Labeled data enables expressive computations. These same labels can also be used to easily create informative plots.

xarray’s plotting capabilities are centered around xarray.DataArray objects. To plot xarray.Dataset objects simply access the relevant DataArrays, ie dset['var1']. Dataset specific plotting routines are also available (see Datasets). Here we focus mostly on arrays 2d or larger. If your data fits nicely into a pandas DataFrame then you’re better off using one of the more developed tools there.

xarray plotting functionality is a thin wrapper around the popular matplotlib library. Matplotlib syntax and function names were copied as much as possible, which makes for an easy transition between the two. Matplotlib must be installed before xarray can plot.

To use xarray’s plotting capabilities with time coordinates containing cftime.datetime objects nc-time-axis v1.2.0 or later needs to be installed.

For more extensive plotting applications consider the following projects:

  • Seaborn: “provides a high-level interface for drawing attractive statistical graphics.” Integrates well with pandas.

  • HoloViews and GeoViews: “Composable, declarative data structures for building even complex visualizations easily.” Includes native support for xarray objects.

  • hvplot: hvplot makes it very easy to produce dynamic plots (backed by Holoviews or Geoviews) by adding a hvplot accessor to DataArrays.

  • Cartopy: Provides cartographic tools.


The following imports are necessary for all of the examples.

In [1]: import numpy as np

In [2]: import pandas as pd

In [3]: import matplotlib.pyplot as plt

In [4]: import xarray as xr

For these examples we’ll use the North American air temperature dataset.

In [5]: airtemps = xr.tutorial.open_dataset('air_temperature')

In [6]: airtemps
Dimensions:  (lat: 25, lon: 53, time: 2920)
  * 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
Data variables:
    air      (time, lat, lon) float32 ...
    Conventions:  COARDS
    title:        4x daily NMC reanalysis (1948)
    description:  Data is from NMC initialized reanalysis\n(4x/day).  These a...
    platform:     Model

# Convert to celsius
In [7]: air = airtemps.air - 273.15

# copy attributes to get nice figure labels and change Kelvin to Celsius
In [8]: air.attrs = airtemps.air.attrs

In [9]: air.attrs['units'] = 'deg C'


Until GH1614 is solved, you might need to copy over the metadata in attrs to get informative figure labels (as was done above).


One Dimension

Simple Example

The simplest way to make a plot is to call the xarray.DataArray.plot() method.

In [10]: air1d = air.isel(lat=10, lon=10)

In [11]: air1d.plot()
Out[11]: [<matplotlib.lines.Line2D at 0x7f0ce562c320>]

xarray uses the coordinate name along with metadata attrs.long_name, attrs.standard_name, and attrs.units (if available) to label the axes. The names long_name, standard_name and units are copied from the CF-conventions spec. When choosing names, the order of precedence is long_name, standard_name and finally The y-axis label in the above plot was constructed from the long_name and units attributes of air1d.

In [12]: air1d.attrs
{'long_name': '4xDaily Air temperature at sigma level 995',
 'units': 'deg C',
 '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': array([185.16, 322.1 ], dtype=float32)}

Additional Arguments

Additional arguments are passed directly to the matplotlib function which does the work. For example, xarray.plot.line() calls matplotlib.pyplot.plot passing in the index and the array values as x and y, respectively. So to make a line plot with blue triangles a matplotlib format string can be used:

In [13]: air1d[:200].plot.line('b-^')
Out[13]: [<matplotlib.lines.Line2D at 0x7f0ce5d49390>]


Not all xarray plotting methods support passing positional arguments to the wrapped matplotlib functions, but they do all support keyword arguments.

Keyword arguments work the same way, and are more explicit.

In [14]: air1d[:200].plot.line(color='purple', marker='o')
Out[14]: [<matplotlib.lines.Line2D at 0x7f0ce43d8780>]

Adding to Existing Axis

To add the plot to an existing axis pass in the axis as a keyword argument ax. This works for all xarray plotting methods. In this example axes is an array consisting of the left and right axes created by plt.subplots.

In [15]: fig, axes = plt.subplots(ncols=2)

In [16]: axes
array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f0ce43fc1d0>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x7f0ce4396c88>], dtype=object)

In [17]: air1d.plot(ax=axes[0])
Out[17]: [<matplotlib.lines.Line2D at 0x7f0ce43803c8>]

In [18]: air1d.plot.hist(ax=axes[1])
(array([  9.,  38., 255., 584., 542., 489., 368., 258., 327.,  50.]),
 array([ 0.95 ,  2.719,  4.488, ..., 15.102, 16.871, 18.64 ], dtype=float32),
 <a list of 10 Patch objects>)

In [19]: plt.tight_layout()

In [20]: plt.draw()

On the right is a histogram created by xarray.plot.hist().

Controlling the figure size

You can pass a figsize argument to all xarray’s plotting methods to control the figure size. For convenience, xarray’s plotting methods also support the aspect and size arguments which control the size of the resulting image via the formula figsize = (aspect * size, size):

In [21]: air1d.plot(aspect=2, size=3)
Out[21]: [<matplotlib.lines.Line2D at 0x7f0ce4387898>]

In [22]: plt.tight_layout()

This feature also works with Faceting. For facet plots, size and aspect refer to a single panel (so that aspect * size gives the width of each facet in inches), while figsize refers to the entire figure (as for matplotlib’s figsize argument).


If figsize or size are used, a new figure is created, so this is mutually exclusive with the ax argument.


The convention used by xarray (figsize = (aspect * size, size)) is borrowed from seaborn: it is therefore not equivalent to matplotlib’s.

Multiple lines showing variation along a dimension

It is possible to make line plots of two-dimensional data by calling xarray.plot.line() with appropriate arguments. Consider the 3D variable air defined above. We can use line plots to check the variation of air temperature at three different latitudes along a longitude line:

In [23]: air.isel(lon=10, lat=[19,21,22]).plot.line(x='time')
[<matplotlib.lines.Line2D at 0x7f0ce431f5f8>,
 <matplotlib.lines.Line2D at 0x7f0ce432e748>,
 <matplotlib.lines.Line2D at 0x7f0ce432e8d0>]

It is required to explicitly specify either

  1. x: the dimension to be used for the x-axis, or

  2. hue: the dimension you want to represent by multiple lines.

Thus, we could have made the previous plot by specifying hue='lat' instead of x='time'. If required, the automatic legend can be turned off using add_legend=False. Alternatively, hue can be passed directly to xarray.plot() as air.isel(lon=10, lat=[19,21,22]).plot(hue=’lat’).

Dimension along y-axis

It is also possible to make line plots such that the data are on the x-axis and a dimension is on the y-axis. This can be done by specifying the appropriate y keyword argument.

In [24]: air.isel(time=10, lon=[10, 11]).plot(y='lat', hue='lon')
[<matplotlib.lines.Line2D at 0x7f0ce42f3c18>,
 <matplotlib.lines.Line2D at 0x7f0ce42f3c50>]

Step plots

As an alternative, also a step plot similar to matplotlib’s plt.step can be made using 1D data.

In [25]: air1d[:20].plot.step(where='mid')
Out[25]: [<matplotlib.lines.Line2D at 0x7f0ce432eb00>]

The argument where defines where the steps should be placed, options are 'pre' (default), 'post', and 'mid'. This is particularly handy when plotting data grouped with xarray.Dataset.groupby_bins().

In [26]: air_grp = air.mean(['time','lon']).groupby_bins('lat',[0,23.5,66.5,90])

In [27]: air_mean = air_grp.mean()

In [28]: air_std = air_grp.std()

In [29]: air_mean.plot.step()
Out[29]: [<matplotlib.lines.Line2D at 0x7f0ce4251400>]

In [30]: (air_mean + air_std).plot.step(ls=':')
Out[30]: [<matplotlib.lines.Line2D at 0x7f0ce4251a58>]

In [31]: (air_mean - air_std).plot.step(ls=':')
Out[31]: [<matplotlib.lines.Line2D at 0x7f0ce4251ac8>]

In [32]: plt.ylim(-20,30)
Out[32]: (-20, 30)

In [33]: plt.title('Zonal mean temperature')
Out[33]: Text(0.5,1,'Zonal mean temperature')

In this case, the actual boundaries of the bins are used and the where argument is ignored.

