cleanup inconsistently used imports

pandas/core/categorical.py

@@ -28,7 +28,6 @@
     is_list_like, is_sequence,
     is_scalar,
     is_dict_like)
-from pandas.core.common import is_null_slice, _maybe_box_datetimelike
 
 from pandas.core.algorithms import factorize, take_1d, unique1d
 from pandas.core.accessor import PandasDelegate
@@ -468,7 +467,7 @@ def tolist(self):
         (for Timestamp/Timedelta/Interval/Period)
         """
         if is_datetimelike(self.categories):
-            return [_maybe_box_datetimelike(x) for x in self]
+            return [com._maybe_box_datetimelike(x) for x in self]
         return np.array(self).tolist()
 
     @property
@@ -1686,7 +1685,7 @@ def _slice(self, slicer):
         # only allow 1 dimensional slicing, but can
         # in a 2-d case be passd (slice(None),....)
         if isinstance(slicer, tuple) and len(slicer) == 2:
-            if not is_null_slice(slicer[0]):
+            if not com.is_null_slice(slicer[0]):
                 raise AssertionError("invalid slicing for a 1-ndim "
                                      "categorical")
             slicer = slicer[1]
@@ -1847,7 +1846,7 @@ def __setitem__(self, key, value):
             # only allow 1 dimensional slicing, but can
             # in a 2-d case be passd (slice(None),....)
             if len(key) == 2:
-                if not is_null_slice(key[0]):
+                if not com.is_null_slice(key[0]):
                     raise AssertionError("invalid slicing for a 1-ndim "
                                          "categorical")
                 key = key[1]

pandas/core/frame.py

@@ -62,12 +62,6 @@
 from pandas.core.dtypes.missing import isna, notna
 
 
-from pandas.core.common import (_try_sort,
-                                _default_index,
-                                _values_from_object,
-                                _maybe_box_datetimelike,
-                                _dict_compat,
-                                standardize_mapping)
 from pandas.core.generic import NDFrame, _shared_docs
 from pandas.core.index import (Index, MultiIndex, _ensure_index,
                                _ensure_index_from_sequences)
@@ -387,9 +381,9 @@ def __init__(self, data=None, index=None, columns=None, dtype=None,
                         if isinstance(data[0], Series):
                             index = _get_names_from_index(data)
                         elif isinstance(data[0], Categorical):
-                            index = _default_index(len(data[0]))
+                            index = com._default_index(len(data[0]))
                         else:
-                            index = _default_index(len(data))
+                            index = com._default_index(len(data))
 
                     mgr = _arrays_to_mgr(arrays, columns, index, columns,
                                          dtype=dtype)
@@ -466,7 +460,7 @@ def _init_dict(self, data, index, columns, dtype=None):
         else:
             keys = list(data.keys())
             if not isinstance(data, OrderedDict):
-                keys = _try_sort(keys)
+                keys = com._try_sort(keys)
             columns = data_names = Index(keys)
             arrays = [data[k] for k in keys]
 
@@ -493,12 +487,12 @@ def _get_axes(N, K, index=index, columns=columns):
             # return axes or defaults
 
             if index is None:
-                index = _default_index(N)
+                index = com._default_index(N)
             else:
                 index = _ensure_index(index)
 
             if columns is None:
-                columns = _default_index(K)
+                columns = com._default_index(K)
             else:
                 columns = _ensure_index(columns)
             return index, columns
@@ -990,7 +984,7 @@ def to_dict(self, orient='dict', into=dict):
                           "columns will be omitted.", UserWarning,
                           stacklevel=2)
         # GH16122
-        into_c = standardize_mapping(into)
+        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))
@@ -1000,13 +994,13 @@ def to_dict(self, orient='dict', into=dict):
             return into_c((('index', self.index.tolist()),
                            ('columns', self.columns.tolist()),
                            ('data', lib.map_infer(self.values.ravel(),
-                                                  _maybe_box_datetimelike)
+                                                  com._maybe_box_datetimelike)
                             .reshape(self.values.shape).tolist())))
         elif orient.lower().startswith('s'):
-            return into_c((k, _maybe_box_datetimelike(v))
+            return into_c((k, com._maybe_box_datetimelike(v))
                           for k, v in compat.iteritems(self))
         elif orient.lower().startswith('r'):
-            return [into_c((k, _maybe_box_datetimelike(v))
+            return [into_c((k, com._maybe_box_datetimelike(v))
                            for k, v in zip(self.columns, np.atleast_1d(row)))
                     for row in self.values]
         elif orient.lower().startswith('i'):
@@ -2006,7 +2000,7 @@ def _get_value(self, index, col, takeable=False):
 
         if takeable:
             series = self._iget_item_cache(col)
-            return _maybe_box_datetimelike(series._values[index])
+            return com._maybe_box_datetimelike(series._values[index])
 
         series = self._get_item_cache(col)
         engine = self.index._engine
@@ -3371,7 +3365,7 @@ def _maybe_casted_values(index, labels=None):
                             values, mask, np.nan)
             return values
 
