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John M. Olin Library, Instruction Room 2

Data Cleaning with Python

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 workshop will cover some common data-cleaning steps using Python and the Pandas library. Basic knowledge of Python syntax is recommended.

Free and open to all; registration is required.

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