Python數(shù)據(jù)可視化，無(wú)法繞過(guò)的matplotlib基礎(chǔ)知識(shí)！！！

conda?install?matplotlib

pip?install?matplotlib

import numpy as npimport matplotlib.pyplot as plt
x = np.linspace(-np.pi, np.pi, 30) # 在區(qū)間內(nèi)生成30個(gè)等差數(shù)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() # 創(chuàng)建一個(gè)新的窗口plt.plot(x, y) # 畫(huà)一個(gè)x與y相關(guān)的曲線(xiàn)plt.show()# 顯示圖像

import numpy as npimport matplotlib.pyplot as plt
x = np.linspace(-np.pi, np.pi, 100) # 在區(qū)間內(nèi)生成21個(gè)等差數(shù)y = np.sin(x)linear_y = 0.2 * x + 0.1
plt.figure(figsize = (8, 6)) # 自定義窗口的大小
plt.plot(x, y)plt.plot(x, linear_y, color = "red", linestyle = '--') # 自定義顏色和表示方式
plt.title('y = sin(x) and y = 0.2x + 0.1') # 定義該曲線(xiàn)的標(biāo)題plt.xlabel('x') # 定義橫軸標(biāo)簽plt.ylabel('y') # 定義縱軸標(biāo)簽
plt.show()

import numpy as npimport matplotlib.pyplot as plt
x = np.linspace(-np.pi, np.pi, 100) # 在區(qū)間內(nèi)生成21個(gè)等差數(shù)y = np.sin(x)linear_y = 0.2 * x + 0.1
plt.figure(figsize = (8, 6)) # 自定義窗口的大小
plt.plot(x, y)plt.plot(x, linear_y, color = "red", linestyle = '--') # 自定義顏色和表示方式
plt.title('y = sin(x) and y = 0.2x + 0.1') # 定義該曲線(xiàn)的標(biāo)題plt.xlabel('x') # 定義橫軸標(biāo)簽plt.ylabel('y') # 定義縱軸標(biāo)簽plt.xlim(-np.pi, np.pi)plt.ylim(-1, 1)
# 重新設(shè)置x軸的刻度# 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()?#?顯示圖像

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() # 獲取坐標(biāo)軸ax.spines['right'].set_color('none') # 隱藏上方和右方的坐標(biāo)軸ax.spines['top'].set_color('none')
# 設(shè)置左方和下方坐標(biāo)軸的位置ax.spines['bottom'].set_position(('data', 0)) # 將下方的坐標(biāo)軸設(shè)置到y(tǒng) = 0的位置ax.spines['left'].set_position(('data', 0)) # 將左方的坐標(biāo)軸設(shè)置到 x = 0 的位置
plt.show() # 顯示圖像

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)) 
# 為曲線(xiàn)加上標(biāo)簽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)) 
# 將曲線(xiàn)的信息標(biāo)識(shí)出來(lái)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) # 指定線(xiàn)的顏色和寬度
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') # 生成柱狀圖，指明圖的寬度，透明度和顏色plt.show()

import numpy as npimport matplotlib.pyplot as plt
plt.figure(figsize = (16, 12))n = 12x = np.arange(n) # 按順序生成從12以?xún)?nèi)的數(shù)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)
# 設(shè)置柱狀圖的顏色以及邊界顏色#+y表示在x軸的上方 -y表示在x軸的下方plt.bar(x, +y1, facecolor = '#9999ff', edgecolor = 'white')plt.bar(x, -y2, facecolor = '#ff9999', edgecolor = 'white')
plt.xlim(-0.5, n) # 設(shè)置x軸的范圍，plt.xticks(()) # 可以通過(guò)設(shè)置刻度為空，消除刻度plt.ylim(-1.25, 1.25) # 設(shè)置y軸的范圍plt.yticks(())
# plt.text()在圖像中寫(xiě)入文本，設(shè)置位置，設(shè)置文本，ha設(shè)置水平方向?qū)ζ浞绞剑瑅a設(shè)置垂直方向?qū)R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)  # 生成網(wǎng)格坐標(biāo) 將x軸與y軸正方形區(qū)域的點(diǎn)全部獲取line_num = 10 # 等高線(xiàn)的數(shù)量
plt.figure(figsize = (16, 12))
#contour 生成等高線(xiàn)的函數(shù)#前倆個(gè)參數(shù)表示點(diǎn)的坐標(biāo)，第三個(gè)參數(shù)表示等成等高線(xiàn)的函數(shù)，第四個(gè)參數(shù)表示生成多少個(gè)等高線(xiàn)C = plt.contour(X, Y, f(X, Y), line_num, colors = 'black', linewidths = 0.5) # 設(shè)置顏色和線(xiàn)段的寬度plt.clabel(C, inline = True, fontsize = 12) # 得到每條等高線(xiàn)確切的值
# 填充顏色, cmap 表示以什么方式填充，hot表示填充熱量的顏色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 # 導(dǎo)入處理圖片的庫(kù)import matplotlib.cm as cm # 導(dǎo)入處理顏色的庫(kù)colormap
plt.figure(figsize = (16, 12))img?=?mpimg.imread('image/fuli.jpg')#?讀取圖片print(img) # numpy數(shù)據(jù)print(img.shape) # 
plt.imshow(img, cmap = 'hot')plt.colorbar() # 得到顏色多對(duì)應(yīng)的數(shù)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]]
 ...
 [[ 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 # 導(dǎo)入處理顏色的庫(kù)colormapimport numpy as np
size =  8# 得到一個(gè)8*8數(shù)值在(0, 1)之間的矩陣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 # 導(dǎo)入Axes3D對(duì)象
fig = plt.figure(figsize = (16, 12))ax = fig.add_subplot(111, projection = '3d') # 得到3d圖像
x = np.arange(-4, 4, 0.25)y = np.arange(-4, 4, 0.25)X, Y = np.meshgrid(x, y) # 生成網(wǎng)格Z = np.sqrt(X ** 2 + Y ** 2)
# 畫(huà)曲面圖              # 行和列對(duì)應(yīng)的跨度         # 設(shè)置顏色ax.plot_surface(X, Y, Z, rstride = 1, cstride = 1, cmap = plt.get_cmap('rainbow'))plt.show()