CODEWITHSUNDEEP
pythonpython-3.xpandaspython-2.7
Mohekar
Mohekar

Reputation: 185

Add new rows to pandas dataframe with reference with other Row?

df:

End_Dt_Id Start_Date     End_Date
18AUG19   12-08-2019    18-08-2019  -- date format DD/MM/YYYY
25AUG19   19-08-2019    25-08-2019
01SEP19   26-08-2019    01-09-2019
08SEP19   02-09-2019    08-09-2019
15SEP19   09-09-2019    15-09-2019
22SEP19   16-09-2019    22-09-2019
12SEP19   23-09-2019    29-09-2019

I need to add 4 more weeks to my dataframe which will depend upon last week in my df, for example the last week would be this row "29SEP19 23-09-2019 29-09-2019" because of max(End_Date), there after i need to add four more weeks as shown in the output.

df_output:

End_Dt_Id Start_Date     End_Date
18AUG19   12-08-2019    18-08-2019  -- date format DD/MM/YYYY
25AUG19   19-08-2019    25-08-2019
01SEP19   26-08-2019    01-09-2019
08SEP19   02-09-2019    08-09-2019
15SEP19   09-09-2019    15-09-2019
22SEP19   16-09-2019    22-09-2019
29SEP19   23-09-2019    29-09-2019
05OCT19   30-09-2019    05-10-2019
12OCT19   06-10-2019    12-10-2019
19OCT19   13-10-2019    19-10-2019
26OCT19   20-10-2019    26-10-2019

Upvotes: 0

Views: 52

Answers (1)

kait
kait

Reputation: 1357

I'm not sure that I understand the logic, but this provides the output you are looking for:

last_row = df.sort_values('End_Date')[-1:]
new_rows = []
start_date = last_row['Start_Date'].iloc[0]
end_date = last_row['End_Date'].iloc[0]
for i in range(1,5):

    start_date = end_date + dt.timedelta(days=1)
    end_date = end_date + dt.timedelta(days=7)
    end_dt_id = dt.datetime.strftime(end_date, '%d%b%y').upper()
    new_rows.append([end_dt_id,start_date, end_date])
    
df = pd.concat([
    df, 
    pd.DataFrame(new_rows, columns=['End_Dt_Id', 'Start_Date', 'End_Date'])
])
for col in ['Start_Date', 'End_Date']:
    df[col] = pd.to_datetime(df[col]).dt.strftime('%d-%m-%Y')

Returns:

End_Dt_Id  Start_Date    End_Date
  18AUG19  12-08-2019  18-08-2019
  25AUG19  19-08-2019  25-08-2019
  01SEP19  26-08-2019  01-09-2019
  08SEP19  02-09-2019  08-09-2019
  15SEP19  09-09-2019  15-09-2019
  22SEP19  16-09-2019  22-09-2019
  12SEP19  23-09-2019  12-09-2019
  19SEP19  13-09-2019  19-09-2019
  26SEP19  20-09-2019  26-09-2019
  03OCT19  27-09-2019  03-10-2019
  10OCT19  04-10-2019  10-10-2019

Upvotes: 1

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