关键词：python, plot, matplotlib, break axes
方法一：
首先介绍一种简单快速的方法――调用包 brokenaxes。
import matplotlib.pyplot as plt
from brokenaxes import brokenaxes
import numpy as np
fig = plt.figure(figsize=(5,2))
bax = brokenaxes(xlims=((0, .1), (.4, .7)), ylims=((-1, .7), (.79, 1)), hspace=.05, despine=False)
x = np.linspace(0, 1, 100)
bax.plot(x, np.sin(10 * x), label="sin")
bax.plot(x, np.cos(10 * x), label="cos")
bax.legend(loc=3)
bax.set_xlabel("time")
bax.set_ylabel("value")
效果如下：
方法二：
拼接法，该种方法代码更繁琐，但更有可能满足个性化的需求。
请点击参考链接
"""
Broken axis example, where the y-axis will have a portion cut out.
"""
import matplotlib.pyplot as plt
import numpy as np
# 30 points between [0, 0.2) originally made using np.random.rand(30)*.2
pts = np.array([
0.015, 0.166, 0.133, 0.159, 0.041, 0.024, 0.195, 0.039, 0.161, 0.018,
0.143, 0.056, 0.125, 0.096, 0.094, 0.051, 0.043, 0.021, 0.138, 0.075,
0.109, 0.195, 0.050, 0.074, 0.079, 0.155, 0.020, 0.010, 0.061, 0.008])
# Now let"s make two outlier points which are far away from everything.
pts[[3, 14]] += .8
# If we were to simply plot pts, we"d lose most of the interesting
# details due to the outliers. So let"s "break" or "cut-out" the y-axis
# into two portions - use the top (ax) for the outliers, and the bottom
# (ax2) for the details of the majority of our data
f, (ax, ax2) = plt.subplots(2, 1, sharex=True)
# plot the same data on both axes
ax.plot(pts)
ax2.plot(pts)
# zoom-in / limit the view to different portions of the data
ax.set_ylim(.78, 1.) # outliers only
ax2.set_ylim(0, .22) # most of the data
# hide the spines between ax and ax2
ax.spines["bottom"].set_visible(False)
ax2.spines["top"].set_visible(False)
ax.xaxis.tick_top()
ax.tick_params(labeltop="off") # don"t put tick labels at the top
ax2.xaxis.tick_bottom()
# This looks pretty good, and was fairly painless, but you can get that
# cut-out diagonal lines look with just a bit more work. The important
# thing to know here is that in axes coordinates, which are always
# between 0-1, spine endpoints are at these locations (0,0), (0,1),
# (1,0), and (1,1). Thus, we just need to put the diagonals in the
# appropriate corners of each of our axes, and so long as we use the
# right transform and disable clipping.
d = .015 # how big to make the diagonal lines in axes coordinates
# arguments to pass to plot, just so we don"t keep repeating them
kwargs = dict(transform=ax.transAxes, color="k", clip_on=False)
ax.plot((-d, +d), (-d, +d), **kwargs) # top-left diagonal
ax.plot((1 - d, 1 + d), (-d, +d), **kwargs) # top-right diagonal
kwargs.update(transform=ax2.transAxes) # switch to the bottom axes
ax2.plot((-d, +d), (1 - d, 1 + d), **kwargs) # bottom-left diagonal
ax2.plot((1 - d, 1 + d), (1 - d, 1 + d), **kwargs) # bottom-right diagonal
# What"s cool about this is that now if we vary the distance between
# ax and ax2 via f.subplots_adjust(hspace=...) or plt.subplot_tool(),
# the diagonal lines will move accordingly, and stay right at the tips
# of the spines they are "breaking"
plt.show()
效果如下：
补充：python绘制折线图--纵坐标y轴截断
看代码吧~
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 4 21:50:38 2019
@author: muli
"""
import matplotlib.pyplot as plt
from pylab import *
mpl.rcParams["font.sans-serif"] = ["SimHei"] #支持中文
names = ["1","2","3","4","5"] # 刻度值命名
x = [1,2,3,4,5] # 横坐标
y3= [2,3,1,4,5] # 纵坐标
y4= [4,6,8,5,9] # 纵坐标
y5=[24,27,22,26,28] # 纵坐标
f, (ax3, ax) = plt.subplots(2, 1, sharex=False) # 绘制两个子图
plt.subplots_adjust(wspace=0,hspace=0.08) # 设置 子图间距
ax.plot(x, y3, color="red", marker="o", linestyle="solid",label=u"1") # 绘制折线
ax.plot(x, y4, color="g", marker="o", linestyle="solid",label=u"2") # 绘制折线
plt.xticks(x, names, rotation=45) # 刻度值
ax3.xaxis.set_major_locator(plt.NullLocator()) # 删除坐标轴的刻度显示
ax3.plot(x, y5, color="blue", marker="o", linestyle="solid",label=u"3") # 绘制折线
ax3.plot(x, y3, color="red", marker="o", linestyle="solid",label=u"1") # 起图例作用
ax3.plot(x, y4, color="g", marker="o", linestyle="solid",label=u"2") # 起图例作用
ax3.set_ylim(21, 30) # 设置纵坐标范围
ax.set_ylim(0, 10) # 设置纵坐标范围
ax3.grid(axis="both",linestyle="-.") # 打开网格线
ax.grid(axis="y",linestyle="-.") # 打开网格线
ax3.legend() # 让图例生效
plt.xlabel(u"λ") #X轴标签
plt.ylabel("mAP") #Y轴标签
ax.spines["top"].set_visible(False) # 边框控制
ax.spines["bottom"].set_visible(True) # 边框控制
ax.spines["right"].set_visible(False) # 边框控制
ax3.spines["top"].set_visible(False) # 边框控制
ax3.spines["bottom"].set_visible(False) # 边框控制
ax3.spines["right"].set_visible(False) # 边框控制
ax.tick_params(labeltop="off")
# 绘制断层线
d = 0.01 # 断层线的大小
kwargs = dict(transform=ax3.transAxes, color="k", clip_on=False)
ax3.plot((-d, +d), (-d, +d), **kwargs) # top-left diagonal
kwargs.update(transform=ax.transAxes, color="k") # switch to the bottom axes
ax.plot((-d, +d), (1 - d, 1 + d), **kwargs) # bottom-left diagonal
plt.show()
结果如图所示：
以上为个人经验，希望能给大家一个参考，也希望大家多多支持服务器之家。如有错误或未考虑完全的地方，望不吝赐教。
原文链接：https://blog.csdn.net/maryyu8873/article/details/84313423