Other axes kwargs

The keyword arguments xincrease and yincrease let you control the axes direction.

In [34]: air.isel(time=10, lon=[10, 11]).plot.line(y='lat', hue='lon', xincrease=False, yincrease=False)
[<matplotlib.lines.Line2D at 0x7f0ce4210be0>,
 <matplotlib.lines.Line2D at 0x7f0ce4210c18>]

In addition, one can use xscale, yscale to set axes scaling; xticks, yticks to set axes ticks and xlim, ylim to set axes limits. These accept the same values as the matplotlib methods Axes.set_(x,y)scale(), Axes.set_(x,y)ticks(), Axes.set_(x,y)lim() respectively.

Two Dimensions

Simple Example

The default method xarray.DataArray.plot() calls xarray.plot.pcolormesh() by default when the data is two-dimensional.

In [35]: air2d = air.isel(time=500)

In [36]: air2d.plot()
Out[36]: <matplotlib.collections.QuadMesh at 0x7f0ce5da1438>

All 2d plots in xarray allow the use of the keyword arguments yincrease and xincrease.

In [37]: air2d.plot(yincrease=False)
Out[37]: <matplotlib.collections.QuadMesh at 0x7f0ce586c860>


We use xarray.plot.pcolormesh() as the default two-dimensional plot method because it is more flexible than xarray.plot.imshow(). However, for large arrays, imshow can be much faster than pcolormesh. If speed is important to you and you are plotting a regular mesh, consider using imshow.

Missing Values

xarray plots data with Missing values.

In [38]: bad_air2d = air2d.copy()

In [39]: bad_air2d[dict(lat=slice(0, 10), lon=slice(0, 25))] = np.nan

In [40]: bad_air2d.plot()
Out[40]: <matplotlib.collections.QuadMesh at 0x7f0ce50e1080>

Nonuniform Coordinates

It’s not necessary for the coordinates to be evenly spaced. Both xarray.plot.pcolormesh() (default) and xarray.plot.contourf() can produce plots with nonuniform coordinates.

In [41]: b = air2d.copy()

# Apply a nonlinear transformation to one of the coords
In [42]: b.coords['lat'] = np.log(b.coords['lat'])

In [43]: b.plot()
Out[43]: <matplotlib.collections.QuadMesh at 0x7f0ce50ef8d0>

Calling Matplotlib

Since this is a thin wrapper around matplotlib, all the functionality of matplotlib is available.

In [44]: air2d.plot(
Out[44]: <matplotlib.collections.QuadMesh at 0x7f0ce41769e8>

In [45]: plt.title('These colors prove North America\nhas fallen in the ocean')
Out[45]: Text(0.5,1,'These colors prove North America\nhas fallen in the ocean')

In [46]: plt.ylabel('latitude')
Out[46]: Text(0,0.5,'latitude')

In [47]: plt.xlabel('longitude')
Out[47]: Text(0.5,0,'longitude')

In [48]: plt.tight_layout()

In [49]: plt.draw()


xarray methods update label information and generally play around with the axes. So any kind of updates to the plot should be done after the call to the xarray’s plot. In the example below, plt.xlabel effectively does nothing, since d_ylog.plot() updates the xlabel.

In [50]: plt.xlabel('Never gonna see this.')
Out[50]: Text(0.5,0,'Never gonna see this.')

In [51]: air2d.plot()
Out[51]: <matplotlib.collections.QuadMesh at 0x7f0ce40de0f0>

In [52]: plt.draw()


xarray borrows logic from Seaborn to infer what kind of color map to use. For example, consider the original data in Kelvins rather than Celsius:

In [53]: airtemps.air.isel(time=0).plot()
Out[53]: <matplotlib.collections.QuadMesh at 0x7f0ce4046208>

The Celsius data contain 0, so a diverging color map was used. The Kelvins do not have 0, so the default color map was used.


