[ Index | Exercise 8.1 | Exercise 8.3 ]
Objectives:
- Using generators to set up processing pipelines
Files Created: ticker.py
Note
For this exercise the stocksim.py program should still be
running in the background. You're going to use the follow()
function you wrote in the previous exercise.
A major power of generators is that they allow you to create programs that set up processing pipelines--much like pipes on Unix systems. Experiment with this concept by performing these steps:
>>> from follow import follow
>>> import csv
>>> lines = follow('Data/stocklog.csv')
>>> rows = csv.reader(lines)
>>> for row in rows:
print(row)
['BA', '98.35', '6/11/2007', '09:41.07', '0.16', '98.25', '98.35', '98.31', '158148']
['AA', '39.63', '6/11/2007', '09:41.07', '-0.03', '39.67', '39.63', '39.31', '270224']
['XOM', '82.45', '6/11/2007', '09:41.07', '-0.23', '82.68', '82.64', '82.41', '748062']
['PG', '62.95', '6/11/2007', '09:41.08', '-0.12', '62.80', '62.97', '62.61', '454327']
...Well, that's interesting. What you're seeing here is that the output of the
follow() function has been piped into the csv.reader() function and we're
now getting a sequence of split rows.
In a file ticker.py, define the following class (using your structure code from before) and set up
a pipeline:
# ticker.py
from structure import Structure
class Ticker(Structure):
name = String()
price = Float()
date = String()
time = String()
change = Float()
open = Float()
high = Float()
low = Float()
volume = Integer()
if __name__ == '__main__':
from follow import follow
import csv
lines = follow('Data/stocklog.csv')
rows = csv.reader(lines)
records = (Ticker.from_row(row) for row in rows)
for record in records:
print(record)When you run this, you should see some output like this:
Ticker('IBM',103.53,'6/11/2007','09:53.59',0.46,102.87,103.53,102.77,541633)
Ticker('MSFT',30.21,'6/11/2007','09:54.01',0.16,30.05,30.21,29.95,7562516)
Ticker('AA',40.01,'6/11/2007','09:54.01',0.35,39.67,40.15,39.31,576619)
Ticker('T',40.1,'6/11/2007','09:54.08',-0.16,40.2,40.19,39.87,1312959)
Oh, you can do better than that. Let's plug this into your table generation code. Change the program to the following:
# ticker.py
...
if __name__ == '__main__':
from follow import follow
import csv
from tableformat import create_formatter, print_table
formatter = create_formatter('text')
lines = follow('Data/stocklog.csv')
rows = csv.reader(lines)
records = (Ticker.from_row(row) for row in rows)
negative = (rec for rec in records if rec.change < 0)
print_table(negative, ['name','price','change'], formatter)This should produce some output that looks like this:
name price change
---------- ---------- ----------
C 53.12 -0.21
UTX 70.04 -0.19
AXP 62.86 -0.18
MMM 85.72 -0.22
MCD 51.38 -0.03
WMT 49.85 -0.23
KO 51.6 -0.07
AIG 71.39 -0.14
PG 63.05 -0.02
HD 37.76 -0.19
Now, THAT is crazy! And pretty awesome.
Discussion
Some lessons learned: You can create various generator functions and chain them together to perform processing involving data-flow pipelines.
A good mental model for generator functions might be Lego blocks. You can make a collection of small iterator patterns and start stacking them together in various ways. It can be an extremely powerful way to program.
[ Solution | Index | Exercise 8.1 | Exercise 8.3 ]
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