.. currentmodule:: xarray
Labeled data enables expressive computations. These same labels can also be used to easily create informative plots.
xarray's plotting capabilities are centered around
:py:class:`DataArray` objects.
To plot :py:class:`Dataset` objects
simply access the relevant DataArrays, i.e. dset['var1'].
Dataset specific plotting routines are also available (see :ref:`plot-dataset`).
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.
.. ipython:: python
:suppress:
# 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.
.. ipython:: 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.
.. ipython:: 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 :issue:`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 :py:func:`DataArray.plot()` method.
.. ipython:: 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.
.. ipython:: python
air1d.attrs
Additional arguments are passed directly to the matplotlib function which does the work. For example, :py:func:`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:
.. ipython:: 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.
.. ipython:: 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.
.. ipython:: 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 :py:func:`xarray.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):
.. ipython:: python
air1d.plot(aspect=2, size=3)
@savefig plotting_example_size_and_aspect.png
plt.tight_layout()
.. ipython:: python
:suppress:
# create a dummy figure so sphinx plots everything below normally
plt.figure()
This feature also works with :ref:`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.
Per default dimension coordinates are used for the x-axis (here the time coordinates). However, you can also use non-dimension coordinates, MultiIndex levels, and dimensions without coordinates along the x-axis. To illustrate this, let's calculate a 'decimal day' (epoch) from the time and assign it as a non-dimension coordinate:
.. ipython:: python
decimal_day = (air1d.time - air1d.time[0]) / pd.Timedelta("1d")
air1d_multi = air1d.assign_coords(decimal_day=("time", decimal_day))
air1d_multi
To use 'decimal_day' as x coordinate it must be explicitly specified:
.. ipython:: python
air1d_multi.plot(x="decimal_day")
Creating a new MultiIndex named 'date' from 'time' and 'decimal_day',
it is also possible to use a MultiIndex level as x-axis:
.. ipython:: python
air1d_multi = air1d_multi.set_index(date=("time", "decimal_day"))
air1d_multi.plot(x="decimal_day")
Finally, if a dataset does not have any coordinates it enumerates all data points:
.. ipython:: python
air1d_multi = air1d_multi.drop("date")
air1d_multi.plot()
The same applies to 2D plots below.
It is possible to make line plots of two-dimensional data by calling :py:func:`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:
.. ipython:: 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 :py:func:`xarray.plot.line` as air.isel(lon=10, lat=[19,21,22]).plot.line(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.
.. ipython:: 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.
.. ipython:: python
:okwarning:
@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 :py:meth:`Dataset.groupby_bins`.
.. ipython:: 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.
.. ipython:: 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 :py:meth:`DataArray.plot` calls :py:func:`xarray.plot.pcolormesh` by default when the data is two-dimensional.
.. ipython:: 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.
.. ipython:: python
@savefig 2d_simple_yincrease.png width=4in
air2d.plot(yincrease=False)
Note
We use :py:func:`xarray.plot.pcolormesh` as the default two-dimensional plot
method because it is more flexible than :py:func:`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.
xarray plots data with :ref:`missing_values`.
.. ipython:: 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 :py:func:`xarray.plot.pcolormesh` (default) and :py:func:`xarray.plot.contourf` can produce plots with nonuniform coordinates.
.. ipython:: 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.
.. ipython:: python
air2d.plot(cmap=plt.cm.Blues)
plt.title("These colors prove North America\nhas 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.
.. ipython:: 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:
.. ipython:: 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.
.. ipython:: 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.
.. ipython:: 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:
.. ipython:: 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:
.. ipython:: 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:
.. ipython:: 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).
.. ipython:: python
:okwarning:
@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. The general approach to plotting here is called “small multiples”, where the same kind of plot is repeated multiple times, and the specific use of small multiples to display the same relationship conditioned on one ore more other variables is often called a “trellis plot”.
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.
.. ipython:: 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
:py:class:`xarray.plot.FacetGrid` object.
.. ipython:: 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.
.. ipython:: 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.
.. ipython:: 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.
.. ipython:: python
:suppress:
plt.close("all")
.. ipython:: 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"},
)
The object returned, g in the above examples, is a :py:class:`~xarray.plot.FacetGrid` object
that links a :py:class:`DataArray` to a matplotlib figure with a particular structure.
This object can be 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.
.. ipython:: python
g.axes
g.name_dicts
It's possible to select the :py:class:`xarray.DataArray` or
:py:class:`xarray.Dataset` corresponding to the FacetGrid through the
name_dicts.
.. ipython:: 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.
.. ipython:: 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()
:py:class:`~xarray.plot.FacetGrid` objects have methods that let you customize the automatically generated axis labels, axis ticks and plot titles. See :py:meth:`~xarray.plot.FacetGrid.set_titles`, :py:meth:`~xarray.plot.FacetGrid.set_xlabels`, :py:meth:`~xarray.plot.FacetGrid.set_ylabels` and :py:meth:`~xarray.plot.FacetGrid.set_ticks` for more information. Plotting functions can be applied to each subset of the data by calling :py:meth:`~xarray.plot.FacetGrid.map_dataarray` or to each subplot by calling :py:meth:`~xarray.plot.FacetGrid.map`.
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
.. ipython:: python
ds = xr.tutorial.scatter_example_dataset()
ds
Suppose we want to scatter A against B
.. ipython:: python
@savefig ds_simple_scatter.png
ds.plot.scatter(x="A", y="B")
The hue kwarg lets you vary the color by variable value
.. ipython:: python
@savefig ds_hue_scatter.png
ds.plot.scatter(x="A", y="B", hue="w")
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).
.. ipython:: python
ds = ds.assign(w=[1, 2, 3, 5])
@savefig ds_discrete_legend_hue_scatter.png
ds.plot.scatter(x="A", y="B", hue="w", hue_style="discrete")
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.
.. ipython:: python
@savefig ds_hue_size_scatter.png
ds.plot.scatter(x="A", y="B", hue="z", hue_style="discrete", markersize="z")
Faceting is also possible
.. ipython:: python
@savefig ds_facet_scatter.png
ds.plot.scatter(x="A", y="B", col="x", row="z", hue="w", hue_style="discrete")
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.
.. ipython:: python
import cartopy.crs as ccrs
air = xr.tutorial.open_dataset("air_temperature").air
p = air.isel(time=0).plot(
subplot_kws=dict(projection=ccrs.Orthographic(-80, 35), facecolor="gray"),
transform=ccrs.PlateCarree())
p.axes.set_global()
@savefig plotting_maps_cartopy.png width=100%
p.axes.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:
.. ipython:: python
:okwarning:
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.
.. ipython:: 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 :py:meth:`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:
| Dimensions | Plotting function |
| 1 | :py:func:`xarray.plot.line` |
| 2 | :py:func:`xarray.plot.pcolormesh` |
| Anything else | :py:func:`xarray.plot.hist` |
If you'd like to find out what's really going on in the coordinate system, read on.
.. ipython:: 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.
.. ipython:: 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: :ref:`/examples/multidimensional-coords.ipynb`.
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:
.. ipython:: 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 (:issue:`781`). This is why the default is to not follow this convention when plotting on a map:
.. ipython:: 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())
ax.coastlines()
@savefig plotting_example_2d_irreg_map.png width=4in
ax.gridlines(draw_labels=True)
You can however decide to infer the cell boundaries and use the
infer_intervals keyword:
.. ipython:: python
ax = plt.subplot(projection=ccrs.PlateCarree())
da.plot.pcolormesh("lon", "lat", ax=ax, infer_intervals=True)
ax.scatter(lon, lat, transform=ccrs.PlateCarree())
ax.coastlines()
@savefig plotting_example_2d_irreg_map_infer.png width=4in
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.
.. ipython:: 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])