iterkeys
()
Iterator over all keys
itervalues
()
Iterator over all values
keys
()
Copy of all keys
poptiem
(k)
Returns and removes item with key k
update
([e])
Updates items with items from e
values
()
Copy of all values
Sets
The last data structure we will consider is the set object. Although set theory is a
cornerstone of mathematics and also finance theory, there are not too many practical
applications for set objects. The objects are unordered collections of other objects,
containing every element only once:
In [ 74 ]: s = set([‘u’, ‘d’, ‘ud’, ‘du’, ‘d’, ‘du’])
s
Out[74]: {‘d’, ‘du’, ‘u’, ‘ud’}
In [ 75 ]: t = set([‘d’, ‘dd’, ‘uu’, ‘u’])
With set objects, you can implement operations as you are used to in mathematical set
theory. For example, you can generate unions, intersections, and differences:
In [ 76 ]: s.union(t) # all of s and t
Out[76]: {‘d’, ‘dd’, ‘du’, ‘u’, ‘ud’, ‘uu’}
In [ 77 ]: s.intersection(t) # both in s and t
Out[77]: {‘d’, ‘u’}
In [ 78 ]: s.difference(t) # in s but not t
Out[78]: {‘du’, ‘ud’}
In [ 79 ]: t.difference(s) # in t but not s
Out[79]: {‘dd’, ‘uu’}
In [ 80 ]: s.symmetric_difference(t) # in either one but not both
Out[80]: {‘dd’, ‘du’, ‘ud’, ‘uu’}
One application of set objects is to get rid of duplicates in a list object. For example:
In [ 81 ]: from random import randint
l = [randint( 0 , 10 ) for i in range( 1000 )]
# 1,000 random integers between 0 and 10
len(l) # number of elements in l
Out[81]: 1000
In [ 82 ]: l[: 20 ]
Out[82]: [8, 3, 4, 9, 1, 7, 5, 5, 6, 7, 4, 4, 7, 1, 8, 5, 0, 7, 1, 9]
In [ 83 ]: s = set(l)
s
Out[83]: {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}
