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Leer Challenge: Slicing and Search Drills | Strings
Data Types in Python

bookChallenge: Slicing and Search Drills

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Fill in the expressions to compute each result using only the taught tools (string methods, slicing, in/find/count, and f-strings).

Compute:

  1. name_clean: trim leading/trailing spaces from full_name.
  2. has_quick: True if "quick" appears anywhere in sentence (case-insensitive).
  3. inside_parens: the substring inside the first pair of parentheses in sentence.
  4. o_count: how many times the letter 'o' appears in sentence (case-insensitive).
  5. id_prefix, id_number, id_suffix: from id_code = "USR-00042-xy" extract "USR", "00042", and "xy" via slicing.
  6. domain: from email, after trimming and lowercasing, take everything after @.
  7. report: build "{name_clean} | {domain} | {id_number} | {o_count}" using an f-string and the provided SEP.

Oplossing

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Sectie 3. Hoofdstuk 6
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bookChallenge: Slicing and Search Drills

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Taak

Swipe to start coding

Fill in the expressions to compute each result using only the taught tools (string methods, slicing, in/find/count, and f-strings).

Compute:

  1. name_clean: trim leading/trailing spaces from full_name.
  2. has_quick: True if "quick" appears anywhere in sentence (case-insensitive).
  3. inside_parens: the substring inside the first pair of parentheses in sentence.
  4. o_count: how many times the letter 'o' appears in sentence (case-insensitive).
  5. id_prefix, id_number, id_suffix: from id_code = "USR-00042-xy" extract "USR", "00042", and "xy" via slicing.
  6. domain: from email, after trimming and lowercasing, take everything after @.
  7. report: build "{name_clean} | {domain} | {id_number} | {o_count}" using an f-string and the provided SEP.

Oplossing

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Was alles duidelijk?

Hoe kunnen we het verbeteren?

Bedankt voor je feedback!

close

Awesome!

Completion rate improved to 5
Sectie 3. Hoofdstuk 6
single

single

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