目錄

1.繪制概率圖

2.繪制雷達圖

3.繪制流向圖

4.繪制極坐標圖

5.繪制詞云圖

---

1.繪制概率圖

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()

結果圖：

2.繪制雷達圖

import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
# 某學生的課程與成績
courses = ['數據結構', '數據可視化', '高數', '英語', '軟件工程', '組成原理', 'C語言', '體育']
scores = [82, 95, 78, 85, 45, 88, 76, 88]
dataLength = len(scores)               # 數據長度
# angles數組把圓周等分為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])  # 閉合
# 繪制雷達圖
plt.polar(angles,              # 設置角度scores,            # 設置各角度上的數據'bv--',             # 設置顏色、線型和端點符號linewidth=2)       # 設置線寬
# 設置角度網格標簽
plt.thetagrids(angles*180/np.pi, courses, fontproperties='simhei', fontsize=12)
# 填充雷達圖內部
plt.fill(angles, scores, facecolor='g', alpha=0.2)
plt.show()

結果圖：

3.繪制流向圖

import numpy as np
import matplotlib.pyplot as pltY, 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()

結果圖：

4.繪制極坐標圖

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'$\theta$')

結果圖：

?

5.繪制詞云圖

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)

結果圖：

?