Question

从昨天开始，我一直在寻找将 SQL 查询的输出转换为 Pandas 数据帧的方法。

例如执行此操作的代码：

data = select * from table

我已经尝试了很多我在互联网上找到的代码，但似乎没有任何效果。

请注意，我的数据库存储在 Azure DataBricks 中，我只能使用其 URL 访问该表。

非常感谢！

Answer 1

希望能帮到你。插入和选择都在此代码中以供参考。

def db_insert_user_level_info(table_name):
    #Call Your DF Here , as an argument in the function or pass directly
    df=df_parameter
    params = urllib.parse.quote_plus("DRIVER={SQL Server};SERVER=DESKTOP-ITAJUJ2;DATABASE=githubAnalytics")
    engine = create_engine("mssql+pyodbc:///?odbc_connect=%s" % params) 
    engine.connect() 
    table_row_count=select_row_count(table_name)
    df_row_count=df.shape[0]
    if table_row_count == df_row_count:
        print("Data Cannot Be Inserted Because The Row Count is Same")
    else:
        df.to_sql(name=table_name,con=engine, index=False, if_exists='append')
        print("********************************** DONE EXECTUTED SUCCESSFULLY ***************************************************")
        
def select_row_count(table_name):
    cnxn = pyodbc.connect("Driver={SQL Server Native Client 11.0};"
                            "Server=DESKTOP-ITAJUJ2;"
                            "Database=githubAnalytics;"
                            "Trusted_Connection=yes;")

    cur = cnxn.cursor()
    try:
        db_cmd = "SELECT count(*) FROM "+table_name
        res = cur.execute(db_cmd)
        # Do something with your result set, for example print out all the results:
        for x in res:
            return x[0]
    except:
        print("Table is not Available , Please Wait...")

Answer 2

使用sqlalchemy连接数据库，使用pandas的内置方法read_sql_query直接进入DataFrame：

import pandas as pd
from sqlalchemy import create_engine

engine = create_engine(url)
connection = engine.connect()
query = "SELECT * FROM table"
df = pd.read_sql_query(query,connection)

将 SQL 查询输出转换为 Pandas 数据帧

2 个答案: