Python’s `set.copy()`

method creates and returns a flat copy of this set.

Here’s a minimal example where you copy a set with two integers and a string value:

>>> s = {1, 2, 'Alice'} >>> s.copy() {1, 2, 'Alice'}

## Syntax

Let’s dive into the formal syntax of the `set.copy()`

method.

set.copy()

Argument | Data Type | Explanation |
---|---|---|

`-` | – | – |

The `set.copy()`

method doesn’t take any argument. If you try to pass an argument, Python will raise a `TypeError: copy() takes no arguments (1 given)`

. You can fix this error by not passing an argument into the method.

>>> {1, 2, 3}.copy({'Alice', 'Bob'}) Traceback (most recent call last): File "<pyshell#3>", line 1, in <module> {1, 2, 3}.copy({'Alice', 'Bob'}) TypeError: copy() takes no arguments (1 given)

## Return Value of Set copy()

The return value of `set.copy()`

is a new `set`

object that contains a reference to all set elements in the original set.

## Shallow vs Deep Set copy()

The `set.add()`

copy operation performs a shallow copy of the set which means that each element in the set is not copied itself. The new set holds the same elements as the original set—it’s just a different *container*.

This has led some people to ask: **how to perform a deep copy of a set?**

? However, the ** concept of a deep copy doesn’t make any sense for sets** because sets are only allowed to hold immutable objects that cannot be changed after creation. There’s no practical benefit of copying an immutable object—but tangible practical disadvantages such as memory wastage. Due to the

**, the program would behave exactly the same when working on a copy compared to working on the original object. That’s why you shouldn’t even copy immutable objects but reuse them.**

*read-only access patterns of immutable objects*The following code shows that the `set.copy()`

function creates a shallow copy. As checked using the “is” keyword, the copied set points to another memory location but the only set element, a tuple, points to the same memory location in both sets.

>>> s1 = {(1, 2, 3)} >>> s2 = s1.copy() >>> s1.pop() is s2.pop() True

## What is the Time Complexity of Set copy()?

The runtime complexity of the `set.copy()`

function on a set with n elements is * O(n)* because Python creates a new empty set and adds one element at a time to the set. This procedure is repeated

*n*times. Each add operation is

*O(1)*so the total runtime complexity is

*O(1) * O(n)*which yields a linear runtime complexity.

You can see this in the following simple experiment where we run the set method multiple times for an increasing number of set elements.

I ran this experiment on my ** Acer Aspire 5 notebook** (I know) with

**(8th Gen) processor and 16GB of memory. Here’s the code of the experiment:**

*Intel Core i7*import matplotlib.pyplot as plt import random import time sizes = [i * 10**5 for i in range(50)] runtimes = [] for size in sizes: s = set(range(size)) # Start track time ... t1 = time.time() s.copy() t2 = time.time() # ... end track time runtimes.append(t2-t1) plt.plot(sizes, runtimes) plt.ylabel('Runtime (s)') plt.xlabel('Set Size') plt.show()

## Other Python Set Methods

All set methods are called on a given set. For example, if you created a set `s = {1, 2, 3}`

, you’d call `s.clear()`

to remove all elements of the set. We use the term ** “this set”** to refer to the set on which the method is executed.

`add()` | Add an element to this set |

`clear()` | Remove all elements from this set |

`copy()` | Create and return a flat copy of this set |

`difference()` | Create and return a new set containing all elements of this set except the ones in the given set arguments. The resulting set has at most as many elements as any other. |

`difference_update()` | Remove all elements from this set that are members of any of the given set arguments. |

`discard()` | Remove an element from this set if it is a member, otherwise do nothing. |

`intersection()` | Create and return a new set that contains all elements that are members of all sets: this and the specified as well. . |

`intersection_update()` | Removes all elements from this set that are not members in all other specified sets. |

`isdisjoint()` | Return `True` if no element from this set is a member of any other specified set. Sets are disjoint if and only if their intersection is the empty set. |

`issubset()` | Return `True` if all elements of this set are members of the specified set argument. |

`issuperset()` | Return `True` if all elements of the specified set argument are members of this set. |

`pop()` | Remove and return a random element from this set. If the set is empty, it’ll raise a `KeyError` . |

`remove()` | Remove and return a specific element from this set as defined in the argument. If the set doesn’t contain the element, it’ll raise a `KeyError` . |

`symmetric_difference()` | Return a new set with elements in either this set or the specified set argument, but not elements that are members of both. |

`symmetric_difference_update()` | Replace this set with the symmetric difference, i.e., elements in either this set or the specified set argument, but not elements that are members of both. |

`union()` | Create and return a new set with all elements that are in this set, or in any of the specified set arguments. |

`update()` | Update this set with all elements that are in this set, or in any of the specified set arguments. The resulting set has at least as many elements as any other. |

While working as a researcher in distributed systems, Dr. Christian Mayer found his love for teaching computer science students.

To help students reach higher levels of Python success, he founded the programming education website Finxter.com that has taught exponential skills to millions of coders worldwide. He’s the author of the best-selling programming books Python One-Liners (NoStarch 2020), The Art of Clean Code (NoStarch 2022), and The Book of Dash (NoStarch 2022). Chris also coauthored the Coffee Break Python series of self-published books. He’s a computer science enthusiast, freelancer, and owner of one of the top 10 largest Python blogs worldwide.

His passions are writing, reading, and coding. But his greatest passion is to serve aspiring coders through Finxter and help them to boost their skills. You can join his free email academy here.