Python史上最全种类数据库操作方法分享

更新时间：2023年07月05日 14:59:48 作者：techlead_krischang

本文将详细探讨如何在Python中连接全种类数据库以及实现相应的CRUD（创建，读取，更新，删除）操作，文中的示例代码讲解详细，需要的可以参考一下

本文将详细探讨如何在Python中连接全种类数据库以及实现相应的CRUD（创建，读取，更新，删除）操作。我们将逐一解析连接MySQL，SQL Server，Oracle，PostgreSQL，MongoDB，SQLite，DB2，Redis，Cassandra，Microsoft Access，ElasticSearch，Neo4j，InfluxDB，Snowflake，Amazon DynamoDB，Microsoft Azure CosMos DB数据库的方法，并演示相应的CRUD操作。

MySQL

连接数据库

Python可以使用mysql-connector-python库连接MySQL数据库：

import mysql.connector
conn = mysql.connector.connect(user='username', password='password', host='127.0.0.1', database='my_database')
print("Opened MySQL database successfully")
conn.close()

CRUD操作

接下来，我们将展示在MySQL中如何进行基本的CRUD操作。

创建（Create）

conn = mysql.connector.connect(user='username', password='password', host='127.0.0.1', database='my_database')
cursor = conn.cursor()
cursor.execute("CREATE TABLE Employees (ID INT PRIMARY KEY NOT NULL, NAME TEXT NOT NULL, AGE INT, ADDRESS CHAR(50), SALARY REAL)")
print("Table created successfully")
conn.close()

读取（Retrieve）

conn = mysql.connector.connect(user='username', password='password', host='127.0.0.1', database='my_database')
cursor = conn.cursor()
cursor.execute("SELECT id, name, address, salary from Employees")
rows = cursor.fetchall()
for row in rows:
    print("ID = ", row[0])
    print("NAME = ", row[1])
    print("ADDRESS = ", row[2])
    print("SALARY = ", row[3])
conn.close()

更新（Update）

conn = mysql.connector.connect(user='username', password='password', host='127.0.0.1', database='my_database')
cursor = conn.cursor()
cursor.execute("UPDATE Employees set SALARY = 25000.00 where ID = 1")
conn.commit()
print("Total number of rows updated :", cursor.rowcount)
conn.close()

删除（Delete）

conn = mysql.connector.connect(user='username', password='password', host='127.0.0.1', database='my_database')
cursor = conn.cursor()
cursor.execute("DELETE from Employees where ID = 1")
conn.commit()
print("Total number of rows deleted :", cursor.rowcount)
conn.close()

SQL Server

连接数据库

Python可以使用pyodbc库连接SQL Server数据库：

import pyodbc
conn = pyodbc.connect('DRIVER={SQL Server};SERVER=localhost;DATABASE=my_database;UID=username;PWD=password')
print("Opened SQL Server database successfully")
conn.close()

CRUD操作

接下来，我们将展示在SQL Server中如何进行基本的CRUD操作。

创建（Create）

conn = pyodbc.connect('DRIVER={SQL Server};SERVER=localhost;DATABASE=my_database;UID=username;PWD=password')
cursor = conn.cursor()
cursor.execute("CREATE TABLE Employees (ID INT PRIMARY KEY NOT NULL, NAME VARCHAR(20) NOT NULL, AGE INT, ADDRESS CHAR(50), SALARY REAL)")
conn.commit()
print("Table created successfully")
conn.close()

读取（Retrieve）

conn = pyodbc.connect('DRIVER={SQL Server};SERVER=localhost;DATABASE=my_database;UID=username;PWD=password')
cursor = conn.cursor()
cursor.execute("SELECT id, name, address, salary from Employees")
rows = cursor.fetchall()
for row in rows:
    print("ID = ", row[0])
    print("NAME = ", row[1])
    print("ADDRESS = ", row[2])
    print("SALARY = ", row[3])
conn.close()

