Standardizing steps for two dataframes

I have two separate dfs - Df1 and Df2. These Dfs are the same except that one covers a date range of 1 year while the other covers the date range for 5 years.

Now I want to apply the same steps to both the DFs, some steps being below:-

    DF = DF.fillna(method='ffill')
    DF = DF.fillna('0')
    DF = DF.T
    DF.index.name = 'Sector'
    DF = DF.round(0).astype(int)
    DF = DF.sort_index(axis=1)
    
    DF.columns = pd.to_datetime(DF.columns)
    list_month = np.array(pd.DatetimeIndex(DF.columns).month)
    DF.columns = pd.Series(DF.columns[:]).apply(lambda x: x.strftime('%b %Y')).to_list()

Is there a way I can repeat the above steps for both the DFs without having to write the same code block twice, one for each df? I think a for loop might be useful here but I am unsure about how to use it.

The most general way to create reusable code is to write a function. However, if you know in advance that you have exactly two dataframes, you can also use the looping capability of a list comprehension:

    DF1, DF2 = [
        ( DF
        .rename(index=pd.to_datetime(DF.index.to_series()).to_dict())
        .sort_index()
        .pipe( lambda df: df.rename(index=df.index.to_series().dt.strftime('%b %Y').to_dict()) )
        .fillna(method='ffill').fillna('0').T.rename_axis(index='Sector')
        .round(0).astype(int) )
    for DF in [DF1, DF2]]

Sample input:

DF1:
           auto   tech  util  agri
2/20/2023   7.7  10.10   NaN   NaN
3/21/2022   7.7    NaN  1.01   NaN
6/25/2021   7.7  11.11   NaN   NaN
5/24/2020   7.7    NaN  2.02   NaN
4/23/2019   7.7  12.12   NaN   1.0

DF2:
           auto   tech   util  agri
2/20/2023   7.7  10.10    NaN   NaN
3/21/2022   7.7    NaN   1.01   NaN
6/25/2021   7.7  11.11    NaN   NaN
5/24/2020   7.7    NaN   2.02   NaN
4/23/2019   7.7  12.12    NaN   1.0
4/23/2018   8.8  20.10    NaN   NaN
4/23/2017   8.8    NaN  11.01   NaN
4/23/2015   8.8  21.11    NaN   NaN
4/23/2016   8.8    NaN  22.02   NaN
4/23/2014   8.8  22.12    NaN   NaN

Output:

DF1:
        Apr 2019  May 2020  Jun 2021  Mar 2022  Feb 2023
Sector
auto           7         7         7         7         7
tech          12        12        11        11        10
util           0         2         2         1         1
agri           1         1         1         1         1

DF2:
        Apr 2014  Apr 2015  Apr 2016  Apr 2017  Apr 2018  Apr 2019  May 2020  Jun 2021  Mar 2022  Feb 2023
Sector
auto           8         8         8         8         8         7         7         7         7         7
tech          22        21        21        21        20        12        12        11        11        10
util           0         0        22        11        11        11         2         2         1         1
agri           0         0         0         0         0         1         1         1         1         1