I am trying to use Pandas.DataFrame as the intermediate result dataset between two consequent SQL queries.
I imagine it looks like:
import pandas.io.sql as pisql
import pyodbc
SQL_command1 = """
select * from tab_A
"""
result = pisql.read_frame(SQL_command1)
SQL_command2 = """
select *
from ? A
inner join B
on A.id = B.id
"""
pyodbc.cursor.execute(SQL_command2, result)
The SQL_command2
in above code is simply a pseudo code, where ? takes in the result
as the input and given a alias name as A
.
This is my first time using Pandas
, so I'm not confident if my idea is feasible or efficient. Can anyone enlight me please?
Many thanks.
The pseudo code would look like this
import pandas as pd
df_a = pd.read_csv('tab_a.csv') #or read_sql or other read engine
df_b = pd.read_csv('tab_b.csv')
result = pd.merge(left=df_a,
right=df_b,
how='inner',
on='id') #assuming 'id' is in both table
And to select columns of pandas dataframe, it would be something like df_a[['col1','col2','col3']]
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