概念 >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
散点图显示两组数据的值, 每个点的坐标位置由变量的值决定.
由一组不连接的点完成, 用于观察两种变量的相关性.
例如身高 - 体重, 温度 - 纬度, 等等.
- >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
- import numpy as np
- import matplotlib.pyplot as plt
- height = [161,170,182,175,173,165]
- weight = [50,58,80,70,69,55]
- plt.scatter(height,weight)
- plt.show()
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
股票前后两天的涨跌关系
- import numpy as np
- import matplotlib.pyplot as plt
- open,close = np.loadtxt("000001.csv",delimiter=",",skiprows=1,usecols=(1,4),unpack= True)
- change = close - open
- yesterday = change[:-1]
- today = change[1:]
- plt.scatter(yesterday,today)
- plt.show()
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
图的外观
颜色, c
点大小, s
透明度, alpha
点形状, marker
plt.scatter(yesterday,today,s = 100,c = "r",marker="<",alpha=0.5)
作业
使用 000001.SH 数据.
计算最高价和开盘价之差 diff.
绘出前后两天 diff 的散点图, 研究是否有相关性.
- import numpy as np
- import matplotlib.pyplot as plt
- open,high = np.loadtxt("000001.csv",delimiter=",",skiprows=1,usecols=(1,2),unpack= True)
- diff = high - open
- yesterday = diff[:-1]
- today = diff[1:]
- # plt.scatter(yesterday,today)
- # plt.show()
- plt.scatter(yesterday,today,s = 400,c= 'y',marker="2",alpha= 0.3)
- plt.show()
来源: http://www.bubuko.com/infodetail-2927192.html