探索性数据分析，主要针对原始数据进行初次了解。了解数据的分布情况、了解分析方向、排除该单个变量的异常值 等。此脚本读取的是 SQL Server ，只需给定表名或视图名称，如果有数据，将输出每个字段符合要求的每张数据分布图。

# -*- coding: UTF-8 -*-# python 3.5.0# 探索性数据分析(Exploratory Data Analysis,EDA)__author__ = 'HZC'import mathimport sqlalchemyimport numpy as npimport pandas as pdimport matplotlib.pyplot as pltclass EDA:    def __init__(self,d):        self.engine = sqlalchemy.create_engine("mssql+pymssql://%s:%s@%s/%s" %(d['user'],d['pwd'],d['ins'],d['db']))            def get_df_from_table(self,table_name):        df = pd.read_sql_table(table_name, self.engine)        return df        def get_df_from_query(self,sql):        df = pd.read_sql_query(sql, self.engine)        return df        #读取表各字段数据类型    def get_table_type(self,table_name):        sql = """select c.name as colname,t.name as typename        from sys.sysobjects o inner join syscolumns c on o.id=c.id and o.name<>'dtproperties'         inner join sys.systypes t on c.xusertype=t.xusertype         where o.name='%s'""" % table_name        df = self.get_df_from_query(sql)        return df        #绘图    def eda_plot(self,table_name):        list_char = ['char','nchar','varchar','nvarchar','text','ntext','sysname']        list_num = ['tinyint','smallint','int','real','money','float','decimal','numeric','smallmoney','bigint']        df_type = self.get_table_type(table_name)        df_date = self.get_df_from_table(table_name)        date_count = df_date.shape[0]        k = 0        for row in df_type.itertuples():            k = k + 1            #字符类型，绘柱状图            if row.typename in list_char:                col = df_date.groupby([row.colname]).agg({row.colname:['count']})                row_count = col.shape[0]                #col_count = col.shape[1]                col = col.sort_index()                val = col.values.tolist()                #只绘不重复数占总数比小于 5% 的                if math.floor(row_count*100/date_count) <5:                    df_ = pd.DataFrame(col.index.values.tolist(), columns=[row.colname])                    df_['count'] = list(i[0] for i in val)                    x_axle = range(len(df_[row.colname]))                    y_axle = df_['count'].tolist()                    x_label = df_[row.colname].tolist()                    fig, (ax1, ax2) = plt.subplots(2)                    ax1.bar(x_axle,y_axle)                    ax1.set_xticks(x_axle)                    ax1.set_xticklabels(x_label)                    ax1.set_title('表[%s]  %s  分布' % (table_name,row.colname))                    ax2.pie(y_axle,labels=x_label, autopct='%1.2f%%')                                #数值类型，其他分布图                elif row.typename in list_num:                df__ = pd.DataFrame(df_date[row.colname])                df__ = df__[(df__[row.colname].notnull())].sort_values(row.colname, ascending=True).reset_index(drop=True)                k = k + 1                plt.figure(k)                plt.subplot(1,3,1)                plt.hist(df__[row.colname])                plt.subplot(1,3,2)                plt.boxplot(df__[row.colname])                plt.gca().set_title('表[%s]  %s  分布' % (table_name,row.colname))                plt.subplot(1,3,3)                plt.violinplot(df__[row.colname])                plt.tight_layout()            else:                pass        plt.show()if __name__ == "__main__":    #conn = {'user':'kk','pwd':'kk','ins':'HYH0109-189\CAT2014','db':'CSMS3'}     conn = {
   'user':'用户名','pwd':'密码','ins':'实例','db':'数据库'}     eda = EDA(conn)    eda.eda_plot("表或视图名")

显示图分为字符型（离散型）和数值型（连续型），示例结果如下：