Practical String Processing.

You've learned to make decisions and repeat work. Now apply that control flow directly to text: find a symbol in an address, count vowels, extract part of a ticket ID and inspect fictional log lines.

• Use len(), zero-based indices and negative indices to inspect text safely.
• Slice strings with start, stop and step, including reverse slices.
• Use in and not in for substring membership and case-insensitive checks.
• Distinguish find(), index() and count() when locating text.
• Identify prefixes and suffixes with startswith() and endswith().
• Clean and combine text with strip(), lower(), upper(), replace(), split() and join().
• Write a character-by-character loop that counts matching letters.
• Build a small independent text-processing script and test varied inputs.

Python sees text as a sequence, not a mysterious single object. len(text) reports the number of characters, with spaces and punctuation included. Positions begin at zero: name[0] is the first character and name[-1] is the last. Indexing an empty string or an out-of-range position raises IndexError. This is different from slicing, which safely returns shorter strings when a boundary goes beyond the text.

device = 'SW-01'
print(len(device))
print(device[0])
print(device[-1])

A slice uses text[start:stop:step] to build a new string. Start is included, stop is excluded, and the step controls the movement. Omitting start or stop defaults to the relevant end; a negative step can reverse characters. Because strings are immutable, slicing creates a new string and never modifies the original. For beginners, first reason through a simple positive-range slice, then experiment with [-3:] and [::-1].

ticket = 'INC-2047'
print(ticket[:3])
print(ticket[-4:])
print(ticket[::-1])

Membership testing is one of the most useful beginner programming skills. The expression '@' in address evaluates to True or False; it does not require a loop or complicated regular expression. not in expresses the opposite. Matching is case-sensitive, so normalize with lower() before case-insensitive checks. One symbol is not enough to validate an email address: for now, we are only checking whether a marker exists.

email = 'student@example.com'
message = 'ERROR: timeout'
print('@' in email)
print('error' in message.lower())
print('offline' not in message.lower())

Sometimes you need the location of a substring instead of a Boolean membership result. text.find(part) returns the first start index, or -1 if absent. text.index(part) is similar but raises ValueError when no match exists. text.count(part) counts non-overlapping occurrences. Choose based on whether an absent match is expected or exceptional. A simple membership test is clearer when you only need True or False.

message = 'error|warning|error'
print(message.find('warning'))
print(message.find('offline'))
print(message.count('error'))

Filenames, ticket identifiers, and log lines often have known beginnings and endings. startswith(prefix) and endswith(suffix) return Booleans. They avoid hand-counting positions for simple prefix/suffix tests. They are case-sensitive and do not validate every detail of a filename; combine with strip() or lower() when the problem requires normalization.

filename = '2026-11-08-alert.log'
print(filename.startswith('2026'))
print(filename.endswith('.log'))
print(filename.endswith('.csv'))

Text from exports or terminals may contain leading spaces, inconsistent capitalization or separators. strip() removes surrounding whitespace; lower() and upper() return transformed versions; replace(old, new) returns a new string with matching substrings replaced. None of these methods changes the original string, because Python strings are immutable. Be deliberate with replacements: replacing every hyphen in a hostname could change its meaning.

raw = '  ap-02 | OFFLINE  '
cleaned = raw.strip().replace(' | ', ': ').upper()
print(cleaned)
print(raw == cleaned)

A line such as 'SW-01, offline, HQ' contains several fields separated by commas. split(',') produces a list of parts (including spaces around values), while strip() cleans each one. A delimiter's join(items) creates one string from a sequence of strings. A split is convenient for simple, controlled text formats; for real CSV with quoted commas, use csv.DictReader in the Files lesson.

raw = 'SW-01, offline, HQ'
parts = [item.strip() for item in raw.split(',')]
host, status, site = parts
print(f'{host} @ {site}: {status}')

A string itself is iterable. You can write for letter in message: to inspect each character in order. Convert the message to lowercase to avoid repeating uppercase comparisons, then use membership letter in 'aeiou'. Keep the counter outside the loop and increment it only for matches. This combines Control Flow with string processing and mirrors the kind of unfamiliar problem a beginner may see in an exam or work task.

message = 'Education'
vowel_count = 0
for letter in message.lower():
    if letter in 'aeiou':
        vowel_count += 1
print(vowel_count)

Combine the techniques: scan fictional log lines, select those beginning with WARN, extract hostnames and return an alphabetized list of distinct hosts. This exercise introduces a little list handling from the earlier Control Flow lesson, which Working with Data will explore more thoroughly. Do not confuse the presence of a word in text with a validated operational fact. You're writing a local parser for a known fictional format, not examining real devices.

events = ['WARN AP-02 offline', 'INFO SW-01 up']
for event in events:
    if event.startswith('WARN '):
        parts = event.split()
        print(parts[1])

The lesson begins with a guided exercise; complete the full coding clinic afterward. Edit real Python, read the console and trace, then check the result with the given inputs. Change the inputs and test other cases too.

Use the official Python documentation to confirm exact syntax or behavior. The browser workbench and the local Python interpreter should agree for these basic operations.