In this tutorial, we have captured the important terms used in Python programming. If you are learning Python, it is good to be aware of different programming concepts and slang related to Python.
Please note that these terms form the foundation of Python programming, and a solid understanding of them is essential for effective development and problem-solving in the language. So, let’s delve in to know what all of these are.
Firstly, let’s focus on the pure programming terms or concepts commonly used in Python.
Common Python Programming Terms
1. Python
Python is a smart and flexible language that’s easy to read and use. It can do different types of programming, like making websites or solving math problems. Whether you’re building something for the web or doing complex calculations, Python has got you covered!
Example 1: Python for Web Development
import tornado.ioloop as ioloop
import tornado.web as web
class M(web.RequestHandler):
def get(self):
self.write("Welcome to the Tornado-powered Web App!")
def make_app():
return web.Application([(r'/', M)])
if __name__ == "__main__":
app = make_app()
app.listen(8888)
ioloop.IOLoop.current().start()Example 2: Python for Scientific Computing
# Using NumPy for numerical operations
import numpy as np
# Creating an array and doing operations
arr = np.array([1, 2, 3, 4, 5])
mean_value = np.mean(arr)
print(f"Mean: {mean_value}")2. Interpreter
Python is a type of language that doesn’t need to be turned into a special code before running. Instead, a tool called the Python interpreter reads and runs the instructions directly. It translates the instructions into a form that the computer can understand, doing it line by line. This makes it quick to develop and fix issues in our code.
Example: Basic Python Interpreter Usage
# Interactive Python interpreter session
>>> print("Hello, Python!")
Hello, Python!
>>> x = 5
>>> x + 3
83. Syntax
Python’s syntax is clean and straightforward, emphasizing readability. Indentation is used for code blocks instead of braces, promoting consistent formatting and reducing clutter. It ensures Python code is easy to understand and maintain.
Example: Pythonic Code with Clean Syntax
# Pythonic way of swapping values
a, b = 5, 10
a, b = b, a
print(f"a: {a}, b: {b}")4. Variables
In Python, variables are used to store and use data. They are dynamically typed, allowing flexibility in assigning values without explicit type declarations. Variable names should follow the rules for identifiers and be descriptive for clarity in code.
Example: Dynamic Typing in Python
# Dynamic typing in Python
variable = 42
print(f"The variable is of type: {type(variable)}")
variable = "Hello, Python!"
print(f"Now the variable is of type: {type(variable)}")5. Data Types
Data types are essential in programming because they define how data is stored, manipulated, and used. Without data types, it would be impossible to write meaningful and functional code.
Python data types include integers, floats, strings, lists, tuples, dictionaries, and more. Understanding and appropriately using these types is essential for effective data manipulation and program functionality.
Example: Using Various Data Types
# Examples of different data types
int_var = 42
float_var = 3.14
str_var = "Python"
list_var = [1, 2, 3, 4]
tuple_var = (10, 20, 30)
dict_var = {'key': 'value'}
print(f"Types: {type(int_var)}, {type(float_var)}, {type(str_var)}, {type(list_var)}, {type(tuple_var)}, {type(dict_var)}")6. Functions
Functions in Python are like toolkits that do specific jobs and can be used over and over. You create them using the word “def” and give them a name. This helps keep your code organized and easy to understand. You can also give them some extra information (parameters) to work with and get results back (return values). It’s like having a set of instructions that you can use whenever you need that particular job done.
Example: Defining and Using Functions
# Simple function definition and usage
def greet(name):
return f"Hello, {name}!"
# Using the function
result = greet("Python Developer")
print(result)7. Control Flow
Python helps you control how your program runs with things like if statements, loops (for and while), and exceptions. These are tools that let you decide what parts of your code should run and how many times. They’re really important for making your program do specific things based on conditions or repeat actions when needed.
Example: Using if-else Statements
# Example of if-else statement
num = 7
if num % 2 == 0:
print(f"{num} is even.")
else:
print(f"{num} is odd.")8. Lists and Dictionaries
In Python, lists are like containers that can hold lots of things in a specific order. You can change, add, or remove these things easily. On the other hand, dictionaries are like special lists that not only store things but also label them with keys. This way, you can quickly find and organize your stuff. So, lists are for keeping things in order, and dictionaries are for smartly organizing things with labels.
