Interoperability

Interoperable with the scientific Python ecosystem including NumPy, Dask, Pandas, and Matplotlib.

Apply operations over named dimensions

Select values by label instead of integer location

Vectorized operations

Mathematical operations vectorize across multiple dimensions (array broadcasting) based on dimension names, not shape.

GroupBy operations

Flexible split-apply-combine operations with groupby.

Database like operations

Database like alignment based on coordinate labels that smoothly handles missing values.

Arbitrary metadata tracking

Keep track of arbitrary metadata in the form of a Python dictionary.

Flexible and Extensible I/O backend API

Read and write data to and from NetCDF, HDF, Zarr, OpenDAP, and GRIB.

1"""
2To try Xarray in the browser,
3use the console located 👉 or 👇:
41. Type code in the input cell and press
5   Shift + Enter to execute
62. Or copy paste the code, and click on
7   the "Run" ▶ button in the toolbar
8"""
9import xarray as xr
10import pandas as pd
11import numpy as np
12
13data = xr.DataArray(
14    np.random.randn(3, 2, 3),
15    dims=("time", "lat", "lon"),
16    coords={
17        "lat": [10, 20],
18        "time": pd.date_range(
19            "2020-01", periods=3, freq="MS"
20        ),
21    },
22)
23
24# positional and by integer label, like numpy
25data[0, :]
26
27# loc or "location": positional and
28# coordinate label, like pandas
29data.loc[:, 10]
30
31# isel or "integer select": by dimension name
32# and integer label
33data.isel(lat=0)
34
35# sel or "select": by dimension name and
36# coordinate label
37data.sel(time="2020-01")
38
39# Data aggregations uses dimension names
40# instead of axis numbers
41data.mean(dim=["time", "lat"])
42
43# quick and convenient visualizations
44data.isel(lon=0).plot();
45
46# Pretty neat, eh? :)
47# For more, head over to the documentation page