Python / Core Python Fundamentals Interview Questions
Which Python string methods are most useful for cleaning and parsing data payloads?
String manipulation is the backbone of text-based data processing. Python strings are immutable, so every method returns a new string.
raw = ' Hello, World! ' # Trimming whitespace raw.strip() # 'Hello, World!' â both ends raw.lstrip() # 'Hello, World! ' raw.rstrip() # ' Hello, World!' # Case operations 'Python'.lower() # 'python' 'python'.upper() # 'PYTHON' 'hello world'.title() # 'Hello World' # Splitting and joining 'a,b,c'.split(',') # ['a', 'b', 'c'] ' a b c '.split() # ['a', 'b', 'c'] â splits on any whitespace ','.join(['a', 'b', 'c']) # 'a,b,c' # Checking content 'hello123'.isalpha() # False (has digits) 'hello123'.isalnum() # True ' '.isspace() # True 'hello'.startswith('he') # True 'world'.endswith('ld') # True # Replacing and finding 'banana'.replace('a', '@') # 'b@n@n@' 'hello world'.find('world') # 6 (-1 if not found) 'hello world'.count('l') # 3
For parsing structured text formats, regular expressions (import re) extend beyond what string methods can do. But for simple cleaning — stripping, case-folding, splitting on a fixed delimiter — the built-in methods are faster and more readable than regex. A common data-cleaning pipeline: value.strip().lower().replace('-', '_') in one chained call.
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