美文网首页Python数据分析与展示
(六)pyplot基础图表函数(学习笔记)|python数据分析

(六)pyplot基础图表函数(学习笔记)|python数据分析

作者: 努力奋斗的durian | 来源:发表于2018-01-29 10:39 被阅读49次

    1.pyplot基础图表函数概述
    2.pyplot图饼的绘制
    3.pyplot直方图的绘制
    4.pyplot极坐标图的绘制
    5.pyplot散点图的绘制
    6.单元小结
    [网页链接【Python数据分析与展示】.MOOC. 北京理工大学
    https://www.bilibili.com/video/av10101509/?from=search&seid=8584212945516406240#page=27)

    最近更新:2018-01-29

    1.pyplot基础图表函数概述

    重点是选什么样的图形与数据相对应




    2.pyplot图饼的绘制

    2.1扁形饼图

    import matplotlib.pyplot as plt
    labels="Frogs","Hogs","Dogs","Logs"
    sizes=[35,30,45,10]
    explode=(0,0.1,0,0)
    plt.pie(sizes,explode=explode,labels=labels,autopct="%1.1f%%",shadow=False,startangle=99)
    plt.show()
    

    2.2圆饼图

    扁形之间的区别是,增加了一行代码,plt.axis("equal")

    import matplotlib.pyplot as plt
    labels="Frogs","Hogs","Dogs","Logs"
    sizes=[35,30,45,10]
    explode=(0,0.1,0,0)
    plt.pie(sizes,explode=explode,labels=labels,autopct="%1.1f%%",shadow=False,startangle=99)
    plt.axis("equal")
    plt.show()
    

    3.pyplot直方图的绘制

    import matplotlib.pyplot as plt
    import numpy as np
    
    np.random.seed(0)
    mu,sigma=100,20
    a=np.random.normal(mu,sigma,size=100)
    plt.hist(a,20,normed=1,histtype="stepfilled",facecolor="b",alpha=0.75)
    plt.title("Histogram")
    plt.show()
    

    将hist代码中的hist由原来的20分别改为10,40



    hist改为10的代码及图像

    import matplotlib.pyplot as plt
    import numpy as np
    
    np.random.seed(0)
    mu,sigma=100,20
    a=np.random.normal(mu,sigma,size=100)
    plt.hist(a,10,normed=1,histtype="stepfilled",facecolor="b",alpha=0.75)
    plt.title("Histogram")
    plt.show()
    

    hist改为40的代码及图像

    import matplotlib.pyplot as plt
    import numpy as np
    
    np.random.seed(0)
    mu,sigma=100,20
    a=np.random.normal(mu,sigma,size=100)
    plt.hist(a,40,normed=1,histtype="stepfilled",facecolor="b",alpha=0.75)
    plt.title("Histogram")
    plt.show()
    

    理解直方图最关键的地方就是理解直方图的个数.

    4.pyplot极坐标图的绘制

    import matplotlib.pyplot as plt
    import numpy as np
    
    N=20
    theta=np.linspace(0.0,2*np.pi,N,endpoint=False)
    radii=10*np.random.rand(N)
    width=np.pi/4*np.random.rand(N)
    
    ax=plt.subplot(111,projection="polar")
    bars=ax.bar(theta,radii,width=width,bottom=0.0)
    
    for r,bar in zip(radii,bars):
        bar.set_facecolor(plt.cm.viridis(r/10.))
        bar.set_alpha(0.5)
        
    plt.show()
    


    最关键的代码行ax
    修改参数,将n由原来的20改为10,pi/4改为pi/2,


    import matplotlib.pyplot as plt
    import numpy as np
    
    N=10
    theta=np.linspace(0.0,2*np.pi,N,endpoint=False)
    radii=10*np.random.rand(N)
    width=np.pi/2*np.random.rand(N)
    
    ax=plt.subplot(111,projection="polar")
    bars=ax.bar(theta,radii,width=width,bottom=0.0)
    
    for r,bar in zip(radii,bars):
        bar.set_facecolor(plt.cm.viridis(r/10.))
        bar.set_alpha(0.5)
        
    plt.show()
    

    5.pyplot散点图的绘制

    import matplotlib.pyplot as plt
    import numpy as np
    
    fig,ax=plt.subplots()
    ax.plot(10*np.random.randn(100),10*np.random.randn(100),"o")
    ax.set_title("Simple Scatter")
        
    plt.show()
    

    6.单元小结

    相关文章

      网友评论

        本文标题:(六)pyplot基础图表函数(学习笔记)|python数据分析

        本文链接:https://www.haomeiwen.com/subject/gtatzxtx.html