Solutions: Practice Exercises — Python String Methods (Novice-Friendly)

Solutions: Practice Exercises — Python String Methods (Novice-Friendly)#

These solutions avoid list comprehensions and exceptions, and use clear step-by-step code. Each solution includes one or two short comments explaining what’s happening.

1. Clean up messy district names#

districts = ["  Scarborough  ", "North York", " Etobicoke ", "York  "]

clean_districts = []
for d in districts:
    # strip() removes spaces (and other whitespace) from the start and end
    cleaned = d.strip()
    clean_districts.append(cleaned)

clean_districts

2. Parse a simple CSV line (name + value)#

line = "Scarborough,  632098 "

# split into two parts around the comma
parts = line.split(",")
district_name = parts[0].strip()  # clean up extra spaces

pop_text = parts[1].strip()
population = int(pop_text)        # convert digits to an integer

district_name, population

3. Build a dictionary from multiple CSV lines#

lines = [
    "Scarborough, 632098",
    "North York,   869401 ",
    "Etobicoke,  365143",
]

pop_by_district = {}

for line in lines:
    # split into name and population (at the first comma)
    name_text, pop_text = line.split(",", 1)
    name = name_text.strip()
    pop = int(pop_text.strip())
    
    # store the result in the dictionary
    pop_by_district[name] = pop

pop_by_district

4. Handle bad lines while parsing (no exceptions)#

lines = [
    "Scarborough, 632098",
    "North York, ",
    "Etobicoke, 365143",
    "York, not_available"
]

valid_pops = []

for line in lines:
    parts = line.split(",", 1)
    value_text = parts[1].strip()
    
    # only keep the value if it is made of digits (e.g., "365143")
    if value_text.isdigit():
        valid_pops.append(int(value_text))

valid_pops

5. Replace inconsistent separators#

lines = [
    "Scarborough;632098",
    "North York,869401",
    "Etobicoke; 365143"
]

pop_by_district = {}

for line in lines:
    # replace semicolons with commas so we can split consistently
    normalized = line.replace(";", ",")
    name_text, pop_text = normalized.split(",", 1)
    
    name = name_text.strip()
    pop = int(pop_text.strip())
    pop_by_district[name] = pop

pop_by_district

6. Extract the domain from email addresses#

emails = ["ana@utoronto.ca", "bob@gmail.com", "c.chen@geography.org"]

domains = []
for e in emails:
    # split on '@' and take the part after it
    parts = e.split("@", 1)
    domain = parts[1]
    domains.append(domain)

domains

7. Turn “Last, First” into “First Last”#

names = ["Nguyen, Linh", "Smith, Jordan", "Patel, Asha"]

fixed = []
for n in names:
    # split into last and first around the comma
    last_text, first_text = n.split(",", 1)
    last = last_text.strip()
    first = first_text.strip()
    
    fixed.append(first + " " + last)

fixed

8. Count categories in coded survey responses#

responses = ["car", "Car ", " transit", "bike", "CAR", "transit", "walk "]

counts = {}

for r in responses:
    # normalize by trimming spaces and making lowercase
    key = r.strip().lower()
    
    # update the count (initialize to 0 if key not seen yet)
    if key in counts:
        counts[key] = counts[key] + 1
    else:
        counts[key] = 1

counts

9. Find and clean unit numbers in addresses#

addresses = [
    "12-100 King St W",
    " 55 Queen St E",
    "8-77 Bloor St W ",
]

street_addresses = []
has_unit = []

for addr in addresses:
    s = addr.strip()  # clean up whitespace first
    
    # if there's a dash, treat the part before the dash as a unit number
    if "-" in s:
        unit_part, street_part = s.split("-", 1)
        street_addresses.append(street_part.strip())
        has_unit.append(True)
    else:
        street_addresses.append(s)
        has_unit.append(False)

street_addresses, has_unit

10. Reconstruct a “slug” for filenames#

titles = [
    "Income Inequality in Toronto",
    "Public Transit & Access",
    "Housing Prices: 2015 to 2025"
]

def make_slug(title):
    # lower() makes everything lowercase
    s = title.lower()
    
    # remove colons and replace '&' with 'and'
    s = s.replace(":", "")
    s = s.replace("&", "and")
    
    # split() breaks on any whitespace; join back with underscores
    words = s.split()
    slug = "_".join(words)
    return slug

slugs = []
for t in titles:
    slugs.append(make_slug(t))

slugs
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