Today
Create a dataframe column containing the current date:
import datetime as dt
orders['today'] = pd.to_datetime(dt.date.today())
Now
Create a dataframe column containing the current date and time:
orders['now'] = dt.datetime.now()
Date
Create a dataframe column containing the date 2020-02-01:
orders['date'] =
dt.datetime(2020, 2, 1)
Pandas dt (Year, Month, Day, Hour etc)
Extract date and time features from the "order_date" datetime column in Pandas dataframe :
orders['year'] = orders['Order_Date'].dt.year
orders['qtr'] = orders['Order_Date'].dt.quarter
orders['month'] = orders['Order_Date'].dt.month
orders['week'] = orders['Order_Date'].dt.week
orders['day'] = orders['Order_Date'].dt.day
orders['weekday'] = orders['Order_Date'].dt.weekday
orders['hour'] = orders['Order_Date'].dt.hour
orders['minute'] = orders['Order_Date'].dt.minute
Weekend
Create a dataframe column indicating if the date in the "order_date" column occured at the weekend:
orders['weekend'] =
orders['weekday'].apply(lambda
x: 1 if x == 5
or x == 6
else 0)
Days Between Dates
Create a dataframe column containing the number of
days between the dates in the "order_date" and "today" columns:
orders['days_since_order'] =
(orders['today'] -
orders['Order_Date']).dt.days
Dates Before and After
Create two dataframe columns containing the dates 7 days
after and 7 days before the date in the "order_date" column:
orders['week_after_order'] =
orders['Order_Date'] +
dt.timedelta(days=7)
orders['week_before_order'] =
orders['Order_Date'] -
dt.timedelta(days=7)
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