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# Overview: Why xarray?#

Xarray introduces labels in the form of dimensions, coordinates and attributes on top of raw NumPy-like multidimensional arrays, which allows for a more intuitive, more concise, and less error-prone developer experience.

## What labels enable#

Multi-dimensional (a.k.a. N-dimensional, ND) arrays (sometimes called â€śtensorsâ€ť) are an essential part of computational science. They are encountered in a wide range of fields, including physics, astronomy, geoscience, bioinformatics, engineering, finance, and deep learning. In Python, NumPy provides the fundamental data structure and API for working with raw ND arrays. However, real-world datasets are usually more than just raw numbers; they have labels which encode information about how the array values map to locations in space, time, etc.

Xarray doesnâ€™t just keep track of labels on arrays â€“ it uses them to provide a powerful and concise interface. For example:

• Apply operations over dimensions by name: `x.sum('time')`.

• Select values by label (or logical location) instead of integer location: `x.loc['2014-01-01']` or `x.sel(time='2014-01-01')`.

• Mathematical operations (e.g., `x - y`) vectorize across multiple dimensions (array broadcasting) based on dimension names, not shape.

• Easily use the split-apply-combine paradigm with `groupby`: `x.groupby('time.dayofyear').mean()`.

• Database-like alignment based on coordinate labels that smoothly handles missing values: `x, y = xr.align(x, y, join='outer')`.

• Keep track of arbitrary metadata in the form of a Python dictionary: `x.attrs`.

The N-dimensional nature of xarrayâ€™s data structures makes it suitable for dealing with multi-dimensional scientific data, and its use of dimension names instead of axis labels (`dim='time'` instead of `axis=0`) makes such arrays much more manageable than the raw numpy ndarray: with xarray, you donâ€™t need to keep track of the order of an arrayâ€™s dimensions or insert dummy dimensions of size 1 to align arrays (e.g., using `np.newaxis`).

The immediate payoff of using xarray is that youâ€™ll write less code. The long-term payoff is that youâ€™ll understand what you were thinking when you come back to look at it weeks or months later.

## Core data structures#

Xarray has two core data structures, which build upon and extend the core strengths of NumPy and pandas. Both data structures are fundamentally N-dimensional:

• `DataArray` is our implementation of a labeled, N-dimensional array. It is an N-D generalization of a `pandas.Series`. The name `DataArray` itself is borrowed from Fernando Perezâ€™s datarray project, which prototyped a similar data structure.

• `Dataset` is a multi-dimensional, in-memory array database. It is a dict-like container of `DataArray` objects aligned along any number of shared dimensions, and serves a similar purpose in xarray to the `pandas.DataFrame`.

The value of attaching labels to numpyâ€™s `numpy.ndarray` may be fairly obvious, but the dataset may need more motivation.

The power of the dataset over a plain dictionary is that, in addition to pulling out arrays by name, it is possible to select or combine data along a dimension across all arrays simultaneously. Like a `DataFrame`, datasets facilitate array operations with heterogeneous data â€“ the difference is that the arrays in a dataset can have not only different data types, but also different numbers of dimensions.

This data model is borrowed from the netCDF file format, which also provides xarray with a natural and portable serialization format. NetCDF is very popular in the geosciences, and there are existing libraries for reading and writing netCDF in many programming languages, including Python.

Xarray distinguishes itself from many tools for working with netCDF data in-so-far as it provides data structures for in-memory analytics that both utilize and preserve labels. You only need to do the tedious work of adding metadata once, not every time you save a file.

## Goals and aspirations#

Xarray contributes domain-agnostic data-structures and tools for labeled multi-dimensional arrays to Pythonâ€™s SciPy ecosystem for numerical computing. In particular, xarray builds upon and integrates with NumPy and pandas:

• Our user-facing interfaces aim to be more explicit versions of those found in NumPy/pandas.

• Compatibility with the broader ecosystem is a major goal: it should be easy to get your data in and out.

• We try to keep a tight focus on functionality and interfaces related to labeled data, and leverage other Python libraries for everything else, e.g., NumPy/pandas for fast arrays/indexing (xarray itself contains no compiled code), Dask for parallel computing, matplotlib for plotting, etc.

Xarray is a collaborative and community driven project, run entirely on volunteer effort (see Contributing to xarray). Our target audience is anyone who needs N-dimensional labeled arrays in Python. Originally, development was driven by the data analysis needs of physical scientists (especially geoscientists who already know and love netCDF), but it has become a much more broadly useful tool, and is still under active development. See our technical Development roadmap for more details, and feel free to reach out with questions about whether xarray is the right tool for your needs.