Interaction. Assuming Python is configured properly, the interaction should look something like the following (you can run this any way you like (in IDLE, from a shell prompt, and so on):

% python
...copyright information lines...
>>> "Hello World!"
'Hello World!'
>>>                 # Use Ctrl-D or Ctrl-Z to exit, or close window

Programs. Your code (i.e., module) file module1.py and the operating system shell interactions should look like this:

print('Hello module world!')

% python module1.py
Hello module world!

Again, feel free to run this other ways—by clicking the file’s icon, by using IDLE’s Run→Run Module menu option, and so on.

Modules. The following interaction listing illustrates running a module file by importing it:

% python
>>> import module1
Hello module world!
>>>

Remember that you will need to reload the module to run it again without stopping and restarting the interpreter. The question about moving the file to a different directory and importing it again is a trick question: if Python generates a module1.pyc file in the original directory, it uses that when you import the module, even if the source code (.py) file has been moved to a directory not in Python’s search path. The .pyc file is written automatically if Python has access to the source file’s directory; it contains the compiled byte code version of a module. See Chapter 3 for more on modules.

Scripts. Assuming your platform supports the #! trick, your solution will look like the following (although your #! line may need to list another path on your machine):

#!/usr/local/bin/python          (or #!/usr/bin/env python)
print('Hello module world!')
% chmod +x module1.py

% module1.py
Hello module world!

Errors. The following interaction (run in Python 3.0) demonstrates the sorts of error messages you’ll get when you complete this exercise. Really, you’re triggering Python exceptions; the default exception-handling behavior terminates the running Python program and prints an error message and stack trace on the screen The stack trace shows where you were in a program when the exception occurred (if function calls are active when the error happens, the “Traceback” section displays all active call levels). In Part VII, you will learn that you can catch exceptions using try statements and process them arbitrarily; you’ll also see there that Python includes a full-blown source code debugger for special error-detection requirements. For now, notice that Python gives meaningful messages when programming errors occur, instead of crashing silently:

% python
>>> 2 ** 500
32733906078961418700131896968275991522166420460430647894832913680961337964046745
54883270092325904157150886684127560071009217256545885393053328527589376
>>>
>>> 1 / 0
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ZeroDivisionError: int division or modulo by zero
>>>
>>> spam
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
NameError: name 'spam' is not defined

Breaks and cycles. When you type this code:

L = [1, 2]
L.append(L)

you create a cyclic data structure in Python. In Python releases before 1.5.1, the Python printer wasn’t smart enough to detect cycles in objects, and it would print an unending stream of [1, 2, [1, 2, [1, 2, [1, 2, and so on, until you hit the break-key combination on your machine (which, technically, raises a keyboard-interrupt exception that prints a default message). Beginning with Python 1.5.1, the printer is clever enough to detect cycles and prints [[...]] instead to let you know that it has detected a loop in the object’s structure and avoided getting stuck printing forever.

The reason for the cycle is subtle and requires information you will glean in Part II, so this is something of a preview. But in short, assignments in Python always generate references to objects, not copies of them. You can think of objects as chunks of memory and of references as implicitly followed pointers. When you run the first assignment above, the name L becomes a named reference to a two-item list object—a pointer to a piece of memory. Python lists are really arrays of object references, with an append method that changes the array in-place by tacking on another object reference at the end. Here, the append call adds a reference to the front of L at the end of L, which leads to the cycle illustrated in Figure B-1: a pointer at the end of the list that points back to the front of the list.

Besides being printed specially, as you’ll learn in Chapter 6 cyclic objects must also be handled specially by Python’s garbage collector, or their space will remain unreclaimed even when they are no longer in use. Though rare in practice, in some programs that traverse arbitrary objects or structures you might have to detect such cycles yourself by keeping track of where you’ve been to avoid looping. Believe it or not, cyclic data structures can sometimes be useful, despite their special-case printing.

The basics. Here are the sorts of results you should get, along with a few comments about their meaning. Again, note that ; is used in a few of these to squeeze more than one statement onto a single line (the ; is a statement separator), and commas build up tuples displayed in parentheses. Also keep in mind that the / division result near the top differs in Python 2.6 and 3.0 (see Chapter 5 for details), and the list wrapper around dictionary method calls is needed to display results in 3.0, but not 2.6 (see Chapter 8):

# Numbers

>>> 2 ** 16                           # 2 raised to the power 16
65536
>>> 2 / 5, 2 / 5.0                    # Integer / truncates in 2.6, but not 3.0
(0.40000000000000002, 0.40000000000000002)

# Strings

>>> "spam" + "eggs"                   # Concatenation
'spameggs'
>>> S = "ham"
>>> "eggs " + S
'eggs ham'
>>> S * 5                             # Repetition
'hamhamhamhamham'
>>> S[:0]                             # An empty slice at the front -- [0:0]
''                                    # Empty of same type as object sliced

