Data wrangling, also known as data munging, is the process of transforming and manipulating data into a more meaningful, useful, and structured format to enable more effective data analysis and data exploration. It is a common step in the workflow for processing data, especially within the field of data science. It involves adding, subtracting, sorting, organizing, and transforming data into the format needed for a specific purpose.
Data wrangling can take place in many ways. It often requires programming skills due to its technical complexity, including manually coding data wrangling through languages such as Python, R, and SQL. Some existing tools are available for data wrangling, such as Microsoft Excel, Pandas, and Apache Spark, and are used to clean up datasets in a more automated fashion.
When done properly, data wrangling can help create meaningful analysis from large and complex datasets. It can allow discovering patterns and correlations that weren’t apparent before, uncovering valuable insights. It can also reduce the amount of noise in datasets and save time, as data wrangling with proper code and tools can complete the task much faster than manual coding.
Data wrangling is not a single task, but a series of tasks. It is used extensively in preparing data for Machine Learning models, where quality data is the key for accurate results. Data wrangling is also necessary for natural language processing, where cleaning and transforming text data is often required before running models.
In summary, data wrangling is an important step in data analysis and is especially important for data science applications. By transforming datasets into a more structured form, it can reduce the amount of noise, uncover hidden correlations and insights, and enable more meaningful analysis. It is also a relatively time-consuming task as coding is often involved, and there are multiple tasks that must be completed.
