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.MaxNLocator
The matplotlib.ticker.MaxNLocator class is used to select no more than N intervals at nice locations. It is a subclass of matplotlib.ticker.Locator.
Syntax: class matplotlib.ticker.MaxNLocator(*args, **kwargs)
Parameter:
- 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.
Methods of the class:
- set_params(self, **kwargs): It sets parameters for the locator.
- tick_values(self, vmin, vmax): It returns the values of the located ticks given vmin and vmax.
- view_limits(self, dmin, dmax): It is used to select a scale for the range from vmin to vmax.
Example 1:
import matplotlib.pyplot as plt from matplotlib import ticker import numpy as np N = 10x = np.arange(N) y = np.random.randn(N) fig = plt.figure() ax = fig.add_subplot(111) ax.plot(x, y) # Create your ticker object with M ticks M = 3yticks = ticker.MaxNLocator(M) # Set the yaxis major locator using # your ticker object. ax.yaxis.set_major_locator(yticks) plt.show() |
Output:
Example 2:
import matplotlib.pyplot as plt from matplotlib.ticker import MaxNLocator, IndexFormatter ax = df.plot() ax.xaxis.set_major_locator(MaxNLocator(11)) ax.xaxis.set_major_formatter(IndexFormatter(df.index)) ax.grid(which ='minor', alpha = 0.2) ax.grid(which ='major', alpha = 0.5) ax.legend().set_visible(False) plt.xticks(rotation = 75) plt.tight_layout() plt.show() |
Output:
