Common Coding Mistakes to Avoid
Even experienced programmers make mistakes.

Undefined variables:

Ensure all variables are declared and initialized before use.

Type coercion:

Be mindful of JavaScript's automatic type conversion, which can lead to unexpected results.

Incorrect scope:

Understand the difference between global and local scope to avoid unintended variable access.

Logical errors:

Carefully review your code for logical inconsistencies that might lead to incorrect output.

Off-by-one errors:

Pay attention to array indices and loop conditions to prevent errors in indexing and iteration.

Infinite loops:

Avoid creating loops that never terminate due to incorrect conditions or missing exit points.

Example:
// Undefined variable error
let result = x + 5; // Assuming x is not declared

// Type coercion error
let age = "30";
let isAdult = age >= 18; // Age will be converted to a number

By being aware of these common pitfalls, you can write more robust and error-free code.

Do you have any specific coding mistakes you've encountered recently?

#javascript #coding #bestpractices

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O(1) - Constant Time: Simple tasks that take the same amount of time no matter how much data you have, like finding an item in a list by its position.

O(log n) - Logarithmic Time: Tasks that take less time as the data grows, like finding an item in a sorted list by repeatedly dividing it in half.

O(n) - Linear Time: Tasks that take more time as the data grows, like counting all items in a list by checking each one.

O(n log n) - Linearithmic Time: Tasks that get a bit slower as the data grows, like sorting a list using efficient methods such as merge sort or quick sort.

O(n²) - Quadratic Time: Tasks that get noticeably slower as the data grows, like sorting a list using simpler methods like bubble sort or finding all pairs in a list.

O(2^n) - Exponential Time: Tasks that get much slower as the data grows, like finding all subsets of a set or solving complex problems like the traveling salesman using a basic approach.

O(n!) - Factorial Time: Tasks that get extremely slow as the data grows, like solving problems that involve checking every possible arrangement of items.

Expert Python Programming (2021)

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