Merge remote-tracking branch 'upstream/master' into bisect

Author: simonjayhawkins

asv_bench/benchmarks/groupby.py

@@ -486,7 +486,7 @@ def setup(self):
         tmp2 = (np.random.random(10000) * 10.0).astype(np.float32)
         tmp = np.concatenate((tmp1, tmp2))
         arr = np.repeat(tmp, 10)
-        self.df = DataFrame(dict(a=arr, b=arr))
+        self.df = DataFrame({"a": arr, "b": arr})
 
     def time_sum(self):
         self.df.groupby(["a"])["b"].sum()

asv_bench/benchmarks/join_merge.py

@@ -132,6 +132,9 @@ def time_join_dataframe_index_single_key_small(self, sort):
     def time_join_dataframe_index_shuffle_key_bigger_sort(self, sort):
         self.df_shuf.join(self.df_key2, on="key2", sort=sort)
 
+    def time_join_dataframes_cross(self, sort):
+        self.df.loc[:2000].join(self.df_key1, how="cross", sort=sort)
+
 
 class JoinIndex:
     def setup(self):
@@ -205,6 +208,9 @@ def time_merge_dataframe_integer_2key(self, sort):
     def time_merge_dataframe_integer_key(self, sort):
         merge(self.df, self.df2, on="key1", sort=sort)
 
+    def time_merge_dataframes_cross(self, sort):
+        merge(self.left.loc[:2000], self.right.loc[:2000], how="cross", sort=sort)
+
 
 class I8Merge:
 

doc/source/development/contributing.rst

@@ -146,7 +146,7 @@ Creating a development environment
 ----------------------------------
 
 To test out code changes, you'll need to build pandas from source, which
-requires a C compiler and Python environment. If you're making documentation
+requires a C/C++ compiler and Python environment. If you're making documentation
 changes, you can skip to :ref:`contributing.documentation` but you won't be able
 to build the documentation locally before pushing your changes.
 
@@ -195,6 +195,13 @@ operations. To install pandas from source, you need to compile these C
 extensions, which means you need a C compiler. This process depends on which
 platform you're using.
 
+If you have setup your environment using ``conda``, the packages ``c-compiler``
+and ``cxx-compiler`` will install a fitting compiler for your platform that is
+compatible with the remaining conda packages. On Windows and macOS, you will
+also need to install the SDKs as they have to be distributed separately.
+These packages will be automatically installed by using ``pandas``'s
+``environment.yml``.
+
 **Windows**
 
 You will need `Build Tools for Visual Studio 2017
@@ -206,12 +213,33 @@ You will need `Build Tools for Visual Studio 2017
 	scrolling down to "All downloads" -> "Tools for Visual Studio 2019".
 	In the installer, select the "C++ build tools" workload.
 
+You can install the necessary components on the commandline using
+`vs_buildtools.exe <https://aka.ms/vs/16/release/vs_buildtools.exe>`_:
+
+.. code::
+
+    vs_buildtools.exe --quiet --wait --norestart --nocache ^
+        --installPath C:\BuildTools ^
+        --add "Microsoft.VisualStudio.Workload.VCTools;includeRecommended" ^
+        --add Microsoft.VisualStudio.Component.VC.v141 ^
+        --add Microsoft.VisualStudio.Component.VC.v141.x86.x64 ^
+        --add Microsoft.VisualStudio.Component.Windows10SDK.17763
+
+To setup the right paths on the commandline, call
+``"C:\BuildTools\VC\Auxiliary\Build\vcvars64.bat" -vcvars_ver=14.16 10.0.17763.0``.
+
 **macOS**
 
-Information about compiler installation can be found here:
+To use the ``conda``-based compilers, you will need to install the
+Developer Tools using ``xcode-select --install``. Otherwise
+information about compiler installation can be found here:
 https://devguide.python.org/setup/#macos
 
-**Unix**
+**Linux**
+
+For Linux-based ``conda`` installations, you won't have to install any
+additional components outside of the conda environment. The instructions
+below are only needed if your setup isn't based on conda environments.
 
