第三回:布局格式定方圆
import numpy as np import pandas as pd import matplotlib.pyplot as plt plt.rcParams['font.sans-serif'] = ['SimHei'] #用于正常显示中文标签 plt.rcParams['axes.unicode_minus'] = False #用于正常显示负号
一、子图?
1. 使用 plt.subplots 均匀状态下绘制子图
返回元素是由画布和子图组成的列表。第一个数字是行，第二个是列，默认值为1
figsize 整个画布的大小可以指定参数
sharex 和 sharey 分别表示是否共享横轴和纵轴的刻度
tight_layout 函数可以调整子图的相对大小，使字符不重叠
fig, axs = plt.subplots(2, 5, figsize=(10, 4), sharex=True, sharey=True) fig.suptitle样例1， size=20) for i in range(2): for j in range(5): axs[i][j].scatter(np.random.randn(10), np.random.randn(10)) axs[i][j].set_title('第%d行，第%d列'%(i 1,j 1)) axs[i][j].set_xlim(-5,5) axs[i][j].set_ylim(-5,5) if i==1: axs[i][j].set_xlabel(横坐标) if j==0: axs[i][j].set_ylabel(纵坐标) fig.tight_layout() 
subplots是基于OO模式写作，显式创建一个或多个axes对象，然后在相应的子图对象上绘图。 另一种方法是使用subplot这样基于pyplot模式的写作方法，每次在指定位置新建子图，后续的绘图操作将指向当前子图，本质上subplot也是Figure.add_subplot一种包装。
在调用subplot一般需要输入三位数，分别代表总行数、总列数和当前子图index
plt.figure() # 子图1 plt.subplot(2,2,1) plt.plot([1,2], 'r') # 子图2 plt.subplot(2,2,2) plt.plot([1,2], 'b') #子图3 plt.subplot(224) # 当三位数小于10时，中间逗号可以省略，这个命令等于plt.subplot(2,2,4) plt.plot([1,2], 'g'); 除常规直角坐标系外，还可通过projection方法创建极坐标系下的图表
N = 150 r = 2 * np.random.rand(N) theta = 2
* np
.pi
* np
.random
.rand
(N
) area
=
200
* r
**
2 colors
= theta plt
.subplot
(projection
=
'polar'
) plt
.scatter
(theta
, r
, c
=colors
, s
=area
, cmap
=
'hsv'
, alpha
=
0.75
)
;
用极坐标系画出玫瑰图
from matplotlib.patches import Wedge
from matplotlib.collections import PatchCollection
# 可以借助上一章所学习的楔形图来绘制玫瑰图，此处我们随机生成36个分支，每个分支的长度从0.1开始，逐步增加0.01，最终生成如下所示的玫瑰图：
fig = plt.figure(figsize=(9,9))
ax1 = fig.add_subplot(111)
theta1 = 0
num = 36
patches = []
patches = [
Wedge((0.5, 0.5), 0.01 + 0.01*i, i* 360/num, (1 + i) * 360/num) for i in range(num)
]
colors = 100 * np.random.rand(len(patches))
p = PatchCollection(patches, alpha=0.8)
p.set_array(colors)
ax1.add_collection(p);
2. 使用 GridSpec 绘制非均匀子图¶
所谓非均匀包含两层含义，第一是指图的比例大小不同但没有跨行或跨列，第二是指图为跨列或跨行状态
利用 add_gridspec 可以指定相对宽度比例 width_ratios 和相对高度比例参数 height_ratios
fig = plt.figure(figsize=(10, 4))
spec = fig.add_gridspec(nrows=2, ncols=5, width_ratios=[1,2,3,4,5], height_ratios=[1,3])
fig.suptitle('样例2', size=20)
for i in range(2):
for j in range(5):
ax = fig.add_subplot(spec[i, j])
ax.scatter(np.random.randn(10), np.random.randn(10))
ax.set_title('第%d行，第%d列'%(i+1,j+1))
if i==1: ax.set_xlabel('横坐标')
if j==0: ax.set_ylabel('纵坐标')
fig.tight_layout()
在上面的例子中出现了 spec[i, j] 的用法，事实上通过切片就可以实现子图的合并而达到跨图的共能 (切片方括号内用两个：分隔三个参数，下例中是用了两个参，其中是spec的分隔x，y)
fig = plt.figure(figsize=(10, 4))
spec = fig.add_gridspec(nrows=2, ncols=6, width_ratios=[2,2.5,3,1,1.5,2], height_ratios=[1,2])
fig.suptitle('样例3', size=20)
# sub1
ax = fig.add_subplot(spec[0, :3])
ax.scatter(np.random.randn(10), np.random.randn(10))
# sub2
ax = fig.add_subplot(spec[0, 3:5])
ax.scatter(np.random.randn(10), np.random.randn(10))
# sub3
ax = fig.add_subplot(spec[:, 5])
ax.scatter(np.random.randn(10), np.random.randn(10))
# sub4
ax = fig.add_subplot(spec[1, 0])
ax.scatter(np.random.randn(10), np.random.randn(10))
# sub5
ax = fig.add_subplot(spec[1, 1:5])
ax.scatter(np.random.randn(10), np.random.randn(10))
fig.tight_layout()
(5条消息) Python Matplotlib scatter函数：绘制散点图_人生苦短， 还不用Python？-CSDN博客_matplotlib scatter
二、子图上的方法¶
补充介绍一些子图上的方法
常用直线的画法为： axhline, axvline, axline （horizontal, vertical and any）
-
构造函数
axhline(y=0, xmin=0, xmax=1, **kwargs) axvline(x=0, ymin=0, ymax=1, **kwargs) axline(xy1, xy2=None, *, slope=None, **kwargs) -
Cases
fig, ax = plt.subplots(figsize=(4,3))
ax.axhline(0.5,0.2,0.8)
ax.axvline(0.5,0.2,0.8)
ax.axline([0.3,0.3],[0.7,0.7]);
这里的x(y)min,x(y)max指的是显示的比例，如下例
fig, ax = plt.subplots(figsize=(4,3))
ax.axhline(0.3,0,0.6)
ax.axvline(0.5,0.1,0.9)
ax.axline([0.3,0.3],[0.7,0.7]);
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)]
使用 grid 可以加灰色网格
fig, ax = plt.subplots(figsize=(4,3))
ax.grid(True)
使用 set_xscale 可以设置坐标轴的规度（指对数坐标等）
fig, axs = plt.subplots(1, 2, figsize=(10, 4))
for j in range(2):
axs[j].plot(list('abcd'), [10**i for i in range(4)])
if j==0:
axs[j].set_yscale('log')
else:
pass
fig.tight_layout()
plt.subplots(1, 2, figsize=(10, 4)) for j in range(2): axs[j].plot(list(‘abcd’), [10**i for i in range(4)]) if j==0: axs[j].set_yscale(‘log’) else: pass fig.tight_layout()
