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‎README.md

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# pandas: powerful Python data analysis toolkit
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[![PyPI Latest Release](https://img.shields.io/pypi/v/pandas.svg)](https://pypi.org/project/pandas/)
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[![Conda Latest Release](https://anaconda.org/conda-forge/pandas/badges/version.svg)](https://anaconda.org/anaconda/pandas/)
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[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3509134.svg)](https://doi.org/10.5281/zenodo.3509134)
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[![Package Status](https://img.shields.io/pypi/status/pandas.svg)](https://pypi.org/project/pandas/)
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[![License](https://img.shields.io/pypi/l/pandas.svg)](https://github.com/pandas-dev/pandas/blob/master/LICENSE)
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[![Travis Build Status](https://travis-ci.org/pandas-dev/pandas.svg?branch=master)](https://travis-ci.org/pandas-dev/pandas)

‎asv_bench/asv.conf.json

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// followed by the pip installed packages).
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"matrix": {
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"numpy": [],
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"Cython": [],
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"Cython": ["0.29.16"],
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"matplotlib": [],
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"sqlalchemy": [],
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"scipy": [],

‎asv_bench/benchmarks/array.py

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@@ -9,6 +9,11 @@ def setup(self):
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self.values_float = np.array([1.0, 0.0, 1.0, 0.0])
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self.values_integer = np.array([1, 0, 1, 0])
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self.values_integer_like = [1, 0, 1, 0]
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self.data = np.array([True, False, True, False])
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self.mask = np.array([False, False, True, False])
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def time_constructor(self):
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pd.arrays.BooleanArray(self.data, self.mask)
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def time_from_bool_array(self):
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pd.array(self.values_bool, dtype="boolean")
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def time_from_float_array(self):
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pd.array(self.values_float, dtype="boolean")
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class IntegerArray:
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def setup(self):
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self.values_integer = np.array([1, 0, 1, 0])
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self.data = np.array([1, 2, 3, 4], dtype="int64")
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self.mask = np.array([False, False, True, False])
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def time_constructor(self):
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pd.arrays.IntegerArray(self.data, self.mask)
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def time_from_integer_array(self):
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pd.array(self.values_integer, dtype="Int64")

‎ci/code_checks.sh

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@@ -292,10 +292,6 @@ if [[ -z "$CHECK" || "$CHECK" == "doctests" ]]; then
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pytest -q --doctest-modules pandas/core/generic.py
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RET=$(($RET + $?)) ; echo $MSG "DONE"
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MSG='Doctests groupby.py' ; echo $MSG
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pytest -q --doctest-modules pandas/core/groupby/groupby.py -k"-cumcount -describe -pipe"
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RET=$(($RET + $?)) ; echo $MSG "DONE"
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MSG='Doctests series.py' ; echo $MSG
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pytest -q --doctest-modules pandas/core/series.py
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RET=$(($RET + $?)) ; echo $MSG "DONE"
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pytest -q --doctest-modules pandas/core/dtypes/
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RET=$(($RET + $?)) ; echo $MSG "DONE"
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MSG='Doctests groupby' ; echo $MSG
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pytest -q --doctest-modules pandas/core/groupby/
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RET=$(($RET + $?)) ; echo $MSG "DONE"
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MSG='Doctests indexes' ; echo $MSG
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pytest -q --doctest-modules pandas/core/indexes/
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RET=$(($RET + $?)) ; echo $MSG "DONE"

‎ci/deps/azure-36-minimum_versions.yaml

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- numpy=1.13.3
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- openpyxl=2.5.7
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- pytables=3.4.2
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- python-dateutil=2.6.1
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- python-dateutil=2.7.3
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- pytz=2017.2
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- scipy=0.19.0
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- xlrd=1.1.0

‎ci/deps/azure-37-numpydev.yaml

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- pytz
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- pip
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- pip:
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- cython>=0.29.16
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- cython==0.29.16
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# GH#33507 cython 3.0a1 is causing TypeErrors 2020-04-13
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- "git+git://github.com/dateutil/dateutil.git"
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- "-f https://7933911d6844c6c53a7d-47bd50c35cd79bd838daf386af554a83.ssl.cf2.rackcdn.com"
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- "--pre"

‎ci/deps/azure-macos-36.yaml

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- openpyxl
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- pyarrow>=0.13.0
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- pytables
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- python-dateutil==2.6.1
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- python-dateutil==2.7.3
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- pytz
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- xarray
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- xlrd

‎doc/source/getting_started/install.rst

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================================================================ ==========================
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`setuptools <https://setuptools.readthedocs.io/en/latest/>`__ 24.2.0
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`NumPy <https://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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`python-dateutil <https://dateutil.readthedocs.io/en/stable/>`__ 2.7.3
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`pytz <https://pypi.org/project/pytz/>`__ 2017.2
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================================================================ ==========================
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‎doc/source/getting_started/intro_tutorials/03_subset_data.rst

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<div class="card-body">
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<p class="card-text">
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This tutorial uses the titanic data set, stored as CSV. The data
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This tutorial uses the Titanic data set, stored as CSV. The data
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consists of the following data columns:
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- PassengerId: Id of every passenger.
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<ul class="task-bullet">
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<li>
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I’m interested in the age of the titanic passengers.
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I’m interested in the age of the Titanic passengers.
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.. ipython:: python
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<ul class="task-bullet">
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<li>
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I’m interested in the age and sex of the titanic passengers.
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I’m interested in the age and sex of the Titanic passengers.
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.. ipython:: python
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selection brackets ``[]``. Only rows for which the value is ``True``
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We now from before that the original titanic ``DataFrame`` consists of
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We know from before that the original Titanic ``DataFrame`` consists of
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891 rows. Let’s have a look at the amount of rows which satisfy the
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condition by checking the ``shape`` attribute of the resulting
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``DataFrame`` ``above_35``:
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<ul class="task-bullet">
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I’m interested in the titanic passengers from cabin class 2 and 3.
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I’m interested in the Titanic passengers from cabin class 2 and 3.
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.. ipython:: python
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‎doc/source/user_guide/io.rst

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Quoting, compression, and file format
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+++++++++++++++++++++++++++++++++++++
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compression : {``'infer'``, ``'gzip'``, ``'bz2'``, ``'zip'``, ``'xz'``, ``None``}, default ``'infer'``
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compression : {``'infer'``, ``'gzip'``, ``'bz2'``, ``'zip'``, ``'xz'``, ``None``, ``dict``}, default ``'infer'``
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For on-the-fly decompression of on-disk data. If 'infer', then use gzip,
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bz2, zip, or xz if filepath_or_buffer is a string ending in '.gz', '.bz2',
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'.zip', or '.xz', respectively, and no decompression otherwise. If using 'zip',
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the ZIP file must contain only one data file to be read in.
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Set to ``None`` for no decompression.
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Set to ``None`` for no decompression. Can also be a dict with key ``'method'``
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set to one of {``'zip'``, ``'gzip'``, ``'bz2'``}, and other keys set to
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compression settings. As an example, the following could be passed for
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faster compression: ``compression={'method': 'gzip', 'compresslevel': 1}``.
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.. versionchanged:: 0.24.0 'infer' option added and set to default.
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.. versionchanged:: 1.1.0 dict option extended to support ``gzip`` and ``bz2``.
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thousands : str, default ``None``
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The compression parameter can also be a ``dict`` in order to pass options to the
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compression protocol. It must have a ``'method'`` key set to the name
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of the compression protocol, which must be one of
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{``'zip'``, ``'gzip'``, ``'bz2'``}. All other key-value pairs are passed to
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the underlying compression library.
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.. ipython:: python
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Passing options to the compression protocol in order to speed up compression:
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.. ipython:: python
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df.to_pickle(
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"data.pkl.gz",
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compression={"method": "gzip", 'compresslevel': 1}
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)
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.. ipython:: python
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:suppress:
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‎doc/source/user_guide/timeseries.rst

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.. versionadded:: 1.1.0
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You may obtain the year, week and day components of the ISO year from the ISO 8601 standard:
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.. ipython:: python
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idx = pd.date_range(start='2019-12-29', freq='D', periods=4)
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idx.to_series().dt.isocalendar()
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DateOffset objects

‎doc/source/whatsnew/index.rst

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{{ header }}
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*************
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Release Notes
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Release notes
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*************
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This is the list of changes to pandas between each release. For full details,

‎doc/source/whatsnew/v0.10.0.rst

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.. _whatsnew_0100:
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v0.10.0 (December 17, 2012)
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---------------------------
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Version 0.10.0 (December 17, 2012)
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----------------------------------
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{{ header }}
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however, query terms using the prior (undocumented) methodology are unsupported. You must read in the entire
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N dimensional Panels (experimental)
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N dimensional panels (experimental)
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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‎doc/source/whatsnew/v0.10.1.rst

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.. _whatsnew_0101:
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v0.10.1 (January 22, 2013)
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---------------------------
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Version 0.10.1 (January 22, 2013)
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---------------------------------
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{{ header }}
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‎doc/source/whatsnew/v0.11.0.rst

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.. _whatsnew_0110:
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v0.11.0 (April 22, 2013)
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------------------------
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Version 0.11.0 (April 22, 2013)
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-------------------------------
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{{ header }}
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‎doc/source/whatsnew/v0.12.0.rst

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.. _whatsnew_0120:
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v0.12.0 (July 24, 2013)
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------------------------
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Version 0.12.0 (July 24, 2013)
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------------------------------
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{{ header }}
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``__repr__``). Plus string safety throughout. Now employed in many places
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I/O enhancements
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~~~~~~~~~~~~~~~~
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IO enhancements
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~~~~~~~~~~~~~~~
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‎doc/source/whatsnew/v0.13.0.rst

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.. _whatsnew_0130:
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v0.13.0 (January 3, 2014)
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---------------------------
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Version 0.13.0 (January 3, 2014)
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--------------------------------
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{{ header }}
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‎doc/source/whatsnew/v0.13.1.rst

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.. _whatsnew_0131:
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v0.13.1 (February 3, 2014)
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--------------------------
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Version 0.13.1 (February 3, 2014)
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---------------------------------
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‎doc/source/whatsnew/v0.14.0.rst

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.. _whatsnew_0140.slicers:
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MultiIndexing using slicers
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Multiindexing using slicers
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~~~~~~~~~~~~~~~~~~~~~~~~~~~
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In 0.14.0 we added a new way to slice MultiIndexed objects.
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.. _whatsnew_0140.bug_fixes:
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Bug Fixes
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Bug fixes
908908
~~~~~~~~~
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‎doc/source/whatsnew/v0.15.0.rst

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.. _whatsnew_0150.sql:
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Improvements in the sql io module
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Improvements in the SQL io module
604604
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- Added support for a ``chunksize`` parameter to ``to_sql`` function. This allows DataFrame to be written in chunks and avoid packet-size overflow errors (:issue:`8062`).

‎doc/source/whatsnew/v0.18.0.rst

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.. _whatsnew_0180.bug_fixes:
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Bug Fixes
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Bug fixes
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‎doc/source/whatsnew/v0.18.1.rst

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.. _whatsnew_0181.numpy_compatibility:
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numpy function compatibility
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NumPy function compatibility
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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Compatibility between pandas array-like methods (e.g. ``sum`` and ``take``) and their ``numpy``

‎doc/source/whatsnew/v0.19.0.rst

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.. _whatsnew_0190.gbq:
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Google BigQuery Enhancements
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Google BigQuery enhancements
381381
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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- The :func:`read_gbq` method has gained the ``dialect`` argument to allow users to specify whether to use BigQuery's legacy SQL or BigQuery's standard SQL. See the `docs <https://pandas-gbq.readthedocs.io/en/latest/reading.html>`__ for more details (:issue:`13615`).
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- The :func:`~DataFrame.to_gbq` method now allows the DataFrame column order to differ from the destination table schema (:issue:`11359`).
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.. _whatsnew_0190.errstate:
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Fine-grained numpy errstate
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Fine-grained NumPy errstate
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^^^^^^^^^^^^^^^^^^^^^^^^^^^
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Previous versions of pandas would permanently silence numpy's ufunc error handling when ``pandas`` was imported. Pandas did this in order to silence the warnings that would arise from using numpy ufuncs on missing data, which are usually represented as ``NaN`` s. Unfortunately, this silenced legitimate warnings arising in non-pandas code in the application. Starting with 0.19.0, pandas will use the ``numpy.errstate`` context manager to silence these warnings in a more fine-grained manner, only around where these operations are actually used in the pandas code base. (:issue:`13109`, :issue:`13145`)
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.. _whatsnew_0190.sparse:
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Sparse Changes
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Sparse changes
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^^^^^^^^^^^^^^
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These changes allow pandas to handle sparse data with more dtypes, and for work to make a smoother experience with data handling.

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