>>> "green %s and %s" % ("eggs", S)   # Formatting
'green eggs and ham'
>>> 'green {0} and {1}'.format('eggs', S)
'green eggs and ham'

# Tuples

>>> ('x',)[0]                         # Indexing a single-item tuple
'x'
>>> ('x', 'y')[1]                     # Indexing a 2-item tuple
'y'

# Lists

>>> L = [1,2,3] + [4,5,6]             # List operations
>>> L, L[:], L[:0], L[-2], L[-2:]
([1, 2, 3, 4, 5, 6], [1, 2, 3, 4, 5, 6], [], 5, [5, 6])
>>> ([1,2,3]+[4,5,6])[2:4]
[3, 4]
>>> [L[2], L[3]]                      # Fetch from offsets; store in a list
[3, 4]
>>> L.reverse(); L                    # Method: reverse list in-place
[6, 5, 4, 3, 2, 1]
>>> L.sort(); L                       # Method: sort list in-place
[1, 2, 3, 4, 5, 6]
>>> L.index(4)                        # Method: offset of first 4 (search)
3

# Dictionaries

>>> {'a':1, 'b':2}['b']               # Index a dictionary by key
2
>>> D = {'x':1, 'y':2, 'z':3}
>>> D['w'] = 0                        # Create a new entry
>>> D['x'] + D['w']
1
>>> D[(1,2,3)] = 4                    # A tuple used as a key (immutable)

>>> D
{'w': 0, 'z': 3, 'y': 2, (1, 2, 3): 4, 'x': 1}

>>> list(D.keys()), list(D.values()), (1,2,3) in D         # Methods, key test
(['w', 'z', 'y', (1, 2, 3), 'x'], [0, 3, 2, 4, 1], True)

# Empties

>>> [[]], ["",[],(),{},None]          # Lots of nothings: empty objects
([[]], ['', [], (), {}, None])

Indexing and slicing. Indexing out of bounds (e.g., L[4]) raises an error; Python always checks to make sure that all offsets are within the bounds of a sequence.

On the other hand, slicing out of bounds (e.g., L[-1000:100]) works because Python scales out-of-bounds slices so that they always fit (the limits are set to zero and the sequence length, if required).

Extracting a sequence in reverse, with the lower bound greater than the higher bound (e.g., L[3:1]), doesn’t really work. You get back an empty slice ([ ]) because Python scales the slice limits to make sure that the lower bound is always less than or equal to the upper bound (e.g., L[3:1] is scaled to L[3:3], the empty insertion point at offset 3). Python slices are always extracted from left to right, even if you use negative indexes (they are first converted to positive indexes by adding the sequence length). Note that Python 2.3’s three-limit slices modify this behavior somewhat. For instance, L[3:1:-1] does extract from right to left:

>>> L = [1, 2, 3, 4]
>>> L[4]
Traceback (innermost last):
  File "<stdin>", line 1, in ?
IndexError: list index out of range
>>> L[-1000:100]
[1, 2, 3, 4]
>>> L[3:1]
[]
>>> L
[1, 2, 3, 4]
>>> L[3:1] = ['?']
>>> L
[1, 2, 3, '?', 4]

Indexing, slicing, and del. Your interaction with the interpreter should look something like the following code. Note that assigning an empty list to an offset stores an empty list object there, but assigning an empty list to a slice deletes the slice. Slice assignment expects another sequence, or you’ll get a type error; it inserts items inside the sequence assigned, not the sequence itself:

>>> L = [1,2,3,4]
>>> L[2] = []
>>> L
[1, 2, [], 4]
>>> L[2:3] = []
>>> L
[1, 2, 4]
>>> del L[0]
>>> L
[2, 4]
>>> del L[1:]
>>> L
[2]
>>> L[1:2] = 1
Traceback (innermost last):
  File "<stdin>", line 1, in ?
TypeError: illegal argument type for built-in operation

Tuple assignment. The values of X and Y are swapped. When tuples appear on the left and right of an assignment symbol (=), Python assigns objects on the right to targets on the left according to their positions. This is probably easiest to understand by noting that the targets on the left aren’t a real tuple, even though they look like one; they are simply a set of independent assignment targets. The items on the right are a tuple, which gets unpacked during the assignment (the tuple provides the temporary assignment needed to achieve the swap effect):

>>> X = 'spam'
>>> Y = 'eggs'
>>> X, Y = Y, X
>>> X
'eggs'
>>> Y
'spam'

Dictionary keys. Any immutable object can be used as a dictionary key, including integers, tuples, strings, and so on. This really is a dictionary, even though some of its keys look like integer offsets. Mixed-type keys work fine, too:

>>> D = {}
>>> D[1] = 'a'
>>> D[2] = 'b'
>>> D[(1, 2, 3)] = 'c'
>>> D
{1: 'a', 2: 'b', (1, 2, 3): 'c'}

