.items():
                     new_data[item][col] = s
             data = new_data
         elif orient != 'items':  # pragma: no cover
@@ -1500,7 +1500,7 @@ def _homogenize_dict(self, frames, intersect=True, dtype=None):
             result = OrderedDict()
 
         adj_frames = OrderedDict()
-        for k, v in compat.iteritems(frames):
+        for k, v in frames.items():
             if isinstance(v, dict):
                 adj_frames[k] = self._constructor_sliced(v)
             else:
@@ -1512,7 +1512,7 @@ def _homogenize_dict(self, frames, intersect=True, dtype=None):
 
         reindex_dict = {self._AXIS_SLICEMAP[a]: axes_dict[a] for a in axes}
         reindex_dict['copy'] = False
-        for key, frame in compat.iteritems(adj_frames):
+        for key, frame in adj_frames.items():
             if frame is not None:
                 result[key] = frame.reindex(**reindex_dict)
             else:

pandas/core/reshape/melt.py

@@ -10,7 +10,6 @@
 from pandas.core.dtypes.generic import ABCMultiIndex
 from pandas.core.dtypes.missing import notna
 
-from pandas import compat
 from pandas.core.arrays import Categorical
 from pandas.core.frame import _shared_docs
 from pandas.core.indexes.base import Index
@@ -173,7 +172,7 @@ def lreshape(data, groups, dropna=True, label=None):
         for c in pivot_cols:
             mask &= notna(mdata[c])
         if not mask.all():
-            mdata = {k: v[mask] for k, v in compat.iteritems(mdata)}
+            mdata = {k: v[mask] for k, v in mdata.items()}
 
     return data._constructor(mdata, columns=id_cols + pivot_cols)