Xarray Interpolation, Groupby, Resample, Rolling, and Coarsen #

import numpy as np
import xarray as xr
from matplotlib import pyplot as plt
%xmode Minimal
Interpolation#
In the previous lesson on Xarray, we learned how to select data based on its dimension coordinates and align data with dimension different coordinates.
But what if we want to estimate the value of the data variables at different coordinates.
This is where interpolation comes in.
# we write it out explicitly so we can see each point.
x_data = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
f = xr.DataArray(x_data**2, dims=['x'], coords={'x': x_data})
<xarray.DataArray (x: 11)>
array([  0,   1,   4,   9,  16,  25,  36,  49,  64,  81, 100])
Coordinates:
  * x        (x) int64 0 1 2 3 4 5 6 7 8 9 10

f.plot(marker='o')
We only have data on the integer points in x.
But what if we wanted to estimate the value at, say, 4.5?
f.sel(x=4.5)
array(20.5)
Coordinates:
    x        float64 4.5

Interpolation uses scipy.interpolate under the hood.
There are different modes of interpolation.
# default
f.interp(x=4.5, method='linear').values
x_new = x_data + 0.5
f_interp_linear = f.interp(x=x_new, method='linear')
f_interp_cubic = f.interp(x=x_new, method='cubic')
f.plot(marker='o', label='original')
f_interp_linear.plot(marker='o', label='linear')
f_interp_cubic.plot(marker='o', label='cubic')
plt.legend()
You can apply interpolation to any dimension, and even to multiple dimensions at a time.
(Multidimensional interpolation only supports mode='nearest' and mode='linear'.)
But keep in mind that Xarray has no built-in understanding of geography.
If you use interp on lat / lon coordinates, it will just perform naive interpolation of the lat / lon values.
More sophisticated treatment of spherical geometry requires another package such as xesmf.
Groupby#
Xarray copies Pandas’ very useful groupby functionality, enabling the “split / apply / combine” workflow on xarray DataArrays and Datasets. In the first part of the lesson, we will learn to use groupby by analyzing sea-surface temperature data.
First we load a dataset. We will use the NOAA Extended Reconstructed Sea Surface Temperature (ERSST) v5 product, a widely used and trusted gridded compilation of of historical data going back to 1854.
Since the data is provided via an OPeNDAP server, we can load it directly without downloading anything:
url = 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/noaa.ersst.v5/sst.mnmean.nc'
ds = xr.open_dataset(url, drop_variables=['time_bnds'])
ds = ds.sel(time=slice('1960', '2018')).load()
Dimensions:  (lat: 89, lon: 180, time: 708)
Coordinates:
  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0
  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0
  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01
Data variables:
    sst      (time, lat, lon) float32 -1.8 -1.8 -1.8 -1.8 ... nan nan nan nan
Attributes: (12/38)
    climatology:                     Climatology is based on 1971-2000 SST, X...
    description:                     In situ data: ICOADS2.5 before 2007 and ...
    keywords_vocabulary:             NASA Global Change Master Directory (GCM...
    keywords:                        Earth Science > Oceans > Ocean Temperatu...
    instrument:                      Conventional thermometers
    source_comment:                  SSTs were observed by conventional therm...
    ...                              ...
    license:                         No constraints on data access or use
    comment:                         SSTs were observed by conventional therm...
    summary:                         ERSST.v5 is developed based on v4 after ...
    dataset_title:                   NOAA Extended Reconstructed SST V5
    data_modified:                   2021-11-07
    DODS_EXTRA.Unlimited_Dimension:  time

Let’s do some basic visualizations of the data, just to make sure it looks reasonable.
ds.sst[0].plot(vmin=-2, vmax=30)
Note that xarray correctly parsed the time index, resulting in a Pandas datetime index on the time dimension.
ds.time
<xarray.DataArray 'time' (time: 708)>
array(['1960-01-01T00:00:00.000000000', '1960-02-01T00:00:00.000000000',
       '1960-03-01T00:00:00.000000000', ..., '2018-10-01T00:00:00.000000000',
       '2018-11-01T00:00:00.000000000', '2018-12-01T00:00:00.000000000'],
      dtype='datetime64[ns]')
Coordinates:
  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01
Attributes:
    long_name:        Time
    delta_t:          0000-01-00 00:00:00
    avg_period:       0000-01-00 00:00:00
    prev_avg_period:  0000-00-07 00:00:00
    standard_name:    time
    axis:             T
    actual_range:     [19723. 80992.]
    _ChunkSizes:      1

