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 es:todos menos la primera letra de _ pl:_ bez pierwszej litery

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 es:todas menos la primera palabra de _ pl:_ bez pierwszego słowa

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 es:todos menos la última letra de _ pl:_ bez ostatniej litery

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 es:todos menos la última palabra de _ pl:_ bez ostatniego słowa

All but last of empty sentence.

pt:a primeira palavra de _ ca:primera paraula de _ es:primera palabra de _ pl:pierwsze słowo _

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

pt:o último caractere de _ ca:primera lletra de _ es:última letra de _ pl:ostatnia litera _ 0

pt:a última palavra de _ ca:darrera paraula de _ es:última palabra de _ pl:ostatnie słowo _

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? _ es:¿palabra vacía? _ pl:puste słowo? _ 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? _ es:¿oración vacía? _ pl:puste zdanie? _

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 _ pl:połącz słowa _ 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 _ pl:lista $arrowRight zdanie _

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 _ pl:zdanie $arrowRight lista _

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 _ pl:słowo $arrowRight lista _

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 _ pl:lista $arrowRight słowo _

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 _ pl:zdanie _

pt:lança o erro _ pl:błąd _

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 _ pl:tekstowa _ (

)

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.

es:requerir no vacío _ _

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

pt:a primeira palavra de _ ca:primera lletra de _ es:primera letra de _ pl:pierwsza litera _

returns a frequency analysis of the argument list, represented as a two-column table, where the first column contains the unique values and the second column their occurrences

pt:$flash as frequências dos itens de _ ca:$flash analitza _ pl:$flash analizuj _

pt:o agrupamento dos itens de _ de acordo com _ ca:$flash agrupa _ per _ pl:$flash grupuj _ według _

pt:uma ordenação de _ com critério _ ca:$flash ordena _ per _ pl:$flash sortuj _ według _

pt:o histograma de _ entre _ e _ com intervalo _ ca:histograma de _ des de: _ fins: _ interval: _ pl:wykres _ od: _ do: _ odstęp: _ 1

pt:o histograma de _ agrupado de acordo com _ entre _ e _ com passo _ ca:histograma de _ agrupat per: _ des de: _ fins: _ interval: _ pl:wykres _ pogrupowany według: _ od: _ do: _ odstęp: _

add missing entries to a sorted list. Used for histograms

pt:os itens de _ com a chave _ entre _ e _ com passo _ completados por _ ca:omple _ clau: _ des de: _ fins: _ interval: _ amplada: _ pl:wypełnij _ klucz: _ od: _ do: _ odstęp: _ z: _ 11

pt:_ em minúsculas ca:_ $arrowRight minúscules pl:_ $arrowRight małe litery

pt:desenha gráfico de barras de _ em (x: _ , y: _ ) com largura _ e altura _ ca:dibuixa _ _ a x: _ y: _ i ample: _ alçada: _ pl:wykres _ _ x: _ y: _ szerokość: _ wysokość: _ barsbars lines pie chart-200-10040020022100startsettingratio1step2lines

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 _ pl:$flash sortuj _ za pomocą _

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 _ _ pl:$flash skojarz _ _

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 _ _ pl:_ dla każdego _ z _ _

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.

pl:$flash spakuj _

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 _ pl:mapuj wielokrotnie _ na _

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3080