Python Jumpstart by Building 10 Apps Transcripts
Chapter: App 9: Real Estate Analysis App
Lecture: CSV Processing with the CSV module
0:00 Ok, so that was kind of a cool start, but let's just save this here
0:04 and call this load file basic or something like that,
0:07 I'll leave it commented you can have that if you like,
0:10 but now what we are really going to do is something a little bit better,
0:14 we are going to go over here and use a module called csv,
0:18 so there is a couple of formats that have built in support in Python,
0:21 one of them is comma separated values, another one is Json,
0:25 Json is actually my favorite text based format, and we also have xml.
0:30 So we can actually import this module up here at the top,
0:34 I just wrote import csv at the top as you know,
0:37 and then I can come over here and there is a reader,
0:39 the most natural thing would be to say create me a reader,
0:42 and what do we need to do, we need to pass an iterable,
0:45 well, what is that iterable, here is one, fin,
0:49 so we want to get the header out, like so,
0:54 and we'll capture the reader,
0:57 like so and then we can say for row in reader I can just print row,
1:02 let's see what happens here.
1:05 Perfect, look at that,
1:07 it's already done the parsing and the reader has a lot of support
1:10 for things like the delimiter and we could say like the delimiter is this
1:14 and we could say what the escape character is the quote character to escape
1:19 and things that might have a comma in them, like names and so on,
1:23 so now you can see if we use this delimiter nothing gets separated,
1:26 but if we use commas, boom, now it's separated again, right,
1:30 these are lists of individual bits of data, split on that,
1:33 just kind of like we did in our own code.
1:36 However, what we get back if I just print out the type here,
1:38 with the comma how about that, is a list and that still means
1:45 if I want to work with the bed I have to say something like row of 4
1:51 and I need to know that the fourth item is the fourth column is beds,
1:57 or whatever right, 0, 1, 2, 3, 4, but if somebody changes this over time
2:02 and they add another column like country or something up front, all those indexes break,
2:07 our code is super fragile and we have to know like oh yeah,
2:11 what if 4 means beds, of course it does, right,
2:14 that means nothing to humans,
2:16 so we can come over here and actually do better than this,
2:18 we can come over and say reader=csv.dict reader.
2:26 Now, dict reader doesn't return rows it returns a dictionary for each row,
2:31 so instead of using numbers to find the data, we use the names,
2:35 what names- the column names,
2:37 so we come over here and give it the same iterable
2:40 and then let's loop over these again,
2:46 now notice we have a dictionary and the type is residential, the zip is this,
2:49 the latitude is that, this is so much easier, let me show you how to use it really quick,
2:54 then we'll take a moment and look at the concept of dictionaries in more depth.
2:58 So instead of using numbers, we can use names
3:00 so if I just wanted to print out bed count,
3:03 that would be super easy now and I don't care about the order or how many rows
3:07 or how it revolves over time, I just say row of beds,
3:13 bed count 3, bed count 4, beautiful,
3:16 and you can see over here in our data there are those.
3:19 So this dict reader is really the way
3:20 I recommend you work with comma separated value files.
3:23 It gives you a lot more durability you don't have to know that like
3:26 the fifth element is actually the bed count and if that changes it's fragile,
3:31 all that sort of stuff, it just detects it and then builds up these dictionaries for you.
3:35 So what are dictionaries, let's take a moment and look at this core concept.