ds.sst.sel(lon=300, lat=50).plot()
As we can see from the plot, the timeseries at any one point is totally dominated by the seasonal cycle. We would like to remove this seasonal cycle (called the “climatology”) in order to better see the long-term variaitions in temperature. We will accomplish this using groupby.
The syntax of Xarray’s groupby is almost identical to Pandas.
We will first apply groupby to a single DataArray.
ds.sst.groupby?
----------
group : str, DataArray or IndexVariable
    Array whose unique values should be used to group this array. If a
    string, must be the name of a variable contained in this dataset.
squeeze : bool, optional
    If "group" is a dimension of any arrays in this dataset, `squeeze`
    controls whether the subarrays have a dimension of length 1 along
    that dimension or if the dimension is squeezed out.
restore_coord_dims : bool, optional
    If True, also restore the dimension order of multi-dimensional
    coordinates.
Returns
-------
grouped
    A `GroupBy` object patterned after `pandas.GroupBy` that can be
    iterated over in the form of `(unique_value, grouped_array)` pairs.
Examples
--------
Calculate daily anomalies for daily data:
>>> da = xr.DataArray(
...     np.linspace(0, 1826, num=1827),
...     coords=[pd.date_range("1/1/2000", "31/12/2004", freq="D")],
...     dims="time",
... )
<xarray.DataArray (time: 1827)>
array([0.000e+00, 1.000e+00, 2.000e+00, ..., 1.824e+03, 1.825e+03,
       1.826e+03])
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2004-12-31
>>> da.groupby("time.dayofyear") - da.groupby("time.dayofyear").mean("time")
<xarray.DataArray (time: 1827)>
array([-730.8, -730.8, -730.8, ...,  730.2,  730.2,  730.5])
Coordinates:
  * time       (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2004-12-31
    dayofyear  (time) int64 1 2 3 4 5 6 7 8 ... 359 360 361 362 363 364 365 366
See Also
--------
core.groupby.DataArrayGroupBy
core.groupby.DatasetGroupBy
File:      /srv/conda/envs/notebook/lib/python3.8/site-packages/xarray/core/common.py
Type:      method
Split Step#
The most important argument is group: this defines the unique values we will us to “split” the data for grouped analysis. We can pass either a DataArray or a name of a variable in the dataset. Lets first use a DataArray. Just like with Pandas, we can use the time indexe to extract specific components of dates and times. Xarray uses a special syntax for this .dt, called the DatetimeAccessor.
ds.time.dt
<xarray.DataArray 'month' (time: 708)>
array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,
        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,
       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,
        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,
        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,
        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,
        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,
       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,
        5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9,
       10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,
        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,
        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,
        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,
        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,
       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,
        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,
        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,
        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,
        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,
       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,
        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,
        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,
        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,
        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,
       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,
        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,
        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,
        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,
        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,
       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,
        5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9,
       10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,
        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,
        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,
        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,
        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,
       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,
        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,
        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,
        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])
Coordinates:
  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01

ds.time.dt.year
We can use these arrays in a groupby operation:
gb = ds.sst.groupby(ds.time.dt.month)
Xarray also offers a more concise syntax when the variable you’re grouping on is already present in the dataset. This is identical to the previous line:
gb = ds.sst.groupby('time.month')
Now that the data are split, we can manually iterate over the group. The iterator returns the key (group name) and the value (the actual dataset corresponding to that group) for each group.
for group_name, group_da in gb:
    # stop iterating after the first loop
    break 
print(group_name)
group_da
<xarray.DataArray 'sst' (time: 59, lat: 89, lon: 180)>
array([[[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],
        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],
        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],
        [ nan,  nan,  nan, ...,  nan,  nan,  nan],
        [ nan,  nan,  nan, ...,  nan,  nan,  nan],
        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],
       [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],
        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],
        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],
        [ nan,  nan,  nan, ...,  nan,  nan,  nan],
        [ nan,  nan,  nan, ...,  nan,  nan,  nan],
        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],
       [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],
        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],
        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],
        [ nan,  nan,  nan, ...,  nan,  nan,  nan],
        [ nan,  nan,  nan, ...,  nan,  nan,  nan],
        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],
       [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],
        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],
        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],
        [ nan,  nan,  nan, ...,  nan,  nan,  nan],
        [ nan,  nan,  nan, ...,  nan,  nan,  nan],
        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],
       [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],
        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],
        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],
        [ nan,  nan,  nan, ...,  nan,  nan,  nan],
        [ nan,  nan,  nan, ...,  nan,  nan,  nan],
        [ nan,  nan,  nan, ...,  nan,  nan,  nan]]], dtype=float32)
Coordinates:
  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0
  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0
  * time     (time) datetime64[ns] 1960-01-01 1961-01-01 ... 2018-01-01
Attributes:
    long_name:     Monthly Means of Sea Surface Temperature
    units:         degC
    var_desc:      Sea Surface Temperature
    level_desc:    Surface
    statistic:     Mean
    dataset:       NOAA Extended Reconstructed SST V5
    parent_stat:   Individual Values
    actual_range:  [-1.8     42.32636]
    valid_range:   [-1.8 45. ]
    _ChunkSizes:   [  1  89 180]