-        new_index = _default_index(len(new_obj))
+        new_index = com._default_index(len(new_obj))
         if level is not None:
             if not isinstance(level, (tuple, list)):
                 level = [level]
@@ -6084,7 +6078,7 @@ def extract_index(data):
                            (lengths[0], len(index)))
                     raise ValueError(msg)
             else:
-                index = _default_index(lengths[0])
+                index = com._default_index(lengths[0])
 
     return _ensure_index(index)
 
@@ -6155,7 +6149,7 @@ def _to_arrays(data, columns, coerce_float=False, dtype=None):
                                          dtype=dtype)
     elif isinstance(data[0], Categorical):
         if columns is None:
-            columns = _default_index(len(data))
+            columns = com._default_index(len(data))
         return data, columns
     elif (isinstance(data, (np.ndarray, Series, Index)) and
           data.dtype.names is not None):
@@ -6179,7 +6173,7 @@ def _masked_rec_array_to_mgr(data, index, columns, dtype, copy):
     if index is None:
         index = _get_names_from_index(fdata)
         if index is None:
-            index = _default_index(len(data))
+            index = com._default_index(len(data))
     index = _ensure_index(index)
 
     if columns is not None:
@@ -6239,14 +6233,14 @@ def _list_of_series_to_arrays(data, columns, coerce_float=False, dtype=None):
     for s in data:
         index = getattr(s, 'index', None)
         if index is None:
-            index = _default_index(len(s))
+            index = com._default_index(len(s))
 
         if id(index) in indexer_cache:
             indexer = indexer_cache[id(index)]
         else:
             indexer = indexer_cache[id(index)] = index.get_indexer(columns)
 
-        values = _values_from_object(s)
+        values = com._values_from_object(s)
         aligned_values.append(algorithms.take_1d(values, indexer))
 
     values = np.vstack(aligned_values)
@@ -6276,7 +6270,7 @@ def _list_of_dict_to_arrays(data, columns, coerce_float=False, dtype=None):
 
 def _convert_object_array(content, columns, coerce_float=False, dtype=None):
     if columns is None:
-        columns = _default_index(len(content))
+        columns = com._default_index(len(content))
     else:
         if len(columns) != len(content):  # pragma: no cover
             # caller's responsibility to check for this...
@@ -6298,7 +6292,7 @@ def convert(arr):
 def _get_names_from_index(data):
     has_some_name = any(getattr(s, 'name', None) is not None for s in data)
     if not has_some_name:
-        return _default_index(len(data))
+        return com._default_index(len(data))
 
     index = lrange(len(data))
     count = 0
@@ -6333,7 +6327,7 @@ def _homogenize(data, index, dtype=None):
                     oindex = index.astype('O')
 
                 if isinstance(index, (DatetimeIndex, TimedeltaIndex)):
-                    v = _dict_compat(v)
+                    v = com._dict_compat(v)
                 else:
                     v = dict(v)
                 v = lib.fast_multiget(v, oindex.values, default=np.nan)

pandas/core/generic.py

@@ -30,9 +30,7 @@
 from pandas.core.dtypes.inference import is_hashable
 from pandas.core.dtypes.missing import isna, notna
 from pandas.core.dtypes.generic import ABCSeries, ABCPanel, ABCDataFrame
-from pandas.core.common import (_count_not_none,
-                                _maybe_box_datetimelike, _values_from_object,
-                                AbstractMethodError, SettingWithCopyError,
+from pandas.core.common import (AbstractMethodError, SettingWithCopyError,
                                 SettingWithCopyWarning)
 
 from pandas.core.base import PandasObject, SelectionMixin
@@ -1026,7 +1024,7 @@ def _indexed_same(self, other):
                    for a in self._AXIS_ORDERS)
 
     def __neg__(self):
-        values = _values_from_object(self)
+        values = com._values_from_object(self)
         if values.dtype == np.bool_:
             arr = operator.inv(values)
         else:
@@ -1035,7 +1033,7 @@ def __neg__(self):
 
     def __invert__(self):
         try:
-            arr = operator.inv(_values_from_object(self))
+            arr = operator.inv(com._values_from_object(self))
             return self.__array_wrap__(arr)
         except Exception:
 
