de-privatize names imported across modules

Author: jbrockmendel

asv_bench/benchmarks/sparse.py

@@ -71,7 +71,7 @@ def setup(self):
         s.index = MultiIndex.from_product([range(10)] * 4)
         self.ss = s.astype("Sparse")
 
-    def time_sparse_series_to_coo(self):
+    def timesparse_series_to_coo(self):
         self.ss.sparse.to_coo(row_levels=[0, 1], column_levels=[2, 3], sort_labels=True)
 
 

pandas/__init__.py

@@ -20,9 +20,9 @@
 
 # numpy compat
 from pandas.compat.numpy import (
-    _np_version_under1p17,
-    _np_version_under1p18,
-    _is_numpy_dev,
+    is_numpy_dev as _is_numpy_dev,
+    np_version_under1p17 as _np_version_under1p17,
+    np_version_under1p18 as _np_version_under1p18,
 )
 
 try:
@@ -37,148 +37,134 @@
     ) from e
 
 from pandas._config import (
-    get_option,
-    set_option,
-    reset_option,
     describe_option,
+    get_option,
     option_context,
     options,
+    reset_option,
+    set_option,
 )
 
+from pandas.util._print_versions import show_versions
+from pandas.util._tester import test
+
+import pandas.api
+import pandas.arrays
+from pandas.core.arrays.sparse import SparseDtype
+from pandas.core.computation.api import eval
+
 # let init-time option registration happen
 import pandas.core.config_init
+from pandas.core.reshape.api import (
+    concat,
+    crosstab,
+    cut,
+    get_dummies,
+    lreshape,
+    melt,
+    merge,
+    merge_asof,
+    merge_ordered,
+    pivot,
+    pivot_table,
+    qcut,
+    wide_to_long,
+)
+import pandas.testing
 
-from pandas.core.api import (
-    # dtype
+from pandas.io.json import _json_normalize as json_normalize
+from pandas.tseries import offsets
+from pandas.tseries.api import infer_freq
+
+# use the closest tagged version if possible
+from ._version import get_versions
+
+from pandas.core.api import (  # dtype; missing; indexes; tseries; conversion; misc
+    NA,
+    BooleanDtype,
+    Categorical,
+    CategoricalDtype,
+    CategoricalIndex,
+    DataFrame,
+    DateOffset,
+    DatetimeIndex,
+    DatetimeTZDtype,
+    Flags,
+    Float64Index,
+    Grouper,
+    Index,
+    IndexSlice,
     Int8Dtype,
     Int16Dtype,
     Int32Dtype,
     Int64Dtype,
+    Int64Index,
+    Interval,
+    IntervalDtype,
+    IntervalIndex,
+    MultiIndex,
+    NamedAgg,
+    NaT,
+    Period,
+    PeriodDtype,
+    PeriodIndex,
+    RangeIndex,
+    Series,
+    StringDtype,
+    Timedelta,
+    TimedeltaIndex,
+    Timestamp,
     UInt8Dtype,
     UInt16Dtype,
     UInt32Dtype,
     UInt64Dtype,
-    CategoricalDtype,
-    PeriodDtype,
-    IntervalDtype,
-    DatetimeTZDtype,
-    StringDtype,
-    BooleanDtype,
-    # missing
-    NA,
+    UInt64Index,
+    array,
+    bdate_range,
+    date_range,
+    factorize,
+    interval_range,
     isna,
     isnull,
     notna,
     notnull,
-    # indexes
-    Index,
-    CategoricalIndex,
-    Int64Index,
-    UInt64Index,
-    RangeIndex,
-    Float64Index,
-    MultiIndex,
-    IntervalIndex,
-    TimedeltaIndex,
-    DatetimeIndex,
-    PeriodIndex,
-    IndexSlice,
-    # tseries
-    NaT,
-    Period,
     period_range,
-    Timedelta,
+    set_eng_float_format,
     timedelta_range,
-    Timestamp,
-    date_range,
-    bdate_range,
-    Interval,
-    interval_range,
-    DateOffset,
-    # conversion
-    to_numeric,
     to_datetime,
+    to_numeric,
     to_timedelta,
-    # misc
-    Flags,
-    Grouper,
-    factorize,
     unique,
     value_counts,
-    NamedAgg,
-    array,
-    Categorical,
-    set_eng_float_format,
-    Series,
-    DataFrame,
-)
-
-from pandas.core.arrays.sparse import SparseDtype
-
-from pandas.tseries.api import infer_freq
-from pandas.tseries import offsets
-
-from pandas.core.computation.api import eval
-
-from pandas.core.reshape.api import (
-    concat,
-    lreshape,
-    melt,
-    wide_to_long,
-    merge,
-    merge_asof,
-    merge_ordered,
-    crosstab,
-    pivot,
-    pivot_table,
-    get_dummies,
-    cut,
-    qcut,
 )
 
-import pandas.api
-from pandas.util._print_versions import show_versions
 
-from pandas.io.api import (
-    # excel
+from pandas.io.api import (  # excel; parsers; pickle; pytables; sql; misc
     ExcelFile,
     ExcelWriter,
-    read_excel,
-    # parsers
-    read_csv,
-    read_fwf,
-    read_table,
-    # pickle
-    read_pickle,
-    to_pickle,
-    # pytables
     HDFStore,
-    read_hdf,
-    # sql
-    read_sql,
-    read_sql_query,
-    read_sql_table,
-    # misc
     read_clipboard,
-    read_parquet,
-    read_orc,
+    read_csv,
+    read_excel,
     read_feather,
+    read_fwf,
     read_gbq,
+    read_hdf,
     read_html,
     read_json,
-    read_stata,
+    read_orc,
+    read_parquet,
+    read_pickle,
     read_sas,
     read_spss,
+    read_sql,
+    read_sql_query,
+    read_sql_table,
+    read_stata,
+    read_table,
+    to_pickle,
 )
 
-from pandas.io.json import _json_normalize as json_normalize
-
-from pandas.util._tester import test
-import pandas.testing
-import pandas.arrays
-
-# use the closest tagged version if possible
-from ._version import get_versions
 
 v = get_versions()
 __version__ = v.get("closest-tag", v["version"])

pandas/_testing.py

@@ -2713,7 +2713,7 @@ def use_numexpr(use, min_elements=None):
     if min_elements is None:
         min_elements = expr._MIN_ELEMENTS
 
-    olduse = expr._USE_NUMEXPR
+    olduse = expr.USE_NUMEXPR
     oldmin = expr._MIN_ELEMENTS
     expr.set_use_numexpr(use)
     expr._MIN_ELEMENTS = min_elements

pandas/compat/numpy/__init__.py

@@ -8,11 +8,11 @@
 # numpy versioning
 _np_version = np.__version__
 _nlv = LooseVersion(_np_version)
-_np_version_under1p17 = _nlv < LooseVersion("1.17")
-_np_version_under1p18 = _nlv < LooseVersion("1.18")
+np_version_under1p17 = _nlv < LooseVersion("1.17")
+np_version_under1p18 = _nlv < LooseVersion("1.18")
 _np_version_under1p19 = _nlv < LooseVersion("1.19")
 _np_version_under1p20 = _nlv < LooseVersion("1.20")
-_is_numpy_dev = ".dev" in str(_nlv)
+is_numpy_dev = ".dev" in str(_nlv)
 _min_numpy_ver = "1.16.5"
 
 
@@ -65,6 +65,6 @@ def np_array_datetime64_compat(arr, *args, **kwargs):
 __all__ = [
     "np",
     "_np_version",
-    "_np_version_under1p17",
-    "_is_numpy_dev",
+    "np_version_under1p17",
+    "is_numpy_dev",
 ]

pandas/core/array_algos/masked_reductions.py

@@ -8,7 +8,7 @@
 import numpy as np
 
 from pandas._libs import missing as libmissing
-from pandas.compat.numpy import _np_version_under1p17
+from pandas.compat.numpy import np_version_under1p17
 
 from pandas.core.nanops import check_below_min_count
 
@@ -46,7 +46,7 @@ def _sumprod(
         if check_below_min_count(values.shape, mask, min_count):
             return libmissing.NA
 
-        if _np_version_under1p17:
+        if np_version_under1p17:
             return func(values[~mask])
         else:
             return func(values, where=~mask)

pandas/core/arrays/sparse/accessor.py

@@ -88,9 +88,9 @@ def from_coo(cls, A, dense_index=False):
         dtype: Sparse[float64, nan]
         """
         from pandas import Series
-        from pandas.core.arrays.sparse.scipy_sparse import _coo_to_sparse_series
+        from pandas.core.arrays.sparse.scipy_sparse import coo_to_sparse_series
 
-        result = _coo_to_sparse_series(A, dense_index=dense_index)
+        result = coo_to_sparse_series(A, dense_index=dense_index)
         result = Series(result.array, index=result.index, copy=False)
 
         return result
@@ -168,9 +168,9 @@ def to_coo(self, row_levels=(0,), column_levels=(1,), sort_labels=False):
         >>> columns
         [('a', 0), ('a', 1), ('b', 0), ('b', 1)]
         """
-        from pandas.core.arrays.sparse.scipy_sparse import _sparse_series_to_coo
+        from pandas.core.arrays.sparse.scipy_sparse import sparse_series_to_coo
 
-        A, rows, columns = _sparse_series_to_coo(
+        A, rows, columns = sparse_series_to_coo(
             self._parent, row_levels, column_levels, sort_labels=sort_labels
         )
         return A, rows, columns

pandas/core/arrays/sparse/scipy_sparse.py

@@ -85,7 +85,7 @@ def _get_index_subset_to_coord_dict(index, subset, sort_labels=False):
     return values, i_coord, j_coord, i_labels, j_labels
 
 
-def _sparse_series_to_coo(ss, row_levels=(0,), column_levels=(1,), sort_labels=False):
+def sparse_series_to_coo(ss, row_levels=(0,), column_levels=(1,), sort_labels=False):
     """
     Convert a sparse Series to a scipy.sparse.coo_matrix using index
     levels row_levels, column_levels as the row and column
@@ -113,7 +113,7 @@ def _sparse_series_to_coo(ss, row_levels=(0,), column_levels=(1,), sort_labels=F
     return sparse_matrix, rows, columns
 
 
-def _coo_to_sparse_series(A, dense_index: bool = False):
+def coo_to_sparse_series(A, dense_index: bool = False):
     """
     Convert a scipy.sparse.coo_matrix to a SparseSeries.
 

pandas/core/common.py

@@ -16,7 +16,7 @@
 
 from pandas._libs import lib, tslibs
 from pandas._typing import AnyArrayLike, Scalar, T
-from pandas.compat.numpy import _np_version_under1p18
+from pandas.compat.numpy import np_version_under1p18
 
 from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
 from pandas.core.dtypes.common import (
@@ -425,7 +425,7 @@ def random_state(state=None):
     if (
         is_integer(state)
         or is_array_like(state)
-        or (not _np_version_under1p18 and isinstance(state, np.random.BitGenerator))
+        or (not np_version_under1p18 and isinstance(state, np.random.BitGenerator))
     ):
         return np.random.RandomState(state)
     elif isinstance(state, np.random.RandomState):

pandas/core/computation/engines.py

@@ -6,11 +6,11 @@
 from typing import Dict, Type
 
 from pandas.core.computation.align import align_terms, reconstruct_object
-from pandas.core.computation.ops import _mathops, _reductions
+from pandas.core.computation.ops import MATHOPS, REDUCTIONS
 
 import pandas.io.formats.printing as printing
 
-_ne_builtins = frozenset(_mathops + _reductions)
+_ne_builtins = frozenset(MATHOPS + REDUCTIONS)
 
 
 class NumExprClobberingError(NameError):