Python数据分析（8）matplotlib库的使用：基本图像类型（直方图，条形图，饼图）

傷城~ 2022-05-22 00:54 239阅读 0赞

#coding=gbk
    import matplotlib.pyplot as plt
    import numpy as np
    #图像的基本 类型
    #1.线性图
    x=np.arange(-2*np.pi,2*np.pi,0.01)
    y1=np.sin(3*x)/x
    y2=np.sin(2*x)/x
    y3=np.sin(4*x)/x
    plt.plot(x,y1)
    plt.plot(x,y2,'k--',linewidth=2)    #调整线宽，k为黑色的
    plt.plot(x,y3,'m--')
    plt.title('线性表',fontproperties='SimHei',fontsize=25,color='red')
    plt.show()      #画多条线时，会自动调整不同的颜色

![20180611223052331][]

#横坐标修改为pi
    x=np.arange(-2*np.pi,2*np.pi,0.01)
    y1=np.sin(3*x)/x
    y2=np.sin(2*x)/x
    y3=np.sin(4*x)/x
    plt.plot(x,y1)
    plt.plot(x,y2,'k--',linewidth=2)    #调整线宽，k为黑色的
    plt.plot(x,y3,'m--')
    plt.title('线性表',fontproperties='SimHei',fontsize=15,color='red')
    plt.xticks([-2*np.pi,-np.pi,0,np.pi, 2*np.pi],
               [r'$-2\pi$',r'$-\pi$',r'$0$',r'$\pi$',r'$2\pi$'])#修改横坐标的数字
    plt.yticks([-1,0,+1,+2,+3],[r'$-1$',r'$0$',r'$+1$',r'$+2$',r'$+3$'])
    plt.show()

![20180611223107914][]

#将pandas库的DataFrame对象画图
    import pandas as pd
    data={'series1':[1,2,4,8,13],
          'series2':[2,5,8,12,16],
          'series3':[4,6,2,7,9]}
    df=pd.DataFrame(data)
    x=np.arange(5)
    plt.axis([0,5,0,17])    #设置横坐标，纵轴长度
    plt.plot(x,df)
    plt.legend(df,loc=0)    #此处使用df 和 data 都可以
    plt.show()

![20180611223140455][]

#2直方图
    np.random.seed(123) #设定随机种子
    p=np.random.randint(0,100,100)
    plt.hist(p,35,color='yellow')  #分成35个区间
    plt.show()

![20180611223206369][]

#3条状图
    # 带误差线的条状图
     x=np.arange(5)
     y=[4,6,3,8,9]
     std=[0.3,0.8,0.6,1.2,1.8]   #设为标准差
     plt.title('bar')
     plt.xticks(x,['a','b','c','d','e'])
     plt.bar(x,y,yerr=std,alpha=0.8,label='first',error_kw={'ecolor':'0.3','capsize':5})
     #    ecolor表示为黑线的透明度，capsize为横线的长度
     plt.legend(loc=2)
     plt.show()

> 颜色参数：

![20180611223518143][]

#水平条状图
    #改变参数 yticks 和 xerr
    x=np.arange(5)
    y=[4,6,3,8,9]
    std=[0.3,0.8,0.6,1.2,1.8]   #设为标准差
    plt.title('bar')
    plt.yticks(x,['a','b','c','d','e'])
    plt.barh(x,y,xerr=std,alpha=0.8,label='first',error_kw={'ecolor':'0.3','capsize':5})
    #    ecolor表示为黑线的透明度，capsize为横线的长度
    plt.legend(loc=4)
    plt.show()

![20180611223254952][]

#多序列条状图
    x=np.arange(5)
    y1=[4,6,3,8,9]
    y2=[2,3,6,7,10]
    y3=[1,6,3,9,15]
    plt.title('bar')
    bw=0.3      #间隔为bw
    plt.xticks(x+bw,['a','b','c','d','e'])
    plt.bar(x,y1,bw,color='b')
    plt.bar(x+bw,y2,bw,color='g')
    plt.bar(x+2*bw,y3,bw,color='r')
    plt.show()

![20180611223311107][]

#为DataFrame数据结构画bar图
    data={'series1':[1,2,4,8,13],
          'series2':[2,5,8,12,16],
          'series3':[4,6,2,7,9]}
    df=pd.DataFrame(data)
    df.plot(kind='bar')
    plt.show()

![20180611223331470][]

#多序列堆积图
    x=np.arange(5)
    y1=np.array([4,6,3,8,9])    #需要定义为array数组
    y2=np.array([2,3,6,7,10])
    y3=np.array([1,6,3,9,5])
    plt.xticks(x+0.2,['a','b','c','d','e'])
    plt.axis([0,6,0,25])
    plt.bar(x,y1,color='b')
    plt.bar(x,y2,color='g',bottom=y1)   #以y1为低，bottom
    plt.bar(x,y3,color='r',bottom=(y1+y2))
    plt.show()

![20180611223344416][]

#为dataFrame 数据结构画堆积图
    data={'series1':[1,2,4,8,13],
          'series2':[2,5,8,12,16],
          'series3':[4,6,2,7,9]}
    df=pd.DataFrame(data)
    df.plot(kind='bar',stacked=True)
    plt.show()

![20180611223403319][]

#3饼图
    values=[12,24,45,60,12]
    labels=['book','pen','pencil','notebook','desk']
    color=['red','yellow','black','blue','green']
    explode=[0.3,0,0,0,0]   #脱离圆的程度，0为为脱离，1为完全脱离
    plt.pie(values,labels=labels,colors=color,explode=explode,startangle=90,
            autopct='%1.1f% %',shadow=True)  #autopct表示显示每个标签的百分比
    plt.axis('equal')
    plt.show()

![20180611223416552][]

#为dataFrame 数据结构画饼图
    data={'series1':[1,2,4,8,13],
          'series2':[2,5,8,12,16],
          'series3':[4,6,2,7,9]}
    df=pd.DataFrame(data)
    df['series3'].plot(kind='pie',figsize=(6,6))
    plt.show()

![20180611223424736][]

[20180611223052331]: /images/20220522/14de106954214464981e2397970577a2.png
[20180611223107914]: /images/20220522/3f1c90aca59a4d22b7e0bb9465eed000.png
[20180611223140455]: /images/20220522/180a060920fc4816ad9f5200dbd57733.png
[20180611223206369]: /images/20220522/be0b8167687b4947a7f4b850c1e9390e.png
[20180611223518143]: /images/20220522/d5264f58165e4fa7bff98c4f7e6bfc57.png
[20180611223254952]: /images/20220522/31228867934c46869907e9317d3c818b.png
[20180611223311107]: /images/20220522/a72992719b584196bd6c0b9071eaa63d.png
[20180611223331470]: /images/20220522/a00cdce3115f4aca872f66dd084e862f.png
[20180611223344416]: /images/20220522/d7b1f301229045b5a51ae9f595858f8a.png
[20180611223403319]: /images/20220522/c66be5ec50de4b2db78f842658bb6c31.png
[20180611223416552]: /images/20220522/f314c87864dd451b8072bdabb8049376.png
[20180611223424736]: /images/20220522/24209bd3b55e4741ac9380f7f3bfdc69.png