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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0:00 It's time for application number 9.
0:03 This one I am calling the real estate data miner app.
0:06 So what we are going to do we are going to take
0:08 a whole bunch of historical real estate data
0:11 feed it to this application, and be able to answer pretty interesting questions
0:14 like what was the average price of a two bedroom house
0:18 sold in this region during this time, things like that,
0:21 so what it's going to look like- well,
0:24 you'll see something like this, standard header,
0:27 and it's going to load up a comma separate value,
0:29 a csv file of historical real estate data
0:32 and it will detect the header so we'll use this,
0:36 basically define the columns that we'll use to answer our questions later,
0:40 it can be somewhat dynamic with this,
0:42 and then we can answer questions like what was the most expensive house sold period,
0:46 well that's some kind of 4 bedroom, 3 bath house for almost million dollars in Wilton
0:50 And the least expensive house was oh my gosh,
0:54 like a house for a 1500 dollars, 3 bedroom 3 bath in Lincoln.
0:58 Something is going on there, who knows the history of that one.
1:01 But the average price was 234,000 dollars
1:03 for 2.9 on average bedroom, 1.8 bath on average, right.
1:09 And if we restrict ourselves to say well let's just talk about only 2 bedroom houses
1:13 and give me some stats on that,
1:15 well the average price was 165,000 dollars
1:18 obviously two bedrooms in 2 bedroom house and 1.4 baths.
1:23 So what are we going to focus on when we build this app.
1:26 The primary thing we are going to look at is something called
1:28 list comprehensions and something very closely related to them
1:32 called generator expression.
1:34 These two pythonic concepts allow you to take
1:37 what would otherwise be loops and condense them down into much shorter,
1:40 more concise types of set based operations,
1:43 in some sense it will move from procedural programming to declarative programming.
1:47 We also get to look again at the string and representation magic methods,
1:50 string parsing, we'll specifically look at the csv file format
1:55 but this concept could be applied to many different types of formats really,
1:59 somewhat like we saw in app number 8,
2:02 we'll be able to use these generator expressions and data pipelines
2:05 similar to the way we did for generator methods.
2:08 You can think of generator expressions as
2:11 simplified inline generator methods if you will.
2:15 Finally, we will get a chance to look at a challenge
2:17 of writing code that has to run on both,
2:20 on Python 2 and Python 3, as you saw,
2:23 we are talking about averages and there is a nice statistics module
2:25 that was introduced in Python 3.4, it is not available in Pyhton 2,
2:29 so what will we do there? Well, you'll see that we can write the same basic code
2:32 and just adjust our imports and make it work.