DOC: whatsnew 0.24.0 corrections & edits & remove some warnings

doc/source/api.rst

@@ -113,6 +113,7 @@ HDFStore: PyTables (HDF5)
    HDFStore.select
    HDFStore.info
    HDFStore.keys
+   HDFStore.groups
    HDFStore.walk
 
 Feather
@@ -611,6 +612,16 @@ These can be accessed like ``Series.dt.<property>``.
    Series.dt.month_name
    Series.dt.day_name
 
+**Period Properties**
+
+.. autosummary::
+   :toctree: generated/
+   :template: autosummary/accessor_attribute.rst
+
+   Series.dt.qyear
+   Series.dt.start_time
+   Series.dt.end_time
+
 **Timedelta Properties**
 
 .. autosummary::
@@ -1648,7 +1659,14 @@ Categorical Components
    CategoricalIndex.set_categories
    CategoricalIndex.as_ordered
    CategoricalIndex.as_unordered
+
+Modifying and Computations
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. autosummary::
+   :toctree: generated/
+
    CategoricalIndex.map
+   CategoricalIndex.equals
 
 .. _api.intervalindex:
 
@@ -1741,7 +1759,6 @@ MultiIndex Components
    MultiIndex.swaplevel
    MultiIndex.reorder_levels
    MultiIndex.remove_unused_levels
-   MultiIndex.unique
 
 MultiIndex Selecting
 ~~~~~~~~~~~~~~~~~~~~
@@ -1750,6 +1767,7 @@ MultiIndex Selecting
    :toctree: generated/
 
    MultiIndex.get_loc
+   MultiIndex.get_loc_level
    MultiIndex.get_indexer
    MultiIndex.get_level_values
 
@@ -2292,6 +2310,7 @@ Function application
    :toctree: generated/
 
    GroupBy.apply
+   GroupBy.agg
    GroupBy.aggregate
    GroupBy.transform
    GroupBy.pipe
@@ -2336,7 +2355,6 @@ application to columns of a specific data type.
 .. autosummary::
    :toctree: generated/
 
-   DataFrameGroupBy.agg
    DataFrameGroupBy.all
    DataFrameGroupBy.any
    DataFrameGroupBy.bfill

doc/source/basics.rst

@@ -88,7 +88,7 @@ may involve copying data and coercing values.
 
 Getting the "raw data" inside a :class:`DataFrame` is possibly a bit more
 complex. When your ``DataFrame`` only has a single data type for all the
-columns, :atr:`DataFrame.to_numpy` will return the underlying data:
+columns, :attr:`DataFrame.to_numpy` will return the underlying data:
 
 .. ipython:: python
 
@@ -1931,7 +1931,7 @@ Categorical         :class:`CategoricalDtype` (none)             :class:`Categor
 period (time spans) :class:`PeriodDtype`      :class:`Period`    :class:`arrays.PeriodArray`   :ref:`timeseries.periods`
 sparse              :class:`SparseDtype`      (none)             :class:`arrays.SparseArray`   :ref:`sparse`
 intervals           :class:`IntervalDtype`    :class:`Interval`  :class:`arrays.IntervalArray` :ref:`advanced.intervalindex`
-nullable integer    :clsas:`Int64Dtype`, ...  (none)             :class:`arrays.IntegerArray`  :ref:`integer_na`
+nullable integer    :class:`Int64Dtype`, ...  (none)             :class:`arrays.IntegerArray`  :ref:`integer_na`
 =================== ========================= ================== ============================= =============================
 
 Pandas uses the ``object`` dtype for storing strings.
@@ -2324,4 +2324,4 @@ All NumPy dtypes are subclasses of ``numpy.generic``:
 .. note::
 
     Pandas also defines the types ``category``, and ``datetime64[ns, tz]``, which are not integrated into the normal
-    NumPy hierarchy and won't show up with the above function.
+    NumPy hierarchy and won't show up with the above function.

doc/source/conf.py

@@ -369,7 +369,7 @@
 
 intersphinx_mapping = {
     'statsmodels': ('http://www.statsmodels.org/devel/', None),
-    'matplotlib': ('http://matplotlib.org/', None),
+    'matplotlib': ('https://matplotlib.org/', None),
     'pandas-gbq': ('https://pandas-gbq.readthedocs.io/en/latest/', None),
     'python': ('https://docs.python.org/3/', None),
     'numpy': ('https://docs.scipy.org/doc/numpy/', None),

doc/source/io.rst

@@ -1863,7 +1863,9 @@ Writing to a file, with a date index and a date column:
    dfj2['bools'] = True
    dfj2.index = pd.date_range('20130101', periods=5)
    dfj2.to_json('test.json')
-   open('test.json').read()
+
+   with open('test.json') as fh:
+       print(fh.read())
 
 Fallback Behavior
 +++++++++++++++++
@@ -2321,6 +2323,11 @@ indicate missing values and the subsequent read cannot distinguish the intent.
    new_df = pd.read_json('test.json', orient='table')
    print(new_df.index.name)
 
+.. ipython:: python
+   :suppress:
+
+   os.remove('test.json')
+
 .. _Table Schema: https://specs.frictionlessdata.io/json-table-schema/
 
 HTML
@@ -4766,6 +4773,11 @@ this file into a ``DataFrame``.
 Passing ``index=True`` will *always* write the index, even if that's not the
 underlying engine's default behavior.
 
+.. ipython:: python
+   :suppress:
+
+   os.remove('test.parquet')
+
 
 Partitioning Parquet files
 ''''''''''''''''''''''''''