reading-notes

Class 1 - Introduction to Python

Lab 1 - Ensuring our computers and minds are prepared for Python

Overview

Today you’ll begin working on a command line utility which will mimic the functionality of a point of sale restaurant system using your basic Python tools and understanding of the basics of the language.

Feature Tasks and Requirements

$ python snakes_cafe.py
**************************************
**    Welcome to the Snakes Cafe!   **
**    Please see our menu below.    **
**
** To quit at any time, type "quit" **
**************************************

Appetizers
----------
Wings
Cookies
Spring Rolls

Entrees
-------
Salmon
Steak
Meat Tornado
A Literal Garden

Desserts
--------
Ice Cream
Cake
Pie

Drinks
------
Coffee
Tea
Unicorn Tears

***********************************
** What would you like to order? **
***********************************
>

Entering an order

***********************************
** What would you like to order? **
***********************************
> Cake

** 1 order of Cake has been added to your meal **

> Cake

** 2 orders of Cake have been added to your meal **

Exiting

> quit

Stretch Goals

Configuration

Create a project named snakes-cafe.

Create a snakes_cafe.py file inside the project folder. Note the hyphen vs. underscore

Your file structure should look like this.

├── snakes-cafe
│   ├── README.md
│   └── snakes_cafe.py

Repository set-up

Create a repository on Github with the exact name of snakes-cafe. See the Github section of Lab Submission Instructions for additional details.

Submission Instructions

Refer to the Lab Submission Instructions for the complete lab submission process and expectations. You may have noticed many references to Lab Submission Instructions. That’s on purpose. Getting the steps exactly right is crucial. So make sure you follow them closely.

Setup

Python Setup guide

Creating Project

mkdir example-lab
cd example-lab
touch README.md

Create virtual environment

python3 -m venv .venv

NOTE: Replace python3 with a more specific version as needed.

Activate virtual environment

Mac/Linux:

source .venv/bin/activate

Windows:

source .venv/Scripts/activate

Create modules and scripts

mkdir example_lab
touch example_lab/example_script.py

Note the underscore vs hyphen

Install packages

For example:

pip install favorite-library

Record package dependencies

pip freeze > requirements.txt

Should result in this file tree:

└── example-lab
    ├── README.md
    ├── requirements.txt
    └── example_lab
        └── example_script.py

Tests

Many labs will require automated testing. If your lab requires it, then install pytest or pytest-watch.

pip install pytest # or pytest-watch
pip freeze > requirements.txt
touch tests/__init__.py # (Note: 2 underscores on both sides.)
touch tests/test_example.py

Should result in a file tree like this:

└── example-lab
    ├── README.md
    ├── requirements.txt
    ├── example_lab
    │   └── example_script.py
    └── tests
        ├── __init__.py
        └── test_example.py

README

Your project’s README.md should match the structure of the template README.

Git

On Local System

git init
touch .gitignore

Add .venv folder to .gitignore

git add .
git commit -m "first commit"

On Github site

Create an EMPTY repository example-lab on Github. DO NOT initialize with README, license, or gitignore. Those will be added soon.

The next screen will have a “Quick Setup” section with a URL available to copy. Copy it ;)

On local system (again)

git remote add origin the_url_you_copied_that_ends_with_git
git push -u origin main

Now everything is wired up between the local machine and Github.

Canvas Submission

Submit a link to the README.md from your assignment branch in Canvas.

Resubmits

Any commits made to the submission branch will be updated in the PR.

In the event of assignment resubmission, submit the submission branch PR on canvas.

Github Actions

This step is optional early in the course. The instructor will inform you when it is required.

Setup “Github Actions” so that your code can be properly tested in Github as you make new pushes to your branches and pull requests to master.

Include the following YAML code:

name: Run Python Tests

on:
  push:
    branches:
      - main
    paths:
      - "python/**"
  pull_request:
    branches:
      - main
    paths:
      - "python/**"

jobs:
  ci:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Set up Python 3.9
        uses: actions/setup-python@v2
        with:
          python-version: 3.9
      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install -r requirements.txt
        working-directory: ./python
      - name: Test with pytest
        run: pytest -vv
        working-directory: ./python

Code Challenge

Reverse an Array

Specifications

NOTE: This challenge is whiteboard only. Write out code as part of your whiteboard process, but don’t worry about creating external program files.

Feature Tasks

Write a function called reverseArray which takes an array as an argument. Without utilizing any of the built-in methods available to your language, return an array with elements in reversed order.

Example
Input Output
[1, 2, 3, 4, 5, 6] [6, 5, 4, 3, 2, 1]
[89, 2354, 3546, 23, 10, -923, 823, -12] [-12, 823, -923, 10, 23, 3546, 2354, 89]
[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, …] [199, 197, 193, 191, 181, … 7, 5, 3, 2]

Structure and Testing

Stretch Goal

Once you’ve achieved a working solution, implement the same feature with a different methodology. (Hint: what different techniques do you have when working with arrays? Recursion, loops, indexes, modifying the array input directly…) In other words, use a different algorithm & pseudocode to solve the same problem. Then compare approaches for efficiency, readability, flexibility, etc.

