Move from Excel to Python with Pandas Transcripts
Chapter: Appendix: Python language concepts
Lecture: Concept: list comprehensions

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0:01 Python has a great declarative way to process a set of items
0:05 and either turn it into a list, a dictionary, a set or a generator.
0:09 Let's look at the list version through an example.
0:13 Here we have some get_active_customers method,
0:16 maybe it goes to a database, maybe it just goes to some data structure,
0:19 it doesn't really matter, but it comes back with an iterable set of users,
0:23 so we could loop over all of the users, using a "" loop to find the users
0:29 who have paid today and get their usernames and put that into a list.
0:33 So what we do is we create a list, some name paying usernames
0:38 and we'd "" over those to loop over all of them and then we do a test,
0:41 we'd say if that particular user's last purchase was today
0:45 then append to their username to this paying usernames list.
0:50 And then in the end, we'd have a list, which is all the usernames
0:53 of the customers you bought something from us today.
0:56 This would be an imperative users' search,
0:59 an imperative style of programming, where you explicitly say all the steps,
1:02 let's see how we could do this instead with the list comprehension.
1:05 Here you'll see many of the same elements,
1:09 and it looks like we are declaring a list, so [ ] in Python means declare an empty list,
1:14 but there is stuff in the middle.
1:16 The way you read this you kind of got to piece it together,
1:18 maybe top to bottom is not necessarily the best way to put this all together
1:22 but let's go top to bottom for a minute and then I'll pull out the pieces for you.
1:25 So, we are going to get the name of the user,
1:27 and we are going to later introduce a variable called "u",
1:30 which is the individual user for the set we are going through, so we'd say,
1:33 that's like our projection that we want, and there is a "" statement,
1:37 like we had before, where we get the active customers
1:40 and we are going to process them,
1:42 and then there is some kind of test whether or not that particular user should be in this set.
1:46 So, we set the source, that's going to be out get_active_customers
1:50 and we are going to express that we are iterating over that for "u" in that set
1:55 and "u" declares the local variable that we are going to work with,
1:58 we are going to filter on that with an "if" test,
2:00 and finally we are going to do some kind of projection,
2:03 we could just say "u" to get all the users, here we want all the usernames
2:06 so we say Now, there are multiple structures like this in Python,
2:11 we could have parenthesis that would generate a generator,
2:13 but as I said before, [ ] represents list, and so when you have the [ ] here,
2:18 you know what is going to come out is a list, and this is a list comprehension.
2:23 Once you get used to it, you'll find this style of programming is a little cleaner
2:26 and a little more concise.
2:29 It's also different in another important way,
2:31 because this can be just part of a larger expression,
2:34 this could be say in inline argument to a method you are calling.
2:38 Or, you could chain it together with other comprehensions,
2:42 or other types of processing.
2:44 The imperative style of programming required separate language structures
2:48 that required their own blocks,
2:51 so you can't really compose "" loops but you can compose these comprehensions
2:54 which makes then really useful in places the "" loop wouldn't be.