data:image/png;base64,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

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 _

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 _

pt:lança o erro _

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 _

pt:o texto multilinha _ ca:multilínia _

Reports the part of the first string up to the first instance of the second string inside it. If the second string isn't found, reports the entire first string.

pt:o texto de _ antes de _ ca:part del text _ anterior a _

Reports the portion of the first input string starting after the first occurrence of the second string. If the second string isn't found in the first string, reports an empty string.

pt:o texto de _ depois de _ ca:part del text _ posterior a _

Reports the character position (starting from 1) of the beginning of the first input string inside the second input string. If it's not found, reports 0.

pt:a posição de _ em _ ca:posició de _ en _

If input is TRUE, comparisons made by functions in the string library will be case-independent (so "FOO" = "foo"). This is the default. If input is FALSE, comparisons will be exact.

pt:altera comparações ignorando distinção minúsculas/maiúsculas para _ ca:ignora la diferenciació de majúscules _

Reports the portion of the first input (string) starting at the position given by the second input (counting from 1, like LETTER n OF) and ending at the position given by the third input (also counting from 1). If the third input is empty, reports the portion from the first position to the end of the string. If a position number is negative, counts from the end backward, so -1 is the last character, -2 is the next to last, etc.

pt:o texto de _ entre as posições _ e _ , inclusive ca:part del text _ des de la posició _ fins _

Reports True if the first input string contains the second input string, otherwise false. Comparison is case-independent by default; use USE CASE-INDEPENDENT COMPARISONS to change that.

pt:o texto de _ antes de _ ca:text _ conté _ ?

Reports the input text with lower case letters instead of capital letters in the input. Uses the user's locale's rules for case conversion.

pt:em minúsculas _ ca:_ a minúscules

ca:comparacions ignorant majúscules? err_reset

Reports a new list whose items are the same as in the input list, except that if two or more equal items appear in the input list, only the last one is kept in the result.

ca:elimina els duplicats de _

Reports a sorted version of the list in its first input slot, using the comparison function in the second input slot. For a list of numbers, using < as the comparison function will sort from low to high; using > will sort from high to low.

ca:ordena _ segons criteri _

The second input is an "association list," a list of two-item lists. Each of those smaller lists has a "key" as its first item and a "value" as its second. ASSOC reports the first key-value pair in the association list whose key matches the first input.

ca:associació _ _

This block carries out the given script for each item of the given list, like the primitive FOR EACH. What's different is that it provides the # variable, which will contain the item number in the list of each item in turn, 1 while processing item 1, and so on.

ca:_ per cada _ de _ _

Takes a (possibly deep) list as input, and reports a human-readable text form of the list (namely, Lisp notation). Will not work on circular lists.

ca:notació textual de _ (

)

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 _

ZIP takes any number of lists as inputs. The lists should all be the same length. ZIP reports a list of lists in which the first item is a list of all the first items, the second item is a list of all the second items, etc. Viewing the inputs as the rows of a matrix, ZIP reports its transpose.

The identity function reports its input.

Takes as input a function of N inputs and N lists. The function is called with item 1 of all the lists as its inputs, with item 2 of all the lists as its inputs, and so on. (The lists should all be the same length.)

ca:multi-mapeja _ sobre _

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 _

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