Map & Combine#
Now that we have groups defined, it’s time to “apply” a calculation to the group. Like in Pandas, these calculations can either be:
aggregation: reduces the size of the group
transformation: preserves the group’s full size
At then end of the apply step, xarray will automatically combine the aggregated / transformed groups back into a single object.
Warning
Xarray calls the “apply” step map. This is different from Pandas!
The most fundamental way to apply is with the .map method.
gb.map?
Signature: gb.map(func, shortcut=False, args=(), **kwargs)
Docstring:
Apply a function to each array in the group and concatenate them
together into a new array.
`func` is called like `func(ar, *args, **kwargs)` for each array `ar`
in this group.
Apply uses heuristics (like `pandas.GroupBy.apply`) to figure out how
to stack together the array. The rule is:
1. If the dimension along which the group coordinate is defined is
   still in the first grouped array after applying `func`, then stack
   over this dimension.
2. Otherwise, stack over the new dimension given by name of this
   grouping (the argument to the `groupby` function).
Parameters
----------
func : callable
    Callable to apply to each array.
shortcut : bool, optional
    Whether or not to shortcut evaluation under the assumptions that:
    (1) The action of `func` does not depend on any of the array
        metadata (attributes or coordinates) but only on the data and
        dimensions.
    (2) The action of `func` creates arrays with homogeneous metadata,
        that is, with the same dimensions and attributes.
    If these conditions are satisfied `shortcut` provides significant
    speedup. This should be the case for many common groupby operations
    (e.g., applying numpy ufuncs).
*args : tuple, optional
    Positional arguments passed to `func`.
**kwargs
    Used to call `func(ar, **kwargs)` for each array `ar`.
Returns
-------
applied : DataArray or DataArray
    The result of splitting, applying and combining this array.
File:      /srv/conda/envs/notebook/lib/python3.8/site-packages/xarray/core/groupby.py
Type:      method
Aggregations#
.apply accepts as its argument a function. We can pass an existing function:
gb.map(np.mean)
<xarray.DataArray 'sst' (month: 12)>
array([13.659641, 13.768647, 13.76488 , 13.684034, 13.642146, 13.713043,
       13.921847, 14.093956, 13.982147, 13.691116, 13.506494, 13.529454],
      dtype=float32)
Coordinates:
  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12

Because we specified no extra arguments (like axis) the function was applied over all space and time dimensions. This is not what we wanted. Instead, we could define a custom function. This function takes a single argument–the group dataset–and returns a new dataset to be combined:
def time_mean(a):
    return a.mean(dim='time')
gb.apply(time_mean)
<xarray.DataArray 'sst' (month: 12, lat: 89, lon: 180)>
array([[[-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,
         -1.8000009, -1.8000009],
        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,
         -1.8000009, -1.8000009],
        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,
         -1.8000009, -1.8000009],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan]],
       [[-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,
         -1.8000009, -1.8000009],
        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,
         -1.8000009, -1.8000009],
        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,
         -1.8000009, -1.8000009],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan]],
       [[-1.7995025, -1.7995973, -1.7998415, ..., -1.7997988,
         -1.7996519, -1.7995045],
        [-1.7995876, -1.7997634, -1.8000009, ..., -1.8000009,
         -1.7998358, -1.7996247],
        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,
         -1.8000009, -1.8000009],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan]]], dtype=float32)
Coordinates:
  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0
  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0
  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12