@@ -1490,7 +1488,7 @@ def __round__(self, decimals=0):
     # Array Interface
 
     def __array__(self, dtype=None):
-        return _values_from_object(self)
+        return com._values_from_object(self)
 
     def __array_wrap__(self, result, context=None):
         d = self._construct_axes_dict(self._AXIS_ORDERS, copy=False)
@@ -2696,7 +2694,7 @@ def xs(self, key, axis=0, level=None, drop_level=True):
             # that means that their are list/ndarrays inside the Series!
             # so just return them (GH 6394)
             if not is_list_like(new_values) or self.ndim == 1:
-                return _maybe_box_datetimelike(new_values)
+                return com._maybe_box_datetimelike(new_values)
 
             result = self._constructor_sliced(
                 new_values, index=self.columns,
@@ -3557,7 +3555,7 @@ def filter(self, items=None, like=None, regex=None, axis=None):
         """
         import re
 
-        nkw = _count_not_none(items, like, regex)
+        nkw = com._count_not_none(items, like, regex)
         if nkw > 1:
             raise TypeError('Keyword arguments `items`, `like`, or `regex` '
                             'are mutually exclusive')
@@ -6357,7 +6355,7 @@ def _where(self, cond, other=np.nan, inplace=False, axis=None, level=None,
                         if try_quick:
 
                             try:
-                                new_other = _values_from_object(self).copy()
+                                new_other = com._values_from_object(self).copy()
                                 new_other[icond] = other
                                 other = new_other
                             except Exception:
@@ -7318,7 +7316,7 @@ def pct_change(self, periods=1, fill_method='pad', limit=None, freq=None,
         rs = (data.div(data.shift(periods=periods, freq=freq, axis=axis,
                                   **kwargs)) - 1)
         if freq is None:
-            mask = isna(_values_from_object(self))
+            mask = isna(com._values_from_object(self))
             np.putmask(rs.values, mask, np.nan)
         return rs
 
@@ -7778,7 +7776,7 @@ def cum_func(self, axis=None, skipna=True, *args, **kwargs):
         else:
             axis = self._get_axis_number(axis)
 
-        y = _values_from_object(self).copy()
+        y = com._values_from_object(self).copy()
 
         if (skipna and
                 issubclass(y.dtype.type, (np.datetime64, np.timedelta64))):

pandas/core/indexes/datetimelike.py

@@ -10,6 +10,7 @@
 from pandas.core.tools.timedeltas import to_timedelta
 
 import numpy as np
+
 from pandas.core.dtypes.common import (
     _ensure_int64,
     is_dtype_equal,
@@ -29,6 +30,8 @@
 from pandas.core.dtypes.generic import (
     ABCIndex, ABCSeries, ABCPeriodIndex, ABCIndexClass)
 from pandas.core.dtypes.missing import isna
+import pandas.core.dtypes.concat as _concat
+
 from pandas.core import common as com, algorithms
 from pandas.core.algorithms import checked_add_with_arr
 from pandas.core.common import AbstractMethodError
@@ -41,7 +44,6 @@
 
 from pandas.core.indexes.base import Index, _index_shared_docs
 from pandas.util._decorators import Appender, cache_readonly
-import pandas.core.dtypes.concat as _concat
 import pandas.tseries.frequencies as frequencies
 
 import pandas.core.indexes.base as ibase

pandas/core/panel.py

@@ -31,7 +31,7 @@
 from pandas.core.internals import (BlockManager,
                                    create_block_manager_from_arrays,
                                    create_block_manager_from_blocks)
-from pandas.core.ops import _op_descriptions
+
 from pandas.core.series import Series
 from pandas.core.reshape.util import cartesian_product
 from pandas.util._decorators import Appender
@@ -1545,9 +1545,9 @@ def na_op(x, y):
                 result = missing.fill_zeros(result, x, y, name, fill_zeros)
                 return result
 
-            if name in _op_descriptions:
+            if name in ops._op_descriptions:
                 op_name = name.replace('__', '')
-                op_desc = _op_descriptions[op_name]
+                op_desc = ops._op_descriptions[op_name]
                 if op_desc['reversed']:
                     equiv = 'other ' + op_desc['op'] + ' panel'
                 else: