Improve Subplot Size/Spacing with Many Subplots in Matplotlib

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.*)

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()

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

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()

• Resolving this issue when plotting a dataframe with pandas.DataFrame.plot, which uses matplotlib as the default backend.
  • The following works for whichever kind= is specified (e.g. 'bar', 'scatter', 'hist', etc.)
• Tested in python 3.8.12, pandas 1.3.4, matplotlib 3.4.3

Imports and sample data

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# sinusoidal sample data
sample_length = range(1, 15+1)
rads = np.arange(0, 2*np.pi, 0.01)
data = np.array([np.sin(t*rads) for t in sample_length])
df = pd.DataFrame(data.T, index=pd.Series(rads.tolist(), name='radians'), columns=[f'freq: {i}x' for i in sample_length])

# display(df.head(3))
         freq: 1x  freq: 2x  freq: 3x  freq: 4x  freq: 5x  freq: 6x  freq: 7x  freq: 8x  freq: 9x  freq: 10x  freq: 11x  freq: 12x  freq: 13x  freq: 14x  freq: 15x
radians                                                                                                                                                            
0.00     0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000
0.01     0.010000  0.019999  0.029996  0.039989  0.049979  0.059964  0.069943  0.079915  0.089879   0.099833   0.109778   0.119712   0.129634   0.139543   0.149438
0.02     0.019999  0.039989  0.059964  0.079915  0.099833  0.119712  0.139543  0.159318  0.179030   0.198669   0.218230   0.237703   0.257081   0.276356   0.295520

# default plot with subplots; each column is a subplot
axes = df.plot(subplots=True)