These are all taken from this great 3-hr YouTube tutorial by Ben Root from SciPy 2018. I’ve condensed the main takeaways of the talk into the following list of key concepts / tricks that I hadn’t previously been aware of.

General

To set figure aspect ratio to 2:1:

fig, ax = plt.subplots(figsize=plt.figaspect(0.5))

To loop through axes without axes[0][0] notation:

fig, axes = plt.subplots(nrows=2, ncols=2)
for ax in axes.flat:
	ax.plot(x,y)

To change z-order of plot (higher value = on top):

ax.plot(x, y, zorder=1)
ax.fill_between(x, y1, y2, zorder=2)

To get aes() ggplot-like dataframe processing:

df = {
  'x' : [0],
  'y' : [1],
}
ax.plot('x', 'y', data=df)

To modify the aesthetics of any “artist” (i.e. visual element on a plot) after creation:

bars = ax.bar(x, y, color='k', align='center')
for b, y in zip(bars, y):
	if y < 0:
		bar.set(color='blue')

To plot a bunch of points with the same marker style (regardless of whether you show a line or not), use plot(). Otherwise, use scatter()

To get LaTex, use an r string with $ for math:

plt.title(r'$\sigma_i=1$')

To add/remove padding to plot:

ax.margins(x=0.1,y=0)

To get x-data to take up same number of pixels as equivalent range in y-axis:

ax.axis('equal')

To get chart element to not appear in legend, use the _nolegend_ label:

ax.plot(x, y, label="_nolegend_")

To have two plots share axes:

fig, axes = plt.subplots(1,2, sharex=True, sharey=True)

To have one plot have two different y-axis scales (“twinning”):

fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
ax1.plot(x,y)
ax2.plot(x2,y2)

Artists

An artist is anything that is visible on the plot.

There are two types of artists:

1. Primitives – Shapes
   • e.g. Rectangle, Polygon, Circle, Line
2. Containers – They are given a list of primitives to draw
   • e.g. Figure, Axes, Collection

To get a list of properties for an artist $X$:

plt.getp(X)

Limits

Always set limits after you plot your data

Ticks

• Tick (ticks) = location of tick label
• Tick Line (ticklines) = line that denotes location of the Tick
• Tick Label (ticklabels) = text dislabed at Tick
• Ticker = automatically determines Ticks + formats Tick Labels

Colors

Colormaps

Colorbars must be added to fig, not ax b/c it will occur outside of the ax and “steal” room from it. By default, it steals space from the last ax

im = ax.imshow(data)
fig.colorbar(im)

To use bottom-left to be origin of image (rather than top-left):

im = ax.imshow(data, origin='lower')
fig.colorbar(im)

To get to be centered at 0 (i.e. map white to 0 for the red/blue seismic colormap), use vim, vmax to specify the range of the colormap

X = Some constant
im = ax.imshow(data, cmap='seismic', vmin=-X, vmax=X)

To get an array from a colormap:

from matplotlib import cm

# To get 'Blues' divided into 10 equally spaced shades, using full spectrum
colors = cm.get_cmap('Blues', 10)(np.linspace(0, 1, 10))

Markers + Lines

References

1. Anatomy of Matplotlib (Beginner Level) | SciPy 2018 Tutorial | Benjamin Root & Hannah Aizenman