Merge branch 'master' of https://github.com/pandas-dev/pandas into bool_ops3

Author: jbrockmendel

asv_bench/benchmarks/io/excel.py

@@ -25,13 +25,12 @@ def setup(self, engine):
         self.writer_read.save()
         self.bio_read.seek(0)
 
-        self.bio_write = BytesIO()
-        self.bio_write.seek(0)
-        self.writer_write = ExcelWriter(self.bio_write, engine=engine)
-
     def time_read_excel(self, engine):
         read_excel(self.bio_read)
 
     def time_write_excel(self, engine):
-        self.df.to_excel(self.writer_write, sheet_name='Sheet1')
-        self.writer_write.save()
+        bio_write = BytesIO()
+        bio_write.seek(0)
+        writer_write = ExcelWriter(bio_write, engine=engine)
+        self.df.to_excel(writer_write, sheet_name='Sheet1')
+        writer_write.save()

doc/source/api.rst

@@ -2388,15 +2388,23 @@ Style Export and Import
    Styler.to_excel
 
 Plotting
-~~~~~~~~
+--------
 
-.. currentmodule:: pandas
+.. currentmodule:: pandas.plotting
+
+The following functions are contained in the `pandas.plotting` module.
 
 .. autosummary::
    :toctree: generated/
 
-   plotting.register_matplotlib_converters
-   plotting.deregister_matplotlib_converters
+   andrews_curves
+   bootstrap_plot
+   deregister_matplotlib_converters
+   lag_plot
+   parallel_coordinates
+   radviz
+   register_matplotlib_converters
+   scatter_matrix
 
 .. currentmodule:: pandas
 

doc/source/install.rst

@@ -6,7 +6,7 @@
 Installation
 ============
 
-The easiest way for the majority of users to install pandas is to install it
+The easiest way to install pandas is to install it
 as part of the `Anaconda <http://docs.continuum.io/anaconda/>`__ distribution, a
 cross platform distribution for data analysis and scientific computing.
 This is the recommended installation method for most users.
@@ -40,7 +40,7 @@ packages that make up the `SciPy <http://www.scipy.org/>`__ stack
 (Linux, Mac OS X, Windows) Python distribution for data analytics and
 scientific computing.
 
-After running a simple installer, the user will have access to pandas and the
+After running the installer, the user will have access to pandas and the
 rest of the `SciPy <http://www.scipy.org/>`__ stack without needing to install
 anything else, and without needing to wait for any software to be compiled.
 
@@ -51,9 +51,9 @@ A full list of the packages available as part of the
 `Anaconda <http://docs.continuum.io/anaconda/>`__ distribution
 `can be found here <http://docs.continuum.io/anaconda/pkg-docs.html>`__.
 
-An additional advantage of installing with Anaconda is that you don't require
-admin rights to install it, it will install in the user's home directory, and
-this also makes it trivial to delete Anaconda at a later date (just delete
+Another advantage to installing Anaconda is that you don't need
+admin rights to install it. Anaconda can install in the user's home directory,
+which makes it trivial to delete Anaconda if you decide (just delete
 that folder).
 
 .. _install.miniconda:
@@ -85,9 +85,9 @@ downloading and running the `Miniconda
 will do this for you. The installer
 `can be found here <http://conda.pydata.org/miniconda.html>`__
 
-The next step is to create a new conda environment (these are analogous to a
-virtualenv but they also allow you to specify precisely which Python version
-to install also). Run the following commands from a terminal window::
+The next step is to create a new conda environment. A conda environment is like a
+virtualenv that allows you to specify a specific version of Python and set of libraries.
+Run the following commands from a terminal window::
 
     conda create -n name_of_my_env python
 
@@ -118,8 +118,8 @@ distribution::
 
     conda install anaconda
 
-If you require any packages that are available to pip but not conda, simply
-install pip, and use pip to install these packages::
+If you need packages that are available to pip but not conda, then
+install pip, and then use pip to install those packages::
 
     conda install pip
     pip install django
@@ -134,15 +134,12 @@ pandas can be installed via pip from
 
     pip install pandas
 
-This will likely require the installation of a number of dependencies,
-including NumPy, will require a compiler to compile required bits of code,
-and can take a few minutes to complete.
 
 Installing using your Linux distribution's package manager.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 The commands in this table will install pandas for Python 3 from your distribution.
-To install pandas for Python 2 you may need to use the package ``python-pandas``.
+To install pandas for Python 2, you may need to use the ``python-pandas`` package.
 