Outliers often have an extreme effect on the output of the plot. Here we add two bad data points. This affects the color scale, washing out the plot.

In [54]: air_outliers = airtemps.air.isel(time=0).copy()

In [55]: air_outliers[0, 0] = 100

In [56]: air_outliers[-1, -1] = 400

In [57]: air_outliers.plot()
Out[57]: <matplotlib.collections.QuadMesh at 0x7f0ce4025748>

This plot shows that we have outliers. The easy way to visualize the data without the outliers is to pass the parameter robust=True. This will use the 2nd and 98th percentiles of the data to compute the color limits.

In [58]: air_outliers.plot(robust=True)
Out[58]: <matplotlib.collections.QuadMesh at 0x7f0ce3f99630>

Observe that the ranges of the color bar have changed. The arrows on the color bar indicate that the colors include data points outside the bounds.

Discrete Colormaps

It is often useful, when visualizing 2d data, to use a discrete colormap, rather than the default continuous colormaps that matplotlib uses. The levels keyword argument can be used to generate plots with discrete colormaps. For example, to make a plot with 8 discrete color intervals:

In [59]: air2d.plot(levels=8)
Out[59]: <matplotlib.collections.QuadMesh at 0x7f0ce3f76eb8>

It is also possible to use a list of levels to specify the boundaries of the discrete colormap:

In [60]: air2d.plot(levels=[0, 12, 18, 30])
Out[60]: <matplotlib.collections.QuadMesh at 0x7f0ce40376a0>

You can also specify a list of discrete colors through the colors argument:

In [61]: flatui = ["#9b59b6", "#3498db", "#95a5a6", "#e74c3c", "#34495e", "#2ecc71"]

In [62]: air2d.plot(levels=[0, 12, 18, 30], colors=flatui)
Out[62]: <matplotlib.collections.QuadMesh at 0x7f0ce3e4fef0>

Finally, if you have Seaborn installed, you can also specify a seaborn color palette to the cmap argument. Note that levels must be specified with seaborn color palettes if using imshow or pcolormesh (but not with contour or contourf, since levels are chosen automatically).

In [63]: air2d.plot(levels=10, cmap='husl')
Out[63]: <matplotlib.collections.QuadMesh at 0x7f0ce3e33e48>

In [64]: plt.draw()


Faceting here refers to splitting an array along one or two dimensions and plotting each group. xarray’s basic plotting is useful for plotting two dimensional arrays. What about three or four dimensional arrays? That’s where facets become helpful.

Consider the temperature data set. There are 4 observations per day for two years which makes for 2920 values along the time dimension. One way to visualize this data is to make a separate plot for each time period.

The faceted dimension should not have too many values; faceting on the time dimension will produce 2920 plots. That’s too much to be helpful. To handle this situation try performing an operation that reduces the size of the data in some way. For example, we could compute the average air temperature for each month and reduce the size of this dimension from 2920 -> 12. A simpler way is to just take a slice on that dimension. So let’s use a slice to pick 6 times throughout the first year.

In [65]: t = air.isel(time=slice(0, 365 * 4, 250))

In [66]: t.coords
  * 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 ... 2013-11-09T12:00:00

Simple Example

The easiest way to create faceted plots is to pass in row or col arguments to the xarray plotting methods/functions. This returns a xarray.plot.FacetGrid object.

In [67]: g_simple = t.plot(x='lon', y='lat', col='time', col_wrap=3)

Faceting also works for line plots.

In [68]: g_simple_line = t.isel(lat=slice(0,None,4)).plot(x='lon', hue='lat', col='time', col_wrap=3)

4 dimensional

For 4 dimensional arrays we can use the rows and columns of the grids. Here we create a 4 dimensional array by taking the original data and adding a fixed amount. Now we can see how the temperature maps would compare if one were much hotter.

