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Merge remote-tracking branch 'upstream/master' into 26760-converter
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‎LICENSES/HAVEN_LICENSE

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YEAR: 2013-2016
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COPYRIGHT HOLDER: Hadley Wickham; RStudio; and Evan Miller

‎LICENSES/HAVEN_MIT

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Based on http://opensource.org/licenses/MIT
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This is a template. Complete and ship as file LICENSE the following 2
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lines (only)
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YEAR:
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COPYRIGHT HOLDER:
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and specify as
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License: MIT + file LICENSE
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Copyright (c) <YEAR>, <COPYRIGHT HOLDER>
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Permission is hereby granted, free of charge, to any person obtaining
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a copy of this software and associated documentation files (the
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"Software"), to deal in the Software without restriction, including
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without limitation the rights to use, copy, modify, merge, publish,
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distribute, sublicense, and/or sell copies of the Software, and to
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permit persons to whom the Software is furnished to do so, subject to
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the following conditions:
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The above copyright notice and this permission notice shall be
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included in all copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
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EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
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MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
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NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
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LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
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OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
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WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

‎asv_bench/benchmarks/sparse.py

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import itertools
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import numpy as np
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import scipy.sparse
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from pandas import (SparseSeries, SparseDataFrame, SparseArray, Series,
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date_range, MultiIndex)
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import pandas as pd
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from pandas import MultiIndex, Series, SparseArray, date_range
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def make_array(size, dense_proportion, fill_value, dtype):
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data = np.random.randn(N)[:-i]
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idx = rng[:-i]
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data[100:] = np.nan
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self.series[i] = SparseSeries(data, index=idx)
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self.series[i] = pd.Series(pd.SparseArray(data), index=idx)
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def time_series_to_frame(self):
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SparseDataFrame(self.series)
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pd.DataFrame(self.series)
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class SparseArrayConstructor:
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N = 1000
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self.arr = np.arange(N)
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self.sparse = scipy.sparse.rand(N, N, 0.005)
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self.dict = dict(zip(range(N), itertools.repeat([0])))
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def time_constructor(self):
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SparseDataFrame(columns=self.arr, index=self.arr)
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def time_from_scipy(self):
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SparseDataFrame(self.sparse)
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def time_from_dict(self):
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SparseDataFrame(self.dict)
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pd.DataFrame.sparse.from_spmatrix(self.sparse)
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class FromCoo:
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shape=(100, 100))
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def time_sparse_series_from_coo(self):
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SparseSeries.from_coo(self.matrix)
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pd.Series.sparse.from_coo(self.matrix)
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class ToCoo:
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s[100] = -1.0
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s[999] = 12.1
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s.index = MultiIndex.from_product([range(10)] * 4)
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self.ss = s.to_sparse()
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self.ss = s.astype("Sparse")
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def time_sparse_series_to_coo(self):
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self.ss.to_coo(row_levels=[0, 1],
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column_levels=[2, 3],
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sort_labels=True)
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self.ss.sparse.to_coo(row_levels=[0, 1],
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column_levels=[2, 3],
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sort_labels=True)
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class Arithmetic:

‎azure-pipelines.yml

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parameters:
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name: macOS
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vmImage: xcode9-macos10.13
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- template: ci/azure/posix.yml
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parameters:
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name: Linux
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- script: |
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export PATH=$HOME/miniconda3/bin:$PATH
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source activate pandas-dev
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doc/make.py
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# Next we should simply have `doc/make.py --warnings-are-errors`, everything else is required because the ipython directive doesn't fail the build on errors (https://github.com/ipython/ipython/issues/11547)
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doc/make.py --warnings-are-errors | tee sphinx.log ; SPHINX_RET=${PIPESTATUS[0]}
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grep -B1 "^<<<-------------------------------------------------------------------------$" sphinx.log ; IPY_RET=$(( $? != 1 ))
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exit $(( $SPHINX_RET + $IPY_RET ))
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displayName: 'Build documentation'
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- script: |

