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.AutoLocator
The matplotlib.ticker.AutoLocator class is a subclass of matplotlib.ticker.MaxNLocator, and has parameters nbins = ‘auto’ and steps = [1, 2, 2.5, 5, 10]. It is used to dynamically find major tick positions.
Syntax:class matplotlib.ticker.AutoLocator
Parameters:
- nbins: It is either an integer or ‘auto’, where the integer value represents the maximum number of intervals; one less than max number of ticks. The number of bins gets automatically determined on the basis of the length of the axis.It is an optional argument and has a default value of 10.
- steps: It is an optional parameter representing a nice number sequence that starts from 1 and ends with 10.
- integer: It is an optional boolean value. If set True, the ticks accepts only integer values, provided at least min_n_ticks integers are within the view limits.
- symmetric: It is an optional value. If set to True, auto-scaling will result in a range symmetric about zero.
- prune: It is an optional parameter that accepts either of the four values: {‘lower’, ‘upper’, ‘both’, None}. By default it is None.
Example 1:
Python3
import matplotlibimport matplotlib.pyplot as pltimport numpy as npfig, axes = plt.subplots(3, 4, sharex = 'row', sharey = 'row', squeeze = False)data = np.random.rand(20, 2, 10)for ax in axes.flatten()[:-1]: ax.plot(*np.random.randn(2, 10), marker ="o", ls ="")# Now remove axes[1, 5] from # the grouper for xaxisaxes[2, 3].get_shared_x_axes().remove(axes[2, 3])# Create and assign new tickerxticker = matplotlib.axis.Ticker()axes[2, 3].xaxis.major = xticker# The new ticker needs new locator # and formattersxloc = matplotlib.ticker.AutoLocator()xfmt = matplotlib.ticker.ScalarFormatter()axes[2, 3].xaxis.set_major_locator(xloc)axes[2, 3].xaxis.set_major_formatter(xfmt)# Now plot to the "ungrouped" axesaxes[2, 3].plot(np.random.randn(10)*100 + 100, np.linspace(-3, 3, 10), marker ="o", ls ="", color ="green")plt.show() |
Output:
Example 2:
Python3
import pylab as plfrom matplotlib import ticker# helper functiondef AutoLocatorInit(self): ticker.MaxNLocator.__init__(self, nbins = 4, steps =[1, 2, 5, 10])ticker.AutoLocator.__init__ = AutoLocatorInitpl.plot(pl.randn(100))pl.figure()pl.hist(pl.randn(1000), bins = 40)pl.show() |
Output:
