请将C:\path\to\your\database.accdb替换为你的Access数据库文件的实际路径。 建立连接: 使用pyodbc.connect()函数建立与Access数据库的连接。 python conn = pyodbc.connect(conn_str) 读取数据到DataFrame: 使用Pandas的read_sql_query函数执行SQL查询,并将查询结果读取到DataFrame中。 python query = "SELECT ...
query ="SELECT * FROM user_to_role"engine = create_engine("mysql+pymysql://")# 这里我们将 user_id 改成了字符串,当然我们改成字符串反而是不对的,这里只是演示这个功能df = pl.read_database(query, engine, schema_overrides={"user_id": pl.String})print(df)""" shape: (9, 2) ┌──...
( username=username, password=password, host=host, port=port, database=database ) ) # 查询所有 def search_all(): sql = "select * from goods" df = pd.read_sql(sql=sql, con=engine) data_lst = df.to_dict("records") return data_lst # 通过名字查询 def search_by_name(name): sql...
2、读取excel文件 fpath = "./datas/crazyant/access_pvuv.xlsx" pvuv = pd.read_excel(fpath) pvuv 3、读取MySQL数据库 import pymysql conn =.connect( host='127.0.0.1', user='root', password='12345678', database='test', charset='utf8' ) mysql_page = pd.read_sql("select * from cra...
uv'] ) pvuv2、读取excel文件fpath = "./datas/crazyant/access_pvuv.xlsx" pvuv = pd.read...
Importing Data is the first important step in any data science project. Learn how pandas' read_csv() function is perfect for this.
复制 fpath = "./datas/crazyant/access_pvuv.txt" In [10]: 代码语言:javascript 代码运行次数:0 运行 复制 pvuv = pd.read_csv( fpath, sep="\t", header=None, names=['pdate', 'pv', 'uv'] ) In [11]: 代码语言:javascript 代码运行次数:0 运行 复制 pvuv Out[11]: pdate pv uv ...
fpath = "./datas/crazyant/access_pvuv.xlsx" pvuv = pd.read_excel(fpath) In [ ] pvuv 3、读取MySQL数据库 In [ ] import pymysql conn = pymysql.connect( host='127.0.0.1', user='root', password='12345678', database='test', charset='utf8' ) In [ ] mysql_page = pd.read_sql...
use database_name 如果要删除数据库,可以使用以下命令删除数据库: drop database database_name 这是MySQL 命令行: 让我们练习管理数据库。 我们可以使用以下命令创建数据库: create database mydb 要查看所有数据库,我们可以使用以下命令: show databases 这里有多个数据库,其中一些来自其他项目,但是正如您所...
In this SQLite database we have a table calledpurchases, and our index is in a column called "index". By passing a SELECT query and ourcon, we can read from thepurchasestable: df = pd.read_sql_query("SELECT * FROM purchases", con) ...