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