Cleaning and Transforming Data with Python: Introduction to pandas (Python 2)
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Cleaning and Transforming Data with Python: Introduction to pandas (Python 2) Online
Are you ready to learn about one of Python's most popular data libraries? This class will explore the basics of pandas, including its ability to read in, clean, and transform data while also incorporating and reviewing skills from earlier Python classes offered at the Medical Library.
In this 2-hour online session, you'll learn how to:
- Determine what you want to achieve with data cleaning
- Work with pandas data structures (dataframes, series)
- Perform routine data cleaning tasks with pandas (e.g., dealing with missing data, converting data types, cleaning up text issues, removing and adding data, etc.)
- Determine whether and what data transformation (e.g., grouping, subsetting, reshaping) might be needed for next steps
Technical requirements:
A computer or device with the following installed:
- Python 3
- pandas Python library
- A Python environment, such as Jupyter Notebook, PyCharm, etc. -- the instructor will use Jupyter Notebook
- Or: do all of the above at once by installing Anaconda (which comes with Python 3, pandas, and Jupyter Notebook installed).
Note: the Medical Library has many items available for borrowing as well as desktops on site, if you need access to equipment.
Prerequisites:
In order to get the most out of this workshop, it is strongly recommended that you have already attended an introductory Python training (such as the Medical Library's Python 1), and/or that you understand Python fundamentals, including variables, data types (strings, lists, dictionaries), libraries, slicing and indexing, and applying functions to datasets (such as with numpy or matplotlib). While anyone is welcome to attend, this session will be more useful if you already know Python fundamentals.
What else to know about this session:
- This training will be hands-on; come ready to code alongside the instructor.
- Please note that registration is required for this event.