Data Cleaning with Python Workshop
–
Presented by the University Libraries Data can be messy. Researchers commonly lament the amount of time spent preparing data before any analysis can take place. Potential steps for preparing or “cleaning” data include changing data types, adjusting values for consistency and validation, splitting or joining values, and removing extraneous records, to name a few. This […]