Matplotlib.ticker.AutoMinorLocator Class in Python

Matplotlib is an amazing visualization library in Python for 2D plots of arrays. Matplotlib is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack.

matplotlib.ticker.AutoMinorLocator

The matplotlib.ticker.AutoMinorLocator class is used to find minor tick positions based on the positions of major ticks dynamically. The major ticks need to be evenly spaced along with a linear scale.

Syntax: class matplotlib.ticker.AutoMinorLocator(n=None)

parameter:

• n: it represents the number of subdivisions of the interval between major ticks. If n is omitted or None, it automatically sets to 5 or 4.

Methods of the class:

• tick_values(self, vmin, vmax): Given vmin and vmax it returns the value of the located ticks.

Example 1:

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import ticker

data = [
    ('Area 1', 'Bar 1', 2, 2),
    ('Area 2', 'Bar 2', 1, 3),
    ('Area 1', 'Bar 3', 3, 2),
    ('Area 2', 'Bar 4', 2, 3),
]

df = pd.DataFrame(data, columns =('A', 'B',
                                  'D1', 'D2'))

df = df.set_index(['A', 'B'])
df.sort_index(inplace = True)

# Remove the index names for the plot,
# or it'll be used as the axis label
df.index.names = ['', '']

ax = df.plot(kind ='barh', stacked = True)

minor_locator = ticker.AutoMinorLocator(2)

ax.yaxis.set_minor_locator(minor_locator)

ax.set_yticklabels(df.index.get_level_values(1))
ax.set_yticklabels(df.index.get_level_values(0).unique(),
                   minor = True)

ax.set_yticks(np.arange(0.5, len(df), 2),
              minor = True)

ax.tick_params(axis ='y', which ='minor',
               direction ='out', pad = 50)

plt.show()

Output:

Example 2:

from pylab import *
import matplotlib
import matplotlib.ticker as ticker


# Setting minor ticker size to 0,
# globally.
matplotlib.rcParams['xtick.minor.size'] = 0

# Create a figure with just one
# subplot.
fig = figure()
ax = fig.add_subplot(111)

# Set both X and Y limits so that
# matplotlib
ax.set_xlim(0, 800)

# Fixes the major ticks to the places
# where desired (one every hundred units)
ax.xaxis.set_major_locator(ticker.FixedLocator(range(0,
                                                     801,
                                                     100)))
ax.xaxis.set_major_formatter(ticker.NullFormatter())

# Add minor tickers AND labels for them
ax.xaxis.set_minor_locator(ticker.AutoMinorLocator(n = 2))
ax.xaxis.set_minor_formatter(ticker.FixedFormatter(['AB %d' % x
                                                    for x in range(1, 9)]))

ax.set_ylim(-2000, 6500, auto = False)

# common attributes for the bar plots
bcommon = dict(
    height = [8500],
    bottom = -2000,
    width = 100)


bars = [[600, 'green'],
        [700, 'red']]
for left, clr in bars:
    bar([left], color = clr, **bcommon)


show()

Output: