# -*- coding:utf-8 -*-
import pandas as pd
import MySQLdb
from warnings import filterwarnings
# 由于create table if not exists总会抛出warning，因此使用filterwarnings消除
filterwarnings('ignore', category = MySQLdb.Warning)
from sqlalchemy import create_engine
import sys
if sys.version_info.major<3:
reload(sys)
sys.setdefaultencoding("utf-8")
# 此脚本适用于python2和python3
host,port,user,passwd,db,charset="192.168.1.193",3306,"leo","mysql","test","utf8"
def get_df():
global host,port,user,passwd,db,charset
conn_config={"host":host, "port":port, "user":user, "passwd":passwd, "db":db,"charset":charset}
conn = MySQLdb.connect(**conn_config)
result_df=pd.read_sql('select UserName,Subject,Score from TEST',conn)
return result_df
def pivot(result_df):
df_pivoted_init=result_df.pivot('UserName','Subject','Score')
df_pivoted = df_pivoted_init.reset_index() # 将行索引也作为DataFrame值的一部分，以方便存储数据库
return df_pivoted_init,df_pivoted
# 返回的两个DataFrame，一个是以姓名作index的，一个是以数字序列作index，前者用于unpivot，后者用于save_to_mysql
def unpivot(df_pivoted_init):
# unpivot需要进行df_pivoted_init二维表格的行、列索引遍历，需要拼SQL因此不能使用save_to_mysql存数据，这里使用SQL和MySQLdb接口存
insert_sql="insert into test_unpivot(UserName,Subject,Score) values "
# 处理值为NaN的情况
df_pivoted_init=df_pivoted_init.add(0,fill_value=0)
for col in df_pivoted_init.columns:
for index in df_pivoted_init.index:
value=df_pivoted_init.at[index,col]
if value!=0:
insert_sql=insert_sql+"('%s','%s',%s)" %(index,col,value)+','
insert_sql = insert_sql.strip(',')
global host, port, user, passwd, db, charset
conn_config = {"host": host, "port": port, "user": user, "passwd": passwd, "db": db, "charset": charset}
conn = MySQLdb.connect(**conn_config)
cur=conn.cursor()
cur.execute("create table if not exists test_unpivot like TEST")
cur.execute(insert_sql)
conn.commit()
conn.close()
def save_to_mysql(df_pivoted,tablename):
global host, port, user, passwd, db, charset
"""
只有使用sqllite时才能指定con=connection实例，其他数据库需要使用sqlalchemy生成engine，engine的定义可以添加?来设置字符集和其他属性
"""
conn="mysql://%s:%s@%s:%d/%s?charset=%s" %(user,passwd,host,port,db,charset)
mysql_engine = create_engine(conn)
df_pivoted.to_sql(name=tablename, con=mysql_engine, if_exists='replace', index=False)
# 从TEST表读取源数据至DataFrame结构
result_df=get_df()
# 将源数据行转列为二维表格形式
df_pivoted_init,df_pivoted=pivot(result_df)
# 将二维表格形式的数据存到新表test中
save_to_mysql(df_pivoted,'test')
# 将被行转列的数据unpivot，存入test_unpivot表中
unpivot(df_pivoted_init)
