Scheduled weekly dependency update for week 03

[pyup-bot]

Updates

Here's a list of all the updates bundled in this pull request. I've added some links to make it easier for you to find all the information you need.

django-braces	1.11.0	»	1.12.0	PyPI | Changelog | Repo
django-model-utils	3.0.0	»	3.1.1	PyPI | Changelog | Repo
Pillow	4.3.0	»	5.0.0	PyPI | Changelog | Homepage
argon2-cffi	16.3.0	»	18.1.0	PyPI | Changelog | Docs
boto3	1.4.7	»	1.5.19	PyPI | Changelog | Repo
pandas	0.21.0	»	0.22.0	PyPI | Changelog | Homepage
numpy	1.13.3	»	1.14.0	PyPI | Changelog | Homepage
raven	6.3.0	»	6.5.0	PyPI | Changelog | Repo
django-tables2	1.14.2	»	1.17.1	PyPI | Changelog | Repo
Sphinx	1.6.5	»	1.6.6	PyPI | Changelog | Homepage
django-extensions	1.9.7	»	1.9.9	PyPI | Changelog | Repo | Docs
Werkzeug	0.12.2	»	0.14.1	PyPI | Changelog | Homepage
django-test-plus	1.0.20	»	1.0.22	PyPI | Changelog | Repo
django-debug-toolbar	1.8	»	1.9.1	PyPI | Changelog | Repo

Changelogs

django-model-utils 3.0.0 -> 3.1.1

3.1.1

---

• Update classifiers and README via GH-306, fixes GH-305

3.1.0

---

• Support for Django 2.0 via GH-298, fixes GH-297
• Remove old travis script via GH-300
• Fix codecov and switch to py.test 301

Pillow 4.3.0 -> 5.0.0

5.0.0

---

• Docs: Added docstrings from documentation 2914
  [radarhere]

• Test: Switch from nose to pytest 2815
  [hugovk]

• Rework Source directory layout, preventing accidental import of PIL. 2911
  [wiredfool]

• Dynamically link libraqm 2753
  [wiredfool]

• Removed scripts directory 2901
  [wiredfool]

• TIFF: Run all compressed tiffs through libtiff decoder 2899
  [wiredfool]

• GIF: Add disposal option when saving GIFs 2902
  [linnil1, wiredfool]

• EPS: Allow for an empty line in EPS header data 2903
  [radarhere]

• PNG: Add support for sRGB and cHRM chunks, permit sRGB when no iCCP chunk present 2898
  [wiredfool]

• Dependencies: Update Tk Tcl to 8.6.8 2905
  [radarhere]

• Decompression bomb error now raised for images 2x larger than a decompression bomb warning 2583
  [wiredfool]

• Test: avoid random failure in test_effect_noise 2894
  [hugovk]

• Increased epsilon for test_file_eps.py:test_showpage due to Arch update. 2896
  [wiredfool]

• Removed check parameter from _save in BmpImagePlugin, PngImagePlugin, ImImagePlugin, PalmImagePlugin, and PcxImagePlugin. 2873
  [radarhere]

• Make PngImagePlugin.add_text() zip argument type bool 2890
  [jdufresne]

• Depends: Updated libwebp to 0.6.1 2880
  [radarhere]

• Remove unnecessary bool() calls in Image.registered_extensions and skipKnownBadTests 2891
  [jdufresne]

• Fix count of BITSPERSAMPLE items in broken TIFF files 2883
  [homm]

• Fillcolor parameter for Image.Transform 2852
  [wiredfool]

• Test: Display differences for test failures 2862
  [wiredfool]

• Added executable flag to file with shebang line 2884
  [radarhere]

• Setup: Specify compatible Python versions for pip 2877
  [hugovk]

• Dependencies: Updated libimagequant to 2.11.4 2878
  [radarhere]

• Setup: Warn if trying to install for Py3.7 on Windows 2855
  [hugovk]

• Doc: Fonts can be loaded from a file-like object, not just filename 2861
  [robin-norwood]

• Add eog support for Ubuntu Image Viewer 2864
  [NafisFaysal]

• Test: Test on 3.7-dev on Travis.ci 2870
  [hugovk]

• Dependencies: Update libtiff to 4.0.9 2871
  [radarhere]

• Setup: Replace deprecated platform.dist with file existence check 2869
  [wiredfool]

• Build: Fix setup.py on Debian 2853
  [wiredfool]

• Docs: Correct error in ImageDraw documentation 2858
  [meribold]

• Test: Drop Ubuntu Precise, Fedora 24, Fedora 25, add Fedora 27, Centos 7, Amazon v2 CI Support 2854, 2843, 2895, 2897
  [wiredfool]

• Dependencies: Updated libimagequant to 2.11.3 2849
  [radarhere]

• Test: Fix test_image.py to use tempfile 2841
  [radarhere]

• Replace PIL.OleFileIO deprecation warning with descriptive ImportError 2833
  [hugovk]

• WebP: Add support for animated WebP files 2761
  [jd20]

• PDF: Set encoderinfo for images when saving multi-page PDF. Fixes 2804. 2805
  [ixio]

• Allow the olefile dependency to be optional 2789
  [jdufresne]

• GIF: Permit LZW code lengths up to 12 bits in GIF decode 2813
  [wiredfool]

• Fix unterminiated string and unchecked exception in _font_text_asBytes. 2825
  [wiredfool]

• PPM: Use fixed list of whitespace, rather relying on locale, fixes 272. 2831
  [markmiscavage]

• Added support for generators when using append_images 2829, 2835
  [radarhere]

• Doc: Correct PixelAccess.rst 2824
  [hasahmed]

• Depends: Update raqm to 0.3.0 2822
  [radarhere]

• Docs: Link to maintained version of aggdraw 2809
  [hugovk]

• Include license file in the generated wheel packages 2801
  [jdufresne]

• Depends: Update openjpeg to 2.3.0 2791
  [radarhere]

• Add option to Makefile to build and install with C coverage 2781
  [hugovk]

• Add context manager support to ImageFile.Parser and PngImagePlugin.ChunkStream 2793
  [radarhere]

• ImageDraw.textsize: fix zero length error 2788
  [wiredfool, hugovk]

argon2-cffi 16.3.0 -> 18.1.0

18.1.0

---

Vendoring Argon2 670229c <https://github.com/P-H-C/phc-winner-argon2/tree/670229c849b9fe882583688b74eb7dfdc846f9f6>_ (20171227)

Changes:
^^^^^^^^

• It is now possible to use the argon2_cffi bindings against an Argon2 library that is provided by the system.

---

boto3 1.4.7 -> 1.5.19

1.5.19

======

• api-change:glue: [botocore] Update glue client to latest version
• api-change:transcribe: [botocore] Update transcribe client to latest version

1.5.18

======

• api-change:sagemaker: [botocore] Update sagemaker client to latest version

1.5.17

======

• api-change:ec2: [botocore] Update ec2 client to latest version
• api-change:autoscaling-plans: [botocore] Update autoscaling-plans client to latest version

1.5.16

======

• api-change:application-autoscaling: [botocore] Update application-autoscaling client to latest version
• api-change:autoscaling-plans: [botocore] Update autoscaling-plans client to latest version
• api-change:rds: [botocore] Update rds client to latest version

1.5.15

======

• api-change:lambda: [botocore] Update lambda client to latest version
• enhancement:cloudformation get_template template body ordering: [botocore] fixes boto/boto31378 <https://github.com/boto/boto3/issues/1378>__

1.5.14

======

• api-change:glue: [botocore] Update glue client to latest version

1.5.13

======

• api-change:ssm: [botocore] Update ssm client to latest version
• api-change:elbv2: [botocore] Update elbv2 client to latest version
• api-change:rds: [botocore] Update rds client to latest version
• api-change:elb: [botocore] Update elb client to latest version

1.5.12

======

• api-change:kms: [botocore] Update kms client to latest version

1.5.11

======

• api-change:ds: [botocore] Update ds client to latest version

1.5.10

======

• api-change:route53: [botocore] Update route53 client to latest version
• api-change:discovery: [botocore] Update discovery client to latest version
• api-change:codedeploy: [botocore] Update codedeploy client to latest version

1.5.9

=====

• api-change:ssm: [botocore] Update ssm client to latest version
• api-change:inspector: [botocore] Update inspector client to latest version
• api-change:snowball: [botocore] Update snowball client to latest version

1.5.8

=====

• api-change:rds: [botocore] Update rds client to latest version

1.5.7

=====

• api-change:workspaces: [botocore] Update workspaces client to latest version

1.5.6

=====

• api-change:ecs: [botocore] Update ecs client to latest version
• api-change:ec2: [botocore] Update ec2 client to latest version
• api-change:inspector: [botocore] Update inspector client to latest version
• api-change:sagemaker: [botocore] Update sagemaker client to latest version

1.5.5

=====

• api-change:ec2: [botocore] Update ec2 client to latest version
• enhancement:Paginator: [botocore] Added paginator support for lambda list aliases operation.
• api-change:kinesisanalytics: [botocore] Update kinesisanalytics client to latest version
• api-change:codebuild: [botocore] Update codebuild client to latest version

1.5.4

=====

• api-change:iot: [botocore] Update iot client to latest version
• api-change:config: [botocore] Update config client to latest version

1.5.3

=====

• api-change:route53: [botocore] Update route53 client to latest version
• api-change:apigateway: [botocore] Update apigateway client to latest version
• api-change:mediastore-data: [botocore] Update mediastore-data client to latest version

1.5.2

=====

• bugfix:presigned-url: [botocore] Fixes a bug where content-type would be set on presigned requests for query services.
• api-change:cloudwatch: [botocore] Update cloudwatch client to latest version

1.5.1

=====

• api-change:appstream: [botocore] Update appstream client to latest version

1.5.0

=====

• bugfix:Filters: Fixes a bug where parameters passed to resource collections could be mutated after the collections were created.
• api-change:ses: [botocore] Update ses client to latest version
• enhancement:credentials: [botocore] Moved the JSONFileCache from the CLI into botocore so that it can be used without importing from the cli.
• feature:botocore dependency: Update dependency strategy to always take a floor on the most recent version of botocore. This means whenever there is a release of botocore, boto3 will release as well to account for the new version of botocore.
• api-change:apigateway: [botocore] Update apigateway client to latest version

1.4.8

=====

• enhancement:botocore: Raised minor version dependency for botocore

pandas 0.21.0 -> 0.22.0

0.22.0

---

This is a major release from 0.21.1 and includes a single, API-breaking change.
We recommend that all users upgrade to this version after carefully reading the
release note (singular!).

.. _whatsnew_0220.api_breaking:

Backwards incompatible API changes

Pandas 0.22.0 changes the handling of empty and all-NA sums and products. The
summary is that

• The sum of an empty or all-NA Series is now 0
• The product of an empty or all-NA Series is now 1
• We've added a min_count parameter to .sum() and .prod() controlling
  the minimum number of valid values for the result to be valid. If fewer than
  min_count non-NA values are present, the result is NA. The default is
  0. To return NaN, the 0.21 behavior, use min_count=1.

Some background: In pandas 0.21, we fixed a long-standing inconsistency
in the return value of all-NA series depending on whether or not bottleneck
was installed. See :ref:whatsnew_0210.api_breaking.bottleneck. At the same
time, we changed the sum and prod of an empty Series to also be NaN.

Based on feedback, we've partially reverted those changes.

Arithmetic Operations
^^^^^^^^^^^^^^^^^^^^^

The default sum for empty or all-NA Series is now 0.

pandas 0.21.x

.. code-block:: ipython

In [1]: pd.Series([]).sum()
Out[1]: nan

In [2]: pd.Series([np.nan]).sum()
Out[2]: nan

pandas 0.22.0

.. ipython:: python

pd.Series([]).sum()
pd.Series([np.nan]).sum()

The default behavior is the same as pandas 0.20.3 with bottleneck installed. It
also matches the behavior of NumPy's np.nansum on empty and all-NA arrays.

To have the sum of an empty series return NaN (the default behavior of
pandas 0.20.3 without bottleneck, or pandas 0.21.x), use the min_count
keyword.

.. ipython:: python

pd.Series([]).sum(min_count=1)

Thanks to the skipna parameter, the .sum on an all-NA
series is conceptually the same as the .sum of an empty one with
skipna=True (the default).

.. ipython:: python

pd.Series([np.nan]).sum(min_count=1) skipna=True by default

The min_count parameter refers to the minimum number of non-null values
required for a non-NA sum or product.

:meth:Series.prod has been updated to behave the same as :meth:Series.sum,
returning 1 instead.

.. ipython:: python

pd.Series([]).prod()
pd.Series([np.nan]).prod()
pd.Series([]).prod(min_count=1)

These changes affect :meth:DataFrame.sum and :meth:DataFrame.prod as well.
Finally, a few less obvious places in pandas are affected by this change.

Grouping by a Categorical
^^^^^^^^^^^^^^^^^^^^^^^^^

Grouping by a Categorical and summing now returns 0 instead of
NaN for categories with no observations. The product now returns 1
instead of NaN.

pandas 0.21.x

.. code-block:: ipython

In [8]: grouper = pd.Categorical(['a', 'a'], categories=['a', 'b'])

In [9]: pd.Series([1, 2]).groupby(grouper).sum()
Out[9]:
a 3.0
b NaN
dtype: float64

pandas 0.22

.. ipython:: python

grouper = pd.Categorical(['a', 'a'], categories=['a', 'b'])
pd.Series([1, 2]).groupby(grouper).sum()

To restore the 0.21 behavior of returning NaN for unobserved groups,
use min_count>=1.

.. ipython:: python

pd.Series([1, 2]).groupby(grouper).sum(min_count=1)

Resample
^^^^^^^^

The sum and product of all-NA bins has changed from NaN to 0 for
sum and 1 for product.

pandas 0.21.x

.. code-block:: ipython

In [11]: s = pd.Series([1, 1, np.nan, np.nan],
...: index=pd.date_range('2017', periods=4))
...: s
Out[11]:
2017-01-01 1.0
2017-01-02 1.0
2017-01-03 NaN
2017-01-04 NaN
Freq: D, dtype: float64

In [12]: s.resample('2d').sum()
Out[12]:
2017-01-01 2.0
2017-01-03 NaN
Freq: 2D, dtype: float64

pandas 0.22.0

.. ipython:: python

s = pd.Series([1, 1, np.nan, np.nan],
index=pd.date_range('2017', periods=4))
s.resample('2d').sum()

To restore the 0.21 behavior of returning NaN, use min_count>=1.

.. ipython:: python

s.resample('2d').sum(min_count=1)

In particular, upsampling and taking the sum or product is affected, as
upsampling introduces missing values even if the original series was
entirely valid.

pandas 0.21.x

.. code-block:: ipython

In [14]: idx = pd.DatetimeIndex(['2017-01-01', '2017-01-02'])

In [15]: pd.Series([1, 2], index=idx).resample('12H').sum()
Out[15]:
2017-01-01 00:00:00 1.0
2017-01-01 12:00:00 NaN
2017-01-02 00:00:00 2.0
Freq: 12H, dtype: float64

pandas 0.22.0

.. ipython:: python

idx = pd.DatetimeIndex(['2017-01-01', '2017-01-02'])
pd.Series([1, 2], index=idx).resample("12H").sum()

Once again, the min_count keyword is available to restore the 0.21 behavior.

.. ipython:: python

pd.Series([1, 2], index=idx).resample("12H").sum(min_count=1)

Rolling and Expanding
^^^^^^^^^^^^^^^^^^^^^

Rolling and expanding already have a min_periods keyword that behaves
similar to min_count. The only case that changes is when doing a rolling
or expanding sum with min_periods=0. Previously this returned NaN,
when fewer than min_periods non-NA values were in the window. Now it
returns 0.

pandas 0.21.1

.. code-block:: ipython

In [17]: s = pd.Series([np.nan, np.nan])

In [18]: s.rolling(2, min_periods=0).sum()
Out[18]:
0 NaN
1 NaN
dtype: float64

pandas 0.22.0

.. ipython:: python

s = pd.Series([np.nan, np.nan])
s.rolling(2, min_periods=0).sum()

The default behavior of min_periods=None, implying that min_periods
equals the window size, is unchanged.

Compatibility

If you maintain a library that should work across pandas versions, it
may be easiest to exclude pandas 0.21 from your requirements. Otherwise, all your
sum() calls would need to check if the Series is empty before summing.

With setuptools, in your setup.py use::

install_requires=['pandas!=0.21.*', ...]

With conda, use

.. code-block:: yaml

requirements:
run:
- pandas !=0.21.0,!=0.21.1

Note that the inconsistency in the return value for all-NA series is still
there for pandas 0.20.3 and earlier. Avoiding pandas 0.21 will only help with
the empty case.

.. _whatsnew_0211:

0.21.1

---

This is a minor bug-fix release in the 0.21.x series and includes some small regression fixes,
bug fixes and performance improvements.
We recommend that all users upgrade to this version.

Highlights include:

• Temporarily restore matplotlib datetime plotting functionality. This should
  resolve issues for users who implicitly relied on pandas to plot datetimes
  with matplotlib. See :ref:here <whatsnew_0211.converters>.
• Improvements to the Parquet IO functions introduced in 0.21.0. See
  :ref:here <whatsnew_0211.enhancements.parquet>.

.. contents:: What's new in v0.21.1
:local:
:backlinks: none

.. _whatsnew_0211.converters:

Restore Matplotlib datetime Converter Registration

Pandas implements some matplotlib converters for nicely formatting the axis
labels on plots with datetime or Period values. Prior to pandas 0.21.0,
these were implicitly registered with matplotlib, as a side effect of import pandas.

In pandas 0.21.0, we required users to explicitly register the
converter. This caused problems for some users who relied on those converters
being present for regular matplotlib.pyplot plotting methods, so we're
temporarily reverting that change; pandas 0.21.1 again registers the converters on
import, just like before 0.21.0.

We've added a new option to control the converters:
pd.options.plotting.matplotlib.register_converters. By default, they are
registered. Toggling this to False removes pandas' formatters and restore
any converters we overwrote when registering them (:issue:18301).

We're working with the matplotlib developers to make this easier. We're trying
to balance user convenience (automatically registering the converters) with
import performance and best practices (importing pandas shouldn't have the side
effect of overwriting any custom converters you've already set). In the future
we hope to have most of the datetime formatting functionality in matplotlib,
with just the pandas-specific converters in pandas. We'll then gracefully
deprecate the automatic registration of converters in favor of users explicitly
registering them when they want them.

.. _whatsnew_0211.enhancements:

New features

.. _whatsnew_0211.enhancements.parquet:

Improvements to the Parquet IO functionality
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

• :func:DataFrame.to_parquet will now write non-default indexes when the
  underlying engine supports it. The indexes will be preserved when reading
  back in with :func:read_parquet (:issue:18581).
• :func:read_parquet now allows to specify the columns to read from a parquet file (:issue:18154)
• :func:read_parquet now allows to specify kwargs which are passed to the respective engine (:issue:18216)

.. _whatsnew_0211.enhancements.other:

Other Enhancements
^^^^^^^^^^^^^^^^^^

• :meth:Timestamp.timestamp is now available in Python 2.7. (:issue:17329)
• :class:Grouper and :class:TimeGrouper now have a friendly repr output (:issue:18203).

.. _whatsnew_0211.deprecations:

Deprecations

• pandas.tseries.register has been renamed to
  :func:pandas.plotting.register_matplotlib_converters`` (:issue:18301`)

.. _whatsnew_0211.performance:

Performance Improvements

• Improved performance of plotting large series/dataframes (:issue:18236).

.. _whatsnew_0211.bug_fixes:

Bug Fixes

Conversion
^^^^^^^^^^

• Bug in :class:TimedeltaIndex subtraction could incorrectly overflow when NaT is present (:issue:17791)
• Bug in :class:DatetimeIndex subtracting datetimelike from DatetimeIndex could fail to overflow (:issue:18020)
• Bug in :meth:IntervalIndex.copy when copying and IntervalIndex with non-default closed (:issue:18339)
• Bug in :func:DataFrame.to_dict where columns of datetime that are tz-aware were not converted to required arrays when used with orient='records', raising``TypeError (:issue:18372`)
• Bug in :class:DateTimeIndex and :meth:date_range where mismatching tz-aware start and end timezones would not raise an err if end.tzinfo is None (:issue:18431)
• Bug in :meth:Series.fillna which raised when passed a long integer on Python 2 (:issue:18159).

Indexing
^^^^^^^^

• Bug in a boolean comparison of a datetime.datetime and a datetime64[ns] dtype Series (:issue:17965)
• Bug where a MultiIndex with more than a million records was not raising AttributeError when trying to access a missing attribute (:issue:18165)
• Bug in :class:IntervalIndex constructor when a list of intervals is passed with non-default closed (:issue:18334)
• Bug in Index.putmask when an invalid mask passed (:issue:18368)
• Bug in masked assignment of a timedelta64[ns] dtype Series, incorrectly coerced to float (:issue:18493)

I/O
^^^

• Bug in class:~pandas.io.stata.StataReader not converting date/time columns with display formatting addressed (:issue:17990). Previously columns with display formatting were normally left as ordinal numbers and not converted to datetime objects.
• Bug in :func:read_csv when reading a compressed UTF-16 encoded file (:issue:18071)
• Bug in :func:read_csv for handling null values in index columns when specifying na_filter=False (:issue:5239)
• Bug in :func:read_csv when reading numeric category fields with high cardinality (:issue:18186)
• Bug in :meth:DataFrame.to_csv when the table had MultiIndex columns, and a list of strings was passed in for header (:issue:5539)
• Bug in parsing integer datetime-like columns with specified format in read_sql (:issue:17855).
• Bug in :meth:DataFrame.to_msgpack when serializing data of the numpy.bool_ datatype (:issue:18390)
• Bug in :func:read_json not decoding when reading line deliminted JSON from S3 (:issue:17200)
• Bug in :func:pandas.io.json.json_normalize to avoid modification of meta (:issue:18610)
• Bug in :func:to_latex where repeated multi-index values were not printed even though a higher level index differed from the previous row (:issue:14484)
• Bug when reading NaN-only categorical columns in :class:HDFStore (:issue:18413)
• Bug in :meth:DataFrame.to_latex with longtable=True where a latex multicolumn always spanned over three columns (:issue:17959)

Plotting
^^^^^^^^

• Bug in DataFrame.plot() and Series.plot() with :class:DatetimeIndex where a figure generated by them is not pickleable in Python 3 (:issue:18439)

Groupby/Resample/Rolling
^^^^^^^^^^^^^^^^^^^^^^^^

• Bug in DataFrame.resample(...).apply(...) when there is a callable that returns different columns (:issue:15169)
• Bug in DataFrame.resample(...) when there is a time change (DST) and resampling frequecy is 12h or higher (:issue:15549)
• Bug in pd.DataFrameGroupBy.count() when counting over a datetimelike column (:issue:13393)
• Bug in rolling.var where calculation is inaccurate with a zero-valued array (:issue:18430)

Reshaping
^^^^^^^^^

• Error message in pd.merge_asof() for key datatype mismatch now includes datatype of left and right key (:issue:18068)
• Bug in pd.concat when empty and non-empty DataFrames or Series are concatenated (:issue:18178 :issue:18187)
• Bug in DataFrame.filter(...) when :class:unicode is passed as a condition in Python 2 (:issue:13101)
• Bug when merging empty DataFrames when np.seterr(divide='raise') is set (:issue:17776)

Numeric
^^^^^^^

• Bug in pd.Series.rolling.skew() and rolling.kurt() with all equal values has floating issue (:issue:18044)

Categorical
^^^^^^^^^^^

• Bug in :meth:DataFrame.astype where casting to 'category' on an empty DataFrame causes a segmentation fault (:issue:18004)
• Error messages in the testing module have been improved when items have different CategoricalDtype (:issue:18069)
• CategoricalIndex can now correctly take a pd.api.types.CategoricalDtype as its dtype (:issue:18116)
• Bug in Categorical.unique() returning read-only codes array when all categories were NaN (:issue:18051)
• Bug in DataFrame.groupby(axis=1) with a CategoricalIndex (:issue:18432)

String
^^^^^^

• :meth:Series.str.split() will now propagate NaN values across all expanded columns instead of None (:issue:18450)

.. _whatsnew_060:

numpy 1.13.3 -> 1.14.0

1.14.0

==========================

Numpy 1.14.0 is the result of seven months of work and contains a large number
of bug fixes and new features, along with several changes with potential
compatibility issues. The major change that users will notice are the
stylistic changes in the way numpy arrays and scalars are printed, a change
that will affect doctests. See below for details on how to preserve the
old style printing when needed.

A major decision affecting future development concerns the schedule for
dropping Python 2.7 support in the runup to 2020. The decision has been made to
support 2.7 for all releases made in 2018, with the last release being
designated a long term release with support for bug fixes extending through
2019. In 2019 support for 2.7 will be dropped in all new releases. More details
can be found in the relevant NEP_.

This release supports Python 2.7 and 3.4 - 3.6.

.. _NEP: https://github.com/numpy/numpy/blob/master/doc/neps/dropping-python2.7-proposal.rst

Highlights

• The np.einsum function uses BLAS when possible

• genfromtxt, loadtxt, fromregex and savetxt can now handle
  files with arbitrary Python supported encoding.

• Major improvements to printing of NumPy arrays and scalars.

New functions

• parametrize: decorator added to numpy.testing

• chebinterpolate: Interpolate function at Chebyshev points.

• format_float_positional and format_float_scientific : format
  floating-point scalars unambiguously with control of rounding and padding.

• PyArray_ResolveWritebackIfCopy and PyArray_SetWritebackIfCopyBase,
  new C-API functions useful in achieving PyPy compatibity.

Deprecations

• Using np.bool_ objects in place of integers is deprecated. Previously
  operator.index(np.bool_) was legal and allowed constructs such as
  [1, 2, 3][np.True_]. That was misleading, as it behaved differently from
  np.array([1, 2, 3])[np.True_].

• Truth testing of an empty array is deprecated. To check if an array is not
  empty, use array.size > 0.

• Calling np.bincount with minlength=None is deprecated.
  minlength=0 should be used instead.

• Calling np.fromstring with the default value of the sep argument is
  deprecated. When that argument is not provided, a broken version of
  np.frombuffer is used that silently accepts unicode strings and -- after
  encoding them as either utf-8 (python 3) or the default encoding
  (python 2) -- treats them as binary data. If reading binary data is
  desired, np.frombuffer should be used directly.

• The style option of array2string is deprecated in non-legacy printing mode.

• PyArray_SetUpdateIfCopyBase has been deprecated. For NumPy versions >= 1.14
  use PyArray_SetWritebackIfCopyBase instead, see C API changes below for
  more details.

• The use of UPDATEIFCOPY arrays is deprecated, see C API changes below
  for details. We will not be dropping support for those arrays, but they are
  not compatible with PyPy.

Future Changes

• np.issubdtype will stop downcasting dtype-like arguments.
  It might be expected that issubdtype(np.float32, 'float64') and
  issubdtype(np.float32, np.float64) mean the same thing - however, there
  was an undocumented special case that translated the former into
  issubdtype(np.float32, np.floating), giving the surprising result of True.

This translation now gives a warning that explains what translation is
occurring. In the future, the translation will be disabled, and the first
example will be made equivalent to the second.

• np.linalg.lstsq default for rcond will be changed. The rcond
  parameter to np.linalg.lstsq will change its default to machine precision
  times the largest of the input array dimensions. A FutureWarning is issued
  when rcond is not passed explicitly.

• a.flat.__array__() will return a writeable copy of a when a is
  non-contiguous. Previously it returned an UPDATEIFCOPY array when a was
  writeable. Currently it returns a non-writeable copy. See gh-7054 for a
  discussion of the issue.

• Unstructured void array's .item method will return a bytes object. In the
  future, calling .item() on arrays or scalars of np.void datatype will
  return a bytes object instead of a buffer or int array, the same as
  returned by bytes(void_scalar). This may affect code which assumed the
  return value was mutable, which will no longer be the case. A
  FutureWarning is now issued when this would occur.

Compatibility notes

The mask of a masked array view is also a view rather than a copy

There was a FutureWarning about this change in NumPy 1.11.x. In short, it is
now the case that, when changing a view of a masked array, changes to the mask
are propagated to the original. That was not previously the case. This change
affects slices in particular. Note that this does not yet work properly if the
mask of the original array is nomask and the mask of the view is changed.
See gh-5580 for an extended discussion. The original behavior of having a copy
of the mask can be obtained by calling the unshare_mask method of the view.

np.ma.masked is no longer writeable

Attempts to mutate the masked constant now error, as the underlying arrays
are marked readonly. In the past, it was possible to get away with::

emulating a function that sometimes returns np.ma.masked

val = random.choice([np.ma.masked, 10])
var_arr = np.asarray(val)
val_arr += 1 now errors, previously changed np.ma.masked.data

np.ma functions producing fill_values have changed

Previously, np.ma.default_fill_value would return a 0d array, but
np.ma.minimum_fill_value and np.ma.maximum_fill_value would return a
tuple of the fields. Instead, all three methods return a structured np.void
object, which is what you would already find in the .fill_value attribute.

Additionally, the dtype guessing now matches that of np.array - so when
passing a python scalar x, maximum_fill_value(x) is always the same as
maximum_fill_value(np.array(x)). Previously x = long(1) on Python 2
violated this assumption.

a.flat.__array__() returns non-writeable arrays when a is non-contiguous

The intent is that the UPDATEIFCOPY array previously returned when a was
non-contiguous will be replaced by a writeable copy in the future. This
temporary measure is aimed to notify folks who expect the underlying array be
modified in this situation that that will no longer be the case. The most
likely places for this to be noticed is when expressions of the form
np.asarray(a.flat) are used, or when a.flat is passed as the out
parameter to a ufunc.

np.tensordot now returns zero array when contracting over 0-length dimension

Previously np.tensordot raised a ValueError when contracting over 0-length
dimension. Now it returns a zero array, which is consistent with the behaviour
of np.dot and np.einsum.

numpy.testing reorganized

This is not expected to cause problems, but possibly something has been left
out. If you experience an unexpected import problem using numpy.testing
let us know.

np.asfarray no longer accepts non-dtypes through the dtype argument

This previously would accept dtype=some_array, with the implied semantics
of dtype=some_array.dtype. This was undocumented, unique across the numpy
functions, and if used would likely correspond to a typo.

1D np.linalg.norm preserves float input types, even for arbitrary orders

Previously, this would promote to float64 when arbitrary orders were
passed, despite not doing so under the simple cases::

>>> f32 = np.float32([1, 2])
>>> np.linalg.norm(f32, 2.0).dtype
dtype('float32')
>>> np.linalg.norm(f32, 2.0001).dtype
dtype('float64') numpy 1.13
dtype('float32') numpy 1.14

This change affects only float32 and float16 arrays.

count_nonzero(arr, axis=()) now counts over no axes, not all axes

Elsewhere, axis==() is always understood as "no axes", but
count_nonzero had a special case to treat this as "all axes". This was
inconsistent and surprising. The correct way to count over all axes has always
been to pass axis == None.

__init__.py files added to test directories

This is for pytest compatibility in the case of duplicate test file names in
the different directories. As a result, run_module_suite no longer works,
i.e., python <path-to-test-file> results in an error.

.astype(bool) on unstructured void arrays now calls bool on each element

On Python 2, void_array.astype(bool) would always return an array of
True, unless the dtype is V0. On Python 3, this operation would usually
crash. Going forwards, astype matches the behavior of bool(np.void),
considering a buffer of all zeros as false, and anything else as true.
Checks for V0 can still be done with arr.dtype.itemsize == 0.

MaskedArray.squeeze never returns np.ma.masked

np.squeeze is documented as returning a view, but the masked variant would
sometimes return masked, which is not a view. This has been fixed, so that
the result is always a view on the original masked array.
This breaks any code that used masked_arr.squeeze() is np.ma.masked, but
fixes code that writes to the result of .squeeze().

Renamed first parameter of can_cast from from to from_

The previous parameter name from is a reserved keyword in Python, which made
it difficult to pass the argument by name. This has been fixed by renaming
the parameter to from_.

isnat raises TypeError when passed wrong type

The ufunc isnat used to raise a ValueError when it was not passed
variables of type datetime or timedelta. This has been changed to
raising a TypeError.

dtype.__getitem__ raises TypeError when passed wrong type

When indexed with a float, the dtype object used to raise ValueError.

User-defined types now need to implement __str__ and __repr__

Previously, user-defined types could fall back to a default implementation of
__str__ and __repr__ implemented in numpy, but this has now been
removed. Now user-defined types will fall back to the python default
object.__str__ and object.__repr__.

Many changes to array printing, disableable with the new "legacy" printing mode

The str and repr of ndarrays and numpy scalars have been changed in
a variety of ways. These changes are likely to break downstream user's
doctests.

These new behaviors can be disabled to mostly reproduce numpy 1.13 behavior by
enabling the new 1.13 "legacy" printing mode. This is enabled by calling
np.set_printoptions(legacy="1.13"), or using the new legacy argument to
np.array2string, as np.array2string(arr, legacy='1.13').

In summary, the major changes are:

• For floating-point types:

• The repr of float arrays often omits a space previously printed
  in the sign position. See the new sign option to np.set_printoptions.
• Floating-point arrays and scalars use a new algorithm for decimal
  representations, giving the shortest unique representation. This will
  usually shorten float16 fractional output, and sometimes float32 and
  float128 output. float64 should be unaffected. See the new
  floatmode option to np.set_printoptions.
• Float arrays printed in scientific notation no longer use fixed-precision,
  and now instead show the shortest unique representation.
• The str of floating-point scalars is no longer truncated in python2.

• For other data types:

• Non-finite complex scalars print like nanj instead of nan*j.
• NaT values in datetime arrays are now properly aligned.
• Arrays and scalars of np.void datatype are now printed using hex
  notation.

• For line-wrapping:

• The "dtype" part of ndarray reprs will now be printed on the next line
  if there isn't space on the last line of array output.
• The linewidth format option is now always respected.
  The repr or str of an array will never exceed this, unless a single
  element is too wide.
• The last line of an array string will never have more elements than earlier
  lines.
• An extra space is no longer inserted on the first line if the elements are
  too wide.

• For summarization (the use of ... to shorten long arrays):

• A trailing comma is no longer inserted for str.
  Previously, str(np.arange(1001)) gave
  '[ 0 1 2 ..., 998 999 1000]', which has an extra comma.
• For arrays of 2-D and beyond, when ... is printed on its own line in
  order to summarize any but the last axis, newlines are now appended to that
  line to match its leading newlines and a trailing space character is
  removed.

• MaskedArray arrays now separate printed elements with commas, always
  print the dtype, and correctly wrap the elements of long arrays to multiple
  lines. If there is more than 1 dimension, the array attributes are now
  printed in a new "left-justified" printing style.
• recarray arrays no longer print a trailing space before their dtype, and
  wrap to the right number of columns.
• 0d arrays no longer have their own idiosyncratic implementations of str
  and repr. The style argument to np.array2string is deprecated.
• Arrays of bool datatype will omit the datatype in the repr.
• User-defined dtypes (subclasses of np.generic) now need to
  implement __str__ and __repr__.

You may want to do something like::

FIXME: Set numpy array str/repr to legacy behaviour on numpy > 1.13

try:
np.set_printoptions(legacy='1.13')
except TypeError:
pass

after ::

import numpy as np

Some of these changes are described in more detail below.

C API changes

PyPy compatible alternative to UPDATEIFCOPY arrays

UPDATEIFCOPY arrays are contiguous copies of existing arrays, possibly with
different dimensions, whose contents are copied back to the original array when
their refcount goes to zero and they are deallocated. Because PyPy does not use
refcounts, they do not function correctly with PyPy. NumPy is in the process of
eliminating their use internally and two new C-API functions,

• PyArray_SetWritebackIfCopyBase
• PyArray_ResolveWritebackIfCopy,

have been added together with a complimentary flag,
NPY_ARRAY_WRITEBACKIFCOPY. Using the new functionality also requires that
some flags be changed when new arrays are created, to wit:
NPY_ARRAY_INOUT_ARRAY should be replaced by NPY_ARRAY_INOUT_ARRAY2 and
NPY_ARRAY_INOUT_FARRAY should be replaced by NPY_ARRAY_INOUT_FARRAY2.
Arrays created with these new flags will then have the WRITEBACKIFCOPY
semantics.

If PyPy compatibility is not a concern, these new functions can be ignored,
although there will be a DeprecationWarning. If you do wish to pursue PyPy
compatibility, more information on these functions and their use may be found
in the c-api_ documentation and the example in how-to-extend_.

.. _c-api: https://github.com/numpy/numpy/blob/master/doc/source/reference/c-api.array.rst
.. _how-to-extend: https://github.com/numpy/numpy/blob/master/doc/source/user/c-info.how-to-extend.rst

New Features

Encoding argument for text IO functions

genfromtxt, loadtxt, fromregex and savetxt can now handle files
with arbitrary encoding supported by Python via the encoding argument.
For backward compatibility the argument defaults to the special bytes value
which continues to treat text as raw byte values and continues to pass latin1
encoded bytes to custom converters.
Using any other value (including None for system default) will switch the
functions to real text IO so one receives unicode strings instead of bytes in
the resulting arrays.

External nose plugins are usable by numpy.testing.Tester

numpy.testing.Tester is now aware of nose plugins that are outside the
nose built-in ones. This allows using, for example, nose-timer like
so: np.test(extra_argv=['--with-timer', '--timer-top-n', '20']) to
obtain the runtime of the 20 slowest tests. An extra keyword timer was
also added to Tester.test, so np.test(timer=20) will also report the 20
slowest tests.

parametrize decorator added to numpy.testing

A basic parametrize decorator is now available in numpy.testing. It is
intended to allow rewriting yield based tests that have been deprecated in
pytest so as to facilitate the transition to pytest in the future. The nose
testing framework has not been supported for several years and looks like
abandonware.

The new parametrize decorator does not have the full functionality of the
one in pytest. It doesn't work for classes, doesn't support nesting, and does
not substitute variable names. Even so, it should be adequate to rewrite the
NumPy tests.

chebinterpolate function added to numpy.polynomial.chebyshev

The new chebinterpolate function interpolates a given function at the
Chebyshev points of the first kind. A new Chebyshev.interpolate class
method adds support for interpolation over arbitrary intervals using the scaled
and shifted Chebyshev points of the first kind.

Support for reading lzma compressed text files in Python 3

With Python versions containing the lzma module the text IO functions can
now transparently read from files with xz or lzma extension.

sign option added to np.setprintoptions and np.array2string

This option controls printing of the sign of floating-point types, and may be
one of the characters '-', '+' or ' '. With '+' numpy always prints the sign of
positive values, with ' ' it always prints a space (whitespace character) in
the sign position of positive values, and with '-' it will omit the sign
character for positive values. The new default is '-'.

This new default changes the float output relative to numpy 1.13. The old
behavior can be obtained in 1.13 "legacy" printing mode, see compatibility
notes above.

hermitian option added tonp.linalg.matrix_rank

The new hermitian option allows choosing between standard SVD based matrix
rank calculation and the more efficient eigenvalue based method for
symmetric/hermitian matrices.

threshold and edgeitems options added to np.array2string

These options could previously be controlled using np.set_printoptions, but
now can be changed on a per-call basis as arguments to np.array2string.

concatenate and stack gained an out argument

A preallocated buffer of the desired dtype can now be used for the output of
these functions.

Support for PGI flang compiler on Windows

The PGI flang compiler is a Fortran front end for LLVM released by NVIDIA under
the Apache 2 license. It can be invoked by ::

python setup.py config --compiler=clang --fcompiler=flang install

There is little experience with this new compiler, so any feedback from people
using it will be appreciated.

Improvements

Numerator degrees of freedom in random.noncentral_f need only be positive.

Prior to NumPy 1.14.0, the numerator degrees of freedom needed to be > 1, but
the distribution is valid for values > 0, which is the new requirement.

The GIL is released for all np.einsum variations

Some specific loop structures which have an accelerated loop version
did not release the GIL prior to NumPy 1.14.0. This oversight has been
fixed.

The np.einsum function will use BLAS when possible and optimize by default

The np.einsum function will now call np.tensordot when appropriate.
Because np.tensordot uses BLAS when possible, that will speed up execution.
By default, np.einsum will also attempt optimization as the overhead is
small relative to the potential improvement in speed.

f2py now handles arrays of dimension 0

f2py now allows for the allocation of arrays of dimension 0. This allows
for more consistent handling of corner cases downstream.

numpy.distutils supports using MSVC and mingw64-gfortran together

Numpy distutils now supports using Mingw64 gfortran and MSVC compilers
together. This enables the production of Python extension modules on Windows
containing Fortran code while retaining compatibility with the
binaries distributed by Python.org. Not all use cases are supported,
but most common ways to wrap Fortran for Python are functional.

Compilation in this mode is usually enabled automatically, and can be
selected via the --fcompiler and --compiler options to
setup.py. Moreover, linking Fortran codes to static OpenBLAS is
supported; by default a gfortran compatible static archive
openblas.a is looked for.

np.linalg.pinv now works on stacked matrices

Previously it was limited to a single 2d array.

numpy.save aligns data to 64 bytes instead of 16

Saving NumPy arrays in the npy format with numpy.save inserts
padding before the array data to align it at 64 bytes. Previously
this was only 16 bytes (and sometimes less due to a bug in the code
for version 2). Now the alignment is 64 bytes, which matches the
widest SIMD instruction set commonly available, and is also the most
common cache line size. This makes npy files easier to use in
programs which open them with mmap, especially on Linux where an
mmap offset must be a multiple of the page size.

NPZ files now can be written without using temporary files

In Python 3.6+ numpy.savez and numpy.savez_compressed now write
directly to a ZIP file, without creating intermediate temporary files.

Better support for empty structured and string types

Structured types can contain zero fields, and string dtypes can contain zero
characters. Zero-length strings still cannot be created directly, and must be
constructed through structured dtypes::

str0 = np.empty(10, np.dtype([('v', str, N)]))['v']
void0 = np.empty(10, np.void)

It was always possible to work with these, but the following operations are
now supported for these arrays:

• arr.sort()
• arr.view(bytes)
• arr.resize(...)
• pickle.dumps(arr)

Support for decimal.Decimal in np.lib.financial

Unless otherwise stated all functions within the financial package now
support using the decimal.Decimal built-in type.

Float printing now uses "dragon4" algorithm for shortest decimal representation

The str and repr of floating-point values (16, 32, 64 and 128 bit) are
now printed to give the shortest decimal representation which uniquely
identifies the value from others of the same type. Previously this was only
true for float64 values. The remaining float types will now often be shorter
than in numpy 1.13. Arrays printed in scientific notation now also use the
shortest scientific representation, instead of fixed precision as before.

Additionally, the str of float scalars scalars will no longer be truncated
in python2, unlike python2 floats. np.double scalars now have a str
and repr identical to that of a python3 float.

New functions np.format_float_scientific and np.format_float_positional
are provided to generate these decimal representations.

A new option floatmode has been added to np.set_printoptions and
np.array2string, which gives control over uniqueness and rounding of
printed elements in an array. The new default is floatmode='maxprec' with
precision=8, which will print at most 8 fractional digits, or fewer if an
element can be uniquely represented with fewer. A useful new mode is
floatmode="unique", which will output enough digits to specify the array
elements uniquely.

Numpy complex-floating-scalars with values like inf*j or nan*j now
print as infj and nanj, like the pure-python complex type.

The FloatFormat and LongFloatFormat classes are deprecated and should
both be replaced by FloatingFormat. Similarly ComplexFormat and
LongComplexFormat should be replaced by ComplexFloatingFormat.

void datatype elements are now printed in hex notation

A hex representation compatible with the python bytes type is now printed
for unstructured np.void elements, e.g., V4 datatype. Previously, in
python2 the raw void data of the element was printed to stdout, or in python3
the integer byte values were shown.

printing style for void datatypes is now independently customizable

The printing style of np.void arrays is now independently customizable
using the formatter argument to np.set_printoptions, using the
'void' key, instead of the catch-all numpystr key as before.

Reduced memory usage of np.loadtxt

np.loadtxt now reads files in chunks instead of all at once which decreases
its memory usage significantly for large files.

Changes

Multiple-field indexing/assignment of structured arrays

The indexing and assignment of structured arrays with multiple fields has
changed in a number of ways, as warned about in previous releases.

First, indexing a structured array with multiple fields, e.g.,
arr[['f1', 'f3']], returns a view into the original array instead of a
copy. The returned view will have extra padding bytes corresponding to
intervening fields in the original array, unlike the copy in 1.13, which will
affect code such as arr[['f1', 'f3']].view(newdtype).

Second, assignment between structured arrays will now occur "by position"
instead of "by field name". The Nth field of the destination will be set to the
Nth field of the source regardless of field name, unlike in numpy versions 1.6
to 1.13 in which fields in the destination array were set to the
identically-named field in the source array or to 0 if the source did not have
a field.

Correspondingly, the order of fields in a structured dtypes now matters when
computing dtype equality. For example, with the dtypes ::

x = dtype({'names': ['A', 'B'], 'formats': ['i4', 'f4'], 'offsets': [0, 4]})
y = dtype({'names': ['B', 'A'], 'formats': ['f4', 'i4'], 'offsets': [4, 0]})

the expression x == y will now return False, unlike before.
This makes dictionary based dtype specifications like
dtype({'a': ('i4', 0), 'b': ('f4', 4)}) dangerous in python < 3.6
since dict key order is not preserved in those versions.

Assignment from a structured array to a boolean array now raises a ValueError,
unlike in 1.13, where it always set the destination elements to True.

Assignment from structured array with more than one field to a non-structured
array now raises a ValueError. In 1.13 this copied just the first field of the
source to the destination.

Using field "titles" in multiple-field indexing is now disallowed, as is
repeating a field name in a multiple-field index.

The documentation for structured arrays in the user guide has been
significantly updated to reflect these changes.

Integer and Void scalars are now unaffected by np.set_string_function

Previously, unlike most other numpy scalars, the str and repr of
integer and void scalars could be controlled by np.set_string_function.
This is no longer possible.

0d array printing changed, style arg of array2string deprecated

Previously the str and repr of 0d arrays had idiosyncratic
implementations which returned str(a.item()) and 'array(' + repr(a.item()) + ')' respectively for 0d array a, unlike both numpy
scalars and higher dimension ndarrays.

Now, the str of a 0d array acts like a numpy scalar using str(a[()])
and the repr acts like higher dimension arrays using formatter(a[()]),
where formatter can be specified using np.set_printoptions. The
style argument of np.array2string is deprecated.

This new behavior is disabled in 1.13 legacy printing mode, see compatibility
notes above.

Seeding RandomState using an array requires a 1-d array

RandomState previously would accept empty arrays or arrays with 2 or more
dimensions, which resulted in either a failure to seed (empty arrays) or for
some of the passed values to be ignored when setting the seed.

MaskedArray objects show a more useful repr

The repr of a MaskedArray is now closer to the python code that would
produce it, with arrays now being shown with commas and dtypes. Like the other
formatting changes, this can be disabled with the 1.13 legacy printing mode in
order to help transition doctests.

The repr of np.polynomial classes is more explicit

It now shows the domain and window parameters as keyword arguments to make
them more clear::

>>> np.polynomial.Polynomial(range(4))
Polynomial([0., 1., 2., 3.], domain=[-1, 1], window=[-1, 1])

==========================

raven 6.3.0 -> 6.5.0

6.5.0

---

• [Core] Fixed missing deprecation on processors.SanitizePasswordsProcessor
• [Core] Improve exception handling in Serializer.transform
• [Core] Fixed celery.register_logger_signal ignoring subclasses
• [Core] Fixed sanitizer skipping byte instances
• [Lambda] Fixed AttributeError when requestContext not present

6.4.0

---

• [Core] Support for defining sanitized_keys on the client (pr/990)
• [Django] Support for Django 2.0 Urlresolver
• [Docs] Several fixes and improvements

django-tables2 1.14.2 -> 1.17.1

1.17.1

• Fix typo in setup.py for extras_require.

1.17.0

• Dropped support for Django 1.8, 1.9 and 1.10.
• Add extra_context argument to TemplateColumn 509 by ad-m
• Remove unnecessary cast of record to str 514, fixes 511
• Use django.test.TestCase for all tests, and remove dependency on pytest and reorganized some tests 515
• Remove traces of django-haystack tests from the tests, there were no actual tests.

1.16.0

This is the last version supporting Django 1.8, 1.9 and 1.10. Django 1.8 is only supported until april 2018, so consider upgrading to Django 1.11!

• Added tf dictionary to Column.attrs with default values for the footer, so footers now have class attribute by default 501 by mpasternak

1.15.0

• Added as=varname keyword argument to the {% querystring %} template tag,
  fixes 481
• Updated the tutorial to reflect current state of Django a bit better.
• Used OrderedDict rather than dict as the parent for utils.AttributeDict to make the rendered html more consistant accross python versions.
• Allow reading column attrs from a column's attribute, allowing easier reuse of custom column attributes (fixes 241)
• value and record are optionally passed to the column attrs callables for data rows. 503

[pyup-bot]

Closing this in favor of #76