多选的 Pandas read_sql 查询
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Pandas read_sql query with multiple selects
提问by Austin B
Can read_sql query handle a sql script with multiple select statements?
read_sql 查询可以处理具有多个选择语句的 sql 脚本吗？
I have a MSSQL query that is performing different tasks, but I don't want to have to write an individual query for each case. I would like to write just the one query and pull in the multiple tables.
我有一个执行不同任务的 MSSQL 查询，但我不想为每种情况编写单独的查询。我只想编写一个查询并拉入多个表。
I want the multiple queries in the same script because the queries are related, and it making updating the script easier.
我希望在同一个脚本中进行多个查询，因为这些查询是相关的，这使得更新脚本更容易。
For example:
例如：
SELECT ColumnX_1, ColumnX_2, ColumnX_3
FROM Table_X
INNER JOIN (Etc etc...)
----------------------
SELECT ColumnY_1, ColumnY_2, ColumnY_3
FROM Table_Y
INNER JOIN (Etc etc...)
Which leads to two separate query results.
这会导致两个单独的查询结果。
The subsequent python code is:
后续的python代码为：
scriptFile = open('.../SQL Queries/SQLScript.sql','r')
script = scriptFile.read()
engine = sqlalchemy.create_engine("mssql+pyodbc://UserName:PW!@Table")
connection = engine.connect()
df = pd.read_sql_query(script,connection)
connection.close()
Only the first table from the query is brought in.
只引入查询中的第一个表。
Is there anyway I can pull in both query results (maybe with a dictionary) that will prevent me from having to separate the query into multiple scripts.
无论如何，我是否可以同时提取两个查询结果（可能使用字典），这样我就不必将查询分成多个脚本。
采纳答案by Andy Hayden
You could do the following:
您可以执行以下操作：
queries = """
SELECT ColumnX_1, ColumnX_2, ColumnX_3
FROM Table_X
INNER JOIN (Etc etc...)
---
SELECT ColumnY_1, ColumnY_2, ColumnY_3
FROM Table_Y
INNER JOIN (Etc etc...)
""".split("---")
Now you can query each table and concat the result:
现在您可以查询每个表并连接结果：
df = pd.concat([pd.read_sql_query(q, connection) for q in queries])
Another option is to use UNION on the two results i.e. do the concat in SQL.
另一种选择是在两个结果上使用 UNION，即在 SQL 中进行连接。
