data:image/png;base64,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

item

Takes a text string as input, and reports a new text string containing all but the first character of the input.

pt:_ sem o primeiro caractere ca:_ sense la primera lletra

All but first of empty word.

Takes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing all but the first word, with one space between words and no spaces at the beginning or end. (Note: consider using SENTENCE➞LIST and processing the resulting list instead of doing recursion on sentences in text string form. List operations are faster.)

pt:_ sem a primeira palavra ca:_ sense la primera paraula

All but first of empty sentence.

Takes a text string as input, and reports a new text string containing all but the last letter of the input.

pt:_ sem o último caractere ca:_ sense la darrera lletra

All but first of empty word.

Takes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing all but the last word, with one space between words and no spaces at the beginning or end. (Note: consider using SENTENCE->LIST and processing the resulting list instead of doing recursion on sentences in text string form. List operations are faster.)

pt:_ sem a última palavra ca:_ sense la darrera paraula

All but last of empty sentence.

pt:a primeira palavra de _ ca:primera paraula de _

Takes a text string as input, and reports the last character in the string.

pt:o último caractere de _ ca:primera lletra de _ 0

pt:a última palavra de _ ca:darrera paraula de _

Last of empty sentence.

Takes a text string as input, and reports TRUE if the string has no characters in it of any kind, otherwise false.

pt:a palavra _ está vazia ca:paraula buida? _ 0

Takes a text string as input, and reports TRUE if the input contains no characters other than spaces (therefore, no words when the string is considered as a sentence), otherwise FALSE.

pt:a frase _ está vazia ca:frase buida? _

Like JOIN, takes any number of words (text strings) and reports a sentence with its inputs concatenated, but inserts a blank space between the inputs. Consider using SENTENCE (Lists palette) instead.

de:füge Wörter zusammen _ ca:uneix les paraules _ es:unir las palabras _ fr:fusionne les mots _ pt:uma frase com as palavras _ firstbffirst1

1bf

Takes a sentence in list form and reports the sentence as a text string.

de:Liste $arrowRight Satz _ ca:llista $arrowRight frase _ es:lista $arrowRight frase _ fr:liste $arrowRight phrase _ pt:uma frase com as palavras em _

Takes a sentence in text string form and reports the sentence as a list of its words.

de:Satz $arrowRight Liste _ ca:frase $arrowRight llista _ es:frase $arrowRight lista _ fr:phrase $arrowRight liste _ pt:uma lista com as palavras da frase _

report a list in which each item is one letter from the input word

de:Wort $arrowRight Liste _ ca:paraula $arrowRight llista _ es:palabra $arrowRight lista _ fr:mot $arrowRight liste _ pt:uma lista com os caracteres da palavra _

join all the items of the input list into a single word, and report it

de:Liste $arrowRight Wort _ ca:llista $arrowRight paraula _ es:lista $arrowRight palabra _ fr:liste $arrowRight mot _ pt:uma palavra com os caracteres em _

SENTENCE is the main constructor for sentences, represented as lists of words. It takes zero or more inputs, each of which can be either a list or a text string. If a list, the input is assumed to be a list of words. If a text string, it is converted to a list of words using SENTENCE→LIST. Then all the lists of words are appended to form a new list of words. If the inputs are lists of lists rather than lists of words, SENTENCE, like APPEND, does only one level of flattening, reporting a list of all the items of all the input lists.

ca:frase _

pt:lança o erro _

Takes a (possibly deep) list as input, and reports a human-readable text form of the list (namely, Lisp notation).

ca:notació textual de _ (

)

Helper function for word/sentence library. Reports its first input, unless that input is empty, in which case it gives its second input as an error message.

Takes a text string as input, and reports the first character in the string.

pt:a primeira palavra de _ ca:primera lletra de _

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KBgAAH6CYJECBAgIAA+wECBAgQIBAICHCAbpIAAQIECByxcQFpoRMBzwAAAABJRU5ErkJggg==Person 2Person 2.guyfury