('chocolate'); ...篇幅有限，详细参考源码... """) 我们可以通过下面的代码来查看新建的表格，并且转换成DataFrame格式的数据集，代码如下 df_sweets = pd.read_sql...个数据集，主要是涉及到了甜品、甜品的种类以及加工和仓储的数据，而例如甜品的数据集当中主要包括的有甜品的重量、糖分的含量、生产的日期和过期的时间、成本等数据，以及 df_manufacturers = pd.read_sql...("SELECT * FROM storehouses", connector) output 还有甜品的种类数据集， df_sweets_types = pd.read_sql("SELECT *...output Milty Mikus Mivi Mi Misa Maltik Macus 当然在SQL语句当中的通配符，%表示匹配任意数量的字母，而_表示匹配任意一个字母，具体的区别如下 # SQL pd.read_sql...("SELECT name FROM sweets WHERE name LIKE 'M%'", connector) output pd.read_sql("SELECT name FROM sweets
charset=utf8") 03 执行sql语句 # 方法一:使用pd.read_sql() 主要参数如下所示pd.read_sql(sql, #需要使用的sql语句或者数据表con, #sqlalchemy...charset=gbk") data = pd.read_sql(sql = 'select * from orderitem limit 10',con=eng,index_col='SDate')data...charset=gbk") data = pd.read_sql(sql = "category",con=eng) # 此方法会读取指定表中的全部数据，如果表数据量比较大,会造成读取数据慢,慎用。...charset=gbk") data = pd.read_sql(sql = 'select * from orderitem limit 10',con=eng)dataOperationalError...()方法读入数据库文件,返回数据框结构,可以快速浏览数据汇总; pd.read_sql()使用con参数使用pymsql.connect()方法,sql参数不能直接使用表名称,需要使用完整的sql语句;
charset=utf8") 03 执行sql语句 # 方法一:使用pd.read_sql() 主要参数如下所示 pd.read_sql( sql, #需要使用的sql语句或者数据表 con, #sqlalchemy...charset=gbk") data = pd.read_sql(sql = 'select * from orderitem limit 10',con=eng,index_col='SDate')...charset=gbk") data = pd.read_sql(sql = "category",con=eng) # 此方法会读取指定表中的全部数据，如果表数据量比较大,会造成读取数据慢,慎用。...charset=gbk") data = pd.read_sql(sql = 'select * from orderitem limit 10',con=eng) data OperationalError...()方法读入数据库文件,返回数据框结构,可以快速浏览数据汇总; pd.read_sql()使用con参数使用pymsql.connect()方法,sql参数不能直接使用表名称,需要使用完整的sql语句;
query_sql = "select srcip from table_name where cnt_date <= '%s' limit 1" % day try: df = pd.read_sql...break mysqlcur.execute(delete_sql) mysqlconn.commit() df = pd.read_sql...s' limit 1" % day optimize_sql = "OPTIMIZE TABLE g_visit_relation_asset" try: df = pd.read_sql...break mysqlcur.execute(delete_sql) mysqlconn.commit() df = pd.read_sql...expired_day = (expired_day - timedelta(days = 1)).strftime("%Y-%m-%d") df = pd.read_sql
user = "用户名", password = '密码', db = "数据库名", charset='utf8') # charset用于修正中文输出为问号的问题 df = pd.read_sql..., db = "test", charset='utf8') #charset用于修正中文输出为问号的问题 sql = "select * from score;" df = pd.read_sql
charset=utf8") # sql 命令 sql = "SELECT * FROM stu" df = pd.read_sql(sql=sql, con=engine) print(df) # 第二种...='localhost', user='root', password='root', database='data', charset='utf8', use_unicode=True) df = pd.read_sql
35分钟左右 for code in get_code(): data=get_data(code) insert_sql(data,'stock_data') #读取整张表数据 df=pd.read_sql...('stock_data',engine) print(len(df)) #输出结果：270998 #选取ts_code=000001.SZ的股票数据 df=pd.read_sql("select *...d.max()) print(d.min()) 2019-04-25T00:00:00.000000000 2018-01-02T00:00:00.000000000 #获取交易日2019年4月25日数据 pd.read_sql...engine = create_engine('postgresql+psycopg2://postgres:123456@localhost:5432/postgres') data=pd.read_sql...engine = create_engine('postgresql+psycopg2://postgres:123456@localhost:5432/postgres') data=pd.read_sql
用户名 password="cueb",#密码 port=3306,#端口号 charset='utf8' ) s = "select * from user"； data = pd.read_sql
'user', conn, schema='mytest', if_exists='append') # # 执行“select * from words;”SQL语句读取数据库中的数据 df1 = pd.read_sql...session.execute("delete from user where id=4") session.commit() # # 执行“select * from words;”SQL语句读取数据库中的数据 df = pd.read_sql...charset=utf8') # # 执行“select * from words;”SQL语句读取数据库中的数据 df = pd.read_sql('select * from user;', con
where a.provincename=b.provincename and a.reportyear=2010 group by b.areaname order by 2 ''' df = pd.read_sql...where a.provincename=b.provincename and a.reportyear=2010 group by b.areaname order by 2 ''' df = pd.read_sql...a,proviceinfo b where a.provincename=b.provincename and a.reportyear=2010 order by 2 ''' df = pd.read_sql...a,proviceinfo b where a.provincename=b.provincename and a.reportyear=2010 order by 2 ''' df = pd.read_sql...a,proviceinfo b where a.provincename=b.provincename and a.reportyear=2010 order by 2 ''' df = pd.read_sql
conn = create_engine('mysql+pymysql://user:passwd@ip:3306/temp_data_2',encoding='utf8') jxb_sx_head3 = pd.read_sql...#检查是否插入成功 conn = pymysql.connect(host='ip',user = "用户名", passwd = "密码", db = "test") cs_add_date = pd.read_sql...date_pl.to_sql(name='jlkj_cs', con=conn, if_exists='append', index=False, index_label=False) cs_add_date2 = pd.read_sql
import pandas as pd #这里即遵循sql语句规则 sql = "select * from 要查询的表格" df0 = pd.read_sql(sql,conn) df=pd.DataFrame...and JSON_VALUE(detail,'$.level_code')='0A' order by created_on desc """ df0 = pd.read_sql
数据库操作 4.1 读取数据库表 使用 pd.read_sql() 方法读取数据库表： # 读取数据库表 query = 'SELECT * FROM your_table' df_sql = pd.read_sql
DB_PORT, DATABASE) #1 engine = create_engine(connect_info) # sql 命令 sql_cmd = "SELECT * FROM table" df = pd.read_sql...localhost, user=username, password=password, database=dbname, charset='utf8', use_unicode=True) df = pd.read_sql
_tags') as tags from hn_items_raw """)with connect(DB_PATH) as db: hn_items_fields = pd.read_sql...hn_items_rawwhere json_extract(data, '$.author') = 'luu'"""%%timeitwith connect(DB_PATH) as db: luu_df = pd.read_sql...DB_PATH) as db: db.execute(create_author_idx_query)%%timeitwith connect(DB_PATH) as db: luu_df = pd.read_sql
user_name', 14 password = 'password' 15 db = 'db_name') 16 sql = 'SELECT * FROM tb_name' 17 data = pd.read_sql
pymysql.connect(host='localhost', user='root', passwd='123456', db='test', port=3306, charset='utf8') jianshu = pd.read_sql
gradeTable"fields_name = "name, course, grade" sql = "Select {0} From {1}".format(fields_name, table_name)df = pd.read_sql
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