Python中的 Matplotlib.ticker.IndexFormatter 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.IndexFormatter

matplotlib.ticker.IndexFormatter类是matplotlib.ticker类的子类，用于格式化位置 x，它是最近的第 i 个标签，其中 i = int(x + 0.5)。 i len(list) 的位置有 0 个刻度标签。

Syntax: class matplotlib.ticker.IndexFormatter(labels)

Parameter :

• labels: It is a list of labels.

示例 1：

import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
  
   
# create dummy data    
x = ['str{}'.format(k) for k in range(20)]
y = np.random.rand(len(x))
   
# create an IndexFormatter 
# with labels x
x_fmt = mpl.ticker.IndexFormatter(x)
   
fig,ax = plt.subplots()
  
ax.plot(y)
  
# set our IndexFormatter to be
# responsible for major ticks
ax.xaxis.set_major_formatter(x_fmt)

输出：

示例 2：

from matplotlib.ticker import IndexFormatter, IndexLocator
import pandas as pd
import matplotlib.pyplot as plt
  
  
years = range(2015, 2018)
fields = range(4)
days = range(4)
bands = ['R', 'G', 'B']
  
index = pd.MultiIndex.from_product(
    [years, fields], names =['year', 'field'])
  
columns = pd.MultiIndex.from_product(
    [days, bands], names =['day', 'band'])
  
df = pd.DataFrame(0, index = index, columns = columns)
  
df.loc[(2015, ), (0, )] = 1
df.loc[(2016, ), (1, )] = 1
df.loc[(2017, ), (2, )] = 1
ax = plt.gca()
plt.spy(df)
  
xbase = len(bands)
xoffset = xbase / 2
xlabels = df.columns.get_level_values('day')
  
ax.xaxis.set_major_locator(IndexLocator(base = xbase,
                                        offset = xoffset))
  
ax.xaxis.set_major_formatter(IndexFormatter(xlabels))
  
plt.xlabel('Day')
ax.xaxis.tick_bottom()
  
ybase = len(fields)
yoffset = ybase / 2
ylabels = df.index.get_level_values('year')
  
ax.yaxis.set_major_locator(IndexLocator(base = ybase, 
                                        offset = yoffset))
  
ax.yaxis.set_major_formatter(IndexFormatter(ylabels))
  
plt.ylabel('Year')
  
plt.show()

输出：