# Frequently Asked Questions

## Why is pandas not enough?

pandas is a fantastic library for analysis of low-dimensional labelled data - if it can be sensibly described as “rows and columns”, pandas is probably the right choice. However, sometimes we want to use higher dimensional arrays (ndim > 2), or arrays for which the order of dimensions (e.g., columns vs rows) shouldn’t really matter. For example, climate and weather data is often natively expressed in 4 or more dimensions: time, x, y and z.

Pandas has historically supported N-dimensional panels, but deprecated them in version 0.20 in favor of Xarray data structures. There are now built-in methods on both sides to convert between pandas and Xarray, allowing for more focussed development effort. Xarray objects have a much richer model of dimensionality - if you were using Panels:

- You need to create a new factory type for each dimensionality.
- You can’t do math between NDPanels with different dimensionality.
- Each dimension in a NDPanel has a name (e.g., ‘labels’, ‘items’, ‘major_axis’, etc.) but the dimension names refer to order, not their meaning. You can’t specify an operation as to be applied along the “time” axis.
- You often have to manually convert collections of pandas arrays (Series, DataFrames, etc) to have the same number of dimensions. In contrast, this sort of data structure fits very naturally in an xarray `Dataset`.

You can read about switching from Panels to Xarray here. Pandas gets a lot of things right, but scientific users need fully multi- dimensional data structures.

## How do xarray data structures differ from those found in pandas?

The main distinguishing feature of xarray’s `DataArray` over labeled arrays in pandas is that dimensions can have names (e.g., “time”, “latitude”, “longitude”). Names are much easier to keep track of than axis numbers, and xarray uses dimension names for indexing, aggregation and broadcasting. Not only can you write `x.sel(time='2000-01-01')` and `x.mean(dim='time')`, but operations like `x - x.mean(dim='time')` always work, no matter the order of the “time” dimension. You never need to reshape arrays (e.g., with `np.newaxis`) to align them for arithmetic operations in xarray.

## Should I use xarray instead of pandas?

It’s not an either/or choice! xarray provides robust support for converting back and forth between the tabular data-structures of pandas and its own multi-dimensional data-structures.

That said, you should only bother with xarray if some aspect of data is fundamentally multi-dimensional. If your data is unstructured or one-dimensional, stick with pandas.

## Why don’t aggregations return Python scalars?

xarray tries hard to be self-consistent: operations on a `DataArray` (resp. `Dataset`) return another `DataArray` (resp. `Dataset`) object. In particular, operations returning scalar values (e.g. indexing or aggregations like `mean` or `sum` applied to all axes) will also return xarray objects.

Unfortunately, this means we sometimes have to explicitly cast our results from xarray when using them in other libraries. As an illustration, the following code fragment

    In [1]: arr = xr.DataArray([1, 2, 3])

    In [2]: pd.Series({'x': arr[0], 'mean': arr.mean(), 'std': arr.std()})
    Out[2]:
    mean                  <xarray.DataArray ()>\narray(2.0)
    std     <xarray.DataArray ()>\narray(0.816496580927726)
    x                       <xarray.DataArray ()>\narray(1)
    dtype: object

does not yield the pandas DataFrame we expected. We need to specify the type conversion ourselves:

    In [3]: pd.Series({'x': arr[0], 'mean': arr.mean(), 'std': arr.std()}, dtype=float)
    Out[3]:
    mean    2.000000
    std     0.816497
    x       1.000000
    dtype: float64

Alternatively, we could use the `item` method or the `float` constructor to convert values one at a time

    In [4]: pd.Series({'x': arr[0].item(), 'mean': float(arr.mean())})
    Out[4]:
    mean    2.0
    x       1.0
    dtype: float64

## What is your approach to metadata?

We are firm believers in the power of labeled data! In addition to dimensions and coordinates, xarray supports arbitrary metadata in the form of global (Dataset) and variable specific (DataArray) attributes (`attrs`).

Automatic interpretation of labels is powerful but also reduces flexibility. With xarray, we draw a firm line between labels that the library understands (`dims` and `coords`) and labels for users and user code (`attrs`). For example, we do not automatically interpret and enforce units or CF conventions. (An exception is serialization to and from netCDF files.)

An implication of this choice is that we do not propagate `attrs` through most operations unless explicitly flagged (some methods have a `keep_attrs` option). Similarly, xarray does not check for conflicts between `attrs` when combining arrays and datasets, unless explicitly requested with the option `compat='identical'`. The guiding principle is that metadata should not be allowed to get in the way.

## What other netCDF related Python libraries should I know about?

netCDF4-python provides a lower level interface for working with netCDF and OpenDAP datasets in Python. We use netCDF4-python internally in xarray, and have contributed a number of improvements and fixes upstream. xarray does not yet support all of netCDF4-python’s features, such as writing to netCDF groups or modifying files on-disk.

Iris (supported by the UK Met office) provides similar tools for in- memory manipulation of labeled arrays, aimed specifically at weather and climate data needs. Indeed, the Iris `Cube` was direct inspiration for xarray’s `DataArray`. xarray and Iris take very different approaches to handling metadata: Iris strictly interprets CF conventions. Iris particularly shines at mapping, thanks to its integration with Cartopy.

UV-CDAT is another Python library that implements in-memory netCDF-like variables and tools for working with climate data.

We think the design decisions we have made for xarray (namely, basing it on pandas) make it a faster and more flexible data analysis tool. That said, Iris and CDAT have some great domain specific functionality, and we would love to have support for converting their native objects to and from xarray (see GH37 and GH133)

## How should I cite xarray?

If you are using xarray and would like to cite it in academic publication, we would certainly appreciate it. We recommend two citations.

> 1. At a minimum, we recommend citing the xarray overview journal article, published in the Journal of Open Research Software.

>    - Hoyer, S. & Hamman, J., (2017). xarray: N-D labeled Arrays and Datasets in Python. Journal of Open Research Software. 5(1), p.10. DOI: http://doi.org/10.5334/jors.148

>      Here’s an example of a BibTeX entry:

>          @article{hoyer2017xarray,
>            title     = {xarray: {N-D} labeled arrays and datasets in {Python}},
>            author    = {Hoyer, S. and J. Hamman},
>            journal   = {Journal of Open Research Software},
>            volume    = {5},
>            number    = {1},
>            year      = {2017},
>            publisher = {Ubiquity Press},
>            doi       = {10.5334/jors.148},
>            url       = {http://doi.org/10.5334/jors.148}
>          }

> 2. You may also want to cite a specific version of the xarray package. We provide a Zenodo citation and DOI for this purpose:

>    > An example BibTeX entry:

>    >     @misc{xarray_v0_8_0,
>    >           author = {Stephan Hoyer and Clark Fitzgerald and Joe Hamman and others},
>    >           title  = {xarray: v0.8.0},
>    >           month  = aug,
>    >           year   = 2016,
>    >           doi    = {10.5281/zenodo.59499},
>    >           url    = {http://dx.doi.org/10.5281/zenodo.59499}
>    >          }