SQLAlchemy 连接 MySql 数据库
sqlalchemy 库
from sqlalchemy import create_engine
engine = create_engine('mysql+pymysql://root:981221@localhost/testdb?charset=utf8mb4')
print(engine)
Engine(mysql+pymysql://root:***@localhost/testdb?charset=utf8mb4)
pandas 库
- pandas 的3 个函数用于读取数据库数据
- read_sql_table ---------------读取都一个表格
- read_sql_query ---------------实现查询
- read_sql ------------------上面两者的综合
读 / 写数据库数据
- read_sql_table
- read_sql_query
- read_sql
import pandas as pd
formlist = pd.read_sql_query('show tables', con=engine)
print('testdb数据库表清单为:','\n',formlist)
testdb数据库表清单为:
Tables_in_testdb
0 meal_order_detail1
1 meal_order_detail2
2 meal_order_detail3
3 test1
detail1 = pd.read_sql_table('meal_order_detail1',con=engine)
print('使用read_sql_table 读取订单详情的长度为:',len(detail1))
使用read_sql_table 读取订单详情的长度为: 2779
detail2 = pd.read_sql('select * from meal_order_detail2',con=engine)
print('使用read_sql 函数 + SQL语句 读取的订单详情表长度为:',len(detail2))
detail3 = pd.read_sql('meal_order_detail3',con=engine)
print('使用read_sql 函数 + 表格名称 读取的订单详情表长度为:',len(detail3))
使用read_sql 函数 + SQL语句 读取的订单详情表长度为: 3647
使用read_sql 函数 + 表格名称 读取的订单详情表长度为: 3611
数据库数据存储
detail1.to_sql('test1', con = engine, index = False, if_exists = 'replace')
formlist1 = pd.read_sql_query('show tables', con=engine)
print('新增一个表格后，testdb数据库数据表清单为:','\n',formlist1)
新增一个表格后，testdb数据库数据表清单为:
Tables_in_testdb
0 meal_order_detail1
1 meal_order_detail2
2 meal_order_detail3
3 test1
读 / 写文本文件
order = pd.read_table('D:/pandas/meal_order_info.csv',sep=',',encoding='gbk')
print('使用read_table读取的菜品订单信息表长度为：',len(order))
使用read_table读取的菜品订单信息表长度为： 945
order1 = pd.read_csv('D:/pandas/meal_order_info.csv',encoding='gbk')
print('使用read_csv读取菜品订单信息表的长度为为：',len(order1))
使用read_csv读取菜品订单信息表的长度为为： 945
更改参数读取菜品订单信息表
- header 用于指定猎命，默认infer-------自动识别，如果是None----------添加默认列名
- sep ------指定文本的分隔符，如果不符合----------读取结果是一片数据
order2 = pd.read_table('D:/pandas/meal_order_info.csv',sep=';',encoding='gbk')
print('分隔符是；时，菜品订单信息表为：','\n', order2)
分隔符是；时，菜品订单信息表为：
info_id,"emp_id","number_consumers","mode","dining_table_id","dining_table_name","expenditure","dishes_count","accounts_payable","use_start_time","check_closed","lock_time","cashier_id","pc_id","order_number","org_id","print_doc_bill_num","lock_table_info","order_status","phone","name"
0 417,1442,4,NA,1501,1022,165,5,165,"2016/8/1 11...
1 301,1095,3,NA,1430,1031,321,6,321,"2016/8/1 11...
2 413,1147,6,NA,1488,1009,854,15,854,"2016/8/1 1...
3 415,1166,4,NA,1502,1023,466,10,466,"2016/8/1 1...
4 392,1094,10,NA,1499,1020,704,24,704,"2016/8/1 ...
.. ...
940 641,1095,8,NA,1492,1013,679,12,679,"2016/8/31 ...
941 672,1089,6,NA,1489,1010,800,24,800,"2016/8/31 ...
942 692,1155,8,NA,1492,1013,735,10,735,"2016/8/31 ...
943 647,1094,4,NA,1485,1006,262,9,262,"2016/8/31 2...
944 570,1113,8,NA,1517,1038,589,13,589,"2016/8/31 ...
[945 rows x 1 columns]
order3 = pd.read_csv('D:/pandas/meal_order_info.csv',sep=',',header=None,encoding='gbk')
print('header为None时菜品订单信息表为：',order3)
header为None时菜品订单信息表为： 0 1 2 3 4 \
0 info_id emp_id number_consumers mode dining_table_id
1 417 1442 4 NaN 1501
2 301 1095 3 NaN 1430
3 413 1147 6 NaN 1488
4 415 1166 4 NaN 1502
.. ... ... ... ... ...
941 641 1095 8 NaN 1492
942 672 1089 6 NaN 1489
943 692 1155 8 NaN 1492
944 647 1094 4 NaN 1485
945 570 1113 8 NaN 1517
5 6 7 8 \
0 dining_table_name expenditure dishes_count accounts_payable
1 1022 165 5 165
2 1031 321 6 321
3 1009 854 15 854
4 1023 466 10 466
.. ... ... ... ...
941 1013 679 12 679
942 1010 800 24 800
943 1013 735 10 735
944 1006 262 9 262
945 1038 589 13 589
9 ... 11 12 13 \
0 use_start_time ... lock_time cashier_id pc_id
1 2016/8/1 11:05:36 ... 2016/8/1 11:11:46 NaN NaN
2 2016/8/1 11:15:57 ... 2016/8/1 11:31:55 NaN NaN
3 2016/8/1 12:42:52 ... 2016/8/1 12:54:37 NaN NaN
4 2016/8/1 12:51:38 ... 2016/8/1 13:08:20 NaN NaN
.. ... ... ... ... ...
941 2016/8/31 21:23:48 ... 2016/8/31 21:31:48 NaN NaN
942 2016/8/31 21:24:12 ... 2016/8/31 21:56:12 NaN NaN
943 2016/8/31 21:25:18 ... 2016/8/31 21:33:34 NaN NaN
944 2016/8/31 21:37:39 ... 2016/8/31 21:55:39 NaN NaN
945 2016/8/31 21:41:56 ... 2016/8/31 21:32:56 NaN NaN
14 15 16 17 18 \
0 order_number org_id print_doc_bill_num lock_table_info order_status
1 NaN 330 NaN NaN 1
2 NaN 328 NaN NaN 1
3 NaN 330 NaN NaN 1
4 NaN 330 NaN NaN 1
.. ... ... ... ... ...
941 NaN 330 NaN NaN 1
942 NaN 330 NaN NaN 1
943 NaN 330 NaN NaN 1
944 NaN 330 NaN NaN 1
945 NaN 330 NaN NaN 1
19 20
0 phone name
1 18688880641 苗宇怡
2 18688880174 赵颖
3 18688880276 徐毅凡
4 18688880231 张大鹏
.. ... ...
941 18688880307 李靖
942 18688880305 莫言
943 18688880327 习一冰
944 18688880207 章春华
945 18688880313 唐雅嘉
[946 rows x 21 columns]
文本文件存储
- to_csv ( path_or_buf=None, sep=’,’, index= True…)
import os
print('菜品订单信息表写入文本文件之前---------文件列表:','\n',os.listdir('D:/pandas/tmp'))
order.to_csv('D:/pandas/tmp/orderInfo.csv',sep=';',index=False)
print('菜品订单信息表写入文本文件之后---------文件列表:','\n',os.listdir('D:/pandas/tmp'))
菜品订单信息表写入文本文件之前---------文件列表:
['orderInfo.csv']
菜品订单信息表写入文本文件之后---------文件列表:
['orderInfo.csv']
读 / 写 Excel 文件
- read_excel ( io, sheetname=0, encoding='gbk…)
- 要需要 pandas库
- excel 文件转换需要安装 xlrd 库，下面的代码才可以运行
user = pd.read_excel('D:/pandas/users.xlsx')
print('客户信息表长度:',len(user))
客户信息表长度: 734
Excel 文件存储
- to_excel (excel_writer=None, sheetname=‘None’…)
print('客户信息表写入Excel文件前-----目录列表：','\n',os.listdir('D:/pandas/tmp'))
user.to_excel('D:/pandas/tmp/userInfro.xlsx')
print('客户信息表写入Excel文件后-----目录列表：','\n',os.listdir('D:/pandas/tmp'))
客户信息表写入Excel文件前-----目录列表：
['orderInfo.csv']
客户信息表写入Excel文件后-----目录列表：
['orderInfo.csv', 'userInfro.xlsx']