Example: Working with Lists and Dictionaries
# Examples of lists and dictionaries
a_seq = [1, 2, 3, 4, 5]
a_dict = {'Job': 'Engineer', 'Exp': 3, 'Company': 'Google'}
# Accessing elements
print(f"List: {a_seq[2]}, Dictionary: {a_dict['name']}")9. Classes and Objects
In Python, we use something called classes. Think of classes as templates that define how things should be made. These things are called objects. So, a class is like a blueprint, and objects are the actual things made using that blueprint. Classes package together both information (data) and actions (behavior). This way, we can organize our code neatly, reuse it, and keep things separate. It’s like having a set of instructions to create different objects in our programs.
Example: Creating a Class and Object in Python
# Simple class defn and object instantiation
class Car:
def __init__(self, brand, model):
self.brand = brand
self.model = model
# Creating an object
my_car = Car(brand="Toyota", model="Camry")
print(f"My car: {my_car.brand} {my_car.model}")10. Modules and Libraries
In Python, we like to keep things organized and reusable. We use modules, which are like files containing Python code. These modules help us structure our code neatly. Additionally, we have libraries, such as NumPy, pandas, and TensorFlow. Libraries are like toolboxes that give Python extra abilities for specific jobs, making it easier to create big and complex programs. So, modules help us organize, and libraries give us extra tools to do cool things with Python!
Example: Using NumPy for Math Operations
# Using NumPy for math operations
import numpy as np
# Creating arrays and doing operations
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
result = np.add(arr1, arr2)
print(f"Result of addition: {result}")11. Exception Handling
In Python, we have a way to deal with problems in our code. We use something called try and except blocks. It’s like a plan: we try to do something, and if there’s a problem, we have a backup plan (except) to handle it. Finally, is like a step we always take, whether there’s a problem or not. This way, our programs can deal with errors in a structured and organized manner, making them more reliable.
Example: Handling Divide-by-Zero Exception
def meow_translator(meow):
"""Attempts to translate a cat's meow to human language."""
try:
if meow == "Meow":
return "Greeting."
elif meow == "Mrrrooow":
return "I'm hungry."
elif meow == "Mrrrrowww?":
return "What's that?"
else:
raise ValueError("Unrecognized meow!")
except ValueError as e:
print(f"Oops, couldn't understand that meow: {e}")
return "Consult a meow-expert!"
# Test the translator
cat_meow = "Mrrrrowww?"
translation = meow_translator(cat_meow)
print(f"Your cat says: {translation}")12. File Handling
Python provides built-in functions and methods for reading from and writing to files. Understanding Python file handling is crucial for tasks like data persistence, configuration management, and interaction with external data sources.
Example: Reading and Writing to a File
# Reading and writing to a file
with open("example.txt", "w") as file:
file.write("Hello, File!\nPython is amazing.")
with open("example.txt", "r") as file:
content = file.read()
print(content)Commonly Used in Abbreviations in Python
1. IDE (Integrated Development Environment)
An IDE is like a special program that helps you do everything when you’re coding in Python. It has tools for writing code, fixing mistakes (debugging), and testing your programs. Some examples of these programs are PyCharm, Visual Studio Code, and Jupyter Notebook. They make it easier to write and work with Python code.
Example: Using PyCharm for Python Development
# PyCharm example code
def greet(name):
return f"Hello, {name}!"
result = greet("PyCharm User")
print(result)2. PEP (Python Enhancement Proposal)
PEPs are like documents that tell everyone in the Python community about new things or ideas for the language. They might suggest adding cool features or sharing important information. When we follow PEPs, it’s like everyone using the same rule book. This keeps things consistent and standard across all the Python stuff people create. So, PEPs help us work together better and make sure Python stays awesome and organized!
Example: Following PEP 8 Guidelines
# PEP 8 compliant code
class PEP8Class:
def __init__(self, name):
self.name = name
def display_info(self):
print(f"Hello, {self.name}!")
# Creating an instance and calling the method
pep8 = PEP8Class("PEP 8")
pep8.display_info()3. API (Application Programming Interface)
APIs are like sets of rules that say how different programs can talk to each other. In Python, we have a lot of these rules for various things like making websites, working with data, or doing machine learning. So, APIs help Python programs connect and work with other cool tools and services easily. It’s like having a common language that different programs understand, making it simpler for them to share and use information.
Example: Using an API for Weather Data
from requests import get # Import only the 'get' function from the 'requests' module
resp = get("https://api.weather.com/data/2.5/weather?q=Vatican&appid=YOUR_API_KEY")
info = resp.json()
print(f"Weather in Vatican: {info['weather'][0]['description']}")4. GUI (Graphical User Interface)
GUIs help make computer programs that you can interact with using buttons, menus, and other visual stuff. In Python, we use libraries like Tkinter and PyQt to easily create these interactive programs. These libraries have tools that make it simpler to design and build the visual parts of your program. So, with GUIs, you can make programs that not only do cool stuff but also look good and are easy for people to use. It’s like giving your program a friendly face!
