Data wrangling, also known as data munging, is the process of cleaning, structuring, and enriching raw data into a usable format. It’s one of the most time-consuming but critical steps in any data analysis or machine learning project.

Common data wrangling tasks:

1. Handling Missing Values
   • Use techniques like imputation (mean, median) or deletion based on the context and volume of missing data.
2. Removing Duplicates
   • Identify and remove repeated records to avoid skewing results.
3. Data Type Conversion
   • Convert columns to appropriate types (e.g., dates, integers, categories).
4. Standardizing Formats
   • Normalize text (e.g., lowercasing), fix date formats, and ensure consistency.
5. Filtering and Subsetting
   • Focus analysis on relevant portions of data using logical filters.
6. Merging and Joining Datasets
   • Combine related data from different sources using keys or indexes.
7. Feature Engineering
   • Create new variables from existing ones, such as extracting day or hour from a timestamp.

Tools like Pandas, OpenRefine, and Power Query simplify these tasks and automate large portions of the wrangling process.

Data wrangling may not be glamorous, but it is foundational. Without clean and well-prepared data, even the most advanced models or visualizations will fail to deliver meaningful insights. Mastering these techniques ensures your analyses are reliable and actionable.