Python 中的 matplotlib . ticker . autominorlocator 类
原文:https://www . geeksforgeeks . org/matplotlib-ticker-autominorlocator-in-class-python/
Matplotlib 是 Python 中一个惊人的可视化库，用于数组的 2D 图。Matplotlib 绘图库是一个基于 NumPy 阵列的多平台数据可视化库，旨在与更广泛的 SciPy 堆栈协同工作。
matplotlib.ticker .自动转换器
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。主要刻度需要以线性刻度均匀分布。
语法:class matplotlib . ticker . autominorlocator(n =无)
参数:
- n: 表示主要蜱类间隔的细分数。如果省略 n 或无，它会自动设置为 5 或 4。
该类方法:
- tick_values(self，vmin，vmax): 给定 vmin 和 vmax，它返回找到的 tick 的值。
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:
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:
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