REF: implement _shared_docs to de-circularize dependencies

pandas/core/frame.py

@@ -135,6 +135,7 @@
     sanitize_index,
     to_arrays,
 )
+from pandas.core.reshape.melt import melt
 from pandas.core.series import Series
 from pandas.core.sorting import ensure_key_mapped
 
@@ -7069,104 +7070,6 @@ def unstack(self, level=-1, fill_value=None):
 
         return unstack(self, level, fill_value)
 
-    _shared_docs[
-        "melt"
-    ] = """
-    Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.
-
-    This function is useful to massage a DataFrame into a format where one
-    or more columns are identifier variables (`id_vars`), while all other
-    columns, considered measured variables (`value_vars`), are "unpivoted" to
-    the row axis, leaving just two non-identifier columns, 'variable' and
-    'value'.
-    %(versionadded)s
-    Parameters
-    ----------
-    id_vars : tuple, list, or ndarray, optional
-        Column(s) to use as identifier variables.
-    value_vars : tuple, list, or ndarray, optional
-        Column(s) to unpivot. If not specified, uses all columns that
-        are not set as `id_vars`.
-    var_name : scalar
-        Name to use for the 'variable' column. If None it uses
-        ``frame.columns.name`` or 'variable'.
-    value_name : scalar, default 'value'
-        Name to use for the 'value' column.
-    col_level : int or str, optional
-        If columns are a MultiIndex then use this level to melt.
-
-    Returns
-    -------
-    DataFrame
-        Unpivoted DataFrame.
-
-    See Also
-    --------
-    %(other)s : Identical method.
-    pivot_table : Create a spreadsheet-style pivot table as a DataFrame.
-    DataFrame.pivot : Return reshaped DataFrame organized
-        by given index / column values.
-    DataFrame.explode : Explode a DataFrame from list-like
-            columns to long format.
-
-    Examples
-    --------
-    >>> df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},
-    ...                    'B': {0: 1, 1: 3, 2: 5},
-    ...                    'C': {0: 2, 1: 4, 2: 6}})
-    >>> df
-       A  B  C
-    0  a  1  2
-    1  b  3  4
-    2  c  5  6
-
-    >>> %(caller)sid_vars=['A'], value_vars=['B'])
-       A variable  value
-    0  a        B      1
-    1  b        B      3
-    2  c        B      5
-
-    >>> %(caller)sid_vars=['A'], value_vars=['B', 'C'])
-       A variable  value
-    0  a        B      1
-    1  b        B      3
-    2  c        B      5
-    3  a        C      2
-    4  b        C      4
-    5  c        C      6
-
-    The names of 'variable' and 'value' columns can be customized:
-
-    >>> %(caller)sid_vars=['A'], value_vars=['B'],
-    ...         var_name='myVarname', value_name='myValname')
-       A myVarname  myValname
-    0  a         B          1
-    1  b         B          3
-    2  c         B          5
-
-    If you have multi-index columns:
-
-    >>> df.columns = [list('ABC'), list('DEF')]
-    >>> df
-       A  B  C
-       D  E  F
-    0  a  1  2
-    1  b  3  4
-    2  c  5  6
-
-    >>> %(caller)scol_level=0, id_vars=['A'], value_vars=['B'])
-       A variable  value
-    0  a        B      1
-    1  b        B      3
-    2  c        B      5
-
-    >>> %(caller)sid_vars=[('A', 'D')], value_vars=[('B', 'E')])
-      (A, D) variable_0 variable_1  value
-    0      a          B          E      1
-    1      b          B          E      3
-    2      c          B          E      5
-    """
-
     @Appender(
         _shared_docs["melt"]
         % dict(
@@ -7183,7 +7086,6 @@ def melt(
         value_name="value",
         col_level=None,
     ) -> "DataFrame":
-        from pandas.core.reshape.melt import melt
 
         return melt(
             self,

pandas/core/generic.py

@@ -97,6 +97,7 @@
 from pandas.core.internals import BlockManager
 from pandas.core.missing import find_valid_index
 from pandas.core.ops import _align_method_FRAME
+from pandas.core.shared_docs import _shared_docs
 
 from pandas.io.formats import format as fmt
 from pandas.io.formats.format import DataFrameFormatter, format_percentiles
@@ -109,7 +110,6 @@
 
