CLN: deprivatize names in pd.core.common (#27741)

* deprivative funcs in pd.core.common

* simplify all/any funcs

Author: topper-123

pandas/core/common.py

@@ -165,51 +165,39 @@ def cast_scalar_indexer(val):
     return val
 
 
-def _not_none(*args):
+def not_none(*args):
     """
     Returns a generator consisting of the arguments that are not None.
     """
     return (arg for arg in args if arg is not None)
 
 
-def _any_none(*args):
+def any_none(*args):
     """
     Returns a boolean indicating if any argument is None.
     """
-    for arg in args:
-        if arg is None:
-            return True
-    return False
+    return any(arg is None for arg in args)
 
 
-def _all_none(*args):
+def all_none(*args):
     """
     Returns a boolean indicating if all arguments are None.
     """
-    for arg in args:
-        if arg is not None:
-            return False
-    return True
+    return all(arg is None for arg in args)
 
 
-def _any_not_none(*args):
+def any_not_none(*args):
     """
     Returns a boolean indicating if any argument is not None.
     """
-    for arg in args:
-        if arg is not None:
-            return True
-    return False
+    return any(arg is not None for arg in args)
 
 
-def _all_not_none(*args):
+def all_not_none(*args):
     """
     Returns a boolean indicating if all arguments are not None.
     """
-    for arg in args:
-        if arg is None:
-            return False
-    return True
+    return all(arg is not None for arg in args)
 
 
 def count_not_none(*args):
@@ -447,7 +435,7 @@ def random_state(state=None):
         )
 
 
-def _pipe(obj, func, *args, **kwargs):
+def pipe(obj, func, *args, **kwargs):
     """
     Apply a function ``func`` to object ``obj`` either by passing obj as the
     first argument to the function or, in the case that the func is a tuple,
@@ -482,7 +470,7 @@ def _pipe(obj, func, *args, **kwargs):
         return func(obj, *args, **kwargs)
 
 
-def _get_rename_function(mapper):
+def get_rename_function(mapper):
     """
     Returns a function that will map names/labels, dependent if mapper
     is a dict, Series or just a function.

pandas/core/generic.py

@@ -1124,7 +1124,7 @@ def rename(self, *args, **kwargs):
             v = axes.get(self._AXIS_NAMES[axis])
             if v is None:
                 continue
-            f = com._get_rename_function(v)
+            f = com.get_rename_function(v)
             baxis = self._get_block_manager_axis(axis)
             if level is not None:
                 level = self.axes[axis]._get_level_number(level)
@@ -1312,7 +1312,7 @@ class  name
                 if non_mapper:
                     newnames = v
                 else:
-                    f = com._get_rename_function(v)
+                    f = com.get_rename_function(v)
                     curnames = self._get_axis(axis).names
                     newnames = [f(name) for name in curnames]
                 result._set_axis_name(newnames, axis=axis, inplace=True)
@@ -4993,7 +4993,7 @@ def sample(
 
     @Appender(_shared_docs["pipe"] % _shared_doc_kwargs)
     def pipe(self, func, *args, **kwargs):
-        return com._pipe(self, func, *args, **kwargs)
+        return com.pipe(self, func, *args, **kwargs)
 
     _shared_docs["aggregate"] = dedent(
         """

pandas/core/groupby/generic.py

@@ -361,7 +361,7 @@ def _wrap_applied_output(self, keys, values, not_indexed_same=False):
         # GH12824.
         def first_not_none(values):
             try:
-                return next(com._not_none(*values))
+                return next(com.not_none(*values))
             except StopIteration:
                 return None
 

pandas/core/groupby/groupby.py

@@ -590,7 +590,7 @@ def __getattr__(self, attr):
     )
     @Appender(_pipe_template)
     def pipe(self, func, *args, **kwargs):
-        return com._pipe(self, func, *args, **kwargs)
+        return com.pipe(self, func, *args, **kwargs)
 
     plot = property(GroupByPlot)
 
@@ -928,7 +928,7 @@ 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 com._not_none(*values):
+            for v in com.not_none(*values):
                 ax = v._get_axis(self.axis)
                 ax._reset_identity()
             return values

pandas/core/indexes/api.py

@@ -283,7 +283,7 @@ def _get_consensus_names(indexes):
 
     # find the non-none names, need to tupleify to make
     # the set hashable, then reverse on return
-    consensus_names = {tuple(i.names) for i in indexes if com._any_not_none(*i.names)}
+    consensus_names = {tuple(i.names) for i in indexes 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

@@ -3588,8 +3588,8 @@ def _join_multi(self, other, how, return_indexers=True):
         from pandas.core.reshape.merge import _restore_dropped_levels_multijoin
 
         # figure out join names
-        self_names = set(com._not_none(*self.names))
-        other_names = set(com._not_none(*other.names))
+        self_names = set(com.not_none(*self.names))
+        other_names = set(com.not_none(*other.names))
         overlap = self_names & other_names
 
         # need at least 1 in common

pandas/core/indexes/datetimes.py

@@ -1569,7 +1569,7 @@ def date_range(
                   dtype='datetime64[ns]', freq='D')
     """
 
-    if freq is None and com._any_none(periods, start, end):
+    if freq is None and com.any_none(periods, start, end):
         freq = "D"
 
     dtarr = DatetimeArray._generate_range(

pandas/core/indexes/interval.py

@@ -1318,7 +1318,7 @@ def _is_type_compatible(a, b):
         (is_number(a) and is_number(b))
         or (is_ts_compat(a) and is_ts_compat(b))
         or (is_td_compat(a) and is_td_compat(b))
-        or com._any_none(a, b)
+        or com.any_none(a, b)
     )
 
 
@@ -1416,7 +1416,7 @@ def interval_range(
     end = com.maybe_box_datetimelike(end)
     endpoint = start if start is not None else end
 
-    if freq is None and com._any_none(periods, start, end):
+    if freq is None and com.any_none(periods, start, end):
         freq = 1 if is_number(endpoint) else "D"
 
     if com.count_not_none(start, end, periods, freq) != 3:
@@ -1463,7 +1463,7 @@ def interval_range(
 
     if is_number(endpoint):
         # force consistency between start/end/freq (lower end if freq skips it)
-        if com._all_not_none(start, end, freq):
+        if com.all_not_none(start, end, freq):
             end -= (end - start) % freq
 
         # compute the period/start/end if unspecified (at most one)
@@ -1475,7 +1475,7 @@ def interval_range(
             end = start + (periods - 1) * freq
 
         breaks = np.linspace(start, end, periods)
-        if all(is_integer(x) for x in com._not_none(start, end, freq)):
+        if all(is_integer(x) for x in com.not_none(start, end, freq)):
             # np.linspace always produces float output
             breaks = maybe_downcast_to_dtype(breaks, "int64")
     else:

pandas/core/indexes/range.py

@@ -110,7 +110,7 @@ def __new__(
             return cls._simple_new(start, dtype=dtype, name=name)
 
         # validate the arguments
-        if com._all_none(start, stop, step):
+        if com.all_none(start, stop, step):
             raise TypeError("RangeIndex(...) must be called with integers")
 
         start = ensure_python_int(start) if start is not None else 0

pandas/core/indexes/timedeltas.py

@@ -780,7 +780,7 @@ def timedelta_range(
                 '5 days 00:00:00'],
                dtype='timedelta64[ns]', freq=None)
     """
-    if freq is None and com._any_none(periods, start, end):
+    if freq is None and com.any_none(periods, start, end):
         freq = "D"
 
     freq, freq_infer = dtl.maybe_infer_freq(freq)