Python, dataframe sql join - python-3.x

Question

I have two python functions that query a database directly.
Is there a way to join these 2 functions within python?
I want to do a couple of joins not really sure how to do that in python.
Query 1:
def query1(businessDate):
con = pyodbc.connect(r'DSN='+'Stack',autocommit=True)
print('working')
#businessDate = r"'2019-03-13'"
#remember business date should be entered like "'2019-03-13'"
sql = f"""
SELECT
iddate,
businessdate,
stack, identifier
FROM stackoverflow
where stack is not null
and businessdate = {businessDate}
"""
df_stack = pd.read_sql(sql,con)
con.close()
return(df_stack)
query 2:
def superuser(businessDate):
con = pyodbc.connect(r'DSN='+'super',autocommit=True)
print('working')
#remember business date should be entered like "'2019-03-13'"
sql = f"""
SELECT
iddate,
businessdate,
stack, identifier
FROM superuser
WHERE stack is not null
and businessdate = {businessDate}
"""
df_super = pd.read_sql(sql,con)
con.close()
return(df_super)
I'd want to do a left outer join table 1 with table 2 on identifier, stack, iddate and businessdate
Trying:
def testjoin():
con = pyodbc.connect(r'DSN='+'Stack',autocommit=True)
print('working')
pd.merge(df_stack,df_super, on = ['identifier','stack','iddate'])
df_test = pd.read_sql(sql,con)
con.close()
return(df_test)
trying 2:
def testjoin():
con = pyodbc.connect(r'DSN='+'Stack',autocommit=True)
print('working')
df_stack= query1("'2019-03-13'")
df_super= superuser("'2019-03-13'")
pd.merge(df_stack,df_super, on = ['identifier','stack','iddate'])
df_test = pd.read_sql(sql,con)
con.close()
return(df_test)
getting error name 'sql' is not defined'

Answer 1

Left Outer Join
SELECT *
FROM df_stack
LEFT OUTER JOIN df_super
ON df_stack.stack = df_super.stack
ON df_stack.identifier= df_super.identifier
ON df_stack.iddate = df_super.iddate
ON df_stack.businessdate = df_super.businessdate;
pd.merge(df_stack,df_super,
on=['iddate','businessdate', 'stack', 'identifier'],
how='left')

Answer 2

Ok I'm going to post this as an answer instead of the comments as there are several ways which to do what you are asking. sql is not defined as I noted in the comment because it is outside the scope of the function testjoin().
One way to is to treat the SQL string as a global variable and then access it inside the function.
sql = '''
SELECT
iddate,
businessdate,
stack, identifier
FROM stackoverflow
where stack is not null
and businessdate = {businessDate}
'''
def testjoin():
con = pyodbc.connect(r'DSN='+'Stack',autocommit=True)
print('working')
df_stack= query1("'2019-03-13'")
df_super= superuser("'2019-03-13'")
pd.merge(df_stack,df_super, on = ['identifier','stack','iddate'])
df_test = pd.read_sql(sql.format(businessDate="'2019-03-14'"),con)
con.close()
return(df_test)
The reason I used .format() instead of an f string is that an f string requires the variable to be declared at the time the f string is created. Which if you did not have businessdate as a variable it would be an error. .format() allows you to place the variable in the string and then change it's value whenever you want. I would do it this way if the main part of your query isn't going to change that much and you just need to filter by date.
The other way would to build the string outside the function and then pass it in as a parameter
businessdate = "'2019-03-13'"
sql = f'''
SELECT
iddate,
businessdate,
stack, identifier
FROM stackoverflow
where stack is not null
and businessdate = {businessDate}
'''
def testjoin(sql_string):
con = pyodbc.connect(r'DSN='+'Stack',autocommit=True)
print('working')
df_stack= query1("'2019-03-13'")
df_super= superuser("'2019-03-13'")
pd.merge(df_stack,df_super, on = ['identifier','stack','iddate'])
df_test = pd.read_sql(sql_string,con)
con.close()
return(df_test)
test_df = testjoin(sql)
You could also continue building the string inside each function as well but given the Don't Repeat Yourself paradigm in coding, and the fact you are already building it in two other functions it would be best to avoid that.