Part 18: Data Manipulation in Performance Optimization

The provided code demonstrates a comprehensive approach to optimizing and analyzing large financial transaction datasets using Python's Pandas library, along with NumPy for numerical operations. Here’s a breakdown of the key steps and optimizations applied:

Step-by-Step Breakdown

1. Data Generation:

   • Generates realistic financial transactions data with various attributes such as transaction_id, customer_id, merchant_id, amount, category, timestamp, is_fraud, and merchant_country.

2. Optimizing Data Types:

   • Converts columns to more memory-efficient types (e.g., unsigned integers for IDs, categorical types for categories and countries).
   • This significantly reduces the overall memory footprint of the DataFrame.

3. Setting Index for Time-Based Queries:

   • Sets timestamp as the index and sorts it.
   • This allows efficient time-based queries and resampling operations.

4. Performing Fraud Analysis Using Vectorized Operations:

   • Aggregates data by category to calculate fraud statistics like count, rate, total amount, average amount, and standard deviation using vectorized groupby operations.

5. Complex Filtering with query() Method:

   • Uses the `

Read the full article at Towards AI - Medium

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