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Merge branch 'master' of git://github.com/pandas-dev/pandas into clip
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‎.travis.yml

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# In allow_failures
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- os: linux
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env:
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- JOB="3.5_DOC" DOC=true
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- JOB="3.6_DOC" DOC=true
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addons:
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apt:
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packages:
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- xsel
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allow_failures:
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- os: linux
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env:
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- JOB="3.6_NUMPY_DEV" TEST_ARGS="--skip-slow --skip-network" PANDAS_TESTING_MODE="deprecate"
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- os: linux
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env:
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- JOB="3.5_DOC" DOC=true
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- JOB="3.6_DOC" DOC=true
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before_install:
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- echo "before_install"

‎AUTHORS.md

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About the Copyright Holders
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===========================
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* Copyright (c) 2008-2011 AQR Capital Management, LLC
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AQR Capital Management began pandas development in 2008. Development was
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led by Wes McKinney. AQR released the source under this license in 2009.
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* Copyright (c) 2011-2012, Lambda Foundry, Inc.
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Wes is now an employee of Lambda Foundry, and remains the pandas project
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lead.
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* Copyright (c) 2011-2012, PyData Development Team
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The PyData Development Team is the collection of developers of the PyData
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project. This includes all of the PyData sub-projects, including pandas. The
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core team that coordinates development on GitHub can be found here:
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http://github.com/pydata.
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Full credits for pandas contributors can be found in the documentation.
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Our Copyright Policy
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====================
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PyData uses a shared copyright model. Each contributor maintains copyright
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over their contributions to PyData. However, it is important to note that
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these contributions are typically only changes to the repositories. Thus,
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the PyData source code, in its entirety, is not the copyright of any single
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person or institution. Instead, it is the collective copyright of the
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entire PyData Development Team. If individual contributors want to maintain
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a record of what changes/contributions they have specific copyright on,
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they should indicate their copyright in the commit message of the change
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when they commit the change to one of the PyData repositories.
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With this in mind, the following banner should be used in any source code
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file to indicate the copyright and license terms:
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```
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#-----------------------------------------------------------------------------
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# Copyright (c) 2012, PyData Development Team
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# All rights reserved.
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#
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# Distributed under the terms of the BSD Simplified License.
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#
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# The full license is in the LICENSE file, distributed with this software.
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#-----------------------------------------------------------------------------
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```
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Other licenses can be found in the LICENSES directory.
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License
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=======
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pandas is distributed under a 3-clause ("Simplified" or "New") BSD
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license. Parts of NumPy, SciPy, numpydoc, bottleneck, which all have
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BSD-compatible licenses, are included. Their licenses follow the pandas
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license.
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‎LICENSE

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=======
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License
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=======
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BSD 3-Clause License
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pandas is distributed under a 3-clause ("Simplified" or "New") BSD
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license. Parts of NumPy, SciPy, numpydoc, bottleneck, which all have
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BSD-compatible licenses, are included. Their licenses follow the pandas
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license.
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pandas license
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==============
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Copyright (c) 2011-2012, Lambda Foundry, Inc. and PyData Development Team
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All rights reserved.
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Copyright (c) 2008-2011 AQR Capital Management, LLC
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Copyright (c) 2008-2012, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team
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All rights reserved.
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions are
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met:
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* Redistributions of source code must retain the above copyright
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notice, this list of conditions and the following disclaimer.
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* Redistributions in binary form must reproduce the above
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copyright notice, this list of conditions and the following
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disclaimer in the documentation and/or other materials provided
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with the distribution.
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* Neither the name of the copyright holder nor the names of any
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contributors may be used to endorse or promote products derived
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from this software without specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDER AND CONTRIBUTORS
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"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
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OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
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SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
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LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
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THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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modification, are permitted provided that the following conditions are met:
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* Redistributions of source code must retain the above copyright notice, this
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list of conditions and the following disclaimer.
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* Redistributions in binary form must reproduce the above copyright notice,
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this list of conditions and the following disclaimer in the documentation
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and/or other materials provided with the distribution.
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* Neither the name of the copyright holder nor the names of its
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contributors may be used to endorse or promote products derived from
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this software without specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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About the Copyright Holders
48-
===========================
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AQR Capital Management began pandas development in 2008. Development was
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led by Wes McKinney. AQR released the source under this license in 2009.
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Wes is now an employee of Lambda Foundry, and remains the pandas project
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lead.
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The PyData Development Team is the collection of developers of the PyData
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project. This includes all of the PyData sub-projects, including pandas. The
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core team that coordinates development on GitHub can be found here:
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http://github.com/pydata.
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Full credits for pandas contributors can be found in the documentation.
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Our Copyright Policy
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====================
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PyData uses a shared copyright model. Each contributor maintains copyright
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over their contributions to PyData. However, it is important to note that
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these contributions are typically only changes to the repositories. Thus,
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the PyData source code, in its entirety, is not the copyright of any single
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person or institution. Instead, it is the collective copyright of the
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entire PyData Development Team. If individual contributors want to maintain
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a record of what changes/contributions they have specific copyright on,
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they should indicate their copyright in the commit message of the change
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when they commit the change to one of the PyData repositories.
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With this in mind, the following banner should be used in any source code
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file to indicate the copyright and license terms:
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#-----------------------------------------------------------------------------
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# Copyright (c) 2012, PyData Development Team
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# All rights reserved.
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#
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# Distributed under the terms of the BSD Simplified License.
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#
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# The full license is in the LICENSE file, distributed with this software.
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#-----------------------------------------------------------------------------
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Other licenses can be found in the LICENSES directory.

‎asv_bench/benchmarks/hdfstore_bench.py

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stop = self.df2.index[15000]
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self.store.select('table', where="index > start and index < stop")
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def time_store_repr(self):
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repr(self.store)
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def time_store_str(self):
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str(self.store)
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def time_store_info(self):
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self.store.info()
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class HDF5Panel(object):
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goal_time = 0.2

‎ci/requirements-2.7.sh

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echo "install 27"
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conda install -n pandas -c conda-forge feather-format
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conda install -n pandas -c conda-forge feather-format jemalloc=4.4.0

‎ci/requirements-2.7_BUILD_TEST.sh

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echo "install 27 BUILD_TEST"
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conda install -n pandas -c conda-forge pyarrow dask
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conda install -n pandas -c conda-forge pyarrow dask jemalloc=4.4.0

‎ci/requirements-3.5.sh

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echo "install 35"
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conda install -n pandas -c conda-forge feather-format
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conda install -n pandas -c conda-forge feather-format jemalloc=4.4.0

‎ci/requirements-3.6.run

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jinja2
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sqlalchemy
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pymysql
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jemalloc=4.4.0
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feather-format
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# psycopg2 (not avail on defaults ATM)
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beautifulsoup4

‎ci/requirements-3.5_DOC.build renamed to ‎ci/requirements-3.6_DOC.build

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python=3.5*
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python=3.6*
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python-dateutil
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pytz
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numpy

‎ci/requirements-3.5_DOC.run renamed to ‎ci/requirements-3.6_DOC.run

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ipython
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ipykernel
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ipywidgets
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sphinx
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sphinx=1.5*
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nbconvert
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notebook
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beautifulsoup4
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html5lib
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pytables
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openpyxl=1.8.5
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openpyxl
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xlrd
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xlwt
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bottleneck
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statsmodels
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xarray
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pyqt=4.11.4
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pyqt

‎ci/requirements-3.5_DOC.sh renamed to ‎ci/requirements-3.6_DOC.sh

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conda install -n pandas -c conda-forge feather-format nbsphinx pandoc
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conda install -n pandas -c conda-forge feather-format nbsphinx pandoc jemalloc=4.4.0
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conda install -n pandas -c r r rpy2 --yes

‎ci/requirements-3.6_NUMPY_DEV.build

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python-dateutil
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pytz
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‎ci/requirements-3.6_NUMPY_DEV.build.sh

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PRE_WHEELS="https://7933911d6844c6c53a7d-47bd50c35cd79bd838daf386af554a83.ssl.cf2.rackcdn.com"
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# install dateutil from master
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pip install -U git+git://github.com/dateutil/dateutil.git
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‎doc/source/advanced.rst

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KeyError: 'Cannot get right slice bound for non-unique label: 3'
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:meth:`Index.is_monotonic_increasing` and :meth:`Index.is_monotonic_decreasing` only check that
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an index is weakly monotonic. To check for strict montonicity, you can combine one of those with
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:meth:`Index.is_unique`
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.. ipython:: python
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weakly_monotonic = pd.Index(['a', 'b', 'c', 'c'])
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weakly_monotonic
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weakly_monotonic.is_monotonic_increasing
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~~~~~~~~~~~~~~~~~~~~~~~

‎doc/source/api.rst

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HDFStore.info
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Index.is_monotonic
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Index.is_monotonic_increasing
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Index.is_monotonic_decreasing
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Index.is_strictly_monotonic_increasing
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Index.is_strictly_monotonic_decreasing
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Index.is_unique
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Index.has_duplicates
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Index.dtype

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