Would like to backfill nan values in dataframe for a particular date

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Python

MS-Excel

H there,

I would like to Backfill, NaN Values For a given Column, i.e. in this case 'Date', and for a Particular Date from that Column.

I have allready Forward Filled for missing Dates, in my DataFrame in Pandas. I am working with an Excel .xls File , in my DataFrame in Pandas, using Jupyter Notebook.

This is the Line Of Code I used :-

df["Date"].fillna(method='ffill', inplace = True)

But I would like to narrow that down to a particular Date, this time back filling.

Could someone tell me, what I should type to do that ?

Any help would be appreciated

Regards

Eddie Winch

What I have tried:

This is the Date Sorting part of my Code :-

<pre>#return df['Durn'] column datetime64 format to object type
df['Durn'] = pd.to_datetime(df['Durn'], format='%H:%M:%S').dt.time
#print("reverted datatype of column Durn back to ----->",df['Durn'].dtype)
#print("=======")
  
#print("\n\n*** FINAL RESULTSET ***\n\n")
  
df['Date']= pd.to_datetime(df['Date'],format='%Y-%m-%d')
#df['Date']= pd.to_datetime(df['Date']).dt.strftime('%d-%m-%Y')
##added two lines above to convert date format    

df['Date'] = df['Date'].mask(df['Date'].dt.year == 2008, 
                             df['Date'] + pd.offsets.DateOffset(year=2009)) 

df=df.loc[df.Date.dt.strftime('%m%d').astype(int).argsort()]    

df['Date']= pd.to_datetime(df['Date']).dt.strftime('%d-%m-%Y')
    
pd.DataFrame(df)