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Lists, sets, and maps


A list of elements of type t has type sp.list[t]. For example, [1, 2, 3] has type sp.list[].

len(x: sp.list[t]) → sp.nat

Returns the length of a list, e.g. len(["a", "b", "c"]) == 3.

sp.sum(xs: sp.list[]) →
sp.sum(xs: sp.list[sp.nat]) → sp.nat

Returns the sum of all the elements in a list, e.g. sp.sum([1, 2, 3]) == 6. Works on lists of both sp.nat and

l.push(x: t) → 

Adds an element to the start of a list. For example:

s = [1, 2, 3]
# evaluates to `[4, 1, 2, 3]`
sp.cons(x: t, x: sp.list[t]) → sp.list[t]

Returns a new list with an element added to the front. For example sp.cons(1, [2, 3]) evaluates to [1, 2, 3].

sp.range(to: sp.nat) → sp.list[sp.nat]
sp.range(to: → sp.list[]
sp.range(from_: sp.nat, to: sp.nat) → sp.list[sp.nat]
sp.range(from_:, to: → sp.list[]
sp.range(from_: sp.nat, to: sp.nat, step: sp.nat) → sp.list[sp.nat]
sp.range(from_:, to:, step: → sp.list[]

sp.range(3) evaluates to [0, 1, 2].

sp.range(3, 7) evaluates to [3, 4, 5, 6].

sp.range(3, 7, 2) evaluates to [3, 5].


A set of elements of type t has type sp.set[t]. For example, sp.set(1, 2, 3) has type sp.set[].

len(x: sp.set[t]) → sp.nat

Returns the number of elements in a set.

s.contains(x: t) → sp.bool

For a set m of type sp.set[t], returns a boolean value indicating whether x is an element of s.

s.elements() → sp.list[t]

For a set s of type sp.set[t] returns its elements as a list.

s.add(x: t) → 

Adds an element to a set. For example:

s = sp.set(1, 2, 3)
assert s.contains(4)


A map that takes elements of type k to elements of type v has type[k, v]. SmartPy maps are similar to Python's dictionaries.{'a': 65}, {'b': 66}).

m[...]  → 

An entry can be looked up with the m[...] notation, e.g.:

m = {'a': 65, 'b': 66}
assert m['a'] == 65
del  m[key: t] → 

An entry can be deleted from a map using the statement del m[key]. For example:

m = {'a': 65, 'b': 66}
del m['a']
assert not m.contains('a')
m.get(key: k, default=...: v) → v
m.get(key: t, error=...: t) → v

Looks up key in the map m. If key does not have an entry in the map, default is returned or error is raised, according to which keyword argument is given.

len(x:[k, v]) → sp.nat

Returns the size of a map, i.e. the number of its entries.

m.items() → sp.list[sp.record(key=k, value=v)]

{'a': 97, 'b': 98}.items() evaluates to [sp.record(key='a', value=97), sp.record(key='b', value=98)].

m.keys() → sp.list[k]

{'a': 97, 'b': 98}.keys() evaluates to ['a', 'b'].

m.values() → sp.list[k]

{'a': 97, 'b': 98}.values() evaluates to [97, 98].

sp.update_map(key: k, value: sp.option[v], m: map[k, v]) → map[k, v]

Returns a copy of m with a modified entry at key: if value == None, it is removed; if value == sp.Some(v), it is v.


assert sp.update_map('a', sp.Some(3), {})['a'] == 3
assert sp.update_map('a', None, {'a': 2}).get('a', default=-1) == -1
sp.get_and_update(key: k, value: sp.option[v], m: map[k, v]) → sp.pair[sp.option[v], map[k, v]]

Like sp.update_map, but also returns the old value of the map (or None if k had no entry).

Big maps

Analogous to there is sp.big_map[k, v], denoting lazily deserialized maps from k to v. These maps should be used if you intend to store large amounts of data in a map. Using sp.big_map can reduce gas costs significantly compared to standard maps, as data is lazily deserialized. Note however that individual operations on sp.big_map have higher gas costs than those over standard maps. A sp.big_map also has a lower storage cost than a standard map of the same size, when large keys are used, since only the hash of each key is stored in a sp.big_map.

A sp.big_map cannot be iterated over, nor contained in another big map.