 .. csv-table::
     :header: "Distribution", "Status", "Download / Repository Link", "Install method"
@@ -169,9 +166,9 @@ See the :ref:`contributing documentation <contributing>` for complete instructio
 Running the test suite
 ~~~~~~~~~~~~~~~~~~~~~~
 
-pandas is equipped with an exhaustive set of unit tests covering about 97% of
+pandas is equipped with an exhaustive set of unit tests, covering about 97% of
 the codebase as of this writing. To run it on your machine to verify that
-everything is working (and you have all of the dependencies, soft and hard,
+everything is working (and that you have all of the dependencies, soft and hard,
 installed), make sure you have `pytest
 <http://doc.pytest.org/en/latest/>`__ and run:
 
@@ -214,8 +211,8 @@ Recommended Dependencies
 
 .. note::
 
-   You are highly encouraged to install these libraries, as they provide large speedups, especially
-   if working with large data sets.
+   You are highly encouraged to install these libraries, as they provide speed improvements, especially
+   when working with large data sets.
 
 
 .. _install.optional_dependencies:

doc/source/io.rst

@@ -135,8 +135,14 @@ usecols : array-like or callable, default ``None``
   be positional (i.e. integer indices into the document columns) or strings
   that correspond to column names provided either by the user in `names` or
   inferred from the document header row(s). For example, a valid array-like
-  `usecols` parameter would be ``[0, 1, 2]`` or ``['foo', 'bar', 'baz']``. 
-  Element order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``.
+  `usecols` parameter would be ``[0, 1, 2]`` or ``['foo', 'bar', 'baz']``.
+
+  Element order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``. To
+  instantiate a DataFrame from ``data`` with element order preserved use
+  ``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]`` for columns
+  in ``['foo', 'bar']`` order or
+  ``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]`` for
+  ``['bar', 'foo']`` order.
 
   If callable, the callable function will be evaluated against the column names,
   returning names where the callable function evaluates to True:

doc/source/whatsnew/v0.23.0.txt

@@ -345,6 +345,23 @@ Convert to an xarray DataArray
 
    p.to_xarray()
 
+
+
+.. _whatsnew_0230.api_breaking.core_common:
+
+pandas.core.common removals
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The following error & warning messages are removed from ``pandas.core.common`` (:issue:`13634`, :issue:`19769`):
+
+- ``PerformanceWarning``
+- ``UnsupportedFunctionCall``
+- ``UnsortedIndexError``
+- ``AbstractMethodError``
+
+These are available from import from ``pandas.errors`` (since 0.19.0).
+
+
 .. _whatsnew_0230.api_breaking.apply:
 
 Changes to make output of ``DataFrame.apply`` consistent
@@ -556,6 +573,7 @@ Datetimelike API Changes
 - Subtracting ``NaT`` from a :class:`Series` with ``dtype='datetime64[ns]'`` returns a ``Series`` with ``dtype='timedelta64[ns]'`` instead of ``dtype='datetime64[ns]'`` (:issue:`18808`)
 - Operations between a :class:`Series` with dtype ``dtype='datetime64[ns]'`` and a :class:`PeriodIndex` will correctly raises ``TypeError`` (:issue:`18850`)
 - Subtraction of :class:`Series` with timezone-aware ``dtype='datetime64[ns]'`` with mis-matched timezones will raise ``TypeError`` instead of ``ValueError`` (:issue:`18817`)
+- :func:`pandas.merge` provides a more informative error message when trying to merge on timezone-aware and timezone-naive columns (:issue:`15800`)
 
 .. _whatsnew_0230.api.other:
 
@@ -575,7 +593,6 @@ Other API Changes
 - :func:`Series.fillna` now raises a ``TypeError`` instead of a ``ValueError`` when passed a list, tuple or DataFrame as a ``value`` (:issue:`18293`)
 - :func:`pandas.DataFrame.merge` no longer casts a ``float`` column to ``object`` when merging on ``int`` and ``float`` columns (:issue:`16572`)
 - :func:`pandas.merge` now raises a ``ValueError`` when trying to merge on incompatible data types (:issue:`9780`)
-- :func:`pandas.merge` provides a more informative error message when trying to merge on timezone-aware and timezone-naive columns (:issue:`15800`)
 - The default NA value for :class:`UInt64Index` has changed from 0 to ``NaN``, which impacts methods that mask with NA, such as ``UInt64Index.where()`` (:issue:`18398`)
 - Refactored ``setup.py`` to use ``find_packages`` instead of explicitly listing out all subpackages (:issue:`18535`)
 - Rearranged the order of keyword arguments in :func:`read_excel()` to align with :func:`read_csv()` (:issue:`16672`)
@@ -590,6 +607,7 @@ Other API Changes
 - Set operations (union, difference...) on :class:`IntervalIndex` with incompatible index types will now raise a ``TypeError`` rather than a ``ValueError`` (:issue:`19329`)
 - :class:`DateOffset` objects render more simply, e.g. ``<DateOffset: days=1>`` instead of ``<DateOffset: kwds={'days': 1}>`` (:issue:`19403`)
 - Boolean operations ``&, |, ^`` between a ``Series`` and an ``Index`` will no longer raise ``TypeError`` (:issue:`19792`)
+- ``Categorical.fillna`` now validates its ``value`` and ``method`` keyword arguments. It now raises when both or none are specified, matching the behavior of :meth:`Series.fillna` (:issue:`19682`)
 
 .. _whatsnew_0230.deprecations:
 
@@ -611,6 +629,7 @@ Deprecations
 
 - The ``broadcast`` parameter of ``.apply()`` is deprecated in favor of ``result_type='broadcast'`` (:issue:`18577`)
 - The ``reduce`` parameter of ``.apply()`` is deprecated in favor of ``result_type='reduce'`` (:issue:`18577`)
+- The ``order`` parameter of :func:`factorize` is deprecated and will be removed in a future release (:issue:`19727`)
 
 .. _whatsnew_0230.prior_deprecations:
 
@@ -644,6 +663,7 @@ Removal of prior version deprecations/changes
 - The modules ``pandas.tools.hashing`` and ``pandas.util.hashing`` have been removed (:issue:`16223`)
 - The top-level functions ``pd.rolling_*``, ``pd.expanding_*`` and ``pd.ewm*`` have been removed (Deprecated since v0.18).
   Instead, use the DataFrame/Series methods :attr:`~DataFrame.rolling`, :attr:`~DataFrame.expanding` and :attr:`~DataFrame.ewm` (:issue:`18723`)
+- Imports from ``pandas.core.common`` for functions such as ``is_datetime64_dtype`` are now removed. These are located in ``pandas.api.types``. (:issue:`13634`, :issue:`19769`)
 
 .. _whatsnew_0230.performance:
 
@@ -731,6 +751,8 @@ Datetimelike
 - Bug in :class:`Timestamp` and :func:`to_datetime` where a string representing a barely out-of-bounds timestamp would be incorrectly rounded down instead of raising ``OutOfBoundsDatetime`` (:issue:`19382`)
 - Bug in :func:`Timestamp.floor` :func:`DatetimeIndex.floor` where time stamps far in the future and past were not rounded correctly (:issue:`19206`)
 - Bug in :func:`to_datetime` where passing an out-of-bounds datetime with ``errors='coerce'`` and ``utc=True`` would raise ``OutOfBoundsDatetime`` instead of parsing to ``NaT`` (:issue:`19612`)
+- Bug in :class:`DatetimeIndex` and :class:`TimedeltaIndex` addition and subtraction where name of the returned object was not always set consistently. (:issue:`19744`)
+-
 
 Timedelta
 ^^^^^^^^^
@@ -742,8 +764,9 @@ Timedelta
 - Bug in :class:`TimedeltaIndex` where division by a ``Series`` would return a ``TimedeltaIndex`` instead of a ``Series`` (:issue:`19042`)
 - Bug in :func:`Timedelta.__add__`, :func:`Timedelta.__sub__` where adding or subtracting a ``np.timedelta64`` object would return another ``np.timedelta64`` instead of a ``Timedelta`` (:issue:`19738`)
 - Bug in :func:`Timedelta.__floordiv__`, :func:`Timedelta.__rfloordiv__` where operating with a ``Tick`` object would raise a ``TypeError`` instead of returning a numeric value (:issue:`19738`)
+- Bug in :func:`Period.asfreq` where periods near ``datetime(1, 1, 1)`` could be converted incorrectly (:issue:`19643`)
 - Bug in :func:`Timedelta.total_seconds()` causing precision errors i.e. ``Timedelta('30S').total_seconds()==30.000000000000004`` (:issue:`19458`)
-
+-
 
 Timezones
 ^^^^^^^^^
@@ -794,6 +817,8 @@ Indexing
 - Bug in :class:`IntervalIndex` where empty and purely NA data was constructed inconsistently depending on the construction method (:issue:`18421`)
 - Bug in :func:`IntervalIndex.symmetric_difference` where the symmetric difference with a non-``IntervalIndex`` did not raise (:issue:`18475`)
 - Bug in :class:`IntervalIndex` where set operations that returned an empty ``IntervalIndex`` had the wrong dtype (:issue:`19101`)
+- Bug in :meth:`DataFrame.drop_duplicates` where no ``KeyError`` is raised when passing in columns that don't exist on the ``DataFrame`` (issue:`19726`)
+
 
 MultiIndex
 ^^^^^^^^^^

pandas/_libs/src/period_helper.c

@@ -138,7 +138,7 @@ PANDAS_INLINE npy_int64 transform_via_day(npy_int64 ordinal,
 }
 
 static npy_int64 DtoB_weekday(npy_int64 absdate) {
-    return (((absdate) / 7) * 5) + (absdate) % 7 - BDAY_OFFSET;
+    return floordiv(absdate, 7) * 5 + mod_compat(absdate, 7) - BDAY_OFFSET;
 }
 
 static npy_int64 DtoB(struct date_info *dinfo,
@@ -245,7 +245,8 @@ static npy_int64 asfreq_UpsampleWithinDay(npy_int64 ordinal,
 static npy_int64 asfreq_BtoDT(npy_int64 ordinal, asfreq_info *af_info) {
     ordinal += BDAY_OFFSET;
     ordinal =
-        (((ordinal - 1) / 5) * 7 + mod_compat(ordinal - 1, 5) + 1 - ORD_OFFSET);
+        (floordiv(ordinal - 1, 5) * 7 + mod_compat(ordinal - 1, 5) + 1 -
+         ORD_OFFSET);
 
     return upsample_daytime(ordinal, af_info);
 }