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Merge remote-tracking branch 'upstream/master' into doc/substitution-offset
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‎ci/azure/posix.yml

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PATTERN: "not slow and not network"
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${{ if eq(parameters.name, 'Linux') }}:
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py35_compat:
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ENV_FILE: ci/deps/azure-35-compat.yaml
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CONDA_PY: "35"
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PATTERN: "not slow and not network"
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py36_locale_slow_old_np:
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ENV_FILE: ci/deps/azure-36-locale.yaml
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CONDA_PY: "36"
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PATTERN: "slow"
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LOCALE_OVERRIDE: "zh_CN.UTF-8"
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EXTRA_APT: "language-pack-zh-hans"
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py36_locale_slow:
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ENV_FILE: ci/deps/azure-36-locale_slow.yaml
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CONDA_PY: "36"

‎ci/azure/windows.yml

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ENV_FILE: ci/deps/azure-windows-36.yaml
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CONDA_PY: "36"
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py37_np141:
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ENV_FILE: ci/deps/azure-windows-37.yaml
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CONDA_PY: "37"
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steps:
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- task: CondaEnvironment@1
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inputs:

‎ci/deps/azure-35-compat.yaml

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name: pandas-dev
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channels:
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- defaults
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- conda-forge
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dependencies:
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- beautifulsoup4==4.4.1
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- bottleneck=1.2.0
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- cython=0.28.2
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- hypothesis>=3.58.0
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- jinja2=2.8
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- numexpr=2.6.1
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- numpy=1.12.0
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- openpyxl=2.4.0
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- pytables=3.4.2
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- python-dateutil=2.5.0
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- python=3.5*
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- pytz=2015.4
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- scipy=0.18.1
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- xlrd=1.0.0
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- xlsxwriter=0.7.7
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- xlwt=1.0.0
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# universal
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- pytest-xdist
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- pytest-mock
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- isort
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- pip:
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# for python 3.5, pytest>=4.0.2 is not available in conda
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- pytest>=4.0.2
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- html5lib==1.0b2

‎ci/deps/azure-36-locale.yaml

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name: pandas-dev
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channels:
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- defaults
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- conda-forge
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dependencies:
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- beautifulsoup4==4.5.1
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- bottleneck=1.2.0
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- cython=0.28.2
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- lxml
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- matplotlib=2.0.0
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- numpy=1.12.0
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- openpyxl=2.4.0
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- python-dateutil
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- python-blosc
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- python=3.6
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- pytz=2016.10
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- scipy
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- sqlalchemy=1.1.4
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- xlrd=1.0.0
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- xlsxwriter=0.9.4
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- xlwt=1.2.0
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# universal
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- pytest>=4.0.2
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- pytest-xdist
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- pytest-mock
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- hypothesis>=3.58.0
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- isort
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- pip:
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- html5lib==1.0b2

‎ci/deps/azure-macos-35.yaml

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- nomkl
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- numexpr
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- numpy=1.12.0
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- openpyxl=2.5.5
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- openpyxl
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- pyarrow
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- pytables
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- python=3.5*

‎ci/deps/azure-windows-37.yaml

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name: pandas-dev
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channels:
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- defaults
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- conda-forge
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dependencies:
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- beautifulsoup4
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- bottleneck
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- gcsfs
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- html5lib
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- jinja2
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- lxml
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- matplotlib=3.0.1
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- numexpr
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- numpy=1.14.*
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- openpyxl
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- pytables
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- python=3.7.*
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- python-dateutil
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- pytz
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- s3fs
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- scipy
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- sqlalchemy
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- xlrd
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- xlsxwriter
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- xlwt
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# universal
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- cython>=0.28.2
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- pytest>=4.0.2
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- pytest-xdist
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- pytest-mock
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- moto
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- hypothesis>=3.58.0
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- isort

‎ci/deps/travis-36-locale.yaml

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- numpy
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- openpyxl
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- psycopg2
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- pymysql
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- pymysql=0.7.9
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- pytables
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- python-dateutil
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- python=3.6*

‎doc/source/conf.py

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master_doc = 'index'
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# General information about the project.
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project = u'pandas'
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copyright = u'2008-2014, the pandas development team'
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project = 'pandas'
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copyright = '2008-2014, the pandas development team'
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# The version info for the project you're documenting, acts as replacement for
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# |version| and |release|, also used in various other places throughout the

