Dataset Plotting

Xarray has limited support for plotting Dataset variables against each other. Consider this dataset

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import pandas as pd
import xarray as xr
%matplotlib inline

# Load air temperature dataset (needed for complete context)
airtemps = xr.tutorial.open_dataset("air_temperature")

# Convert to celsius
air = airtemps.air - 273.15

# copy attributes to get nice figure labels and change Kelvin to Celsius
air.attrs = airtemps.air.attrs
air.attrs["units"] = "deg C"

ds = xr.tutorial.scatter_example_dataset(seed=42)
ds

<xarray.Dataset> Size: 9kB
Dimensions:  (x: 3, y: 11, z: 4, w: 4)
Coordinates:
  * x        (x) int64 24B 0 1 2
  * y        (y) float64 88B 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
  * z        (z) int64 32B 0 1 2 3
  * w        (w) <U5 80B 'one' 'two' 'three' 'five'
Data variables:
    A        (x, y, z, w) float64 4kB 0.03047 -0.104 ... 4.512e-05 0.01906
    B        (x, y, z, w) float64 4kB 0.0 0.0 0.0 0.0 ... 1.369 1.423 1.428

xarray.Dataset

• Dimensions:
  • x: 3
  • y: 11
  • z: 4
  • w: 4
• Coordinates: (4)
  • x
    (x)
    int64
    0 1 2

    units :

    xunits

    array([0, 1, 2])

  • y
    (y)
    float64
    0.0 0.1 0.2 0.3 ... 0.7 0.8 0.9 1.0

    units :

    yunits

    array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ])

  • z
    (z)
    int64
    0 1 2 3

    units :

    zunits

    array([0, 1, 2, 3])

  • w
    (w)
    <U5
    'one' 'two' 'three' 'five'

    units :

    wunits

    array(['one', 'two', 'three', 'five'], dtype='<U5')

• Data variables: (2)
  • A
    (x, y, z, w)
    float64
    0.03047 -0.104 ... 0.01906

    units :

    Aunits

    array([[[[ 3.04717080e-02, -1.03998411e-01,  7.50451196e-02,
               9.40564716e-02],
             [ 3.04717080e-02, -1.03998411e-01,  7.50451196e-02,
               9.40564716e-02],
             [ 3.04717080e-02, -1.03998411e-01,  7.50451196e-02,
               9.40564716e-02],
             [ 3.04717080e-02, -1.03998411e-01,  7.50451196e-02,
               9.40564716e-02]],
    
            [[ 5.04717080e-02, -8.39984106e-02,  9.50451196e-02,
               1.14056472e-01],
             [ 4.71383746e-02, -8.73317440e-02,  9.17117862e-02,
               1.10723138e-01],
             [ 4.47574223e-02, -8.97126963e-02,  8.93308339e-02,
               1.08342186e-01],
             [ 4.29717080e-02, -9.14984106e-02,  8.75451196e-02,
               1.06556472e-01]],
    
            [[ 7.04717080e-02, -6.39984106e-02,  1.15045120e-01,
               1.34056472e-01],
    ...
             [-6.95282920e-02, -2.03998411e-01, -2.49548804e-02,
              -5.94352836e-03]],
    
            [[ 1.04717080e-02, -1.23998411e-01,  5.50451196e-02,
               7.40564716e-02],
             [-1.95282920e-02, -1.53998411e-01,  2.50451196e-02,
               4.40564716e-02],
             [-4.09568635e-02, -1.75426982e-01,  3.61654815e-03,
               2.26279002e-02],
             [-5.70282920e-02, -1.91498411e-01, -1.24548804e-02,
               6.55647164e-03]],
    
            [[ 3.04717080e-02, -1.03998411e-01,  7.50451196e-02,
               9.40564716e-02],
             [-2.86162536e-03, -1.37331744e-01,  4.17117862e-02,
               6.07231383e-02],
             [-2.66711492e-02, -1.61141268e-01,  1.79022624e-02,
               3.69136145e-02],
             [-4.45282920e-02, -1.78998411e-01,  4.51195806e-05,
               1.90564716e-02]]]])

  • B
    (x, y, z, w)
    float64
    0.0 0.0 0.0 ... 1.369 1.423 1.428

    units :

    Bunits

    array([[[[ 0.        ,  0.        ,  0.        ,  0.        ],
             [ 0.00304717, -0.01039984,  0.00750451,  0.00940565],
             [ 0.00609434, -0.02079968,  0.01500902,  0.01881129],
             [ 0.00914151, -0.03119952,  0.02251354,  0.02821694]],
    
            [[ 0.00316228,  0.00316228,  0.00316228,  0.00316228],
             [ 0.00620945, -0.00723756,  0.01066679,  0.01256792],
             [ 0.00925662, -0.0176374 ,  0.0181713 ,  0.02197357],
             [ 0.01230379, -0.02803725,  0.02567581,  0.03137922]],
    
            [[ 0.01788854,  0.01788854,  0.01788854,  0.01788854],
             [ 0.02093571,  0.0074887 ,  0.02539306,  0.02729419],
             [ 0.02398289, -0.00291114,  0.03289757,  0.03669984],
             [ 0.02703006, -0.01331098,  0.04040208,  0.04610549]],
    
            [[ 0.04929503,  0.04929503,  0.04929503,  0.04929503],
             [ 0.0523422 ,  0.03889519,  0.05679954,  0.05870068],
             [ 0.05538937,  0.02849535,  0.06430405,  0.06810632],
             [ 0.05843654,  0.01809551,  0.07180857,  0.07751197]],
    
    ...
    
            [[ 0.80996341,  0.80996341,  0.80996341,  0.80996341],
             [ 0.81301058,  0.79956357,  0.81746792,  0.81936906],
             [ 0.81605775,  0.78916373,  0.82497244,  0.82877471],
             [ 0.81910493,  0.77876389,  0.83247695,  0.83818035]],
    
            [[ 0.9724334 ,  0.9724334 ,  0.9724334 ,  0.9724334 ],
             [ 0.97548057,  0.96203356,  0.97993791,  0.98183905],
             [ 0.97852774,  0.95163372,  0.98744243,  0.9912447 ],
             [ 0.98157491,  0.94123388,  0.99494694,  1.00065034]],
    
            [[ 1.16843347,  1.16843347,  1.16843347,  1.16843347],
             [ 1.17148064,  1.15803363,  1.17593798,  1.17783912],
             [ 1.17452781,  1.14763379,  1.1834425 ,  1.18724477],
             [ 1.17757498,  1.13723395,  1.19094701,  1.19665041]],
    
            [[ 1.4       ,  1.4       ,  1.4       ,  1.4       ],
             [ 1.40304717,  1.38960016,  1.40750451,  1.40940565],
             [ 1.40609434,  1.37920032,  1.41500902,  1.41881129],
             [ 1.40914151,  1.36880048,  1.42251354,  1.42821694]]]])

• Indexes: (4)
  • x
    PandasIndex

    PandasIndex(Index([0, 1, 2], dtype='int64', name='x'))

  • y
    PandasIndex

    PandasIndex(Index([                0.0,                 0.1,                 0.2,
           0.30000000000000004,                 0.4,                 0.5,
            0.6000000000000001,  0.7000000000000001,                 0.8,
                           0.9,                 1.0],
          dtype='float64', name='y'))

  • z
    PandasIndex

    PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='z'))

  • w
    PandasIndex

    PandasIndex(Index(['one', 'two', 'three', 'five'], dtype='object', name='w'))

• Attributes: (0)

Scatter

Let’s plot the A DataArray as a function of the y coord

with xr.set_options(display_expand_data=False):
    display(ds.A)

<xarray.DataArray 'A' (x: 3, y: 11, z: 4, w: 4)> Size: 4kB
0.03047 -0.104 0.07505 0.09406 0.03047 ... -0.04453 -0.179 4.512e-05 0.01906
Coordinates:
  * x        (x) int64 24B 0 1 2
  * y        (y) float64 88B 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
  * z        (z) int64 32B 0 1 2 3
  * w        (w) <U5 80B 'one' 'two' 'three' 'five'
Attributes:
    units:    Aunits

xarray.DataArray

'A'

• x: 3
• y: 11
• z: 4
• w: 4

• 0.03047 -0.104 0.07505 0.09406 ... -0.04453 -0.179 4.512e-05 0.01906

  array([[[[ 3.04717080e-02, -1.03998411e-01,  7.50451196e-02,
             9.40564716e-02],
           [ 3.04717080e-02, -1.03998411e-01,  7.50451196e-02,
             9.40564716e-02],
           [ 3.04717080e-02, -1.03998411e-01,  7.50451196e-02,
             9.40564716e-02],
           [ 3.04717080e-02, -1.03998411e-01,  7.50451196e-02,
             9.40564716e-02]],
  
          [[ 5.04717080e-02, -8.39984106e-02,  9.50451196e-02,
             1.14056472e-01],
           [ 4.71383746e-02, -8.73317440e-02,  9.17117862e-02,
             1.10723138e-01],
           [ 4.47574223e-02, -8.97126963e-02,  8.93308339e-02,
             1.08342186e-01],
           [ 4.29717080e-02, -9.14984106e-02,  8.75451196e-02,
             1.06556472e-01]],
  
          [[ 7.04717080e-02, -6.39984106e-02,  1.15045120e-01,
             1.34056472e-01],
  ...
           [-6.95282920e-02, -2.03998411e-01, -2.49548804e-02,
            -5.94352836e-03]],
  
          [[ 1.04717080e-02, -1.23998411e-01,  5.50451196e-02,
             7.40564716e-02],
           [-1.95282920e-02, -1.53998411e-01,  2.50451196e-02,
             4.40564716e-02],
           [-4.09568635e-02, -1.75426982e-01,  3.61654815e-03,
             2.26279002e-02],
           [-5.70282920e-02, -1.91498411e-01, -1.24548804e-02,
             6.55647164e-03]],
  
          [[ 3.04717080e-02, -1.03998411e-01,  7.50451196e-02,
             9.40564716e-02],
           [-2.86162536e-03, -1.37331744e-01,  4.17117862e-02,
             6.07231383e-02],
           [-2.66711492e-02, -1.61141268e-01,  1.79022624e-02,
             3.69136145e-02],
           [-4.45282920e-02, -1.78998411e-01,  4.51195806e-05,
             1.90564716e-02]]]])

• Coordinates: (4)
  • x
    (x)
    int64
    0 1 2

    units :

    xunits

    array([0, 1, 2])

  • y
    (y)
    float64
    0.0 0.1 0.2 0.3 ... 0.7 0.8 0.9 1.0

    units :

    yunits

    array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ])

  • z
    (z)
    int64
    0 1 2 3

    units :

    zunits

    array([0, 1, 2, 3])

  • w
    (w)
    <U5
    'one' 'two' 'three' 'five'

    units :

    wunits

    array(['one', 'two', 'three', 'five'], dtype='<U5')

• Indexes: (4)
  • x
    PandasIndex

    PandasIndex(Index([0, 1, 2], dtype='int64', name='x'))

  • y
    PandasIndex

    PandasIndex(Index([                0.0,                 0.1,                 0.2,
           0.30000000000000004,                 0.4,                 0.5,
            0.6000000000000001,  0.7000000000000001,                 0.8,
                           0.9,                 1.0],
          dtype='float64', name='y'))

  • z
    PandasIndex

    PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='z'))

  • w
    PandasIndex

    PandasIndex(Index(['one', 'two', 'three', 'five'], dtype='object', name='w'))

• Attributes: (1)

  units :

  Aunits

ds.A.plot.scatter(x="y");

Same plot can be displayed using the dataset:

ds.plot.scatter(x="y", y="A");

Now suppose we want to scatter the A DataArray against the B DataArray

ds.plot.scatter(x="A", y="B");

The hue kwarg lets you vary the color by variable value

ds.plot.scatter(x="A", y="B", hue="w");

You can force a legend instead of a colorbar by setting add_legend=True, add_colorbar=False.

ds.plot.scatter(x="A", y="B", hue="w", add_legend=True, add_colorbar=False);

ds.plot.scatter(x="A", y="B", hue="w", add_legend=False, add_colorbar=True);

The markersize kwarg lets you vary the point’s size by variable value. You can additionally pass size_norm to control how the variable’s values are mapped to point sizes.

ds.plot.scatter(x="A", y="B", hue="y", markersize="z");

The z kwarg lets you plot the data along the z-axis as well.

ds.plot.scatter(x="A", y="B", z="z", hue="y", markersize="x");

Faceting is also possible

ds.plot.scatter(x="A", y="B", hue="y", markersize="x", row="x", col="w");

And adding the z-axis

ds.plot.scatter(x="A", y="B", z="z", hue="y", markersize="x", row="x", col="w");

For more advanced scatter plots, we recommend converting the relevant data variables to a pandas DataFrame and using the extensive plotting capabilities of seaborn.

Quiver

Visualizing vector fields is supported with quiver plots:

ds.isel(w=1, z=1).plot.quiver(x="x", y="y", u="A", v="B");

where u and v denote the x and y direction components of the arrow vectors. Again, faceting is also possible:

ds.plot.quiver(x="x", y="y", u="A", v="B", col="w", row="z", scale=4);

scale is required for faceted quiver plots. The scale determines the number of data units per arrow length unit, i.e. a smaller scale parameter makes the arrow longer.

Streamplot

Visualizing vector fields is also supported with streamline plots:

ds.isel(w=1, z=1).plot.streamplot(x="x", y="y", u="A", v="B");

where u and v denote the x and y direction components of the vectors tangent to the streamlines. Again, faceting is also possible:

ds.plot.streamplot(x="x", y="y", u="A", v="B", col="w", row="z");