Reduce Circular Imports with pandas.core.reshape.concat

pandas/core/frame.py

@@ -118,6 +118,7 @@
     to_arrays,
 )
 from pandas.core.ops.missing import dispatch_fill_zeros
+from pandas.core.reshape.concat import concat
 from pandas.core.series import Series
 
 from pandas.io.formats import console, format as fmt
@@ -6991,8 +6992,6 @@ def append(self, other, ignore_index=False, verify_integrity=False, sort=None):
             if (self.columns.get_indexer(other.columns) >= 0).all():
                 other = other.reindex(columns=self.columns)
 
-        from pandas.core.reshape.concat import concat
-
         if isinstance(other, (list, tuple)):
             to_concat = [self] + other
         else:
@@ -7130,7 +7129,6 @@ def _join_compat(
         self, other, on=None, how="left", lsuffix="", rsuffix="", sort=False
     ):
         from pandas.core.reshape.merge import merge
-        from pandas.core.reshape.concat import concat
 
         if isinstance(other, Series):
             if other.name is None:
@@ -7291,7 +7289,6 @@ def round(self, decimals=0, *args, **kwargs):
         2   0.7   0.0
         3   0.2   0.0
         """
-        from pandas.core.reshape.concat import concat
 
         def _dict_round(df, decimals):
             for col, vals in df.items():
@@ -8297,8 +8294,6 @@ def isin(self, values):
         dog        False      False
         """
         if isinstance(values, dict):
-            from pandas.core.reshape.concat import concat
-
             values = collections.defaultdict(list, values)
             return concat(
                 (

pandas/core/groupby/generic.py

@@ -67,6 +67,7 @@
 from pandas.core.index import Index, MultiIndex, _all_indexes_same
 import pandas.core.indexes.base as ibase
 from pandas.core.internals import BlockManager, make_block
+from pandas.core.reshape.concat import concat
 from pandas.core.series import Series
 
 from pandas.plotting import boxplot_frame_groupby
@@ -275,8 +276,6 @@ def aggregate(self, func=None, *args, **kwargs):
 
         # _level handled at higher
         if not _level and isinstance(ret, dict):
-            from pandas import concat
-
             ret = concat(ret, axis=1)
         return ret
 
@@ -443,8 +442,6 @@ def transform(self, func, *args, **kwargs):
 
         # check for empty "results" to avoid concat ValueError
         if results:
-            from pandas.core.reshape.concat import concat
-
             result = concat(results).sort_index()
         else:
             result = Series()
@@ -1221,8 +1218,6 @@ def first_not_none(values):
                         # still a series
                         # path added as of GH 5545
                         elif all_indexed_same:
-                            from pandas.core.reshape.concat import concat
-
                             return concat(values)
 
                     if not all_indexed_same:
@@ -1257,8 +1252,6 @@ def first_not_none(values):
                         else:
                             # GH5788 instead of stacking; concat gets the
                             # dtypes correct
-                            from pandas.core.reshape.concat import concat
-
                             result = concat(
                                 values,
                                 keys=key_index,
@@ -1303,8 +1296,6 @@ def first_not_none(values):
             return self._concat_objects(keys, values, not_indexed_same=not_indexed_same)
 
     def _transform_general(self, func, *args, **kwargs):
-        from pandas.core.reshape.concat import concat
-
         applied = []
         obj = self._obj_with_exclusions
         gen = self.grouper.get_iterator(obj, axis=self.axis)
@@ -1653,8 +1644,6 @@ def _iterate_column_groupbys(self):
             )
 
     def _apply_to_column_groupbys(self, func):
-        from pandas.core.reshape.concat import concat
-
         return concat(
             (func(col_groupby) for _, col_groupby in self._iterate_column_groupbys()),
             keys=self._selected_obj.columns,
@@ -1741,8 +1730,6 @@ def groupby_series(obj, col=None):
         if isinstance(obj, Series):
             results = groupby_series(obj)
         else:
-            from pandas.core.reshape.concat import concat
-
             results = [groupby_series(obj[col], col) for col in obj.columns]
             results = concat(results, axis=1)
             results.columns.names = obj.columns.names

pandas/core/groupby/groupby.py

@@ -51,6 +51,7 @@ class providing the base-class of operations.
 from pandas.core.generic import NDFrame
 from pandas.core.groupby import base
 from pandas.core.index import CategoricalIndex, Index, MultiIndex
+from pandas.core.reshape.concat import concat
 from pandas.core.series import Series
 from pandas.core.sorting import get_group_index_sorter
 
@@ -922,8 +923,6 @@ def _python_agg_general(self, func, *args, **kwargs):
         return self._wrap_aggregated_output(output)
 
     def _concat_objects(self, keys, values, not_indexed_same=False):
-        from pandas.core.reshape.concat import concat
-
         def reset_identity(values):
             # reset the identities of the components
             # of the values to prevent aliasing
@@ -1852,7 +1851,6 @@ def quantile(self, q=0.5, interpolation="linear"):
         a    2.0
         b    3.0
         """
-        from pandas import concat
 
         def pre_processor(vals: np.ndarray) -> Tuple[np.ndarray, Optional[Type]]:
             if is_object_dtype(vals):

pandas/core/reshape/concat.py

@@ -6,14 +6,16 @@
 
 import numpy as np
 
-from pandas import DataFrame, Index, MultiIndex, Series
+from pandas.core.dtypes.generic import ABCDataFrame, ABCSeries
+
 from pandas.core import common as com
 from pandas.core.arrays.categorical import (
     _factorize_from_iterable,
     _factorize_from_iterables,
 )
-from pandas.core.generic import NDFrame
 from pandas.core.index import (
+    Index,
+    MultiIndex,
     _all_indexes_same,
     _get_consensus_names,
     _get_objs_combined_axis,
@@ -275,7 +277,7 @@ def __init__(
         copy=True,
         sort=False,
     ):
-        if isinstance(objs, (NDFrame, str)):
+        if isinstance(objs, (ABCDataFrame, ABCSeries, str)):
             raise TypeError(
                 "first argument must be an iterable of pandas "
                 "objects, you passed an object of type "
@@ -322,7 +324,7 @@ def __init__(
         # consolidate data & figure out what our result ndim is going to be
         ndims = set()
         for obj in objs:
-            if not isinstance(obj, (Series, DataFrame)):
+            if not isinstance(obj, (ABCSeries, ABCDataFrame)):
                 msg = (
                     "cannot concatenate object of type '{}';"
                     " only Series and DataFrame objs are valid".format(type(obj))
@@ -348,7 +350,7 @@ def __init__(
             # filter out the empties if we have not multi-index possibilities
             # note to keep empty Series as it affect to result columns / name
             non_empties = [
-                obj for obj in objs if sum(obj.shape) > 0 or isinstance(obj, Series)
+                obj for obj in objs if sum(obj.shape) > 0 or isinstance(obj, ABCSeries)
             ]
 
             if len(non_empties) and (
@@ -362,17 +364,26 @@ def __init__(
         self.objs = objs
 
         # Standardize axis parameter to int
-        if isinstance(sample, Series):
+        # TODO: Should this really require a class import?
+        """
+        if isinstance(sample, ABCSeries):
             axis = DataFrame._get_axis_number(axis)
         else:
             axis = sample._get_axis_number(axis)
+        """
+        # TODO: implement universal axis validation; align with core.generic
+        if not isinstance(axis, int):
+            try:
+                axis = {"index": 0, "rows": 0, "columns": 1}[axis]
+            except KeyError:
+                raise ValueError("No axis named {}".format(axis))
 
         # Need to flip BlockManager axis in the DataFrame special case
-        self._is_frame = isinstance(sample, DataFrame)
+        self._is_frame = isinstance(sample, ABCDataFrame)
         if self._is_frame:
             axis = 1 if axis == 0 else 0
 
-        self._is_series = isinstance(sample, Series)
+        self._is_series = isinstance(sample, ABCSeries)
         if not 0 <= axis <= sample.ndim:
             raise AssertionError(
                 "axis must be between 0 and {ndim}, input was"
@@ -545,7 +556,7 @@ def _get_concat_axis(self):
                 num = 0
                 has_names = False
                 for i, x in enumerate(self.objs):
-                    if not isinstance(x, Series):
+                    if not isinstance(x, ABCSeries):
                         raise TypeError(
                             "Cannot concatenate type 'Series' "
                             "with object of type {type!r}".format(type=type(x).__name__)

pandas/core/reshape/merge.py

@@ -44,6 +44,7 @@
 import pandas.core.common as com
 from pandas.core.frame import _merge_doc
 from pandas.core.internals import _transform_index, concatenate_block_managers
+from pandas.core.reshape.concat import concat
 import pandas.core.sorting as sorting
 from pandas.core.sorting import is_int64_overflow_possible
 
