Learning

Complexity Complexity comes from an accumulation of dependencies and obscurities. Strategic (long-term approach, thinking about design) vs. Tactical programming (short-term approach, ship features as soon as possible). Modules should be deep #module #interface #implementation Module has two parts: interface and implementation. A module is any unit of code that has an interface and an implementation. Each class in OOP is a module. The best modules are those whose interfaces are much simpler than their implementations. ...

Learning anything deeply requires a lot of time and it is an on-going process. Programming is no different. Challenging part is that, it seems like, there is always more and more to learn. For example, learning Python - you can go through basics in 1 hour, but when you start exploring it more and actually start using the language, you always find something new. A new feature, library, module, tool, n different ways of doing a task. ...

Python’s lambda function is a powerful tool for creating small, anonymous functions on the fly. Lambda functions are known as anonymous functions, because: they don’t have a name, are created and used at the same time. I have used the lambda function numerous times, especially when I needed to sort something on a specific key. Some benefits of using lambda functions: Concise code - no need to create new named functions if we are using it only once, Cleaner code - lambda functions are easy to read (with good naming practices), Flexibility - can be used in various situations - as an argument to another function, in a list comprehension or as the key function for sorting, No side effects - lambda functions are pure. This is very important when working with data that needs to be kept in its original state. Lambda functions, as mentioned above, are very handy when sorting more complex iterables. ...

What is Pattern Matching Pattern matching is a newer feature that came with Python 3.10. Using pattern matching simplifies the code and improves readability. The other option is to write multiple if-else statements, but this can quickly become very unreadable. Example Consider these 2 examples. The first one using multiple if-else statements, the second one pattern matching - match/case. Which one is more readable? 1 2 3 4 5 6 7 8 9 10 # if-else statements if x == 'red': print("The color is red") elif x == 'blue': print("The color is blue") elif x == 'green': print("The color is green") else: print("Unknown color") 1 2 3 4 5 6 7 8 9 10 11 # Pattern matching match x: case 'red': print("The color is red") case 'blue': print("The color is blue") case 'green': print("The color is green") case _: print("Unknown color") This is a very simple example, and pattern matching can be also used in more complex situations where more conditions need to be met. ...

There are a lot of tricks one can use in Python. So-called Python one-liners are one of them. It’s a nice way of shortening the length of the code. However, it’s good to know that sometimes, using the longer version might be the better choice when it comes to readability. Swapping two variables 1 2 3 4 a = 10 b = 20 a, b = b, a List comprehension 1 squares = [i*i for i in range(20)] Ternary operator 1 my_value = 30 if 5>3 else 50 Print only elements (unpacking) 1 2 numbers = [1, 2, 3, 4, 5] print(*numbers) Days remaining in a year 1 import datetime;print((datetime.date(2023,12,31)-datetime.date.today()).days) Reversing a list/string 1 2 letters = ['a', 'b', 'c', 'd', 'e'] print(letter[::-1]) Multiple variable assignments 1 age, city, position = 25, 'London', 'Python developer' Convert number strings into ints 1 2 strs = '1 2 3 4 5 6' ints = list(map(int, strs.split())) Read all lines of a file to a list 1 values = [line.strip() for line in open('document.txt', 'r')] Start an HTTP server in a terminal python -m http.server ...

Each programming language has its own peculiarities - knowing these will make your code more reliable, and you will avoid unexpected behaviors and/or bugs. In Python, one such peculiarity, that I learned recently, is that you should not use mutable objects as default arguments. Let’s see why. Example You want a function that appends a number to a list. If the list is not passed, it appends data to a newly created list. ...

I am currently exploring the topic of objects a little more. Once you go a bit deeper into the topic, it can get quite complex and confusing (at least at first). What is pretty cool though is realizing that in Python, everything is an object. Basically, every value or data type is an instance of a class. For example, creating a new string like my_name = 'Peter' actually creates an object of the class str. That’s why we can also use methods such as upper() (and many others) as these come with the class. ...

The Challenge I’m currently working on an application in Django which I hope to deploy in the upcoming weeks, it’s just that I fix one bug and another one appears, then tests pass, I add something new, suddenly nothing is working so I have to redesign the whole thing again. This has also prompted me to explore the topic of debugging a bit more recently. I guess that’s how we learn. ...

Toggle your thinking - before learning, go through the topic quickly to prepare your mind. Look through the chapter before reading it - graphs, graphics, questions, headlines. The Two Modes Focused-mode vs Diffuse-mode Focused mode You are actively focusing on something. Like a flashlight. Diffuse mode Not actively focusing, letting the mind wander. Different areas of the brain connect and return valuable insights. Very effective approach: combine focused session followed up with a diffuse session. Actively focus on something to enter focused mode (I like to use Pomodoro technique), when the break comes enter the diffuse mode by turning on your ‘big picture’ diffuse mode (go for a walk, a run…). ...

Looping through an iterable is the most basic and fundamental ability of any programming language. Python’s for loop is at a first glance very straightforward, but just like with any other tool it can be used in multiple ways - these become handy when you start taking performance into account. Here are 6 things to consider when you need to loop over something. 1. Maybe I don’t actually need a for-loop For loops can be slow. There are many built-in methods optimized for specific tasks. Perhaps a better approach would be to simply use a built-in function. Let’s say you need to sum up a list of numbers. ...