因工作原因，一些获取的行业数据以已知的结构体存储在.mat文件中,

现需要将其存储在数据库中并且能够灵活调用至python dataframe里进行操作

 

原数据的一个例子如下

目标如上:

然后是转化代码:

import scipy.iodata = scipy.io.loadmat(r'C:\Users\wenzhe.tian\Desktop\PTSimA\Doing\MC.mat')import pandas as pddata.pop('__header__')data.pop('__version__')data.pop('__globals__')vehicle_name=data.keys()vehicle_name=list(vehicle_name)for i in vehicle_name:    df = pd.DataFrame(data[i][0])    try:        df=df.astype(float)    except:        for j in list(df):            try:                df[j]=df[j].astype(float)            except:                continue#                df[j]=df[j].astype(str)    if i==vehicle_name[0]:        df1=df;    else:        df1=pd.concat([df,df1],axis=0)df1['MC_name']=vehicle_namedf1['Tips']=df1['Tips'].map(str)+df1['tips'].map(str)df1['Tips']=df1['Tips'].str.replace('nan','')df1=df1.drop(['tips'],axis=1)df1=df1.reset_index();import numpy as np# ndarray需转化为 字符list_transfer=['Speed','Torque','eff','eff_current']for i in list_transfer:    for j in range(len(df1)):        try:            df1[i][j]=df1[i][j].tostring();        except:            continue;

结果如下(df1)：

然后用to_sql的方式将该dataframe 保存至本地sql数据库即可