Dive into advanced NumPy techniques in this comprehensive tutorial from SciPy Japan 2019. Master broadcasting rules, explore strides and stride tricks, and learn advanced indexing methods. Work hands-on with practical examples using Jupyter Notebook, analyzing gene expression data, and manipulating NumPy arrays. Tackle exercises on reads per kilobase, RAM usage, transposing, slicing, and variance calculation. Gain valuable insights from Juan Nunez-Iglesias, a Research Fellow, core developer of scikit-image, and co-author of "Elegant SciPy". Enhance your Python and array computing skills to take your data analysis capabilities to the next level.
Overview
Syllabus
Introduction
Jupiter Notebook
Gene Expression
Expression Data
NumPy Array
Reads per kilobase
RAM
Transpose
Slicing
Exercises
Variance
Broadcasting
Taught by
Enthought