 Some Linux distributions will come with a pre-installed C compiler. To find out
 which compilers (and versions) are installed on your system::
@@ -243,11 +271,10 @@ Let us know if you have any difficulties by opening an issue or reaching out on
 Creating a Python environment
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Now that you have a C compiler, create an isolated pandas development
-environment:
+Now create an isolated pandas development environment:
 
-* Install either `Anaconda <https://www.anaconda.com/download/>`_ or `miniconda
-  <https://conda.io/miniconda.html>`_
+* Install either `Anaconda <https://www.anaconda.com/download/>`_, `miniconda
+  <https://conda.io/miniconda.html>`_, or `miniforge <https://github.com/conda-forge/miniforge>`_
 * Make sure your conda is up to date (``conda update conda``)
 * Make sure that you have :ref:`cloned the repository <contributing.forking>`
 * ``cd`` to the pandas source directory

doc/source/development/policies.rst

@@ -35,7 +35,7 @@ We will not introduce new deprecations in patch releases.
 Deprecations will only be enforced in **major** releases. For example, if a
 behavior is deprecated in pandas 1.2.0, it will continue to work, with a
 warning, for all releases in the 1.x series. The behavior will change and the
-deprecation removed in the next next major release (2.0.0).
+deprecation removed in the next major release (2.0.0).
 
 .. note::
 

doc/source/user_guide/dsintro.rst

@@ -439,7 +439,7 @@ Data Classes as introduced in `PEP557 <https://www.python.org/dev/peps/pep-0557>
 can be passed into the DataFrame constructor.
 Passing a list of dataclasses is equivalent to passing a list of dictionaries.
 
-Please be aware, that that all values in the list should be dataclasses, mixing
+Please be aware, that all values in the list should be dataclasses, mixing
 types in the list would result in a TypeError.
 
 .. ipython:: python

doc/source/user_guide/indexing.rst

@@ -584,48 +584,20 @@ without using a temporary variable.
    (bb.groupby(['year', 'team']).sum()
       .loc[lambda df: df['r'] > 100])
 
-.. _indexing.deprecate_ix:
 
-IX indexer is deprecated
-------------------------
-
-.. warning::
-
-   .. versionchanged:: 1.0.0
-
-   The ``.ix`` indexer was removed, in favor of the more strict ``.iloc`` and ``.loc`` indexers.
+.. _combining_positional_and_label_based_indexing:
 
-``.ix`` offers a lot of magic on the inference of what the user wants to do. To wit, ``.ix`` can decide
-to index *positionally* OR via *labels* depending on the data type of the index. This has caused quite a
-bit of user confusion over the years.
+Combining positional and label-based indexing
+---------------------------------------------
 
-The recommended methods of indexing are:
-
-* ``.loc`` if you want to *label* index.
-* ``.iloc`` if you want to *positionally* index.
+If you wish to get the 0th and the 2nd elements from the index in the 'A' column, you can do:
 
 .. ipython:: python
 
   dfd = pd.DataFrame({'A': [1, 2, 3],
                       'B': [4, 5, 6]},
                      index=list('abc'))
-
   dfd
-
-Previous behavior, where you wish to get the 0th and the 2nd elements from the index in the 'A' column.
-
-.. code-block:: ipython
-
-  In [3]: dfd.ix[[0, 2], 'A']
-  Out[3]:
-  a    1
-  c    3
-  Name: A, dtype: int64
-
-Using ``.loc``. Here we will select the appropriate indexes from the index, then use *label* indexing.
-
-.. ipython:: python
-
   dfd.loc[dfd.index[[0, 2]], 'A']
 
 This can also be expressed using ``.iloc``, by explicitly getting locations on the indexers, and using

doc/source/user_guide/integer_na.rst

@@ -117,7 +117,7 @@ dtype if needed.
    # coerce when needed
    s + 0.01
 
-These dtypes can operate as part of of ``DataFrame``.
+These dtypes can operate as part of ``DataFrame``.
 
 .. ipython:: python
 

doc/source/user_guide/timeseries.rst

@@ -588,45 +588,43 @@ would include matching times on an included date:
 
 .. warning::
 
-   Indexing ``DataFrame`` rows with strings is deprecated in pandas 1.2.0 and will be removed in a future version.  Use ``frame.loc[dtstring]`` instead.
+   Indexing ``DataFrame`` rows with a *single* string with getitem (e.g. ``frame[dtstring]``)
+   is deprecated starting with pandas 1.2.0 (given the ambiguity whether it is indexing
+   the rows or selecting a column) and will be removed in a future version. The equivalent
+   with ``.loc`` (e.g. ``frame.loc[dtstring]``) is still supported.
 
 .. ipython:: python
-   :okwarning:
 
    dft = pd.DataFrame(
        np.random.randn(100000, 1),
        columns=["A"],
        index=pd.date_range("20130101", periods=100000, freq="T"),
    )
    dft
-   dft["2013"]
+   dft.loc["2013"]
 
 This starts on the very first time in the month, and includes the last date and
 time for the month:
 
 .. ipython:: python
-   :okwarning:
 
    dft["2013-1":"2013-2"]
 
 This specifies a stop time **that includes all of the times on the last day**:
 
 .. ipython:: python
-   :okwarning:
 
    dft["2013-1":"2013-2-28"]
 
 This specifies an **exact** stop time (and is not the same as the above):
 
 .. ipython:: python
-   :okwarning:
 
    dft["2013-1":"2013-2-28 00:00:00"]
 
 We are stopping on the included end-point as it is part of the index:
 
 .. ipython:: python
-   :okwarning:
 
    dft["2013-1-15":"2013-1-15 12:30:00"]
 
@@ -652,7 +650,6 @@ We are stopping on the included end-point as it is part of the index:
 Slicing with string indexing also honors UTC offset.
 
 .. ipython:: python
-   :okwarning:
 
     df = pd.DataFrame([0], index=pd.DatetimeIndex(["2019-01-01"], tz="US/Pacific"))
     df
@@ -704,15 +701,14 @@ If index resolution is second, then the minute-accurate timestamp gives a
     series_second.index.resolution
     series_second["2011-12-31 23:59"]
 
-If the timestamp string is treated as a slice, it can be used to index ``DataFrame`` with ``[]`` as well.
+If the timestamp string is treated as a slice, it can be used to index ``DataFrame`` with ``.loc[]`` as well.
 
 .. ipython:: python
-    :okwarning:
 
     dft_minute = pd.DataFrame(
         {"a": [1, 2, 3], "b": [4, 5, 6]}, index=series_minute.index
     )
-    dft_minute["2011-12-31 23"]
+    dft_minute.loc["2011-12-31 23"]
 
 
 .. warning::
@@ -2080,7 +2076,6 @@ You can pass in dates and strings to ``Series`` and ``DataFrame`` with ``PeriodI
 Passing a string representing a lower frequency than ``PeriodIndex`` returns partial sliced data.
 
 .. ipython:: python
-   :okwarning:
 
    ps["2011"]
 
@@ -2090,7 +2085,7 @@ Passing a string representing a lower frequency than ``PeriodIndex`` returns par
        index=pd.period_range("2013-01-01 9:00", periods=600, freq="T"),
    )
    dfp
-   dfp["2013-01-01 10H"]
+   dfp.loc["2013-01-01 10H"]
 
 As with ``DatetimeIndex``, the endpoints will be included in the result. The example below slices data starting from 10:00 to 11:59.
 

doc/source/whatsnew/v0.12.0.rst

@@ -419,7 +419,7 @@ Bug fixes
 ~~~~~~~~~
 
   - Plotting functions now raise a ``TypeError`` before trying to plot anything
-    if the associated objects have have a dtype of ``object`` (:issue:`1818`,
+    if the associated objects have a dtype of ``object`` (:issue:`1818`,
     :issue:`3572`, :issue:`3911`, :issue:`3912`), but they will try to convert object arrays to
     numeric arrays if possible so that you can still plot, for example, an
     object array with floats. This happens before any drawing takes place which
@@ -430,8 +430,8 @@ Bug fixes
 
   - ``Series.str`` now supports iteration (:issue:`3638`). You can iterate over the
     individual elements of each string in the ``Series``. Each iteration yields
-    yields a ``Series`` with either a single character at each index of the
-    original ``Series`` or ``NaN``. For example,
+    a ``Series`` with either a single character at each index of the original
+    ``Series`` or ``NaN``. For example,
 
     .. ipython:: python
         :okwarning:

doc/source/whatsnew/v0.14.0.rst

@@ -923,7 +923,7 @@ Bug fixes
 - ``HDFStore.select_as_multiple`` handles start and stop the same way as ``select`` (:issue:`6177`)
 - ``HDFStore.select_as_coordinates`` and ``select_column`` works with a ``where`` clause that results in filters (:issue:`6177`)
 - Regression in join of non_unique_indexes (:issue:`6329`)
-- Issue with groupby ``agg`` with a single function and a a mixed-type frame (:issue:`6337`)
+- Issue with groupby ``agg`` with a single function and a mixed-type frame (:issue:`6337`)
 - Bug in ``DataFrame.replace()`` when passing a non- ``bool``
   ``to_replace`` argument (:issue:`6332`)
 - Raise when trying to align on different levels of a MultiIndex assignment (:issue:`3738`)

doc/source/whatsnew/v0.15.2.rst

@@ -136,7 +136,7 @@ Enhancements
 
 - Added ability to export Categorical data to Stata (:issue:`8633`).  See :ref:`here <io.stata-categorical>` for limitations of categorical variables exported to Stata data files.
 - Added flag ``order_categoricals`` to ``StataReader`` and ``read_stata`` to select whether to order imported categorical data (:issue:`8836`).  See :ref:`here <io.stata-categorical>` for more information on importing categorical variables from Stata data files.
-- Added ability to export Categorical data to to/from HDF5 (:issue:`7621`). Queries work the same as if it was an object array. However, the ``category`` dtyped data is stored in a more efficient manner. See :ref:`here <io.hdf5-categorical>` for an example and caveats w.r.t. prior versions of pandas.
+- Added ability to export Categorical data to/from HDF5 (:issue:`7621`). Queries work the same as if it was an object array. However, the ``category`` dtyped data is stored in a more efficient manner. See :ref:`here <io.hdf5-categorical>` for an example and caveats w.r.t. prior versions of pandas.
 - Added support for ``searchsorted()`` on ``Categorical`` class (:issue:`8420`).
 
 Other enhancements:

doc/source/whatsnew/v0.16.1.rst

@@ -6,7 +6,7 @@ Version 0.16.1 (May 11, 2015)
 {{ header }}
 
 
-This is a minor bug-fix release from 0.16.0 and includes a a large number of
+This is a minor bug-fix release from 0.16.0 and includes a large number of
 bug fixes along several new features, enhancements, and performance improvements.
 We recommend that all users upgrade to this version.
 
@@ -72,7 +72,7 @@ setting the index of a ``DataFrame/Series`` with a ``category`` dtype would conv
     Out[4]: Index(['c', 'a', 'b'], dtype='object')
 
 
-setting the index, will create create a ``CategoricalIndex``
+setting the index, will create a ``CategoricalIndex``
 
 .. code-block:: ipython
 

doc/source/whatsnew/v0.16.2.rst

@@ -6,7 +6,7 @@ Version 0.16.2 (June 12, 2015)
 {{ header }}
 
 
-This is a minor bug-fix release from 0.16.1 and includes a a large number of
+This is a minor bug-fix release from 0.16.1 and includes a large number of
 bug fixes along some new features (:meth:`~DataFrame.pipe` method), enhancements, and performance improvements.
 
