Data Manipulation
Data manipulation involves cleaning, transforming, and reshaping data to prepare it for analysis and make it suitable for specific tasks.
Overview
Data manipulation focuses on cleaning, transforming, and reshaping data. Real-world data is often messy and requires manipulation before analysis.
Data manipulation includes tasks like filtering, sorting, aggregating, merging, and reshaping data using tools like pandas, dplyr, and SQL.
Key Technologies
Key Concepts
Data Cleaning
Clean data by handling missing values, removing duplicates, and correcting errors.
Data Transformation
Transform data by reshaping, aggregating, and creating derived variables.
Data Merging
Combine data from multiple sources using joins, merges, and concatenation.
Efficient Processing
Process large datasets efficiently using vectorized operations and optimized libraries.