Why ProPlot?

Matplotlib is an extremely powerful plotting package used by academics, engineers, and data scientists far and wide. However, certain plotting tasks can be cumbersome or repetitive for its heaviest users, i.e. users who…

  • …make very rich, complex figures.

  • …want to finely tune their figure annotations and aesthetics.

  • …create new figures nearly every day.

ProPlot’s core mission is to provide a smoother plotting experience for heavy matplotlib users. We do this by expanding upon the object-oriented matplotlib API. ProPlot makes changes that would be hard to justify or difficult to incorporate into matplotlib itself, owing to design choices and backwards compatibility considerations. This page enumerates these changes and explains how they address limitations of the matplotlib API.

Less typing, more plotting


Matplotlib users often need to change lots of plot settings all at once. With the default API, this requires calling a series of one-liner setter methods.

This workflow is quite verbose – it tends to require “boilerplate code” that gets copied and pasted a hundred times. It can also be confusing – it is often unclear whether properties are applied from an Axes setter (e.g. set_title, set_xlabel and set_xticks), an XAxis or YAxis setter (e.g. set_major_locator and set_major_formatter), a Spine setter (e.g. set_bounds), a miscellaneous “bulk” setter (e.g. tick_params), or whether they require tinkering with several different objects. Also, one often needs to loop through lists of subplots to apply identical settings to each subplot.


ProPlot introduces the format method for changing arbitrary settings in bulk. Think of this as an expanded and thoroughly documented version of the Artist update method. format can also be used to update Bulk global settings and various other rc settings for a particular subplot, and to concisely work with verbose classes using the Class constructor functions. Further, The subplot container class can be used to invoke format on several subplots at once.

Together, these features significantly reduce the amount of code needed to create highly customized figures. As an example, it is trivial to see that

import proplot as plot
f, axs = plot.subplots(ncols=2)
axs.format(linewidth=1, color='gray')
axs.format(xticks=20, xtickminor=True, xlabel='x axis', ylabel='y axis')

…is much more succinct than

import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
from matplotlib import rcParams
rcParams['axes.linewidth'] = 1
rcParams['axes.color'] = 'gray'
fig, axs = plt.subplots(ncols=2)
for ax in axs:
   ax.tick_params(width=1, color='gray', labelcolor='gray')
   ax.tick_params(axis='x', which='minor', bottom=True)
   ax.set_xlabel('x axis', color='gray')
   ax.set_ylabel('y axis', color='gray')
plt.style.use('default')  # restore

Class constructor functions


Matplotlib and cartopy introduce a bunch of classes with verbose names like MultipleLocator, FormatStrFormatter, and LambertAzimuthalEqualArea. Since plotting code has a half life of about 30 seconds, typing out all of these extra class names and import statements can be a major drag.

Parts of the matplotlib API were actually designed with this in mind. Backend classes, native axes projections, axis scales, box styles, arrow styles, and arc styles are referenced with “registered” string names, as are basemap projection types. So, why not “register” everything else?


In ProPlot, tick locators, tick formatters, axis scales, cartopy projections, colormaps, and property cyclers are all “registered”. This is done by creating several constructor functions and passing various keyword argument through the constructor functions. This may seem “unpythonic” but it is absolutely invaluable when writing plotting code.

Each constructor function accepts various other input types for your convenience. For example, scalar numbers passed to Locator returns a MultipleLocator instance, lists of strings passed to Formatter returns a FixedFormatter instance, and Colormap and Cycle accept colormap names, individual colors, and lists of colors. When a class instance is passed to the relevant constructor function, it is simply returned. See X and Y axis settings, Colormaps, and Color cycles for details.

The below table lists the constructor functions and the keyword arguments that use them.



Used by

Keyword argument(s)


Axis Locator

format and colorbar

locator=, xlocator=, ylocator=, minorlocator=, xminorlocator=, yminorlocator=, ticks=, xticks=, yticks=, minorticks=, xminorticks=, yminorticks=


Axis Formatter

format and colorbar

formatter=, xformatter=, yformatter=, ticklabels=, xticklabels=, yticklabels=


Axis ScaleBase


xscale=, yscale=


Property Cycler

1d plotting methods



Colormap instance

2d plotting methods



Normalize instance

2d plotting methods



Projection or Basemap



Note that set_xscale and set_yscale now accept instances of ScaleBase thanks to a monkey patch applied by ProPlot.

Automatic dimensions and spacing


Matplotlib plots tend to require lots of “tweaking” when you have more than one subplot in the figure. This is partly because you must specify the physical dimensions of the figure, while the dimensions of the individual subplots are more important:

  1. The subplot aspect ratio is usually more relevant than the figure aspect ratio, e.g. for map projections.

  2. The subplot width and height control the evident thickness of text and other content plotted inside the axes.

Matplotlib has a tight layout algorithm to keep you from having to “tweak” the spacing, but the algorithm cannot apply different amounts of spacing between different subplot row and column boundaries. This limitation often results in unnecessary whitespace, and can be a major problem when you want to put e.g. a legend on the outside of a subplot.


In ProPlot, you can specify the physical dimensions of a reference subplot instead of the figure by passing axwidth, axheight, and/or aspect to Figure. The default behavior is aspect=1 and axwidth=2 (inches). If the aspect ratio mode for the reference subplot is set to 'equal', as with Geographic and polar plots and imshow plots, the imposed aspect ratio will be used instead. Figure dimensions are constrained as follows:

  • When axwidth or axheight are specified, the figure width and height are calculated automatically.

  • When width is specified, the figure height is calculated automatically.

  • When height is specified, the figure width is calculated automatically.

  • When width and height or figsize is specified, the figure dimensions are fixed.

ProPlot also uses a custom tight layout algorithm that automatically determines the left, right, bottom, top, wspace, and hspace GridSpec parameters. This algorithm is simpler because:

  • The new GridSpec class permits variable spacing between rows and columns. It turns out this is critical for putting Colorbars and legends on the outside of subplots.

  • Figures are restricted to have only one GridSpec per figure. This is done by requiring users to draw all of their subplots at once with subplots (see GH#50).

See Automatic layout for details.

Eliminating redundancies


For many of us, figures with just one subplot are a rarity. We tend to need multiple subplots for comparing different datasets and illustrating complex concepts. Unfortunately, it is easy to end up with redundant figure elements when drawing multiple subplots; namely:

  • Repeated axis tick labels.

  • Repeated axis labels.

  • Repeated colorbars.

  • Repeated legends.

These sorts of redundancies are extremely common even in publications, where they waste valuable page space. They arise because this is the path of least resistance for the default API – removing redundancies tends to require a fair amount of extra work.


ProPlot seeks to eliminate redundant elements to help you make clear, concise figures. We tackle this issue using Shared and spanning labels and Figure colorbars and legends.

  • By default, axis tick labels and axis labels are shared between subplots in the same row or column. This is controlled by the sharex, sharey, spanx, and spany subplots keyword args.

  • The new Figure colorbar and legend methods make it easy to draw colorbars and legends intended to reference more than one subplot. For details, see the next section.

Outer colorbars and legends


In matplotlib, it is difficult to draw colorbars and legends intended to reference more than one subplot or along the outside of subplots:

  • To draw legends outside of subplots, you usually need to position the legend manually and adjust various GridSpec spacing properties to make room for the legend.

  • To make colorbars that span multiple subplots, you have to supply colorbar with a cax you drew yourself. This requires so much tinkering that most users just add identical colorbars to every single subplot!

Furthermore, drawing colorbars with fig.colorbar(..., ax=ax) tends to mess up subplot aspect ratios since the space allocated for the colorbar is “stolen” from the parent axes.


ProPlot introduces a brand new framework for drawing Axes colorbars and legends (colorbars and legends inside or along the outside edge of a subplot) and Figure colorbars and legends (colorbars and legends sapnning contiguous subplots along the edge of the figure):

  • Passing an “outer” location to Axes colorbar or Axes legend (e.g. loc='l' or loc='left') draws the colorbar or legend along the outside of the axes. Passing an “inner” location (e.g. loc='ur' or loc='upper right') draws an inset colorbar or legend. And yes, that’s right, you can now draw inset colorbars!

  • To draw a colorbar or legend along the edge of the figure, use Figure colorbar and legend. The col, row, and span keyword args control which GridSpec rows and columns are spanned by the colorbar or legend.

  • Since GridSpec permits variable spacing between subplot rows and columns, “outer” colorbars and legends do not mess up subplot spacing or add extra whitespace. This is critical e.g. if you have a colorbar between columns 1 and 2 but nothing between columns 2 and 3.

  • Figure and Axes colorbar widths are specified in physical units rather than relative units. This makes colorbar thickness independent of subplot size and easier to get just right.

There are also several New colorbar features and New legend features.

The subplot container class


In matplotlib, subplots returns a 2d ndarray for figures with more than one column and row, a 1d ndarray for single-row or single-column figures, or just an Axes instance for single-subplot figures.


In ProPlot, subplots returns a subplot_grid filled with Axes instances. This container lets you call arbitrary methods on arbitrary subplots all at once, which can be useful when you want to style your subplots identically (e.g. axs.format(tickminor=False)). The subplot_grid class also unifies the behavior of the three possible matplotlib.pyplot.subplots return values:

  • subplot_grid permits 2d indexing, e.g. axs[1,0]. Since subplots can generate figures with arbitrarily complex subplot geometry, this 2d indexing is useful only when the arrangement happens to be a clean 2d matrix.

  • subplot_grid also permits 1d indexing, e.g. axs[0], since it is a list subclass. The default order can be switched from row-major to column-major by passing order='F' to subplots.

  • When it is singleton, subplot_grid behaves like a scalar. So when you make a single axes with f, axs = plot.subplots(), axs[0].method(...) is equivalent to axs.method(...).

See Subplot grids for details.

New and improved plotting methods


Certain plotting tasks are quite difficult to accomplish with the default matplotlib API. The seaborn, xarray, and pandas packages offer improvements, but it would be nice to have this functionality build right into matplotlib. There is also room for improvement of the native matplotlib plotting methods that none of these packages address.


ProPlot adds various seaborn, xarray, and pandas features to the Axes plotting methods along with several brand new features designed to make your life easier.

  • The new area and areax methods call fill_between and fill_betweenx. These methods now accept 2D arrays and stack or overlay successive columns, and a negpos keyword argument that can be used to assign separate colors for negative and positive data.

  • The new parametric method draws parametric line plots, where the parametric coordinate is denoted with a colorbar rather than text annotations. This is much cleaner and more aesthetically pleasing than the conventional approach.

  • The new heatmap method invokes pcolormesh and draws ticks at the center of each box. This is more convenient for things like covariance matrices.

  • The bar and barh methods accept 2D arrays and stack or group successive columns. Just like fill_between and fill_betweenx, you will be able to use different colors for positive/negative bars.

  • All 1d plotting can be used to draw On-the-fly error bars using the means, medians, boxdata, and bardata keyword arguments. You no longer have to work with add_errobar method directly.

  • All 1d plotting methods accept a cycle keyword argument interpreted by Cycle and optional legend and colorbar keyword arguments for populating legends and colorbars at the specified location with the result of the plotting command. See Color cycles and Colorbars and legends.

  • All 2d plotting methods accept a cmap keyword argument interpreted by Colormap, a norm keyword argument interpreted by Norm, and an optional colorbar keyword argument for drawing on-the-fly colorbars with the resulting mappable. See Colormaps and Colorbars and legends.

  • All 2d plotting methods accept a labels keyword argument. This is used to draw contour labels or grid box labels on heatmap plots. Labels are colored black or white according to the luminance of the underlying filled contour or grid box color. See 2d plotting for details.

  • ProPlot fixes the irritating white-lines-between-filled-contours, white-lines-between-pcolor-patches, and white-lines-between-colorbar-patches vector graphic issues.

  • Matplotlib requires coordinate centers for contour plots and edges for pcolor plots. If you pass centers to pcolor, matplotlib treats them as edges and silently trims one row/column of your data. Most people don’t realize this! ProPlot changes this behavior: If edges are passed to contour or contourf, centers are calculated from the edges; if centers are passed to pcolor or pcolormesh, edges are estimated from the centers.

Xarray and pandas integration


When you pass the array-like xarray.DataArray, pandas.DataFrame, and pandas.Series containers to matplotlib plotting commands, the metadata is ignored. To create plots that are automatically labeled with this metadata, you must use the dedicated xarray.DataArray.plot, pandas.DataFrame.plot, and pandas.Series.plot tools instead.

This approach is fine for quick plots, but not ideal for complex ones. It requires learning a different syntax from matplotlib, and tends to encourage using the pyplot API rather than the object-oriented API. These tools also introduce features that would be useful additions to matplotlib in their own right, without requiring special data containers and an entirely separate API.


ProPlot reproduces most of the xarray.DataArray.plot, pandas.DataFrame.plot, and pandas.Series.plot features on the Axes plotting methods themselves. Passing an DataArray, DataFrame, or Series through any plotting method automatically updates the axis tick labels, axis labels, subplot titles, and colorbar and legend labels from the metadata. This can be disabled by passing autoformat=False to the plotting method or to subplots.

Also, as described in New and improved plotting methods, ProPlot implements certain features like grouped bar plots, layered area plots, heatmap plots, and on-the-fly colorbars and legends from the xarray and pandas APIs directly on the Axes class.

Cartopy and basemap integration


There are two widely-used engines for plotting geophysical data with matplotlib: cartopy and basemap. Using cartopy tends to be verbose and involve boilerplate code, while using basemap requires you to use plotting commands on a separate Basemap object rather than an axes object.

Also, cartopy and basemap plotting commands assume map projection coordinates unless specified otherwise. For most of us, this choice is very frustrating, since geophysical data are usually stored in longitude-latitude or “Plate Carrée” coordinates.


ProPlot integrates various cartopy and basemap features into the ProjAxes format method. This lets you apply all kinds of geographic plot settings, like coastlines, continents, political boundaries, and meridian and parallel gridlines. ProjAxes also overrides various plotting methods:

  • The new default for all GeoAxes plotting methods is transform=ccrs.PlateCarree().

  • The new default for all BasemapAxes plotting methods is latlon=True.

  • Global coverage over the poles and across the matrix longitude boundaries can be enforced by passing globe=True to any 2d plotting command, e.g. pcolormesh and contourf.

See Geographic and polar plots for details. Note that active development on basemap will halt after 2020. For now, cartopy is missing several features offered by basemap – namely, flexible meridian and parallel gridline labels, drawing physical map scales, and convenience features for adding background images like the “blue marble”. But once these are added to cartopy, ProPlot may remove the basemap integration features.

Colormaps and property cycles


In matplotlib, colormaps are implemented with the ListedColormap and LinearSegmentedColormap classes. They are hard to edit and hard to create from scratch.


In ProPlot, it is easy to manipulate colormaps and property cycles:

  • The Colormap constructor function can be used to slice and merge existing colormaps and/or generate brand new ones.

  • The Cycle constructor function can be used to make color cycles from colormaps! Color cycles can be applied to plots in a variety of ways; see Color cycles for details.

  • The new ListedColormap and LinearSegmentedColormap classes include several convenient methods and have a much nicer REPL string representation.

  • The PerceptuallyUniformColormap class is used to make Perceptually uniform colormaps. These have smooth, aesthetically pleasing color transitions represent your data accurately.

Importing ProPlot also makes all colormap names case-insensitive, and colormaps can be reversed or cyclically shifted by 180 degrees simply by appending '_r' or '_shifted' to the colormap name. This is powered by the CmapDict dictionary, which replaces matplotlib’s native colormap database.

Smarter colormap normalization


In matplotlib, when extend='min', extend='max', or extend='neither' is passed to colorbar , the colormap colors reserved for “out-of-bounds” values are truncated. This can be irritating for plots with very few colormap levels, which are often more desirable (see Discrete colormap levels).

The problem is that matplotlib “discretizes” colormaps by generating low-resolution lookup tables (see LinearSegmentedColormap). While straightforward, this approach has limitations and results in unnecessary plot-specific copies of the colormap. Ideally, the task of discretizing colormap colors should be left to the normalizer; matplotlib provides BoundaryNorm for this purpose, but it is seldom used and its features are limited.


In ProPlot, all colormaps retain a high-resolution lookup table and the BinNorm class is applied to every plot. BinNorm restricts your plot to a subset of lookup table colors matching the number of requested levels. It chooses indices such that the colorbar levels always traverse the full range of colors, no matter the extend setting, and makes sure the end colors on cyclic colormaps are distinct.

Also, before discretization, BinNorm passes values through the continuous normalizer requested by the user with the norm keyword argument (e.g. LogNorm or MidpointNorm). You can thus think of BinNorm as a “meta-normalizer”: BinNorm simply discretizes the result of any arbitrary continuous transformation.

Bulk global settings


In matplotlib, there are several rcParams that you often want to set all at once, like the tick lengths and spine colors. It is also often desirable to change these settings for individual subplots or individual blocks of code rather than globally.


In ProPlot, you can use the rc object to change lots of settings at once with convenient shorthands. This is meant to replace matplotlib’s rcParams. dictionary. Settings can be changed with plot.rc.key = value, plot.rc[key] = value, plot.rc.update(...), with the format method, or with the context method.

For details, see Configuring proplot. The most notable bulk settings are described below.





The color for axes bounds, ticks, and labels.

axes.edgecolor, geoaxes.edgecolor, axes.labelcolor, tick.labelcolor, hatch.color, xtick.color, ytick.color


The width of axes bounds and ticks.

axes.linewidth, geoaxes.linewidth, hatch.linewidth, xtick.major.width, ytick.major.width


Font size for “small” labels.

font.size, tick.labelsize, xtick.labelsize, ytick.labelsize, axes.labelsize, legend.fontsize, geogrid.labelsize


Font size for “large” labels.

abc.size, figure.titlesize, axes.titlesize, suptitle.size, title.size, leftlabel.size, toplabel.size, rightlabel.size, bottomlabel.size


Padding between ticks and labels.

xtick.major.pad, xtick.minor.pad, ytick.major.pad, ytick.minor.pad


Tick direction.

xtick.direction, ytick.direction


Tick length.

xtick.major.size, ytick.major.size, ytick.minor.size * tickratio, xtick.minor.size * tickratio


Ratio between major and minor tick lengths.

xtick.major.size, ytick.major.size, ytick.minor.size * tickratio, xtick.minor.size * tickratio


Margin width when limits not explicitly set.

axes.xmargin, axes.ymargin

Physical units engine


Matplotlib requires users to use inches for the figure size figsize. This may be confusing for users outside of the U.S.

Matplotlib also uses figure-relative units for the margins left, right, bottom, and top, and axes-relative units for the column and row spacing wspace and hspace. Relative units tend to require “tinkering” with numbers until you find the right one. And since they are relative, if you decide to change your figure size or add a subplot, they will have to be readjusted.


ProPlot introduces the physical units engine units for interpreting figsize, width, height, axwidth, axheight, left, right, top, bottom, wspace, hspace, and arguments in a few other places. Acceptable units include inches, centimeters, millimeters, pixels, points, picas, em-heights, and light years (because why not?). Em-heights are particularly useful, as labels already present can be useful rulers for figuring out the amount of space needed.

units is also used to convert settings passed to rc from arbitrary physical units to points – for example, rc.linewidth, rc.ticklen, rc[‘axes.titlesize’], and rc[‘axes.titlepad’]. See Configuring proplot for details.

The .proplot folder


In matplotlib, it can be difficult to design your own colormaps and color cycles, and there is no builtin way to save them for future use. It is also difficult to get matplotlib to use custom .ttc, .ttf, and .otf font files, which may be desirable when you are working on Linux servers with limited font selections.


ProPlot automatically adds colormaps, color cycles, and font files saved in the .proplot/cmaps, .proplot/cycles, and .proplot/fonts folders in your home directory. You can save colormaps and color cycles to these folders simply by passing save=True to Colormap and Cycle. To manually load from these folders, e.g. if you have added files to these folders but you do not want to restart your ipython session, simply call register_cmaps, register_cycles, and register_fonts.