机器学习——pandas,seaborn数据可视化

迷南。 2022-12-26 00:48 197阅读 0赞

## 这是一个数据可视化的demo ##

import warnings
    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt
    import seaborn as sns
    sns.set()
    warnings.filterwarnings('ignore')
    
    df = pd.read_csv(
        'https://labfile.oss.aliyuncs.com/courses/1283/telecom_churn.csv')
    df.head()
    
    #hist()方法绘制直方图
    features = ['Total day minutes','Total intl calls']
    #df[features].hist(figsize=(10,4))
    
    #绘制密度图（density plots）
    #df[features].plot(kind='density',subplots=True,layout=(1,2),sharex=False,figsize=(10,4),legend=False,title=features)
    '''kindstr
    The kind of plot to produce:
    ‘line’ : line plot (default)
    ‘bar’ : vertical bar plot
    ‘barh’ : horizontal bar plot
    ‘hist’ : histogram
    ‘box’ : boxplot
    ‘kde’ : Kernel Density Estimation plot
    ‘density’ : same as ‘kde’
    ‘area’ : area plot
    ‘pie’ : pie plot
    ‘scatter’ : scatter plot
    ‘hexbin’ : hexbin plot.
    '''
    #使用seaborn中的distplot()方法显示直方图和密度图,归一化处理
    #sns.distplot(df['Total intl calls'])
    
    #绘制箱型图
    #sns.boxplot(x='Total intl calls',data=df)
    
    #绘制提琴形图
    #_,axes = plt.subplots(1,2,sharey=True,figsize=(6,4))
    #sharey = True or 'all': x- or y-axis will be shared among all subplots.
    #sns.boxplot(data=df['Total intl calls'],ax=axes[0])
    #Axes object to draw the plot onto, otherwise uses the current Axes.
    #sns.violinplot(data=df['Total intl calls'],ax=axes[1])
    
    #数据描述
    df[feature].describe()
    df['Churn'].value_counts()#得到分布情况，数量
    
    #绘制条形图,查看类别特征
    # _,axes = plt.subplots(1,2,sharey=True,figsize=(10,4))
    # sns.countplot(x='Churn',data=df,ax=axes[0])
    # sns.countplot(x='Customer service calls',data=df,ax=axes[1])
    
    #多变量可视化尝试
    #丢弃非数值变量（bool）
    numerical = list(set(df.columns)-set([
        'state','International plan','Voice mail plan','Area code','Churn','Customer service calls'
    ]))
    #计算绘图,corr()计算出没对特征间的相关性
    #corr_matrix = df[numerical].corr()
    #sns.heatmap(corr_matrix)
    
    #根据计算结果可以得出Total day charge可以通过四个变量得出，去掉
    numerical = list(set(numerical)-set([
        'Total day charge','Total eve charge','Total night charge','Total intl charge'
    ]))
    # corr_matrix = df[numerical].corr()
    # sns.heatmap(corr_matrix)
    
    #散点图
    # plt.scatter(df['Total day minutes'],df['Total night minutes'])
    
    #seaborn中的joinplot()可以在绘制散点图时绘制两张直方图
    # sns.jointplot(x='Total day minutes',y='Total night minutes',data=df,kind='scatter')
    #绘制平滑过的散点直方图
    sns.jointplot('Total day minutes','Total night minutes',data=df,kind='kde',color='g',hue='minutes')
    # kind{  “scatter” | “kde” | “hist” | “hex” | “reg” | “resid” }
    
    #散点图矩阵
    # %config InlineBackend.figure_format = 'png'
    # sns.pairplot(df[numerical])
    
    #数量和类别
    sns.lmplot('Total day minutes','Total night minutes',data=df,hue='Churn',fit_reg=False)
    
    #创建箱型图
    numerical.append('Customer service calls')
    
    fig,axes = plt.subplots(3,4,figsize=(10,6))
    for idx,feat in enumerate(numerical):
        ax = axes[int(idx/4),idx%4]
        sns.boxplot(x='Churn',y=feat,data=df,ax=ax)
        ax.set_xlabel('')
        ax.set_ylabel(feat)
    fig.tight_layout()
    
    #创建箱型图和提琴形图，查看忠实客户和不忠实客户日通话分数
    _,axes = plt.subplots(1,2,sharey=True,figsize=(10,6))
    sns.boxplot(x='Churn',y='Total day minutes',data=df,ax=axes[0])
    sns.violinplot(x='Churn',y='Total day minutes',data=df,ax=axes[1])
    
    #分析两个类别维度下的数量变化
    sns.catplot(x='Churn',y = 'Total day minutes',col='Customer service calls',data=df[df['Customer service calls']<8],
               kind='box',col_wrap=4,height=3,aspect=.8)
    
    #类别和类别
    sns.countplot('Customer service calls',hue='Churn',data=df)
    
    #交叉表
    pd.crosstab(df['State'],df['Churn']).T
    
    #计算离网率，排序
    df.groupby(['State'])['Churn'].agg([np.mean]).sort_values(by='mean',ascending=False).T

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