学习笔记,这个笔记以例子为主。
开发工具:Spyder
文章目录
matplotlib概述
matplotlib是python的一个绘图库。使用它可以很方便的绘制质量级别高的图形。
matplotlib基本绘图
先来几个案例简单了解一下matplotlib
案例1(绘制一条余弦曲线)
- 语法
import numpy as np
import matplotlib.pyplot as mp
# xarray: <序列> 水平坐标序列
# yarray: <序列> 垂直坐标序列
mp.plot(xarray, yarray)
#显示图表
mp.show()
代码:
import numpy as np
import matplotlib.pyplot as mp
#生成一条正弦曲线
x = np.linspace(-np.pi, np.pi, 1000)
print(x.shape)
sin_x = np.sin(x)
#绘制
mp.plot(x, sin_x)
mp.show()
图像:
案例2(绘制水平线与垂直线)
- 语法
import numpy as np
import matplotlib.pyplot as mp
# vertical 绘制垂直线
mp.vlines(vval, ymin, ymax, ...)
#vval为x坐标值,ymin和ymax为垂直线的最小最大值
# horizotal 绘制水平线
mp.hlines(xval, xmin, xmax, ...)
#xval为y坐标值, xmin和xmax为水平线的最小最大值
#显示图表
mp.show()
代码:
import numpy as np
import matplotlib.pyplot as mp
xs = np.arange(6)
ys = np.array([20, 60, 40, 50, 10, 20])
mp.plot(xs, ys)
mp.vlines(3, 20, 50)
mp.hlines(30, 1, 4)
mp.show()
图像:

案例3(绘制多条垂直/水平线)
代码:
import numpy as np
import matplotlib.pyplot as mp
xs = np.arange(6)
ys = np.array([20, 60, 40, 50, 10, 20])
mp.plot(xs, ys)
mp.vlines([3, 5, 7], 20, 50)
mp.hlines(30, 1, 4)
mp.show()
图像:
线型、线宽和颜色
- 语法
mp.plot(xarray, yarray, linestyle='', linewidth=1, color='', alpha=0.5)
参数表:
参数 | 含义 | 参数值 |
---|---|---|
linestyle | 线型 | "-" "--" ":" ".-" |
linewidth | 线宽 | 数字 |
color | 颜色 | 颜色的英文单词 / 常见颜色英文单词首字母 / #495434 / (1,1,1) / (1,1,1,1) |
alpha | 透明度 | 浮点数值 |
颜色表:
- 举个例子
代码:
import numpy as np
import matplotlib.pyplot as mp
x = np.linspace(-np.pi, np.pi, 1000)
sin_x = np.sin(x)
cos_x = np.cos(x)
mp.plot(x, sin_x, linestyle=':', alpha = 0.8,
linewidth= 2, color='dodgerblue')
mp.plot(x, cos_x, linestyle='-.', alpha = 0.8,
linewidth= 2, color='orangered')
mp.show()
图像:
设置坐标轴范围
- 语法
mp.xlim(x_limt_min, x_limit_max)
#x_limt_min: <float> x轴范围最小值
#x_limit_max: <float> x轴范围最大值
mp.ylim(y_limt_min, y_limit_max)
#y_limt_min: <float> y轴范围最小值
#y_limit_max: <float> y轴范围最大值
- 举个例子(设置坐标轴范围)
代码:
import numpy as np
import matplotlib.pyplot as mp
x = np.linspace(-np.pi, np.pi, 1000)
sin_x = np.sin(x)
cos_x = np.cos(x)
mp.plot(x, sin_x, linestyle=':', alpha = 0.8,
linewidth= 2, color='dodgerblue')
mp.plot(x, cos_x, linestyle='-.', alpha = 0.8,
linewidth= 2, color='orangered')
mp.xlim(0, np.pi)
mp.ylim(0, 1)
mp.show()
图像:
设置坐标刻度
- 语法
mp.xticks(x_val_list, x_text_list)
#x_val_list: x轴刻度值序列
#x_text_list: x轴刻度标签文本序列 [可选]
mp.yticks(y_val_list, y_text_list)
#y_val_list: y轴刻度值序列
#y_text_list: y轴刻度标签文本序列 [可选]
举个例子(修改坐标轴刻度)
代码:
import numpy as np
import matplotlib.pyplot as mp
x = np.linspace(-np.pi, np.pi, 1000)
sin_x = np.sin(x)
cos_x = np.cos(x)
mp.plot(x, sin_x, linestyle=':', alpha = 0.8,
linewidth= 2, color='dodgerblue')
mp.plot(x, cos_x, linestyle='-.', alpha = 0.8,
linewidth= 2, color='orangered')
mp.xticks([-np.pi, 0, np.pi],
['-π', '0', 'π'])
mp.show()
图像:
举个例子2(Latex排版语法字符串)
代码:
import numpy as np
import matplotlib.pyplot as mp
x = np.linspace(-np.pi, np.pi, 1000)
sin_x = np.sin(x)
cos_x = np.cos(x)
mp.plot(x, sin_x, linestyle=':', alpha = 0.8,
linewidth= 2, color='dodgerblue')
mp.plot(x, cos_x, linestyle='-.', alpha = 0.8,
linewidth= 2, color='orangered')
mp.xticks(
[-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi],
[r'$-\pi$', r'$-\frac{\pi}{2}$', '0',
