Merge remote-tracking branch 'origin/master' into list_of_namedtuples3

* origin/master: (52 commits)
  CLN: Prune unnecessary indexing code (#27576)
  CLN: get parts of Block.replace out of try/except (#27408)
  Groupby transform cleanups (#27467)
  TYPING: add type hints to pandas\io\formats\printing.py (#27579)
  TYPING: Partial typing of Categorical (#27318)
  REF: collect indexing methods (#27588)
  API: Add entrypoint for plotting (#27488)
  REF: implement module for shared constructor functions (#27551)
  CLN: more assorted cleanups (#27555)
  CLN: pandas\io\formats\format.py (#27577)
  TYPING: some type hints for core.dtypes.common (#27564)
  DEPR: remove .ix from tests/indexing/multiindex/test_ix.py (#27565)
  DEPR: remove .ix from tests/indexing/test_partial.py (#27566)
  TST: add regression test for slicing IntervalIndex MI level with scalars (#27572)
  DEPR: remove .ix from tests/indexing/multiindex/test_setitem.py (#27574)
  BUG: display.precision of negative complex numbers (#27511)
  Removed ABCs from pandas._typing (#27424)
  DEPR: remove .ix from tests/indexing/test_indexing.py (#27535)
  BUG: fix+test quantile with empty DataFrame, closes #23925 (#27436)
  BUG: maybe_convert_objects mixed datetimes and timedeltas  (#27438)
  ...

Author: pilkibun

.gitignore

@@ -66,6 +66,9 @@ coverage_html_report
 # hypothesis test database
 .hypothesis/
 __pycache__
+# pytest-monkeytype
+monkeytype.sqlite3
+
 
 # OS generated files #
 ######################

.travis.yml

@@ -1,4 +1,3 @@
-sudo: false
 language: python
 python: 3.5
 

Makefile

@@ -15,7 +15,7 @@ lint-diff:
 	git diff upstream/master --name-only -- "*.py" | xargs flake8
 
 black:
-	black . --exclude '(asv_bench/env|\.egg|\.git|\.hg|\.mypy_cache|\.nox|\.tox|\.venv|_build|buck-out|build|dist)'
+	black . --exclude '(asv_bench/env|\.egg|\.git|\.hg|\.mypy_cache|\.nox|\.tox|\.venv|_build|buck-out|build|dist|setup.py)'
 
 develop: build
 	python setup.py develop

ci/code_checks.sh

@@ -56,7 +56,7 @@ if [[ -z "$CHECK" || "$CHECK" == "lint" ]]; then
     black --version
 
     MSG='Checking black formatting' ; echo $MSG
-	black . --check --exclude '(asv_bench/env|\.egg|\.git|\.hg|\.mypy_cache|\.nox|\.tox|\.venv|_build|buck-out|build|dist)'
+	black . --check --exclude '(asv_bench/env|\.egg|\.git|\.hg|\.mypy_cache|\.nox|\.tox|\.venv|_build|buck-out|build|dist|setup.py)'
     RET=$(($RET + $?)) ; echo $MSG "DONE"
 
     # `setup.cfg` contains the list of error codes that are being ignored in flake8

ci/deps/travis-36-cov.yaml

@@ -39,7 +39,7 @@ dependencies:
   - xlsxwriter
   - xlwt
   # universal
-  - pytest>=4.0.2
+  - pytest
   - pytest-xdist
   - pytest-cov
   - pytest-mock

doc/source/development/extending.rst

@@ -441,5 +441,22 @@ This would be more or less equivalent to:
 The backend module can then use other visualization tools (Bokeh, Altair,...)
 to generate the plots.
 
+Libraries implementing the plotting backend should use `entry points <https://setuptools.readthedocs.io/en/latest/setuptools.html#dynamic-discovery-of-services-and-plugins>`__
+to make their backend discoverable to pandas. The key is ``"pandas_plotting_backends"``. For example, pandas
+registers the default "matplotlib" backend as follows.
+
+.. code-block:: python
+
+   # in setup.py
+   setup(  # noqa: F821
+       ...,
+       entry_points={
+           "pandas_plotting_backends": [
+               "matplotlib = pandas:plotting._matplotlib",
+           ],
+       },
+   )
+
+
 More information on how to implement a third-party plotting backend can be found at
 https://github.com/pandas-dev/pandas/blob/master/pandas/plotting/__init__.py#L1.

doc/source/getting_started/basics.rst

@@ -1422,8 +1422,6 @@ The :meth:`~DataFrame.rename` method also provides an ``inplace`` named
 parameter that is by default ``False`` and copies the underlying data. Pass
 ``inplace=True`` to rename the data in place.
 
