Data Wrangling
Data wrangling is the process of cleaning, structuring, and enriching raw data into a format suitable for analysis. It involves tasks like removing inconsistencies, handling missing values, standardizing formats, and combining datasets to prepare them for data-driven decision-making or modeling. It is a critical step in data science, analytics, and machine learning workflows.
Also known as: Data munging, data preparation.
Comparisons
- Data Wrangling vs. Data Cleaning: Data wrangling is broader, encompassing cleaning and restructuring, while data cleaning focuses on error correction and quality improvement.
- Data Wrangling vs. ETL: ETL is a systematic pipeline for moving and transforming data, whereas wrangling is often more exploratory and manual.
Pros
- Prepares data for analysis: Ensures datasets are ready for insights or modeling.
- Enhances data usability: Makes raw data meaningful and actionable.
- Customizable workflows: Adapts to the unique needs of specific datasets and goals.
Cons
- Time-intensive: Can require significant manual effort for complex datasets.
- Prone to human error: Manual processes increase the risk of mistakes.
Example
A data analyst prepares a sales dataset for visualization:
- Original Dataset: Contains missing values, duplicate entries, and inconsistent date formats.
- Wrangling Process:
- Fill missing sales amounts with averages or placeholders.
- Remove duplicate records.
- Standardize dates to a consistent format (e.g., YYYY-MM-DD).
- Merge sales data with marketing spend data for enriched analysis.
- Result: A clean and well-structured dataset ready for visualization in a dashboard tool, enabling insights into sales trends and marketing ROI.
Data wrangling bridges the gap between raw data and actionable insights, making it indispensable for analytics and decision-making.