Python database module sqlite3 operation example (very practical)

sqlite3Modules are a very common and powerful tool when using Python for SQLite database operations . It provides a set of functions and methods for creating, connecting, querying and managing databases. Here are some common usage examples:

import sqlite3

#连接到现有数据库Connection个新数据库
conn = sqlite3.connect('stockssqlite3.db')

# 创建内存中的数据库（不保存到磁盘）
# conn = sqlite3.connect(':memory:')
# 获取数据库游标
cursor = conn.cursor()

# 创建表
# cursor.execute('''CREATE TABLE stocks2(date text, symbol text, price real)''')

# 插入数据
# cursor.execute("INSERT INTO stocks2 VALUES ('2023-07-01', 'WL22', 140.34)")
# cursor.execute("INSERT INTO stocks VALUES ('2023-07-02', 'HengDa', 2515.62)")

# 提交更改
conn.commit()

# # 更新数据
cursor.execute("UPDATE stocks SET price =888 WHERE symbol = 'WL'")
# conn.commit()

# 删除数据
cursor.execute("DELETE FROM stocks WHERE symbol = 'HengDa'")
conn.commit()

# 使用参数化查询防止SQL注入
symbol = 'WL'
cursor.execute("SELECT * FROM stocks WHERE symbol=?", (symbol,))
rows2 = cursor.fetchall()
# 遍历查询结果
for row in rows2:
    print('WL: ',row)


# # 开始事务
# conn.execute("BEGIN TRANSACTION")
# # 执行多个操作
# cursor.execute("INSERT INTO stocks VALUES ('2023-07-03', 'MSFT', 278.21)")
# cursor.execute("INSERT INTO stocks VALUES ('2023-07-04', 'AMZN', 3606.02)")

# # 提交事务
# conn.commit()

# 批量插入数据
# data = [('2023-07-05', 'SanAN', 144.50),
#         ('2023-07-05', 'ZXGJ', 2540.10),
#         ('2023-07-05', 'MaoTai', 280.50)]

# cursor.executemany("INSERT INTO stocks VALUES (?, ?, ?)", data)
# conn.commit()

try:
    # 开始事务
    conn.execute("BEGIN TRANSACTION")
    # 执行一些操作
    cursor.execute("INSERT INTO stocks VALUES ('2023-07-06', 'WuHanD', 148.20)")
    cursor.execute("INSERT INTO stocks VALUES ('2023-07-06', 'GOOGL', 2570.80,'55')")
    # 抛出异常，触发回滚
    raise Exception("Some error occurred")
    # 提交事务
    conn.commit()
except Exception as e:
    # 发生异常，回滚事务
    conn.rollback()
    print("Transaction rolled back:", str(e))

# # 使用命名参数化查询
# symbol = 'AAPL'
# price = 150.25
# cursor.execute("SELECT * FROM stocks WHERE symbol=:symbol AND price>=:price", {'symbol': symbol, 'price': price})

# 查询数据
cursor.execute("SELECT * FROM stocks")
rows = cursor.fetchall()
# 遍历查询结果 获取所有行数据
for row in rows:
    print(row)

# 获取部分行数据
cursor.execute("SELECT * FROM stocks2 LIMIT 5")
rows = cursor.fetchmany(3)
for row in rows:
    print('stocks2 fetchmany(3): ',row)

# 获取查询结果的列信息
cursor.execute("SELECT * FROM stocks")
columns = [description[0] for description in cursor.description]
print(columns)

# 创建索引
# cursor.execute("CREATE INDEX idx_symbol ON stocks (symbol)")
conn.commit()
# 执行查询（使用索引）
cursor.execute("SELECT * FROM stocks WHERE symbol = 'WK'")
result = cursor.fetchall()
print(result)

# 创建触发器
# 该语句指定了触发器的名称update_price
# 触发的事件AFTER UPDATE OF price ON stocks，以及在触发时要执行的操作。
# 触发器是强大的数据库功能，可以用于在特定事件发生时自动执行复杂的操作。它们可以用于实现数据完整性约束、记录日志、更新其他表的数据等
# cursor.execute("""
#     CREATE TRIGGER update_price
#     AFTER UPDATE OF price ON stocks
#     BEGIN
#         -- 触发时执行的操作
#         UPDATE stocks2 SET price = 5555 WHERE symbol = 'WL22';
#     END
# """)
# conn.commit()

# # 删除触发器
# cursor.execute("DROP TRIGGER IF EXISTS update_price")

# # 提交更改
# conn.commit()

# 查询触发器信息
cursor.execute("SELECT name, tbl_name, sql FROM sqlite_master WHERE type='trigger'")
triggers = cursor.fetchall()

# 打印触发器信息
for trigger in triggers:
    name, tbl_name, sql = trigger
    print(f"Name: {name}")
    print(f"Table: {tbl_name}")
    print(f"SQL: {sql}")
    print("-" * 30)

# 获取数据库版本号
cursor.execute("SELECT sqlite_version()")
version = cursor.fetchone()[0]
print("SQLite version:", version)

# 获取数据库中的表列表
cursor.execute("SELECT name FROM sqlite_master WHERE type='table'")
tables = cursor.fetchall()
for table in tables:
    print("Table name:", table[0])

# 获取表的列信息
cursor.execute("PRAGMA table_info(stocks)")
columns = cursor.fetchall()
for column in columns:
    print("Column name:", column[1])
    print("Column type:", column[2])

# 定义用户自定义函数
def calculate_total(a, b):
    return a + b

# 注册用户自定义函数
conn.create_function("total", 2, calculate_total)

# 使用用户自定义函数
cursor.execute("SELECT total(10, 5)")
result = cursor.fetchone()[0]
print("Total:", result)

import re

# 创建自定义函数来进行正则表达式匹配
def regex_match(pattern, string):
    return bool(re.match(pattern, string))

# 注册自定义函数
conn.create_function("regex_match", 2, regex_match)

# 使用正则表达式进行查询
cursor.execute("SELECT * FROM my_table WHERE regex_match('^A.*', name)")

import sqlite3

# 连接到数据库
conn = sqlite3.connect("mydatabase.db")
cursor = conn.cursor()

# 删除触发器
cursor.execute("DROP TRIGGER IF EXISTS update_price")

# 提交更改
conn.commit()

# 关闭连接
conn.close()

 

import sqlite3

# 连接到数据库
conn = sqlite3.connect("mydatabase.db")
cursor = conn.cursor()

# 查询触发器信息
cursor.execute("SELECT name, tbl_name, sql FROM sqlite_master WHERE type='trigger'")
triggers = cursor.fetchall()

# 打印触发器信息
for trigger in triggers:
    name, tbl_name, sql = trigger
    print(f"Name: {name}")
    print(f"Table: {tbl_name}")
    print(f"SQL: {sql}")
    print("-" * 30)

# 关闭连接
conn.close()

sqlite-utils, sqlitebiterand other similar libraries can provide more advanced features, including execution optimization of prepared statements and management of database connection pools. These libraries can be used as sqlite3module extensions to provide more convenient and efficient SQLite database operations. Here is a brief introduction to these libraries:

1. sqlite-utils: It is a Python library that provides many tools and functions to simplify the operation of SQLite database. It supports creating, connecting, querying and managing databases, and also provides advanced functions, such as execution optimization of precompiled statements, data import and export, database migration, etc. You can use sqlite-utilsto perform advanced operations to improve the performance and functionality of your SQLite database.

2. sqlitebiter: It is a command-line tool and Python library for converting different data sources (such as CSV, Excel, JSON, etc.) into SQLite databases. It can automatically infer the table structure and import the data into the SQLite database. sqlitebiterOptions are also provided to optimize the import process, including using prepared statements and bulk inserts, among others. This makes the data import process faster and more efficient.