What is the primary purpose of data wrangling?

The primary purpose of data wrangling is to prepare data for analysis. This process involves cleaning, structuring, and enriching raw data into a desired format to make it suitable for further analysis. Data wrangling typically includes tasks such as removing duplicates, correcting errors, handling missing values, and transforming data types. By ensuring that the data is organized and formatted correctly, analysts can derive insights more effectively and make informed decisions based on accurate information.

Focusing on data preparation is crucial for any data-driven project, as the quality of insights is directly influenced by the quality of the data itself. Without thorough data wrangling, analysts may encounter challenges, such as inaccurate results or difficulties in querying and processing the data, which can lead to flawed analysis.