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Apply blackdoc to the documentation #4012

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Apply blackdoc to the documentation #4012

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62 changes: 32 additions & 30 deletions doc/combining.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -4,11 +4,12 @@ Combining data
--------------

.. ipython:: python
:suppress:
:suppress:

import numpy as np
import pandas as pd
import xarray as xr

np.random.seed(123456)

* For combining datasets or data arrays along a single dimension, see concatenate_.
Expand All @@ -28,11 +29,10 @@ that dimension:

.. ipython:: python

arr = xr.DataArray(np.random.randn(2, 3),
[('x', ['a', 'b']), ('y', [10, 20, 30])])
arr = xr.DataArray(np.random.randn(2, 3), [("x", ["a", "b"]), ("y", [10, 20, 30])])
arr[:, :1]
# this resembles how you would use np.concatenate
xr.concat([arr[:, :1], arr[:, 1:]], dim='y')
xr.concat([arr[:, :1], arr[:, 1:]], dim="y")

In addition to combining along an existing dimension, ``concat`` can create a
new dimension by stacking lower dimensional arrays together:
Expand All @@ -41,30 +41,30 @@ new dimension by stacking lower dimensional arrays together:

arr[0]
# to combine these 1d arrays into a 2d array in numpy, you would use np.array
xr.concat([arr[0], arr[1]], 'x')
xr.concat([arr[0], arr[1]], "x")

If the second argument to ``concat`` is a new dimension name, the arrays will
be concatenated along that new dimension, which is always inserted as the first
dimension:

.. ipython:: python

xr.concat([arr[0], arr[1]], 'new_dim')
xr.concat([arr[0], arr[1]], "new_dim")

The second argument to ``concat`` can also be an :py:class:`~pandas.Index` or
:py:class:`~xarray.DataArray` object as well as a string, in which case it is
used to label the values along the new dimension:

.. ipython:: python

xr.concat([arr[0], arr[1]], pd.Index([-90, -100], name='new_dim'))
xr.concat([arr[0], arr[1]], pd.Index([-90, -100], name="new_dim"))

Of course, ``concat`` also works on ``Dataset`` objects:

.. ipython:: python

ds = arr.to_dataset(name='foo')
xr.concat([ds.sel(x='a'), ds.sel(x='b')], 'x')
ds = arr.to_dataset(name="foo")
xr.concat([ds.sel(x="a"), ds.sel(x="b")], "x")

:py:func:`~xarray.concat` has a number of options which provide deeper control
over which variables are concatenated and how it handles conflicting variables
Expand All @@ -84,16 +84,16 @@ To combine variables and coordinates between multiple ``DataArray`` and/or

.. ipython:: python

xr.merge([ds, ds.rename({'foo': 'bar'})])
xr.merge([xr.DataArray(n, name='var%d' % n) for n in range(5)])
xr.merge([ds, ds.rename({"foo": "bar"})])
xr.merge([xr.DataArray(n, name="var%d" % n) for n in range(5)])

If you merge another dataset (or a dictionary including data array objects), by
default the resulting dataset will be aligned on the **union** of all index
coordinates:

.. ipython:: python

other = xr.Dataset({'bar': ('x', [1, 2, 3, 4]), 'x': list('abcd')})
other = xr.Dataset({"bar": ("x", [1, 2, 3, 4]), "x": list("abcd")})
xr.merge([ds, other])

This ensures that ``merge`` is non-destructive. ``xarray.MergeError`` is raised
Expand All @@ -116,7 +116,7 @@ used in the :py:class:`~xarray.Dataset` constructor:

.. ipython:: python

xr.Dataset({'a': arr[:-1], 'b': arr[1:]})
xr.Dataset({"a": arr[:-1], "b": arr[1:]})

.. _combine:

Expand All @@ -131,8 +131,8 @@ are filled with ``NaN``. For example:

.. ipython:: python

ar0 = xr.DataArray([[0, 0], [0, 0]], [('x', ['a', 'b']), ('y', [-1, 0])])
ar1 = xr.DataArray([[1, 1], [1, 1]], [('x', ['b', 'c']), ('y', [0, 1])])
ar0 = xr.DataArray([[0, 0], [0, 0]], [("x", ["a", "b"]), ("y", [-1, 0])])
ar1 = xr.DataArray([[1, 1], [1, 1]], [("x", ["b", "c"]), ("y", [0, 1])])
ar0.combine_first(ar1)
ar1.combine_first(ar0)

Expand All @@ -152,7 +152,7 @@ variables with new values:

