Читать книгу Earth Observation Using Python - Rebekah B. Esmaili - Страница 26

Python contains intrinsic structure and mathematical commands, but its capabilities can be dramatically increased using modules. Modules are written in Python or a compiled language like C to help simplify common, general, or redundant tasks. For instance, the datetime module helps programmers manipulate calendar dates and times using a variety of units. Packages contain one or more modules, which are often designed to facilitate tasks that follow a central theme. Some other terms used interchangeably for packages are libraries and distributions.

At the time of writing, there are over 200,000 Python packages registered on pipy.org and more that live on the internet in code repositories such as GitHub (https://github.com/). Many of the most popular packages are often developed and maintained by large online communities. This massive effort benefits you as a scientist because many common tasks have already been developed in Python by someone else. This can also create a dilemma for scientists and researchers – the trade‐off between using existing code to save time against time spent researching and vetting so many code options. Additionally, because many of these packages do not have full‐time staff support, the projects can be abandoned by their development teams, and your code could eventually become obsolete.

In your research, I suggest you use three rules when choosing packages to learn and work with:

1 Use established packages.

2 Use packages that have a large community of support.

3 Use code that is efficient with respect to reduced coding time and increased speed of performance.

Following is a list of the main Python packages that I will cover in this text.