This course is delivered through the Jupyter notebook application.
It begins with a comprehensive overview of the Pandas library. The two main data structures in this library – the series and dataframe and associated methods are explained with clarifying examples on how to select, filter, aggregate and merge data.
Keys tasks like handling null values, applying functions and plotting are also highlighted. Each section has exercises for participants to attempt to consolidate their learning.
Participants should become knowledgeable in how to deal with tabular data through using the specialised data structures found in the Pandas library and how to perform key analysis through methods available in this library.
- Describe specialised data structures – the series and dataframe in the Pandas library;
- Select, filter, aggregate, merge data in the Pandas dataframe;
- Execute specific operations to handle null values and apply functions;
- Plot data in a dataframe column(s).
Online learning – available
Self learning – available
Face to face – available
Basic literacy in Python code. Participants must have completed Introduction to Python.
Course materials are available on our Github.
To discuss booking this course for remote delivery, please contact the Data Science Campus Faculty.