Vectorization using Numpy and (Geo)Pandas

Vectorization using Numpy and (Geo)Pandas#

In this exercise, we will learn to…

  1. Use vectorization in Python using numpy and (geo)pandas.

  2. Explain how vectorization is used to speed up geometric operations.

  3. Measure the execution time and memory consumption of Python script.

Note

To work on this exercise, go to this Gitlab repository and clone it to your computer: https://courses.gistools.geog.uni-heidelberg.de/advancedgeoscripting/02_vectorization

To avoid technical issues on your computers, run this notebook using Binder

Installation#

All required packages are contained in the environment.yml file.

conda env create -f environment.yml

Usage#

All exercises are contained in the two jupyter notebooks.

An overview of packages for profiling is given in the profiling/README.md.

References#

The notebook ‘MinneAnalytics Big Data notebook.ipynb’ was taken from Sofia Heisler’s repository PyCon 2017: Optimizing Pandas Code for Performance.

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