‎doc/source/install.rst

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* `PyTables <http://www.pytables.org>`__: necessary for HDF5-based storage, Version 3.4.2 or higher
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* `pyarrow <http://arrow.apache.org/docs/python/>`__ (>= 0.9.0): necessary for feather-based storage.
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* `Apache Parquet <https://parquet.apache.org/>`__, either `pyarrow <http://arrow.apache.org/docs/python/>`__ (>= 0.7.0) or `fastparquet <https://fastparquet.readthedocs.io/en/latest>`__ (>= 0.2.1) for parquet-based storage. The `snappy <https://pypi.org/project/python-snappy>`__ and `brotli <https://pypi.org/project/brotlipy>`__ are available for compression support.
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* `SQLAlchemy <http://www.sqlalchemy.org>`__: for SQL database support. Version 1.0.8 or higher recommended. Besides SQLAlchemy, you also need a database specific driver. You can find an overview of supported drivers for each SQL dialect in the `SQLAlchemy docs <http://docs.sqlalchemy.org/en/latest/dialects/index.html>`__. Some common drivers are:
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* `SQLAlchemy <http://www.sqlalchemy.org>`__: for SQL database support. Version 1.1.4 or higher recommended. Besides SQLAlchemy, you also need a database specific driver. You can find an overview of supported drivers for each SQL dialect in the `SQLAlchemy docs <http://docs.sqlalchemy.org/en/latest/dialects/index.html>`__. Some common drivers are:
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* `psycopg2 <http://initd.org/psycopg/>`__: for PostgreSQL
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* `pymysql <https://github.com/PyMySQL/PyMySQL>`__: for MySQL.

‎doc/source/user_guide/advanced.rst

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In [11]: df3 = df3.set_index('B')
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In [11]: df3.index
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Out[11]: CategoricalIndex([u'a', u'a', u'b', u'b', u'c', u'a'], categories=[u'a', u'b', u'c'], ordered=False, name=u'B', dtype='category')
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Out[11]: CategoricalIndex(['a', 'a', 'b', 'b', 'c', 'a'], categories=['a', 'b', 'c'], ordered=False, name='B', dtype='category')
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TypeError: categories must match existing categories when appending

‎doc/source/user_guide/groupby.rst

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So depending on the path taken, and exactly what you are grouping. Thus the grouped columns(s) may be included in
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the output as well as set the indices.
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.. warning::
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In the current implementation apply calls func twice on the
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first group to decide whether it can take a fast or slow code
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path. This can lead to unexpected behavior if func has
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side-effects, as they will take effect twice for the first
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group.
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.. ipython:: python
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d = pd.DataFrame({"a": ["x", "y"], "b": [1, 2]})
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def identity(df):
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print(df)
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return df
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d.groupby("a").apply(identity)
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Other useful features
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---------------------

‎doc/source/user_guide/options.rst

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.. ipython:: python
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df = pd.DataFrame({u'国籍': ['UK', u'日本'], u'名前': ['Alice', u'しのぶ']})
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df = pd.DataFrame({'国籍': ['UK', '日本'], '名前': ['Alice', 'しのぶ']})
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df
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.. image:: ../_static/option_unicode01.png
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.. ipython:: python
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df = pd.DataFrame({'a': ['xxx', u'¡¡'], 'b': ['yyy', u'¡¡']})
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df = pd.DataFrame({'a': ['xxx', '¡¡'], 'b': ['yyy', '¡¡']})
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df
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.. image:: ../_static/option_unicode03.png

‎doc/source/user_guide/reshaping.rst

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In [2]: pd.factorize(x, sort=True)
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Out[2]:
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(array([ 2, 2, -1, 3, 0, 1]),
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Index([3.14, inf, u'A', u'B'], dtype='object'))
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Index([3.14, inf, 'A', 'B'], dtype='object'))
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Out[3]: (array([3, 3, 0, 4, 1, 2]), array([nan, 3.14, inf, 'A', 'B'], dtype=object))

‎doc/source/whatsnew/v0.25.0.rst

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df = pd.DataFrame([0], index=pd.DatetimeIndex(['2019-01-01'], tz='US/Pacific'))
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df['2019-01-01 12:00:00+04:00':'2019-01-01 13:00:00+04:00']
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.. _whatsnew_0250.api_breaking.groupby_apply_first_group_once:
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GroupBy.apply on ``DataFrame`` evaluates first group only once
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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The implementation of :meth:`DataFrameGroupBy.apply() <pandas.core.groupby.DataFrameGroupBy.apply>`
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previously evaluated the supplied function consistently twice on the first group
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to infer if it is safe to use a fast code path. Particularly for functions with
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side effects, this was an undesired behavior and may have led to surprises.
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(:issue:`2936`, :issue:`2656`, :issue:`7739`, :issue:`10519`, :issue:`12155`,
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:issue:`20084`, :issue:`21417`)
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Now every group is evaluated only a single time.
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.. ipython:: python
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df = pd.DataFrame({"a": ["x", "y"], "b": [1, 2]})
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df
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def func(group):
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print(group.name)
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return group
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*Previous Behaviour*:
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.. code-block:: python
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In [3]: df.groupby('a').apply(func)
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x
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x
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y
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Out[3]:
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a b
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0 x 1
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1 y 2
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*New Behaviour*:
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.. ipython:: python
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df.groupby("a").apply(func)
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Concatenating Sparse Values
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^^^^^^^^^^^^^^^^^^^^^^^^^^^
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df = pd.DataFrame({"A": pd.SparseArray([0, 1])})
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*Previous Behavior:*
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*Previous Behavior*:
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.. code-block:: ipython
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In [2]: type(pd.concat([df, df]))
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pandas.core.sparse.frame.SparseDataFrame
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*New Behavior:*
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*New Behavior*:
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.. ipython:: python
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+=================+=================+==========+
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| beautifulsoup4 | 4.4.1 | |
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+-----------------+-----------------+----------+
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| openpyxl | 2.2.6 | |
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| openpyxl | 2.4.0 | |
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+-----------------+-----------------+----------+
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| pymysql | 0.6.6 | |
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| pymysql | 0.7.9 | |
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+-----------------+-----------------+----------+
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| pytz | 2015.4 | |
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+-----------------+-----------------+----------+
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| sqlalchemy | 1.0.8 | |
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| sqlalchemy | 1.1.4 | |
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+-----------------+-----------------+----------+
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| xlsxwriter | 0.7.7 | |
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+-----------------+-----------------+----------+
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int8/int16/int32 and the searched key is within the integer bounds for the dtype (:issue:`22034`)
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- Improved performance of :meth:`pandas.core.groupby.GroupBy.quantile` (:issue:`20405`)
178222
- Improved performance of :meth:`read_csv` by faster tokenizing and faster parsing of small float numbers (:issue:`25784`)
223+
- Improved performance of :meth:`read_csv` by faster parsing of N/A and boolean values (:issue:`25804`)
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.. _whatsnew_0250.bug_fixes:
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- Bug in :meth:`to_numeric` in which numbers were being coerced to float, even though ``errors`` was not ``coerce`` (:issue:`24910`)
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- Bug in error messages in :meth:`DataFrame.corr` and :meth:`Series.corr`. Added the possibility of using a callable. (:issue:`25729`)
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- Bug in :meth:`Series.divmod` and :meth:`Series.rdivmod` which would raise an (incorrect) ``ValueError`` rather than return a pair of :class:`Series` objects as result (:issue:`25557`)
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-
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- Raises a helpful exception when a non-numeric index is sent to :meth:`interpolate` with methods which require numeric index. (:issue:`21662`)
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-
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-
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Interval
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^^^^^^^^
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-
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- Construction of :class:`Interval` is restricted to numeric, :class:`Timestamp` and :class:`Timedelta` endpoints (:issue:`23013`)
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-
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-
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Indexing
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^^^^^^^^
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-
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- Improved exception message when calling :meth:`DataFrame.iloc` with a list of non-numeric objects (:issue:`25753`).
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-
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-
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- Bug in :func:`merge` when merging by index name would sometimes result in an incorrectly numbered index (:issue:`24212`)
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- :func:`to_records` now accepts dtypes to its `column_dtypes` parameter (:issue:`24895`)
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- Bug in :func:`concat` where order of ``OrderedDict`` (and ``dict`` in Python 3.6+) is not respected, when passed in as ``objs`` argument (:issue:`21510`)
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- Bug in :func:`concat` where the resulting ``freq`` of two :class:`DatetimeIndex` with the same ``freq`` would be dropped (:issue:`3232`).
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Sparse
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^^^^^^

‎doc/sphinxext/announce.py

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def get_authors(revision_range):
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pat = u'^.*\\t(.*)$'
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pat = '^.*\\t(.*)$'
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# authors, in current release and previous to current release.
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pre.discard('Homu')
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# Append '+' to new authors.
73-
authors = [s + u' +' for s in cur - pre] + [s for s in cur & pre]
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authors = [s + ' +' for s in cur - pre] + [s for s in cur & pre]
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authors.sort()
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return authors
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# From regular merges
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merges = this_repo.git.log(
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'--oneline', '--merges', revision_range)
84-
issues = re.findall(u"Merge pull request \\#(\\d*)", merges)
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issues = re.findall("Merge pull request \\#(\\d*)", merges)
8585
prnums.extend(int(s) for s in issues)
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# From Homu merges (Auto merges)
88-
issues = re. findall(u"Auto merge of \\#(\\d*)", merges)
88+
issues = re. findall("Auto merge of \\#(\\d*)", merges)
8989
prnums.extend(int(s) for s in issues)
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# From fast forward squash-merges
9292
commits = this_repo.git.log(
9393
'--oneline', '--no-merges', '--first-parent', revision_range)
94-
issues = re.findall(u'^.*\\(\\#(\\d+)\\)$', commits, re.M)
94+
issues = re.findall('^.*\\(\\#(\\d+)\\)$', commits, re.M)
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# get PR data from github repo

‎pandas/__init__.py

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from datetime import datetime
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from pandas._config import (get_option, set_option, reset_option,
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describe_option, option_context, options)
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# let init-time option registration happen
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import pandas.core.config_init
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