📅 2023-01-23 — Session: Optimized Census Data Processing with Dask and Pandas
🕒 01:45–03:00
🏷️ Labels: Data Processing, Dask, Pandas, Optimization, Census Data
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal:
The primary objective of this session was to optimize the processing of census data using Dask and Pandas, focusing on efficient data loading, filtering, and merging operations.
Key Activities:
- Data Loading and Filtering: Demonstrated loading and filtering of census data from CSV files using Dask, specifically targeting housing, households, and individuals datasets.
- Data Processing with Dask: Implemented a workflow using Dask for handling large datasets, including merging, sampling, and computing ratios based on population projections.
- Code Correction and Optimization: Corrected and optimized Python code for processing census data, emphasizing the use of the
compute()function and optimizing data merging processes to enhance performance. - Progress Tracking: Utilized a progress bar to track data sampling and merging operations, ensuring efficient execution.
Achievements:
- Successfully loaded and filtered census data using Dask and Pandas.
- Optimized data processing workflows by implementing the
compute()function and reducing unnecessary computations. - Enhanced code efficiency and performance in data merging tasks.
Pending Tasks:
- Further optimization of data processing workflows to ensure scalability and efficiency.
- Exploration of additional data processing techniques to improve performance further.