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Merge remote-tracking branch 'upstream/master' into pandas-rosy-new
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‎asv_bench/benchmarks/eval.py

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@@ -45,7 +45,7 @@ def setup(self):
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index = pd.date_range('20010101', periods=N, freq='T')
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s = pd.Series(index)
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self.ts = s.iloc[halfway]
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self.df = pd.DataFrame({'a': np.random.randn(N), 'dates': s},
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self.df = pd.DataFrame({'a': np.random.randn(N), 'dates': index},
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index=index)
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data = np.random.randn(N)
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self.min_val = data.min()

‎ci/deps/travis-27.yaml

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@@ -22,7 +22,7 @@ dependencies:
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- patsy
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- psycopg2
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- py
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- pyarrow=0.7.0
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- pyarrow=0.9.0
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- PyCrypto
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- pymysql=0.6.3
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- pytables

‎doc/source/extending.rst

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@@ -28,8 +28,14 @@ decorate a class, providing the name of attribute to add. The class's
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@pd.api.extensions.register_dataframe_accessor("geo")
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class GeoAccessor(object):
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def __init__(self, pandas_obj):
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self._validate(pandas_obj)
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self._obj = pandas_obj
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@staticmethod
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def _validate(obj):
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if 'lat' not in obj.columns or 'lon' not in obj.columns:
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raise AttributeError("Must have 'lat' and 'lon'.")
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@property
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def center(self):
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# return the geographic center point of this DataFrame
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If you write a custom accessor, make a pull request adding it to our
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:ref:`ecosystem` page.
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We highly recommend validating the data in your accessor's `__init__`.
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In our ``GeoAccessor``, we validate that the data contains the expected columns,
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raising an ``AttributeError`` when the validation fails.
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For a ``Series`` accessor, you should validate the ``dtype`` if the accessor
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applies only to certain dtypes.
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.. _extending.extension-types:
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Extension Types

‎doc/source/install.rst

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@@ -257,7 +257,7 @@ Optional Dependencies
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* `SciPy <http://www.scipy.org>`__: miscellaneous statistical functions, Version 0.18.1 or higher
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* `xarray <http://xarray.pydata.org>`__: pandas like handling for > 2 dims, needed for converting Panels to xarray objects. Version 0.7.0 or higher is recommended.
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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/>`__ (>= 0.7.0): necessary for feather-based storage.
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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 0.8.1 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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‎doc/source/whatsnew/v0.24.0.rst

