data:image/png;base64,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data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAeAAAAFoCAYAAACPNyggAAACtUlEQVR4nO3BMQEAAADCoPVPbQwfoAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA+Bo3+AAF/RMkcAAAAAElFTkSuQmCC

25500

roundround flat

GETGET POST PUT DELETEhttp://snap.berkeley.edu

PUT/properties/

Acceptapplication/json

21/properties/

10230

Take any number of input lists, and create a new list containing the items of the input lists. So APPEND [A B] [C D] where the [...] are lists reports the list [A B C D] not [[A B] [C D]].

ca:concatena _ 1111

Reports a new list containing the items of the input list, but in the opposite order.

ca:capgira _ 1inputresult

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 _ 1

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 _ copy of datasplitmerge11#1#2

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 _ _ 11

This block applies the given function to each item of the given list, like the primitive MAP block, but with two differences: 1. The # variable provided will contain the item number of the item currently being used in the given function: 1 for item 1, and so on. You read the variable in your function; don't set it. 2. You can provide more than one input list. If so, the lengths of all the lists should be the same, and the function you supply must expect as many inputs as you have lists (i.e., that many empty input slots in the function).

ca:_ mapeja _ sobre _ #11datacountmapmany#

1data listscount

This block makes a new list containing selected items from the given list, like the primitive KEEP block. What's different is that it provides the # variable for use in your predicate function. It will contain the item number (in the original list) of the item being examined by the predicate.

ca:_ manté els elements on _ de _ #1datacount1

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 _ _

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