# Sets - Advanced Python 04

A Set is an unordered collection data type that is unindexed, mutable, and has no duplicate elements. Sets are created with braces.

`my_set = {"apple", "banana", "cherry"}`

### Create a set

Use curly braces or the built-in set function.

```
my_set = {"apple", "banana", "cherry"}
print(my_set)
# or use the set function and create from an iterable, e.g. list, tuple, string
my_set_2 = set(["one", "two", "three"])
my_set_2 = set(("one", "two", "three"))
print(my_set_2)
my_set_3 = set("aaabbbcccdddeeeeeffff")
print(my_set_3)
# careful: an empty set cannot be created with {}, as this is interpreted as dict
# use set() instead
a = {}
print(type(a))
a = set()
print(type(a))
```

```
{'banana', 'apple', 'cherry'}
{'three', 'one', 'two'}
{'b', 'c', 'd', 'e', 'f', 'a'}
<class 'dict'>
<class 'set'>
```

### Add elements

```
my_set = set()
# use the add() method to add elements
my_set.add(42)
my_set.add(True)
my_set.add("Hello")
# note: the order does not matter, and might differ when printed
print(my_set)
# nothing happens when the element is already present:
my_set.add(42)
print(my_set)
```

```
{True, 42, 'Hello'}
{True, 42, 'Hello'}
```

### Remove elements

```
# remove(x): removes x, raises a KeyError if element is not present
my_set = {"apple", "banana", "cherry"}
my_set.remove("apple")
print(my_set)
# KeyError:
# my_set.remove("orange")
# discard(x): removes x, does nothing if element is not present
my_set.discard("cherry")
my_set.discard("blueberry")
print(my_set)
# clear() : remove all elements
my_set.clear()
print(my_set)
# pop() : return and remove a random element
a = {True, 2, False, "hi", "hello"}
print(a.pop())
print(a)
```

```
{'banana', 'cherry'}
{'banana'}
set()
False
{True, 2, 'hi', 'hello'}
```

### Check if element is in Set

```
my_set = {"apple", "banana", "cherry"}
if "apple" in my_set:
print("yes")
```

```
yes
```

### Iterating

```
# Iterating over a set by using a for in loop
# Note: order is not important
my_set = {"apple", "banana", "cherry"}
for i in my_set:
print(i)
```

```
banana
apple
cherry
```

### Union and Intersection

```
odds = {1, 3, 5, 7, 9}
evens = {0, 2, 4, 6, 8}
primes = {2, 3, 5, 7}
# union() : combine elements from both sets, no duplication
# note that this does not change the two sets
u = odds.union(evens)
print(u)
# intersection(): take elements that are in both sets
i = odds.intersection(evens)
print(i)
i = odds.intersection(primes)
print(i)
i = evens.intersection(primes)
print(i)
```

```
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}
set()
{3, 5, 7}
{2}
```

### Difference of sets

```
setA = {1, 2, 3, 4, 5, 6, 7, 8, 9}
setB = {1, 2, 3, 10, 11, 12}
# difference() : returns a set with all the elements from the setA that are not in setB.
diff_set = setA.difference(setB)
print(diff_set)
# A.difference(B) is not the same as B.difference(A)
diff_set = setB.difference(setA)
print(diff_set)
# symmetric_difference() : returns a set with all the elements that are in setA and setB but not in both
diff_set = setA.symmetric_difference(setB)
print(diff_set)
# A.symmetric_difference(B) = B.symmetric_difference(A)
diff_set = setB.symmetric_difference(setA)
print(diff_set)
```

```
{4, 5, 6, 7, 8, 9}
{10, 11, 12}
{4, 5, 6, 7, 8, 9, 10, 11, 12}
{4, 5, 6, 7, 8, 9, 10, 11, 12}
```

### Updating sets

```
setA = {1, 2, 3, 4, 5, 6, 7, 8, 9}
setB = {1, 2, 3, 10, 11, 12}
# update() : Update the set by adding elements from another set.
setA.update(setB)
print(setA)
# intersection_update() : Update the set by keeping only the elements found in both
setA = {1, 2, 3, 4, 5, 6, 7, 8, 9}
setA.intersection_update(setB)
print(setA)
# difference_update() : Update the set by removing elements found in another set.
setA = {1, 2, 3, 4, 5, 6, 7, 8, 9}
setA.difference_update(setB)
print(setA)
# symmetric_difference_update() : Update the set by only keeping the elements found in either set, but not in both
setA = {1, 2, 3, 4, 5, 6, 7, 8, 9}
setA.symmetric_difference_update(setB)
print(setA)
# Note: all update methods also work with other iterables as argument, e.g lists, tuples
# setA.update([1, 2, 3, 4, 5, 6])
```

```
{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}
{1, 2, 3}
{4, 5, 6, 7, 8, 9}
{4, 5, 6, 7, 8, 9, 10, 11, 12}
```

### Copying

```
set_org = {1, 2, 3, 4, 5}
# this just copies the reference to the set, so be careful
set_copy = set_org
# now modifying the copy also affects the original
set_copy.update([3, 4, 5, 6, 7])
print(set_copy)
print(set_org)
# use copy() to actually copy the set
set_org = {1, 2, 3, 4, 5}
set_copy = set_org.copy()
# now modifying the copy does not affect the original
set_copy.update([3, 4, 5, 6, 7])
print(set_copy)
print(set_org)
```

```
{1, 2, 3, 4, 5, 6, 7}
{1, 2, 3, 4, 5, 6, 7}
{1, 2, 3, 4, 5, 6, 7}
{1, 2, 3, 4, 5}
```

### Subset, Superset, and Disjoint

```
setA = {1, 2, 3, 4, 5, 6}
setB = {1, 2, 3}
# issubset(setX): Returns True if setX contains the set
print(setA.issubset(setB))
print(setB.issubset(setA)) # True
# issuperset(setX): Returns True if the set contains setX
print(setA.issuperset(setB)) # True
print(setB.issuperset(setA))
# isdisjoint(setX) : Return True if both sets have a null intersection, i.e. no same elements
setC = {7, 8, 9}
print(setA.isdisjoint(setB))
print(setA.isdisjoint(setC))
```

```
False
True
True
False
False
True
```

### Frozenset

Frozen set is just an immutable version of normal set. While elements of a set can be modified at any time, elements of frozen set remains the same after creation. Creation with: `my_frozenset = frozenset(iterable)`

```
a = frozenset([0, 1, 2, 3, 4])
# The following is not allowed:
# a.add(5)
# a.remove(1)
# a.discard(1)
# a.clear()
# Also no update methods are allowed:
# a.update([1,2,3])
# Other set operations work
odds = frozenset({1, 3, 5, 7, 9})
evens = frozenset({0, 2, 4, 6, 8})
print(odds.union(evens))
print(odds.intersection(evens))
print(odds.difference(evens))
```

```
frozenset({0, 1, 2, 3, 4, 5, 6, 7, 8, 9})
frozenset()
frozenset({1, 3, 5, 7, 9})
```

**Join My Newsletter! **Get Python and ML tips emailed directly to your inbox. Each month you’ll get a summary of all the content I created, including the newest videos, articles, promotions, tips, and more.

# ML From Scratch

Implement popular Machine Learning algorithms from scratch using only built-in Python modules and numpy.

# Advanced Python

Advanced Python Tutorials. It covers topics like collections, decorators, generators, multithreading, logging, and much more.

# PyTorch Beginner

Learn all the necessary basics to get started with this deep learning framework.