Dictionary indexing. Indexing a nonexistent key (D['d']) raises an error; assigning to a nonexistent key (D['d']='spam') creates a new dictionary entry. On the other hand, out-of-bounds indexing for lists raises an error too, but so do out-of-bounds assignments. Variable names work like dictionary keys; they must have already been assigned when referenced, but they are created when first assigned. In fact, variable names can be processed as dictionary keys if you wish (they’re made visible in module namespace or stack-frame dictionaries):

>>> D = {'a':1, 'b':2, 'c':3}
>>> D['a']
1
>>> D['d']
Traceback (innermost last):
  File "<stdin>", line 1, in ?
KeyError: d
>>> D['d'] = 4
>>> D
{'b': 2, 'd': 4, 'a': 1, 'c': 3}
>>>
>>> L = [0, 1]
>>> L[2]
Traceback (innermost last):
  File "<stdin>", line 1, in ?
IndexError: list index out of range
>>> L[2] = 3
Traceback (innermost last):
  File "<stdin>", line 1, in ?
IndexError: list assignment index out of range

Generic operations. Question answers:

String indexing. This is a bit of a trick question—Because strings are collections of one-character strings, every time you index a string, you get back a string that can be indexed again. S[0][0][0][0][0] just keeps indexing the first character over and over. This generally doesn’t work for lists (lists can hold arbitrary objects) unless the list contains strings:

>>> S = "spam"
>>> S[0][0][0][0][0]
's'
>>> L = ['s', 'p']
>>> L[0][0][0]
's'

Immutable types. Either of the following solutions works. Index assignment doesn’t, because strings are immutable:

>>> S = "spam"
>>> S = S[0] + 'l' + S[2:]
>>> S
'slam'
>>> S = S[0] + 'l' + S[2] + S[3]
>>> S
'slam'

Nesting. Here is a sample:

>>> me = {'name':('John', 'Q', 'Doe'), 'age':'?', 'job':'engineer'}
>>> me['job']
'engineer'
>>> me['name'][2]
'Doe'

Files. Here’s one way to create and read back a text file in Python (ls is a Unix command; use dir on Windows):

# File: maker.py
file = open('myfile.txt', 'w')
file.write('Hello file world!
')        # Or: open().write()
file.close()                             # close not always needed

# File: reader.py
file = open('myfile.txt')                # 'r' is default open mode
print(file.read())                       # Or print(open().read())

% python maker.py
% python reader.py
Hello file world!

% ls -l myfile.txt
-rwxrwxrwa   1 0        0             19 Apr 13 16:33 myfile.txt

Coding basic loops. As you work through this exercise, you’ll wind up with code that looks like the following:

>>> S = 'spam'
>>> for c in S:
...     print(ord(c))
...
115
112
97
109

>>> x = 0
>>> for c in S: x += ord(c)             # Or: x = x + ord(c)
...
>>> x
433

>>> x = []
>>> for c in S: x.append(ord(c))
...
>>> x
[115, 112, 97, 109]

>>> list(map(ord, S))                   # list() required in 3.0, not 2.6
[115, 112, 97, 109]

Backslash characters. The example prints the bell character (a) 50 times; assuming your machine can handle it, and when it’s run outside of IDLE, you may get a series of beeps (or one sustained tone, if your machine is fast enough). Hey—I warned you.

Sorting dictionaries. Here’s one way to work through this exercise (see Chapter 8 or Chapter 14 if this doesn’t make sense). Remember, you really do have to split up the keys and sort calls like this because sort returns None. In Python 2.2 and later, you can iterate through dictionary keys directly without calling keys (e.g., for key in D:), but the keys list will not be sorted like it is by this code. In more recent Pythons, you can achieve the same effect with the sorted built-in, too:

>>> D = {'a':1, 'b':2, 'c':3, 'd':4, 'e':5, 'f':6, 'g':7}
>>> D
{'f': 6, 'c': 3, 'a': 1, 'g': 7, 'e': 5, 'd': 4, 'b': 2}
>>>
>>> keys = list(D.keys())              # list() required in 3.0, not in 2.6
>>> keys.sort()
>>> for key in keys:
...     print(key, '=>', D[key])
...
a => 1
b => 2
c => 3
d => 4
e => 5
f => 6
g => 7

>>> for key in sorted(D):              # Better, in more recent Pythons
...     print(key, '=>', D[key])

Program logic alternatives. Here’s some sample code for the solutions. For step e, assign the result of 2 ** X to a variable outside the loops of steps a and b, and use it inside the loop. Your results may vary a bit; this exercise is mostly designed to get you playing with code alternatives, so anything reasonable gets full credit:

# a

L = [1, 2, 4, 8, 16, 32, 64]
X = 5

i = 0
while i < len(L):
    if 2 ** X == L[i]:
        print('at index', i)
        break
    i += 1
else:
    print(X, 'not found')


