python uses dataset to quickly use SQLite

Table of contents

1. Official website address

2. Installation

3. Quick use

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1. Official website address

GitHub - pudo/dataset: Easy-to-use data handling for SQL data stores with support for implicit table creation, bulk loading, and transactions.

2. Installation

 pip install dataset
如果是mysql，则多安装一个依赖：pip install mysqlclient

3. Quick use

import dataset

if __name__ == '__main__':
    """
    先天支持sqlite
    如果是mysql，则多安装一个依赖：pip install mysqlclient
    """
    db = dataset.connect('sqlite:///mydatabase.db')
    # 建表，如果表，则dataset会自动创建。
    table = db['user']
    # 新增
    table.insert(dict(name="张三丰", age=18, country='China'))
    # 新增
    table.insert(dict(name='Jane Doe', age=37, country='France', gender='female'))
    # 修改数据
    table.update(dict(name='张三丰', age=34), ['name'])  # 根据name值过滤进行修改
    # 快速事务，显式使用事务参考官网
    with dataset.connect('sqlite:///mydatabase.db') as tx:
        tx['user'].insert(dict(name='John Doe', age=46, country='China'))
    # 所有表
    tables = db.tables
    # 表字段
    columns = table.columns
    # 总行数
    count = len(table)
    # 所有数据
    users = table.all()
    # 搜索
    users_china = table.users_in(country='China')
    # 获取特定数据
    one = table.find_one(name='John Doe')
    # 查找多个
    users_in = table.find(id=[1, 3, 7])
    # 比较查找
    elderly_users1 = table.find(age={'>=': 70})
    possible_customers = table.find(age={'between': [21, 80]})
    elderly_users2 = table.find(table.table.columns.age >= 70)

    # 自定义SQL
    result = db.query('SELECT country, COUNT(*) c FROM user GROUP BY country')