以下是一个直接利用DataFrame新建数据库的case，可以参考以下

def create_table_by_df(self, dbPath=None, tableName=None, partitionColumns=None):
        """
        通过pandas读取一个样例文件，通过样例数据创建一个表

        :param dbPath:
        :param tableName:
        :return:
        """
        dataPath = "D://work//data//stock//test//second//20191212.csv"

        colfloat = ['ask1', 'ask2', 'ask3', 'ask4', 'ask5', 'bid1', 'bid2', 'bid3', 'bid4', 'bid5']
        colint = ['asize1', 'asize2', 'asize3', 'asize4', 'asize5', 'bsize1', 'bsize2', 'bsize3', 'bsize4', 'bsize5']
        cols = ['ask1', 'ask2', 'ask3', 'ask4', 'ask5', 'bid1', 'bid2',
                'bid3', 'bid4', 'bid5', 'asize1', 'asize2', 'asize3', 'asize4',
                'asize5', 'bsize1', 'bsize2', 'bsize3', 'bsize4', 'bsize5']
        df = pd.read_csv(dataPath, encoding='utf-8')
        df['symbol'] = df['symbol'].astype(str)
        to_datetime64_for_df(df, ['DateTime', 'Date'])
        df['Time'] = df['Time'].apply(lambda x: datetime.strptime(str(x), "%H:%M:%S.%f"))
        df[colfloat] = df[colfloat].astype('float32')
        df[colint] = df[colint].astype('int64')
        s.upload({"newdf": df})
        s.run("""dbt=database("{}")""".format(dbPath))
        s.run(
            """newdf=select symbol(symbol) as symbol,nanotimestamp(DateTime) as DateTime,date(Date) as Date,nanotime(Time) as Time ,{} from newdf""".format(
                ','.join(cols)))
        s.run("""t=dbt.createPartitionedTable(newdf,"{}",`{})""".format(tableName, '`'.join(partitionColumns)))
        s.run("t.append!(newdf)")

后续导入到此表的，先在python中进行初步的数据转化，再通过pythonAPI的函数tableAppender导入，参考文档https://gitee.com/dolphindb/api_python3#614-%E4%BD%BF%E7%94%A8-tableappender-%E5%AF%B9%E8%B1%A1%E8%BF%BD%E5%8A%A0%E6%95%B0%E6%8D%AE%E6%97%B6%E8%87%AA%E5%8A%A8%E8%BD%AC%E6%8D%A2%E6%97%B6%E9%97%B4%E7%B1%BB%E5%9E%8B

 def import_df_to_dolphindb_tableAppender(self, dbPath=None, tableName=None, allFiles=None):
        """
        本例为利用tableAppender把文件通过DataFrame加载到数据表中的样例;
        通过tableAppender导入数据,使用tableAppender对象追加数据时自动转换时间类型,
        但是其他的数据类型转换需要在python中显示转换为相应的类型,
        如表中存储为int, 则在python端需要显示转化为int32
        如表中存储为long, 则在python端需要显示转化为int64
        :param dbPath:
        :param tableName:
        :param  allFiles:
        """
        ###2.通过tableAppender导入数据,使用tableAppender对象追加数据时自动转换时间类型
        colfloat = ['ask1', 'ask2', 'ask3', 'ask4', 'ask5', 'bid1', 'bid2', 'bid3', 'bid4', 'bid5']
        colint = ['asize1', 'asize2', 'asize3', 'asize4', 'asize5', 'bsize1', 'bsize2', 'bsize3', 'bsize4', 'bsize5']
        # # #
        Appender = ddb.tableAppender(dbPath=dbPath, tableName=tableName, ddbSession=s, action="fitColumnType")
        for ifile in allFiles:
            df = pd.read_csv(ifile, encoding='utf-8')
            df['symbol'] = df['symbol'].astype(str)
            to_datetime64_for_df(df, ['DateTime', 'Date'])
            df['Time'] = df['Time'].apply(lambda x: datetime.strptime(str(x), "%H:%M:%S.%f"))
            df[colfloat] = df[colfloat].astype('float32')
            df[colint] = df[colint].fillna(0).astype('int64')
            Appender.append(df)