removeExtraSpaces

1/**
2 * Removes leading, trailing, and multiple consecutive spaces from a string.
3 *
4 * @param str - The input string.
5 * @returns A cleaned string with single spaces between words.
6 */
7export function removeExtraSpaces(str: string): string {
8  return str.trim().replace(/\s+/g, ' ');
9}

• Cleans Up Whitespace Efficiently

  Combines trim() and a regular expression to remove leading/trailing whitespace and collapse multiple internal spaces into a single space.

• Improves Consistency in Text

  Ensures uniform spacing, which is essential for comparing, storing, or displaying user input cleanly.

• Simple and Lightweight

  One-liner solution that is easy to understand and performant even on large strings.

• Language-Agnostic & Safe

  Handles spacing consistently across languages, including those with non-Latin characters.

Tests | Examples

1test('removeExtraSpaces - trims and reduces internal spaces', () => {
2  expect(removeExtraSpaces('  Hello   world  ')).toBe('Hello world');
3});
4
5test('removeExtraSpaces - handles tabs and newlines', () => {
6  expect(removeExtraSpaces('\tHello\n\nworld\t')).toBe('Hello world');
7});
8
9test('removeExtraSpaces - returns same string if no extra spaces', () => {
10  expect(removeExtraSpaces('Hello world')).toBe('Hello world');
11});
12
13test('removeExtraSpaces - empty string remains empty', () => {
14  expect(removeExtraSpaces('')).toBe('');
15});
16
17test('removeExtraSpaces - only whitespace', () => {
18  expect(removeExtraSpaces('   \n\t   ')).toBe('');
19});

Common Use Cases

• User Input Normalization

  Clean form inputs like names, addresses, or messages before saving to a database.

• Search Optimization

  Standardize strings before performing text comparisons or full-text searches.

• Display Formatting

  Present clean text in the UI by removing excessive or accidental spacing.

• Data Cleanup

  Useful in ETL (Extract, Transform, Load) pipelines or preprocessing CSV/TSV content with inconsistent spacing.

• Natural Language Processing (NLP)

  Preprocess text before tokenization or analysis in NLP models.