Trying to insert pandas dataframe to temporary table - sql-server

Question

I'm looking to create a temp table and insert a some data into it. I have used pyodbc extensively to pull data but I am not familiar with writing data to SQL from a python environment. I am doing this at work so I dont have the ability to create tables, but I can create temp and global temp tables. My intent is to insert a relatively small dataframe (150rows x 4cols)into a temp table and reference it throughout my session, my program structure makes it so that a global variable in the session will not suffice.I am getting the following error when trying the piece below, what am I doing wrong?
pyodbc.ProgrammingError: ('42S02', "[42S02] [Microsoft][ODBC SQL Server Driver][SQL Server]Invalid object name 'sqlite_master'. (208) (SQLExecDirectW); [42S02] [Microsoft][ODBC SQL Server Driver][SQL Server]Statement(s) could not be prepared. (8180)")
import numpy as np
import pandas as pd
import pyodbc
conn = pyodbc.connect('Driver={SQL Server};'
'Server=SERVER;'
'Database=DATABASE;'
'Trusted_Connection=yes;')
cursor = conn.cursor()
temp_creator = '''CREATE TABLE #rankings (Col1 int, Col2 int)'''
cursor.execute(temp_creator)
df_insert = pd.DataFrame({'Col1' : [1, 2, 3], 'Col2':[4,5,6]})
df_insert.to_sql(r'#rankings', conn, if_exists='append')
read_query = '''SELECT * FROM #rankings'''
df_back = pd.read_sql(read_query,conn)

Answer 1

Pandas.to_sql is failing there. But for SQL Server 2016+/Azure SQL Database there's a better way in any case. Instead of having pandas insert each row, send the whole dataframe to the server in JSON format and insert it in a single statement. Like this:
import numpy as np
import pandas as pd
import pyodbc
conn = pyodbc.connect('Driver={Sql Server};'
'Server=localhost;'
'Database=tempdb;'
'Trusted_Connection=yes;')
cursor = conn.cursor()
temp_creator = '''CREATE TABLE #rankings (Col1 int, Col2 int);'''
cursor.execute(temp_creator)
df_insert = pd.DataFrame({'Col1' : [1, 2, 3], 'Col2':[4,5,6]})
df_json = df_insert.to_json(orient='records')
print(df_json)
load_df = """\
insert into #rankings(Col1, Col2)
select Col1, Col2
from openjson(?)
with
(
Col1 int '$.Col1',
Col2 int '$.Col2'
);
"""
cursor.execute(load_df,df_json)
#df_insert.to_sql(r'#rankings', conn, if_exists='append')
read_query = '''SELECT * FROM #rankings'''
df_back = pd.read_sql(read_query,conn)
print(df_back)
which outputs
[{"Col1":1,"Col2":4},{"Col1":2,"Col2":5},{"Col1":3,"Col2":6}]
Col1 Col2
0 1 4
1 2 5
2 3 6
Press any key to continue . . .

Answer 2

Inserting into temp table using sqlalchemy works great:
import pandas as pd
from sqlalchemy import create_engine
engine = create_engine('mssql://sql-server/MY_DB?trusted_connection=yes&driver=ODBC+Driver+17+for+SQL+Server')
df1 = pd.DataFrame({'a':[1,2,3], 'b':[4,5,6]})
df1.to_sql(name='#my_temp_table', con=engine)
df2 = pd.read_sql_query(sql='select * from #my_temp_table', con=engine)
# Now we can test they are the same:
pd.testing.assert_frame_equal(df1,df2.drop(columns=['index']))