Use pandas.core.common for None checks

Author: jschendel

pandas/core/common.py

@@ -233,6 +233,20 @@ def _any_none(*args):
     return False
 
 
+def _all_none(*args):
+    for arg in args:
+        if arg is not None:
+            return False
+    return True
+
+
+def _any_not_none(*args):
+    for arg in args:
+        if arg is not None:
+            return True
+    return False
+
+
 def _all_not_none(*args):
     for arg in args:
         if arg is None:
@@ -459,13 +473,6 @@ def _apply_if_callable(maybe_callable, obj, **kwargs):
     return maybe_callable
 
 
-def _all_none(*args):
-    for arg in args:
-        if arg is not None:
-            return False
-    return True
-
-
 def _where_compat(mask, arr1, arr2):
     if arr1.dtype == _NS_DTYPE and arr2.dtype == _NS_DTYPE:
         new_vals = np.where(mask, arr1.view('i8'), arr2.view('i8'))

pandas/core/generic.py

@@ -29,10 +29,9 @@
 from pandas.core.dtypes.missing import isna, notna
 from pandas.core.dtypes.generic import ABCSeries, ABCPanel, ABCDataFrame
 
-from pandas.core.common import (_values_from_object,
-                                _maybe_box_datetimelike,
-                                SettingWithCopyError, SettingWithCopyWarning,
-                                AbstractMethodError)
+from pandas.core.common import (_count_not_none, _maybe_box_datetimelike,
+                                _values_from_object, AbstractMethodError,
+                                SettingWithCopyError, SettingWithCopyWarning)
 
 from pandas.core.base import PandasObject, SelectionMixin
 from pandas.core.index import (Index, MultiIndex, _ensure_index,
@@ -3126,7 +3125,7 @@ def filter(self, items=None, like=None, regex=None, axis=None):
         """
         import re
 
-        nkw = sum([x is not None for x in [items, like, regex]])
+        nkw = _count_not_none(items, like, regex)
         if nkw > 1:
             raise TypeError('Keyword arguments `items`, `like`, or `regex` '
                             'are mutually exclusive')

pandas/core/groupby.py

@@ -39,7 +39,8 @@
 from pandas.core.dtypes.missing import isna, notna, _maybe_fill
 
 from pandas.core.common import (_values_from_object, AbstractMethodError,
-                                _default_index)
+                                _default_index, _not_none, _get_callable_name,
+                                _asarray_tuplesafe)
 
 from pandas.core.base import (PandasObject, SelectionMixin, GroupByError,
                               DataError, SpecificationError)
@@ -60,7 +61,6 @@
 from pandas.util._validators import validate_kwargs
 
 import pandas.core.algorithms as algorithms
-import pandas.core.common as com
 from pandas.core.config import option_context
 
 from pandas.plotting._core import boxplot_frame_groupby
@@ -877,10 +877,9 @@ def _concat_objects(self, keys, values, not_indexed_same=False):
         def reset_identity(values):
             # reset the identities of the components
             # of the values to prevent aliasing
-            for v in values:
-                if v is not None:
-                    ax = v._get_axis(self.axis)
-                    ax._reset_identity()
+            for v in _not_none(*values):
+                ax = v._get_axis(self.axis)
+                ax._reset_identity()
             return values
 
         if not not_indexed_same:
@@ -1806,7 +1805,7 @@ def apply(self, f, data, axis=0):
         group_keys = self._get_group_keys()
 
         # oh boy
-        f_name = com._get_callable_name(f)
+        f_name = _get_callable_name(f)
         if (f_name not in _plotting_methods and
                 hasattr(splitter, 'fast_apply') and axis == 0):
             try:
@@ -2533,7 +2532,7 @@ def __init__(self, index, grouper=None, obj=None, name=None, level=None,
                 self.grouper = self.obj[self.name]
 
             elif isinstance(self.grouper, (list, tuple)):
-                self.grouper = com._asarray_tuplesafe(self.grouper)
+                self.grouper = _asarray_tuplesafe(self.grouper)
 