@@ -156,8 +157,6 @@ def _groupby_and_merge(by, on, left, right, _merge_pieces, check_duplicates=True
 
     # preserve the original order
     # if we have a missing piece this can be reset
-    from pandas.core.reshape.concat import concat
-
     result = concat(pieces, ignore_index=True)
     result = result.reindex(columns=pieces[0].columns, copy=False)
     return result, lby

pandas/core/reshape/reshape.py

@@ -26,6 +26,7 @@
 from pandas.core.construction import extract_array
 from pandas.core.frame import DataFrame
 from pandas.core.index import Index, MultiIndex
+from pandas.core.reshape.concat import concat
 from pandas.core.series import Series
 from pandas.core.sorting import (
     compress_group_index,
@@ -469,8 +470,6 @@ def _unstack_extension_series(series, level, fill_value):
     # 2. Followup with a columnwise take.
     # We use the dummy take to discover newly-created missing values
     # introduced by the reshape.
-    from pandas.core.reshape.concat import concat
-
     dummy_arr = np.arange(len(series))
     # fill_value=-1, since we will do a series.values.take later
     result = _Unstacker(
@@ -855,8 +854,6 @@ def get_dummies(
     1  0.0  1.0  0.0
     2  0.0  0.0  1.0
     """
-    from pandas.core.reshape.concat import concat
-
     dtypes_to_encode = ["object", "category"]
 
     if isinstance(data, DataFrame):
@@ -946,8 +943,6 @@ def _get_dummies_1d(
     drop_first=False,
     dtype=None,
 ):
-    from pandas.core.reshape.concat import concat
-
     # Series avoids inconsistent NaN handling
     codes, levels = _factorize_from_iterable(Series(data))
 

pandas/core/series.py

@@ -75,6 +75,7 @@
 from pandas.core.indexes.timedeltas import TimedeltaIndex
 from pandas.core.indexing import check_bool_indexer
 from pandas.core.internals import SingleBlockManager
+from pandas.core.reshape.concat import concat
 from pandas.core.strings import StringMethods
 from pandas.core.tools.datetimes import to_datetime
 
@@ -2705,8 +2706,6 @@ def append(self, to_append, ignore_index=False, verify_integrity=False):
         ...
         ValueError: Indexes have overlapping values: [0, 1, 2]
         """
-        from pandas.core.reshape.concat import concat
-
         if isinstance(to_append, (list, tuple)):
             to_concat = [self]
             to_concat.extend(to_append)

pandas/core/window/common.py

@@ -11,6 +11,7 @@
 from pandas.core.generic import _shared_docs
 from pandas.core.groupby.base import GroupByMixin
 from pandas.core.index import MultiIndex
+from pandas.core.reshape.concat import concat
 
 _shared_docs = dict(**_shared_docs)
 _doc_template = """
@@ -128,8 +129,6 @@ def dataframe_from_int_dict(data, frame_template):
                                 *_prep_binary(arg1.iloc[:, i], arg2.iloc[:, j])
                             )
 
-                from pandas import concat
-
                 result_index = arg1.index.union(arg2.index)
                 if len(result_index):
 

pandas/io/formats/format.py

@@ -71,6 +71,7 @@
 from pandas.core.index import Index, ensure_index
 from pandas.core.indexes.datetimes import DatetimeIndex
 from pandas.core.indexes.timedeltas import TimedeltaIndex
+from pandas.core.reshape.concat import concat
 
 from pandas.io.common import _stringify_path
 from pandas.io.formats.printing import adjoin, justify, pprint_thing
@@ -260,8 +261,6 @@ def __init__(
         self._chk_truncate()
 
     def _chk_truncate(self) -> None:
-        from pandas.core.reshape.concat import concat
-
         min_rows = self.min_rows
         max_rows = self.max_rows
         # truncation determined by max_rows, actual truncated number of rows
@@ -609,8 +608,6 @@ def _chk_truncate(self) -> None:
         Checks whether the frame should be truncated. If so, slices
         the frame up.
         """
-        from pandas.core.reshape.concat import concat
-
         # Cut the data to the information actually printed
         max_cols = self.max_cols
         max_rows = self.max_rows