‎ci/deps/azure-35-compat.yaml

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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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- beautifulsoup4=4.6.0
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- bottleneck=1.2.1
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- jinja2=2.8
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- numexpr=2.6.2
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- numpy=1.13.3
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- openpyxl=2.4.0
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- openpyxl=2.4.8
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- pytables=3.4.2
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- python-dateutil=2.5.0
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- python-dateutil=2.6.1
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- python=3.5.*
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- pytz=2015.4
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- pytz=2017.2
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- scipy=0.19.0
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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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- xlrd=1.1.0
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- xlsxwriter=0.9.8
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- xlwt=1.2.0
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# universal
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- cython=0.28.2
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- hypothesis>=3.58.0

‎ci/deps/azure-36-locale.yaml

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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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- beautifulsoup4==4.6.0
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- bottleneck=1.2.*
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- cython=0.28.2
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- lxml
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- matplotlib=2.2.2
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- numpy=1.14.*
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- openpyxl=2.4.0
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- openpyxl=2.4.8
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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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- pytz=2017.2
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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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- xlrd=1.1.0
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- xlsxwriter=0.9.8
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- xlwt=1.2.0
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# universal
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- pytest>=4.0.2

‎ci/deps/azure-macos-35.yaml

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- pyarrow
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- pytables
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- python=3.5.*
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- python-dateutil==2.6.1
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- pytz
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- xarray
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- xlrd
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- xlsxwriter
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- xlwt
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- pip:
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- python-dateutil==2.5.3
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- pyreadstat
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# universal
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- pytest==4.5.0
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- pytest-xdist

‎ci/deps/azure-windows-37.yaml

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- pytest-mock
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- moto
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- hypothesis>=3.58.0
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- pyreadstat

‎ci/deps/travis-37.yaml

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- hypothesis>=3.58.0
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- s3fs
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- pip
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- pyreadstat
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- pip:
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- moto

‎doc/source/development/contributing.rst

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# Use an ENV_DIR of your choice. We'll use ~/virtualenvs/pandas-dev
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# Any parent directories should already exist
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python3 -m venv ~/virtualenvs/pandas-dev
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# Activate the virtulaenv
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# Activate the virtualenv
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. ~/virtualenvs/pandas-dev/bin/activate
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# Install the build dependencies

‎doc/source/getting_started/10min.rst

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plt.close('all')
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.. ipython:: python
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:okwarning:
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ts = pd.Series(np.random.randn(1000),
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index=pd.date_range('1/1/2000', periods=1000))

‎doc/source/index.rst.template

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:maxdepth: 3
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:hidden:
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{% endif %}
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{% if not single_doc -%}
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{% if not single_doc %}
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What's New in 0.25.0 <whatsnew/v0.25.0>
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install
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getting_started/index
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{% if not single_doc -%}
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development/index
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whatsnew/index
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{% endif -%}
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{% endif %}
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* :doc:`whatsnew/v0.25.0`
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* :doc:`install`