Example: Creating a GUI with Tkinter
# A Short Sample of Tkinter GUI
import tkinter as tk
base = tk.Tk()
out = tk.Label(base, text="Hello, Tkinter!")
out.pack()
base.mainloop()5. ORM (Object-Relational Mapping)
ORM tools, such as Django ORM, help us interact with databases in Python by treating tables as Python objects. This simplifies tasks like adding or retrieving data, making database operations easier and faster without delving into intricate details.
Example: Using Django ORM for DB connection
from django.db import models as m
# Model defn
class User(m.Model):
id = m.AutoField(primary_key=True)
name = m.CharField(max_length=50)
# DB com
try:
user = User(name='JaneDoe')
user.save()
except Exception as e:
print(f"Error: {e}")
# Query
result = User.objects.filter(name='JaneDoe').first()
print(f"User profile in DB: {result.name}" if result else "No user profile found.")6. HTTP (Hypertext Transfer Protocol)
Python is popular for making websites, and to do that well, we need to understand something called HTTP. It’s like the language computers use to talk to each other on the web. In Python, we have helpful tools like Flask and Django that make it easier to work with HTTP. These tools handle the talking part, dealing with requests (asking for things) and responses (getting things back). So, with Python and these tools, we can build and use web services smoothly. It’s like having a translator making sure our websites communicate effectively!
Example: Using Python for Writing Web App
from starlette.applications import Starlette as Srv
from starlette.routing import Route as Rt
app = Srv()
async def home(request):
return {"msg": "Welcome to the Python Web App!"}
app.routes = [Rt('/', home)]
if __name__ == '__main__':
import uvicorn as uv
uv.run(app, host='127.0.0.1', port=8000)7. JSON (JavaScript Object Notation)
JSON is a simple way for computers to share information. In Python, we have a tool called the JSON module that helps us easily send and receive this kind of data. It’s like having a translator that makes sure different systems understand each other when they exchange information. So, with JSON and Python, our programs can talk to other systems without any confusion. It’s like having a common language that everyone understands for sharing data.
Example: Encoding and Decoding JSON in Python
# Encoding and decoding JSON
import json as js
# Original data
data = {'title': 'OpenAI', 'version': 3.5, 'lang': 'Python'}
try:
# Encoding to JSON
js_data = js.dumps(data)
print(f"Encoded JSON: {js_data}")
# Decoding from JSON
decod = js.loads(js_data)
print(f"Decoded Data: {decod}")
# Data consistency check
print("Consistent" if data == decod else "Inconsistent")
except js.JSONDecodeError as e:
print(f"Error decoding JSON: {e}")
except js.JSONEncodeError as e:
print(f"Error encoding JSON: {e}")8. OOP (Object-Oriented Programming)
OOP is a way of organizing code in Python. It’s like a method that encourages grouping code into classes and objects. Classes are like blueprints, telling Python how to create things, and objects are the actual things created from those blueprints. This method helps keep code neat and organized, and it promotes the reuse of code. So, with OOP, we can build programs more efficiently by using ready-made pieces and keeping everything in order. It’s like having a handy toolbox for building different things in Python!
Example: Illustrating OOP Principles in Python
# Example of OOP in Python
class Animal:
def __init__(self, name):
self.name = name
def make_sound(self):
pass
class Dog(Animal):
def make_sound(self):
return "Woof!"
# Creating instances
inst1 = Animal("Generic Animal")
inst2 = Dog("Buddy")
# Making sounds
print(inst1.make_sound()) # Output: None (abstract)
print(inst2.make_sound()) # Output: Woof!9. MVC (Model-View-Controller)
MVC is like a plan we follow when building web stuff with Python, especially in frameworks like Django. This plan divides our work into three parts:
- Model: This part deals with the data.
- View: This part is about what users see and interact with.
- Controller: This part manages the logic and user input.
So, with MVC, we have a clear way to organize our work, making it easier to build and maintain web applications. It’s like having a roadmap that guides us in creating well-structured and user-friendly websites using Python.
