What is Data Wrangling?
Data wrangling, also known as data munging, is the process of cleaning, transforming, and organizing data in a way that makes it suitable for analysis. It is a crucial step in the data science process and typically requires significant effort and time.
Data wrangling tasks can include:
- Data cleaning: This involves identifying and removing errors, outliers, or inconsistencies in the data. This step is important to ensure that the data is accurate and reliable for analysis.
- Data transformation: This involves converting data into a format that is more suitable for analysis. This can include converting data types, normalizing data, or aggregating data.
- Data integration: This involves combining data from multiple sources into a single dataset. This can be a complex task, particularly when the data is stored in different formats or structures.
- Data reduction: This involves reducing the amount of data by removing irrelevant or redundant information. This step can help to improve the performance of data analysis and machine learning algorithms.
- Data enrichment: This involves adding additional information to the data, such as geographic coordinates or demographic information, to make it more useful for analysis.
Data wrangling is a challenging task, as it requires a deep understanding of the data, the business problem, and the data analysis technique that will be used. It also requires a good knowledge of programming and data manipulation tools such as SQL, Python pandas, and R data.table.