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 :

1. Fill missing sales amounts with averages or placeholders.
2. Remove duplicate records.
3. Standardize dates to a consistent format (e.g., YYYY-MM-DD).
4. 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.