‎doc/source/install.rst

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==================== 12130 passed, 12 skipped in 368.339 seconds =====================
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.. _install.dependencies:
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Dependencies
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------------
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* `setuptools <https://setuptools.readthedocs.io/en/latest/>`__: 24.2.0 or higher
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* `NumPy <http://www.numpy.org>`__: 1.13.3 or higher
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* `python-dateutil <https://dateutil.readthedocs.io/en/stable/>`__: 2.5.0 or higher
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* `pytz <http://pytz.sourceforge.net/>`__: 2015.4 or higher
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================================================================ ==========================
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Package Minimum supported version
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================================================================ ==========================
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`setuptools <https://setuptools.readthedocs.io/en/latest/>`__ 24.2.0
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`NumPy <http://www.numpy.org>`__ 1.13.3
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`python-dateutil <https://dateutil.readthedocs.io/en/stable/>`__ 2.6.1
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`pytz <http://pytz.sourceforge.net/>`__ 2017.2
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================================================================ ==========================
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.. _install.recommended_dependencies:
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========================= ================== =============================================================
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Dependency Minimum Version Notes
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========================= ================== =============================================================
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BeautifulSoup4 4.4.1 HTML parser for read_html (see :ref:`note <optional_html>`)
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BeautifulSoup4 4.6.0 HTML parser for read_html (see :ref:`note <optional_html>`)
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Jinja2 Conditional formatting with DataFrame.style
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PyQt4 Clipboard I/O
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PyQt5 Clipboard I/O
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PyTables 3.4.2 HDF5-based reading / writing
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SQLAlchemy 1.1.4 SQL support for databases other than sqlite
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SciPy 0.19.0 Miscellaneous statistical functions
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XLsxWriter Excel writing
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XLsxWriter 0.9.8 Excel writing
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blosc Compression for msgpack
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fastparquet 0.2.1 Parquet reading / writing
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gcsfs 0.1.0 Google Cloud Storage access
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html5lib HTML parser for read_html (see :ref:`note <optional_html>`)
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lxml HTML parser for read_html (see :ref:`note <optional_html>`)
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matplotlib 2.2.2 Visualization
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openpyxl 2.4.0 Reading / writing for xlsx files
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openpyxl 2.4.8 Reading / writing for xlsx files
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pandas-gbq 0.8.0 Google Big Query access
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psycopg2 PostgreSQL engine for sqlalchemy
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pyarrow 0.9.0 Parquet and feather reading / writing
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pymysql MySQL engine for sqlalchemy
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pyreadstat SPSS files (.sav) reading
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qtpy Clipboard I/O
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s3fs 0.0.8 Amazon S3 access
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xarray 0.8.2 pandas-like API for N-dimensional data
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xclip Clipboard I/O on linux
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xlrd 1.0.0 Excel reading
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xlwt 2.4.0 Excel writing
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xlrd 1.1.0 Excel reading
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xlwt 1.2.0 Excel writing
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xsel Clipboard I/O on linux
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zlib Compression for msgpack
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========================= ================== =============================================================

‎doc/source/reference/arrays.rst

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.. autosummary::
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:toctree: api/
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:template: autosummary/class_without_autosummary.rst
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arrays.DatetimeArray
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:toctree: api/
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:template: autosummary/class_without_autosummary.rst
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arrays.TimedeltaArray
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:toctree: api/
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:template: autosummary/class_without_autosummary.rst
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arrays.PeriodArray
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.. autosummary::
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:toctree: api/
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:template: autosummary/class_without_autosummary.rst
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arrays.IntervalArray
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IntervalDtype
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.. Those attributes and methods are included in the API because the docstrings
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.. of IntervalIndex and IntervalArray are shared. Including it here to make
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.. sure a docstring page is built for them to avoid warnings
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..
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.. autosummary::
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:toctree: api/
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arrays.IntervalArray.left
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arrays.IntervalArray.right
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arrays.IntervalArray.closed
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arrays.IntervalArray.mid
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arrays.IntervalArray.length
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arrays.IntervalArray.is_non_overlapping_monotonic
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arrays.IntervalArray.from_arrays
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arrays.IntervalArray.from_tuples
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arrays.IntervalArray.from_breaks
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arrays.IntervalArray.overlaps
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arrays.IntervalArray.set_closed
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arrays.IntervalArray.to_tuples
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.. _api.arrays.integer_na:
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Nullable Integer
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.. autosummary::
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:toctree: api/
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:template: autosummary/class_without_autosummary.rst
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arrays.IntegerArray
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.. autosummary::
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:toctree: api/
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:template: autosummary/class_without_autosummary.rst
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SparseArray
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‎doc/source/reference/extensions.rst

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api.extensions.register_index_accessor
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api.extensions.ExtensionDtype
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api.extensions.ExtensionArray
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.. autosummary::
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:toctree: api/
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:template: autosummary/class_without_autosummary.rst
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arrays.PandasArray

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