Python: Import multiple dataframes using for loop - python-3.x

Question

I have the following code which works to import a dataframe.
#read tblA
tbl = 'a'
cols = 'imp_a'
usecols = dfDD[dfDD[cols].notnull()][cols].values.tolist()
dfa = getdf(tbl, dfRT, sfsession)
dfa = dfa[usecols]
#read tblB
tbl = 'b'
cols = 'imp_sb'
usecols = dfDD[dfDD[cols].notnull()][cols].values.tolist()
dfb = getdf(tbl, dfRT, sfsession)
dfb = dfb[usecols]
#importing a few more tables in the steps as above two...
Is there a way to shorten this code and avoiding writing the same thing multiple times. The values that change are tbl, cols, dataframe name (df..)
I tried a few different things including putting all the changing attributes into a dictionary, but wasn't able to make it work. I could create a function, but the function would require a few more parameters - dfDD, dfRT, sfsession. I don't think it's a great solution. There has to be a better way to write this.

Answer 1

The loop should be fairly simple like this -
import pandas as pd
Create a dictionary that will store your dataframes.
df_dict = {}
config = {'tblA': {'tbl': 'a', 'cols': 'imp_a'},
'tblB': {'tbl': 'b', 'cols': 'imp_sb'}}
# Loop through the config
for key, val in config.items():
tbl = val['tbl']
cols = val['cols']
usecols = dfDD[dfDD[cols].notnull()][cols].values.tolist()
df = getdf(tbl, dfRT, sfsession)[usecols]
df_dict [key] = df # Store your dataframe in the dictionary
print(f"Created dataframe for table - {key} ({tbl} | {cols})")