更新（Update）

conn = pyodbc.connect('DRIVER={SQL Server};SERVER=localhost;DATABASE=my_database;UID=username;PWD=password')
cursor = conn.cursor()
cursor.execute("UPDATE Employees set SALARY = 25000.00 where ID = 1")
conn.commit()
print("Total number of rows updated :", cursor.rowcount)
conn.close()

删除（Delete）

conn = pyodbc.connect('DRIVER={SQL Server};SERVER=localhost;DATABASE=my_database;UID=username;PWD=password')
cursor = conn.cursor()
cursor.execute("DELETE from Employees where ID = 1")
conn.commit()
print("Total number of rows deleted :", cursor.rowcount)
conn.close()

Oracle

连接数据库

Python可以使用cx_Oracle库连接Oracle数据库：

import cx_Oracle
dsn_tns = cx_Oracle.makedsn('localhost', '1521', service_name='my_database') 
conn = cx_Oracle.connect(user='username', password='password', dsn=dsn_tns)
print("Opened Oracle database successfully")
conn.close()

CRUD操作

接下来，我们将展示在Oracle中如何进行基本的CRUD操作。

创建（Create）

dsn_tns = cx_Oracle.makedsn('localhost', '1521', service_name='my_database') 
conn = cx_Oracle.connect(user='username', password='password', dsn=dsn_tns)
cursor = conn.cursor()
cursor.execute("CREATE TABLE Employees (ID NUMBER(10) NOT NULL PRIMARY KEY, NAME VARCHAR2(20) NOT NULL, AGE NUMBER(3), ADDRESS CHAR(50), SALARY NUMBER(10, 2))")
conn.commit()
print("Table created successfully")
conn.close()

读取（Retrieve）

dsn_tns = cx_Oracle.makedsn('localhost', '1521', service_name='my_database') 
conn = cx_Oracle.connect(user='username', password='password', dsn=dsn_tns)
cursor = conn.cursor()
cursor.execute("SELECT id, name, address, salary from Employees")
rows = cursor.fetchall()
for row in rows:
    print("ID = ", row[0])
    print("NAME = ", row[1])
    print("ADDRESS = ", row[2])
    print("SALARY = ", row[3])
conn.close()

更新（Update）

dsn_tns = cx_Oracle.makedsn('localhost', '1521', service_name='my_database') 
conn = cx_Oracle.connect(user='username', password='password', dsn=dsn_tns)
cursor = conn.cursor()
cursor.execute("UPDATE Employees set SALARY = 25000.00 where ID = 1")
conn.commit()
print("Total number of rows updated :", cursor.rowcount)
conn.close()

删除（Delete）

dsn_tns = cx_Oracle.makedsn('localhost', '1521', service_name='my_database') 
conn = cx_Oracle.connect(user='username', password='password', dsn=dsn_tns)
cursor = conn.cursor()
cursor.execute("DELETE from Employees where ID = 1")
conn.commit()
print("Total number of rows deleted :", cursor.rowcount)
conn.close()

PostgreSQL

连接数据库

Python可以使用psycopg2库连接PostgreSQL数据库：

import psycopg2
conn = psycopg2.connect(database="my_database", user="username", password="password", host="127.0.0.1", port="5432")
print("Opened PostgreSQL database successfully")
conn.close()

CRUD操作

接下来，我们将展示在PostgreSQL中如何进行基本的CRUD操作。

创建（Create）

conn = psycopg2.connect(database="my_database", user="username", password="password", host="127.0.0.1", port="5432")
cursor = conn.cursor()
cursor.execute('''CREATE TABLE Employees
      (ID INT PRIMARY KEY     NOT NULL,
      NAME           TEXT    NOT NULL,
      AGE            INT     NOT NULL,
      ADDRESS        CHAR(50),
      SALARY         REAL);''')
conn.commit()
print("Table created successfully")
conn.close()

读取（Retrieve）

conn = psycopg2.connect(database="my_database", user="username", password="password", host="127.0.0.1", port="5432")
cursor = conn.cursor()
cursor.execute("SELECT id, name, address, salary from Employees")
rows = cursor.fetchall()
for row in rows:
    print("ID = ", row[0])
    print("NAME = ", row[1])
    print("ADDRESS = ", row[2])
    print("SALARY = ", row[3])
conn.close()

更新（Update）

conn = psycopg2.connect(database="my_database", user="username", password="password", host="127.0.0.1", port="5432")
cursor = conn.cursor()
cursor.execute("UPDATE Employees set SALARY = 25000.00 where ID = 1")
conn.commit()
print("Total number of rows updated :", cursor.rowcount)
conn.close()

删除（Delete）

conn = psycopg2.connect(database="my_database", user="username", password="password", host="127.0.0.1", port="5432")
cursor = conn.cursor()
cursor.execute("DELETE from Employees where ID = 1")
conn.commit()
print("Total number of rows deleted :", cursor.rowcount)
conn.close()

MongoDB

连接数据库

Python可以使用pymongo库连接MongoDB数据库：

from pymongo import MongoClient
client = MongoClient("mongodb://localhost:27017/")
db = client["my_database"]
print("Opened MongoDB database successfully")
client.close()

CRUD操作

接下来，我们将展示在MongoDB中如何进行基本的CRUD操作。

创建（Create）

在MongoDB中，文档的创建操作通常包含在插入操作中：

client = MongoClient("mongodb://localhost:27017/")
db = client["my_database"]
employees = db["Employees"]
employee = {"id": "1", "name": "John", "age": "30", "address": "New York", "salary": "1000.00"}
employees.insert_one(employee)
print("Document inserted successfully")
client.close()

读取（Retrieve）

client = MongoClient("mongodb://localhost:27017/")
db = client["my_database"]
employees = db["Employees"]
cursor = employees.find()
for document in cursor:
    print(document)
client.close()

更新（Update）

client = MongoClient("mongodb://localhost:27017/")
db = client["my_database"]
employees = db["Employees"]
query = { "id": "1" }
new_values = { "$set": { "salary": "25000.00" } }
employees.update_one(query, new_values)
print("Document updated successfully")
client.close()

删除（Delete）

client = MongoClient("mongodb://localhost:27017/")
db = client["my_database"]
employees = db["Employees"]
query = { "id": "1" }
employees.delete_one(query)
print("Document deleted successfully")
client.close()

SQLite

连接数据库

Python使用sqlite3库连接SQLite数据库：

import sqlite3
conn = sqlite3.connect('my_database.db')
print("Opened SQLite database successfully")
conn.close()

CRUD操作

接下来，我们将展示在SQLite中如何进行基本的CRUD操作。

创建（Create）

conn = sqlite3.connect('my_database.db')
cursor = conn.cursor()
cursor.execute('''CREATE TABLE Employees
      (ID INT PRIMARY KEY     NOT NULL,
      NAME           TEXT    NOT NULL,
      AGE            INT     NOT NULL,
      ADDRESS        CHAR(50),
      SALARY         REAL);''')
conn.commit()
print("Table created successfully")
conn.close()

读取（Retrieve）

conn = sqlite3.connect('my_database.db')
cursor = conn.cursor()
cursor.execute("SELECT id, name, address, salary from Employees")
rows = cursor.fetchall()
for row in rows:
    print("ID = ", row[0])
    print("NAME = ", row[1])
    print("ADDRESS = ", row[2])
    print("SALARY = ", row[3])
conn.close()

更新（Update）

conn = sqlite3.connect('my_database.db')
cursor = conn.cursor()
cursor.execute("UPDATE Employees set SALARY = 25000.00 where ID = 1")
conn.commit()
print("Total number of rows updated :", cursor.rowcount)
conn.close()

删除（Delete）

conn = sqlite3.connect('my_database.db')
cursor = conn.cursor()
cursor.execute("DELETE from Employees where ID = 1")
conn.commit()
print("Total number of rows deleted :", cursor.rowcount)
conn.close()

DB2

连接数据库

Python可以使用ibm_db库连接DB2数据库：

import ibm_db
dsn = (
    "DRIVER={{IBM DB2 ODBC DRIVER}};"
    "DATABASE=my_database;"
    "HOSTNAME=127.0.0.1;"
    "PORT=50000;"
    "PROTOCOL=TCPIP;"
    "UID=username;"
    "PWD=password;"
)
conn = ibm_db.connect(dsn, "", "")
print("Opened DB2 database successfully")
ibm_db.close(conn)

