The most of contents borrowed from the book 『Python Data Science Handbook: Essential Tools for Working with Data, Jake VanderPlas, O'REILLY, 2017』. I have just added somewhat object-oriented or matlab style codes.

Customizing Ticks

Major and Minor Ticks

In [5]:
%matplotlib inline

import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import numpy as np
In [6]:
ax = plt.axes(xscale = 'log', yscale='log')
In [7]:
print(ax.xaxis.get_major_locator())
print(ax.xaxis.get_minor_locator())

<matplotlib.ticker.LogLocator object at 0x00000265265A72C8>
<matplotlib.ticker.LogLocator object at 0x00000265265A7748>
In [8]:
print(ax.xaxis.get_major_formatter())
print(ax.xaxis.get_minor_formatter())

<matplotlib.ticker.LogFormatterSciNotation object at 0x00000265265A7608>
<matplotlib.ticker.LogFormatterSciNotation object at 0x00000265264C1648>

Hiding Ticks or Labels

In [9]:
ax = plt.axes()
ax.plot(np.random.rand(50))

ax.yaxis.set_major_locator(plt.NullLocator())
ax.xaxis.set_major_formatter(plt.NullFormatter())

Grid of Images

  • Notice that each image has own axes,and we've set the locators to null because the tick values (pixel number in this case) do not convey relevant information for this particular visualization.
In [10]:
fig, ax = plt.subplots(5, 5, figsize=(5,5))
fig.subplots_adjust(hspace=0, wspace=0)

# Get some face data from scikit-learn
from sklearn.datasets import fetch_olivetti_faces
faces = fetch_olivetti_faces().images

for i in range(5):
    for j in range(5):
        ax[i, j].xaxis.set_major_locator(plt.NullLocator())
        ax[i, j].yaxis.set_major_locator(plt.NullLocator())
        ax[i, j].imshow(faces[10*i + j], cmap='bone')

downloading Olivetti faces from https://ndownloader.figshare.com/files/5976027 to C:\Users\actruce\scikit_learn_data

Reducing or Increasing the Number of Ticks

In [11]:
fig, ax = plt.subplots(4, 4, sharex = True, sharey = True)
In [13]:
# For every axis, set the x and y major locator
for axi in ax.flat:
    axi.xaxis.set_major_locator(plt.MaxNLocator(3))
    axi.yaxis.set_major_locator(plt.MaxNLocator(3))
fig
Out[13]:

Fancy Tick Formats

In [14]:
# Plot a sine and cosine curve
fig, ax = plt.subplots()
x = np.linspace(0, 3*np.pi, 1000)
ax.plot(x, np.sin(x), lw=3, label='Sine')
ax.plot(x, np.cos(x), lw=3, label='Cosine')

# Set up grid, legend, and limits
ax.grid(True)
ax.legend(frameon=False)
ax.axis('equal')
ax.set_xlim(0, 3*np.pi)
Out[14]:
(0, 9.42477796076938)
In [17]:
# 1. To space the ticks and grid lines in multiples of pi.
ax.xaxis.set_major_locator(plt.MultipleLocator(np.pi / 2))
ax.xaxis.set_minor_locator(plt.MultipleLocator(np.pi / 4))
fig
Out[17]:
In [20]:
# 2. To fix decimal representation, use plt.FuncFormatter
def format_func(value, tick_number):
    # find number of multiples of pi/2
    N = int(np.round(2*value / np.pi))
    if N == 0:
        return '0'
    elif N == 1:
        return r'$\pi/2$'
    elif N % 2 > 0:
        return r'${}\pi/2$'.format(N)
    else:
        return r'${}\pi$'.format(N // 2)

ax.xaxis.set_major_formatter(plt.FuncFormatter(format_func))
fig
Out[20]:
In [ ]: