Want to understand Python’s comprehensions? Think in Excel or SQL.

Comprehensions are among the most useful constructs in Python. They merge the old, trusty “map” and “filter” functions into a single piece of compact, elegant syntax, allowing us to expression complex ideas in a minimum of code. Comprehensions are one of the most important tools in a Pythonista’s toolbox.

And yet, I have found that a very large number of Python programmers, including some experienced developers, are not completely comfortable with comprehensions. There are two reasons for this: First, it’s not obvious when to use them, and what sorts of problems they solve. The second problem, which is at least as important, is that the syntax is hard for people to remember and understand.

I’ve started to use a new explanation and introduction to comprehensions in my Python classes, and have found that it helps to lower the learning curve to some degree. In this post, I’m publicizing this explanation, in the hopes that it’ll help Python developers to understand when, where, and how to use comprehensions.

Let’s take a simple problem: I want to take a list of five integers, and get a list of their squares. If you give this problem to a new (or even intermediate) Python programmer, the answer would look something like this:

numbers = range(5)
output = [ ]
for number in numbers:
    output.append(number * number)
print(output)

Now, the thing is that this does work. (In my courses, I often use the phrase, “Unfortunately, this works.”) Often, when I talk about comprehensions, I talk about functional programming, the idea of immutable data structures, the idea that we don’t want to change things, and the benefits of thinking in terms of mapreduce.

But let’s ignore all of that, and ask a simpler question: If you were to give this problem to your accountant, how would they solve the problem?

Almost certainly, an accountant would fire up Excel, and put the numbers in a column:

A
-
0
1
2
3
4

Let’s assume that the above numbers are in the spreadsheet’s column A. The Excel user would, given this task, then tell Excel that column B should be calculated as A*A. And it would be done:

A  B
-  -
0  0
1  1
2  4
3  9
4  16

You could argue that the difference here is that Excel has a GUI, and Python doesn’t. But that’s missing the point. The real difference is that our accountant told Excel how to transform the first column into the second column, whereas our Python developer wrote a program that describe how to carry out that transformation.

We can think about this in a different way, too: Rather than solving the problem serially, as in the above for loop, the accountant is thinking in a parallel manner, applying a single expression to a large data set. The Excel user doesn’t care, or even know, the order in which the numbers are handed to the expression. The important thing is that the expression is applied once to each of the numbers, and that the final result appears in the correct order.

We might laugh at Excel, and dismiss its users as technical neophytes. And certainly, many users of Excel would deny that they possess serious programming chops. But this sort of thinking, which is so fundamental and natural to Excel users, is alien to many programmers. Which is a shame, because it allows us to express a very large number of ideas in a simple way.

To summarize this approach:

  • Think of your input as an iterable source of data
  • Think of what operation you want to apply to each element of that source
  • Get a new sequence out

That’s what the traditional “map” function does. Python does have a “map” function, but today, we typically use list comprehensions instead.

Let’s try to make this a bit more concrete, using the example that I used above: Let’s say that we have a list of five numbers, and we want to turn that list into a list of its squares. The list-comprehension syntax looks as follows:

[number * number for number in range(5) ]

Yikes. No wonder people are scared off by this syntax.  Let’s take the above syntax apart:

  • First of all, we’re going to get a list back. (It’s called a “list comprehension” for a reason.) That’s because of the square brackets, which are mandatory, and which tell Python what sort of object to create.
  • The data source will be “range(5),” which returns a list.
  • Each element in the data source will be assigned, in turn, to the iteration variable “number.”
  • We’ll invoke the operation “number * number” on each element of the data source.

In other words, we’re creating a new list, the elements of which are the result of applying our expression to each element of the source. This sounds suspiciously like what our accountant did above, using Excel: We’re telling Python what we want, and how to transform our source to that result. But how are things done internally? How is the list created? We neither know nor care.

List-comprehension syntax can be daunting for people to understand, in part because the order of the operations seems unusual. I’ve found that it can help to rewrite list comprehensions in the following way:

[number * number
 for number in range(5) ]

Yes, that’s right — I now spread list comprehensions across two lines; the first describes the operation I want to invoke, and the second line describes the data source. If this still seems unfamiliar, let’s try to bring it into a context with which you might have some experience:

[number * number           # SELECT
 for number in range(5) ]  # FROM

While they’re not directly equivalent, there are a fair number of similarities between a SELECT query in SQL, the placement of its SELECT expression and FROM clause, and our list comprehension.  The FROM clause in an SQL query describes our data source, which is typically going to be a table, but can also be a view or even the result of a function call. And the initial part of the SELECT is often the name of a column, but  can include function calls and operators.

On the one hand, the SELECT-FROM combination seems almost too simple to mention, in that you’re just retrieving a selected set of values from a data source.  On the other hand, such queries form the backbone of the database industry. In the same way, such functionality forms the backbone of many Python programs, iterating over a data structure, and plucking out part of it, transforming that part, and then returning a new list.

One of my favorite examples (and an exercise in my ebook, “Practice Makes Python“) is to take the /etc/passwd file used in Unix, and get the usernames contained within that file. /etc/passwd consists of one record per line, and the fields are separated by colons. Here are several lines from the /etc/passwd on my computer:

nobody:*:-2:-2::0:0:Unprivileged User:/var/empty:/usr/bin/false
root:*:0:0::0:0:System Administrator:/var/root:/bin/sh
daemon:*:1:1::0:0:System Services:/var/root:/usr/bin/false
_uucp:*:4:4::0:0:Unix to Unix Copy Protocol:/var/spool/uucp:/usr/sbin/uucico

We might normally think of a file as a collection of bytes, to which we give semantic meaning when we read it. But in Python, we’re encouraged to see a file as an ordered, iterable collection of lines of text. True, I can read from a file based on bytes, but it’s so common to want to read files by line that the language provides several constructs to do so.

We know that we can iterate over the lines of a file:

for line in open('/etc/passwd'):
    print(line)

This demonstrates that a file is iterable, which means that it can serve as a data source for a list comprehension. This means that the above code can be rewritten as:

[line
 for line in open('/etc/passwd')]

Again, the first line in our list comprehension represents the expression we want to apply to every element of our data source. In this case, the expression is just the line.  If we want to get the username from each of  these lines, we just need to apply the “split” method on the string, returning a list — and then retrieve index 0 from the resulting list.  For example:

[line.split(":")[0]
 for line in open('/etc/passwd')]

Again, we can think of it in terms of an SQL query:

SELECT username
FROM users

But of course, “username” in the above is a column name.  A more equivalent query to my list comprehension would be a “Users” table with an “info” column, queried as follows:

SELECT split_part(info, ':', 1)
FROM users;

Note that in this case, I’m using the built-in PostgreSQLsplit_part” operator to perform the equivalent operation to the str.split method in Python.

Remember that in the case of my SQL query, the result of a query always looks and acts like a table. The number and types of columns returned will depend on the number and types of expressions that I have in the SELECT  statement.  But the result set will have one or more columns, and zero or more rows.

In the same way, the result of a list comprehension is always going to be a list.  You can have whatever expression you want inside of the list comprehension; the expression represents one item in a list, not the list itself.

For example, let’s assume that I want to turn the usernames in /etc/passwd into a list of dictionaries. This doesn’t require a dictionary comprehension, which creates a single dictionary.  Rather, it requires a list  comprehension, in which the expression creates a dictionary.  Here’s a simple-minded such list comprehension:

[ {'name':line.split(":")[0]}
   for line in open('/etc/passwd')]

The above will work, in that it creates a list of dictionaries. And each dictionary has a single key-value pair.  But it seems a bit silly to do the above.  Rather, I’d probably want to have a dictionary containing the username and the numeric user ID, which is at index 2. I can then write:

[ {'name':line.split(":")[0], 'id':line.split(":")[2]}
for line in open('/etc/passwd')]

Again, we can think about this in terms of Excel, or even in terms of SQL: My query now produces a single column of results, but each column contains a text string. Or we can even say that the query produces two columns of results, which is not at all unusual in the world of SQL.

Let’s ignore the efficiency (or lack thereof) of invoking str.split twice in one comprehension: When I run this code on my Mac, it results in an exception, claiming that an index is out of range.

The reason is simple: I split each line into a list. But if there’s a line that doesn’t contain any : characters, it’ll be turned into a single-element list. I thus need to weed out any lines that won’t conform. Specifically, on my Mac at least, I need to remove any lines in /etc/passwd that are comments, meaning that they start with the ‘#’ character.

In the world of list comprehensions, I say the following:

[ {'name':line.split(":")[0], 'id':line.split(":")[2]}
for line in open('/etc/passwd')
if not line.startswith("#")]

Let’s extend our earlier SQL analogy further, adding the equivalent SQL syntax in comments after our Python code:

[ {'name':line.split(":")[0], 'id':line.split(":")[2]}    # SELECT
for line in open('/etc/passwd')                           # FROM
if not line.startswith("#")]                              # WHERE

Of course, when the first line of our comprehension becomes this long, it’s often a good idea to use a function, instead. And since the first line can be any legitimate Python expression, a function is often a good idea:

def get_user_info(line):
    name, passwd, id, rest = line.split(":", 3)   # max 4 fields
    return {'name':name, 'id':id}

[ get_user_info(line)             # SELECT
for line in open('/etc/passwd')   # FROM
if not line.startswith("#")]      # WHERE

A list comprehension thus gives you power similar to an SQL SELECT query — except that you’re not querying data in a table, but rather any object that conforms to Python’s iteration protocol, which includes a very  large number of built-in and custom-made objects.

Now, when would you want to use a list comprehension? And how does it differ from a for loop?

Using a list comprehension is appropriate whenever you want to transform data. That is, you have an iterable data source, and you want to create a new list whose elements are based on those of the data source. For  example, let’s assume that (for some reason) I want to find out how many times each character is used in /etc/passwd.  I can thus do the following, using collections.Counter:

from collections import Counter
counts = [Counter(line)
          for line in open('/etc/passwd')
          if not line.startswith("#")]

We know that “counts” is a list, because I used a list comprehension to create it. It is a list containing many Counter objects, one for each non-comment line in /etc/passwd. What if I want to find out what the most  popular character is in each line? I can modify my expression, asking the Counter object for the most common character:

counts = [Counter(line).most_common(1)
          for line in open('/etc/passwd')
          if not line.startswith("#")]

I can extend my expression even more, to get the most popular character from each line (inside of a two-element tuple in a one-element list):

counts = [Counter(line).most_common(1)[0][0]
          for line in open('/etc/passwd')
          if not line.startswith("#")]

And now I can find out how many times each most-popular character appears:

Counter([Counter(line).most_common(1)[0][0]
          for line in open('/etc/passwd')
          if not line.startswith("#")])

On my computer, the answer is:

Counter({':': 71, 'e': 4, 's': 1})

Meaning that in 71 non-comment lines, “:” is the most common, but in 4 lines it’s “e”, and in one line it’s “s”.  Now, could I have done this with a for loop?  Yes, of course — but because I’m dealing with iterables, and  because I’m using objects that work with such iterables, I can chain them together to get an answer in a way that doesn’t require me to tell Python how to do its job. I’m doing things like our accountant did, back at the  start of this article — I’m saying what I want, and letting Python do the hard work of dealing with this for me.

When would I use a for loop, then? The distinction is between whether you want to get a list back, and whether you want to execute a command a number of times.  If you want to build a list, and if it’s built on an iterable that already exists, then I’d say a list comprehension is almost certainly going the be the best bet.  But if you want to execute something a number of times without creating a list, then a comprehension is the a bad way to do it; you should use a “for” loop, instead.

It’s true that list comprehensions are faster than for loops. But most of the time, for loops are used for different things than list comprehensions. “for” loops shouldn’t be used when you want to turn one iterable structure into another; that’s for comprehensions. And you shouldn’t execute something (e.g., print) many times via a list comprehension, even if you can do so via a called function.  I’ve found that the dividing line between when to use a “for” loop, and when to use a comprehension, is clearly delineated in the minds of experienced Python developers, but very hazy among newcomers to the language, and to these ideas.

So, to summarize:

  • If you want to execute a command numerous times, use a “for” loop.
  • If you have an iterable, and want to create a new iterable, then a list comprehension is probably your best bet.
  • Building a list comprehension is sort of like working in Excel: You start with a set of data, and you create a new set of data. Any expression can be used to map from one to the other.  You don’t care about how Python does things behind the scenes; you just want to get your new data back.
  • A list comprehension consists of either two or three parts, which are often easier to understand if you put them on separate lines: (1) the expression, (2) the data source, and (3) an optional “if” statement.
  • These three lines are analogous to SQL’s SELECT, FROM, and WHERE clauses in a query.  And just as each of those (SELECT, FROM, and WHERE) can use arbitrary expressions, so too can Python’s list comprehensions use arbitrary expressions. A list comprehension will always return a list, though — just as a SELECT will always return a table-like result set.
  • Do you want to create a set, or perhaps a dictionary, rather than a list?  Then you can use a set comprehension or a dict comprehension. The idea is the same as everything I’ve said about list comprehensions, except that your result will be a single set or a single dictionary.

Do you find it difficult to work with list comprehensions?  If so, what’s hard for you about them?  And does the above help to make their use, and their syntax easier to remember?  I’m eager to hear your reactions, so that I can improve these explanations even further.

13 thoughts on “Want to understand Python’s comprehensions? Think in Excel or SQL.”

  1. Your article uses the SQL language analogies more consistently and convincingly than does your accountant analogy. Perhaps this article should be renamed “Want to understand Python’s comprehensions? Think like a SQL programmer.”

    1. I thought about this some more, and decided to change the title somewhat, to end with, “think in Excel or SQL.” I really do appreciate the feedback, and hope that this reflects the content a bit more closely.

      1. I really like the Excel example. Many Excel users use dynamic ranges within named Excel tables, which means they write a formula once and then Excel automatically copies it down for every row in the table. The map analogy, implementing one operation across all your data, works extremely well there. I will definitely cite this post when explaining Map/Reduce in the future

  2. Thank you, I think the sql analogy really helped me understand list comprehensions properly for the first time. Really great post.

  3. Hi Reuven, it would be great to expand on nested comprehensions as well since those thoroughly confused me. For example I needed to remove any list in a dictionary of list of lists that contained a number less than 0.5 in the third item in any given list. For example, if I have the following..

    mydict = { ‘file’: [ [ 1.00, 7.42, 0.25, 5.5, 1.25], [ 2.3, 5.2, 1.25, 7.44], [8.0, 4.4, 9.12, 8.12] ]}

    Above, I wanted to remove the 1st list from the list of lists in the dict since 0.25 is less than 0.5. Eventually, someone gave me a solution for this but it would be great if in the future you could expand on nested comprehensions as well.

    1. Funny you should mention this — I plan to write about nested comprehensions in the very near future, because they’re so ridiculously confusing, which makes them an attractive target for me. 🙂

  4. Hey Reuven thanks so much for writing this! I know I walked away with a deeper understanding of list comprehensions which I sorely needed!

    I do in fact think the article title change helps a good bit. The analogy that really clicked for me was the SQL one, and having that mentioned in the title.

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