r'$\frac{\pi}{2}$', r'$\pi$'])
mp.yticks([-1.0, -0.5, 0, 0.5, 1])
mp.show()
图像:
设置坐标轴
- 语法
# 获取当前坐标轴字典,{'left':左轴,'right':右轴,'bottom':下轴,'top':上轴 }
ax = mp.gca()
# 获取其中某个坐标轴
axis = ax.spines['坐标轴名']
#坐标轴名:left/right/bottom/top
# 设置坐标轴的位置。 该方法需要传入2个元素的元组作为参数
axis.set_position((type, val))
# type: <str> 移动坐标轴的参照类型 一般设置为'data' (以数据的值作为移动参照值)
# val: 参照值
# 设置坐标轴的颜色
axis.set_color(color)
# color: <str> 颜色值字符串
#若需要隐藏掉坐标轴,则可设置为color参数为'none'
- 举个例子
代码:
import numpy as np
import matplotlib.pyplot as mp
x = np.linspace(-np.pi, np.pi, 1000)
sin_x = np.sin(x)
cos_x = np.cos(x)
mp.plot(x, sin_x, linestyle=':', alpha = 0.8,
linewidth= 2, color='dodgerblue')
mp.plot(x, cos_x, linestyle='-.', alpha = 0.8,
linewidth= 2, color='orangered')
# 设置坐标轴
ax = mp.gca()
ax.spines['top'].set_color('none')
ax.spines['right'].set_color('none')
ax.spines['left'].set_position(('data', 0))
ax.spines['bottom'].set_position(('data', 0))
mp.show()
图像:
图例
- 语法
mp.legend(loc = 0)
位置(loc)标识:
- 举个例子
代码:
import numpy as np
import matplotlib.pyplot as mp
x = np.linspace(-np.pi, np.pi, 1000)
sin_x = np.sin(x)
cos_x = np.cos(x)
mp.plot(x, sin_x, linestyle=':', alpha = 0.8,
linewidth= 2, color='dodgerblue',
label = r'sin(x)')
mp.plot(x, cos_x, linestyle='-.', alpha = 0.8,
linewidth= 2, color='orangered',
label = r'cos(x)')
mp.legend(loc = 2)
mp.show()
图像:
特殊点
- 语法
# xarray: <序列> 所有需要标注点的水平坐标组成的序列
# yarray: <序列> 所有需要标注点的垂直坐标组成的序列
mp.scatter(xarray, yarray,
marker='', #点型 ~ matplotlib.markers
s='', #大小
edgecolor='', #边缘色
facecolor='', #填充色
zorder=3) #绘制图层编号 (编号越大,图层越靠上,就会把图层较小的图层覆盖掉)
点型图:
- 举个例子
代码(只贴绘制特殊点的代码):
# 绘制特殊点
px = [3 / 4 * np.pi, 3 / 4 * np.pi]
py = [np.sin(px[0]), np.cos(px[1])]
mp.scatter(px, py, marker='o', color='red',
s=70, label='Points', zorder=3)
图像:
备注
- 语法
# 在图表中为某个点添加备注。包含备注文本,备注箭头等图像的设置。
mp.annotate(
r'$\frac{\pi}{2}$', #备注中显示的文本内容
xycoords='data', #备注目标点所使用的坐标系(data表示数据坐标系)
xy=(x, y), #备注目标点的坐标
textcoords='offset points', #备注文本所使用的坐标系(offset points表示参照点的偏移坐标系)
xytext=(x, y), #备注文本的坐标
fontsize=14, #备注文本的字体大小
arrowprops=dict() #使用字典定义文本指向目标点的箭头样式
)
arrowprops参数使用字典定义指向目标点的箭头样式:
#arrowprops字典参数的常用key
arrowprops=dict(
arrowstyle='', #定义箭头样式
connectionstyle='' #定义连接线的样式
)
箭头样式(arrowstyle)字符串如下:
'-' |
None |
'->' |
head_length=0.4,head_width=0.2 |
'-[' |
widthB=1.0,lengthB=0.2,angleB=None |
'|-|' |
widthA=1.0,widthB=1.0 |
'-|>' |
head_length=0.4,head_width=0.2 |
'<-' |
head_length=0.4,head_width=0.2 |
'<->' |
head_length=0.4,head_width=0.2 |
'<|-' |
head_length=0.4,head_width=0.2 |
'<|-|>' |
head_length=0.4,head_width=0.2 |
'fancy' |
head_length=0.4,head_width=0.4,tail_width=0.4 |
'simple' |
head_length=0.5,head_width=0.5,tail_width=0.2 |
'wedge' |
tail_width=0.3,shrink_factor=0.5 |
连接线样式(connectionstyle)字符串如下:
‘angle’ | angleA=90,angleB=0,rad=0.0 |
‘angle3’ | angleA=90,angleB=0` |
‘arc’ | angleA=0,angleB=0,armA=None,armB=None,rad=0.0 |
‘arc3’ | rad=0.0 |
‘bar’ | armA=0.0,armB=0.0,fraction=0.3,angle=None |
- 举个例子
代码(只贴备注部分的代码):
mp.annotate(
r'$[\frac{3\pi}{4}, cos(\frac{3\pi}{4})]$',
xycoords='data',
xy=(3/4 * np.pi, np.cos(px[1])),
textcoords='offset points',
xytext=(-80, -30),
fontsize=14,
arrowprops=dict(
arrowstyle='-|>',
connectionstyle='angle3'
))
图像: