Assign value by coordinate

How to assign to individual pixels of a xr.DataArray by coordinate. This is detailed for .sel(...) in the xarray documentation, but works similar for .loc[...].

import xarray as xr
import numpy as np

from util import generate_3d_dataset

print(xr.__version__)
print(np.__version__)

2025.3.1
2.2.0

Generate some dummy data. Needs to be loaded for the value assignment/indexing to work.

ds = generate_3d_dataset(lat = 100, lon=200, time=1).load()

ds

<xarray.Dataset> Size: 162kB
Dimensions:  (lat: 100, lon: 200, time: 1)
Coordinates:
  * lat      (lat) int64 800B 0 1 2 3 4 5 6 7 8 9 ... 91 92 93 94 95 96 97 98 99
  * lon      (lon) int64 2kB 0 1 2 3 4 5 6 7 ... 192 193 194 195 196 197 198 199
  * time     (time) datetime64[ns] 8B 2021-01-01
Data variables:
    test     (lat, lon, time) float64 160kB 0.4465 0.2039 ... 0.9729 0.8818

xarray.Dataset

• Dimensions:
  • lat: 100
  • lon: 200
  • time: 1
• Coordinates: (3)
  • lat
    (lat)
    int64
    0 1 2 3 4 5 6 ... 94 95 96 97 98 99

    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
           18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
           36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
           54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
           72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
           90, 91, 92, 93, 94, 95, 96, 97, 98, 99])

  • lon
    (lon)
    int64
    0 1 2 3 4 5 ... 195 196 197 198 199

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,
           196, 197, 198, 199])

  • time
    (time)
    datetime64[ns]
    2021-01-01

    array(['2021-01-01T00:00:00.000000000'], dtype='datetime64[ns]')

• Data variables: (1)
  • test
    (lat, lon, time)
    float64
    0.4465 0.2039 ... 0.9729 0.8818

    array([[[4.46482927e-01],
            [2.03900510e-01],
            [7.20135394e-01],
            ...,
            [6.52326889e-01],
            [9.47479271e-01],
            [8.09652550e-01]],
    
           [[3.27093992e-01],
            [6.73690998e-01],
            [3.32913621e-01],
            ...,
            [7.25037608e-01],
            [3.78979079e-01],
            [5.44877090e-01]],
    
           [[1.16501764e-01],
            [6.31263455e-01],
            [8.00208251e-01],
            ...,
    ...
            ...,
            [4.93377478e-01],
            [6.77727493e-01],
            [9.65014763e-02]],
    
           [[8.03787379e-02],
            [9.82801631e-01],
            [4.35146764e-01],
            ...,
            [5.49695137e-01],
            [5.93275732e-01],
            [5.29804412e-01]],
    
           [[6.00388515e-04],
            [4.61689868e-01],
            [5.37447201e-01],
            ...,
            [4.85692459e-01],
            [9.72852272e-01],
            [8.81813591e-01]]], shape=(100, 200, 1))

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
           18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
           36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
           54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
           72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
           90, 91, 92, 93, 94, 95, 96, 97, 98, 99],
          dtype='int64', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           190, 191, 192, 193, 194, 195, 196, 197, 198, 199],
          dtype='int64', name='lon', length=200))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2021-01-01'], dtype='datetime64[ns]', name='time', freq='D'))

• Attributes: (0)

Generate target locations. In the following these will be used for orthogonal and pointwise indexing.

lon_coords = np.array([3, 20, 75])
lat_coords = np.array([0, 32, 99])

Orthogonal indexing

ds.loc[dict(lon=lon_coords, lat=lat_coords)]

<xarray.Dataset> Size: 128B
Dimensions:  (lat: 3, lon: 3, time: 1)
Coordinates:
  * lat      (lat) int64 24B 0 32 99
  * lon      (lon) int64 24B 3 20 75
  * time     (time) datetime64[ns] 8B 2021-01-01
Data variables:
    test     (lat, lon, time) float64 72B 0.3392 0.6619 0.7458 ... 0.5027 0.7492

xarray.Dataset

• Dimensions:
  • lat: 3
  • lon: 3
  • time: 1
• Coordinates: (3)
  • lat
    (lat)
    int64
    0 32 99

    array([ 0, 32, 99])