-.. versionadded:: 0.18.0
-
 Finally, :meth:`~Series.rename` also accepts a scalar or list-like
 for altering the ``Series.name`` attribute.
 
@@ -2063,8 +2061,6 @@ Convert a subset of columns to a specified type using :meth:`~DataFrame.astype`.
    dft
    dft.dtypes
 
-.. versionadded:: 0.19.0
-
 Convert certain columns to a specific dtype by passing a dict to :meth:`~DataFrame.astype`.
 
 .. ipython:: python

doc/source/getting_started/dsintro.rst

@@ -251,8 +251,6 @@ Series can also have a ``name`` attribute:
 The Series ``name`` will be assigned automatically in many cases, in particular
 when taking 1D slices of DataFrame as you will see below.
 
-.. versionadded:: 0.18.0
-
 You can rename a Series with the :meth:`pandas.Series.rename` method.
 
 .. ipython:: python

doc/source/user_guide/advanced.rst

@@ -810,15 +810,10 @@ values **not** in the categories, similarly to how you can reindex **any** panda
 Int64Index and RangeIndex
 ~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. warning::
-
-   Indexing on an integer-based Index with floats has been clarified in 0.18.0, for a summary of the changes, see :ref:`here <whatsnew_0180.float_indexers>`.
-
-:class:`Int64Index` is a fundamental basic index in pandas.
-This is an immutable array implementing an ordered, sliceable set.
-Prior to 0.18.0, the ``Int64Index`` would provide the default index for all ``NDFrame`` objects.
+:class:`Int64Index` is a fundamental basic index in pandas. This is an immutable array
+implementing an ordered, sliceable set.
 
-:class:`RangeIndex` is a sub-class of ``Int64Index`` added in version 0.18.0, now providing the default index for all ``NDFrame`` objects.
+:class:`RangeIndex` is a sub-class of ``Int64Index``  that provides the default index for all ``NDFrame`` objects.
 ``RangeIndex`` is an optimized version of ``Int64Index`` that can represent a monotonic ordered set. These are analogous to Python `range types <https://docs.python.org/3/library/stdtypes.html#typesseq-range>`__.
 
 .. _indexing.float64index:
@@ -880,16 +875,6 @@ In non-float indexes, slicing using floats will raise a ``TypeError``.
    In [1]: pd.Series(range(5))[3.5:4.5]
    TypeError: the slice start [3.5] is not a proper indexer for this index type (Int64Index)
 
-.. warning::
-
-   Using a scalar float indexer for ``.iloc`` has been removed in 0.18.0, so the following will raise a ``TypeError``:
-
-   .. code-block:: ipython
-
-      In [3]: pd.Series(range(5)).iloc[3.0]
-      TypeError: cannot do positional indexing on <class 'pandas.indexes.range.RangeIndex'> with these indexers [3.0] of <type 'float'>
-
-
 Here is a typical use-case for using this type of indexing. Imagine that you have a somewhat
 irregular timedelta-like indexing scheme, but the data is recorded as floats. This could, for
 example, be millisecond offsets.

doc/source/user_guide/categorical.rst

@@ -834,8 +834,6 @@ See also the section on :ref:`merge dtypes<merging.dtypes>` for notes about pres
 Unioning
 ~~~~~~~~
 
-.. versionadded:: 0.19.0
-
 If you want to combine categoricals that do not necessarily have the same
 categories, the :func:`~pandas.api.types.union_categoricals` function will
 combine a list-like of categoricals. The new categories will be the union of

doc/source/user_guide/computation.rst

@@ -408,9 +408,7 @@ For some windowing functions, additional parameters must be specified:
 Time-aware rolling
 ~~~~~~~~~~~~~~~~~~
 
-.. versionadded:: 0.19.0
-
-New in version 0.19.0 are the ability to pass an offset (or convertible) to a ``.rolling()`` method and have it produce
+It is possible to pass an offset (or convertible) to a ``.rolling()`` method and have it produce
 variable sized windows based on the passed time window. For each time point, this includes all preceding values occurring
 within the indicated time delta.
 
@@ -893,10 +891,9 @@ Therefore, there is an assumption that :math:`x_0` is not an ordinary value
 but rather an exponentially weighted moment of the infinite series up to that
 point.
 
-One must have :math:`0 < \alpha \leq 1`, and while since version 0.18.0
-it has been possible to pass :math:`\alpha` directly, it's often easier
-to think about either the **span**, **center of mass (com)** or **half-life**
-of an EW moment:
+One must have :math:`0 < \alpha \leq 1`, and while it is possible to pass
+:math:`\alpha` directly, it's often easier to think about either the
+**span**, **center  of mass (com)** or **half-life** of an EW moment:
 
 .. math::
 

doc/source/user_guide/enhancingperf.rst

@@ -601,8 +601,6 @@ This allows for *formulaic evaluation*.  The assignment target can be a
 new column name or an existing column name, and it must be a valid Python
 identifier.
 
-.. versionadded:: 0.18.0
-
 The ``inplace`` keyword determines whether this assignment will performed
 on the original ``DataFrame`` or return a copy with the new column.
 
@@ -630,8 +628,6 @@ new or modified columns is returned and the original frame is unchanged.
    df.eval('e = a - c', inplace=False)
    df
 
-.. versionadded:: 0.18.0
-
 As a convenience, multiple assignments can be performed by using a
 multi-line string.
 
@@ -652,9 +648,7 @@ The equivalent in standard Python would be
    df['a'] = 1
    df
 
-.. versionadded:: 0.18.0
-
-The ``query`` method gained the ``inplace`` keyword which determines
+The ``query`` method has a ``inplace`` keyword which determines
 whether the query modifies the original frame.
 
 .. ipython:: python

doc/source/user_guide/groupby.rst

@@ -827,13 +827,10 @@ and that the transformed data contains no NAs.
 
 .. _groupby.transform.window_resample:
 
-New syntax to window and resample operations
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. versionadded:: 0.18.1
+Window and resample operations
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Working with the resample, expanding or rolling operations on the groupby
-level used to require the application of helper functions. However,
-now it is possible to use ``resample()``, ``expanding()`` and
+It is possible to use ``resample()``, ``expanding()`` and
 ``rolling()`` as methods on groupbys.
 
 The example below will apply the ``rolling()`` method on the samples of

doc/source/user_guide/indexing.rst

@@ -36,10 +36,6 @@ this area.
    should be avoided. See :ref:`Returning a View versus Copy
    <indexing.view_versus_copy>`.
 
-.. warning::
-
-   Indexing on an integer-based Index with floats has been clarified in 0.18.0, for a summary of the changes, see :ref:`here <whatsnew_0180.float_indexers>`.
-
 See the :ref:`MultiIndex / Advanced Indexing <advanced>` for ``MultiIndex`` and more advanced indexing documentation.
 
 See the :ref:`cookbook<cookbook.selection>` for some advanced strategies.
@@ -67,8 +63,6 @@ of multi-axis indexing.
     * A ``callable`` function with one argument (the calling Series or DataFrame) and
       that returns valid output for indexing (one of the above).
 
-      .. versionadded:: 0.18.1
-
   See more at :ref:`Selection by Label <indexing.label>`.
 
 * ``.iloc`` is primarily integer position based (from ``0`` to
@@ -85,8 +79,6 @@ of multi-axis indexing.
     * A ``callable`` function with one argument (the calling Series or DataFrame) and
       that returns valid output for indexing (one of the above).
 
-      .. versionadded:: 0.18.1
-
   See more at :ref:`Selection by Position <indexing.integer>`,
   :ref:`Advanced Indexing <advanced>` and :ref:`Advanced
   Hierarchical <advanced.advanced_hierarchical>`.
@@ -538,8 +530,6 @@ A list of indexers where any element is out of bounds will raise an
 Selection by callable
 ---------------------
 
-.. versionadded:: 0.18.1
-
 ``.loc``, ``.iloc``, and also ``[]`` indexing can accept a ``callable`` as indexer.
 The ``callable`` must be a function with one argument (the calling Series or DataFrame) that returns valid output for indexing.
 
@@ -1105,9 +1095,7 @@ This is equivalent to (but faster than) the following.
    df2 = df.copy()
    df.apply(lambda x, y: x.where(x > 0, y), y=df['A'])
 
-.. versionadded:: 0.18.1
-
-Where can accept a callable as condition and ``other`` arguments. The function must
+``where`` can accept a callable as condition and ``other`` arguments. The function must
 be with one argument (the calling Series or DataFrame) and that returns valid output
 as condition and ``other`` argument.
 

doc/source/user_guide/io.rst

@@ -87,8 +87,6 @@ delim_whitespace : boolean, default False
   If this option is set to ``True``, nothing should be passed in for the
   ``delimiter`` parameter.
 
