Python中的 Matplotlib.ticker.AutoMinorLocator 类

Matplotlib是Python中用于数组二维图的惊人可视化库。 Matplotlib 是一个基于 NumPy 数组构建的多平台数据可视化库，旨在与更广泛的 SciPy 堆栈配合使用。

matplotlib.ticker.AutoMinorLocator

matplotlib.ticker.AutoMinorLocator类用于根据主要刻度的位置动态查找次要刻度位置。主要刻度需要与线性刻度均匀分布。

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.

编程需要懂一点英语

类的方法：

tick_values(self, vmin, vmax)：给定 vmin 和 vmax，它返回定位的刻度值。

示例 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()

输出：

示例 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()

输出：