1.绘制概率图
from scipy.stats import norm fig,ax = plt.subplots() plt.rcParams['font.family'] = ['SimHei'] np.random.seed() mu = 100 sigma = 15 x = mu+sigma*np.random.randn(437) num_bins = 50 n,bins,patches = ax.hist(x,num_bins,density = 1,color='c') y=norm.pdf(bins,mu,sigma) ax.plot(bins,y,'r--') fig.tight_layout() plt.show()
结果图：
2.绘制雷达图
import matplotlib.pyplot as plt import numpy as np %matplotlib inline # 某学生的课程与成绩 courses = ['数据结构', '数据可视化', '高数', '英语', '软件工程', '组成原理', 'C语言', '体育'] scores = [82, 95, 78, 85, 45, 88, 76, 88] dataLength = len(scores) # 数据长度 # angles数组把圆周等分为dataLength份 angles = np.linspace(0, 2*np.pi, dataLength, endpoint=False) courses.append(courses[0]) scores.append(scores[0]) angles = np.append(angles,angles[0]) # 闭合 # 绘制雷达图 plt.polar(angles, # 设置角度 scores, # 设置各角度上的数据 'bv--', # 设置颜色、线型和端点符号 linewidth=2) # 设置线宽 # 设置角度网格标签 plt.thetagrids(angles*180/np.pi, courses, fontproperties='simhei', fontsize=12) # 填充雷达图内部 plt.fill(angles, scores, facecolor='g', alpha=0.2) plt.show()
结果图：
3.绘制流向图
import numpy as np import matplotlib.pyplot as plt Y, X = np.mgrid[-3:3:100j, -3:3:100j] U = -1 - X**2 + Y V = 1 + X - Y**2 speed = np.sqrt(U*U + V*V) plt.streamplot(X, Y, U, V, color=U, linewidth=2, cmap=plt.cm.autumn) plt.colorbar() f, (ax1, ax2) = plt.subplots(ncols=2) ax1.streamplot(X, Y, U, V, density=[0.5, 1]) lw = 5*speed/speed.max() ax2.streamplot(X, Y, U, V, density=0.6, color='k', linewidth=lw) plt.show()
结果图：
4.绘制极坐标图
r = np.linspace(0, 2, 100) theta = 2 * np.pi * r fig = plt.figure(figsize=(13, 4)) ax1 = plt.subplot(121, projection='polar') ax1.scatter(theta, r, label = 'Polar Projection', s = 10) ax1.legend(bbox_to_anchor = (0.85, 1.35)) ax2 = plt.subplot(122) ax2.scatter(theta, r, label = 'Planar Projection', s = 5) ax2.legend(bbox_to_anchor = (0.85, 1.35)) ax2.set_xlabel('R') ax2.set_ylabel(r'$\theta$')
结果图：
5.绘制词云图
import matplotlib.pyplot as plt from wordcloud import WordCloud,STOPWORDS # from pyecharts.charts import WordCloud import PIL.Image as image import numpy as np def get_wordList(): f = open("data//text.txt") wordList = f.read() return wordList def get_wordClound(mylist): pic_path = 'data//myimg.jpg' img_mask = np.array(image.open(pic_path)) wordcloud = WordCloud(background_color="white",mask=img_mask).generate(mylist) plt.imshow(wordcloud) plt.axis("off") wordList = get_wordList() get_wordClound(wordList)
结果图：
