Plotting

Introduction

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

Xarray’s plotting capabilities are centered around DataArray objects. To plot Dataset objects simply access the relevant DataArrays, i.e. 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 v1.3.0 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.

Imports

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
Out[6]: 
<xarray.Dataset> Size: 31MB
Dimensions:  (lat: 25, time: 2920, lon: 53)
Coordinates:
  * lat      (lat) float32 100B 75.0 72.5 70.0 67.5 65.0 ... 22.5 20.0 17.5 15.0
  * lon      (lon) float32 212B 200.0 202.5 205.0 207.5 ... 325.0 327.5 330.0
  * time     (time) datetime64[ns] 23kB 2013-01-01 ... 2014-12-31T18:00:00
Data variables:
    air      (time, lat, lon) float64 31MB ...
Attributes:
    Conventions:  COARDS
    title:        4x daily NMC reanalysis (1948)
    description:  Data is from NMC initialized reanalysis\n(4x/day).  These a...
    platform:     Model
    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...

# 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"

Note

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

DataArrays

One Dimension

Simple Example

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

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

In [11]: air1d.plot()
Out[11]: [<matplotlib.lines.Line2D at 0x7fe6c6fe9940>]

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.

In [12]: air1d.attrs
Out[12]: 
{'long_name': '4xDaily Air temperature at sigma level 995',
 'units': 'deg C',
 'precision': np.int16(2),
 'GRIB_id': np.int16(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 0x7fe6c7bf1c10>]

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.

In [14]: air1d[:200].plot.line(color="purple", marker="o")
Out[14]: [<matplotlib.lines.Line2D at 0x7fe6c7c0e390>]

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 axs is an array consisting of the left and right axes created by plt.subplots.

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

In [16]: axs
Out[16]: array([<Axes: >, <Axes: >], dtype=object)

In [17]: air1d.plot(ax=axs[0])
Out[17]: [<matplotlib.lines.Line2D at 0x7fe6c8018a40>]

In [18]: air1d.plot.hist(ax=axs[1])
Out[18]: 
(array([  9.,  38., 255., 584., 542., 489., 368., 258., 327.,  50.]),
 array([ 0.95 ,  2.719,  4.488, ..., 15.102, 16.871, 18.64 ]),
 <BarContainer object of 10 artists>)

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 0x7fe6c6f905c0>]

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).

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.

Determine x-axis values

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:

In [23]: decimal_day = (air1d.time - air1d.time[0]) / pd.Timedelta("1d")

In [24]: air1d_multi = air1d.assign_coords(decimal_day=("time", decimal_day.data))

In [25]: air1d_multi
Out[25]: 
<xarray.DataArray 'air' (time: 2920)> Size: 23kB
array([7.64, 7.85, 8.05, ..., 9.34, 9.34, 9.54])
Coordinates:
    lat          float32 4B 50.0
    lon          float32 4B 225.0
  * time         (time) datetime64[ns] 23kB 2013-01-01 ... 2014-12-31T18:00:00
    decimal_day  (time) float64 23kB 0.0 0.25 0.5 0.75 ... 729.2 729.5 729.8
Attributes:
    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 ]

To use 'decimal_day' as x coordinate it must be explicitly specified:

In [26]: air1d_multi.plot(x="decimal_day")
Out[26]: [<matplotlib.lines.Line2D at 0x7fe6c84154c0>]

Creating a new MultiIndex named 'date' from 'time' and 'decimal_day', it is also possible to use a MultiIndex level as x-axis:

In [27]: air1d_multi = air1d_multi.set_index(date=("time", "decimal_day"))

In [28]: air1d_multi.plot(x="decimal_day")
Out[28]: [<matplotlib.lines.Line2D at 0x7fe6c8415760>]

Finally, if a dataset does not have any coordinates it enumerates all data points:

In [29]: air1d_multi = air1d_multi.drop_vars(["date", "time", "decimal_day"])

In [30]: air1d_multi.plot()
Out[30]: [<matplotlib.lines.Line2D at 0x7fe6c7bf2060>]

The same applies to 2D plots below.

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 [31]: air.isel(lon=10, lat=[19, 21, 22]).plot.line(x="time")
Out[31]: 
[<matplotlib.lines.Line2D at 0x7fe6c8414350>,
 <matplotlib.lines.Line2D at 0x7fe6c8452930>,
 <matplotlib.lines.Line2D at 0x7fe6c8498c50>]

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.line() as air.isel(lon=10, lat=[19,21,22]).plot.line(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 [32]: air.isel(time=10, lon=[10, 11]).plot(y="lat", hue="lon")
Out[32]: 
[<matplotlib.lines.Line2D at 0x7fe6c80dc830>,
 <matplotlib.lines.Line2D at 0x7fe6c840dbb0>]

Step plots

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

In [33]: air1d[:20].plot.step(where="mid")
Out[33]: [<matplotlib.lines.Line2D at 0x7fe6c8417bf0>]

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 Dataset.groupby_bins().

In [34]: air_grp = air.mean(["time", "lon"]).groupby_bins("lat", [0, 23.5, 66.5, 90])

In [35]: air_mean = air_grp.mean()

In [36]: air_std = air_grp.std()

In [37]: air_mean.plot.step()
Out[37]: [<matplotlib.lines.Line2D at 0x7fe6c8433a10>]

In [38]: (air_mean + air_std).plot.step(ls=":")
Out[38]: [<matplotlib.lines.Line2D at 0x7fe6c84163c0>]

In [39]: (air_mean - air_std).plot.step(ls=":")
Out[39]: [<matplotlib.lines.Line2D at 0x7fe6c8430890>]

In [40]: plt.ylim(-20, 30)
Out[40]: (-20.0, 30.0)

In [41]: plt.title("Zonal mean temperature")
Out[41]: Text(0.5, 1.0, '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 [42]: air.isel(time=10, lon=[10, 11]).plot.line(
   ....:     y="lat", hue="lon", xincrease=False, yincrease=False
   ....: )
   ....: 
Out[42]: 
[<matplotlib.lines.Line2D at 0x7fe6c84a4a70>,
 <matplotlib.lines.Line2D at 0x7fe6c84a4a10>]

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 ax.set_(x,y)scale(), ax.set_(x,y)ticks(), ax.set_(x,y)lim(), respectively.

Two Dimensions

Simple Example

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

In [43]: air2d = air.isel(time=500)

In [44]: air2d.plot()
Out[44]: <matplotlib.collections.QuadMesh at 0x7fe6c8433200>

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

In [45]: air2d.plot(yincrease=False)
Out[45]: <matplotlib.collections.QuadMesh at 0x7fe6c857ac30>

Note

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 [46]: bad_air2d = air2d.copy()

In [47]: bad_air2d[dict(lat=slice(0, 10), lon=slice(0, 25))] = np.nan

In [48]: bad_air2d.plot()
Out[48]: <matplotlib.collections.QuadMesh at 0x7fe6c8591d00>

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 [49]: b = air2d.copy()

# Apply a nonlinear transformation to one of the coords
In [50]: b.coords["lat"] = np.log(b.coords["lat"])

In [51]: b.plot()
Out[51]: <matplotlib.collections.QuadMesh at 0x7fe6c8521550>

Other types of plot

There are several other options for plotting 2D data.

Contour plot using DataArray.plot.contour()

In [52]: air2d.plot.contour()
Out[52]: <matplotlib.contour.QuadContourSet at 0x7fe6c84a4c20>

Filled contour plot using DataArray.plot.contourf()

In [53]: air2d.plot.contourf()
Out[53]: <matplotlib.contour.QuadContourSet at 0x7fe6c87f1eb0>

Surface plot using DataArray.plot.surface()

# transpose just to make the example look a bit nicer
In [54]: air2d.T.plot.surface()
Out[54]: <mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x7fe6c86db170>

Calling Matplotlib

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

In [55]: air2d.plot(cmap=plt.cm.Blues)
Out[55]: <matplotlib.collections.QuadMesh at 0x7fe6c84f9640>

In [56]: plt.title("These colors prove North America\nhas fallen in the ocean")
Out[56]: Text(0.5, 1.0, 'These colors prove North America\nhas fallen in the ocean')

In [57]: plt.ylabel("latitude")
Out[57]: Text(0, 0.5, 'latitude')

In [58]: plt.xlabel("longitude")
Out[58]: Text(0.5, 0, 'longitude')

In [59]: plt.tight_layout()

In [60]: 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.

In [61]: plt.xlabel("Never gonna see this.")
Out[61]: Text(0.5, 0, 'Never gonna see this.')

In [62]: air2d.plot()
Out[62]: <matplotlib.collections.QuadMesh at 0x7fe6c8747050>

In [63]: plt.draw()

Colormaps

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 [64]: airtemps.air.isel(time=0).plot()
Out[64]: <matplotlib.collections.QuadMesh at 0x7fe6c88eb7a0>

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.

Robust

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

In [66]: air_outliers[0, 0] = 100

In [67]: air_outliers[-1, -1] = 400

In [68]: air_outliers.plot()
Out[68]: <matplotlib.collections.QuadMesh at 0x7fe6c8734470>

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 [69]: air_outliers.plot(robust=True)
Out[69]: <matplotlib.collections.QuadMesh at 0x7fe6c86d96a0>

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 [70]: air2d.plot(levels=8)
Out[70]: <matplotlib.collections.QuadMesh at 0x7fe6c88e3f50>

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

In [71]: air2d.plot(levels=[0, 12, 18, 30])
Out[71]: <matplotlib.collections.QuadMesh at 0x7fe6c88eabd0>

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

In [72]: flatui = ["#9b59b6", "#3498db", "#95a5a6", "#e74c3c", "#34495e", "#2ecc71"]

In [73]: air2d.plot(levels=[0, 12, 18, 30], colors=flatui)
Out[73]: <matplotlib.collections.QuadMesh at 0x7fe6c88feb40>

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 [74]: air2d.plot(levels=10, cmap="husl")
Out[74]: <matplotlib.collections.QuadMesh at 0x7fe6c89904a0>

In [75]: plt.draw()

Faceting

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 or 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.

In [76]: t = air.isel(time=slice(0, 365 * 4, 250))

In [77]: t.coords
Out[77]: 
Coordinates:
  * lat      (lat) float32 100B 75.0 72.5 70.0 67.5 65.0 ... 22.5 20.0 17.5 15.0
  * lon      (lon) float32 212B 200.0 202.5 205.0 207.5 ... 325.0 327.5 330.0
  * time     (time) datetime64[ns] 48B 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 [78]: g_simple = t.plot(x="lon", y="lat", col="time", col_wrap=3)

Faceting also works for line plots.

In [79]: 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 [80]: t2 = t.isel(time=slice(0, 2))

In [81]: t4d = xr.concat([t2, t2 + 40], pd.Index(["normal", "hot"], name="fourth_dim"))

# This is a 4d array
In [82]: t4d.coords
Out[82]: 
Coordinates:
  * lat         (lat) float32 100B 75.0 72.5 70.0 67.5 ... 22.5 20.0 17.5 15.0
  * lon         (lon) float32 212B 200.0 202.5 205.0 207.5 ... 325.0 327.5 330.0
  * time        (time) datetime64[ns] 16B 2013-01-01 2013-03-04T12:00:00
  * fourth_dim  (fourth_dim) object 16B 'normal' 'hot'

In [83]: t4d.plot(x="lon", y="lat", col="time", row="fourth_dim")
Out[83]: <xarray.plot.facetgrid.FacetGrid at 0x7fe6c8b62120>

Other features

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

In [84]: hasoutliers = t.isel(time=slice(0, 5)).copy()

In [85]: hasoutliers[0, 0, 0] = -100

In [86]: hasoutliers[-1, -1, -1] = 400

In [87]: g = hasoutliers.plot.pcolormesh(
   ....:     x="lon",
   ....:     y="lat",
   ....:     col="time",
   ....:     col_wrap=3,
   ....:     robust=True,
   ....:     cmap="viridis",
   ....:     cbar_kwargs={"label": "this has outliers"},
   ....: )
   ....: 

FacetGrid Objects

The object returned, g in the above examples, is a FacetGrid object that links a 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 axs and name_dicts attributes, both 2d NumPy object arrays.

In [88]: g.axs
Out[88]: 
array([[<Axes: title={'center': 'time = 2013-01-01'}, ylabel='lat'>,
        <Axes: title={'center': 'time = 2013-03-04T12:00:00'}>,
        <Axes: title={'center': 'time = 2013-05-06'}>],
       [<Axes: title={'center': 'time = 2013-07-07T12:00:00'}, xlabel='lon', ylabel='lat'>,
        <Axes: title={'center': 'time = 2013-09-08'}, xlabel='lon'>, <Axes: xlabel='lon'>]],
      dtype=object)

In [89]: g.name_dicts
Out[89]: 
array([[{'time': np.datetime64('2013-01-01T00:00:00.000000000')},
        {'time': np.datetime64('2013-03-04T12:00:00.000000000')},
        {'time': np.datetime64('2013-05-06T00:00:00.000000000')}],
       [{'time': np.datetime64('2013-07-07T12:00:00.000000000')},
        {'time': np.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 [90]: g.data.loc[g.name_dicts[0, 0]]
Out[90]: 
<xarray.DataArray 'air' (lat: 25, lon: 53)> Size: 11kB
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]])
Coordinates:
  * lat      (lat) float32 100B 75.0 72.5 70.0 67.5 65.0 ... 22.5 20.0 17.5 15.0
  * lon      (lon) float32 212B 200.0 202.5 205.0 207.5 ... 325.0 327.5 330.0
    time     datetime64[ns] 8B 2013-01-01
Attributes:
    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 [91]: g = t.plot.imshow(x="lon", y="lat", col="time", col_wrap=3, robust=True)

In [92]: for i, ax in enumerate(g.axs.flat):
   ....:     ax.set_title("Air Temperature %d" % i)
   ....: 

In [93]: bottomright = g.axs[-1, -1]

In [94]: bottomright.annotate("bottom right", (240, 40))
Out[94]: Text(240, 40, 'bottom right')

In [95]: plt.draw()

FacetGrid objects have methods that let you customize the automatically generated axis labels, axis ticks and plot titles. See set_titles(), set_xlabels(), set_ylabels() and set_ticks() for more information. Plotting functions can be applied to each subset of the data by calling map_dataarray() or to each subplot by calling map().

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

Datasets

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

In [96]: ds = xr.tutorial.scatter_example_dataset(seed=42)

In [97]: ds
Out[97]: 
<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

Scatter

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

In [98]: ds.A
Out[98]: 
<xarray.DataArray 'A' (x: 3, y: 11, z: 4, w: 4)> Size: 4kB
array([[[[ 3.047e-02, -1.040e-01,  7.505e-02,  9.406e-02],
         [ 3.047e-02, -1.040e-01,  7.505e-02,  9.406e-02],
         [ 3.047e-02, -1.040e-01,  7.505e-02,  9.406e-02],
         [ 3.047e-02, -1.040e-01,  7.505e-02,  9.406e-02]],

        [[ 5.047e-02, -8.400e-02,  9.505e-02,  1.141e-01],
         [ 4.714e-02, -8.733e-02,  9.171e-02,  1.107e-01],
         [ 4.476e-02, -8.971e-02,  8.933e-02,  1.083e-01],
         [ 4.297e-02, -9.150e-02,  8.755e-02,  1.066e-01]],

        [[ 7.047e-02, -6.400e-02,  1.150e-01,  1.341e-01],
         [ 6.381e-02, -7.067e-02,  1.084e-01,  1.274e-01],
         [ 5.904e-02, -7.543e-02,  1.036e-01,  1.226e-01],
         [ 5.547e-02, -7.900e-02,  1.000e-01,  1.191e-01]],

        ...,

        [[ 1.905e-01,  5.600e-02,  2.350e-01,  2.541e-01],
         [ 1.638e-01,  2.933e-02,  2.084e-01,  2.274e-01],
         [ 1.448e-01,  1.029e-02,  1.893e-01,  2.083e-01],
...
         [-1.362e-01, -2.707e-01, -9.162e-02, -7.261e-02],
         [-1.410e-01, -2.754e-01, -9.638e-02, -7.737e-02],
         [-1.445e-01, -2.790e-01, -9.995e-02, -8.094e-02]],

        ...,

        [[-9.528e-03, -1.440e-01,  3.505e-02,  5.406e-02],
         [-3.619e-02, -1.707e-01,  8.378e-03,  2.739e-02],
         [-5.524e-02, -1.897e-01, -1.067e-02,  8.342e-03],
         [-6.953e-02, -2.040e-01, -2.495e-02, -5.944e-03]],

        [[ 1.047e-02, -1.240e-01,  5.505e-02,  7.406e-02],
         [-1.953e-02, -1.540e-01,  2.505e-02,  4.406e-02],
         [-4.096e-02, -1.754e-01,  3.617e-03,  2.263e-02],
         [-5.703e-02, -1.915e-01, -1.245e-02,  6.556e-03]],

        [[ 3.047e-02, -1.040e-01,  7.505e-02,  9.406e-02],
         [-2.862e-03, -1.373e-01,  4.171e-02,  6.072e-02],
         [-2.667e-02, -1.611e-01,  1.790e-02,  3.691e-02],
         [-4.453e-02, -1.790e-01,  4.512e-05,  1.906e-02]]]])
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

In [99]: ds.A.plot.scatter(x="y")
Out[99]: <matplotlib.collections.PathCollection at 0x7fe6c8c4d0a0>

Same plot can be displayed using the dataset:

In [100]: ds.plot.scatter(x="y", y="A")
Out[100]: <matplotlib.collections.PathCollection at 0x7fe6c9520a40>

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

In [101]: ds.plot.scatter(x="A", y="B")
Out[101]: <matplotlib.collections.PathCollection at 0x7fe6c8c4fec0>

The hue kwarg lets you vary the color by variable value

In [102]: ds.plot.scatter(x="A", y="B", hue="w")
Out[102]: <matplotlib.collections.PathCollection at 0x7fe6c8db0080>

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

In [103]: ds.plot.scatter(x="A", y="B", hue="w", add_legend=True, add_colorbar=False)
Out[103]: <matplotlib.collections.PathCollection at 0x7fe6c8b4f8f0>

In [104]: ds.plot.scatter(x="A", y="B", hue="w", add_legend=False, add_colorbar=True)
Out[104]: <matplotlib.collections.PathCollection at 0x7fe6c8b79040>

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 [105]: ds.plot.scatter(x="A", y="B", hue="y", markersize="z")
Out[105]: <matplotlib.collections.PathCollection at 0x7fe6c6329040>

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

In [106]: ds.plot.scatter(x="A", y="B", z="z", hue="y", markersize="x")
Out[106]: <mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x7fe6c9ad7ef0>

Faceting is also possible

In [107]: ds.plot.scatter(x="A", y="B", hue="y", markersize="x", row="x", col="w")
Out[107]: <xarray.plot.facetgrid.FacetGrid at 0x7fe6c89402c0>

And adding the z-axis

In [108]: ds.plot.scatter(x="A", y="B", z="z", hue="y", markersize="x", row="x", col="w")
Out[108]: <xarray.plot.facetgrid.FacetGrid at 0x7fe6c604d580>

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:

In [109]: ds.isel(w=1, z=1).plot.quiver(x="x", y="y", u="A", v="B")
Out[109]: <matplotlib.quiver.Quiver at 0x7fe6c8a17980>

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

In [110]: ds.plot.quiver(x="x", y="y", u="A", v="B", col="w", row="z", scale=4)
Out[110]: <xarray.plot.facetgrid.FacetGrid at 0x7fe6c849a2d0>

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:

In [111]: ds.isel(w=1, z=1).plot.streamplot(x="x", y="y", u="A", v="B")
Out[111]: <matplotlib.collections.LineCollection at 0x7fe6c590de80>

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

In [112]: ds.plot.streamplot(x="x", y="y", u="A", v="B", col="w", row="z")
Out[112]: <xarray.plot.facetgrid.FacetGrid at 0x7fe6c5a76a50>

Maps

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

This script will plot the air temperature on a map.

In [113]: import cartopy.crs as ccrs

In [114]: air = xr.tutorial.open_dataset("air_temperature").air

In [115]: p = air.isel(time=0).plot(
   .....:     subplot_kws=dict(projection=ccrs.Orthographic(-80, 35), facecolor="gray"),
   .....:     transform=ccrs.PlateCarree(),
   .....: )
   .....: 

In [116]: p.axes.set_global()

In [117]: p.axes.coastlines()
Out[117]: <cartopy.mpl.feature_artist.FeatureArtist at 0x7fe6c70b2990>

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

In [119]: for ax in p.axs.flat:
   .....:     ax.coastlines()
   .....:     ax.gridlines()
   .....: 

In [120]: plt.draw()

Details

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 [121]: import xarray.plot as xplt

In [122]: da = xr.DataArray(range(5))

In [123]: fig, axs = plt.subplots(ncols=2, nrows=2)

In [124]: da.plot(ax=axs[0, 0])
Out[124]: [<matplotlib.lines.Line2D at 0x7fe6c98b0f20>]

In [125]: da.plot.line(ax=axs[0, 1])
Out[125]: [<matplotlib.lines.Line2D at 0x7fe6d40070b0>]

In [126]: xplt.plot(da, ax=axs[1, 0])
Out[126]: [<matplotlib.lines.Line2D at 0x7fe6c6399130>]

In [127]: xplt.line(da, ax=axs[1, 1])
Out[127]: [<matplotlib.lines.Line2D at 0x7fe6c6399520>]

In [128]: plt.tight_layout()

In [129]: 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:

Dimensions	Plotting function
1	xarray.plot.line()
2	xarray.plot.pcolormesh()
Anything else	xarray.plot.hist()

Coordinates

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

In [130]: a0 = xr.DataArray(np.zeros((4, 3, 2)), dims=("y", "x", "z"), name="temperature")

In [131]: a0[0, 0, 0] = 1

In [132]: a = a0.isel(z=0)

In [133]: a
Out[133]: 
<xarray.DataArray 'temperature' (y: 4, x: 3)> Size: 96B
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 [134]: a.plot()
Out[134]: <matplotlib.collections.QuadMesh at 0x7fe6c7da1550>

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

In [136]: lon += lat / 10

In [137]: lat += lon / 10

In [138]: da = xr.DataArray(
   .....:     np.arange(20).reshape(4, 5),
   .....:     dims=["y", "x"],
   .....:     coords={"lat": (("y", "x"), lat), "lon": (("y", "x"), lon)},
   .....: )
   .....: 

In [139]: da.plot.pcolormesh(x="lon", y="lat")
Out[139]: <matplotlib.collections.QuadMesh at 0x7fe6c848faa0>

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 [140]: import cartopy.crs as ccrs

In [141]: ax = plt.subplot(projection=ccrs.PlateCarree())

In [142]: da.plot.pcolormesh(x="lon", y="lat", ax=ax)
Out[142]: <cartopy.mpl.geocollection.GeoQuadMesh at 0x7fe6c6284a40>

In [143]: ax.scatter(lon, lat, transform=ccrs.PlateCarree())
Out[143]: <matplotlib.collections.PathCollection at 0x7fe6c500ffb0>

In [144]: ax.coastlines()
Out[144]: <cartopy.mpl.feature_artist.FeatureArtist at 0x7fe6c50e35c0>

In [145]: ax.gridlines(draw_labels=True)
Out[145]: <cartopy.mpl.gridliner.Gridliner at 0x7fe6c61cee70>

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

In [146]: ax = plt.subplot(projection=ccrs.PlateCarree())

In [147]: da.plot.pcolormesh(x="lon", y="lat", ax=ax, infer_intervals=True)
Out[147]: <cartopy.mpl.geocollection.GeoQuadMesh at 0x7fe6c61cf3b0>

In [148]: ax.scatter(lon, lat, transform=ccrs.PlateCarree())
Out[148]: <matplotlib.collections.PathCollection at 0x7fe6c4efcf80>

In [149]: ax.coastlines()
Out[149]: <cartopy.mpl.feature_artist.FeatureArtist at 0x7fe6c4effbf0>

In [150]: ax.gridlines(draw_labels=True)
Out[150]: <cartopy.mpl.gridliner.Gridliner at 0x7fe6c4ff3dd0>

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.

In [151]: f, ax = plt.subplots(2, 1)

In [152]: da.plot.line(x="lon", hue="y", ax=ax[0])
Out[152]: 
[<matplotlib.lines.Line2D at 0x7fe6c51b3320>,
 <matplotlib.lines.Line2D at 0x7fe6c51b3f20>,
 <matplotlib.lines.Line2D at 0x7fe6c5dc3b60>,
 <matplotlib.lines.Line2D at 0x7fe6c4ff16d0>]

In [153]: da.plot.line(x="lon", hue="x", ax=ax[1])
Out[153]: 
[<matplotlib.lines.Line2D at 0x7fe6c5061df0>,
 <matplotlib.lines.Line2D at 0x7fe6c5063800>,
 <matplotlib.lines.Line2D at 0x7fe6c5063da0>,
 <matplotlib.lines.Line2D at 0x7fe6c5063c20>,
 <matplotlib.lines.Line2D at 0x7fe6c5063c50>]