python-mastery/Exercises/ex8_1.md

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Exercise 8.1

Objectives:

• Learn how to customize iteration using generators

Files Modified: structure.py

Files Created: follow.py

(a) A Simple Generator

If you ever find yourself wanting to customize iteration, you should always think generator functions. They’re easy to write—simply make a function that carries out the desired iteration logic and uses yield to emit values.

For example, try this generator that allows you to iterate over a range of numbers with fractional steps (something not supported by the range() builtin):

>>> def frange(start,stop,step):
        while start < stop:
            yield start
            start += step

>>> for x in frange(0, 2, 0.25):
        print(x, end=' ')

0 0.25 0.5 0.75 1.0 1.25 1.5 1.75
>>>

Iterating on a generator is a one-time operation. For example, here’s what happen if you try to iterate twice:

>>> f = frange(0, 2, 0.25)
>>> for x in f:
        print(x, end=' ')

0 0.25 0.5 0.75 1.0 1.25 1.5 1.75
>>> for x in f:
        print(x, end=' ')

>>>

If you want to iterate over the same sequence, you need to recreate the generator by calling frange() again. Alternative, you could package everything into a class:

>>> class FRange:
        def __init__(self, start, stop, step):
            self.start = start
            self.stop = stop
            self.step = step
        def __iter__(self):
            n = self.start
            while n < self.stop:
                yield n
                n += self.step

>>> f = FRange(0, 2, 0.25)
>>> for x in f:
        print(x, end=' ')

0 0.25 0.5 0.75 1.0 1.25 1.5 1.75
>>> for x in f:
        print(x, end=' ')

0 0.25 0.5 0.75 1.0 1.25 1.5 1.75
>>>

(b) Adding Iteration to Objects

If you’ve created a custom class, you can make it support iteration by defining an __iter__() special method. __iter__() returns an iterator as a result. As shown in the previous example, an easy way to do it is to define __iter__() as a generator.

In earlier exercises, you defined a Structure base class. Add an __iter__() method to this class that produces the attribute values in order. For example:

class Structure(metaclass=StructureMeta):
    ...
    def __iter__(self):
        for name in self._fields:
            yield getattr(self, name)
    ...

Once you’ve done this, you should be able to iterate over the instance attributes like this:

>>> from stock import Stock
>>> s = Stock('GOOG', 100, 490.1)
>>> for val in s:
        print(val)
GOOG
100
490.1
>>>

(c) The Surprising Power of Iteration

Python uses iteration in ways you might not expect. Once you’ve added __iter__() to the Structure class, you’ll find that it is easy to do all sorts of new operations. For example, conversions to sequences and unpacking:

>>> s = Stock('GOOG', 100, 490.1)
>>> list(s)
['GOOG', 100, 490.1]
>>> tuple(s)
('GOOG', 100, 490.1)
>>> name, shares, price = s
>>> name
'GOOG'
>>> shares
100
>>> price
490.1
>>> 

While we’re at it, we can now add a comparison operator to our Structure class:

# structure.py
class Structure(metaclass=StructureMeta):
    ...
    def __eq__(self, other):
        return isinstance(other, type(self)) and tuple(self) == tuple(other)
    ...

You should now be able to compare objects:

>>> a = Stock('GOOG', 100, 490.1)
>>> b = Stock('GOOG', 100, 490.1)
>>> a == b
True
>>>

Try running your teststock.py unit tests again. Everything should be passing now. Excellent.

(d) Monitoring a streaming data source

Generators can also be a useful way to simply produce a stream of data. In this part, we’ll explore this idea by writing a generator to watch a log file. To start, follow the next instructions carefully.

The program Data/stocksim.py is a program that simulates stock market data. As output, the program constantly writes real-time data to a file stocklog.csv. In a command window (not IDLE) go into the Data/ directory and run this program:

% python3 stocksim.py

If you are on Windows, just locate the stocksim.py program and double-click on it to run it. Now, forget about this program (just let it run). Again, just let this program run in the background—it will run for several hours (you shouldn’t need to worry about it).

Once the above program is running, let’s write a little program to open the file, seek to the end, and watch for new output. Create a file follow.py and put this code in it:

# follow.py
import os
import time
f = open('Data/stocklog.csv')
f.seek(0, os.SEEK_END)   # Move file pointer 0 bytes from end of file

while True:
    line = f.readline()
    if line == '':
        time.sleep(0.1)   # Sleep briefly and retry
        continue
    fields = line.split(',')
    name = fields[0].strip('"')
    price = float(fields[1])
    change = float(fields[4])
    if change < 0:
        print('%10s %10.2f %10.2f' % (name, price, change))

If you run the program, you’ll see a real-time stock ticker. Under the covers, this code is kind of like the Unix tail -f command that’s used to watch a log file.

Note: The use of the readline() method in this example is somewhat unusual in that it is not the usual way of reading lines from a file (normally you would just use a for-loop). However, in this case, we are using it to repeatedly probe the end of the file to see if more data has been added (readline() will either return new data or an empty string).

If you look at the code carefully, the first part of the code is producing lines of data whereas the statements at the end of the while loop are consuming the data. A major feature of generator functions is that you can move all of the data production code into a reusable function.

Modify the code so that the file-reading is performed by a generator function follow(filename). Make it so the following code works:

>>> for line in follow('Data/stocklog.csv'):
          print(line, end='')
   
... Should see lines of output produced here ...

Modify the stock ticker code so that it looks like this:

for line in follow('Data/stocklog.csv'):
    fields = line.split(',')
    name = fields[0].strip('"')
    price = float(fields[1])
    change = float(fields[4])
    if change < 0:
        print('%10s %10.2f %10.2f' % (name, price, change))

Discussion

Something very powerful just happened here. You moved an interesting iteration pattern (reading lines at the end of a file) into its own little function. The follow() function is now this completely general purpose utility that you can use in any program. For example, you could use it to watch server logs, debugging logs, and other similar data sources. That’s kind of cool.

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