# 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")
    PermissionError: [Errno 13] Permission denied: '/sbuild-nonexistent' | Pooch could not create data cache folder '/sbuild-nonexistent/.cache/xarray_tutorial_data'. Will not be able to download data files.

    In [6]: airtemps
    NameError: name 'airtemps' is not defined

    # Convert to celsius
    In [7]: air = airtemps.air - 273.15
    NameError: name 'airtemps' is not defined

    # copy attributes to get nice figure labels and change Kelvin to Celsius
    In [8]: air.attrs = airtemps.air.attrs
    NameError: name 'airtemps' is not defined

    In [9]: air.attrs["units"] = "deg C"
    NameError: name 'air' is not defined

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)
    NameError: name 'air' is not defined

    In [11]: air1d.plot()
    NameError: name 'air1d' is not defined

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
    NameError: name 'air1d' is not defined

#### 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-^")
    NameError: name 'air1d' is not defined

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")
    NameError: name 'air1d' is not defined

#### 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])
    NameError: name 'air1d' is not defined

    In [18]: air1d.plot.hist(ax=axs[1])
    NameError: name 'air1d' is not defined

    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)
    NameError: name 'air1d' is not defined

    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")
    NameError: name 'air1d' is not defined

    In [24]: air1d_multi = air1d.assign_coords(decimal_day=("time", decimal_day.data))
    NameError: name 'air1d' is not defined

    In [25]: air1d_multi
    NameError: name 'air1d_multi' is not defined

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

    In [26]: air1d_multi.plot(x="decimal_day")
    NameError: name 'air1d_multi' is not defined

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"))
    NameError: name 'air1d_multi' is not defined

    In [28]: air1d_multi.plot(x="decimal_day")
    NameError: name 'air1d_multi' is not defined

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"])
    NameError: name 'air1d_multi' is not defined

    In [30]: air1d_multi.plot()
    NameError: name 'air1d_multi' is not defined

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")
    NameError: name 'air' is not defined

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")
    NameError: name 'air' is not defined

#### 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")
    NameError: name 'air1d' is not defined

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])
    NameError: name 'air' is not defined

    In [35]: air_mean = air_grp.mean()
    NameError: name 'air_grp' is not defined

    In [36]: air_std = air_grp.std()
    NameError: name 'air_grp' is not defined

    In [37]: air_mean.plot.step()
    NameError: name 'air_mean' is not defined

    In [38]: (air_mean + air_std).plot.step(ls=":")
    NameError: name 'air_mean' is not defined

    In [39]: (air_mean - air_std).plot.step(ls=":")
    NameError: name 'air_mean' is not defined

    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
       ....: )
       ....:
    NameError: name 'air' is not defined

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)
    NameError: name 'air' is not defined

    In [44]: air2d.plot()
    NameError: name 'air2d' is not defined

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

    In [45]: air2d.plot(yincrease=False)
    NameError: name 'air2d' is not defined

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()
    NameError: name 'air2d' is not defined

    In [47]: bad_air2d[dict(lat=slice(0, 10), lon=slice(0, 25))] = np.nan
    NameError: name 'bad_air2d' is not defined

    In [48]: bad_air2d.plot()
    NameError: name 'bad_air2d' is not defined

#### 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()
    NameError: name 'air2d' is not defined

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

    In [51]: b.plot()
    Out[51]: [<matplotlib.lines.Line2D at 0x7c5f8e828050>]

#### Other types of plot

There are several other options for plotting 2D data.

Contour plot using `DataArray.plot.contour()`

    In [52]: air2d.plot.contour()
    NameError: name 'air2d' is not defined

Filled contour plot using `DataArray.plot.contourf()`

    In [53]: air2d.plot.contourf()
    NameError: name 'air2d' is not defined

Surface plot using `DataArray.plot.surface()`

    # transpose just to make the example look a bit nicer
    In [54]: air2d.T.plot.surface()
    NameError: name 'air2d' is not defined

#### 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)
    NameError: name 'air2d' is not defined

    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()
    NameError: name 'air2d' is not defined

    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()
    NameError: name 'airtemps' is not defined

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()
    NameError: name 'airtemps' is not defined

    In [66]: air_outliers[0, 0] = 100
    NameError: name 'air_outliers' is not defined

    In [67]: air_outliers[-1, -1] = 400
    NameError: name 'air_outliers' is not defined

    In [68]: air_outliers.plot()
    NameError: name 'air_outliers' is not defined

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)
    NameError: name 'air_outliers' is not defined

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)
    NameError: name 'air2d' is not defined

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])
    NameError: name 'air2d' is not defined

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)
    NameError: name 'air2d' is not defined

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")
    NameError: name 'air2d' is not defined

    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))
    NameError: name 'air' is not defined

    In [77]: t.coords
    AttributeError: 'numpy.ndarray' object has no attribute 'coords'

#### 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)
    AttributeError: 'numpy.ndarray' object has no attribute 'plot'

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
       ....: )
       ....:
    AttributeError: 'numpy.ndarray' object has no attribute 'isel'

#### 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))
    AttributeError: 'numpy.ndarray' object has no attribute 'isel'

    In [81]: t4d = xr.concat([t2, t2 + 40], pd.Index(["normal", "hot"], name="fourth_dim"))
    NameError: name 't2' is not defined

    # This is a 4d array
    In [82]: t4d.coords
    NameError: name 't4d' is not defined

    In [83]: t4d.plot(x="lon", y="lat", col="time", row="fourth_dim")
    NameError: name 't4d' is not defined

#### Other features

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

    In [84]: hasoutliers = t.isel(time=slice(0, 5)).copy()
    AttributeError: 'numpy.ndarray' object has no attribute 'isel'

    In [85]: hasoutliers[0, 0, 0] = -100
    NameError: name 'hasoutliers' is not defined

    In [86]: hasoutliers[-1, -1, -1] = 400
    NameError: name 'hasoutliers' is not defined

    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"},
       ....: )
       ....:
    NameError: name 'hasoutliers' is not defined

#### 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
    NameError: name 'g' is not defined

    In [89]: g.name_dicts
    NameError: name 'g' is not defined

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]]
    NameError: name 'g' is not defined

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)
    AttributeError: 'numpy.ndarray' object has no attribute 'plot'

    In [92]: for i, ax in enumerate(g.axs.flat):
       ....:     ax.set_title("Air Temperature %d" % i)
       ....:
    NameError: name 'g' is not defined

    In [93]: bottomright = g.axs[-1, -1]
    NameError: name 'g' is not defined

    In [94]: bottomright.annotate("bottom right", (240, 40))
    NameError: name 'bottomright' is not defined

    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]]]], shape=(3, 11, 4, 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'
    Attributes:
        units:    Aunits

    In [99]: ds.A.plot.scatter(x="y")
    Out[99]: <matplotlib.collections.PathCollection at 0x7c5f817812b0>

Same plot can be displayed using the dataset:

    In [100]: ds.plot.scatter(x="y", y="A")
    Out[100]: <matplotlib.collections.PathCollection at 0x7c5f8e8a07d0>

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 0x7c5f8e780cd0>

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 0x7c5f8e7a4f50>

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 0x7c5f8e780910>

    In [104]: ds.plot.scatter(x="A", y="B", hue="w", add_legend=False, add_colorbar=True)
    Out[104]: <matplotlib.collections.PathCollection at 0x7c5f8e829590>

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 0x7c5f8e6c8050>

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 0x7c5f8e51f390>

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 0x7c5f83cabb60>

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 0x7c5f8dfcc910>

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 0x7c5f83caa510>

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 0x7c5f8dd89950>

`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 0x7c5f8d88d450>

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 0x7c5f8c30b360>

## 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
    PermissionError: [Errno 13] Permission denied: '/sbuild-nonexistent' | Pooch could not create data cache folder '/sbuild-nonexistent/.cache/xarray_tutorial_data'. Will not be able to download data files.

    In [115]: p = air.isel(time=0).plot(
       .....:     subplot_kws=dict(projection=ccrs.Orthographic(-80, 35), facecolor="gray"),
       .....:     transform=ccrs.PlateCarree(),
       .....: )
       .....:
    NameError: name 'air' is not defined

    In [116]: p.axes.set_global()
    NameError: name 'p' is not defined

    In [117]: p.axes.coastlines()
    NameError: name 'p' is not defined

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)},
       .....: )
       .....:
    NameError: name 'air' is not defined

    In [119]: for ax in p.axs.flat:
       .....:     ax.coastlines()
       .....:     ax.gridlines()
       .....:
    NameError: name 'p' is not defined

    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 0x7c5f8dd3f390>]

    In [125]: da.plot.line(ax=axs[0, 1])
    Out[125]: [<matplotlib.lines.Line2D at 0x7c5f8dd3f250>]

    In [126]: xplt.plot(da, ax=axs[1, 0])
    Out[126]: [<matplotlib.lines.Line2D at 0x7c5f8d9460d0>]

    In [127]: xplt.line(da, ax=axs[1, 1])
    Out[127]: [<matplotlib.lines.Line2D at 0x7c5f8d945e50>]

    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.]], shape=(4, 3))
    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 0x7c5f80a6f610>

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 0x7c5f80beb9d0>

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 0x7c5f80a065d0>

    In [143]: ax.scatter(lon, lat, transform=ccrs.PlateCarree())
    Out[143]: <matplotlib.collections.PathCollection at 0x7c5f80b72e90>

    In [144]: ax.coastlines()
    Out[144]: <cartopy.mpl.feature_artist.FeatureArtist at 0x7c5f81781010>

    In [145]: ax.gridlines(draw_labels=True)
    Out[145]: <cartopy.mpl.gridliner.Gridliner at 0x7c5f81780440>

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 0x7c5f80b547d0>

    In [148]: ax.scatter(lon, lat, transform=ccrs.PlateCarree())
    Out[148]: <matplotlib.collections.PathCollection at 0x7c5f80b23b10>

    In [149]: ax.coastlines()
    Out[149]: <cartopy.mpl.feature_artist.FeatureArtist at 0x7c5f80b23ed0>

    In [150]: ax.gridlines(draw_labels=True)
    Out[150]: <cartopy.mpl.gridliner.Gridliner at 0x7c5f80b23d90>

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 0x7c5f801bce10>,
     <matplotlib.lines.Line2D at 0x7c5f801bcf50>,
     <matplotlib.lines.Line2D at 0x7c5f801bd090>,
     <matplotlib.lines.Line2D at 0x7c5f801bd1d0>]

    In [153]: da.plot.line(x="lon", hue="x", ax=ax[1])
    Out[153]:
    [<matplotlib.lines.Line2D at 0x7c5f801bf4d0>,
     <matplotlib.lines.Line2D at 0x7c5f801bf610>,
     <matplotlib.lines.Line2D at 0x7c5f801bf750>,
     <matplotlib.lines.Line2D at 0x7c5f801bf890>,
     <matplotlib.lines.Line2D at 0x7c5f801bf9d0>]