.. ipython:: python

ds.update({'space': ('space', [10.2, 9.4, 3.9])})
ds.update({"space": ("space", [10.2, 9.4, 3.9])})

However, dimensions are still required to be consistent between different
Dataset variables, so you cannot change the size of a dimension unless you
Expand All @@ -170,7 +170,7 @@ syntax:

.. ipython:: python

ds['baz'] = xr.DataArray([9, 9, 9, 9, 9], coords=[('x', list('abcde'))])
ds["baz"] = xr.DataArray([9, 9, 9, 9, 9], coords=[("x", list("abcde"))])
ds.baz

Equals and identical
Expand All @@ -193,16 +193,16 @@ object:

.. ipython:: python

arr.identical(arr.rename('bar'))
arr.identical(arr.rename("bar"))

:py:attr:`~xarray.Dataset.broadcast_equals` does a more relaxed form of equality
check that allows variables to have different dimensions, as long as values
are constant along those new dimensions:

.. ipython:: python

left = xr.Dataset(coords={'x': 0})
right = xr.Dataset({'x': [0, 0, 0]})
left = xr.Dataset(coords={"x": 0})
right = xr.Dataset({"x": [0, 0, 0]})
left.broadcast_equals(right)

Like pandas objects, two xarray objects are still equal or identical if they have
Expand Down Expand Up @@ -231,9 +231,9 @@ coordinates as long as any non-missing values agree or are disjoint:

.. ipython:: python

ds1 = xr.Dataset({'a': ('x', [10, 20, 30, np.nan])}, {'x': [1, 2, 3, 4]})
ds2 = xr.Dataset({'a': ('x', [np.nan, 30, 40, 50])}, {'x': [2, 3, 4, 5]})
xr.merge([ds1, ds2], compat='no_conflicts')
ds1 = xr.Dataset({"a": ("x", [10, 20, 30, np.nan])}, {"x": [1, 2, 3, 4]})
ds2 = xr.Dataset({"a": ("x", [np.nan, 30, 40, 50])}, {"x": [2, 3, 4, 5]})
xr.merge([ds1, ds2], compat="no_conflicts")

Note that due to the underlying representation of missing values as floating
point numbers (``NaN``), variable data type is not always preserved when merging
Expand Down Expand Up @@ -273,10 +273,12 @@ datasets into a doubly-nested list, e.g:

.. ipython:: python

arr = xr.DataArray(name='temperature', data=np.random.randint(5, size=(2, 2)), dims=['x', 'y'])
arr = xr.DataArray(
name="temperature", data=np.random.randint(5, size=(2, 2)), dims=["x", "y"]
)
arr
ds_grid = [[arr, arr], [arr, arr]]
xr.combine_nested(ds_grid, concat_dim=['x', 'y'])
xr.combine_nested(ds_grid, concat_dim=["x", "y"])

:py:func:`~xarray.combine_nested` can also be used to explicitly merge datasets
with different variables. For example if we have 4 datasets, which are divided
Expand All @@ -286,10 +288,10 @@ we wish to use ``merge`` instead of ``concat``:

.. ipython:: python

temp = xr.DataArray(name='temperature', data=np.random.randn(2), dims=['t'])
precip = xr.DataArray(name='precipitation', data=np.random.randn(2), dims=['t'])
temp = xr.DataArray(name="temperature", data=np.random.randn(2), dims=["t"])
precip = xr.DataArray(name="precipitation", data=np.random.randn(2), dims=["t"])
ds_grid = [[temp, precip], [temp, precip]]
xr.combine_nested(ds_grid, concat_dim=['t', None])
xr.combine_nested(ds_grid, concat_dim=["t", None])

:py:func:`~xarray.combine_by_coords` is for combining objects which have dimension
coordinates which specify their relationship to and order relative to one
Expand All @@ -302,8 +304,8 @@ coordinates, not on their position in the list passed to ``combine_by_coords``.
.. ipython:: python
:okwarning:

x1 = xr.DataArray(name='foo', data=np.random.randn(3), coords=[('x', [0, 1, 2])])
x2 = xr.DataArray(name='foo', data=np.random.randn(3), coords=[('x', [3, 4, 5])])
x1 = xr.DataArray(name="foo", data=np.random.randn(3), coords=[("x", [0, 1, 2])])
x2 = xr.DataArray(name="foo", data=np.random.randn(3), coords=[("x", [3, 4, 5])])
xr.combine_by_coords([x2, x1])

These functions can be used by :py:func:`~xarray.open_mfdataset` to open many
Expand Down
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