Example: Illustrating MVC in Django
# Django MVC Sample Demo
# Model (models.py)
from django.db import models
class Book(models.Model):
title = models.CharField(max_length=100)
author = models.CharField(max_length=50)
# View (views.py)
from django.shortcuts import render
from .models import Book
def book_list(request):
books = Book.objects.all()
return render(request, 'books/book_list.html', {'books': books})
# Controller (urls.py)
from django.urls import path
from .views import book_list
urlpatterns = [
path('books/', book_list, name='book_list'),
]
# HTML template (books/book_list.html)
<!DOCTYPE html>
<html>
<head>
<title>Book List</title>
</head>
<body>
<h1>Book List</h1>
<ul>
{% for book in books %}
<li>{{ book.title }} by {{ book.author }}</li>
{% endfor %}
</ul>
</body>
</html>10. VM (Virtual Machine)
Python code isn’t directly understood by computers. Instead, it’s first converted into something called bytecode. This bytecode is then read and executed by a virtual machine (like CPython, Jython, or IronPython). Knowing about this virtual machine is important to make our Python code run faster and smoother. It’s like having a middleman (the virtual machine) that helps our code speak the computer’s language effectively, making sure our programs run well and perform efficiently.
Example: Understanding the Python Virtual Machine (CPython)
# Using CPython (Python Virtual Machine)
# Python bytecode sample
def multiply(a, b):
return a * b
# Disassembling the bytecode
import dis
dis.dis(multiply)11. PEP8 (Python Enhancement Proposal 8)
PEP8 is like a rulebook for writing neat Python code. It gives suggestions on how to organize code, name things, and more. Following PEP8 helps keep Python projects looking tidy and makes the code easy to read. It’s like having a guide that everyone in the Python community can follow, ensuring that code looks similar and is easy to understand, no matter who wrote it. So, sticking to PEP8 is good practice for writing clear and consistent Python programs.
12. GIL (Global Interpreter Lock)
The GIL is like a traffic cop for Python programs running on CPython. It stands for Global Interpreter Lock. This lock makes sure only one task (or thread) can work on Python code at a time. This impacts how multiple things happen at once in Python programs. It’s important to know about the GIL because, in multi-threaded programs, it can affect performance. So, when we’re optimizing our Python code for speed, we need to keep an eye on this GIL to make sure things run smoothly.
Example: Considering the Global Interpreter Lock (GIL) in CPython
# GIL Impact on Multi-threading
import threading as td
ctr = 0
def inc():
global ctr
ctr += sum(1 for _ in range(1000000))
# Creating two threads
t1 = td.Thread(target=inc)
t2 = td.Thread(target=inc)
# Starting and waiting for threads to finish
[t.start() for t in (t1, t2)]
[t.join() for t in (t1, t2)]
# Expected counter value (may vary due to the GIL)
print(f"Counter: {ctr}")Programming Slang Terms Used in Python
Programming slang, often referred to as “jargon,” is an integral part of the developer culture. While not exhaustive, here are some programming slang terms commonly used in the Python programming community:
1. BDFL (Benevolent Dictator For Life)
This term refers to Guido van Rossum, the person who created Python. It’s a playful way of recognizing his job in making the last decisions about how Python should evolve.
Example: Guido van Rossum’s Role as BDFL
# Acknowledging Guido van Rossum as the BDFL
# (Benevolent Dictator For Life)
print("Guido van Rossum is the BDFL of Python.")2. Zen of Python
Although not slang, the “Zen of Python” is a set of guidelines for writing computer programs in Python. It was written by Tim Peters and is known for its practical and humorous advice on Python programming style.
Example: Embracing the Zen of Python
# Applying the Zen of Python principles
import this3. Pythonic
Describes code that follows the rules and style of the Python language. Writing “Pythonic” code focuses on making it easy to read, keeping things simple, and naturally using Python’s features.
Example: Writing Pythonic Code
# Pythonic code example
# List compr to find even numbers
nums = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
even_numbers = [num for num in nums if num % 2 == 0]
print(f"Even numbers: {even_numbers}")4. Spam and Eggs
This playful term is used to talk about a Python conference or meetup where they serve spam and eggs for breakfast. It’s a nod to the Monty Python sketch called “Spam.”
Example: Playful Reference to Monty Python
# Playful reference to Monty Python
print("Welcome to the Spam and Eggs Python Conference!")5. Duck Typing
It is one of the lesser-known terms used in Python programming. It denotes a coding principle that cares more about what an object does than what type it is. It’s like saying, “If it acts like a duck, moves like a duck, and sounds like a duck, it’s probably a duck.”
Example: Applying Duck Typing
# Let's illustrate
class Duck:
def quack(self):
return "Quack!"
class Dog:
def quack(self):
return "Woof!"
def sound(animal):
return animal.quack()
# Creating objects
duck = Duck()
dog = Dog()
# Making sounds
print(sound(duck)) # Output: Quack!
print(sound(dog)) # Output: Woof!6. Snake Case and Camel Case
These terms talk about how we name things. Snake case puts underscores between words (like snake_case), while camel case capitalizes the first letter of each word except the first one (like camelCase).
