When working on python projects, using "virtual environments" allows for your project to be run using its own isolated python binary. The packages that this project uses also exist separately from the python packages installed locally on one's system. This allows for greater organization while working within projects.

Virtual Environments

Concurrency and parallelism in Python are mainly handled by 2 built-in modules: Threading and multiprocessing. Threading allows Python to make use of multiple threads in order to speed up programs that are I/O dependent. Alternatively, multiprocessing allows Python to use multiple processes to speed up CPU bound scenarios.

Additionally, web servers, databases, and distributed networks also make use of the asyncio module. Asyncio allows for concurrent code so that the aforementioned systems don't have wait on execution

Concurrency and Paralellism in Python

Python is widely used for deep learning  with various reasons.

1. Python offers concise and readable code.

2. To reduce development time, programmers turn to a number of Python frameworks and libraries like kearas, TensorfFlow, and NumPy.

3.  Python is supported by many platforms including Linux, Windows, and macOS.

4. Python is the language that people Google more than any other.


Deep learning with Python

Quantum computing is an emerging field that appears to be more and more difficult to avoid in a vast number of fields including physics, business, mathematics, chemistry, and more notably, data science. One such aspect of quantum computing that attracts the minds of many, especially data scientists, is its potential in advancing our knowledge in optimization problems. As such, it may be invaluable to understand the basics of quantum computing and how to start quantum programming now, even before the widespread availability of quantum computers. Even better, companies like Google, Microsoft, IBM, Rigetti, and more have developed their own open-source development kits to begin learning quantum computing. One such software package is called QuTip within Python.

Quantum Computing with Python

- Anaconda
- Pycharm
- Jupyter Notebook
- Command-Line Interface (CLI)
- pyenv 
- PyInstaller
- pylint

Popular Tools for Python

When developing python applications, you often need to use libraries or packages that aren't included in python's standard library. In order to keep track of the required packages and libraries that your project depends on, you can make use of a package manager to install packages and manage dependencies. Python's documentation officially recommends the use of pipenv, but other package managers exist as well, such as pip and poetry.

Package Management

Python has an assortment of tools that are interwoven with the Internet. The language is frequently used both for web development and for interacting directly with the web.

Python and the Web

Python currently ranks third in usage behind C and Java. The original Python was developed in the 1980s by Guido van Rossum and was first released in 1991. Python 2.0 saw release in 2000. The current Python 3.0 was released in 2008 and represented a major revision emphasizing the design philosophy of code readability. 

Python Usage Statistics and History


OpenCV is a very useful tool for image recognition. It is often used in tandem with machine learning libraries, but also allow for relatively simple image manipulation tools such as rotating, resizing and changing data values. 

The child nodes will break into Basic Tools and Advanced Concepts.

Computer Vision with OpenCV

- Bubble Sort
- Selection Sort
- Merge Sort
- Quick Sort
- Shell Sort
- Radix Sort
- Bitonic Sort
- Bucket Sort
- Quickselect
- Heap Sort

Sorting & Selection Algorithms

Python is the scripting language of ArcGIS. ArcGIS includes a Python API, ArcPy, that gives users access to all geoprocessing tools as well as scripting functions and specialized modules that help them automate GIS tasks. Users can write scripts that use ArcPy in conjunction with a diverse array of functions provided through Python's standard and third-party libraries. They can execute Python commands and scripts with ArcGIS Notebooks, in the Python window, through script tools, or run Python outside of ArcGIS. However Python is run, the commands work the same way and use the same geoprocessing tools.
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Geoprocessing with Python

Python libraries are called "modules". They are a reusable chunk of codes that can be included in your programs to provide various functionalities in the form of different objects or functions. For example, there are modules that have functions you can use in your code to test if certain files exist on your hard drive. Likewise,  there are other modules that have functions for implementing web-server or web-browser functionality or to work with images or create charts and graphs; etc.
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The Python standard library comes with the Python package and installs with it when Python is installed on your computer. 
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Additionally, there is a growing number of Python's third-party libraries that canbe imported into your scripts to executes tasks that outstrip the limits of the standard library.
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Python Libraries

This is a Python port of Google's libphonenumber library It supports Python 2.5-2.7 and Python 3.x (in the same codebase, with no 2to3 conversion needed).
Original Java code is Copyright (C) 2009-2015 The Libphonenumber Authors.
Release HISTORY, derived from upstream release notes.

PyPI: https://pypi.org/project/phonenumbers/

phonenumbers Library

Pillow is a fork of the discontinued Python Imaging Library (PIL) and is the most popular package for image processing. It allows for the creation, editing, and exporting of images, as well as including support for a large number of image file formats. Pillow is not included with a Python installation and must be installed with a package manager such as pip.

Image Processing with Pillow

PyMongo is a Python distribution containing tools for working with MongoDB, and is the recommended way to work with MongoDB from Python. The documentation is referenced below. 

PyMongo Distribution

Python offers many different libraries and frameworks for the development of Graphical User Interfaces (GUIs). Many of these tools can create cross platform GUI applications with varying degrees of customizability. At a base level, these frameworks provide the user the ability to create windows, labels, buttons, and canvases (for drawing things) among other graphical features for their applications.

GUI Development with Python

- Virtual Environments
- Package Management
- Python and the Web
- Concurrency and Paralellism
- Deep Learning
- Quantum Computing
- GUI Development
- Popular Tools for Python
- Computer Vision (OpenCV)
- Sorting Algorithms
- Image Processing

University of Michigan - Ann Arbor

Saint Olaf College

Python is an interpreted, object-oriented, high-level programming language. It's known for its built in data structures and robust libraries. Despite this, Python was developed to have an easy to learn syntax which emphasizes readability and whitespace. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and standard library are available without charge for all major platforms, and can be freely distributed.

Python Programming Language

Python Documentation

- Coursera Python Specializations
- Runestone Python 3 Course 
- Python for Everybody MOOC 
- W3Schools Python Tutorial
- Python Wiki
- PEP 8 Code Style Guide
- PyPI

Python Programming Language References

- Values 
- Operators & Operands
- Functions
- Statements
- Expressions
- Debugging
- Classes and Objects
- Data Structures

Python Programming Language Foundational Concepts 

Python is a powerful language that allows data scientists to perform data manipulation, analysis, and visualizations through its vast number of libraries such as numpy and pandas.

Data Science with Python

Python is widely used for machine learning various reasons.
- It is relatively easy to learn.
- The data handling capacity is better than most other languages.
- The community is large, as well as support for other platforms.
- Its’s Open Source.
- It supports developers with frameworks and libraries such as TensorFlow


Machine Learning with Python

Python Programming Advanced Topics

Python is an interpreted language, meaning that code written in the language is not converted into machine-readable code, but instead byte code. This byte code is then translated by an interpreter called the Python Virtual Machine whenever the program runs. On the fly, the interpreter translates the byte code into machine-readable into code that the CPU executes.

Interpretation

Python is an object-oriented programming language. OOP is a programming paradigm that provides a means of structuring programs so that properties and behaviors are bundled into individual objects. For instance, an object could represent a person with properties like a name, age, and address and behaviors such as walking, talking, breathing, and running. Object-oriented programming is an approach for modeling concrete, real-world things, like cars, as well as relations between things, like companies and employees, students and teachers, and so on. OOP models real-world entities as software objects that have some data associated with them and can perform certain functions.

Python as an Object Oriented Programming (OOP) Language

Imperative programming is a programming paradigm where computation is executed step-by-step as it is defined in the code. This approach makes it easier to design new machine learning models because developers can intuitively utilize standard control flow constructs (such as loops and conditionals) and tap into vast, existing software ecosystems, like that of Python. Additionally, imperative programming simplifies the debugging process, as developers can readily access and inspect all intermediate variable values during execution.

Imperative Programming

Conventionally, when evaluating deep neural networks, the Python interpreter must execute the code layer by layer to generate instructions that are then forwarded to a CPU or GPU. While this eager execution is fine for a single fast device, the single-threaded nature of the Python interpreter becomes a severe bottleneck when scaling to advanced multi-GPU servers (e.g., an 8-GPU instance). In such setups, Python struggles to generate instructions fast enough to keep all GPUs fully utilized. This bottleneck can be resolved by using hybrid programming to compile sequential models into optimized symbolic representations, thereby bypassing the Python interpreter during execution.

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