Day 61 of #100DaysOfCode — Python Refresher Part 1

When I started my #100DaysOfCode journey, I began with frontend development using React, then moved into backend development with Node.js and Express. After that, I explored databases to understand how data is stored and managed, followed by building full-stack applications with Next.js. It is now time to start learning Python, not from scratch, but as a refresher to strengthen my fundamentals and expand my backend skillset.

Learning Python strengthens my core programming skills and offers a new perspective beyond JavaScript. It aligns with backend development, data handling, and automation, allowing me to build on my existing knowledge and become a more versatile developer.

Today, for Day 61, I focused on revisiting the core building blocks of Python.

Core Syntax

Variables & Data Types

Variables are used to store data, and Python automatically assigns the data type based on the value.

name = "Ali" # string age = 22 # integer price = 99.99 # float is_active = True # boolean

name = "Ali" # string age = 22 # integer price = 99.99 # float is_active = True # boolean

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You don’t need to declare types explicitly like in some other languages; Python handles it dynamically.

Conditionals (if/elif/else)

Conditionals let your program make decisions.

age = 20

if age >= 18: print("Adult") elif age > 12: print("Teen") else: print("Child")`

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This is fundamental for controlling program flow, especially in backend logic (auth checks, validations, etc.).

Loops (for, while)

Loops allow you to repeat actions.

for i in range(3):  print(i)

for i in range(3):  print(i)

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count = 0 while count < 3:  print(count)  count += 1

count = 0 while count < 3:  print(count)  count += 1

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for is used more often in Python, especially when working with collections.

Functions

Functions group logic into reusable blocks.

def add(a, b):  return a + b

def add(a, b):  return a + b

print(add(2, 3))`

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They help keep your code clean and modular.

Data Structures

Lists

Lists store multiple items in order.

numbers = [1, 2, 3, 4] print(numbers[0]) # 1

numbers = [1, 2, 3, 4] print(numbers[0]) # 1

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Dictionaries

Dictionaries store data in key-value pairs, similar to objects in JavaScript.

user = {  "name": "Ali",  "age": 22 }

user = {  "name": "Ali",  "age": 22 }

print(user["name"])`

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Must Know:

Looping through dicts

for key, value in user.items():  print(key, value)

for key, value in user.items():  print(key, value)

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Nested data handling

data = {  "user": {  "name": "Ali"  } }

data = {  "user": {  "name": "Ali"  } }

print(data["user"]["name"])`

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👉 This directly maps to:

• JSON (backend APIs)

• Database data

Sets

Sets store unique values.

nums = {1, 2, 2, 3} print(nums) # {1, 2, 3}

nums = {1, 2, 2, 3} print(nums) # {1, 2, 3}

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Tuples

Tuples are like lists, but immutable (cannot be changed).

point = (10, 20)

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Loops & Iteration

Looping through collections:

numbers = [1, 2, 3]

for num in numbers: print(num)`

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Using range():

for i in range(5):  print(i)

for i in range(5):  print(i)

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Nested loops (useful for complex data):

matrix = [[1, 2], [3, 4]]

for row in matrix: for item in row: print(item)`

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Functions

Functions are where your code becomes structured and reusable:

Functions make your code reusable and structured.

def greet(name="User"):  return f"Hello, {name}"

def greet(name="User"):  return f"Hello, {name}"

print(greet()) print(greet("Ali"))`

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👉 Think:

“How do I structure logic cleanly?”

Good function design = cleaner code + easier debugging + better scalability.

Working with JSON (IMPORTANT FOR BACKEND)

import json

data = json.loads(json_string) json_string = json.dumps(data)`

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JSON is how data is exchanged between frontend and backend.

Example:

import json

json_string = '{"name": "Ali"}' data = json.loads(json_string)

print(data["name"])`

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👉 This is directly used in:

• APIs

• Django / Flask

Basic File Handling

Working with files is a fundamental skill in backend:

• Reading files

• Writing files

with open("file.txt", "r") as f:  data = f.read()

with open("file.txt", "r") as f:  data = f.read()

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👉 Useful for:

• Logs

• Data processing

• Simple storage tasks

List Comprehension (VERY USEFUL)

squares = [x*x for x in range(10)]*

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A shorter and cleaner way to write loops.

Equivalent version:

squares = [] for x in range(10):  squares.append(x*x)

squares = [] for x in range(10):  squares.append(x*x)

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👉 Cleaner + faster code

Final Thoughts

Today wasn’t about learning something completely new; it was about reinforcing the fundamentals. Python feels simple on the surface, but mastering these basics is what makes you effective when building real-world applications.

Thanks for reading. Feel free to share your thoughts!