Reputation: 1267
With a dataframe df
like:
YEAR MONTH VALUE
(2017,1) 2017 1 1
(2017,2) 2017 2 1
(2017,3) 2017 3 1
How can I automatically generate new incremental rows onto the dataframes such as:
YEAR MONTH VALUE
(2017,1) 2017 1 1
(2017,2) 2017 2 1
(2017,3) 2017 3 1
(2017,4) 2017 4 1
(2017,5) 2017 5 1
(2017,6) 2017 6 1
I see that one can add new rows by creating a panda Series and appending it onto df
but I have alot of rows to add and this method seems super manual.
I've set MONTH
and YEAR
as datetime
using
df["MONTH"]=pd.to_datetime(df["MONTH"], format='%m')
df["YEAR"]=pd.to_datetime(df["YEAR"], format='%Y')
But i'm unsure how to automatically increment new rows onto the existing dataframe.
Upvotes: 2
Views: 810
Reputation: 1637
You can try this
import dateutil.relativedelta
last_row = df.iloc[-1]
last_value = last_row['VALUE'] # or change it
last_date = datetime.datetime(last_row['YEAR'], last_row['MONTH'], 1)
t = [last_date + dateutil.relativedelta.relativedelta(months = k + 1) for k in range(12)]
df1 = [{'YEAR' : k.year, 'MONTH' : k.month, 'VALUE' : last_value} for k in t]
df1 = pandas.DataFrame(df1, index = [(k.year, k.month) for k in t])
dfnew = df.append(df1)
print(dfnew)
MONTH VALUE YEAR
(2017, 1) 1 1 2017
(2017, 2) 2 1 2017
(2017, 3) 3 1 2017
(2017, 4) 4 1 2017
(2017, 5) 5 1 2017
(2017, 6) 6 1 2017
(2017, 7) 7 1 2017
(2017, 8) 8 1 2017
(2017, 9) 9 1 2017
(2017, 10) 10 1 2017
(2017, 11) 11 1 2017
(2017, 12) 12 1 2017
(2018, 1) 1 1 2018
(2018, 2) 2 1 2018
(2018, 3) 3 1 2018
Upvotes: 1