A collection of
Sets are unordered, unique data set that is inside the hash element (immutable type), but the hash itself is not set (it is set dictionary do not bond). The following is a collection of the most important points:
De-emphasis, the list becomes a collection, automatically go heavier.
Relationship test, before the intersection of two sets of test data, poor, union and other relations.
1, to create the collection.
set1 = set({1,2,'barry'})
set2 = {1,2,'barry'}
print(set1,set2) # {1, 2, 'barry'} {1, 2, 'barry'}
2, increasing the collection.
set1.add ( 'View Goddess') Print (setl) #update: Iterative increased set1.update ( 'A') Print (setl) set1.update ( 'teacher') Print (setl) set1.update ([. 1, 2, 3]) Print (SET1)
3, delete the collection.
= {setl 'Alex', 'wusir', 'ritian', 'Egon', 'Barry'}
set1.remove ( 'Alex') # removes an element
print (setl)
set1.pop () # delete a random element
print (SET1)
set1.clear () # empty set of
print (set1)
del SET1 # delete a collection
print (set1)
4, other sets of operations:
4.1 intersection. (Or & intersection)
set1 = {1,2,3,4,5}
set2 = {4,5,6,7,8}
print(set1 & set2) # {4, 5}
print(set1.intersection(set2)) # {4, 5}
4.2 union. (| Or union)
set1 = {1,2,3,4,5}
set2 = {4,5,6,7,8}
print(set1 | set2) # {1, 2, 3, 4, 5, 6, 7,8}
print(set2.union(set1)) # {1, 2, 3, 4, 5, 6, 7,8}
4.3 difference sets. (- or difference)
set1 = {1,2,3,4,5}
set2 = {4,5,6,7,8}
print(set1 - set2) # {1, 2, 3}
print(set1.difference(set2)) # {1, 2, 3}
4.4 Anti intersection. (^ Or symmetric_difference)
set1 = {1,2,3,4,5}
set2 = {4,5,6,7,8}
print(set1 ^ set2) # {1, 2, 3, 6, 7, 8}
print(set1.symmetric_difference(set2)) # {1, 2, 3, 6, 7, 8}
4.5 subset superset
l, 2,3} = {set1
SET2 = {1,2,3,4,5,6}
Print (set1 <SET2)
Print (set1.issubset (SET2)) # two identical, are described set1 is set2 subset.
Print (set2> set1)
Print (set2.issuperset (set1)) # two identical, are explained set1 set2 is a superset.
5, frozenset immutable collections, so the set becomes immutable types.
s = frozenset('barry')
print(s,type(s)) # frozenset({'a', 'y', 'b', 'r'}) <class 'frozenset'>
Second, copy depth
1, look at the assignment operator.
l1 = [1,2,3,['barry','alex']] l2 = l1 l1[0] = 111 print(l1) # [111, 2, 3, ['barry', 'alex']] print(l2) # [111, 2, 3, ['barry', 'alex']] l1[3][0] = 'wusir' print(l1) # [111, 2, 3, ['wusir', 'alex']] print(l2) # [111, 2, 3, ['wusir', 'alex']]
For the assignment operator is, l1 and l2 points to the same memory address, so they are exactly the same.
2, shallow copy copy.
l1 = [1,2,3,['barry','alex']]
l2 = l1.copy() print(l1,id(l1)) # [1, 2, 3, ['barry', 'alex']] 2380296895816 print(l2,id(l2)) # [1, 2, 3, ['barry', 'alex']] 2380296895048
l1[1] = 222
print(l1,id(l1)) # [1, 222, 3, ['barry', 'alex']] 2593038941128
print(l2,id(l2)) # [1, 2, 3, ['barry', 'alex']] 2593038941896
l1[3][0] = 'wusir' print(l1,id(l1[3])) # [1, 2, 3, ['wusir', 'alex']] 1732315659016 print(l2,id(l2[3])) # [1, 2, 3, ['wusir', 'alex']] 1732315659016
For shallow copy, the first layer of creating a new memory address, while the second layer from the beginning, is point to the same memory address, so, for the second layer and the deeper layers, the consistency .
3, deep copy deepcopy.
import copy l1 = [1,2,3,['barry','alex']] l2 = copy.deepcopy(l1) print(l1,id(l1)) # [1, 2, 3, ['barry', 'alex']] 2915377167816 print(l2,id(l2)) # [1, 2, 3, ['barry', 'alex']] 2915377167048 l1[1] = 222 print(l1,id(l1)) # [1, 222, 3, ['barry', 'alex']] 2915377167816 print(l2,id(l2)) # [1, 2, 3, ['barry', 'alex']] 2915377167048 l1[3][0] = 'wusir' print(l1,id(l1[3])) # [1, 222, 3, ['wusir', 'alex']] 2915377167240 print(l2,id(l2[3])) # [1, 2, 3, ['barry', 'alex']] 2915377167304
For deep copy, the two are completely separate, change any element of any one of (no matter how many layers), another absolutely does not change.