# b

L = [1, 2, 4, 8, 16, 32, 64]
X = 5

for p in L:
    if (2 ** X) == p:
        print((2 ** X), 'was found at', L.index(p))
        break
else:
    print(X, 'not found')


# c

L = [1, 2, 4, 8, 16, 32, 64]
X = 5

if (2 ** X) in L:
    print((2 ** X), 'was found at', L.index(2 ** X))
else:
    print(X, 'not found')


# d

X = 5
L = []
for i in range(7): L.append(2 ** i)
print(L)

if (2 ** X) in L:
    print((2 ** X), 'was found at', L.index(2 ** X))
else:
    print(X, 'not found')


# f

X = 5
L = list(map(lambda x: 2**x, range(7)))      # or [2**x for x in range(7)]
print(L)                                     # list() to print all in 3.0, not 2.6

if (2 ** X) in L:
    print((2 ** X), 'was found at', L.index(2 ** X))
else:
    print(X, 'not found')

The basics. There’s not much to this one, but notice that using print (and hence your function) is technically a polymorphic operation, which does the right thing for each type of object:

% python
>>> def func(x): print(x)
...
>>> func("spam")
spam
>>> func(42)
42
>>> func([1, 2, 3])
[1, 2, 3]
>>> func({'food': 'spam'})
{'food': 'spam'}

Arguments. Here’s a sample solution. Remember that you have to use print to see results in the test calls because a file isn’t the same as code typed interactively; Python doesn’t normally echo the results of expression statements in files:

def adder(x, y):
    return x + y

print(adder(2, 3))
print(adder('spam', 'eggs'))
print(adder(['a', 'b'], ['c', 'd']))

% python mod.py
5
spameggs
['a', 'b', 'c', 'd']

varargs. Two alternative adder functions are shown in the following file, adders.py. The hard part here is figuring out how to initialize an accumulator to an empty value of whatever type is passed in. The first solution uses manual type testing to look for an integer, and an empty slice of the first argument (assumed to be a sequence) if the argument is determined not to be an integer. The second solution uses the first argument to initialize and scan items 2 and beyond, much like one of the min function variants shown in Chapter 18.

The second solution is better. Both of these assume all arguments are of the same type, and neither works on dictionaries (as we saw in Part II, + doesn’t work on mixed types or dictionaries). You could add a type test and special code to allow dictionaries, too, but that’s extra credit.

def adder1(*args):
    print('adder1', end=' ')
    if type(args[0]) == type(0):              # Integer?
         sum = 0                              # Init to zero
    else:                                     # else sequence:
         sum = args[0][:0]                    # Use empty slice of arg1
    for arg in args:
        sum = sum + arg
    return sum

def adder2(*args):
    print('adder2', end=' ')
    sum = args[0]                             # Init to arg1
    for next in args[1:]:
        sum += next                           # Add items 2..N
    return sum

for func in (adder1, adder2):
    print(func(2, 3, 4))
    print(func('spam', 'eggs', 'toast'))
    print(func(['a', 'b'], ['c', 'd'], ['e', 'f']))

% python adders.py
adder1 9
adder1 spameggstoast
adder1 ['a', 'b', 'c', 'd', 'e', 'f']
adder2 9
adder2 spameggstoast
adder2 ['a', 'b', 'c', 'd', 'e', 'f']

Keywords. Here is my solution to the first and second parts of this exercise (coded in the file mod.py). To iterate over keyword arguments, use the **args form in the function header and use a loop (e.g., for x in args.keys(): use args[x]), or use args.values() to make this the same as summing *args positionals:

def adder(good=1, bad=2, ugly=3):
    return good + bad + ugly

print(adder())
print(adder(5))
print(adder(5, 6))
print(adder(5, 6, 7))
print(adder(ugly=7, good=6, bad=5))

% python mod.py
6
10
14
18
18


# Second part solutions

def adder1(*args):                  # Sum any number of positional args
    tot = args[0]
    for arg in args[1:]:
        tot += arg
    return tot

def adder2(**args):                 # Sum any number of keyword args
    argskeys = list(args.keys())    # list needed in 3.0!
    tot = args[argskeys[0]]
    for key in argskeys[1:]:
        tot += args[key]
    return tot

def adder3(**args):                 # Same, but convert to list of values
    args = list(args.values())      # list needed to index in 3.0!
    tot = args[0]
    for arg in args[1:]:
        tot += arg
    return tot

def adder4(**args):                 # Same, but reuse positional version
    return adder1(*args.values())

print(adder1(1, 2, 3),       adder1('aa', 'bb', 'cc'))
print(adder2(a=1, b=2, c=3), adder2(a='aa', b='bb', c='cc'))
print(adder3(a=1, b=2, c=3), adder3(a='aa', b='bb', c='cc'))
print(adder4(a=1, b=2, c=3), adder4(a='aa', b='bb', c='cc'))