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@@ -439,7 +439,7 @@ Pandas 0.24.0 includes a number of API breaking changes.
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Dependencies have increased minimum versions
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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We have updated our minimum supported versions of dependencies (:issue:`21242`, :issue:`18742`, :issue:`23774`).
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We have updated our minimum supported versions of dependencies (:issue:`21242`, :issue:`18742`, :issue:`23774`, :issue:`24767`).
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If installed, we now require:
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+-----------------+-----------------+----------+
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+-----------------+-----------------+----------+
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| pandas-gbq | 0.8.0 | |
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+-----------------+-----------------+----------+
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| pyarrow | 0.7.0 | |
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| pyarrow | 0.9.0 | |
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+-----------------+-----------------+----------+
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| pytables | 3.4.2 | |
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+-----------------+-----------------+----------+
@@ -1160,6 +1160,7 @@ Other API Changes
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- :meth:`CategoricalIndex.reindex` now raises a ``ValueError`` if the target index is non-unique and not equal to the current index. It previously only raised if the target index was not of a categorical dtype (:issue:`23963`).
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- :func:`Series.to_list` and :func:`Index.to_list` are now aliases of ``Series.tolist`` respectively ``Index.tolist`` (:issue:`8826`)
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- The result of ``SparseSeries.unstack`` is now a :class:`DataFrame` with sparse values, rather than a :class:`SparseDataFrame` (:issue:`24372`).
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- :class:`DatetimeIndex` and :class:`TimedeltaIndex` no longer ignore the dtype precision. Passing a non-nanosecond resolution dtype will raise a ``ValueError`` (:issue:`24753`)
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.. _whatsnew_0240.api.extension:
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- :meth:`Series.nonzero` is deprecated and will be removed in a future version (:issue:`18262`)
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- Passing an integer to :meth:`Series.fillna` and :meth:`DataFrame.fillna` with ``timedelta64[ns]`` dtypes is deprecated, will raise ``TypeError`` in a future version. Use ``obj.fillna(pd.Timedelta(...))`` instead (:issue:`24694`)
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- ``Series.cat.categorical``, ``Series.cat.name`` and ``Sersies.cat.index`` have been deprecated. Use the attributes on ``Series.cat`` or ``Series`` directly. (:issue:`24751`).
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- Passing a dtype without a precision like ``np.dtype('datetime64')`` or ``timedelta64`` to :class:`Index`, :class:`DatetimeIndex` and :class:`TimedeltaIndex` is now deprecated. Use the nanosecond-precision dtype instead (:issue:`24753`).
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.. _whatsnew_0240.deprecations.datetimelike_int_ops:
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- Bug in :meth:`DatetimeIndex.astype`, :meth:`PeriodIndex.astype` and :meth:`TimedeltaIndex.astype` ignoring the sign of the ``dtype`` for unsigned integer dtypes (:issue:`24405`).
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- Fixed bug in :meth:`Series.max` with ``datetime64[ns]``-dtype failing to return ``NaT`` when nulls are present and ``skipna=False`` is passed (:issue:`24265`)
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- Bug in :func:`to_datetime` where arrays of ``datetime`` objects containing both timezone-aware and timezone-naive ``datetimes`` would fail to raise ``ValueError`` (:issue:`24569`)
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- Bug in :func:`to_datetime` with invalid datetime format doesn't coerce input to ``NaT`` even if ``errors='coerce'`` (:issue:`24763`)
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Timedelta
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^^^
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- Bug in :func:`read_csv` in which a column specified with ``CategoricalDtype`` of boolean categories was not being correctly coerced from string values to booleans (:issue:`20498`)
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- Bug in :func:`read_csv` in which unicode column names were not being properly recognized with Python 2.x (:issue:`13253`)
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- Bug in :meth:`DataFrame.to_sql` when writing timezone aware data (``datetime64[ns, tz]`` dtype) would raise a ``TypeError`` (:issue:`9086`)
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- Bug in :meth:`DataFrame.to_sql` where a naive :class:`DatetimeIndex` would be written as ``TIMESTAMP WITH TIMEZONE`` type in supported databases, e.g. PostgreSQL (:issue:`23510`)
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- Bug in :meth:`read_excel()` when ``parse_cols`` is specified with an empty dataset (:issue:`9208`)

‎doc/source/whatsnew/v0.24.1.rst

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:orphan:
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.. _whatsnew_0241:
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Whats New in 0.24.1 (February XX, 2019)
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---------------------------------------
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.. warning::
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The 0.24.x series of releases will be the last to support Python 2. Future feature
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releases will support Python 3 only. See :ref:`install.dropping-27` for more.
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{{ header }}
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These are the changes in pandas 0.24.1. See :ref:`release` for a full changelog
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including other versions of pandas.
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.. _whatsnew_0241.enhancements:
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Enhancements
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^^^^^^^^^^^^
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.. _whatsnew_0241.bug_fixes:
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Bug Fixes
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~~~~~~~~~
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**Conversion**
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**Indexing**
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**I/O**
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**Categorical**
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.. _whatsnew_0.241.contributors:
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Contributors
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~~~~~~~~~~~~
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.. contributors:: v0.24.0..v0.24.1

‎doc/source/whatsnew/v0.25.0.rst

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.. _whatsnew_0250:
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What's New in 0.25.0 (April XX, 2019)
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-------------------------------------
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{{ header }}
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These are the changes in pandas 0.25.0. See :ref:`release` for a full changelog
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including other versions of pandas.
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.. _whatsnew_0250.enhancements.other:
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Other Enhancements
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Backwards incompatible API changes
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. _whatsnew_0250.api.other:
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Other API Changes
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.. _whatsnew_0250.deprecations:
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Deprecations
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. _whatsnew_0250.performance:
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Performance Improvements
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‎environment.yml

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- nbsphinx
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- openpyxl
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- pyarrow>=0.7.0
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- pyarrow>=0.9.0
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- pytables>=3.4.2
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- pytest-cov
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- pytest-xdist

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