【Python】Introduction to some IDEs and software packages - "Scientific Computing with Python"

title: '【Python】Part 1: Introduction to Some IDEs and Packages - "Scientific Computing with Python"'
date: 2017-06-06
permalink: /posts/2017/06/【Python】Part 1: Introduction to Some IDEs and Packages - "Scientific Computing with Python"/

  • Python

I came across a detailed Python tutorial. With the intention of strengthening my foundation, I took a look and decided to jot down some notes. (The truth is, my fiancée wants to learn Python.)


It's not that by reading these tutorials, you will be able to write code and solve problems right away. The purpose of these tutorials is twofold:

  1. To familiarize you with the structure and form of Python code, so that you no longer feel intimidated when you see Python code. At the very least, you will be able to recognize familiar content.

  2. Although only a few packages are covered, these packages are definitely among the most frequently used ones for data processing in the future.


Let me introduce two IDEs first. The quality of each IDE depends on personal preference.

Pycharm has a high level of integration and debugging is relatively easy. You can choose the interface layout. You can download the free version from the official website.

Spyder has a simple interface, similar to Matlab. You can directly view and modify parameters during debugging. You can install it using pip:

sudo pip install spyder

After installation, type "spyder" in the terminal and press enter.

Python is also a scripting language, and the default Python shell can meet most needs. However, I still recommend using iPython because it is more convenient, with features such as auto-completion and auto-indentation. You can also install it using pip:

sudo pip install ipython

After installation, run "ipython" directly in the terminal.

Below are introductions to some libraries:

1. Numerical Computing Library#

NumPy: Provides fast multidimensional array processing capabilities and a rich library of functions.
SciPy: Adds scientific computing tools on top of NumPy.

2. Symbolic Computing Library#

SymPy: Helps with formula derivation and symbolic solving.

3. User Interface Design#


4. Plotting and Visualization#

Chaco: Closely connected to the Traits library, making it easy to create dynamic interactive graphics.
matplotlib: Quickly plots graphs and supports various output formats, with simple 3D plotting capabilities.

5. Image Processing and Computer Vision#

OpenCV: Need I say more?

All of the above packages can be installed using pip.
I encountered one problem during the installation process, which was not being able to find "swig". You can install it using apt:

sudo apt-get install swig

The above information is referenced from: http://old.sebug.net/paper/books/scipydoc/install.html

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