| 09:00 - 10:30 | Introduction Short Introduction to Programming in Python Starting With Data |
| 10:30 - 10:45 | Break |
| 10:45 - 12:00 | Indexing, Slicing and Subsetting DataFrames in Python Data Types and Formats |
| 12:00 - 13:00 | Break |
| 13:00 - 15:00 | Data Types and Formats ctd. Combining DataFrames with Pandas Data Workflows and Automation |
| 15:00 - 15:15 | Break |
| 15:15 - 17:00 | Data Workflows and Automation ctd. Group Exercise |
Content Contributors: April Wright, Ethan White, John Gosset, Leah Wasser, Mariela Perignon, Tracy Teal
Lesson Maintainers: April Wright, John Gosset, Mateusz Kuzak
Course Instructors: Yara Abu Awad and Chris Sulkowski
Data Carpentry’s aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain. The lessons below were adapted to give you an introduction to Python.
Data for this lesson is from the Portal Project Teaching Database - available on FigShare.
Specifically, the data files we use in these lessons are:
Link to share solutions and notes.
Data Carpentry’s teaching is hands-on, so participants are encouraged to use their own computers to insure the proper setup of tools for an efficient workflow. These lessons assume no prior knowledge of the skills or tools, but working through this lesson requires working copies of the software described below. To most effectively use these materials, please make sure to install everything before working through this lesson.
Participants are required to abide by Data Carpentry’s Code of Conduct.
Data Carpentry is supported by the Gordon and Betty Moore Foundation and a partnership of several NSF-funded BIO Centers (NESCent, iPlant, iDigBio, BEACON and SESYNC) and Software Carpentry, and is sponsored by the Data Observation Network for Earth (DataONE). The structure and objectives of the curriculum as well as the teaching style are informed by Software Carpentry.