Like Pandas, xarray’s groupby object has many built-in aggregation operations (e.g. mean, min, max, std, etc):
# this does the same thing as the previous cell
sst_mm = gb.mean(dim='time')
sst_mm
<xarray.DataArray 'sst' (month: 12, lat: 89, lon: 180)>
array([[[-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,
         -1.8000009, -1.8000009],
        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,
         -1.8000009, -1.8000009],
        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,
         -1.8000009, -1.8000009],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan]],
       [[-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,
         -1.8000009, -1.8000009],
        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,
         -1.8000009, -1.8000009],
        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,
         -1.8000009, -1.8000009],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan]],
       [[-1.7995025, -1.7995973, -1.7998415, ..., -1.7997988,
         -1.7996519, -1.7995045],
        [-1.7995876, -1.7997634, -1.8000009, ..., -1.8000009,
         -1.7998358, -1.7996247],
        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,
         -1.8000009, -1.8000009],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan]]], dtype=float32)
Coordinates:
  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0
  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0
  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12

So we did what we wanted to do: calculate the climatology at every point in the dataset. Let’s look at the data a bit.
Climatlogy at a specific point in the North Atlantic
sst_mm.sel(lon=300, lat=50).plot()
Transformations#
Now we want to remove this climatology from the dataset, to examine the residual, called the anomaly, which is the interesting part from a climate perspective.
Removing the seasonal climatology is a perfect example of a transformation: it operates over a group, but doesn’t change the size of the dataset. Here is one way to code it.
def remove_time_mean(x):
    return x - x.mean(dim='time')
ds_anom = ds.groupby('time.month').apply(remove_time_mean)
ds_anom
Dimensions:  (lat: 89, lon: 180, time: 708)
Coordinates:
  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0
  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0
  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01
Data variables:
    sst      (time, lat, lon) float32 9.537e-07 9.537e-07 9.537e-07 ... nan nan

In the above example, we applied groupby to a Dataset instead of a DataArray.
Xarray makes these sorts of transformations easy by supporting groupby arithmetic.
This concept is easiest explained with an example:
gb = ds.groupby('time.month')
ds_anom = gb - gb.mean(dim='time')
ds_anom
Dimensions:  (lat: 89, lon: 180, time: 708)
Coordinates:
  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0
  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0
  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01
    month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12
Data variables:
    sst      (time, lat, lon) float32 9.537e-07 9.537e-07 9.537e-07 ... nan nan

Now we can view the climate signal without the overwhelming influence of the seasonal cycle.
Timeseries at a single point in the North Atlantic
ds_anom.sst.sel(lon=300, lat=50).plot()
Resample#
Resample in xarray is nearly identical to Pandas.
It can be applied only to time-index dimensions. Here we compute the five-year mean.
It is effectively a group-by operation, and uses the same basic syntax.
Note that resampling changes the length of the the output arrays.
ds_anom_resample = ds_anom.resample(time='5Y').mean(dim='time')
ds_anom_resample
Dimensions:  (time: 13, lat: 89, lon: 180)
Coordinates:
  * time     (time) datetime64[ns] 1960-12-31 1965-12-31 ... 2020-12-31
  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0
  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0
Data variables:
    sst      (time, lat, lon) float32 -0.0005707 -0.0005493 ... nan nan

ds_anom.sst.sel(lon=300, lat=50).plot()
ds_anom_resample.sst.sel(lon=300, lat=50).plot(marker='o')
Rolling is also similar to pandas.
It does not change the length of the arrays.
Instead, it allows a moving window to be applied to the data at each point.
ds_anom_rolling = ds_anom.rolling(time=12, center=True).mean()
ds_anom_rolling
Dimensions:  (lat: 89, lon: 180, time: 708)
Coordinates:
  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0
  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0
  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01
    month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12
Data variables:
    sst      (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan

ds_anom.sst.sel(lon=300, lat=50).plot(label='monthly anom')
ds_anom_resample.sst.sel(lon=300, lat=50).plot(marker='o', label='5 year resample')
ds_anom_rolling.sst.sel(lon=300, lat=50).plot(label='12 month rolling mean', color='k')
plt.legend()
Coarsen#
coarsen is a simple way to reduce the size of your data along one or more axes.
It is very similar to resample when operating on time dimensions; the key differnce is that coarsen only operates on fixed blocks of data, irrespective of the coordinate values, while resample actually looks at the coordinates to figure out, e.g. what month a particular data point is in.
For regularly-spaced monthly data beginning in January, the following should be equivalent to annual resampling.
However, results would different for irregularly-spaced data.
ds.coarsen(time=12).mean()
Dimensions:  (time: 59, lat: 89, lon: 180)
Coordinates:
  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0
  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0
  * time     (time) datetime64[ns] 1960-06-16T08:00:00 ... 2018-06-16T12:00:00
Data variables:
    sst      (time, lat, lon) float32 -1.8 -1.8 -1.8 -1.8 ... nan nan nan nan
Attributes: (12/38)
    climatology:                     Climatology is based on 1971-2000 SST, X...
    description:                     In situ data: ICOADS2.5 before 2007 and ...
    keywords_vocabulary:             NASA Global Change Master Directory (GCM...
    keywords:                        Earth Science > Oceans > Ocean Temperatu...
    instrument:                      Conventional thermometers
    source_comment:                  SSTs were observed by conventional therm...
    ...                              ...
    license:                         No constraints on data access or use
    comment:                         SSTs were observed by conventional therm...
    summary:                         ERSST.v5 is developed based on v4 after ...
    dataset_title:                   NOAA Extended Reconstructed SST V5
    data_modified:                   2021-11-07
    DODS_EXTRA.Unlimited_Dimension:  time

Coarsen also works on spatial coordinates (or any coordiantes).
ds_coarse = ds.coarsen(lon=4, lat=4, boundary='pad').mean()
ds_coarse.sst.isel(time=0).plot(vmin=2, vmax=30, figsize=(12, 5), edgecolor='k')
An Advanced Example#
In this example we will show a realistic workflow with Xarray.
We will
Load a “basin mask” dataset
Interpolate the basins to our SST dataset coordinates
Group the SST by basin
Convert to Pandas Dataframe and plot mean SST by basin
basin = xr.open_dataset('http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NODC/.WOA09/.Masks/.basin/dods')
basin
Dimensions:  (Z: 33, X: 360, Y: 180)
Coordinates:
  * Z        (Z) float32 0.0 10.0 20.0 30.0 50.0 ... 4e+03 4.5e+03 5e+03 5.5e+03
  * X        (X) float32 0.5 1.5 2.5 3.5 4.5 ... 355.5 356.5 357.5 358.5 359.5
  * Y        (Y) float32 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5
Data variables:
    basin    (Z, Y, X) float32 ...
Attributes:
    Conventions:  IRIDL

basin = basin.rename({'X': 'lon', 'Y': 'lat'})
basin
Dimensions:  (Z: 33, lon: 360, lat: 180)
Coordinates:
  * Z        (Z) float32 0.0 10.0 20.0 30.0 50.0 ... 4e+03 4.5e+03 5e+03 5.5e+03
  * lon      (lon) float32 0.5 1.5 2.5 3.5 4.5 ... 355.5 356.5 357.5 358.5 359.5
  * lat      (lat) float32 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5
Data variables:
    basin    (Z, lat, lon) float32 ...
Attributes:
    Conventions:  IRIDL

basin_surf = basin.basin[0]
basin_surf
<xarray.DataArray 'basin' (lat: 180, lon: 360)>
array([[nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       [11., 11., 11., ..., 11., 11., 11.],
       [11., 11., 11., ..., 11., 11., 11.],
       [11., 11., 11., ..., 11., 11., 11.]], dtype=float32)
Coordinates:
    Z        float32 0.0
  * lon      (lon) float32 0.5 1.5 2.5 3.5 4.5 ... 355.5 356.5 357.5 358.5 359.5
  * lat      (lat) float32 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5
Attributes:
    long_name:  basin code
    CLIST:      Atlantic Ocean\nPacific Ocean \nIndian Ocean\nMediterranean S...
    valid_min:  1
    valid_max:  58
    scale_min:  1
    units:      ids
    scale_max:  58

