Another post for data analytics students…
Data wrangling, also known as data manipulation, is the process of preparing data for use, ensuring it’s in a specific shape or format that applications can utilize. It involves skillfully handling and managing data to make it usable for analysis.
Methods for data wrangling include:
• Merging data—Combining datasets through joins, blends, concatenation, and appending.
• Calculating derived and reduced variables—Creating new variables to add meaning, such as flags derived from existing data or aggregate variables to reduce data volume.
• Parsing data—Breaking down large pieces of data into smaller, manageable pieces. For natural language processing, parsing is called tokenization, which involves breaking text into words, each becoming its own token.
• Recoding variables—Translating variables into different formats, such as converting quantitative variables into qualitative ones and vice versa. This includes creating categories based on numerical ranges or using dummy coding to create binary variables for each category.
• Shaping data with common functions—Utilizing tools like conditional operators, working with dates, transposing data, and system functions to shape data. Transposing, for example, involves turning columns into rows and vice versa.