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Merge remote-tracking branch 'upstream/master' into apply-ufunc-example
* upstream/master: Add nanmedian for dask arrays (pydata#3604) added pyinterp to related projects (pydata#3655) Allow incomplete hypercubes in combine_by_coords (pydata#3649) concat keeps attrs from first variable. (pydata#3637) Extend DatetimeAccessor properties and support `.dt` accessor for Timedelta (pydata#3612) update readthedocs.yml (pydata#3639) silence sphinx warnings round 3 (pydata#3602) Fix/quantile wrong errmsg (pydata#3635) Provide shape info in shape mismatch error. (pydata#3619) Minor doc fixes (pydata#3615)
2 parents 9c10c98 + b3d3b44 commit ed06ad2

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doc/api-hidden.rst

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doc/api.rst

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ones_like
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dot
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map_blocks
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show_versions
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set_options
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Dataset
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=======
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Dataset.__setitem__
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Dataset.__delitem__
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Dataset.update
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Dataset.get
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Dataset.items
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Dataset.keys
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Dataset.values
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Dataset contents
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DataArray.unify_chunks
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DataArray.map_blocks
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Coordinates objects
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===================
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.. autosummary::
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:toctree: generated/
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core.coordinates.DataArrayCoordinates
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core.coordinates.DatasetCoordinates
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GroupBy objects
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===============
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core.rolling.DatasetRolling.reduce
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core.rolling_exp.RollingExp
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Coarsen objects
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===============
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.. autosummary::
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:toctree: generated/
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core.rolling.DataArrayCoarsen
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core.rolling.DatasetCoarsen
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Resample objects
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================
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:toctree: generated/
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core.accessor_dt.DatetimeAccessor
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core.accessor_dt.TimedeltaAccessor
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core.accessor_str.StringAccessor
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Custom Indexes
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plot.imshow
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plot.line
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plot.pcolormesh
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plot.FacetGrid
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Faceting
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--------
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plot.FacetGrid.set_xlabels
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plot.FacetGrid.set_ylabels
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Tutorial
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========
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.. autosummary::
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:toctree: generated/
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tutorial.open_dataset
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tutorial.load_dataset
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Testing
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=======
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These backends provide a low-level interface for lazily loading data from
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external file-formats or protocols, and can be manually invoked to create
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arguments for the ``from_store`` and ``dump_to_store`` Dataset methods:
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arguments for the ``load_store`` and ``dump_to_store`` Dataset methods:
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.. autosummary::
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:toctree: generated/
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Deprecated / Pending Deprecation
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================================
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.. autosummary::
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:toctree: generated/
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Dataset.drop
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DataArray.drop
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Dataset.apply

doc/groupby.rst

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~~~~~
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To apply a function to each group, you can use the flexible
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:py:meth:`~xarray.DatasetGroupBy.map` method. The resulting objects are automatically
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:py:meth:`~xarray.core.groupby.DatasetGroupBy.map` method. The resulting objects are automatically
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concatenated back together along the group axis:
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.. ipython:: python
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arr.groupby('letters').map(standardize)
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GroupBy objects also have a :py:meth:`~xarray.DatasetGroupBy.reduce` method and
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methods like :py:meth:`~xarray.DatasetGroupBy.mean` as shortcuts for applying an
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GroupBy objects also have a :py:meth:`~xarray.core.groupby.DatasetGroupBy.reduce` method and
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methods like :py:meth:`~xarray.core.groupby.DatasetGroupBy.mean` as shortcuts for applying an
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aggregation function:
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.. ipython:: python

doc/howdoi.rst

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* - change the order of dimensions
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- :py:meth:`DataArray.transpose`, :py:meth:`Dataset.transpose`
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* - remove a variable from my object
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- :py:meth:`Dataset.drop`, :py:meth:`DataArray.drop`
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- :py:meth:`Dataset.drop_vars`, :py:meth:`DataArray.drop_vars`
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* - remove dimensions of length 1 or 0
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- :py:meth:`DataArray.squeeze`, :py:meth:`Dataset.squeeze`
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* - remove all variables with a particular dimension
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* - write xarray objects with complex values to a netCDF file
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- :py:func:`Dataset.to_netcdf`, :py:func:`DataArray.to_netcdf` specifying ``engine="h5netcdf", invalid_netcdf=True``
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* - make xarray objects look like other xarray objects
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- :py:func:`~xarray.ones_like`, :py:func:`~xarray.zeros_like`, :py:func:`~xarray.full_like`, :py:meth:`Dataset.reindex_like`, :py:meth:`Dataset.interpolate_like`, :py:meth:`Dataset.broadcast_like`, :py:meth:`DataArray.reindex_like`, :py:meth:`DataArray.interpolate_like`, :py:meth:`DataArray.broadcast_like`
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- :py:func:`~xarray.ones_like`, :py:func:`~xarray.zeros_like`, :py:func:`~xarray.full_like`, :py:meth:`Dataset.reindex_like`, :py:meth:`Dataset.interp_like`, :py:meth:`Dataset.broadcast_like`, :py:meth:`DataArray.reindex_like`, :py:meth:`DataArray.interp_like`, :py:meth:`DataArray.broadcast_like`
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* - replace NaNs with other values
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- :py:meth:`Dataset.fillna`, :py:meth:`Dataset.ffill`, :py:meth:`Dataset.bfill`, :py:meth:`Dataset.interpolate_na`, :py:meth:`DataArray.fillna`, :py:meth:`DataArray.ffill`, :py:meth:`DataArray.bfill`, :py:meth:`DataArray.interpolate_na`
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* - extract the year, month, day or similar from a DataArray of time values

doc/indexing.rst

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The arguments to these methods can be any objects that could index the array
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along the dimension given by the keyword, e.g., labels for an individual value,
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Python :py:func:`slice` objects or 1-dimensional arrays.
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Python :py:class:`slice` objects or 1-dimensional arrays.
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.. note::
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doc/interpolation.rst

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# interpolation
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da.interp(time=[2.5, 3.5])
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To interpolate data with a :py:func:`numpy.datetime64` coordinate you can pass a string.
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To interpolate data with a :py:doc:`numpy.datetime64 <reference/arrays.datetime>` coordinate you can pass a string.
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.. ipython:: python
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Interpolation methods
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We use :py:func:`scipy.interpolate.interp1d` for 1-dimensional interpolation and
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We use :py:class:`scipy.interpolate.interp1d` for 1-dimensional interpolation and
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:py:func:`scipy.interpolate.interpn` for multi-dimensional interpolation.
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The interpolation method can be specified by the optional ``method`` argument.

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