(and 6.) Here are my solutions to exercises 5 and 6 (file dicts.py). These are just coding exercises, though, because Python 1.5 added the dictionary methods D.copy() and D1.update(D2) to handle things like copying and adding (merging) dictionaries. (See Python’s library manual or O’Reilly’s Python Pocket Reference for more details.) X[:] doesn’t work for dictionaries, as they’re not sequences (see Chapter 8 for details). Also, remember that if you assign (e = d) rather than copying, you generate a reference to a shared dictionary object; changing d changes e, too:

def copyDict(old):
    new = {}
    for key in old.keys():
        new[key] = old[key]
    return new

def addDict(d1, d2):
    new = {}
    for key in d1.keys():
        new[key] = d1[key]
    for key in d2.keys():
        new[key] = d2[key]
    return new

% python
>>> from dicts import *
>>> d = {1: 1, 2: 2}
>>> e = copyDict(d)
>>> d[2] = '?'
>>> d
{1: 1, 2: '?'}
>>> e
{1: 1, 2: 2}

>>> x = {1: 1}
>>> y = {2: 2}
>>> z = addDict(x, y)
>>> z
{1: 1, 2: 2}

See #5.

More argument-matching examples. Here is the sort of interaction you should get, along with comments that explain the matching that goes on:

def f1(a, b): print(a, b)            # Normal args

def f2(a, *b): print(a, b)           # Positional varargs

def f3(a, **b): print(a, b)          # Keyword varargs

def f4(a, *b, **c): print(a, b, c)   # Mixed modes

def f5(a, b=2, c=3): print(a, b, c)  # Defaults

def f6(a, b=2, *c): print(a, b, c)   # Defaults and positional varargs


% python
>>> f1(1, 2)                         # Matched by position (order matters)
1 2
>>> f1(b=2, a=1)                     # Matched by name (order doesn't matter)
1 2

>>> f2(1, 2, 3)                      # Extra positionals collected in a tuple
1 (2, 3)

>>> f3(1, x=2, y=3)                  # Extra keywords collected in a dictionary
1 {'x': 2, 'y': 3}

>>> f4(1, 2, 3, x=2, y=3)            # Extra of both kinds
1 (2, 3) {'x': 2, 'y': 3}

>>> f5(1)                            # Both defaults kick in
1 2 3
>>> f5(1, 4)                         # Only one default used
1 4 3

>>> f6(1)                            # One argument: matches "a"
1 2 ()
>>> f6(1, 3, 4)                      # Extra positional collected
1 3 (4,)

Primes revisited. Here is the primes example, wrapped up in a function and a module (file primes.py) so it can be run multiple times. I added an if test to trap negatives, 0, and 1. I also changed / to // in this edition to make this solution immune to the Python 3.0 / true division changes we studied in Chapter 5, and to enable it to support floating-point numbers (uncomment the from statement and change // to / to see the differences in 2.6):

#from __future__ import division

def prime(y):
    if y <= 1:                                       # For some y > 1
        print(y, 'not prime')
    else:
        x = y // 2                                   # 3.0 / fails
        while x > 1:
            if y % x == 0:                           # No remainder?
                print(y, 'has factor', x)
                break                                # Skip else
            x -= 1
        else:
            print(y, 'is prime')

prime(13); prime(13.0)
prime(15); prime(15.0)
prime(3);  prime(2)
prime(1);  prime(-3)

Here is the module in action; the // operator allows it to work for floating-point numbers too, even though it perhaps should not:

% python primes.py
13 is prime
13.0 is prime
15 has factor 5
15.0 has factor 5.0
3 is prime
2 is prime
1 not prime
-3 not prime

This function still isn’t very reusable—it could return values, instead of printing—but it’s enough to run experiments. It’s also not a strict mathematical prime (floating points work), and it’s still inefficient. Improvements are left as exercises for more mathematically minded readers. (Hint: a for loop over range(y, 1, −1) may be a bit quicker than the while, but the algorithm is the real bottleneck here.) To time alternatives, use the built-in time module and coding patterns like those used in this general function-call timer (see the library manual for details):

def timer(reps, func, *args):
    import time
    start = time.clock()
    for i in range(reps):
        func(*args)
    return time.clock() - start

List comprehensions. Here is the sort of code you should write; I may have a preference, but I’m not telling:

>>> values = [2, 4, 9, 16, 25]
>>> import math

>>> res = []
>>> for x in values: res.append(math.sqrt(x))
...
>>> res
[1.4142135623730951, 2.0, 3.0, 4.0, 5.0]

>>> list(map(math.sqrt, values))
[1.4142135623730951, 2.0, 3.0, 4.0, 5.0]

>>> [math.sqrt(x) for x in values]
[1.4142135623730951, 2.0, 3.0, 4.0, 5.0]

Timing tools. Here is some code I wrote to time the three square root options, along with the results in 2.6 and 3.0. The last result of each function is printed to verify that all three do the same work:

# File mytimer.py (2.6 and 3.0)

...same as listed in Chapter 20...

