硬肝！超详细matplotlib基础介绍！！！

conda install matplotlib

pip install matplotlib

import numpy as npimport matplotlib.pyplot as plt
x = np.linspace(-np.pi, np.pi, 30) # 在区间内生成30个等差数y = np.sin(x)print('x = ', x)print('y = ', y)

x =  [-3.14159265 -2.92493109 -2.70826953 -2.49160797 -2.2749464  -2.05828484 -1.84162328 -1.62496172 -1.40830016 -1.19163859 -0.97497703 -0.75831547 -0.54165391 -0.32499234 -0.10833078  0.10833078  0.32499234  0.54165391  0.75831547  0.97497703  1.19163859  1.40830016  1.62496172  1.84162328  2.05828484  2.2749464   2.49160797  2.70826953  2.92493109  3.14159265]y =  [-1.22464680e-16 -2.14970440e-01 -4.19889102e-01 -6.05174215e-01 -7.62162055e-01 -8.83512044e-01 -9.63549993e-01 -9.98533414e-01 -9.86826523e-01 -9.28976720e-01 -8.27688998e-01 -6.87699459e-01 -5.15553857e-01 -3.19301530e-01 -1.08119018e-01  1.08119018e-01  3.19301530e-01  5.15553857e-01  6.87699459e-01  8.27688998e-01  9.28976720e-01  9.86826523e-01  9.98533414e-01  9.63549993e-01  8.83512044e-01  7.62162055e-01  6.05174215e-01  4.19889102e-01  2.14970440e-01  1.22464680e-16]

plt.figure() # 创建一个新的窗口plt.plot(x, y) # 画一个x与y相关的曲线plt.show()# 显示图像

import numpy as npimport matplotlib.pyplot as plt
x = np.linspace(-np.pi, np.pi, 100) # 在区间内生成21个等差数y = np.sin(x)linear_y = 0.2 * x + 0.1
plt.figure(figsize = (8, 6)) # 自定义窗口的大小
plt.plot(x, y)plt.plot(x, linear_y, color = "red", linestyle = '--') # 自定义颜色和表示方式
plt.title('y = sin(x) and y = 0.2x + 0.1') # 定义该曲线的标题plt.xlabel('x') # 定义横轴标签plt.ylabel('y') # 定义纵轴标签
plt.show()

import numpy as npimport matplotlib.pyplot as plt
x = np.linspace(-np.pi, np.pi, 100) # 在区间内生成21个等差数y = np.sin(x)linear_y = 0.2 * x + 0.1
plt.figure(figsize = (8, 6)) # 自定义窗口的大小
plt.plot(x, y)plt.plot(x, linear_y, color = "red", linestyle = '--') # 自定义颜色和表示方式
plt.title('y = sin(x) and y = 0.2x + 0.1') # 定义该曲线的标题plt.xlabel('x') # 定义横轴标签plt.ylabel('y') # 定义纵轴标签plt.xlim(-np.pi, np.pi)plt.ylim(-1, 1)
# 重新设置x轴的刻度# plt.xticks(np.linspace(-np.pi, np.pi, 5))x_value_range = np.linspace(-np.pi, np.pi, 5)x_value_strs = [r'$\pi$', r'$-\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'$\pi$']plt.xticks(x_value_range, x_value_strs)plt.show() # 显示图像

import numpy as npimport matplotlib.pyplot as plt
x = np.linspace(-np.pi, np.pi, 100)y = np.sin(x)linear_y = 0.2 * x + 0.1
plt.figure(figsize = (8, 6)) 
plt.plot(x, y)plt.plot(x, linear_y, color = "red", linestyle = '--') 
plt.title('y = sin(x) and y = 0.2x + 0.1')plt.xlabel('x') plt.ylabel('y') plt.xlim(-np.pi, np.pi)plt.ylim(-1, 1)
# plt.xticks(np.linspace(-np.pi, np.pi, 5))x_value_range = np.linspace(-np.pi, np.pi, 5)x_value_strs = [r'$\pi$', r'$-\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'$\pi$']plt.xticks(x_value_range, x_value_strs)
ax = plt.gca() # 获取坐标轴ax.spines['right'].set_color('none') # 隐藏上方和右方的坐标轴ax.spines['top'].set_color('none')
# 设置左方和下方坐标轴的位置ax.spines['bottom'].set_position(('data', 0)) # 将下方的坐标轴设置到y = 0的位置ax.spines['left'].set_position(('data', 0)) # 将左方的坐标轴设置到 x = 0 的位置
plt.show() # 显示图像

import numpy as npimport matplotlib.pyplot as plt
x = np.linspace(-np.pi, np.pi, 100)y = np.sin(x)linear_y = 0.2 * x + 0.1
plt.figure(figsize = (8, 6)) 
# 为曲线加上标签plt.plot(x, y, label = "y = sin(x)")plt.plot(x, linear_y, color = "red", linestyle = '--', label = 'y = 0.2x + 0.1') 
plt.title('y = sin(x) and y = 0.2x + 0.1')plt.xlabel('x') plt.ylabel('y') plt.xlim(-np.pi, np.pi)plt.ylim(-1, 1)
# plt.xticks(np.linspace(-np.pi, np.pi, 5))x_value_range = np.linspace(-np.pi, np.pi, 5)x_value_strs = [r'$\pi$', r'$-\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'$\pi$']plt.xticks(x_value_range, x_value_strs)
ax = plt.gca() ax.spines['right'].set_color('none')ax.spines['top'].set_color('none')

ax.spines['bottom'].set_position(('data', 0)) ax.spines['left'].set_position(('data', 0)) 
# 将曲线的信息标识出来plt.legend(loc = 'lower right', fontsize = 12)plt.show() 

import numpy as npimport matplotlib.pyplot as plt
plt.figure(figsize = (16, 12))x = np.array([1, 2, 3, 4, 5, 6, 7, 8])y = np.array([3, 5, 7, 6, 2, 6, 10, 15])plt.plot(x, y, 'r', lw = 5) # 指定线的颜色和宽度
x = np.array([1, 2, 3, 4, 5, 6, 7, 8])y = np.array([13, 25, 17, 36, 21, 16, 10, 15])plt.bar(x, y, 0.2, alpha = 1, color='b') # 生成柱状图，指明图的宽度，透明度和颜色plt.show()

import numpy as npimport matplotlib.pyplot as plt
plt.figure(figsize = (16, 12))n = 12x = np.arange(n) # 按顺序生成从12以内的数字y1 = (1 - x / float(n)) * np.random.uniform(0.5, 1.0, n)y2 = (1 - x / float(n)) * np.random.uniform(0.5, 1.0, n)
# 设置柱状图的颜色以及边界颜色#+y表示在x轴的上方 -y表示在x轴的下方plt.bar(x, +y1, facecolor = '#9999ff', edgecolor = 'white')plt.bar(x, -y2, facecolor = '#ff9999', edgecolor = 'white')
plt.xlim(-0.5, n) # 设置x轴的范围，plt.xticks(()) # 可以通过设置刻度为空，消除刻度plt.ylim(-1.25, 1.25) # 设置y轴的范围plt.yticks(())
# plt.text()在图像中写入文本，设置位置，设置文本，ha设置水平方向对其方式，va设置垂直方向对齐方式for x1, y in zip(x, y2):    plt.text(x1, -y - 0.05, '%.2f' % y, ha = 'center', va = 'top')for x1, y in zip(x, y1):    plt.text(x1, y + 0.05, '%.2f' % y, ha = 'center', va = 'bottom')plt.show()

import numpy as npimport matplotlib.pyplot as pltN = 50x = np.random.rand(N)y = np.random.rand(N)colors = np.random.rand(N)area = np.pi * (15 * np.random.rand(N))**2plt.scatter(x, y, s = area,c = colors, alpha = 0.8)
plt.show()

import matplotlib.pyplot as pltimport numpy as np
def f(x, y):    return (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-x ** 2 - y ** 2)
n = 256x = np.linspace(-3, 3, n)y = np.linspace(-3, 3, n)X, Y = np.meshgrid(x, y)  # 生成网格坐标 将x轴与y轴正方形区域的点全部获取line_num = 10 # 等高线的数量
plt.figure(figsize = (16, 12))
#contour 生成等高线的函数#前俩个参数表示点的坐标，第三个参数表示等成等高线的函数，第四个参数表示生成多少个等高线C = plt.contour(X, Y, f(X, Y), line_num, colors = 'black', linewidths = 0.5) # 设置颜色和线段的宽度plt.clabel(C, inline = True, fontsize = 12) # 得到每条等高线确切的值
# 填充颜色, cmap 表示以什么方式填充，hot表示填充热量的颜色plt.contourf(X, Y, f(X, Y), line_num, alpha = 0.75, cmap = plt.cm.hot)
plt.show()

import matplotlib.pyplot as pltimport matplotlib.image as mpimg # 导入处理图片的库import matplotlib.cm as cm # 导入处理颜色的库colormap
plt.figure(figsize = (16, 12))img = mpimg.imread('image/fuli.jpg')# 读取图片print(img) # numpy数据print(img.shape) # 
plt.imshow(img, cmap = 'hot')plt.colorbar() # 得到颜色多对应的数值plt.show()

[[[ 11  23  63]  [ 12  24  64]  [  1  13  55]  ...  [  1  12  42]  [  1  12  42]  [  1  12  42]]
 [[ 19  31  71]  [  3  15  55]  [  0  10  52]  ...  [  0  11  39]  [  0  11  39]  [  0  11  39]]
 [[ 22  34  74]  [  3  15  55]  [  7  19  61]  ...  [  0  11  39]  [  0  11  39]  [  0  11  39]]
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 [[ 84 125 217]  [ 80 121 213]  [ 78 118 214]  ...  [ 58  90 191]  [ 54  86 187]  [ 53  85 186]]
 [[ 84 124 220]  [ 79 119 215]  [ 78 117 218]  ...  [ 55  87 188]  [ 55  87 188]  [ 55  87 188]]
 [[ 83 121 220]  [ 80 118 219]  [ 83 120 224]  ...  [ 56  88 189]  [ 58  90 191]  [ 59  91 192]]](728, 516, 3)

import matplotlib.pyplot as pltimport matplotlib.cm as cm # 导入处理颜色的库colormapimport numpy as np
size =  8# 得到一个8*8数值在(0, 1)之间的矩阵a = np.linspace(0, 1, size ** 2).reshape(size, size)
plt.figure(figsize = (16, 12))plt.imshow(a)plt.show()

import numpy as npimport matplotlib.pyplot as pltfrom mpl_toolkits.mplot3d import Axes3D # 导入Axes3D对象
fig = plt.figure(figsize = (16, 12))ax = fig.add_subplot(111, projection = '3d') # 得到3d图像
x = np.arange(-4, 4, 0.25)y = np.arange(-4, 4, 0.25)X, Y = np.meshgrid(x, y) # 生成网格Z = np.sqrt(X ** 2 + Y ** 2)
# 画曲面图              # 行和列对应的跨度         # 设置颜色ax.plot_surface(X, Y, Z, rstride = 1, cstride = 1, cmap = plt.get_cmap('rainbow'))plt.show()