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python-for-applied-computing

Mini jupyter-based "textbook" for learning python for data and scientific computing

What is this?

This is a mini "textbook" derived from the course notes for CIS 161: Computational Science at Grand Valley State University. It is primarily designed to teach Python (specifically Python 3.x) in the context of using Python to solve problems in data, science, and mathematics.

How to use it?

Rather than a traditional textbook, it is meant to be interactive. To allow this, each "chapter" is a jupyter notebook. There are a few different ways you can use this "book".

Static View Online

All of the notebooks are viewable directly online in static form (cannot run and cannot modify). Note, this probably isn't as helpful as running interactively for learning python, but can be helpful if you just need to consult something quick.

Run Online

Option 1 - Google Colab Open in Colab

This will open up the notebook on Google's Colab. Google Colab is a free service that allows you to run jupyter notebooks online.

  • There is no need to install anything
  • You can run cells, modify code, and add new cells
  • To save your changes you have 2 options:
    • save a copy to your Google Drive "File" -> "Save a copy in drive"
    • save a copy to your Github "File" -> "Save a copy in github"
  • Notes:
    • Colab uses a modified version of jupyter notebooks. All of the necessary functionality is there, but some of the options occur in slightly different locations.
    • Because this only copies the notebook (not related data files) you may see minor changes in the code (namely the use of !wget url to grab the datasets as well).

Option 2 - myBinder Launch in Binder

This will open up the notebook in myBinder, a free service that allows you to run jupyter notebooks online. However, unlike Google Colab, the notebooks are static (meaning you can run them, but cannot make and save changes.

Download Notebooks and Work Offline

To download all of the notebooks, click the green "Code" button and either clone it (if you are familiar with git) or click the "Download zip" option to get a zip file of everything.

To download an individual notebook, right click the links the links in the "Download Version" column and select "save link as" (or the equivalent in your browser). To run the notebooks on your local machine, you will need to install python3 and jupyter. There are many ways to install python, but Anaconda Python is often one of the easiest prepackaged python distributions to install that works on Windows, Mac, and Linux.

Order

This "textbook" is meant to be worked with in the order listed below, but you should feel free to jump around or skip sections based on your previous experience with Python.

If you have no previous experience with Python, it is recommended that you work up through section 10 in order. After that, chapters 11 through 15 can be rearranged (with the exception that chapter 13 depends on chapter 12).

Chapters

# Topic Static View Run Online Download Version
01 Intro - coming soon
02 Basic Python - Variables & Operators View Online Open In Colab
Launch in Binder
Work Offline
03 Using Jupyter Notebooks View Online Open In Colab
Launch in Binder
Work Offline
04 Functions View Online Open In Colab
Launch in Binder
Work Offline
05 Math Module View Online Open In Colab
Launch in Binder
Work Offline
06 Conditionals View Online Open In Colab
Launch in Binder
Work Offline
07 Loops View Online Open In Colab
Launch in Binder
Work Offline
08 Random Module View Online Open In Colab
Launch in Binder
Work Offline
09 Lists View Online Open In Colab
Launch in Binder
Work Offline
10 Strings View Online Open In Colab
Launch in Binder
Work Offline
11 Files View Online Open In Colab
Launch in Binder
Work Offline
12 Tuples View Online Open In Colab
Launch in Binder
Work Offline
13 Dictionaries View Online Open In Colab
Launch in Binder
Work Offline
14 Plotting with Matplotlib View Online Open In Colab
Launch in Binder
Work Offline
15 Numpy View Online Open In Colab
Launch in Binder
Work Offline

License Info

Dataset licensing:

The remainder of the work is licensed under CC-BY-NC-SA. See full license text.