单变量分析绘图

怼烎@ 2023-06-20 06:25 8阅读 0赞

1.  基本直方图
    
        %matplotlib inline
        import numpy as np
        import pandas as pd
        from scipy import stats,integrate
        import matplotlib.pyplot as plt
        import seaborn as sns
        
        sns.set(color_codes=True)
        np.random.seed(sum(map(ord,'distributions')))
        x=np.random.normal(size=100)
        sns.distplot(x,kde=False)
    
    运行结果：  
    ![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjI2MDEwMg_size_16_color_FFFFFF_t_70]
2.  自定义bins的大小
    
        sns.distplot(x,bins=20,kde=False)
    
    运行结果：  
    ![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjI2MDEwMg_size_16_color_FFFFFF_t_70 1]
3.  数据分布状况
    
        x=np.random.gamma(6,size=200)
        sns.distplot(x,kde=False,fit=stats.gamma)
    
    运行结果：  
    ![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjI2MDEwMg_size_16_color_FFFFFF_t_70 2]
4.  根据均值和协方差生成数据
    
        mean,cov=[0,1],[(1,.5),(.5,1)]
        data=np.random.multivariate_normal(mean,cov,200)
        df=pd.DataFrame(data,columns=['x','y'])
        df
    
    运行结果：  
    ![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjI2MDEwMg_size_16_color_FFFFFF_t_70 3]
5.  观测两个变量之间的分布关系最好用散点图
    
        sns.jointplot(x='x',y='y',data=df)
    
    运行结果：  
    ![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjI2MDEwMg_size_16_color_FFFFFF_t_70 4]
6.  数据量较大
    
        x,y=np.random.multivariate_normal(mean,cov,1000).T
        with sns.axes_style('white'):
            sns.jointplot(x=x,y=y,kind='hex',color='k')
    
    运行结果：  
    ![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjI2MDEwMg_size_16_color_FFFFFF_t_70 5]

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