CRUD操作

接下来，我们将展示在DB2中如何进行基本的CRUD操作。

创建（Create）

conn = ibm_db.connect(dsn, "", "")
sql = '''CREATE TABLE Employees
      (ID INT PRIMARY KEY     NOT NULL,
      NAME           VARCHAR(20)    NOT NULL,
      AGE            INT     NOT NULL,
      ADDRESS        CHAR(50),
      SALARY         DECIMAL(9, 2));'''
stmt = ibm_db.exec_immediate(conn, sql)
print("Table created successfully")
ibm_db.close(conn)

读取（Retrieve）

conn = ibm_db.connect(dsn, "", "")
sql = "SELECT id, name, address, salary from Employees"
stmt = ibm_db.exec_immediate(conn, sql)
while ibm_db.fetch_row(stmt):
    print("ID = ", ibm_db.result(stmt, "ID"))
    print("NAME = ", ibm_db.result(stmt, "NAME"))
    print("ADDRESS = ", ibm_db.result(stmt, "ADDRESS"))
    print("SALARY = ", ibm_db.result(stmt, "SALARY"))
ibm_db.close(conn)

更新（Update）

conn = ibm_db.connect(dsn, "", "")
sql = "UPDATE Employees set SALARY = 25000.00 where ID = 1"
stmt = ibm_db.exec_immediate(conn, sql)
ibm_db.commit(conn)
print("Total number of rows updated :", ibm_db.num_rows(stmt))
ibm_db.close(conn)

删除（Delete）

conn = ibm_db.connect(dsn, "", "")
sql = "DELETE from Employees where ID = 1"
stmt = ibm_db.exec_immediate(conn, sql)
ibm_db.commit(conn)
print("Total number of rows deleted :", ibm_db.num_rows(stmt))
ibm_db.close(conn)

Microsoft Access

连接数据库

Python可以使用pyodbc库连接Microsoft Access数据库：

import pyodbc
conn_str = (
    r'DRIVER={Microsoft Access Driver (*.mdb, *.accdb)};'
    r'DBQ=path_to_your_access_file.accdb;'
)
conn = pyodbc.connect(conn_str)
print("Opened Access database successfully")
conn.close()

CRUD操作

接下来，我们将展示在Access中如何进行基本的CRUD操作。

创建（Create）

conn = pyodbc.connect(conn_str)
cursor = conn.cursor()
cursor.execute('''CREATE TABLE Employees
      (ID INT PRIMARY KEY     NOT NULL,
      NAME           TEXT    NOT NULL,
      AGE            INT     NOT NULL,
      ADDRESS        CHAR(50),
      SALARY         DECIMAL(9, 2));''')
conn.commit()
print("Table created successfully")
conn.close()

读取（Retrieve）

conn = pyodbc.connect(conn_str)
cursor = conn.cursor()
cursor.execute("SELECT id, name, address, salary from Employees")
rows = cursor.fetchall()
for row in rows:
    print("ID = ", row[0])
    print("NAME = ", row[1])
    print("ADDRESS = ", row[2])
    print("SALARY = ", row[3])
conn.close()

更新（Update）

conn = pyodbc.connect(conn_str)
cursor = conn.cursor()
cursor.execute("UPDATE Employees set SALARY = 25000.00 where ID = 1")
conn.commit()
print("Total number of rows updated :", cursor.rowcount)
conn.close()

删除（Delete）

conn = pyodbc.connect(conn_str)
cursor = conn.cursor()
cursor.execute("DELETE from Employees where ID = 1")
conn.commit()
print("Total number of rows deleted :", cursor.rowcount)
conn.close()

Cassandra

连接数据库

Python可以使用cassandra-driver库连接Cassandra数据库：

from cassandra.cluster import Cluster
cluster = Cluster(['127.0.0.1'])
session = cluster.connect('my_keyspace')
print("Opened Cassandra database successfully")
cluster.shutdown()

