.. currentmodule:: xarray
.. _terminology:
Terminology
===========
*Xarray terminology differs slightly from CF, mathematical conventions, and
pandas; so we've put together a glossary of its terms. Here,* ``arr``
*refers to an xarray* :py:class:`DataArray` *in the examples. For more
complete examples, please consult the relevant documentation.*
.. glossary::
DataArray
A multi-dimensional array with labeled or named
dimensions. ``DataArray`` objects add metadata such as dimension names,
coordinates, and attributes (defined below) to underlying "unlabeled"
data structures such as numpy and Dask arrays. If its optional ``name``
property is set, it is a *named DataArray*.
Dataset
A dict-like collection of ``DataArray`` objects with aligned
dimensions. Thus, most operations that can be performed on the
dimensions of a single ``DataArray`` can be performed on a
dataset. Datasets have data variables (see **Variable** below),
dimensions, coordinates, and attributes.
Variable
A `NetCDF-like variable
`_
consisting of dimensions, data, and attributes which describe a single
array. The main functional difference between variables and numpy arrays
is that numerical operations on variables implement array broadcasting
by dimension name. Each ``DataArray`` has an underlying variable that
can be accessed via ``arr.variable``. However, a variable is not fully
described outside of either a ``Dataset`` or a ``DataArray``.
.. note::
The :py:class:`Variable` class is low-level interface and can
typically be ignored. However, the word "variable" appears often
enough in the code and documentation that is useful to understand.
Dimension
In mathematics, the *dimension* of data is loosely the number of degrees
of freedom for it. A *dimension axis* is a set of all points in which
all but one of these degrees of freedom is fixed. We can think of each
dimension axis as having a name, for example the "x dimension". In
xarray, a ``DataArray`` object's *dimensions* are its named dimension
axes ``da.dims``, and the name of the ``i``-th dimension is ``da.dims[i]``.
If an array is created without specifying dimension names, the default dimension
names will be ``dim_0``, ``dim_1``, and so forth.
Coordinate
An array that labels a dimension or set of dimensions of another
``DataArray``. In the usual one-dimensional case, the coordinate array's
values can loosely be thought of as tick labels along a dimension. We
distinguish :term:`Dimension coordinate` vs. :term:`Non-dimension
coordinate` and :term:`Indexed coordinate` vs. :term:`Non-indexed
coordinate`. A coordinate named ``x`` can be retrieved from
``arr.coords[x]``. A ``DataArray`` can have more coordinates than
dimensions because a single dimension can be labeled by multiple
coordinate arrays. However, only one coordinate array can be a assigned
as a particular dimension's dimension coordinate array.
Dimension coordinate
A one-dimensional coordinate array assigned to ``arr`` with both a name
and dimension name in ``arr.dims``. Usually (but not always), a
dimension coordinate is also an :term:`Indexed coordinate` so that it can
be used for label-based indexing and alignment, like the index found on
a :py:class:`pandas.DataFrame` or :py:class:`pandas.Series`.
Non-dimension coordinate
A coordinate array assigned to ``arr`` with a name in ``arr.coords`` but
*not* in ``arr.dims``. These coordinates arrays can be one-dimensional
or multidimensional, and they are useful for auxiliary labeling. As an
example, multidimensional coordinates are often used in geoscience
datasets when :doc:`the data's physical coordinates (such as latitude
and longitude) differ from their logical coordinates
<../examples/multidimensional-coords>`. Printing ``arr.coords`` will
print all of ``arr``'s coordinate names, with the corresponding
dimension(s) in parentheses. For example, ``coord_name (dim_name) 1 2 3
...``.
Indexed coordinate
A coordinate which has an associated :term:`Index`. Generally this means
that the coordinate labels can be used for indexing (selection) and/or
alignment. An indexed coordinate may have one or more arbitrary
dimensions although in most cases it is also a :term:`Dimension
coordinate`. It may or may not be grouped with other indexed coordinates
depending on whether they share the same index. Indexed coordinates are
marked by an asterisk ``*`` when printing a ``DataArray`` or ``Dataset``.
Non-indexed coordinate
A coordinate which has no associated :term:`Index`. It may still
represent fixed labels along one or more dimensions but it cannot be
used for label-based indexing and alignment.
Index
An *index* is a data structure optimized for efficient data selection
and alignment within a discrete or continuous space that is defined by
coordinate labels (unless it is a functional index). By default, Xarray
creates a :py:class:`~xarray.indexes.PandasIndex` object (i.e., a
:py:class:`pandas.Index` wrapper) for each :term:`Dimension coordinate`.
For more advanced use cases (e.g., staggered or irregular grids,
geospatial indexes), Xarray also accepts any instance of a specialized
:py:class:`~xarray.indexes.Index` subclass that is associated to one or
more arbitrary coordinates. The index associated with the coordinate
``x`` can be retrieved by ``arr.xindexes[x]`` (or ``arr.indexes["x"]``
if the index is convertible to a :py:class:`pandas.Index` object). If
two coordinates ``x`` and ``y`` share the same index,
``arr.xindexes[x]`` and ``arr.xindexes[y]`` both return the same
:py:class:`~xarray.indexes.Index` object.
name
The names of dimensions, coordinates, DataArray objects and data
variables can be anything as long as they are :term:`hashable`. However,
it is preferred to use :py:class:`str` typed names.
scalar
By definition, a scalar is not an :term:`array` and when converted to
one, it has 0 dimensions. That means that, e.g., :py:class:`int`,
:py:class:`float`, and :py:class:`str` objects are "scalar" while
:py:class:`list` or :py:class:`tuple` are not.
duck array
`Duck arrays`__ are array implementations that behave
like numpy arrays. They have to define the ``shape``, ``dtype`` and
``ndim`` properties. For integration with ``xarray``, the ``__array__``,
``__array_ufunc__`` and ``__array_function__`` protocols are also required.
