5. Lighthouse

Level 3 · Format a shelf label

From Thomas Whitaker, Head of Merchandising

“The product name came out of the old system with extra spaces and in lowercase. I need it as a shelf label: name in capitals, a pipe, then the price with a dollar sign and cents.”

Your goal

Set result to the string LINEN THROW PILLOW | $34.00, built from name and price: strip the spaces, uppercase the name, and format the price with exactly 2 decimals.

  • Store a text value (str) in result

Your code

Python

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    Mote checks your result

    Press Run. Each row that matches the goal becomes a firefly in your lantern.

    The lesson

    Strings are text

    A string (str) is text between quotes. Single and double quotes do the same thing:

    product = "Linen Throw Pillow"
city = 'Portland'

    Analysts spend a lot of time on strings, because real data is full of messy text: names typed by customers, codes exported from old systems, cities with trailing spaces.

    String methods

    A method is a function that belongs to a value. You call it with a dot. Strings come with many useful ones:

    raw = "   linen throw pillow  "

raw.strip()          # 'linen throw pillow'   spaces removed at both ends
raw.upper()          # '   LINEN THROW PILLOW  '
raw.strip().title()  # 'Linen Throw Pillow'
"SKU-1042".lower()   # 'sku-1042'
"oak, walnut".replace(",", " /")   # 'oak / walnut'
"Oak Side Table".startswith("Oak")   # True

    Methods return a new string. The original is never changed, because strings in Python cannot be modified in place. If you want to keep the cleaned version, store it:

    name = raw.strip()

    You can chain methods, and they run left to right: raw.strip().upper() strips first, then uppercases the result.

    Joining text with f-strings

    To build a sentence or label from values, put an f before the opening quote and write variables inside curly braces:

    name = "Oak Side Table"
stock = 14
f"{name}: {stock} in stock"     # 'Oak Side Table: 14 in stock'

    Anything inside the braces is evaluated as Python, so you can call methods there too: f"{name.upper()}".

    Formatting numbers

    After the value, a colon starts a format spec. The ones you’ll use constantly:

    price = 34
f"{price:.2f}"        # '34.00'     two decimals
f"{1249.5:,.2f}"      # '1,249.50'  thousands separator
f"{0.237:.1%}"        # '23.7%'     as a percentage

    .2f means “fixed point, two decimals”. It also rounds: f"{49.996:.2f}" gives '50.00'. Note the dollar sign in a label is just a character in the string, so f"${price:.2f}" prints $34.00.

    Why not just use +?

    You can join strings with +, but every piece must already be a string:

    "Price: " + 34          # TypeError: can only concatenate str (not "int") to str
"Price: " + str(34)     # 'Price: 34'

    f-strings convert values for you and let you format them, so they are the modern standard.

    Why analysts clean text first

    Text that looks identical to a person can be different to a computer. "Portland", "portland" and "Portland " are three different values, so a count of customers per city would split one city into three rows. Cleaning text into one consistent form, usually stripped and in a fixed case, is one of the first jobs on almost every dataset. You’ll do it at scale with pandas in the cleaning chapter, using exactly the methods you just learned.

    Common mistakes

    • Calling name.strip() and expecting name itself to change. Strings are immutable; use the returned value.
    • Formatting the price with {price} instead of {price:.2f}, which prints 34 rather than 34.00.
    • Forgetting the f before the quote. Without it, Python prints the braces literally: "{name}" stays "{name}".