Matplotlib is a library in Python and it is numerical – mathematical extension for NumPy library. It is an amazing visualization library in Python for 2D plots of arrays and used for working with the broader SciPy stack.
matplotlib.axis.Axis.set_minor_formatter() Function
The Axis.set_minor_formatter() function in axis module of matplotlib library is used to set the formatter of the minor ticker.
Syntax: Axis.set_minor_formatter(self, formatter)
Parameters: This method accepts the following parameters.
- formatter: This parameter is the Formatter.
Return value: This method does not returns any value.
Below examples illustrate the matplotlib.axis.Axis.set_minor_formatter() function in matplotlib.axis:
Example 1:
Python3
# Implementation of matplotlib function from matplotlib.axis import Axis import matplotlib.pyplot as plt from matplotlib.ticker import MultipleLocator, ScalarFormatter fig, ax = plt.subplots() ax.plot([0, 5, 10, 15, 20], [3, 2, 1, 2, 4]) Axis.set_minor_locator(ax.xaxis, MultipleLocator(1)) Axis.set_minor_formatter(ax.xaxis, ScalarFormatter()) ax.tick_params(axis ='both', which ='major', labelsize = 14, pad = 12, colors ='g') ax.tick_params(axis ='both', which ='minor', labelsize = 8, colors ='b') plt.title("Matplotlib.axis.Axis.set_minor_formatter()\n\ Function Example", fontsize = 12, fontweight ='bold') plt.show() |
Output:
Example 2:
Python3
# Implementation of matplotlib function from matplotlib.axis import Axis import matplotlib.pyplot as plt from matplotlib.ticker import LogFormatter import numpy as np fig, axes = plt.subplots(2) dt = 0.01t = np.arange(dt, 20.0, dt) # first plot doesn't use a formatter axes[0].semilogx(t, np.exp(-t / 5.0)) axes[0].grid() xlims = [[0, 25], [0.2, 8], [0.6, 0.9]] for ax, xlim in zip(axes[1:], xlims): ax.semilogx(t, np.exp(-t / 5.0)) formatter = LogFormatter(labelOnlyBase = False, minor_thresholds = (2, 0.4)) Axis.set_minor_formatter(ax.xaxis, formatter) ax.grid() fig.suptitle("Matplotlib.axis.Axis.set_minor_formatter()\n\ Function Example", fontsize = 12, fontweight ='bold') plt.show() |
Output:
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