In [69]: t2 = t.isel(time=slice(0, 2))

In [70]: t4d = xr.concat([t2, t2 + 40], pd.Index(['normal', 'hot'], name='fourth_dim'))

# This is a 4d array
In [71]: t4d.coords
  * lon         (lon) float32 200.0 202.5 205.0 207.5 ... 325.0 327.5 330.0
  * time        (time) datetime64[ns] 2013-01-01 2013-03-04T12:00:00
  * lat         (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 22.5 20.0 17.5 15.0
  * fourth_dim  (fourth_dim) object 'normal' 'hot'

In [72]: t4d.plot(x='lon', y='lat', col='time', row='fourth_dim')
Out[72]: <xarray.plot.facetgrid.FacetGrid at 0x7f0ce3b0ecf8>

Other features

Faceted plotting supports other arguments common to xarray 2d plots.

In [73]: hasoutliers = t.isel(time=slice(0, 5)).copy()

In [74]: hasoutliers[0, 0, 0] = -100

In [75]: hasoutliers[-1, -1, -1] = 400

In [76]: g = hasoutliers.plot.pcolormesh('lon', 'lat', col='time', col_wrap=3,
   ....:                                 robust=True, cmap='viridis',
   ....:                                 cbar_kwargs={'label': 'this has outliers'})

FacetGrid Objects

xarray.plot.FacetGrid is used to control the behavior of the multiple plots. It borrows an API and code from Seaborn’s FacetGrid. The structure is contained within the axes and name_dicts attributes, both 2d Numpy object arrays.

In [77]: g.axes
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f0ce39c9278>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0ce3987f98>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0ce3a6bba8>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f0ce3960dd8>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0ce3904e48>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f0ce392de48>]], dtype=object)

In [78]: g.name_dicts
array([[{'time': numpy.datetime64('2013-01-01T00:00:00.000000000')},
        {'time': numpy.datetime64('2013-03-04T12:00:00.000000000')},
        {'time': numpy.datetime64('2013-05-06T00:00:00.000000000')}],
       [{'time': numpy.datetime64('2013-07-07T12:00:00.000000000')},
        {'time': numpy.datetime64('2013-09-08T00:00:00.000000000')}, None]], dtype=object)

It’s possible to select the xarray.DataArray or xarray.Dataset corresponding to the FacetGrid through the name_dicts.

In [79]:[g.name_dicts[0, 0]]
<xarray.DataArray 'air' (lat: 25, lon: 53)>
array([[-100.  ,  -30.65,  -29.65, ...,  -40.35,  -37.65,  -34.55],
       [ -29.35,  -28.65,  -28.45, ...,  -40.35,  -37.85,  -33.85],
       [ -23.15,  -23.35,  -24.26, ...,  -39.95,  -36.76,  -31.45],
       [  23.45,   23.05,   23.25, ...,   22.25,   21.95,   21.55],
       [  22.75,   23.05,   23.64, ...,   22.75,   22.75,   22.05],
       [  23.14,   23.64,   23.95, ...,   23.75,   23.64,   23.45]], dtype=float32)
  * 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     datetime64[ns] 2013-01-01
    long_name:     4xDaily Air temperature at sigma level 995
    units:         deg C
    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 ]

Here is an example of using the lower level API and then modifying the axes after they have been plotted.

In [80]: g = t.plot.imshow('lon', 'lat', col='time', col_wrap=3, robust=True)

In [81]: for i, ax in enumerate(g.axes.flat):
   ....:     ax.set_title('Air Temperature %d' % i)

In [82]: bottomright = g.axes[-1, -1]

In [83]: bottomright.annotate('bottom right', (240, 40))
Out[83]: Text(240,40,'bottom right')

In [84]: plt.draw()

TODO: add an example of using the map method to plot dataset variables (e.g., with plt.quiver).