 # goal is to be able to define the docs close to function, while still being
 # able to share
-_shared_docs: Dict[str, str] = dict()
 _shared_doc_kwargs = dict(
     axes="keywords for axes",
     klass="Series/DataFrame",

pandas/core/reshape/concat.py

@@ -3,7 +3,7 @@
 """
 
 from collections import abc
-from typing import Iterable, List, Mapping, Union, overload
+from typing import TYPE_CHECKING, Iterable, List, Mapping, Union, overload
 
 import numpy as np
 
@@ -12,14 +12,14 @@
 from pandas.core.dtypes.concat import concat_compat
 from pandas.core.dtypes.generic import ABCDataFrame, ABCSeries
 
-from pandas import DataFrame, Index, MultiIndex, Series
 from pandas.core.arrays.categorical import (
     factorize_from_iterable,
     factorize_from_iterables,
 )
 import pandas.core.common as com
-from pandas.core.generic import NDFrame
 from pandas.core.indexes.api import (
+    Index,
+    MultiIndex,
     all_indexes_same,
     ensure_index,
     get_consensus_names,
@@ -28,6 +28,9 @@
 import pandas.core.indexes.base as ibase
 from pandas.core.internals import concatenate_block_managers
 
+if TYPE_CHECKING:
+    from pandas import DataFrame
+
 # ---------------------------------------------------------------------
 # Concatenate DataFrame objects
 
@@ -291,7 +294,7 @@ class _Concatenator:
 
     def __init__(
         self,
-        objs,
+        objs: Union[Iterable[FrameOrSeries], Mapping[Label, FrameOrSeries]],
         axis=0,
         join: str = "outer",
         keys=None,
@@ -302,7 +305,7 @@ def __init__(
         copy: bool = True,
         sort=False,
     ):
-        if isinstance(objs, (NDFrame, str)):
+        if isinstance(objs, (ABCSeries, ABCDataFrame, str)):
             raise TypeError(
                 "first argument must be an iterable of pandas "
                 f'objects, you passed an object of type "{type(objs).__name__}"'
@@ -348,7 +351,7 @@ 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 = (
                     f"cannot concatenate object of type '{type(obj)}'; "
                     "only Series and DataFrame objs are valid"
@@ -374,7 +377,7 @@ 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 (
@@ -388,15 +391,15 @@ def __init__(
         self.objs = objs
 
         # Standardize axis parameter to int
-        if isinstance(sample, Series):
-            axis = DataFrame._get_axis_number(axis)
+        if isinstance(sample, ABCSeries):
+            axis = sample._constructor_expanddim._get_axis_number(axis)
         else:
             axis = sample._get_axis_number(axis)
 
         # Need to flip BlockManager axis in the DataFrame special case
         self._is_frame = isinstance(sample, ABCDataFrame)
         if self._is_frame:
-            axis = DataFrame._get_block_manager_axis(axis)
+            axis = sample._get_block_manager_axis(axis)
 
         self._is_series = isinstance(sample, ABCSeries)
         if not 0 <= axis <= sample.ndim:
@@ -543,7 +546,7 @@ def _get_concat_axis(self) -> Index:
                 num = 0
                 has_names = False
                 for i, x in enumerate(self.objs):
-                    if not isinstance(x, Series):
+                    if not isinstance(x, ABCSeries):
                         raise TypeError(
                             f"Cannot concatenate type 'Series' with "
                             f"object of type '{type(x).__name__}'"

pandas/core/reshape/melt.py

@@ -11,27 +11,27 @@
 
 from pandas.core.arrays import Categorical
 import pandas.core.common as com
-from pandas.core.frame import DataFrame, _shared_docs
 from pandas.core.indexes.api import Index, MultiIndex
 from pandas.core.reshape.concat import concat
+from pandas.core.shared_docs import _shared_docs
 from pandas.core.tools.numeric import to_numeric
 
 if TYPE_CHECKING:
-    from pandas import Series  # noqa: F401
+    from pandas import DataFrame, Series  # noqa: F401
 
 
 @Appender(
     _shared_docs["melt"]
     % dict(caller="pd.melt(df, ", versionadded="", other="DataFrame.melt")
 )
 def melt(
-    frame: DataFrame,
+    frame: "DataFrame",
     id_vars=None,
     value_vars=None,
     var_name=None,
     value_name="value",
     col_level=None,
-) -> DataFrame:
+) -> "DataFrame":
     # TODO: what about the existing index?
     # If multiindex, gather names of columns on all level for checking presence
     # of `id_vars` and `value_vars`
@@ -125,7 +125,7 @@ def melt(
 