# File timesqrt.py

import sys, mytimer
reps = 10000
repslist = range(reps)              # Pull out range list time for 2.6

from math import sqrt               # Not math.sqrt: adds attr fetch time
def mathMod():
    for i in repslist:
        res = sqrt(i)
    return res

def powCall():
    for i in repslist:
        res = pow(i, .5)
    return res

def powExpr():
    for i in repslist:
        res = i ** .5
    return res

print(sys.version)
for tester in (mytimer.timer, mytimer.best):
    print('<%s>' % tester.__name__)
    for test in (mathMod, powCall, powExpr):
        elapsed, result = tester(test)
        print ('-'*35)
        print ('%s: %.5f => %s' %
               (test.__name__, elapsed, result))

Following are the test results for Python 3.0 and 2.6. For both, it looks like the math module is quicker than the ** expression, which is quicker than the pow call; however, you should try this with your code and on your own machine and version of Python. Also, note that Python 3.0 is nearly twice as slow as 2.6 on this test; 3.1 or later might perform better (time this in the future to see for yourself):

c:misc> c:python30python timesqrt.py
3.0.1 (r301:69561, Feb 13 2009, 20:04:18) [MSC v.1500 32 bit (Intel)]
<timer>
-----------------------------------
mathMod: 5.33906 => 99.994999875
-----------------------------------
powCall: 7.29689 => 99.994999875
-----------------------------------
powExpr: 5.95770 => 99.994999875
<best>
-----------------------------------
mathMod: 0.00497 => 99.994999875
-----------------------------------
powCall: 0.00671 => 99.994999875
-----------------------------------
powExpr: 0.00540 => 99.994999875


c:misc> c:python26python timesqrt.py
2.6.1 (r261:67517, Dec  4 2008, 16:51:00) [MSC v.1500 32 bit (Intel)]
<timer>
-----------------------------------
mathMod: 2.61226 => 99.994999875
-----------------------------------
powCall: 4.33705 => 99.994999875
-----------------------------------
powExpr: 3.12502 => 99.994999875
<best>
-----------------------------------
mathMod: 0.00236 => 99.994999875
-----------------------------------
powCall: 0.00402 => 99.994999875
-----------------------------------
powExpr: 0.00287 => 99.994999875

To time the relative speeds of Python 3.0 dictionary comprehensions and equivalent for loops interactively, run a session like the following. It appears that the two are roughly the same in this regard under Python 3.0; unlike list comprehensions, though, manual loops are slightly faster than dictionary comprehensions today (though the difference isn’t exactly earth-shattering—at the end we save half a second when making 50 dictionaries of 1,000,000 items each). Again, rather than taking these results as gospel you should investigate further on your own, on your computer and with your Python:

c:misc> c:python30python
>>>
>>> def dictcomp(I):
...     return {i: i for i in range(I)}
...
>>> def dictloop(I):
...     new = {}
...     for i in range(I): new[i] = i
...     return new
...
>>> dictcomp(10)
{0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9}
>>> dictloop(10)
{0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9}
>>>
>>> from mytimer import best, timer
>>> best(dictcomp, 10000)[0]             # 10,000-item dict
0.0013519874732672577
>>> best(dictloop, 10000)[0]
0.001132965223233029
>>>
>>> best(dictcomp, 100000)[0]            # 100,000 items: 10 times slower
0.01816089754424155
>>> best(dictloop, 100000)[0]
0.01643484018219965
>>>
>>> best(dictcomp, 1000000)[0]           # 1,000,000 items: 10X time
0.18685105229855026
>>> best(dictloop, 1000000)[0]           # Time for making one dict
0.1769041177020938
>>>
>>> timer(dictcomp, 1000000, _reps=50)[0]       # 1,000,000-item dict
10.692516087938543
>>> timer(dictloop, 1000000, _reps=50)[0]       # Time for making 50
10.197276050447755

Import basics. When you’re done, your file (mymod.py) and interaction should look similar to the following; remember that Python can read a whole file into a list of line strings, and the len built-in returns the lengths of strings and lists:

def countLines(name):
    file = open(name)
    return len(file.readlines())

def countChars(name):
    return len(open(name).read())

def test(name):                                  # Or pass file object
    return countLines(name), countChars(name)    # Or return a dictionary

% python
>>> import mymod
>>> mymod.test('mymod.py')
(10, 291)

Note that these functions load the entire file in memory all at once, so they won’t work for pathologically large files too big for your machine’s memory. To be more robust, you could read line by line with iterators instead and count as you go:

def countLines(name):
    tot = 0
    for line in open(name): tot += 1
    return tot

def countChars(name):
    tot = 0
    for line in open(name): tot += len(line)
    return tot

A generator expression can have the same effect: sum(len(line) for line in open(name)). On Unix, you can verify your output with a wc command; on Windows, right-click on your file to view its properties. Note that your script may report fewer characters than Windows does—for portability, Python converts Windows line-end markers to , thereby dropping one byte (character) per line. To match byte counts with Windows exactly, you must open in binary mode ('rb'), or add the number of bytes corresponding to the number of lines.

