Time Between Two Events
Given the schema shown below, write a function to identify the user who liked a post in the shortest time after logging in.
user_event +---------------+----------+ | user_id | int | | event | varchar | | timestamp | datetime | +---------------+----------+
Your function should output a result in the following format:
user_id | login | like | time_between --------+----------+----------+-------------- int | datetime | datetime | int
time_betweenshould be rounded to the nearest minute
The key of this solution is the use of a pivot table to get the earliest login and like timestamps for each user.
The use of a pivot table translates the data from this:
To this (after dropping unnecessary columns):
Then it's only a matter of computing the time between the two events:
Finally, we sort the results based on ascending time_between and return the first row, which is the row with the smallest time_between.
Pythondef find_fastest_like(log: pd.DataFrame) -> pd.DataFrame:
# Create pivot table to get the earliest login and like timestamps for each user_id
pivot_df = pd.pivot_table(
data=log,
index='user_id',
columns='event',
values='timestamp',
aggfunc='min'
).reset_index()
# Drop unnecessary columns
pivot_df.columns.name = None
pivot_df = pivot_df[['user_id', 'login', 'like']]
# Calculate the time difference in seconds and round to the nearest minute
pivot_df['time_between'] = ((pivot_df['like'] - pivot_df['login']).dt.total_seconds() / 60).round().astype(int)
# Sort values by time_between
pivot_df = pivot_df.sort_values(by = ['time_between'])
# Return the row with the shortest time_between
result = pivot_df.head(1)
return result
Does this solution consider multiple sessions e.g. multiple logins and likes? Consider the case in which the user logs in at 08:00 and likes at 08:05, then logs out. But then logs in at 08:30 and likes at 08:31. Will the second session be considered?
Can you come up with a solution that considers multiple sessions?
import pandas as pd from datetime import datetime def find_fastest_like(log: pd.DataFrame) -> pd.DataFrame: log=log.sort_values(['user_id','timestamp']) #get the prev event, time by user log['prev_event'] = log.groupby('user_id')['event'].shift(1) log['prev_timestamp'] = log.groupby('user_id')['timestamp'].shift(1) # True only on rows where the previous event was a login # **and** the current event is a like log['login_like'] = (log['prev_event'] == 'login') & (log['event'] == 'like') #filter the dataframe log=log[log['login_like']==True] # get the time difference log['time_between']=log['timestamp']-log['prev_timestamp'] log['time_between']=(log['time_between'].dt.total_seconds()/60).round(0).astype('int') # get the smallest time difference ans=log.nsmallest(1,'time_between') ans.rename(columns={'prev_timestamp':'login','timestamp':'like'}, inplace=True) ans=ans[['user_id','login','like','time_between']] return ans # debug your code below log = pd.DataFrame({ 'user_id': [1, 1, 2, 1, 2, 3, 2, 2, 1, 3, 1, 3, 3], 'event': ['login', 'like', 'login', 'like', 'like', 'login', 'logout', 'like', 'like', 'like', 'logout', 'like', 'logout'], 'timestamp': [ datetime(2024, 7, 25, 10, 0, 0), datetime(2024, 7, 25, 10, 1, 10), datetime(2024, 7, 25, 10, 5, 15), datetime(2024, 7, 25, 10, 15, 0), datetime(2024, 7, 25, 11, 0, 0), datetime(2024, 7, 25, 11, 0, 10), datetime(2024, 7, 25, 11, 2, 30), datetime(2024, 7, 25, 11, 15, 0), datetime(2024, 7, 25, 12, 0, 0), datetime(2024, 7, 25, 12, 0, 20), datetime(2024, 7, 25, 12, 1, 30), datetime(2024, 7, 25, 12, 5, 0), datetime(2024, 7, 25, 12, 10, 0) ] }) result = find_fastest_like(log) print(result.to_string(index=False))