Sets are unordered, unique data set that is inside the hash element (immutable type), but the hash itself is not set (it is set dictionary do not bond). The following is a collection of the most important points:
De-emphasis, the list becomes a collection, automatically go heavier.
Relationship test, before the intersection of two sets of test data, poor, union and other relations.
1, to create the collection.
set1 = set({1,2,'barry'})
set2 = {1,2,'barry'}
print(set1,set2) # {1, 2, 'barry'} {1, 2, 'barry'}
2, increasing the collection.
set1.add ( 'View Goddess') Print (setl) #update: Iterative increased set1.update ( 'A') Print (setl) set1.update ( 'teacher') Print (setl) set1.update ([. 1, 2, 3]) Print (SET1)
3, delete the collection.
= {setl 'Alex', 'wusir', 'ritian', 'Egon', 'Barry'}
set1.remove ( 'Alex') # removes an element
print (setl)
set1.pop () # delete a random element
print (SET1)
set1.clear () # empty set of
print (set1)
del SET1 # delete a collection
print (set1)
4, other sets of operations:
4.1 intersection. (Or & intersection)
set1 = {1,2,3,4,5}
set2 = {4,5,6,7,8}
print(set1 & set2) # {4, 5}
print(set1.intersection(set2)) # {4, 5}
4.2 union. (| Or union)
set1 = {1,2,3,4,5}
set2 = {4,5,6,7,8}
print(set1 | set2) # {1, 2, 3, 4, 5, 6, 7,8}
print(set2.union(set1)) # {1, 2, 3, 4, 5, 6, 7,8}
4.3 difference sets. (- or difference)
set1 = {1,2,3,4,5}
set2 = {4,5,6,7,8}
print(set1 - set2) # {1, 2, 3}
print(set1.difference(set2)) # {1, 2, 3}
4.4 Anti intersection. (^ Or symmetric_difference)
set1 = {1,2,3,4,5}
set2 = {4,5,6,7,8}
print(set1 ^ set2) # {1, 2, 3, 6, 7, 8}
print(set1.symmetric_difference(set2)) # {1, 2, 3, 6, 7, 8}
4.5 subset superset
l, 2,3} = {set1
SET2 = {1,2,3,4,5,6}
Print (set1 <SET2)
Print (set1.issubset (SET2)) # two identical, are described set1 is set2 subset.
Print (set2> set1)
Print (set2.issuperset (set1)) # two identical, are explained set1 set2 is a superset.
5, frozenset immutable collections, so the set becomes immutable types.
s = frozenset('barry')
print(s,type(s)) # frozenset({'a', 'y', 'b', 'r'}) <class 'frozenset'>
Second, copy depth
1, look at the assignment operator.
l1 = [1,2,3,['barry','alex']] l2 = l1 l1[0] = 111 print(l1) # [111, 2, 3, ['barry', 'alex']] print(l2) # [111, 2, 3, ['barry', 'alex']] l1[3][0] = 'wusir' print(l1) # [111, 2, 3, ['wusir', 'alex']] print(l2) # [111, 2, 3, ['wusir', 'alex']]
For the assignment operator is, l1 and l2 points to the same memory address, so they are exactly the same.
2, shallow copy copy.
l1 = [1,2,3,['barry','alex']]
l2 = l1.copy() print(l1,id(l1)) # [1, 2, 3, ['barry', 'alex']] 2380296895816 print(l2,id(l2)) # [1, 2, 3, ['barry', 'alex']] 2380296895048
l1[1] = 222
print(l1,id(l1)) # [1, 222, 3, ['barry', 'alex']] 2593038941128
print(l2,id(l2)) # [1, 2, 3, ['barry', 'alex']] 2593038941896
l1[3][0] = 'wusir' print(l1,id(l1[3])) # [1, 2, 3, ['wusir', 'alex']] 1732315659016 print(l2,id(l2[3])) # [1, 2, 3, ['wusir', 'alex']] 1732315659016
For shallow copy, the first layer of creating a new memory address, while the second layer from the beginning, is point to the same memory address, so, for the second layer and the deeper layers, the consistency .
3, deep copy deepcopy.
import copy l1 = [1,2,3,['barry','alex']] l2 = copy.deepcopy(l1) print(l1,id(l1)) # [1, 2, 3, ['barry', 'alex']] 2915377167816 print(l2,id(l2)) # [1, 2, 3, ['barry', 'alex']] 2915377167048 l1[1] = 222 print(l1,id(l1)) # [1, 222, 3, ['barry', 'alex']] 2915377167816 print(l2,id(l2)) # [1, 2, 3, ['barry', 'alex']] 2915377167048 l1[3][0] = 'wusir' print(l1,id(l1[3])) # [1, 222, 3, ['wusir', 'alex']] 2915377167240 print(l2,id(l2[3])) # [1, 2, 3, ['barry', 'alex']] 2915377167304
For deep copy, the two are completely separate, change any element of any one of (no matter how many layers), another absolutely does not change.