__ https://numpy.org/neps/nep-0022-ndarray-duck-typing-overview.html
.. ipython:: python
:suppress:
import numpy as np
import xarray as xr
Aligning
Aligning refers to the process of ensuring that two or more DataArrays or Datasets
have the same dimensions and coordinates, so that they can be combined or compared properly.
.. ipython:: python
x = xr.DataArray(
[[25, 35], [10, 24]],
dims=("lat", "lon"),
coords={"lat": [35.0, 40.0], "lon": [100.0, 120.0]},
)
y = xr.DataArray(
[[20, 5], [7, 13]],
dims=("lat", "lon"),
coords={"lat": [35.0, 42.0], "lon": [100.0, 120.0]},
)
x
y
Broadcasting
A technique that allows operations to be performed on arrays with different shapes and dimensions.
When performing operations on arrays with different shapes and dimensions, xarray will automatically attempt to broadcast the
arrays to a common shape before the operation is applied.
.. ipython:: python
# 'a' has shape (3,) and 'b' has shape (4,)
a = xr.DataArray(np.array([1, 2, 3]), dims=["x"])
b = xr.DataArray(np.array([4, 5, 6, 7]), dims=["y"])
# 2D array with shape (3, 4)
a + b
Merging
Merging is used to combine two or more Datasets or DataArrays that have different variables or coordinates along
the same dimensions. When merging, xarray aligns the variables and coordinates of the different datasets along
the specified dimensions and creates a new ``Dataset`` containing all the variables and coordinates.
.. ipython:: python
# create two 1D arrays with names
arr1 = xr.DataArray(
[1, 2, 3], dims=["x"], coords={"x": [10, 20, 30]}, name="arr1"
)
arr2 = xr.DataArray(
[4, 5, 6], dims=["x"], coords={"x": [20, 30, 40]}, name="arr2"
)
# merge the two arrays into a new dataset
merged_ds = xr.Dataset({"arr1": arr1, "arr2": arr2})
merged_ds
Concatenating
Concatenating is used to combine two or more Datasets or DataArrays along a dimension. When concatenating,
xarray arranges the datasets or dataarrays along a new dimension, and the resulting ``Dataset`` or ``Dataarray``
will have the same variables and coordinates along the other dimensions.
.. ipython:: python
a = xr.DataArray([[1, 2], [3, 4]], dims=("x", "y"))
b = xr.DataArray([[5, 6], [7, 8]], dims=("x", "y"))
c = xr.concat([a, b], dim="c")
c
Combining
Combining is the process of arranging two or more DataArrays or Datasets into a single ``DataArray`` or
``Dataset`` using some combination of merging and concatenation operations.
.. ipython:: python
ds1 = xr.Dataset(
{"data": xr.DataArray([[1, 2], [3, 4]], dims=("x", "y"))},
coords={"x": [1, 2], "y": [3, 4]},
)
ds2 = xr.Dataset(
{"data": xr.DataArray([[5, 6], [7, 8]], dims=("x", "y"))},
coords={"x": [2, 3], "y": [4, 5]},
)
# combine the datasets
combined_ds = xr.combine_by_coords([ds1, ds2])
combined_ds
lazy
Lazily-evaluated operations do not load data into memory until necessary.Instead of doing calculations
right away, xarray lets you plan what calculations you want to do, like finding the
average temperature in a dataset.This planning is called "lazy evaluation." Later, when
you're ready to see the final result, you tell xarray, "Okay, go ahead and do those calculations now!"
That's when xarray starts working through the steps you planned and gives you the answer you wanted.This
lazy approach helps save time and memory because xarray only does the work when you actually need the
results.
labeled
Labeled data has metadata describing the context of the data, not just the raw data values.
This contextual information can be labels for array axes (i.e. dimension names) tick labels along axes (stored as Coordinate variables) or unique names for each array. These labels
provide context and meaning to the data, making it easier to understand and work with. If you have
temperature data for different cities over time. Using xarray, you can label the dimensions: one for
cities and another for time.
serialization
Serialization is the process of converting your data into a format that makes it easy to save and share.
When you serialize data in xarray, you're taking all those temperature measurements, along with their
labels and other information, and turning them into a format that can be stored in a file or sent over
the internet. xarray objects can be serialized into formats which store the labels alongside the data.
Some supported serialization formats are files that can then be stored or transferred (e.g. netCDF),
whilst others are protocols that allow for data access over a network (e.g. Zarr).
indexing
:ref:`Indexing` is how you select subsets of your data which you are interested in.
- Label-based Indexing: Selecting data by passing a specific label and comparing it to the labels
stored in the associated coordinates. You can use labels to specify what you want like "Give me the
temperature for New York on July 15th."
- Positional Indexing: You can use numbers to refer to positions in the data like "Give me the third temperature value" This is useful when you know the order of your data but don't need to remember the exact labels.
- Slicing: You can take a "slice" of your data, like you might want all temperatures from July 1st
to July 10th. xarray supports slicing for both positional and label-based indexing.