Line Plots (1D)#

The following imports are necessary for all of the examples.

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import xarray as xr

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

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

Syntax Overview#

There are three ways to use the xarray plotting functionality:

Use plot as a convenience method for a DataArray.
Access a specific plotting method from the plot attribute of a DataArray.
Directly from the xarray plot submodule.

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

da = xr.DataArray(range(5))
fig, axs = plt.subplots(ncols=2, nrows=2)
da.plot(ax=axs[0, 0])
da.plot.line(ax=axs[0, 1])
xr.plot.plot(da, ax=axs[1, 0])
xr.plot.line(da, ax=axs[1, 1]);

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()`

Simple Example#

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

air1d = air.isel(lat=10, lon=10)
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.

air1d.attrs

{'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:

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.

air1d[:200].plot.line(color="purple", marker="o");

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.

fig, axs = plt.subplots(ncols=2)

print(axs)

air1d.plot(ax=axs[0])
air1d.plot.hist(ax=axs[1]);

[<Axes: > <Axes: >]

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):

air1d.plot(aspect=2, size=3);

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.

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:

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, or
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.

air.isel(time=10, lon=[10, 11]).plot(y="lat", hue="lon");

Step plots#

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

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 Dataset.groupby_bins().

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)
plt.title("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.

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

Line Plots (1D)#

Syntax Overview#

Simple Example#

Additional Arguments#

Adding to Existing Axis#

Controlling the figure size#

Determine x-axis values#

Multiple lines showing variation along a dimension#

Dimension along y-axis#

Step plots#

Other axes kwargs#