             # a passed Categorical
             elif is_categorical_dtype(self.grouper):
@@ -2739,7 +2738,7 @@ def _get_grouper(obj, key=None, axis=0, level=None, sort=True,
     if not any_callable and not all_in_columns_index and \
        not any_arraylike and not any_groupers and \
        match_axis_length and level is None:
-        keys = [com._asarray_tuplesafe(keys)]
+        keys = [_asarray_tuplesafe(keys)]
 
     if isinstance(level, (tuple, list)):
         if key is None:
@@ -3028,7 +3027,7 @@ def _aggregate_multiple_funcs(self, arg, _level):
                     columns.append(f)
                 else:
                     # protect against callables without names
-                    columns.append(com._get_callable_name(f))
+                    columns.append(_get_callable_name(f))
             arg = lzip(columns, arg)
 
         results = {}
@@ -3686,14 +3685,13 @@ def _wrap_applied_output(self, keys, values, not_indexed_same=False):
         key_names = self.grouper.names
 
         # GH12824.
-        def first_non_None_value(values):
+        def first_not_none(values):
             try:
-                v = next(v for v in values if v is not None)
+                return next(_not_none(*values))
             except StopIteration:
                 return None
-            return v
 
-        v = first_non_None_value(values)
+        v = first_not_none(values)
 
         if v is None:
             # GH9684. If all values are None, then this will throw an error.
@@ -3726,7 +3724,7 @@ def first_non_None_value(values):
                     key_index = None
 
             # make Nones an empty object
-            v = first_non_None_value(values)
+            v = first_not_none(values)
             if v is None:
                 return DataFrame()
             elif isinstance(v, NDFrame):

pandas/core/indexes/api.py

@@ -123,7 +123,7 @@ def _get_consensus_names(indexes):
     # find the non-none names, need to tupleify to make
     # the set hashable, then reverse on return
     consensus_names = set([tuple(i.names) for i in indexes
-                           if any(n is not None for n in i.names)])
+                           if com._any_not_none(*i.names)])
     if len(consensus_names) == 1:
         return list(list(consensus_names)[0])
     return [None] * indexes[0].nlevels

pandas/core/indexes/base.py

@@ -42,16 +42,15 @@
     needs_i8_conversion,
     is_iterator, is_list_like,
     is_scalar)
-from pandas.core.common import (is_bool_indexer,
-                                _values_from_object,
-                                _asarray_tuplesafe)
+from pandas.core.common import (is_bool_indexer, _values_from_object,
+                                _asarray_tuplesafe, _not_none,
+                                _index_labels_to_array)
 
 from pandas.core.base import PandasObject, IndexOpsMixin
 import pandas.core.base as base
 from pandas.util._decorators import (
     Appender, Substitution, cache_readonly, deprecate_kwarg)
 from pandas.core.indexes.frozen import FrozenList
-import pandas.core.common as com
 import pandas.core.dtypes.concat as _concat
 import pandas.core.missing as missing
 import pandas.core.algorithms as algos
@@ -3145,8 +3144,8 @@ def _join_multi(self, other, how, return_indexers=True):
         other_is_mi = isinstance(other, MultiIndex)
 
         # figure out join names
-        self_names = [n for n in self.names if n is not None]
-        other_names = [n for n in other.names if n is not None]
+        self_names = _not_none(*self.names)
+        other_names = _not_none(*other.names)
         overlap = list(set(self_names) & set(other_names))
 
         # need at least 1 in common, but not more than 1
@@ -3691,7 +3690,7 @@ def drop(self, labels, errors='raise'):
         -------
         dropped : Index
         """
-        labels = com._index_labels_to_array(labels)
+        labels = _index_labels_to_array(labels)
         indexer = self.get_indexer(labels)
         mask = indexer == -1
         if mask.any():

pandas/core/indexes/multi.py

@@ -21,7 +21,8 @@
     is_scalar)
 from pandas.core.dtypes.missing import isna, array_equivalent
 from pandas.errors import PerformanceWarning, UnsortedIndexError
-from pandas.core.common import (_values_from_object,
+from pandas.core.common import (_any_not_none,
+                                _values_from_object,
                                 is_bool_indexer,
                                 is_null_slice,
                                 is_true_slices)
@@ -509,7 +510,7 @@ def _format_attrs(self):
                                             max_seq_items=False)),
             ('labels', ibase.default_pprint(self._labels,
                                             max_seq_items=False))]
-        if not all(name is None for name in self.names):
+        if _any_not_none(*self.names):
             attrs.append(('names', ibase.default_pprint(self.names)))
         if self.sortorder is not None:
             attrs.append(('sortorder', ibase.default_pprint(self.sortorder)))

