Optimized Classmate Matching Using Emails

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📅 2024-08-25 — Session: Optimized Classmate Matching Using Emails

🕒 22:10–23:10
🏷️ Labels: Python, Email Lookup, Data Processing, Pandas, Classmates
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to explore and implement a more efficient method for identifying classmates using email addresses instead of traditional legajo numbers.

Key Activities

  • Consideration of Email Addresses for Identification: Evaluated the use of email addresses for lookup purposes, emphasizing their ease of identification and association with individuals.
  • Classmate Lookup by Email in Python: Developed a Python function to find classmates using email addresses, enhancing the lookup process by utilizing unique email identifiers.
  • Classmate Matching by Email: Implemented a function to group results by the number of course matches and display them in a structured DataFrame.
  • Extracting Maximum Match Emails and Weights: Created a Python code snippet to find the maximum value for each row in a DataFrame, extract the corresponding email, and create a new DataFrame with the best match email and weight.
  • Simplifying Data Processing Steps: Streamlined data processing by eliminating unnecessary steps and applying a threshold for filtering.
  • Optimized Classmate Matching in Python: Refined the approach for finding classmates based on email addresses, focusing on a natural long format and filtering classmates based on a match threshold.
  • Generalized Function to Find Connections by Email: Outlined a function to generalize the process of finding connections by email, allowing for customizable output and merging of results.
  • Merging Classmate Data from Friends and Foes: Applied the find_classmates_by_email function to merge results from friends and foes lists.

Achievements

  • Successfully implemented a more efficient and streamlined process for classmate identification using email addresses.
  • Developed modular functions that enhance flexibility and reusability in data processing tasks.

Pending Tasks

  • Further testing and validation of the generalized function for different datasets to ensure robustness and accuracy.
  • Integration of the new approach into the existing data management system for broader application.

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