Python, Appending Dataframe in the right order and printing the dataframe as a whole, timeseries forecasting using LSTM

so i'm currently trying to make a timeseries forecasting using LSTM and i'm still on the early stage where i wanted to make sure my data clean.

for the background:

i'm trying to make a model using LSTM for temperature, rain(?), and humidity (my english not good) for 3 Station, and so if i'm correct there will be 9 models, 3 models each for each station. as of now i'm doing an experiment using 1 year worth of data

the problem:

i named my file based on the index of the month, Jan as 1, Feb as 2, Mar as 3, and so on. Using the os library i managed to loop through the folder for each file and clean the file, drop the column, filling the missing value, etc. But when i'm trying to append the order of the month is not correct, it starts from month 11 then go to 8 etc. what am i doing wrong?

and how to print a full dataframe? currently i succed printing the full dataframe using this method

Here is the code:

Dir_data = '/content/DATA'
excel_clean = pd.DataFrame()
train_data=[]
for i in os.listdir(Dir_data):
  excel_test = pd.read_excel(i)
  #drop column
  excel_test.drop(columns=['ff_avg', 'ddd_x', 'ddd_car', 'ff_avg', 'ff_x','ss','Tn','Tx'],inplace = True)
  #Start Cleaning
  excel_test = excel_test.replace(8888,'x').replace(9999,'x').replace('','x')
  excel_test['RR'] = pd.to_numeric(excel_test['RR'], errors='coerce').astype('float64')
  excel_test['RH_avg'] = pd.to_numeric(excel_test['RH_avg'], errors='coerce').astype('int64')
  excel_test['Tavg'] = pd.to_numeric(excel_test['Tavg'], errors='coerce').astype('float64')
  #excel_test.dtypes
  #Filling Missing Values
  excel_test['RR'] = excel_test['RR'].fillna(excel_test['RR'].mean())
  excel_test['RH_avg'] = excel_test['RH_avg'].fillna(excel_test['RH_avg'].mean())
  excel_test['Tavg'] = excel_test['Tavg'].fillna(excel_test['Tavg'].mean())
  excel_test['RR'] = excel_test['RR'].round(decimals=1)
  excel_test['Tavg'] = excel_test['Tavg'].round(decimals=1)
  excel_clean = excel_clean.append(excel_test)

pd.set_option('max_rows', 99999)
pd.set_option('max_colwidth', 400)
pd.describe_option('max_colwidth')  
excel_clean.reset_index(drop=True,inplace=True)
excel_clean

it's only for 1 station as this is an experiment