Very similar to this question but with the difference that my figure can be as large as it needs to be.

I need to generate a whole bunch of vertically-stacked plots in matplotlib. The result will be saved using figsave and viewed on a webpage, so I don’t care how tall the final image is as long as the subplots are spaced so they don’t overlap.

No matter how big I allow the figure to be, the subplots always seem to overlap.

My code currently looks like

import matplotlib.pyplot as plt
import my_other_module

titles, x_lists, y_lists = my_other_module.get_data()

fig = plt.figure(figsize=(10,60))
for i, y_list in enumerate(y_lists):
    plt.subplot(len(titles), 1, i)
    plt.xlabel("Some X label")
    plt.ylabel("Some Y label")
    plt.title(titles[i])
    plt.plot(x_lists[i],y_list)
fig.savefig('out.png', dpi=100)

Try using plt.tight_layout

As a quick example:

import matplotlib.pyplot as plt

fig, axes = plt.subplots(nrows=4, ncols=4)
fig.tight_layout() # Or equivalently,  "plt.tight_layout()"

plt.show()

Without Tight Layout

With Tight Layout

You can use plt.subplots_adjust to change the spacing between the subplots (source)

call signature:

subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=None)

The parameter meanings (and suggested defaults) are:

left  = 0.125  # the left side of the subplots of the figure
right = 0.9    # the right side of the subplots of the figure
bottom = 0.1   # the bottom of the subplots of the figure
top = 0.9      # the top of the subplots of the figure
wspace = 0.2   # the amount of width reserved for blank space between subplots
hspace = 0.2   # the amount of height reserved for white space between subplots

The actual defaults are controlled by the rc file

I found that subplots_adjust(hspace = 0.001) is what ended up working for me. When I use space = None, there is still white space between each plot. Setting it to something very close to zero however seems to force them to line up. What I’ve uploaded here isn’t the most elegant piece of code, but you can see how the hspace works.

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as tic

fig = plt.figure()

x = np.arange(100)
y = 3.*np.sin(x*2.*np.pi/100.)

for i in range(5):
    temp = 510 + i
    ax = plt.subplot(temp)
    plt.plot(x,y)
    plt.subplots_adjust(hspace = .001)
    temp = tic.MaxNLocator(3)
    ax.yaxis.set_major_locator(temp)
    ax.set_xticklabels(())
    ax.title.set_visible(False)

plt.show()

import matplotlib.pyplot as plt

fig = plt.figure(figsize=(10,60))
plt.subplots_adjust( ... )

The plt.subplots_adjust method:

def subplots_adjust(*args, **kwargs):
    """
    call signature::

      subplots_adjust(left=None, bottom=None, right=None, top=None,
                      wspace=None, hspace=None)

    Tune the subplot layout via the
    :class:`matplotlib.figure.SubplotParams` mechanism.  The parameter
    meanings (and suggested defaults) are::

      left  = 0.125  # the left side of the subplots of the figure
      right = 0.9    # the right side of the subplots of the figure
      bottom = 0.1   # the bottom of the subplots of the figure
      top = 0.9      # the top of the subplots of the figure
      wspace = 0.2   # the amount of width reserved for blank space between subplots
      hspace = 0.2   # the amount of height reserved for white space between subplots

    The actual defaults are controlled by the rc file
    """
    fig = gcf()
    fig.subplots_adjust(*args, **kwargs)
    draw_if_interactive()

or

fig = plt.figure(figsize=(10,60))
fig.subplots_adjust( ... )

The size of the picture matters.

“I’ve tried messing with hspace, but increasing it only seems to make all of the graphs smaller without resolving the overlap problem.”

Thus to make more white space and keep the sub plot size the total image needs to be bigger.

You could try the subplot_tool()

plt.subplot_tool()

Similar to tight_layout matplotlib now (as of version 2.2) provides constrained_layout. In contrast to tight_layout, which may be called any time in the code for a single optimized layout, constrained_layout is a property, which may be active and will optimze the layout before every drawing step.

Hence it needs to be activated before or during subplot creation, such as figure(constrained_layout=True) or subplots(constrained_layout=True).

Example:

import matplotlib.pyplot as plt

fig, axes = plt.subplots(4,4, constrained_layout=True)

plt.show()

constrained_layout may as well be set via rcParams

plt.rcParams['figure.constrained_layout.use'] = True

See the what’s new entry and the Constrained Layout Guide