My df fields are 'User ID' (string), 'Last Login' (%Y-%m-%d %H:%M:%S). I want to know the User IDs that haven't Logged in for the past 3 months. The 'Last Login' field contains 12 months of login history but only the last login per user. I need the User ID field to be preserved to compare against a list of User IDs that are in scope for my analysis.
I have tried a few variations of code using datetime but am struggling to get results.
CodePudding user response:
Get the delta between today's date and last login, then apply a mask on the dataframe to get your final result.
import datetime as dt
current_date = dt.datetime.today()
df['Last Login Days'] = abs(df['Last Login'] - current_date)
absent_users_df = df[df['Last Login Days'] > 90]
CodePudding user response:
import time
import pandas as pd
import numpy as np
now = pd.to_datetime(time.ctime())
idx = (now - df['Last Login']) / np.timedelta64(1, 'M') <= 3
df2 = df[idx]