Lecture 15 — Sets


  • Example: finding all individuals listed in the Internet Movie Database (IMDB)
  • A solution based on lists
  • Sets and set operations
  • A solution based on sets.
  • Efficiency and set representation

Reading is Section 11.1 of Practical Programming.

Finding All Persons in the IMDB file

  • We are given a file extracted from the Internet Movie Database (IMDB) called imdb_data.txt containing, on each line, a person’s name, a movie name, and a year. For example,

    Kishiro, Yukito   | Battle Angel    | 2016
  • Goal:

    • Find all persons named in the file
    • Count the number of different persons named.
    • Ask if a particular person is named in the file
  • The challenge in doing this is that many names appear multiple times.

  • First solution: store names in a list. We’ll start from the following code, posted on the Piazza in lec15_find_names_start.py, which is part of a Lecture 15 zip file.

    imdb_file = input("Enter the name of the IMDB file ==> ").strip()
    name_list = []
    for line in open(imdb_file, encoding = "ISO-8859-1"):
        words = line.strip().split('|')
        name = words[0].strip()

    and complete the code in class.

  • The challenge is that we need to check that a name is not already in the list before adding it.

  • You may access the data files and the starting code .py file from the Resources page of the Piazza site.

How To Test?

  • The file imdb_data.txt has about 260K entries. How will we know our results are correct?
  • Even if we restrict it to movies released in 2010-2012 (the file imdb_2010-12.txt), we still have 25K entries!
  • We need to generate a smaller file with results we can test by hand
    • I have generated hanks.txt for you and will use it to test our program before testing on the larger files.

What Happens?

  • Very slow on the large files because we need to scan through the list to see if a name is already there.
  • We’ll write a faster implementation based on Python sets.
  • We’ll start with the basics of sets.


  • A Python set is an implementation of the mathematical notion of a set:
    • No order to the values (and therefore no indexing)
    • Contains no duplicates
    • Contains whatever type of values we wish; including values of different types.
  • Python set methods are exactly what you would expect.
    • Each has a function call syntax and many have operator syntax in addition.

Set Methods

  • Initialization comes from a list, a range, or from just set():

    >>> s1 = set()
    >>> s1
    >>> s2 = set(range(0,11,2))
    >>> s2
    {0, 2, 4, 6, 8, 10}
    >>> v = [4, 8, 4, 'hello', 32, 64, 'spam', 32, 256]
    >>> s3 = set(v)
    >>> s3
    {32, 64, 4, 'spam', 8, 256, 'hello'}
  • The actual methods are

    • s.add(x) — add an element if it is not already there

    • s.clear() — clear out the set, making it empty

    • s1.difference(s2) — create a new set with the values from s1 that are not in s2.

      • Python also has and “operator syntax” for this:
      s1 - s2
    • s1.intersection(s2) — create a new set that contains only the values that are in both sets. Operator syntax:

      s1 & s2
    • s1.union(s2) — create a new set that contains values that are in either set. Operator syntax:

      s1 | s2
    • s1.issubset(2) —- are all elements of s1 also in s2? Operator syntax:

      s1 <= s2
    • s1.issuperset(s2) — are all elements of s2 also in s1? Operator syntax:

      s1 >= s2
    • s1.symmetric_difference(s2) — create a new set that contains values that are in s1 or s2 but not in both.

      s1 ^ s2
    • x in s - evaluates to True if the value associated with x is in set s.

  • We will explore the intuitions behind these set operations by considering

    • s1 to be the set of actors in comedies,
    • s2 to be the set of actors in action movies

    and then consider who is in the sets

    s1 - s2
    s1 & s2
    s1 | s2
    s1 ^ s2


  1. Sets should be relatively intuitive, so rather than demo them in class, we’ll work through these as an exercise:

    >>> s1 = set(range(0,10))
    >>> s1
    >>> s1.add(6)
    >>> s1.add(10)
    >>> s2 = set(range(4,20,2))
    >>> s2
    >>> s1 - s2
    >>> s1 & s2
    >>> s1 | s2
    >>> s1 <= s2
    >>> s3 = set(range(4,20,4))
    >>> s3 <= s2

Back to Our Problem

  • We’ll modify our code to find the actors in the IMDB. The code is actually very simple and only requires a few set operations.

Side-by-Side Comparison of the Two Solutions

  • Neither the set nor the list is ordered. We can fix this at the end by sorting.
    • The list can be sorted directly.
    • The set must be converted to a list first. The function sorted does this for us.
  • What about speed? The set version is MUCH FASTER — to the point that the list version is essentially useless on a large data set.
    • We’ll use some timings to demonstrate this quantitatively
    • We’ll then explore why in the rest of this lecture.

Comparison of Running Times for Our Two Solutions

  • List-based solution:
    • Each time before a name is added, the code — through the method in — scans through the entire list to decide if it is there.
    • Thus, the work done is proportional to the size of the list.
    • The overall running time is therefore roughly proportional to the square of the number of entries in the list (and the file).
    • Letting the mathematical variable \(N\) represent the length of the list, we write this more formally as \(O(N^2)\), or “the order of N squared”
  • Set-based code
    • For sets, Python uses a technique called hashing to restrict the running time of the add method so that it is independent of size of the set.
      • The details of hashing are covered in CSCI 1200, Data Structures.
    • The overall running time is therefore roughly proportional to the length of the set (and number of entries in the file).
    • We write this as \(O(N)\).
  • We will discuss this type of analysis more later in the semester.
    • It is covered in much greater detail in Data Structures and again in Intro. to Algorithms.


  • Python largely hides the details of the containers — set and list in this case — and therefore it is hard to know which is more efficient and why.
  • For programs applied to small problems involving small data sets, efficiency rarely matters.
  • For longer programs and programs that work on larger data sets, efficiency does matter, sometimes tremendously. What do we do?
    • In some cases, we still use Python and choose the containers and operations that make the code most efficient.
    • In others, we must switch to programming languages, such as C++, that generate and use compiled code.


  • Sets in Python realize the notion of a mathematical set, with all the associated operations.
  • Operations can be used as method calls or, in many cases, operators.
  • The combined core operations of finding if a value is in a set and adding it to the set are much faster when using a set than the corresponding operations using a list.
  • We will continue to see examples of programming with sets when we work with dictionaries.

Extra Practice Problems

  1. Write Python code that implements the following set functions using a combination of loops, the in operator, and the add function. In each case, s1 and s2 are sets and the function call should return a set.
    1. union(s1,s2)
    2. intersection(s1,s2)
    3. symmetric_difference(s1,s2)