 We recommend that all users upgrade to this version.

doc/source/whatsnew/v0.18.0.rst

@@ -610,7 +610,7 @@ Subtraction by ``Timedelta`` in a ``Series`` by a ``Timestamp`` works (:issue:`1
    pd.Timestamp('2012-01-01') - ser
 
 
-``NaT.isoformat()`` now returns ``'NaT'``. This change allows allows
+``NaT.isoformat()`` now returns ``'NaT'``. This change allows
 ``pd.Timestamp`` to rehydrate any timestamp like object from its isoformat
 (:issue:`12300`).
 

doc/source/whatsnew/v0.20.0.rst

@@ -1167,7 +1167,7 @@ Other API changes
 - ``.loc`` has compat with ``.ix`` for accepting iterators, and NamedTuples (:issue:`15120`)
 - ``interpolate()`` and ``fillna()`` will raise a ``ValueError`` if the ``limit`` keyword argument is not greater than 0. (:issue:`9217`)
 - ``pd.read_csv()`` will now issue a ``ParserWarning`` whenever there are conflicting values provided by the ``dialect`` parameter and the user (:issue:`14898`)
-- ``pd.read_csv()`` will now raise a ``ValueError`` for the C engine if the quote character is larger than than one byte (:issue:`11592`)
+- ``pd.read_csv()`` will now raise a ``ValueError`` for the C engine if the quote character is larger than one byte (:issue:`11592`)
 - ``inplace`` arguments now require a boolean value, else a ``ValueError`` is thrown (:issue:`14189`)
 - ``pandas.api.types.is_datetime64_ns_dtype`` will now report ``True`` on a tz-aware dtype, similar to ``pandas.api.types.is_datetime64_any_dtype``
 - ``DataFrame.asof()`` will return a null filled ``Series`` instead the scalar ``NaN`` if a match is not found (:issue:`15118`)
@@ -1315,7 +1315,7 @@ The recommended methods of indexing are:
 - ``.loc`` if you want to *label* index
 - ``.iloc`` if you want to *positionally* index.
 
-Using ``.ix`` will now show a ``DeprecationWarning`` with a link to some examples of how to convert code :ref:`here <indexing.deprecate_ix>`.
+Using ``.ix`` will now show a ``DeprecationWarning`` with a link to some examples of how to convert code `here <https://pandas.pydata.org/pandas-docs/version/1.0/user_guide/indexing.html#ix-indexer-is-deprecated>`__.
 
 
 .. ipython:: python
@@ -1663,11 +1663,11 @@ Indexing
 - Bug in ``.reset_index()`` when an all ``NaN`` level of a ``MultiIndex`` would fail (:issue:`6322`)
 - Bug in ``.reset_index()`` when raising error for index name already present in ``MultiIndex`` columns (:issue:`16120`)
 - Bug in creating a ``MultiIndex`` with tuples and not passing a list of names; this will now raise ``ValueError`` (:issue:`15110`)
-- Bug in the HTML display with with a ``MultiIndex`` and truncation (:issue:`14882`)
+- Bug in the HTML display with a ``MultiIndex`` and truncation (:issue:`14882`)
 - Bug in the display of ``.info()`` where a qualifier (+) would always be displayed with a ``MultiIndex`` that contains only non-strings (:issue:`15245`)
 - Bug in ``pd.concat()`` where the names of ``MultiIndex`` of resulting ``DataFrame`` are not handled correctly when ``None`` is presented in the names of ``MultiIndex`` of input ``DataFrame`` (:issue:`15787`)
 - Bug in ``DataFrame.sort_index()`` and ``Series.sort_index()`` where ``na_position`` doesn't work with a ``MultiIndex`` (:issue:`14784`, :issue:`16604`)
-- Bug in in ``pd.concat()`` when combining objects with a ``CategoricalIndex`` (:issue:`16111`)
+- Bug in ``pd.concat()`` when combining objects with a ``CategoricalIndex`` (:issue:`16111`)
 - Bug in indexing with a scalar and a ``CategoricalIndex`` (:issue:`16123`)
 
 IO

doc/source/whatsnew/v0.21.0.rst

@@ -50,7 +50,7 @@ Parquet is designed to faithfully serialize and de-serialize ``DataFrame`` s, su
 dtypes, including extension dtypes such as datetime with timezones.
 
 This functionality depends on either the `pyarrow <http://arrow.apache.org/docs/python/>`__ or `fastparquet <https://fastparquet.readthedocs.io/en/latest/>`__ library.
-For more details, see see :ref:`the IO docs on Parquet <io.parquet>`.
+For more details, see :ref:`the IO docs on Parquet <io.parquet>`.
 
 
 .. _whatsnew_0210.enhancements.infer_objects: