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:

filter_none

edit
close

play_arrow

link
brightness_4
code

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()

chevron_right


Output:

Example 2:

filter_none

edit
close

play_arrow

link
brightness_4
code

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' %
                                                    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()

chevron_right


Output:




My Personal Notes arrow_drop_up

Check out this Author's contributed articles.

If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.

Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.


Article Tags :

Be the First to upvote.


Please write to us at contribute@geeksforgeeks.org to report any issue with the above content.