python 可视化
宇文云初 人气:0前言：
数据分析初始阶段，通常都要进行可视化处理。数据可视化旨在直观展示信息的分析结果和构思，令某些抽象数据具象化，这些抽象数据包括数据测量单位的性质或数量。本章用的程序库matplotlib是建立在Numpy之上的一个Python图库，它提供了一个面向对象的API和一个过程式类的MATLAB API，他们可以并行使用。
1、
import numpy as np import matplotlib.pyplot as plt scores=np.random.randint(0,100,50) plt.hist(scores,bins=8,histtype=‘stepfilled') plt.title(‘37') plt.show()
2、
x=np.arange(6) y1=np.array([1,4,3,5,6,7]) y2=np.array([3,4,3,5,6,7]) y3=np.array([2,4,3,5,6,7]) plt.stackplot(x,y1,y2,y3) plt.title(‘37') plt.show()
3、
random_state=np.random.RandomState(1231241) random_x=random_state.randn(10000) plt.hist(random_x,bins=25) plt.title(‘37') plt.show()
4、
data=np.array([10,30,15,30,15]) pie_labels=np.array([‘A',‘B',‘C',‘D',‘E']) plt.pie(data,radius=1.5,labels=pie_labels,autopct='%3.1f%%') plt.title(‘37') plt.show()
5、
import matplotlib as mpl mpl.rcParams[‘font.sans-serif']=[‘SimHei'] mpl.rcParams[‘axes.unicode_minus']=False kinds=[‘购物',‘礼尚往来',‘餐饮美食',‘通信',‘生活日用',‘交通出行',‘休闲娱乐',‘其他'] money_scale=[500/1500,123/1500,400/1500,234/1500,300/1500,200/1500,100/1500,150/1500] dev_position=[0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1] plt.pie(money_scale,labels=kinds,autopct='%3.1f%%',shadow=True, explode=dev_position,startangle=90) plt.title(‘37') plt.show()
6、
num=50 x=np.random.rand(num) y=np.random.rand(num) plt.scatter(x,y) plt.title(‘37') plt.show()
7、
num=50 x=np.random.rand(num) y=np.random.rand(num) area=(800*np.random.rand(num)**2) plt.scatter(x,y,s=area) plt.title(‘37') plt.show()
8、
plt.rcParams[‘font.sans-serif']=‘SimHei' plt.rcParams[‘axes.unicode_minus']=False x_speed=np.arange(10,210,10) y_distance=np.array([0.3,0.5,1,3,5,5.5,7,8,9,12,14,15.5,17.8,19,20,23,27,30,31,32]) plt.scatter(x_speed,y_distance,s=50,alpha=0.9) plt.title(‘37') plt.show()
9、
plt.rcParams[‘font.family']= ‘SimHei' plt.rcParams[‘axes.unicode_minus']=False data_2018=np.array([4500,6654.5,5283.4,5107.8,5443.3,5550.6,6400.2,6404.9,5483.1,5330.2,5543,6199.9]) data_2017=np.array([4605.2,4710.3,5168.9,4767.2,4947,5203,6047.4,5945.5,5219.6,5038.1,5196.3,5698.6]) plt.boxplot([data_2018,data_2017],labels=(‘2018年',‘2017年'),meanline=True,widths=0.5,vert=False,patch_artist=True) plt.title(‘37') plt.show()
10、
plt.rcParams[‘font.family']= ‘SimHei' plt.rcParams[‘axes.unicode_minus']=False dim_num=6 data=np.array([[0.50,0.32,0.35,0.30,0.30,0.88], [0.45,0.35,0.30,0.40,0.40,0.30], [0.43,0.99,0.30,0.28,0.22,0.30], [0.30,0.25,0.48,0.95,0.45,0.40], [0.20,0.38,0.87,0.45,0.32,0.28], [0.34,0.31,0.38,0.40,0.92,0.28]]) angles=np.linspace(0, 2 * np.pi, dim_num, endpoint=False) angles=np.concatenate((angles,[angles[0]])) data=np.concatenate((data,[data[0]])) radar_labels=[‘研究型(I)',‘艺术型(A)',‘社会型(S)',‘企业型(E)',‘传统型©',‘现实型®'] radar_labels=np.concatenate((radar_labels, [radar_labels[0]])) plt.polar(angles, data) plt.thetagrids(angles * 180/np.pi, labels=radar_labels) plt.fill(angles, data, alpha=0.25) plt.title(‘37') plt.show()
11、
data =np.array([20,50,10,15,30,55])
pie_labels=np.array([‘A',‘B',‘C',‘D',‘E',‘F'])
plt.pie(data,radius=1.5,wedgeprops={‘width': 0.7},labels=pie_labels,autopct='%3.1f%%',pctdistance=0.75)
plt.title(‘37')
plt.show()
12、
x = np.arange(1,13) y_a = np.array([191,123,234,42,123,432,567,234,231,132,123,134]) y_b = np.array([123,143,234,242,523,232,467,334,131,332,234,345]) y_c = np.array([91,123,534,432,223,332,367,434,111,322,345,560]) plt.stackplot(x,y_a,y_b,y_c) plt.title(‘37') plt.show()
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