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:
