![]() ![]() Print(describe()) # Starts with frog and ends with a flower, with a butterfly in between Print(describe()) # Starts with frog and ends with a butterfly You can use the expression to match an empty sequence and then afterward use to match any other sequence: from typing import SequenceĬase : return "Starts with frog and ends with a flower, with a butterfly in between"Ĭase : return "Starts with frog and ends with a butterfly"Ĭase : return "Ends with a butterfly"Ĭase : return "Starts with frog" For sequence matching, you can use the wildcard *_ expression to match n items. Use this to set specific cases at the top and then get more generic as you go down. With the match statement, cases are evaluated in order. So what is to be gained by using the match statement? Well, the match statement version is 86% faster! This is because the expression for sequence matching compiles into instructions that are far more efficient than a chained if-statement. If isinstance(seq, Sequence) and len(seq) > 0 and seq = "□": The equivalent Python code would be expressed as: def starts_with_frog(seq: Sequence) -> bool: Print(starts_with_frog("ribbit")) # False # You can throw all sorts of junk at the function without it crashing # Also works for tuples without modification ![]() """ Test matching the first element of a sequence is a frog. from typing import Sequenceĭef starts_with_frog(seq: Sequence) -> bool: This function will return true if the input is a sequence that starts with a string literal of a unicode frog. The syntax uses square brackets, but it will match against any Sequence type (tuple, list, bytearray). The match statement has a special syntax for matching sequences. This is called “structural pattern matching” and it’s a feature of many functional programming languages. You can combine one or many of these into a single statement to filter data based on the input.
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