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

In [85]: ds = xr.tutorial.scatter_example_dataset()

In [86]: ds
Dimensions:  (w: 4, x: 3, y: 11, z: 4)
  * x        (x) int64 0 1 2
  * y        (y) float64 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 0 1 2 3
  * w        (w) <U5 'one' 'two' 'three' 'five'
Data variables:
    A        (x, y, z, w) float64 -0.104 0.02719 -0.0425 ... -0.1175 -0.0183
    B        (x, y, z, w) float64 0.0 0.0 0.0 0.0 ... 1.369 1.408 1.387 1.417

Suppose we want to scatter A against B

In [87]: ds.plot.scatter(x='A', y='B')
Out[87]: <matplotlib.collections.PathCollection at 0x7f0ce3786da0>

The hue kwarg lets you vary the color by variable value

In [88]: ds.plot.scatter(x='A', y='B', hue='w')
[<matplotlib.collections.PathCollection at 0x7f0ce35d1d68>,
 <matplotlib.collections.PathCollection at 0x7f0ce35d1c50>,
 <matplotlib.collections.PathCollection at 0x7f0ce35e1780>,
 <matplotlib.collections.PathCollection at 0x7f0ce35d1c18>]

When hue is specified, a colorbar is added for numeric hue DataArrays by default and a legend is added for non-numeric hue DataArrays (as above). You can force a legend instead of a colorbar by setting hue_style='discrete'. Additionally, the boolean kwarg add_guide can be used to prevent the display of a legend or colorbar (as appropriate).

In [89]: ds.w.values = [1, 2, 3, 5]

In [90]: ds.plot.scatter(x='A', y='B', hue='w', hue_style='discrete')
[<matplotlib.collections.PathCollection at 0x7f0ce35a1cc0>,
 <matplotlib.collections.PathCollection at 0x7f0ce35af128>,
 <matplotlib.collections.PathCollection at 0x7f0ce35b6e48>,
 <matplotlib.collections.PathCollection at 0x7f0ce35af080>]

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.

In [91]: ds.plot.scatter(x='A', y='B', hue='z', hue_style='discrete', markersize='z')
[<matplotlib.collections.PathCollection at 0x7f0ce3566ba8>,
 <matplotlib.collections.PathCollection at 0x7f0ce3574668>,
 <matplotlib.collections.PathCollection at 0x7f0ce34fee48>,
 <matplotlib.collections.PathCollection at 0x7f0ce3501780>]

Faceting is also possible

In [92]: ds.plot.scatter(x='A', y='B', col='x', row='z', hue='w', hue_style='discrete')
Out[92]: <xarray.plot.facetgrid.FacetGrid at 0x7f0ce3586da0>

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


To follow this section you’ll need to have Cartopy installed and working.

This script will plot the air temperature on a map.

In [93]: import as ccrs

In [94]: air = xr.tutorial.open_dataset('air_temperature').air

In [95]: ax = plt.axes(projection=ccrs.Orthographic(-80, 35))

In [96]: air.isel(time=0).plot.contourf(ax=ax, transform=ccrs.PlateCarree());

In [97]: ax.set_global(); ax.coastlines();

When faceting on maps, the projection can be transferred to the plot function using the subplot_kws keyword. The axes for the subplots created by faceting are accessible in the object returned by plot:

In [98]: p = air.isel(time=[0, 4]).plot(transform=ccrs.PlateCarree(), col='time',
   ....:                                subplot_kws={'projection': ccrs.Orthographic(-80, 35)})

In [99]: for ax in p.axes.flat:
   ....:     ax.coastlines()
   ....:     ax.gridlines()

In [100]: plt.draw();


Ways to Use

There are three ways to use the xarray plotting functionality:

  1. Use plot as a convenience method for a DataArray.

  2. Access a specific plotting method from the plot attribute of a DataArray.

  3. Directly from the xarray plot submodule.

These are provided for user convenience; they all call the same code.

