Debug

Author: rhshadrach

doc/source/whatsnew/v0.23.0.rst

@@ -102,97 +102,6 @@ A ``DataFrame`` can now be written to and subsequently read back via JSON while
 
 Please note that the string ``index`` is not supported with the round trip format, as it is used by default in ``write_json`` to indicate a missing index name.
 
-.. ipython:: python
-   :okwarning:
-
-   df.index.name = 'index'
-
-   df.to_json('test.json', orient='table')
-   new_df = pd.read_json('test.json', orient='table')
-   new_df
-   new_df.dtypes
-
-.. ipython:: python
-   :suppress:
-
-   import os
-   os.remove('test.json')
-
-
-.. _whatsnew_0230.enhancements.assign_dependent:
-
-
-Method ``.assign()`` accepts dependent arguments
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-The :func:`DataFrame.assign` now accepts dependent keyword arguments for python version later than 3.6 (see also `PEP 468
-<https://www.python.org/dev/peps/pep-0468/>`_). Later keyword arguments may now refer to earlier ones if the argument is a callable. See the
-:ref:`documentation here <dsintro.chained_assignment>` (:issue:`14207`)
-
-.. ipython:: python
-
-    df = pd.DataFrame({'A': [1, 2, 3]})
-    df
-    df.assign(B=df.A, C=lambda x: x['A'] + x['B'])
-
-.. warning::
-
-  This may subtly change the behavior of your code when you're
-  using ``.assign()`` to update an existing column. Previously, callables
-  referring to other variables being updated would get the "old" values
-
-  Previous behavior:
-
-  .. code-block:: ipython
-
-      In [2]: df = pd.DataFrame({"A": [1, 2, 3]})
-
-      In [3]: df.assign(A=lambda df: df.A + 1, C=lambda df: df.A * -1)
-      Out[3]:
-         A  C
-      0  2 -1
-      1  3 -2
-      2  4 -3
-
-  New behavior:
-
-  .. ipython:: python
-
-      df.assign(A=df.A + 1, C=lambda df: df.A * -1)
-
-
-
-.. _whatsnew_0230.enhancements.merge_on_columns_and_levels:
-
-Merging on a combination of columns and index levels
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Strings passed to :meth:`DataFrame.merge` as the ``on``, ``left_on``, and ``right_on``
-parameters may now refer to either column names or index level names.
-This enables merging ``DataFrame`` instances on a combination of index levels
-and columns without resetting indexes. See the :ref:`Merge on columns and
-levels <merging.merge_on_columns_and_levels>` documentation section.
-(:issue:`14355`)
-
-.. ipython:: python
-
-   left_index = pd.Index(['K0', 'K0', 'K1', 'K2'], name='key1')
-
-   left = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
-                        'B': ['B0', 'B1', 'B2', 'B3'],
-                        'key2': ['K0', 'K1', 'K0', 'K1']},
-                       index=left_index)
-
-   right_index = pd.Index(['K0', 'K1', 'K2', 'K2'], name='key1')
-
-   right = pd.DataFrame({'C': ['C0', 'C1', 'C2', 'C3'],
-                         'D': ['D0', 'D1', 'D2', 'D3'],
-                         'key2': ['K0', 'K0', 'K0', 'K1']},
-                        index=right_index)
-
-   left.merge(right, on=['key1', 'key2'])
-
-.. _whatsnew_0230.enhancements.sort_by_columns_and_levels:
 
 .. _whatsnew_0.23.0.contributors: