v0.23.0rc1

This is the first release candidate for pandas 0.23.0

This is a major release from 0.22.0 and includes a number of API changes, new
features, enhancements, and performance improvements along with a large number
of bug fixes.

Highlights include:

• Round-trippable JSON format with 'table' orient
• Instantiation from dicts respects order for Python 3.6+
• Dependent column arguments for assign
• Merging / sorting on a combination of columns and index levels
• Extending Pandas with custom types
• Excluding unobserved categories from groupby

See the full whatsnew for a list of all the changes.

The release candidate can be installed with conda from our development channel (builds for osx-64, linux-64 and win-64 for Python 2.7, Python 3.5, and Python 3.6 are all available):

conda install -c conda-forge --label=rc pandas=0.23.0rc1

Or via PyPI

python3 -m pip install --pre --upgrade pandas

v0.22.0 Final

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.

The only changes are:

• 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.

See the whatsnew for further explanation of all the places in the library this affects.

v0.21.1 Final

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 relied implicitly on pandas to plot datetimes
  with matplotlib. See here
• Improvements to the Parquet IO functions introduced in 0.21.0. See
  here

See the v0.21.1 Whatsnew overview for an extensive list
of all the changes for 0.21.1.

v0.21.0 Final

This is a major release from 0.20.3 and includes a number of API changes, deprecations, new features, enhancements, and performance improvements along with a large number of bug fixes. We recommend that all users upgrade to this version.

Highlights include:

• Integration with Apache Parquet, including a new top-level read_parquet function and a DataFrame.to_parquet method, see here.

• New user-facing dtype pandas.api.types.CategoricalDtype for specifying categoricals independent of the data, see here.

• The behavior of sum and prod on all-NaN Series/DataFrames is now consistent and no longer depends on whether bottleneck is installed, see here.

• Compatibility fixes for pypy, see here.

• Additions to the drop, reindex, and rename API to make them more consistent, see here

• Addition of the new methods DataFrame.infer_objects (see here) and GroupBy.pipe (see here).

• Indexing with a list of labels, where one or more of the labels is missing, is deprecated and will raise a KeyError in a future version, see here.

Check the whatsnew for detailed changes, including backwards incompatible changes and deprecations. Please report any issues you find on the pandas issue tracker.

Binary packages will be available in the defaults and conda-forge channels shortly.

conda install pandas

Wheels and a source distribution are available on PyPI.

pip install --upgrade pip setuptools
pip install --upgrade --upgrade-strategy=only-if-needed pandas

v0.21.0rc1

This is a major release from 0.20.3 and includes a number of API
changes, deprecations, new features, enhancements, and performance
improvements along with a large number of bug fixes. We recommend that
all users upgrade to this version.

Highlights include:

• Integration with Apache Parquet,
  including a new top-level read_parquet function and
  DataFrame.to_parquet method, see here
• New user-facing pandas.api.types.CategoricalDtype for specifying
  categoricals independent of the data, see
  here
• The behavior of sum and prod on all-NaN Series/DataFrames is now
  consistent and no longer depends on whether
  bottleneck is installed,
  see here
• Compatibility fixes for pypy, see here

Check the API Changes whatsnew; and deprecations; before updating.

v0.20.3 Final

This is a minor bug-fix release in the 0.20.x series and includes some small regression fixes
and bug fixes. We recommend that all users upgrade to this version.

See the v0.20.3 Whatsnew page for an overview of all the bugs that have been fixed in 0.20.3.

v0.20.2 Final

This is a minor bug-fix release in the 0.20.x series and includes some small regression fixes,
bug fixes and performance improvements.
We recommend that all users upgrade to this version.

See the v0.20.2 Whatsnew page for an overview of all the bugs that have been fixed in 0.20.2.

v0.20.1 Final

This is a minor release from 0.20.0 and includes a single change for backwards-compatibility with downstream projects using pandas' utils routines. See here.

See the Whatsnew file for more information. Please report any issues here.

The release can be installed with conda from the conda-forge channel (builds for osx-64, linux-64 and win-64 for Python 2.7, Python 3.5, and Python 3.6 are all available):

conda install -c conda-forge pandas

Or via PyPI:

pip install --upgrade pip setuptools
pip install --pre --upgrade --upgrade-strategy=only-if-needed pandas

v0.20.0 Final

This is a major release from 0.19.2 and includes a number of API changes, deprecations, new features,
enhancements, and performance improvements along with a large number of bug fixes. We recommend that all users upgrade to this version.

Highlights include:

• new .agg() API for Series/DataFrame similar to the groupby-rolling-resample API's, see here
• Integration with the feather-format, including a new top-level pd.read_feather() and DataFrame.to_feather() method, see here
• The .ix indexer has been deprecated, see here
• Panel has been deprecated, see here
• Addition of an IntervalIndex and Interval scalar type, see here
• Improved user API when accessing levels in .groupby(), see here
• Improved support for UInt64 dtypes, see here
• A new orient for JSON serialization, orient='table', that uses the Table Schema spec, see here
• Experimental support for exporting DataFrame.style formats to Excel, see here
• Window Binary Corr/Cov operations now return a MultiIndexed DataFrame rather than a Panel, as Panel is now deprecated, see here
• Support for S3 handling now uses s3fs, see here
• Google BigQuery support now uses the pandas-gbq library, see here
• Switched the test framework to use pytest

See the Whatsnew file for more information.

v0.20.0rc2

RELEASE CANDIDATE 2

This is a major release from 0.19.2 and includes a small number of API changes, several new features,
enhancements, and performance improvements along with a large number of bug fixes.

Please try this RC and report any issues on the pandas issue tracker. We will be releasing officially a day or two.

Highlights include:

• new .agg() API for Series/DataFrame similar to the groupby-rolling-resample API's, see here
• Integration with the feather-format, including a new top-level pd.read_feather() and DataFrame.to_feather() method, see here
• The .ix indexer has been deprecated, see here
• Panel has been deprecated, see here
• Addition of an IntervalIndex and Interval scalar type, see here
• Improved user API when accessing levels in .groupby(), see here
• Improved support for UInt64 dtypes, see here
• A new orient for JSON serialization, orient='table', that uses the Table Schema spec, see here
• Experimental support for exporting DataFrame.style formats to Excel, see here
• Window Binary Corr/Cov operations now return a MultiIndexed DataFrame rather than a Panel, as Panel is now deprecated, see here
• Support for S3 handling now uses s3fs, see here
• Google BigQuery support now uses the pandas-gbq library, see here
• Switched the test framework to use pytest

See the Whatsnew file for more information. Please report any issues here.

The release candidate can be installed with conda from our development channel (builds for osx-64, linux-64 and win-64 for Python 2.7, Python 3.5, and Python 3.6 are all available):

conda install -c pandas pandas=0.20.0rc2

Or via PyPI

pip install --upgrade pip setuptools
pip install --pre --upgrade --upgrade-strategy=only-if-needed pandas