Python Jumpstart by Building 10 Apps Transcripts
Chapter: App 9: Real Estate Analysis App
Lecture: Introduction to the Real Estate Data Miner App
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It's time for application number 9.
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This one I am calling the real estate data miner app.
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So what we are going to do we are going to take
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a whole bunch of historical real estate data
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feed it to this application, and be able to answer pretty interesting questions
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like what was the average price of a two bedroom house
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sold in this region during this time, things like that,
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so what it's going to look like- well,
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you'll see something like this, standard header,
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and it's going to load up a comma separate value,
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a csv file of historical real estate data
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and it will detect the header so we'll use this,
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basically define the columns that we'll use to answer our questions later,
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it can be somewhat dynamic with this,
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and then we can answer questions like what was the most expensive house sold period,
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well that's some kind of 4 bedroom, 3 bath house for almost million dollars in Wilton
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And the least expensive house was oh my gosh,
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like a house for a 1500 dollars, 3 bedroom 3 bath in Lincoln.
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Something is going on there, who knows the history of that one.
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But the average price was 234,000 dollars
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for 2.9 on average bedroom, 1.8 bath on average, right.
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And if we restrict ourselves to say well let's just talk about only 2 bedroom houses
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and give me some stats on that,
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well the average price was 165,000 dollars
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obviously two bedrooms in 2 bedroom house and 1.4 baths.
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So what are we going to focus on when we build this app.
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The primary thing we are going to look at is something called
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list comprehensions and something very closely related to them
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called generator expression.
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These two pythonic concepts allow you to take
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what would otherwise be loops and condense them down into much shorter,
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more concise types of set based operations,
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in some sense it will move from procedural programming to declarative programming.
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We also get to look again at the string and representation magic methods,
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string parsing, we'll specifically look at the csv file format
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but this concept could be applied to many different types of formats really,
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somewhat like we saw in app number 8,
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we'll be able to use these generator expressions and data pipelines
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similar to the way we did for generator methods.
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You can think of generator expressions as
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simplified inline generator methods if you will.
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Finally, we will get a chance to look at a challenge
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of writing code that has to run on both,
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on Python 2 and Python 3, as you saw,
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we are talking about averages and there is a nice statistics module
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that was introduced in Python 3.4, it is not available in Pyhton 2,
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so what will we do there? Well, you'll see that we can write the same basic code
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and just adjust our imports and make it work.