 
 @deprecate_kwarg(old_arg_name="label", new_arg_name=None)
-def lreshape(data: DataFrame, groups, dropna: bool = True, label=None) -> DataFrame:
+def lreshape(data: "DataFrame", groups, dropna: bool = True, label=None) -> "DataFrame":
     """
     Reshape long-format data to wide. Generalized inverse of DataFrame.pivot
 
@@ -195,8 +195,8 @@ def lreshape(data: DataFrame, groups, dropna: bool = True, label=None) -> DataFr
 
 
 def wide_to_long(
-    df: DataFrame, stubnames, i, j, sep: str = "", suffix: str = r"\d+"
-) -> DataFrame:
+    df: "DataFrame", stubnames, i, j, sep: str = "", suffix: str = r"\d+"
+) -> "DataFrame":
     r"""
     Wide panel to long format. Less flexible but more user-friendly than melt.
 

pandas/core/shared_docs.py

@@ -0,0 +1,102 @@
+from typing import Dict
+
+_shared_docs: Dict[str, str] = dict()
+
+
+_shared_docs[
+    "melt"
+] = """
+    Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.
+
+    This function is useful to massage a DataFrame into a format where one
+    or more columns are identifier variables (`id_vars`), while all other
+    columns, considered measured variables (`value_vars`), are "unpivoted" to
+    the row axis, leaving just two non-identifier columns, 'variable' and
+    'value'.
+    %(versionadded)s
+    Parameters
+    ----------
+    id_vars : tuple, list, or ndarray, optional
+        Column(s) to use as identifier variables.
+    value_vars : tuple, list, or ndarray, optional
+        Column(s) to unpivot. If not specified, uses all columns that
+        are not set as `id_vars`.
+    var_name : scalar
+        Name to use for the 'variable' column. If None it uses
+        ``frame.columns.name`` or 'variable'.
+    value_name : scalar, default 'value'
+        Name to use for the 'value' column.
+    col_level : int or str, optional
+        If columns are a MultiIndex then use this level to melt.
+
+    Returns
+    -------
+    DataFrame
+        Unpivoted DataFrame.
+
+    See Also
+    --------
+    %(other)s : Identical method.
+    pivot_table : Create a spreadsheet-style pivot table as a DataFrame.
+    DataFrame.pivot : Return reshaped DataFrame organized
+        by given index / column values.
+    DataFrame.explode : Explode a DataFrame from list-like
+            columns to long format.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},
+    ...                    'B': {0: 1, 1: 3, 2: 5},
+    ...                    'C': {0: 2, 1: 4, 2: 6}})
+    >>> df
+       A  B  C
+    0  a  1  2
+    1  b  3  4
+    2  c  5  6
+
+    >>> %(caller)sid_vars=['A'], value_vars=['B'])
+       A variable  value
+    0  a        B      1
+    1  b        B      3
+    2  c        B      5
+
+    >>> %(caller)sid_vars=['A'], value_vars=['B', 'C'])
+       A variable  value
+    0  a        B      1
+    1  b        B      3
+    2  c        B      5
+    3  a        C      2
+    4  b        C      4
+    5  c        C      6
+
+    The names of 'variable' and 'value' columns can be customized:
+
+    >>> %(caller)sid_vars=['A'], value_vars=['B'],
+    ...         var_name='myVarname', value_name='myValname')
+       A myVarname  myValname
+    0  a         B          1
+    1  b         B          3
+    2  c         B          5
+
+    If you have multi-index columns:
+
+    >>> df.columns = [list('ABC'), list('DEF')]
+    >>> df
+       A  B  C
+       D  E  F
+    0  a  1  2
+    1  b  3  4
+    2  c  5  6
+
+    >>> %(caller)scol_level=0, id_vars=['A'], value_vars=['B'])
+       A variable  value
+    0  a        B      1
+    1  b        B      3
+    2  c        B      5
+
+    >>> %(caller)sid_vars=[('A', 'D')], value_vars=[('B', 'E')])
+      (A, D) variable_0 variable_1  value
+    0      a          B          E      1
+    1      b          B          E      3
+    2      c          B          E      5
+    """