basin_surf.plot(vmax=10)
<xarray.DataArray 'sst' (time: 708, basin: 14)>
array([[-1.8       , -1.8       , 23.455315  , ..., -1.8       ,
         3.3971915 , 24.182198  ],
       [-1.8       , -1.8       , 23.722523  , ..., -1.8       ,
         0.03573781, 24.59657   ],
       [-1.8       , -1.8       , 24.601315  , ..., -1.8       ,
        -0.26487017, 26.234186  ],
       [ 0.6758132 ,  6.504184  , 29.279463  , ..., 10.920228  ,
        15.955025  , 29.41976   ],
       [-0.7937442 ,  3.0715032 , 27.608435  , ...,  5.4078875 ,
        10.673693  , 27.7558    ],
       [-1.8       , -0.06063586, 25.881481  , ...,  0.5253569 ,
         7.267694  , 26.163145  ]], dtype=float32)
Coordinates:
  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01
    Z        float32 0.0
  * basin    (basin) float64 1.0 2.0 3.0 4.0 5.0 ... 10.0 11.0 12.0 53.0 56.0
Attributes:
    long_name:     Monthly Means of Sea Surface Temperature
    units:         degC
    var_desc:      Sea Surface Temperature
    level_desc:    Surface
    statistic:     Mean
    dataset:       NOAA Extended Reconstructed SST V5
    parent_stat:   Individual Values
    actual_range:  [-1.8     42.32636]
    valid_range:   [-1.8 45. ]
    _ChunkSizes:   [  1  89 180]

basin_mean_sst = ds.sst.groupby(basin_surf_interp).mean()
basin_mean_sst
<xarray.DataArray 'sst' (time: 708, basin: 14)>
array([[18.585493 , 20.757555 , 21.572067 , ...,  6.238062 ,  6.889794 ,
        26.49982  ],
       [18.705065 , 20.81674  , 21.902279 , ...,  4.8877654,  5.44638  ,
        26.577093 ],
       [18.845842 , 20.865038 , 22.031416 , ...,  4.686406 ,  5.5322194,
        27.908558 ],
       [19.84992  , 21.960493 , 20.389412 , ..., 17.571943 , 18.184528 ,
        29.336565 ],
       [19.424026 , 21.722925 , 21.061403 , ..., 13.461868 , 13.863244 ,
        28.755905 ],
       [19.265354 , 21.512274 , 21.814356 , ...,  9.417906 , 10.607256 ,
        27.905243 ]], dtype=float32)
Coordinates:
  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01
    Z        float32 0.0
  * basin    (basin) float64 1.0 2.0 3.0 4.0 5.0 ... 10.0 11.0 12.0 53.0 56.0

df = basin_mean_sst.mean('time').to_dataframe()
import pandas as pd
basin_names = basin_surf.attrs['CLIST'].split('\n')
basin_df = pd.Series(basin_names, index=np.arange(1, len(basin_names)+1))
basin_df
1                 Atlantic Ocean
2                 Pacific Ocean 
3                   Indian Ocean
4              Mediterranean Sea
5                     Baltic Sea
6                      Black Sea
7                        Red Sea
8                   Persian Gulf
9                     Hudson Bay
10                Southern Ocean
11                  Arctic Ocean
12                  Sea of Japan
13                      Kara Sea
14                      Sulu Sea
15                    Baffin Bay
16            East Mediterranean
17            West Mediterranean
18                Sea of Okhotsk
19                     Banda Sea
20                 Caribbean Sea
21                 Andaman Basin
22               North Caribbean
23                Gulf of Mexico
24                  Beaufort Sea
25               South China Sea
26                   Barents Sea
27                   Celebes Sea
28                Aleutian Basin
29                    Fiji Basin
30          North American Basin
31           West European Basin
32        Southeast Indian Basin
33                     Coral Sea
34             East Indian Basin
35          Central Indian Basin
36      Southwest Atlantic Basin
37      Southeast Atlantic Basin
38       Southeast Pacific Basin
39               Guatemala Basin
40           East Caroline Basin
41                Marianas Basin
42                Philippine Sea
43                   Arabian Sea
44                   Chile Basin
45                  Somali Basin
46               Mascarene Basin
47                  Crozet Basin
48                  Guinea Basin
49                  Brazil Basin
50               Argentine Basin
51                    Tasman Sea
52         Atlantic Indian Basin
53                   Caspian Sea
54                   Sulu Sea II
55               Venezuela Basin
56                 Bay of Bengal
57                      Java Sea
58    East Indian Atlantic Basin
dtype: object