-  .. versionadded:: 0.18.1 support for the Python parser.
-
 Column and index locations and names
 ++++++++++++++++++++++++++++++++++++
 
@@ -298,7 +296,6 @@ compression : {``'infer'``, ``'gzip'``, ``'bz2'``, ``'zip'``, ``'xz'``, ``None``
   the ZIP file must contain only one data file to be read in.
   Set to ``None`` for no decompression.
 
-  .. versionadded:: 0.18.1 support for 'zip' and 'xz' compression.
   .. versionchanged:: 0.24.0 'infer' option added and set to default.
 thousands : str, default ``None``
   Thousands separator.
@@ -456,8 +453,6 @@ worth trying.
 Specifying categorical dtype
 ''''''''''''''''''''''''''''
 
-.. versionadded:: 0.19.0
-
 ``Categorical`` columns can be parsed directly by specifying ``dtype='category'`` or
 ``dtype=CategoricalDtype(categories, ordered)``.
 
@@ -2195,8 +2190,6 @@ With max_level=1 the following snippet normalizes until 1st nesting level of the
 Line delimited json
 '''''''''''''''''''
 
-.. versionadded:: 0.19.0
-
 pandas is able to read and write line-delimited json files that are common in data processing pipelines
 using Hadoop or Spark.
 
@@ -2494,16 +2487,12 @@ Specify values that should be converted to NaN:
 
    dfs = pd.read_html(url, na_values=['No Acquirer'])
 
-.. versionadded:: 0.19
-
 Specify whether to keep the default set of NaN values:
 
 .. code-block:: python
 
    dfs = pd.read_html(url, keep_default_na=False)
 
-.. versionadded:: 0.19
-
 Specify converters for columns. This is useful for numerical text data that has
 leading zeros.  By default columns that are numerical are cast to numeric
 types and the leading zeros are lost. To avoid this, we can convert these
@@ -2515,8 +2504,6 @@ columns to strings.
    dfs = pd.read_html(url_mcc, match='Telekom Albania', header=0,
                       converters={'MNC': str})
 
-.. versionadded:: 0.19
-
 Use some combination of the above:
 
 .. code-block:: python

doc/source/user_guide/merging.rst

@@ -819,8 +819,6 @@ The ``indicator`` argument will also accept string arguments, in which case the
 Merge dtypes
 ~~~~~~~~~~~~
 
-.. versionadded:: 0.19.0
-
 Merging will preserve the dtype of the join keys.
 
 .. ipython:: python
@@ -1386,8 +1384,6 @@ fill/interpolate missing data:
 Merging asof
 ~~~~~~~~~~~~
 
-.. versionadded:: 0.19.0
-
 A :func:`merge_asof` is similar to an ordered left-join except that we match on
 nearest key rather than equal keys. For each row in the ``left`` ``DataFrame``,
 we select the last row in the ``right`` ``DataFrame`` whose ``on`` key is less

doc/source/user_guide/reshaping.rst

@@ -254,8 +254,6 @@ values will be set to ``NaN``.
    df3
    df3.unstack()
 
-.. versionadded:: 0.18.0
-
 Alternatively, unstack takes an optional ``fill_value`` argument, for specifying
 the value of missing data.
 
@@ -486,8 +484,6 @@ not contain any instances of a particular category.
 Normalization
 ~~~~~~~~~~~~~
 
-.. versionadded:: 0.18.1
-
 Frequency tables can also be normalized to show percentages rather than counts
 using the ``normalize`` argument:
 
@@ -630,8 +626,6 @@ the prefix separator. You can specify ``prefix`` and ``prefix_sep`` in 3 ways:
     from_dict = pd.get_dummies(df, prefix={'B': 'from_B', 'A': 'from_A'})
     from_dict
 
-.. versionadded:: 0.18.0
-
 Sometimes it will be useful to only keep k-1 levels of a categorical
 variable to avoid collinearity when feeding the result to statistical models.
 You can switch to this mode by turn on ``drop_first``.

doc/source/user_guide/style.ipynb

@@ -6,10 +6,6 @@
    "source": [
     "# Styling\n",
     "\n",
-    "*New in version 0.17.1*\n",
-    "\n",
-    "<span style=\"color: red\">*Provisional: This is a new feature and still under development. We'll be adding features and possibly making breaking changes in future releases. We'd love to hear your feedback.*</span>\n",
-    "\n",
     "This document is written as a Jupyter Notebook, and can be viewed or downloaded [here](http://nbviewer.ipython.org/github/pandas-dev/pandas/blob/master/doc/source/style.ipynb).\n",
     "\n",
     "You can apply **conditional formatting**, the visual styling of a DataFrame\n",