A note on the explicit vs. implicit interfaces#

Matplotlib has two interfaces. For an explanation of the trade-offs between theexplicit and implicit interfaces seeMatplotlib Application Interfaces (APIs).

In the explicit object-oriented (OO) interface we directly utilize instances ofaxes.Axes to build up the visualization in an instance offigure.Figure. In the implicit interface, inspired by and modeled onMATLAB, we use a global state-based interface which is encapsulated in thepyplot module to plot to the "current Axes". See thepyplottutorials for a more in-depth look at thepyplot interface.

Most of the terms are straightforward but the main thing to rememberis that:

• TheFigure is the final image, and may contain one or moreAxes.

• TheAxes represents an individual plot (not to be confused with

  Axis, which refers to the x-, y-, or z-axis of a plot).

We call methods that do the plotting directly from the Axes, which givesus much more flexibility and power in customizing our plot.

Our data#

We'll use the data from the post from which this tutorial was derived.It contains sales information for a number of companies.

importmatplotlib.pyplotaspltimportnumpyasnpdata={'Barton LLC':109438.50,'Frami, Hills and Schmidt':103569.59,'Fritsch, Russel and Anderson':112214.71,'Jerde-Hilpert':112591.43,'Keeling LLC':100934.30,'Koepp Ltd':103660.54,'Kulas Inc':137351.96,'Trantow-Barrows':123381.38,'White-Trantow':135841.99,'Will LLC':104437.60}group_data=list(data.values())group_names=list(data.keys())group_mean=np.mean(group_data)

Getting started#

This data is naturally visualized as a barplot, with one bar pergroup. To do this with the object-oriented approach, we first generatean instance offigure.Figure andaxes.Axes. The Figure is like a canvas, and the Axesis a part of that canvas on which we will make a particular visualization.

fig,ax=plt.subplots()

Now that we have an Axes instance, we can plot on top of it.

fig,ax=plt.subplots()ax.barh(group_names,group_data)

Controlling the style#

There are many styles available in Matplotlib in order to let you tailoryour visualization to your needs. To see a list of styles, we can usestyle.

print(plt.style.available)

['Solarize_Light2', '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'petroff10', 'petroff6', 'petroff8', 'seaborn-v0_8', 'seaborn-v0_8-bright', 'seaborn-v0_8-colorblind', 'seaborn-v0_8-dark', 'seaborn-v0_8-dark-palette', 'seaborn-v0_8-darkgrid', 'seaborn-v0_8-deep', 'seaborn-v0_8-muted', 'seaborn-v0_8-notebook', 'seaborn-v0_8-paper', 'seaborn-v0_8-pastel', 'seaborn-v0_8-poster', 'seaborn-v0_8-talk', 'seaborn-v0_8-ticks', 'seaborn-v0_8-white', 'seaborn-v0_8-whitegrid', 'tableau-colorblind10']

You can activate a style with the following:

plt.style.use('fivethirtyeight')

Now let's remake the above plot to see how it looks:

fig,ax=plt.subplots()ax.barh(group_names,group_data)

The style controls many things, such as color, linewidths, backgrounds,etc.

Customizing the plot#

Now we've got a plot with the general look that we want, so let's fine-tuneit so that it's ready for print. First let's rotate the labels on the x-axisso that they show up more clearly. We can gain access to these labelswith theaxes.Axes.get_xticklabels() method:

fig,ax=plt.subplots()ax.barh(group_names,group_data)labels=ax.get_xticklabels()

If we'd like to set the property of many items at once, it's useful to usethepyplot.setp() function. This will take a list (or many lists) ofMatplotlib objects, and attempt to set some style element of each one.

fig,ax=plt.subplots()ax.barh(group_names,group_data)labels=ax.get_xticklabels()plt.setp(labels,rotation=45,horizontalalignment='right')

It looks like this cut off some of the labels on the bottom. We cantell Matplotlib to automatically make room for elements in the figuresthat we create. To do this we set theautolayout value of ourrcParams. For more information on controlling the style, layout, andother features of plots with rcParams, seeCustomizing Matplotlib with style sheets and rcParams.

plt.rcParams.update({'figure.autolayout':True})fig,ax=plt.subplots()ax.barh(group_names,group_data)labels=ax.get_xticklabels()plt.setp(labels,rotation=45,horizontalalignment='right')

Next, we add labels to the plot. To do this with the OO interface,we can use theArtist.set() method to set properties of thisAxes object.

fig,ax=plt.subplots()ax.barh(group_names,group_data)labels=ax.get_xticklabels()plt.setp(labels,rotation=45,horizontalalignment='right')ax.set(xlim=[-10000,140000],xlabel='Total Revenue',ylabel='Company',title='Company Revenue')

We can also adjust the size of this plot using thepyplot.subplots()function. We can do this with thefigsize keyword argument.

fig,ax=plt.subplots(figsize=(8,4))ax.barh(group_names,group_data)labels=ax.get_xticklabels()plt.setp(labels,rotation=45,horizontalalignment='right')ax.set(xlim=[-10000,140000],xlabel='Total Revenue',ylabel='Company',title='Company Revenue')

For labels, we can specify custom formatting guidelines in the form offunctions. Below we define a function that takes an integer as input, andreturns a string as an output. When used withAxis.set_major_formatter orAxis.set_minor_formatter, they will automatically create and use aticker.FuncFormatter class.

For this function, thex argument is the original tick label andposis the tick position. We will only usex here but both arguments areneeded.

defcurrency(x,pos):"""The two arguments are the value and tick position"""ifx>=1e6:s=f'${x*1e-6:1.1f}M'else:s=f'${x*1e-3:1.0f}K'returns

We can then apply this function to the labels on our plot. To do this,we use thexaxis attribute of our Axes. This lets you performactions on a specific axis on our plot.

fig,ax=plt.subplots(figsize=(6,8))ax.barh(group_names,group_data)labels=ax.get_xticklabels()plt.setp(labels,rotation=45,horizontalalignment='right')ax.set(xlim=[-10000,140000],xlabel='Total Revenue',ylabel='Company',title='Company Revenue')ax.xaxis.set_major_formatter(currency)

Combining multiple visualizations#

It is possible to draw multiple plot elements on the same instance ofaxes.Axes. To do this we simply need to call another one ofthe plot methods on that Axes object.

fig,ax=plt.subplots(figsize=(8,8))ax.barh(group_names,group_data)labels=ax.get_xticklabels()plt.setp(labels,rotation=45,horizontalalignment='right')# Add a vertical line, here we set the style in the function callax.axvline(group_mean,ls='--',color='r')# Annotate new companiesforgroupin[3,5,8]:ax.text(145000,group,"New Company",fontsize=10,verticalalignment="center")# Now we move our title up since it's getting a little crampedax.title.set(y=1.05)ax.set(xlim=[-10000,140000],xlabel='Total Revenue',ylabel='Company',title='Company Revenue')ax.xaxis.set_major_formatter(currency)ax.set_xticks([0,25e3,50e3,75e3,100e3,125e3])fig.subplots_adjust(right=.1)plt.show()

Saving our plot#

Now that we're happy with the outcome of our plot, we want to save it todisk. There are many file formats we can save to in Matplotlib. To seea list of available options, use:

print(fig.canvas.get_supported_filetypes())

{'eps': 'Encapsulated Postscript', 'gif': 'Graphics Interchange Format', 'jpg': 'Joint Photographic Experts Group', 'jpeg': 'Joint Photographic Experts Group', 'pdf': 'Portable Document Format', 'pgf': 'PGF code for LaTeX', 'png': 'Portable Network Graphics', 'ps': 'Postscript', 'raw': 'Raw RGBA bitmap', 'rgba': 'Raw RGBA bitmap', 'svg': 'Scalable Vector Graphics', 'svgz': 'Scalable Vector Graphics', 'tif': 'Tagged Image File Format', 'tiff': 'Tagged Image File Format', 'webp': 'WebP Image Format'}

We can then use thefigure.Figure.savefig() in order to save the figureto disk. Note that there are several useful flags we show below:

• transparent=True makes the background of the saved figure transparentif the format supports it.

• dpi=80 controls the resolution (dots per square inch) of the output.

• bbox_inches="tight" fits the bounds of the figure to our plot.

# Uncomment this line to save the figure.# fig.savefig('sales.png', transparent=False, dpi=80, bbox_inches="tight")

Total running time of the script: (0 minutes 4.881 seconds)

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