Labeled data enables expressive computations. These same labels can also be used to easily create informative plots.
xarray's plotting capabilities are centered around :pyxarray.DataArray objects. To plot :pyxarray.Dataset objects simply access the relevant DataArrays, ie dset['var1']. 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:
hvplotmakes it very easy to produce dynamic plots (backed byHoloviewsorGeoviews) by adding ahvplotaccessor to DataArrays. - Cartopy: Provides cartographic tools.
python
# Use defaults so we don't get gridlines in generated docs import matplotlib as mpl mpl.rcdefaults()
The following imports are necessary for all of the examples.
python
import numpy as np import pandas as pd import matplotlib.pyplot as plt import xarray as xr
For these examples we'll use the North American air temperature dataset.
python
airtemps = xr.tutorial.open_dataset('air_temperature') airtemps
# 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'
Note
Until 1614 is solved, you might need to copy over the metadata in attrs to get informative figure labels (as was done above).
The simplest way to make a plot is to call the :pyxarray.DataArray.plot() method.
python
air1d = air.isel(lat=10, lon=10)
@savefig plotting_1d_simple.png width=4in air1d.plot()
xarray uses the coordinate name along with metadata attrs.long_name, attrs.standard_name, DataArray.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 DataArray.name. The y-axis label in the above plot was constructed from the long_name and units attributes of air1d.
python
air1d.attrs
Additional arguments are passed directly to the matplotlib function which does the work. For example, :pyxarray.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:
python
@savefig plotting_1d_additional_args.png width=4in air1d[:200].plot.line('b-^')
Note
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.
python
@savefig plotting_example_sin3.png width=4in air1d[:200].plot.line(color='purple', marker='o')
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.
python
fig, axes = plt.subplots(ncols=2)
axes
air1d.plot(ax=axes[0]) air1d.plot.hist(ax=axes[1])
plt.tight_layout()
@savefig plotting_example_existing_axes.png width=6in plt.draw()
On the right is a histogram created by :pyxarray.plot.hist.
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):
python
air1d.plot(aspect=2, size=3) @savefig plotting_example_size_and_aspect.png plt.tight_layout()
python
# create a dummy figure so sphinx plots everything below normally plt.figure()
This feature also works with plotting.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).
Note
If figsize or size are used, a new figure is created, so this is mutually exclusive with the ax argument.
Note
The convention used by xarray (figsize = (aspect * size, size)) is borrowed from seaborn: it is therefore not equivalent to matplotlib's.
It is possible to make line plots of two-dimensional data by calling :pyxarray.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:
python
@savefig plotting_example_multiple_lines_x_kwarg.png air.isel(lon=10, lat=[19,21,22]).plot.line(x='time')
It is required to explicitly specify either
x: the dimension to be used for the x-axis, orhue: 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 :pyxarray.plot as air.isel(lon=10, lat=[19,21,22]).plot(hue='lat').
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.
python
@savefig plotting_example_xy_kwarg.png air.isel(time=10, lon=[10, 11]).plot(y='lat', hue='lon')
As an alternative, also a step plot similar to matplotlib's plt.step can be made using 1D data.
python
@savefig plotting_example_step.png width=4in air1d[:20].plot.step(where='mid')
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 :pyxarray.Dataset.groupby_bins.
python
air_grp = air.mean(['time','lon']).groupby_bins('lat',[0,23.5,66.5,90]) air_mean = air_grp.mean() air_std = air_grp.std() air_mean.plot.step() (air_mean + air_std).plot.step(ls=':') (air_mean - air_std).plot.step(ls=':') plt.ylim(-20,30) @savefig plotting_example_step_groupby.png width=4in plt.title('Zonal mean temperature')
In this case, the actual boundaries of the bins are used and the where argument is ignored.
The keyword arguments xincrease and yincrease let you control the axes direction.
python
@savefig plotting_example_xincrease_yincrease_kwarg.png air.isel(time=10, lon=[10, 11]).plot.line(y='lat', hue='lon', xincrease=False, yincrease=False)
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.
The default method :pyxarray.DataArray.plot calls :pyxarray.plot.pcolormesh by default when the data is two-dimensional.
python
air2d = air.isel(time=500)
@savefig 2d_simple.png width=4in air2d.plot()
All 2d plots in xarray allow the use of the keyword arguments yincrease and xincrease.
python
@savefig 2d_simple_yincrease.png width=4in air2d.plot(yincrease=False)
Note
We use :pyxarray.plot.pcolormesh as the default two-dimensional plot method because it is more flexible than :pyxarray.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.
xarray plots data with missing_values.
python
bad_air2d = air2d.copy()
bad_air2d[dict(lat=slice(0, 10), lon=slice(0, 25))] = np.nan
@savefig plotting_missing_values.png width=4in bad_air2d.plot()
It's not necessary for the coordinates to be evenly spaced. Both :pyxarray.plot.pcolormesh (default) and :pyxarray.plot.contourf can produce plots with nonuniform coordinates.
python
b = air2d.copy() # Apply a nonlinear transformation to one of the coords b.coords['lat'] = np.log(b.coords['lat'])
@savefig plotting_nonuniform_coords.png width=4in b.plot()
Since this is a thin wrapper around matplotlib, all the functionality of matplotlib is available.
python
air2d.plot(cmap=plt.cm.Blues) plt.title('These colors prove North Americanhas fallen in the ocean') plt.ylabel('latitude') plt.xlabel('longitude') plt.tight_layout()
@savefig plotting_2d_call_matplotlib.png width=4in plt.draw()
Note
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.
python
plt.xlabel('Never gonna see this.') air2d.plot()
@savefig plotting_2d_call_matplotlib2.png width=4in 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:
python
@savefig plotting_kelvin.png width=4in airtemps.air.isel(time=0).plot()
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.
python
air_outliers = airtemps.air.isel(time=0).copy() air_outliers[0, 0] = 100 air_outliers[-1, -1] = 400
@savefig plotting_robust1.png width=4in air_outliers.plot()
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.
python
@savefig plotting_robust2.png width=4in air_outliers.plot(robust=True)
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.
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:
python
@savefig plotting_discrete_levels.png width=4in air2d.plot(levels=8)
It is also possible to use a list of levels to specify the boundaries of the discrete colormap:
python
@savefig plotting_listed_levels.png width=4in air2d.plot(levels=[0, 12, 18, 30])
You can also specify a list of discrete colors through the colors argument:
python
flatui = ["#9b59b6", "#3498db", "#95a5a6", "#e74c3c", "#34495e", "#2ecc71"] @savefig plotting_custom_colors_levels.png width=4in air2d.plot(levels=[0, 12, 18, 30], colors=flatui)
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).
python
@savefig plotting_seaborn_palette.png width=4in air2d.plot(levels=10, cmap='husl') 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.
python
t = air.isel(time=slice(0, 365 * 4, 250)) t.coords
The easiest way to create faceted plots is to pass in row or col arguments to the xarray plotting methods/functions. This returns a :pyxarray.plot.FacetGrid object.
python
@savefig plot_facet_dataarray.png g_simple = t.plot(x='lon', y='lat', col='time', col_wrap=3)
Faceting also works for line plots.
python
@savefig plot_facet_dataarray_line.png g_simple_line = t.isel(lat=slice(0,None,4)).plot(x='lon', hue='lat', col='time', col_wrap=3)
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.
python
t2 = t.isel(time=slice(0, 2)) t4d = xr.concat([t2, t2 + 40], pd.Index(['normal', 'hot'], name='fourth_dim')) # This is a 4d array t4d.coords
@savefig plot_facet_4d.png t4d.plot(x='lon', y='lat', col='time', row='fourth_dim')
Faceted plotting supports other arguments common to xarray 2d plots.
python
plt.close('all')
python
hasoutliers = t.isel(time=slice(0, 5)).copy() hasoutliers[0, 0, 0] = -100 hasoutliers[-1, -1, -1] = 400
@savefig plot_facet_robust.png g = hasoutliers.plot.pcolormesh('lon', 'lat', col='time', col_wrap=3, robust=True, cmap='viridis', cbar_kwargs={'label': 'this has outliers'})
:pyxarray.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.
python
g.axes
g.name_dicts
It's possible to select the :pyxarray.DataArray or :pyxarray.Dataset corresponding to the FacetGrid through the name_dicts.
python
g.data.loc[g.name_dicts[0, 0]]
Here is an example of using the lower level API and then modifying the axes after they have been plotted.
python
g = t.plot.imshow('lon', 'lat', col='time', col_wrap=3, robust=True)
- for i, ax in enumerate(g.axes.flat):
ax.set_title('Air Temperature %d' % i)
bottomright = g.axes[-1, -1] bottomright.annotate('bottom right', (240, 40))
@savefig plot_facet_iterator.png plt.draw()
TODO: add an example of using the map method to plot dataset variables (e.g., with plt.quiver).
To follow this section you'll need to have Cartopy installed and working.
This script will plot the air temperature on a map.
python
import cartopy.crs as ccrs air = xr.tutorial.open_dataset('air_temperature').air ax = plt.axes(projection=ccrs.Orthographic(-80, 35)) air.isel(time=0).plot.contourf(ax=ax, transform=ccrs.PlateCarree()); @savefig plotting_maps_cartopy.png width=100% 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:
python
- p = air.isel(time=[0, 4]).plot(transform=ccrs.PlateCarree(), col='time',
subplot_kws={'projection': ccrs.Orthographic(-80, 35)})
- for ax in p.axes.flat:
ax.coastlines() ax.gridlines()
@savefig plotting_maps_cartopy_facetting.png width=100% plt.draw();
There are three ways to use the xarray plotting functionality:
- Use
plotas a convenience method for a DataArray. - Access a specific plotting method from the
plotattribute of a DataArray. - Directly from the xarray plot submodule.
These are provided for user convenience; they all call the same code.
python
import xarray.plot as xplt da = xr.DataArray(range(5)) fig, axes = plt.subplots(ncols=2, nrows=2) da.plot(ax=axes[0, 0]) da.plot.line(ax=axes[0, 1]) xplt.plot(da, ax=axes[1, 0]) xplt.line(da, ax=axes[1, 1]) plt.tight_layout() @savefig plotting_ways_to_use.png width=6in plt.draw()
Here the output is the same. Since the data is 1 dimensional the line plot was used.
The convenience method :pyxarray.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:
| Dimensions | Plotting function |
|---|---|
| 1 | :pyxarray.plot.line |
| 2 | :pyxarray.plot.pcolormesh |
| Anything else | :pyxarray.plot.hist |
If you'd like to find out what's really going on in the coordinate system, read on.
python
- a0 = xr.DataArray(np.zeros((4, 3, 2)), dims=('y', 'x', 'z'),
name='temperature')
a0[0, 0, 0] = 1 a = a0.isel(z=0) a
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.
python
@savefig plotting_example_2d_simple.png width=4in a.plot()
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.
See also: examples.multidim.
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:
python
lon, lat = np.meshgrid(np.linspace(-20, 20, 5), np.linspace(0, 30, 4)) lon += lat/10 lat += lon/10 da = xr.DataArray(np.arange(20).reshape(4, 5), dims=['y', 'x'], coords = {'lat': (('y', 'x'), lat), 'lon': (('y', 'x'), lon)})
@savefig plotting_example_2d_irreg.png width=4in 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 (781). This is why the default is to not follow this convention when plotting on a map:
python
import cartopy.crs as ccrs ax = plt.subplot(projection=ccrs.PlateCarree()); da.plot.pcolormesh('lon', 'lat', ax=ax); ax.scatter(lon, lat, transform=ccrs.PlateCarree()); @savefig plotting_example_2d_irreg_map.png width=4in ax.coastlines(); ax.gridlines(draw_labels=True);
You can however decide to infer the cell boundaries and use the infer_intervals keyword:
python
ax = plt.subplot(projection=ccrs.PlateCarree()); da.plot.pcolormesh('lon', 'lat', ax=ax, infer_intervals=True); ax.scatter(lon, lat, transform=ccrs.PlateCarree()); @savefig plotting_example_2d_irreg_map_infer.png width=4in ax.coastlines(); ax.gridlines(draw_labels=True);
Note
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.
python
f, ax = plt.subplots(2, 1) da.plot.line(x='lon', hue='y', ax=ax[0]); @savefig plotting_example_2d_hue_xy.png da.plot.line(x='lon', hue='x', ax=ax[1]);