DOC: whatsnew 0.24.0 corrections & edits & remove some warnings

Author: jreback

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:
 
@@ -1741,6 +1759,7 @@ MultiIndex Components
  MultiIndex.swaplevel
  MultiIndex.reorder_levels
  MultiIndex.remove_unused_levels
+ MultiIndex.reindex
  MultiIndex.unique
 
 MultiIndex Selecting
@@ -1750,10 +1769,23 @@ MultiIndex Selecting
  :toctree: generated/
 
  MultiIndex.get_loc
+ MultiIndex.get_loc_level
  MultiIndex.get_indexer
  MultiIndex.get_level_values
 
-.. _api.datetimeindex:
+Modifying and Computations
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. autosummary::
+ :toctree: generated/
+
+ MultiIndex.copy
+ MultiIndex.drop_duplicates
+ MultiIndex.duplicated
+ MultiIndex.equals
+ MultiIndex.repeat
+ MultiIndex.where
+
+ .. _api.datetimeindex:
 
 DatetimeIndex
 -------------
@@ -1834,6 +1866,16 @@ Conversion
  DatetimeIndex.to_pydatetime
  DatetimeIndex.to_series
  DatetimeIndex.to_frame
+ DatetimeIndex.shift
+
+Sorting
+~~~~~~~
+.. autosummary::
+ :toctree: generated/
+
+ DatetimeIndex.argsort
+ DatetimeIndex.searchsorted
+ DatetimeIndex.sort_values
 
 TimedeltaIndex
 --------------
@@ -1869,6 +1911,15 @@ Conversion
  TimedeltaIndex.ceil
  TimedeltaIndex.to_frame
 
+Sorting
+~~~~~~~
+.. autosummary::
+ :toctree: generated/
+
+ TimedeltaIndex.argsort
+ TimedeltaIndex.searchsorted
+ TimedeltaIndex.sort_values
+
 .. currentmodule:: pandas
 
 PeriodIndex
@@ -1914,6 +1965,17 @@ Methods
  PeriodIndex.asfreq
  PeriodIndex.strftime
  PeriodIndex.to_timestamp
+ PeriodIndex.shift
+ PeriodIndex.unique
+
+Sorting
+~~~~~~~
+.. autosummary::
+ :toctree: generated/
+
+ PeriodIndex.argsort
+ PeriodIndex.searchsorted
+ PeriodIndex.sort_values
 
 Scalars
 -------
@@ -2292,6 +2354,7 @@ Function application
  :toctree: generated/
 
  GroupBy.apply
+ GroupBy.agg
  GroupBy.aggregate
  GroupBy.transform
  GroupBy.pipe
@@ -2336,7 +2399,6 @@ 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
 ''''''''''''''''''''''''''