More efficient way to manipulate large dataframe

Question

It's my first real Python script, so feel free to make comments in order to improve my code. The purpose of this script is to extract 2 Oracle tables with Python, store them in a dataframe and then join them with pandas.

But for queries returning more than 500k lines I feel that it is slow. Do you know why?

import pandas as pd
from datetime import date
from sqlalchemy import create_engine
import cx_Oracle, time
import pandas as pd
import config

## Variable pour le timer
start = time.time()

## User input en ligne de commande
year = input('Saisir une annee : ')
month = input('Saisir le mois, au fomat MM : ')
societe_var  = input('SA (APPLE,PEACH,BANANA,ANANAS,ALL) : ')

## SA + BU correspondantes aux SA
sa_list = ['APPLE','PEACH','BANANA','ANANAS']
bu_list_MERE = ['006111','1311402','1311403','1311404','1340115','13411106','1311407','1111','6115910','1166157','6811207','8311345','1111','1188100','8118101','8811102','8810113','8811104','8118105','8811106','8811107','8118108','1111']
bu_list_GARE = ['131400','310254']
bu_list_VOYA = ['0151100','1110073','1007115','1311335','1113340','1311341','1113342','1331143']
bu_list_RESO = ['1211345','13111345','11113395','73111345']

#Permet de pointre vers la bonne liste en fonction de la SA saisie
bu_list_map = {
    'APPLE': bu_list_APPLE,
    'PEACH': bu_list_PEACH,
    'BANANA': bu_list_BANANA,
    'ANANAS' : bu_list_ANANAS
}



if societe_var == 'ALL' :
    print('non codé pour le moment')
        

elif societe_var in sa_list :

    bu_list = bu_list_map.get(societe_var)
    sa_var = societe_var
    
    i=1

    for bu in bu_list :

        start_bu = time.time()

        ## On vient ici charger la requête SQL avec les bonnes variables pour gla_va_parametre -- EPOST
        query1 = open('gla_va_parametre - VAR.sql',"r").read()
        query1 = query1.replace('@ANNEE',"'" + year + "'").replace('%MOIS%',"'" + month + "'").replace('%SA%',"'" + societe_var + "'").replace('%BUGL%',"'" + bu + "'").replace('%DIVISION%','"C__en__PS_S1_D_OP_UNIT13".OPERATING_UNIT')

        ## On vient ici charger la requête SQL avec les bonnes variables pour cle-gla_tva -- FPOST
        query2 = open('cle-gla_tva - VAR.sql',"r").read()
        query2 = query2.replace('@ANNEE',"'" + year + "'").replace('%MOIS%',"'" + month + "'").replace('%SA%',"'" + societe_var + "'").replace('%BUGL%',"'" + bu + "'").replace('%DIVISION%','OPERATING_UNIT')

        # Param de connexion
        connection_EPOST = cx_Oracle.connect(user=config.user_EPOST, password=config.password_EPOST, dsn=config.host_EPOST, )
        connection_FPOST = cx_Oracle.connect(user=config.user_FPOST, password=config.password_FPOST, dsn=config.host_FPOST, )

        ## Récup partie EPOST
        with connection_EPOST :
            # On déclare une variable liste vide
            dfl = []  

            # On déclare un DataFrame vide
            dfs = pd.DataFrame()  

            z=1
            # Start Chunking
            for chunk in pd.read_sql(query1, con=connection_EPOST,chunksize=25000) :

                # Start Appending Data Chunks from SQL Result set into List
                dfl.append(chunk)
                print('chunk num : ' + str(z))
                z = z + 1

            # Start appending data from list to dataframe
            dfs = pd.concat(dfl, ignore_index=True)
            print('param récupéré')
            

        ## Récup partie FPOST
        with connection_FPOST :
            # On déclare une variable liste vide
            df2 = []  

            # On déclare un DataFrame vide
            dfs2 = pd.DataFrame()  

            # Start Chunking
            for chunk in pd.read_sql(query2, con=connection_FPOST,chunksize=10000) :

                # Start Appending Data Chunks from SQL Result set into List
                df2.append(chunk)

            # Start appending data from list to dataframe
            dfs2 = pd.concat(df2, ignore_index=True)
            print('clé récupéré')

        print('Début de la jointure')
        jointure = pd.merge(dfs,dfs2,how='left',left_on=['Code_BU_GL','Code_division','Code_ecriture','Date_comptable','Code_ligne_ecriture','UNPOST_SEQ'],right_on=['BUSINESS_UNIT','OPERATING_UNIT','JOURNAL_ID','JOURNAL_DATE','JOURNAL_LINE','UNPOST_SEQ']).drop(columns= ['BUSINESS_UNIT','OPERATING_UNIT','JOURNAL_ID','JOURNAL_DATE','JOURNAL_LINE'])

        jointure.to_csv('out\gla_va_'+year+month+"_"+societe_var+"_"+bu+"_"+date.today().strftime("%Y%m%d")+'.csv', index=False, sep='|')

        print('Fichier ' + str(i) + "/" + str(len(bu_list)) + ' généré en : '+ str(time.time() - start_bu)+' secondes')

        i = i + 1 

print("L'extraction du périmètre de la SA " + societe_var + " s'est effectué en :" + str((time.time() - start)/60) + " min" )