In [101]: import xarray.plot as xplt

In [102]: da = xr.DataArray(range(5))

In [103]: fig, axes = plt.subplots(ncols=2, nrows=2)

In [104]: da.plot(ax=axes[0, 0])
Out[104]: [<matplotlib.lines.Line2D at 0x7f0ce3738630>]

In [105]: da.plot.line(ax=axes[0, 1])
Out[105]: [<matplotlib.lines.Line2D at 0x7f0ce36c0128>]

In [106]: xplt.plot(da, ax=axes[1, 0])
Out[106]: [<matplotlib.lines.Line2D at 0x7f0ce32a7240>]

In [107]: xplt.line(da, ax=axes[1, 1])
Out[107]: [<matplotlib.lines.Line2D at 0x7f0ce35af5c0>]

In [108]: plt.tight_layout()

In [109]: plt.draw()

Here the output is the same. Since the data is 1 dimensional the line plot was used.

The convenience method xarray.DataArray.plot() dispatches to an appropriate plotting function based on the dimensions of the DataArray and whether the coordinates are sorted and uniformly spaced. This table describes what gets plotted:


Plotting function





Anything else



If you’d like to find out what’s really going on in the coordinate system, read on.

In [110]: a0 = xr.DataArray(np.zeros((4, 3, 2)), dims=('y', 'x', 'z'),
   .....:                   name='temperature')

In [111]: a0[0, 0, 0] = 1

In [112]: a = a0.isel(z=0)

In [113]: a
<xarray.DataArray 'temperature' (y: 4, x: 3)>
array([[1., 0., 0.],
       [0., 0., 0.],
       [0., 0., 0.],
       [0., 0., 0.]])
Dimensions without coordinates: y, x

The plot will produce an image corresponding to the values of the array. Hence the top left pixel will be a different color than the others. Before reading on, you may want to look at the coordinates and think carefully about what the limits, labels, and orientation for each of the axes should be.

In [114]: a.plot()
Out[114]: <matplotlib.collections.QuadMesh at 0x7f0ce380dfd0>

It may seem strange that the values on the y axis are decreasing with -0.5 on the top. This is because the pixels are centered over their coordinates, and the axis labels and ranges correspond to the values of the coordinates.

Multidimensional coordinates

See also: Working with Multidimensional Coordinates.

You can plot irregular grids defined by multidimensional coordinates with xarray, but you’ll have to tell the plot function to use these coordinates instead of the default ones:

In [115]: lon, lat = np.meshgrid(np.linspace(-20, 20, 5), np.linspace(0, 30, 4))

In [116]: lon += lat/10

In [117]: lat += lon/10

In [118]: da = xr.DataArray(np.arange(20).reshape(4, 5), dims=['y', 'x'],
   .....:                   coords = {'lat': (('y', 'x'), lat),
   .....:                             'lon': (('y', 'x'), lon)})

In [119]: da.plot.pcolormesh('lon', 'lat');

Note that in this case, xarray still follows the pixel centered convention. This might be undesirable in some cases, for example when your data is defined on a polar projection (GH781). This is why the default is to not follow this convention when plotting on a map:

In [120]: import as ccrs

In [121]: ax = plt.subplot(projection=ccrs.PlateCarree());

In [122]: da.plot.pcolormesh('lon', 'lat', ax=ax);

In [123]: ax.scatter(lon, lat, transform=ccrs.PlateCarree());

In [124]: ax.coastlines(); ax.gridlines(draw_labels=True);

You can however decide to infer the cell boundaries and use the infer_intervals keyword:

In [125]: ax = plt.subplot(projection=ccrs.PlateCarree());

In [126]: da.plot.pcolormesh('lon', 'lat', ax=ax, infer_intervals=True);

In [127]: ax.scatter(lon, lat, transform=ccrs.PlateCarree());

In [128]: ax.coastlines(); ax.gridlines(draw_labels=True);


The data model of xarray does not support datasets with cell boundaries yet. If you want to use these coordinates, you’ll have to make the plots outside the xarray framework.

One can also make line plots with multidimensional coordinates. In this case, hue must be a dimension name, not a coordinate name.

In [129]: f, ax = plt.subplots(2, 1)

In [130]: da.plot.line(x='lon', hue='y', ax=ax[0]);

In [131]: da.plot.line(x='lon', hue='x', ax=ax[1]);