pandas/core/indexes/range.py

@@ -12,6 +12,7 @@
 from pandas import compat
 from pandas.compat import lrange, range
 from pandas.compat.numpy import function as nv
+from pandas.core.common import _all_none
 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
@@ -83,7 +84,7 @@ def _ensure_int(value, field):
 
             return new_value
 
-        if start is None and stop is None and step is None:
+        if _all_none(start, stop, step):
             msg = "RangeIndex(...) must be called with integers"
             raise TypeError(msg)
         elif start is None:

pandas/core/panel.py

@@ -15,13 +15,13 @@
     is_string_like, is_scalar)
 from pandas.core.dtypes.missing import notna
 
-import pandas.core.common as com
 import pandas.core.ops as ops
 import pandas.core.missing as missing
 from pandas import compat
 from pandas.compat import (map, zip, range, u, OrderedDict)
 from pandas.compat.numpy import function as nv
-from pandas.core.common import _try_sort, _default_index
+from pandas.core.common import (_try_sort, _default_index, _all_not_none,
+                                _any_not_none, _apply_if_callable)
 from pandas.core.frame import DataFrame
 from pandas.core.generic import NDFrame, _shared_docs
 from pandas.core.index import (Index, MultiIndex, _ensure_index,
@@ -165,7 +165,7 @@ def _init_data(self, data, copy, dtype, **kwargs):
 
         axes = None
         if isinstance(data, BlockManager):
-            if any(x is not None for x in passed_axes):
+            if _any_not_none(*passed_axes):
                 axes = [x if x is not None else y
                         for x, y in zip(passed_axes, data.axes)]
             mgr = data
@@ -177,7 +177,7 @@ def _init_data(self, data, copy, dtype, **kwargs):
             mgr = self._init_matrix(data, passed_axes, dtype=dtype, copy=copy)
             copy = False
             dtype = None
-        elif is_scalar(data) and all(x is not None for x in passed_axes):
+        elif is_scalar(data) and _all_not_none(*passed_axes):
             values = cast_scalar_to_array([len(x) for x in passed_axes],
                                           data, dtype=dtype)
             mgr = self._init_matrix(values, passed_axes, dtype=values.dtype,
@@ -278,7 +278,7 @@ def from_dict(cls, data, intersect=False, orient='items', dtype=None):
         return cls(**d)
 
     def __getitem__(self, key):
-        key = com._apply_if_callable(key, self)
+        key = _apply_if_callable(key, self)
 
         if isinstance(self._info_axis, MultiIndex):
             return self._getitem_multilevel(key)
@@ -593,7 +593,7 @@ def _box_item_values(self, key, values):
         return self._constructor_sliced(values, **d)
 
     def __setitem__(self, key, value):
-        key = com._apply_if_callable(key, self)
+        key = _apply_if_callable(key, self)
         shape = tuple(self.shape)
         if isinstance(value, self._constructor_sliced):
             value = value.reindex(
@@ -615,7 +615,9 @@ def __setitem__(self, key, value):
 
     def _unpickle_panel_compat(self, state):  # pragma: no cover
         "Unpickle the panel"
-        _unpickle = com._unpickle_array
+        from pandas.io.pickle import _unpickle_array
+
+        _unpickle = _unpickle_array
         vals, items, major, minor = state
 
         items = _unpickle(items)

pandas/core/reshape/concat.py

@@ -241,7 +241,7 @@ def __init__(self, objs, axis=0, join='outer', join_axes=None,
             raise ValueError('No objects to concatenate')
 
         if keys is None:
-            objs = [obj for obj in objs if obj is not None]
+            objs = list(com._not_none(*objs))
         else:
             # #1649
             clean_keys = []

pandas/core/reshape/merge.py

@@ -1550,4 +1550,4 @@ def _should_fill(lname, rname):
 
 
 def _any(x):
-    return x is not None and len(x) > 0 and any([y is not None for y in x])
+    return x is not None and com._any_not_none(*x)