Submission Instructions

Written Class Notes - None

Read 1 - Big O

Resources Link/Pages

Bookmark and Review

Answer

Statement on why this topic matter as it relates to what I’m studying in this module:

Understanding that growth involves some level of discomfort and challenges and you might face a lot of it learning python.

Pain and Suffering

  1. In the context of the reading “Pain and Suffering,” describe the main challenges faced by beginners when learning Python and suggest at least two strategies for overcoming these obstacles.

    • Mental Strain: mentally challenging to think their way through complex programming problems and when confronted with topics that are initially unfamiliar.
    • Emotional Stress: The constant exposure to one’s lack of understanding, collaboration with others, and dealing with uncertainty can lead to emotional stress.

Strategies to Overcome:

Collaborative Learning: collaborate with peers and engage in group discussions. Collaboration not only provides diverse perspectives but also helps in dealing with emotional challenges by fostering a sense of shared experience and collective growth.

Mindset Shift: shift perspective and view the challenges as opportunities for growth rather than obstacles. Emphasize the importance of building a positive mindset that acknowledges the pain of growth but also recognizes it as a temporary phase leading to valuable skills and a better future. Encourage setting clear goals

Beginners Guide to Big O

  1. After reading “Beginners Guide to Big O,” explain the concept of time complexity and space complexity.

    • Time complexity is a measure of the amount of time an algorithm takes to complete as a function of the size of the input data. It provides an understanding of how the algorithm’s performance scales with larger datasets.
    • Space complexity is a measure of the amount of memory or space an algorithm requires concerning the size of the input data. It helps assess how efficiently an algorithm uses memory.

Names and Values in Python

  1. Based on the “Names and Values in Python” reading, explain the difference between mutable and immutable data types in Python.

Things I want to know more about

What exactly is the application of big o notation, where is it used?

Read 1.1 - Data Structures and Algorithms Prep Reading and Videos

Resources Link/Pages

Answer

Statement on why this topic matter as it relates to what I’m studying in this module:

Understanding the characteristics and performance of different data structures allows programmers to select the most appropriate one for a given problem.

Data Structures and Algorithms

  1. What is 1 of the more important things you should consider when deciding which data structure is best suited to solve a particular problem?

    • the efficiency of the operations required to solve the problem. Different data structures excel in different operations. For example, arrays are great for random access, linked lists for sequential access, hash tables for fast lookups, and trees for hierarchical relationships. Analyzing the specific requirements of the problem and understanding the time and space complexity of operations will help in selecting the most appropriate data structure.

  2. How can we ensure that we’ll avoid an infinite recursive call stack?

    • implement a base case in your recursive function. The base case defines a condition under which the function stops calling itself and returns a result. Without a proper base case, the recursion would continue indefinitely, leading to a stack overflow. Ensuring that your recursive function has a well-defined termination condition is essential for preventing infinite recursion.

Things I want to know more about

I want to know more about advanced data structures like Graphs and Heaps

Learning Journal

Today I Learned

One of the most effective tools in adult learning is reflection. By writing coherent summaries of lessons learned, we cement that learning and deepen our understanding of a subject. It also helps us to measure our progress.

Andragogy vs. Pedagogy

Maybe you’ve seen a graphic similar to this before?

Andragogy vs. Pedagogy

Kolb's Adult Learning Cycle

This is a learning style model developed by researcher David Kolb, and it is a core principle in the way our curriculum is designed and our instruction is delivered at Code Fellows. Note the “Reflective Observation” component; it is that piece that is a primary differentiator between the way adults learn and the way children learn.

As an adult, learning is different for you now than it was when you were younger. Do a quick read of this article that describes the differences between andragogy (adult-focused learning) and pedagogy (child-focused learning).

Over the duration of this course, there will be a series of learning journal assignments where you will be prompted to reflect on your learning. The reflection is a critical part of adult learning, and the self-awareness that results is a key component of emotional intelligence. Initially, these journal assignments will give you prompts to get you started, but as time goes by, some assignments will be less structured so that you explore your thoughts with more self-determination and freedom.

Reflection

Write a brief reflection on your learning today, or use the prompt below to get started.

Consider the following quote from the article linked above:

“[Adult learners] are deeply involved not only in planning but also in evaluating their learning, as they know what knowledge they want to acquire.”

In other words, you should continuously evaluate your learning, and your learning journal is an excellent way to keep track of that. How do you see yourself planning and evaluating your learning? What details will you record and/or measure? How often will you evaluate what you want to learn?