The “ambitious” part of this exercise (passing in a file object so you only open the file once), will require you to use the seek method of the built-in file object. It works like C’s fseek call (and calls it behind the scenes): seek resets the current position in the file to a passed-in offset. After a seek, future input/output operations are relative to the new position. To rewind to the start of a file without closing and reopening it, call file.seek(0); the file read methods all pick up at the current position in the file, so you need to rewind to reread. Here’s what this tweak would look like:

def countLines(file):
    file.seek(0)                                 # Rewind to start of file
    return len(file.readlines())

def countChars(file):
    file.seek(0)                                 # Ditto (rewind if needed)
    return len(file.read())

def test(name):
    file = open(name)                            # Pass file object
    return countLines(file), countChars(file)    # Open file only once

>>> import mymod2
>>> mymod2.test("mymod2.py")
(11, 392)

from/from *. Here’s the from * part; replace * with countChars to do the rest:

% python
>>> from mymod import *
>>> countChars("mymod.py")
291

__main__. If you code it properly, it works in either mode (program run or module import):

def countLines(name):
    file = open(name)
    return len(file.readlines())

def countChars(name):
    return len(open(name).read())

def test(name):                                  # Or pass file object
    return countLines(name), countChars(name)    # Or return a dictionary

if __name__ == '__main__':
    print(test('mymod.py'))

% python mymod.py
(13, 346)

This is where I would probably begin to consider using command-line arguments or user input to provide the filename to be counted, instead of hardcoding it in the script (see Chapter 24 for more on sys.argv, and Chapter 10 for more on input):

if __name__ == '__main__':
    print(test(input('Enter file name:'))

if __name__ == '__main__':
    import sys
    print(test(sys.argv[1]))

Nested imports. Here is my solution (file myclient.py):

from mymod import countLines, countChars
print(countLines('mymod.py'), countChars('mymod.py'))

% python myclient.py
13 346

As for the rest of this one, mymod’s functions are accessible (that is, importable) from the top level of myclient, since from simply assigns to names in the importer (it works as if mymod’s defs appeared in myclient). For example, another file can say:

import myclient
myclient.countLines(...)

from myclient import countChars
countChars(...)

If myclient used import instead of from, you’d need to use a path to get to the functions in mymod through myclient:

import myclient
myclient.mymod.countLines(...)

from myclient import mymod
mymod.countChars(...)

In general, you can define collector modules that import all the names from other modules so they’re available in a single convenience module. Using the following code, you get three different copies of the name somename (mod1.somename, collector.somename, and __main__.somename); all three share the same integer object initially, and only the name somename exists at the interactive prompt as is:

# File mod1.py
somename = 42

# File collector.py
from mod1 import *                               # Collect lots of names here
from mod2 import *                               # from assigns to my names
from mod3 import *

>>> from collector import somename

Package imports. For this, I put the mymod.py solution file listed for exercise 3 into a directory package. The following is what I did to set up the directory and its required __init__.py file in a Windows console interface; you’ll need to interpolate for other platforms (e.g., use mv and vi instead of move and edit). This works in any directory (I just happened to run my commands in Python’s install directory), and you can do some of this from a file explorer GUI, too.

When I was done, I had a mypkg subdirectory that contained the files __init__.py and mymod.py. You need an __init__.py in the mypkg directory, but not in its parent; mypkg is located in the home directory component of the module search path. Notice how a print statement coded in the directory’s initialization file fires only the first time it is imported, not the second:

C:python30> mkdir mypkg
C:Python30> move mymod.py mypkgmymod.py
C:Python30> edit mypkg\__init__.py
...coded a print statement...
C:Python30> python
>>> import mypkg.mymod
initializing mypkg
>>> mypkg.mymod.countLines('mypkgmymod.py')
13
>>> from mypkg.mymod import countChars
>>> countChars('mypkgmymod.py')
346

Reloads. This exercise just asks you to experiment with changing the changer.py example in the book, so there’s nothing to show here.

Circular imports. The short story is that importing recur2 first works because the recursive import then happens at the import in recur1, not at a from in recur2.

The long story goes like this: importing recur2 first works because the recursive import from recur1 to recur2 fetches recur2 as a whole, instead of getting specific names. recur2 is incomplete when it’s imported from recur1, but because it uses import instead of from, you’re safe: Python finds and returns the already created recur2 module object and continues to run the rest of recur1 without a glitch. When the recur2 import resumes, the second from finds the name Y in recur1 (it’s been run completely), so no error is reported. Running a file as a script is not the same as importing it as a module; these cases are the same as running the first import or from in the script interactively. For instance, running recur1 as a script is the same as importing recur2 interactively, as recur2 is the first module imported in recur1.

