I am going to go for a Raymond Hettinger style presentation, https://www.cs.odu.edu/~tkennedy/cs330/f20/Public/languageResources/#python-programming-videos.

These materials are web-centric (i.e., do not need to be printed and are available at https://www.cs.odu.edu/~tkennedy/python-workshop).

Who am I?

I have taught various courses, including:

• CS 300T - Computers in Society
• CS 333 - Programming and Problem Solving
• CS 330 - Object Oriented Programming and Design
• CS 350 - Introduction to Software Engineering
• CS 410 - Professional Workforce Development I
• CS 411W - Professional Workforce Development II
• CS 417 - Computational Methods & Software

Most of my free time is spent writing Python 3 and Rust code, tweaking my Vim configuration, or learning a new (programming) language. My current language of interest is Rust (at the time of writing).

Referenced Courses & Materials

I am going to pull from CS 330, CS 350, CS 411W, and CS 417 lecture notes

• CS 330 - Object Oriented Programming & Design
  • S.O.L.I.D
  • Iterators
• CS 350 - Introduction to Software Engineering
• CS 411W - Professional Workforce Development II
  • Git Review
• CS 417 - Computational Methods & Software
  • Python non-linear solver discussion

I will also pull a couple examples from my previous Git workshop, https://www.cs.odu.edu/~tkennedy/git-workshop.

The Broad Strokes

T.B.W

Best Practices

• Design Paradigms
  • S.O.L.I.D
  • Iterators - (CS 330)
• Modern Language Constructs

Tools of the Trade

• Vim (the only editor anyone ever needs)
• Configuration Management
  • External Libraries
  • Cross-compilation
• Continuous Integration & Deployment
  • Jenkins
  • Gitlab CI
  • Travis CI
  • Azure Pipelines
  • AWS CodePipeline
• Documentation
  • Javadoc
  • Pydoc
  • Doxygen
  • Rustdoc
• Code Style
  • Static Code Analysis
  • Code Linters
  • Style Checkers

Testing & Development

• Test Driven Development
• Unit Testing & Integration Testing
• Hamcrest Matchers

Topics

• A quick overview of procedural, object-oriented and functional style programming (very briefly).
• PEP 8, https://www.python.org/dev/peps/pep-0008/ and PEP 20, https://www.python.org/dev/peps/pep-0020/
• Loops, context managers, and list/generator comprehensions, https://www.cs.odu.edu/~tkennedy/cs330/f20/Public/switchingToPython/index.html.
• The basic Python data structures (List, Dictionary, and Set)
• Generator Expressions
• A few Python modules, including Zip, json, and argparse, https://www.cs.odu.edu/~tkennedy/cs330/f20/Public/switchingToPython/index.html#python-includes-batteries
• Writing Pythonic code (e.g., using enumerate), https://www.cs.odu.edu/~tkennedy/cs330/f20/Public/whichLanguageIsIt/index.html#a-little-python.
• Time permitting... a little unit testing.

Procedural, Object-Oriented & Functional Programming

There are generally three styles of code found in Python:

Procedural

point1 = (0, 5)
point2 = (8, 3)
point3 = (1, 7)

points = [point1, point2, point3]

for point in points:
    print(sqrt(point.x ** 2 + point.y ** 2))

Object Oriented

class Point:

    def __init__(self, x, y):
        self.x = x
        self.y = y

    def __eq__(self):
        pass

    def __hash__(self):
        pass

    def __str__(self):
        pass

Functional

point1 = (0, 5)
point2 = (8, 3)
point3 = (1, 7)

points = [point1, point2, point3]

shortest_distance = min((sqrt(point.x ** 2 +  point.y ** 2)) for point in points)
largest_distance = max((sqrt(point.x ** 2 +  point.y ** 2)) for point in points)
average_distance = sum((sqrt(point.x ** 2 +  point.y ** 2)) for point in points) / len(points)

Of course... we should clean it up...

points = [(0, 5), (8, 3), (1, 7)]

distances = [sqrt(point.x ** 2 +  point.y ** 2) for point in points]

shortest_distance = min(distances)
largest_distance  = max(distances)
average_distance  = sum(distances) / len(points)

None of these examples are particuarly well written...

1. No __main__
2. No documentation
3. Everything is in "main"

Pythonic Code

TBW

Loops, Context Managers, and List/Generator Comprehensions

This section is based on notes from CS 330 Object Oriented Programming & Design.

Data Structures

When I work in Python, I generally focus on three core (fundamental) data structures.

• Lists: prime_numbers = [1, 2, 3, 5, 7, 11, 13, 17, 19]
• Dictionaries: favourite_colors = {"Thomas": "Blue", "Jessica": "Purple"}
  • collections.defaultdict
  • collections.Counter
• Sets: some_colors = {"Blue", "Red", "Green", "Cyan", "Teal"}

If we want to map these to (modern) C++, Java, and Rust... we end up with...

Lists & List Comprehensions

The next few discussions will include list comprehensions, dictionary comprehensions and set comprehensions.

Suppose we have a list of programming terms and want to create a second list containing the length of each term. We might take the usual C, C++, or Java approach:

Word Count - Boring C++ Loop

using std::string;
using std::vector;


int main(int argc, char** argv)
{
    vector<string> some_terms {"Hello", "world", "with", "for", "while", "int"};
    vector<int> term_lengths(some_terms.size(), 0);

    for (int i = 0; i < term_lengths.size(); i++) {
        term_lengths[i] = some_terms[i].size();
    }

    return 0;
}

and translate it into Python:

Word Count - Boring Python Loop

def main():
    some_terms = ["Hello", "world", "with", "for", "while", "int"]

    term_lengths = []

    for term in some_terms:
        term_lengths.append(len(term))


if __name__ == "__main__":
    main()

The Python version can (and should) use a list comprehension.

Word Count - Fun Python Loop

def main():
    some_terms = ["Hello", "world", "with", "for", "while", "int"]

    term_lengths = [len(term) for term in some_terms]


if __name__ == "__main__":
    main()

Depending on how many terms we have... a generator expression might be more appropriate:

Word Count - Really Fun Python Loop

def main():
    some_terms = ["Hello", "world", "with", "for", "while", "int"]

    term_lengths = (len(term) for term in some_terms)


if __name__ == "__main__":
    main()

Modern C++ and std::transform

Modern C++11 and newer provide the std::transform method. Combined with lambda functions we can take the original C++ code... and rewrite it as

Word Count - C++ std::transform

using std::string;
using std::vector;


int main(int argc, char** argv)
{
    vector<string> some_terms {"Hello", "world", "with", "for", "while", "int"};
    vector<int> term_lengths;

    std::transform(some_terms.begin(), some_terms.end(), std::back_inserter(term_lengths),
                   [](const string& t) -> int {
                       return t.size();
                   });

    return 0;
}

Java has the java.util.stream package, which provides similar functionality to Python comprehensions and C++ std::transform. However, in Java, we would end up dealing with the Integer wrapper class if we wanted to use a non-array data structure.

Word Count - Java Streams

import java.util.Arrays;
import java.util.List;

public class IntStreamDemo
{
    public static void main(String... args)
    {
        List<String> some_terms = Arrays.asList("Hello", "world", "with",
                                                "for", "while", "int");

        int[] term_lengths = some_terms.stream()
                           .mapToInt(s -> s.length())
                           .toArray();
    }
}

The Python implementation is the most succinct, approachable, and readable.

Context Managers

Python provides the with statement (construct). This allows the setup and teardown involved in using resources (e.g., files, sockets, and database connections) to handled elsewhere.

This has two main benefits:

1. There is less boilerplate code.
2. It is impossible to forget to close/deallocate a resource.

To write to a file, one might write:

Python File IO - Basic

text_file = open("some_file.txt", "w")

for number in range(1, 100):
    text_file.write(f"{number}\n")

Did you notice the missing fclose(text_file)? With one small with the file close operation will be handled automatically.

Python File IO - Using with

with open("some_file.txt", "w") as text_file:
    for number in range(1, 100):
        text_file.write(f"{number}\n")

This also works for other types of files--including compressed files.

Python File IO - Using with and gzip

import gzip

with gzip.open("some_file.txt.gz", "wt") as text_file:
    for number in range(1, 100):
        text_file.write(f"{number}\n")

Python Includes Batteries

For many languages external libraries are usually required for common operations. Python includes batteries.

Libraries & pip

When external libraries are required, the Python pip utility and a requirements.txt can be used for all dependency and configuration management.

In C/C++ we hope for a Linux environment (or Docker). In Java... Gradle is a popular build and configuration management tool.