Xarray supports many of the aggregations methods that numpy has. A partial list includes: all, any, argmax, argmin, max, mean, median, min, prod, sum, std, var.
Whereas the numpy syntax would require scalar axes, xarray can use dimension names:
import xarray as xr
ds = xr.open_dataset("../data/air_temperature.nc")
da = ds['air']
da
<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>
[3869000 values with dtype=float32]
Coordinates:
* lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0
* lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0
* time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00
Attributes:
long_name: 4xDaily Air temperature at sigma level 995
units: degK
precision: 2
GRIB_id: 11
GRIB_name: TMP
var_desc: Air temperature
dataset: NMC Reanalysis
level_desc: Surface
statistic: Individual Obs
parent_stat: Other
actual_range: [185.16 322.1 ]
da.mean()
<xarray.DataArray 'air' ()>
array(281.25513, dtype=float32)
da.mean(dim=['lat', 'lon'])
<xarray.DataArray 'air' (time: 2920)>
array([274.16626, 273.5202 , 273.23352, ..., 272.99994, 273.0445 , 273.41498],
dtype=float32)
Coordinates:
* time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00
da.median(dim='time')
<xarray.DataArray 'air' (lat: 25, lon: 53)>
array([[261.29 , 261.15 , 260.9 , ..., 249.29999, 250.29999, 251.69499],
[266.1 , 266.14502, 265.9 , ..., 248.39 , 250.29 , 253.29999],
[267.19998, 266.345 , 266.1 , ..., 249.29999, 252.39 , 257.1 ],
...,
[297.79 , 297.1 , 296.69998, ..., 296.9 , 296.29 , 295.79 ],
[298.19998, 298. , 297.5 , ..., 296.9 , 296.79 , 296.4 ],
[298.4 , 298.4 , 298.19 , ..., 297.29 , 297.29 , 297.29 ]],
dtype=float32)
Coordinates:
* lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0
* lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0
da.std(dim='time')
<xarray.DataArray 'air' (lat: 25, lon: 53)>
array([[11.747092, 11.693981, 11.698499, ..., 10.668427, 10.772092, 10.894412],
[11.657324, 11.69504 , 11.78264 , ..., 10.910913, 11.066578, 11.024166],
[12.434489, 12.759332, 13.078072, ..., 11.306724, 10.977766, 10.328547],
...,
[ 1.606873, 1.555383, 1.466195, ..., 1.777959, 1.862361, 1.949547],
[ 1.30469 , 1.26994 , 1.253034, ..., 1.760575, 1.911518, 2.038532],
[ 1.036176, 1.04871 , 1.03761 , ..., 1.753763, 1.84843 , 1.925401]],
dtype=float32)
Coordinates:
* lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0
* lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0
%load_ext watermark
%watermark --iversion -g -m -v -u -d
xarray 0.12.1
last updated: 2019-05-17
CPython 3.6.7
IPython 7.5.0
compiler : GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)
system : Darwin
release : 18.2.0
machine : x86_64
processor : i386
CPU cores : 8
interpreter: 64bit
Git hash : 83530e805423a8f36958a61783cbbd9fe388eace