Example: Naming Conventions in Python
# Naming conventions example
snake_case_variable = 42
camelCaseVariable = "Pythonic"
print(f"Snake Case: {snake_case_variable}, Camel Case: {camelCaseVariable}")7. Magic Methods (Dunder Methods)
Methods in Python with double underscores on both sides (like init or str) give special powers. They’re often used for doing special things with operators and customizing how things work.
Example: Using Magic Methods for Customization
# Using magic methods for customization
class MagicClass:
def __init__(self, value):
self.value = value
def __add__(self, other):
return self.value + other.value
# Creating instances
obj1 = MagicClass(5)
obj2 = MagicClass(10)
# Using the magic method
result = obj1 + obj2
print(f"Result of add op: {result}")8. Grokking
To deeply understand or master something in code. For instance, saying, “I finally grokked list comprehensions,” means you get how to use them.
# Example: Filtering even numbers using list comphr
nums = [5, 12, 17, 23, 30, 41, 56, 63, 72, 89]
evens = [z for z in nums if z % 2 == 0]
print("Even numbers:", evens)
# Output: Even numbers: [12, 30, 56, 72]9. Yak Shaving
Completing a big task by doing small, unrelated, and sometimes boring jobs. It’s like solving a puzzle with unexpected steps.
# Yak Shaving example: Fixing a bug with unexpected detours
def calc_sum(a, b):
# Bug: Forgetting to add the numbers
res = a * b
return res
# Yak shaving: Updating the dev env
# ... [some unrelated updates] ...
# Yak shaving: Rearranging code editor settings
# ... [more unrelated tasks] ...
# Finally fixing the bug
result = calc_sum(3, 4)
print("Sum:", result)
# Output: Sum: 1210. Rubber Duck Debugging
In programming slang, a “rubber duck” refers to an inanimate object. Talking to a rubber duck about your code or problem helps you think clearly and find issues. It’s a helpful way to solve problems.
# Rubber Duck Debugging example: Explaining code to a rubber duck
def calc_fact(n):
if n == 0 or n == 1:
return 1
else:
return n * calc_fact(n-1)
# Stuck on a bug, explaining the code to a rubber duck
bug_result = calc_fact(4)
print("Factorial of 4:", bug_result)
# Output: Factorial of 4: 2411. Boilerplate Code
Boilerplate code is like a template that you use over and over in different parts of your program. It’s the standard, repetitive code that doesn’t change much. While it might be necessary, dealing with it can be a bit of a hassle because it doesn’t add anything new or interesting to your code—it’s just there to make things work the way they should. Think of it as the setup you need to do for various tasks, even though it might not be the most exciting part of coding.
# Boilerplate Code example: Setting up user authentication
class User:
def __init__(self, u, p):
self.username = u
self.password = p
# Boilerplate: User authentication function
def auth_user(u, p):
# ... [authentication logic] ...
return True
# Using the boilerplate to authenticate a user
usr = User("john_doe", "password123")
if auth_user(usr.username, usr.password):
print("Authentication successful!")
else:
print("Authentication failed!")
# Output: Authentication successful!12. Heisenbug
A Heisenbug is like a tricky bug that plays hide and seek. When you try to catch and understand it, it mysteriously changes its behavior, making it hard to investigate. It’s named after the Heisenberg Uncertainty Principle from physics, adding a bit of a puzzle to the debugging process.
# Heisenbug example: Rare race condition
import threading as td
ctr = 0
def inc_ctr():
global ctr
for _ in range(1000000):
ctr += 1
# Creating two threads that increment the counter
t1 = td.Thread(target=inc_ctr)
t2 = td.Thread(target=inc_ctr)
# Starting and waiting for both threads to finish
[t.start() for t in (t1, t2)]
[t.join() for t in (t1, t2)]
print("Final Counter:", ctr)
# Output: (not guaranteed due to race condition) Final Counter: [some unpredictable value]These slang terms add a touch of humor and camaraderie to the programming community, fostering a shared understanding among developers.
Summary
Understanding Python-related terms is important because it’s like speaking the language of computers in a friendly way. Python is a versatile tool used for various tasks, from building websites to solving math problems. Knowing the terms helps you communicate effectively with your computer, telling it what to do and how to do it. For example, when you write Python code, you’re giving instructions to the computer in a language it understands. Terms like “variables,” “functions,” and “syntax” are building blocks that help you create cool things with Python. It’s like having a conversation with your computer buddy to get things done!
Happy Coding,
Team TechBeamers
