In the previous solution I have obtained a column "from" in the date format - 20190101. I need to add the column "to" based on the column "from". Basically the range is like this:
from 20190101 to 20190114
from 20190115 to 20190131
from 20190201 to 20190215
from 20190215 to 20190228
etc
How can I automatize this?
How to treat data as dates and operate on them. Eg., I have a date 20181231, I want to add a day 20181231 + 1 = 20190101
Assuming your data are those from the linked question:
df = pd.DataFrame({'id': {1: 'id_2', 2: 'id_3', 3: 'id_4', 4: 'id_5', 0: 'id_1'},
'price': {1: 24.5, 2: 17.5, 3: 149.5, 4: 7.5, 0: 7.5},
'code': {1: 'r', 2: 'r', 3: 'c', 4: 'r', 0: 'r'},
'from': {1: 20190115, 2: 20190101, 3: 20190115, 4: 20190115, 0: 20190115}})
df["from"] = pd.to_datetime(df["from"],format="%Y%m%d")
df["to"] = df["from"]+pd.DateOffset(days=14)
print (df)
#
id price code from to
1 id_2 24.5 r 2019-01-15 2019-01-29
2 id_3 17.5 r 2019-01-01 2019-01-15
3 id_4 149.5 c 2019-01-15 2019-01-29
4 id_5 7.5 r 2019-01-15 2019-01-29
0 id_1 7.5 r 2019-01-15 2019-01-29
strftime to construct your desired format by Series.dt.strftime(“%Y%m%d”).df["to"] = df["from"]+pd.offsets.MonthEnd() to get the last day of a month.As in your example
df:
from
0 2019-01-01
1 2019-01-15
2 2019-02-01
3 2019-02-15
You need subtract 1 day from column from and shift backward and fillna
df['to'] = ((df['from'] - pd.DateOffset(1)).shift(-1)
.fillna(df['from'].tail(1) +
pd.offsets.MonthEnd(0)))
Out[753]:
from to
0 2019-01-01 2019-01-14
1 2019-01-15 2019-01-31
2 2019-02-01 2019-02-14
3 2019-02-15 2019-02-28
from?