CRUD操作

接下来，我们将展示在Cassandra中如何进行基本的CRUD操作。

创建（Create）

cluster = Cluster(['127.0.0.1'])
session = cluster.connect('my_keyspace')
session.execute("""
    CREATE TABLE Employees (
        id int PRIMARY KEY,
        name text,
        age int,
        address text,
        salary decimal
    )
""")
print("Table created successfully")
cluster.shutdown()

读取（Retrieve）

cluster = Cluster(['127.0.0.1'])
session = cluster.connect('my_keyspace')
rows = session.execute('SELECT id, name, address, salary FROM Employees')
for row in rows:
    print("ID = ", row.id)
    print("NAME = ", row.name)
    print("ADDRESS = ", row.address)
    print("SALARY = ", row.salary)
cluster.shutdown()

更新（Update）

cluster = Cluster(['127.0.0.1'])
session = cluster.connect('my_keyspace')
session.execute("UPDATE Employees SET salary = 25000.00 WHERE id = 1")
print("Row updated successfully")
cluster.shutdown()

删除（Delete）

cluster = Cluster(['127.0.0.1'])
session = cluster.connect('my_keyspace')
session.execute("DELETE FROM Employees WHERE id = 1")
print("Row deleted successfully")
cluster.shutdown()

Redis

连接数据库

Python可以使用redis-py库连接Redis数据库：

import redis
r = redis.Redis(host='localhost', port=6379, db=0)
print("Opened Redis database successfully")

CRUD操作

接下来，我们将展示在Redis中如何进行基本的CRUD操作。

创建（Create）

r = redis.Redis(host='localhost', port=6379, db=0)
r.set('employee:1:name', 'John')
r.set('employee:1:age', '30')
r.set('employee:1:address', 'New York')
r.set('employee:1:salary', '1000.00')
print("Keys created successfully")

读取（Retrieve）

r = redis.Redis(host='localhost', port=6379, db=0)
print("NAME = ", r.get('employee:1:name').decode('utf-8'))
print("AGE = ", r.get('employee:1:age').decode('utf-8'))
print("ADDRESS = ", r.get('employee:1:address').decode('utf-8'))
print("SALARY = ", r.get('employee:1:salary').decode('utf-8'))

更新（Update）

r = redis.Redis(host='localhost', port=6379, db=0)

r.set('employee:1:salary', '25000.00')

print("Key updated successfully")

删除（Delete）

r = redis.Redis(host='localhost', port=6379, db=0)

r.delete('employee:1:name', 'employee:1:age', 'employee:1:address', 'employee:1:salary')

print("Keys deleted successfully")

ElasticSearch

连接数据库

Python可以使用elasticsearch库连接ElasticSearch数据库：

from elasticsearch import Elasticsearch
es = Elasticsearch([{'host': 'localhost', 'port': 9200}])
print("Opened ElasticSearch database successfully")

CRUD操作

接下来，我们将展示在ElasticSearch中如何进行基本的CRUD操作。

创建（Create）

es = Elasticsearch([{'host': 'localhost', 'port': 9200}])

employee = {
    'name': 'John',
    'age': 30,
    'address': 'New York',
    'salary': 1000.00
}
res = es.index(index='employees', doc_type='employee', id=1, body=employee)

print("Document created successfully")

读取（Retrieve）

es = Elasticsearch([{'host': 'localhost', 'port': 9200}])

res = es.get(index='employees', doc_type='employee', id=1)
print("Document details:")
for field, details in res['_source'].items():
    print(f"{field.upper()} = ", details)

更新（Update）

es = Elasticsearch([{'host': 'localhost', 'port': 9200}])

res = es.update(index='employees', doc_type='employee', id=1, body={
    'doc': {
        'salary': 25000.00
    }
})

print("Document updated successfully")

删除（Delete）

es = Elasticsearch([{'host': 'localhost', 'port': 9200}])

res = es.delete(index='employees', doc_type='employee', id=1)

print("Document deleted successfully")

Neo4j

连接数据库

Python可以使用neo4j库连接Neo4j数据库：

from neo4j import GraphDatabase
driver = GraphDatabase.driver("bolt://localhost:7687", auth=("neo4j", "password"))
print("Opened Neo4j database successfully")
driver.close()

CRUD操作

接下来，我们将展示在Neo4j中如何进行基本的CRUD操作。

创建（Create）

driver = GraphDatabase.driver("bolt://localhost:7687", auth=("neo4j", "password"))

with driver.session() as session:
    session.run("CREATE (:Employee {id: 1, name: 'John', age: 30, address: 'New York', salary: 1000.00})")

print("Node created successfully")

driver.close()

读取（Retrieve）

driver = GraphDatabase.driver("bolt://localhost:7687", auth=("neo4j", "password"))

with driver.session() as session:
    result = session.run("MATCH (n:Employee) WHERE n.id = 1 RETURN n")
    for record in result:
        print("ID = ", record["n"]["id"])
        print("NAME = ", record["n"]["name"])
        print("ADDRESS = ", record["n"]["address"])
        print("SALARY = ", record["n"]["salary"])

driver.close()

更新（Update）

driver = GraphDatabase.driver("bolt://localhost:7687", auth=("neo4j", "password"))

with driver.session() as session:
    session.run("MATCH (n:Employee) WHERE n.id = 1 SET n.salary = 25000.00")

print("Node updated successfully")

driver.close()

删除（Delete）

driver = GraphDatabase.driver("bolt://localhost:7687", auth=("neo4j", "password"))

with driver.session() as session:
    session.run("MATCH (n:Employee) WHERE n.id = 1 DETACH DELETE n")

print("Node deleted successfully")

driver.close()

InfluxDB

连接数据库

Python可以使用InfluxDB-Python库连接InfluxDB数据库：

from influxdb import InfluxDBClient
client = InfluxDBClient(host='localhost', port=8086)
print("Opened InfluxDB database successfully")
client.close()

CRUD操作

接下来，我们将展示在InfluxDB中如何进行基本的CRUD操作。

创建（Create）

client = InfluxDBClient(host='localhost', port=8086)

json_body = [
    {
        "measurement": "employees",
        "tags": {
            "id": "1"
        },
        "fields": {
            "name": "John",
            "age": 30,
            "address": "New York",
            "salary": 1000.00
        }
    }
]

client.write_points(json_body)

print("Point created successfully")

client.close()

读取（Retrieve）

client = InfluxDBClient(host='localhost', port=8086)

result = client.query('SELECT "name", "age", "address", "salary" FROM "employees"')

for point in result.get_points():
    print("ID = ", point['id'])
    print("NAME = ", point['name'])
    print("AGE = ", point['age'])
    print("ADDRESS = ", point['address'])
    print("SALARY = ", point['salary'])

client.close()

更新（Update）

InfluxDB的数据模型和其他数据库不同，它没有更新操作。但是你可以通过写入一个相同的数据点（即具有相同的时间戳和标签）并改变字段值，实现类似更新操作的效果。

删除（Delete）

同样，InfluxDB也没有提供删除单个数据点的操作。然而，你可以删除整个系列（即表）或者删除某个时间段的数据。

client = InfluxDBClient(host='localhost', port=8086)

# 删除整个系列
client.query('DROP SERIES FROM "employees"')

# 删除某个时间段的数据
# client.query('DELETE FROM "employees" WHERE time &lt; now() - 1d')

print("Series deleted successfully")

client.close()

Snowflake

连接数据库

Python可以使用snowflake-connector-python库连接Snowflake数据库：

from snowflake.connector import connect
con = connect(
    user='username',
    password='password',
    account='account_url',
    warehouse='warehouse',
    database='database',
    schema='schema'
)
print("Opened Snowflake database successfully")
con.close()

CRUD操作

接下来，我们将展示在Snowflake中如何进行基本的CRUD操作。

创建（Create）

con = connect(
    user='username',
    password='password',
    account='account_url',
    warehouse='warehouse',
    database='database',
    schema='schema'
)

cur = con.cursor()
cur.execute("""
CREATE TABLE EMPLOYEES (
    ID INT,
    NAME STRING,
    AGE INT,
    ADDRESS STRING,
    SALARY FLOAT
)
""")

cur.execute("""
INSERT INTO EMPLOYEES (ID, NAME, AGE, ADDRESS, SALARY) VALUES
(1, 'John', 30, 'New York', 1000.00)
""")

print("Table created and row inserted successfully")

con.close()

读取（Retrieve）

con = connect(
    user='username',
    password='password',
    account='account_url',
    warehouse='warehouse',
    database='database',
    schema='schema'
)

cur = con.cursor()
cur.execute("SELECT * FROM EMPLOYEES WHERE ID = 1")

rows = cur.fetchall()

for row in rows:
    print("ID = ", row[0])
    print("NAME = ", row[1])
    print("AGE = ", row[2])
    print("ADDRESS = ", row[3])
    print("SALARY = ", row[4])

con.close()

更新（Update）

con = connect(
    user='username',
    password='password',
    account='account_url',
    warehouse='warehouse',
    database='database',
    schema='schema'
)

cur = con.cursor()
cur.execute("UPDATE EMPLOYEES SET SALARY = 25000.00 WHERE ID = 1")

print("Row updated successfully")

con.close()

删除（Delete）

con = connect(
    user='username',
    password='password',
    account='account_url',
    warehouse='warehouse',
    database='database',
    schema='schema'
)

cur = con.cursor()
cur.execute("DELETE FROM EMPLOYEES WHERE ID = 1")

print("Row deleted successfully")

con.close()

Amazon DynamoDB

连接数据库

Python可以使用boto3库连接Amazon DynamoDB：

import boto3
dynamodb = boto3.resource('dynamodb', region_name='us-west-2',
                          aws_access_key_id='Your AWS Access Key',
                          aws_secret_access_key='Your AWS Secret Key')
print("Opened DynamoDB successfully")

CRUD操作

接下来，我们将展示在DynamoDB中如何进行基本的CRUD操作。

创建（Create）

table = dynamodb.create_table(
    TableName='Employees',
    KeySchema=[
        {
            'AttributeName': 'id',
            'KeyType': 'HASH'
        },
    ],
    AttributeDefinitions=[
        {
            'AttributeName': 'id',
            'AttributeType': 'N'
        },
    ],
    ProvisionedThroughput={
        'ReadCapacityUnits': 5,
        'WriteCapacityUnits': 5
    }
)

table.put_item(
   Item={
        'id': 1,
        'name': 'John',
        'age': 30,
        'address': 'New York',
        'salary': 1000.00
    }
)

print("Table created and item inserted successfully")

读取（Retrieve）

table = dynamodb.Table('Employees')

response = table.get_item(
   Key={
        'id': 1,
    }
)

item = response['Item']
print(item)

更新（Update）

table = dynamodb.Table('Employees')

table.update_item(
    Key={
        'id': 1,
    },
    UpdateExpression='SET salary = :val1',
    ExpressionAttributeValues={
        ':val1': 25000.00
    }
)

print("Item updated successfully")

删除（Delete）

table = dynamodb.Table('Employees')

table.delete_item(
    Key={
        'id': 1,
    }
)

print("Item deleted successfully")

Microsoft Azure CosMos DB

连接数据库

Python可以使用azure-cosmos库连接Microsoft Azure CosMos DB：

from azure.cosmos import CosmosClient, PartitionKey, exceptions
url = 'Cosmos DB Account URL'
key = 'Cosmos DB Account Key'
client = CosmosClient(url, credential=key)
database_name = 'testDB'
database = client.get_database_client(database_name)
container_name = 'Employees'
container = database.get_container_client(container_name)
print("Opened CosMos DB successfully")

CRUD操作

接下来，我们将展示在CosMos DB中如何进行基本的CRUD操作。

创建（Create）

database = client.create_database_if_not_exists(id=database_name)

container = database.create_container_if_not_exists(
    id=container_name, 
    partition_key=PartitionKey(path="/id"),
    offer_throughput=400
)

container.upsert_item({
    'id': '1',
    'name': 'John',
    'age': 30,
    'address': 'New York',
    'salary': 1000.00
})

print("Container created and item upserted successfully")

