Optimized Census Data Processing with Dask and Pandas
- Day: 2023-01-23
- Time: 01:45 to 03:00
- Project: Dev
- Workspace: WP 2: Operational
- Status: Completed
- Priority: MEDIUM
- Assignee: Matías Nehuen Iglesias
- Tags: Data Processing, Dask, Pandas, Optimization, Census Data
Description
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.
Evidence
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- event_ids: []