Tejav
#1890

The Latest Login in 2020

Easy
DatabaseHash MapArray
LeetCode ↗

Approaches

Brute ForceOptimal
Complexity Comparison
Brute ForceOptimal Solution★
Time
O(n²)
O(n)
Space
O(1)
O(n)
💡

Intuition

Time O(n)Space O(n)

Using a single pass through the logins, we can efficiently track the latest login for each user in 2020 by utilizing a hash map. This approach reduces the time complexity significantly.

⚙️

Algorithm

5 steps
  1. 1Step 1: Initialize a hash map to store the latest timestamp for each user.
  2. 2Step 2: Iterate through each login record.
  3. 3Step 3: For each record, check if the timestamp is from 2020.
  4. 4Step 4: If it is, update the hash map with the latest timestamp for that user.
  5. 5Step 5: After processing all records, convert the hash map to the desired output format.
solution.py12 lines
1# Full working Python code
2import pandas as pd
3
4def latest_login(logins):
5    latest = {}
6    for index, row in logins.iterrows():
7        user_id = row['user_id']
8        time_stamp = row['time_stamp']
9        if time_stamp.year == 2020:
10            if user_id not in latest or time_stamp > latest[user_id]:
11                latest[user_id] = time_stamp
12    return pd.DataFrame(latest.items(), columns=['user_id', 'last_stamp'])
ℹ

Complexity note: The time complexity is O(n) because we only make a single pass through the logins, and the space complexity is O(n) due to the hash map storing the latest login for each user.

  • 1Utilizing a hash map allows for efficient tracking of user logins.
  • 2Filtering timestamps by year before processing helps reduce unnecessary comparisons.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.