读取（Retrieve）

for item in container.read_all_items():
    print(item)

更新（Update）

for item in container.read_all_items():
    if item['id'] == '1':
        item['salary'] = 25000.00
        container.upsert_item(item)
        
print("Item updated successfully")

删除（Delete）

for item in container.read_all_items():
    if item['id'] == '1':
        container.delete_item(item, partition_key='1')
        
print("Item deleted successfully")

以上就是Python史上最全种类数据库操作方法分享的详细内容，更多关于Python数据库操作的资料请关注脚本之家其它相关文章

您可能感兴趣的文章:

Python包装异常处理方法
这篇文章主要介绍了Python包装异常处理方法，相比java，python的异常和java中不同，python主要是防止程序异常被中止。一旦被catch后它还行往下执行，本文就分享python相关的异常处理方法，需要的小伙伴可以参考一下
2022-06-06
实现Python与STM32通信方式
今天小编就为大家分享一篇实现Python与STM32通信方式。具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧
2019-12-12
Python ArgumentParse的subparser用法说明
这篇文章主要介绍了Python ArgumentParse的subparser用法说明，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧
2020-04-04
Python实现视频画质增强的示例代码
这篇文章主要为大家详细介绍了如何利用Python语言实现对视频进行画质增强功能，文中的示例代码讲解详细，感兴趣的小伙伴可以尝试一下
2022-04-04
Python对Excel两列数据进行运算的示例代码
本文介绍了如何使用Python中的pandas库对Excel表格中的两列数据进行运算,并提供了详细的代码示例,感兴趣的朋友跟随小编一起看看吧
2024-04-04
Python 确定多项式拟合/回归的阶数实例
今天小编就为大家分享一篇Python 确定多项式拟合/回归的阶数实例，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧
2018-12-12
Python实现监视程序的内存使用情况
我们使用Python和它的数据处理库套件进行大量数据处理时候,可能使用了大量的计算资源,那么如何监视程序的内存使用情况就显得尤为重要,下面我们就来了解一下具体实现方法吧
2023-12-12
基于注解实现 SpringBoot 接口防刷的方法
这篇文章主要介绍了基于注解实现 SpringBoot 接口防刷的方法，文中通过示例代码介绍的非常详细，对大家的学习或者工作具有一定的参考学习价值，需要的朋友们下面随着小编来一起学习学习吧
2021-03-03
解决python给列表里添加字典时被最后一个覆盖的问题
今天小编就为大家分享一篇解决python给列表里添加字典时被最后一个覆盖的问题，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧
2019-01-01
使用Python实现毫秒级抢单功能
年中购物618大狂欢开始了，各大电商又开始了大力度的折扣促销，我们的小胖又给大家谋了一波福利，淘宝APP直接搜索：小胖发福利，每天领取三次粉丝专属现金大红包。这篇文章主要介绍了用Python完成毫秒级抢单，助你秒杀淘宝大单,需要的朋友可以参考下
2019-06-06

Python史上最全种类数据库操作方法分享

目录

MySQL

连接数据库

CRUD操作

SQL Server

连接数据库

CRUD操作

Oracle

连接数据库

CRUD操作

PostgreSQL

连接数据库

CRUD操作

MongoDB

连接数据库

CRUD操作

SQLite

连接数据库

CRUD操作

DB2

连接数据库

CRUD操作

Microsoft Access

连接数据库

CRUD操作

Cassandra

连接数据库

CRUD操作

Redis

连接数据库

CRUD操作

ElasticSearch

连接数据库

CRUD操作

Neo4j

连接数据库

CRUD操作

InfluxDB

连接数据库

CRUD操作

Snowflake

连接数据库

CRUD操作

Amazon DynamoDB

连接数据库

CRUD操作

Microsoft Azure CosMos DB

连接数据库

CRUD操作

相关文章

最新评论

大家感兴趣的内容

最近更新的内容

常用在线小工具