  • lon
    (lon)
    int64
    3 20 75

    array([ 3, 20, 75])

  • time
    (time)
    datetime64[ns]
    2021-01-01

    array(['2021-01-01T00:00:00.000000000'], dtype='datetime64[ns]')

• Data variables: (1)
  • test
    (lat, lon, time)
    float64
    0.3392 0.6619 ... 0.5027 0.7492

    array([[[0.33919176],
            [0.66186497],
            [0.74575776]],
    
           [[0.16406601],
            [0.55243292],
            [0.89205037]],
    
           [[0.80940951],
            [0.50271665],
            [0.74918222]]])

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([0, 32, 99], dtype='int64', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([3, 20, 75], dtype='int64', name='lon'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2021-01-01'], dtype='datetime64[ns]', name='time', freq='D'))

• Attributes: (0)

indexer = xr.Dataset(
    coords={
        "lon": ("queried_points", lon_coords),
        "lat": ("queried_points", lat_coords),
    },
)

indexer

<xarray.Dataset> Size: 48B
Dimensions:  (queried_points: 3)
Coordinates:
    lon      (queried_points) int64 24B 3 20 75
    lat      (queried_points) int64 24B 0 32 99
Dimensions without coordinates: queried_points
Data variables:
    *empty*

xarray.Dataset

• Dimensions:
  • queried_points: 3
• Coordinates: (2)
  • lon
    (queried_points)
    int64
    3 20 75

    array([ 3, 20, 75])

  • lat
    (queried_points)
    int64
    0 32 99

    array([ 0, 32, 99])

• Data variables: (0)
• Indexes: (0)
• Attributes: (0)

Pointwise indexing and value assignment

ds["test"].loc[dict(lon=indexer.lon, lat=indexer.lat)]

<xarray.DataArray 'test' (queried_points: 3, time: 1)> Size: 24B
array([[0.33919176],
       [0.55243292],
       [0.74918222]])
Coordinates:
    lat      (queried_points) int64 24B 0 32 99
    lon      (queried_points) int64 24B 3 20 75
  * time     (time) datetime64[ns] 8B 2021-01-01
Dimensions without coordinates: queried_points

xarray.DataArray

'test'

• queried_points: 3
• time: 1

• 0.3392 0.5524 0.7492

  array([[0.33919176],
         [0.55243292],
         [0.74918222]])

• Coordinates: (3)
  • lat
    (queried_points)
    int64
    0 32 99

    array([ 0, 32, 99])

  • lon
    (queried_points)
    int64
    3 20 75

    array([ 3, 20, 75])

  • time
    (time)
    datetime64[ns]
    2021-01-01

    array(['2021-01-01T00:00:00.000000000'], dtype='datetime64[ns]')

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2021-01-01'], dtype='datetime64[ns]', name='time', freq='D'))

• Attributes: (0)

ds["test"].loc[dict(lon=indexer.lon, lat=indexer.lat)] = 1

ds["test"].loc[dict(lon=indexer.lon, lat=indexer.lat)]

<xarray.DataArray 'test' (queried_points: 3, time: 1)> Size: 24B
array([[1.],
       [1.],
       [1.]])
Coordinates:
    lat      (queried_points) int64 24B 0 32 99
    lon      (queried_points) int64 24B 3 20 75
  * time     (time) datetime64[ns] 8B 2021-01-01
Dimensions without coordinates: queried_points

xarray.DataArray

'test'

• queried_points: 3
• time: 1

• 1.0 1.0 1.0

  array([[1.],
         [1.],
         [1.]])

• Coordinates: (3)
  • lat
    (queried_points)
    int64
    0 32 99

    array([ 0, 32, 99])

  • lon
    (queried_points)
    int64
    3 20 75

    array([ 3, 20, 75])

  • time
    (time)
    datetime64[ns]
    2021-01-01

    array(['2021-01-01T00:00:00.000000000'], dtype='datetime64[ns]')

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2021-01-01'], dtype='datetime64[ns]', name='time', freq='D'))

• Attributes: (0)