Inheritance. Here’s the solution code for this exercise (file adder.py), along with some interactive tests. The __add__ overload has to appear only once, in the superclass, as it invokes type-specific add methods in subclasses:

class Adder:
    def add(self, x, y):
        print('not implemented!')
    def __init__(self, start=[]):
        self.data = start
    def __add__(self, other):                    # Or in subclasses?
        return self.add(self.data, other)        # Or return type?

class ListAdder(Adder):
    def add(self, x, y):
        return x + y

class DictAdder(Adder):
    def add(self, x, y):
        new = {}
        for k in x.keys(): new[k] = x[k]
        for k in y.keys(): new[k] = y[k]
        return new

% python
>>> from adder import *
>>> x = Adder()
>>> x.add(1, 2)
not implemented!
>>> x = ListAdder()
>>> x.add([1], [2])
[1, 2]
>>> x = DictAdder()
>>> x.add({1:1}, {2:2})
{1: 1, 2: 2}

>>> x = Adder([1])
>>> x + [2]
not implemented!
>>>
>>> x = ListAdder([1])
>>> x + [2]
[1, 2]
>>> [2] + x
Traceback (innermost last):
  File "<stdin>", line 1, in ?
TypeError: __add__ nor __radd__ defined for these operands

Notice in the last test that you get an error for expressions where a class instance appears on the right of a +; if you want to fix this, use __radd__ methods, as described in “Operator Overloading” in Chapter 29.

If you are saving a value in the instance anyhow, you might as well rewrite the add method to take just one argument, in the spirit of other examples in this part of the book:

class Adder:
    def __init__(self, start=[]):
        self.data = start
    def __add__(self, other):              # Pass a single argument
        return self.add(other)             # The left side is in self
    def add(self, y):
        print('not implemented!')

class ListAdder(Adder):
    def add(self, y):
        return self.data + y

class DictAdder(Adder):
    def add(self, y):
        pass                               # Change to use self.data instead of x

x = ListAdder([1, 2, 3])
y = x + [4, 5, 6]
print(y)                                   # Prints [1, 2, 3, 4, 5, 6]

Because values are attached to objects rather than passed around, this version is arguably more object-oriented. And, once you’ve gotten to this point, you’ll probably find that you can get rid of add altogether and simply define type-specific __add__ methods in the two subclasses.

Operator overloading. The solution code (file mylist.py) uses a few operator overloading methods that the text didn’t say much about, but they should be straightforward to understand. Copying the initial value in the constructor is important because it may be mutable; you don’t want to change or have a reference to an object that’s possibly shared somewhere outside the class. The __getattr__ method routes calls to the wrapped list. For hints on an easier way to code this in Python 2.2 and later, see Extending Types by Subclassing in Chapter 31:

class MyList:
    def __init__(self, start):
        #self.wrapped = start[:]       # Copy start: no side effects
        self.wrapped = []              # Make sure it's a list here
        for x in start: self.wrapped.append(x)
    def __add__(self, other):
        return MyList(self.wrapped + other)
    def __mul__(self, time):
        return MyList(self.wrapped * time)
    def __getitem__(self, offset):
        return self.wrapped[offset]
    def __len__(self):
        return len(self.wrapped)
    def __getslice__(self, low, high):
        return MyList(self.wrapped[low:high])
    def append(self, node):
        self.wrapped.append(node)
    def __getattr__(self, name):       # Other methods: sort/reverse/etc
        return getattr(self.wrapped, name)
    def __repr__(self):
        return repr(self.wrapped)

if __name__ == '__main__':
    x = MyList('spam')
    print(x)
    print(x[2])
    print(x[1:])
    print(x + ['eggs'])
    print(x * 3)
    x.append('a')
    x.sort()
    for c in x: print(c, end=' ')

% python mylist.py
['s', 'p', 'a', 'm']
a
['p', 'a', 'm']
['s', 'p', 'a', 'm', 'eggs']
['s', 'p', 'a', 'm', 's', 'p', 'a', 'm', 's', 'p', 'a', 'm']
a a m p s

Note that it’s important to copy the start value by appending instead of slicing here, because otherwise the result may not be a true list and so will not respond to expected list methods, such as append (e.g., slicing a string returns another string, not a list). You would be able to copy a MyList start value by slicing because its class overloads the slicing operation and provides the expected list interface; however, you need to avoid slice-based copying for objects such as strings.

Subclassing. My solution (mysub.py) appears below. Your solution should be similar:

from mylist import MyList

class MyListSub(MyList):
    calls = 0                                      # Shared by instances

    def __init__(self, start):
        self.adds = 0                              # Varies in each instance
        MyList.__init__(self, start)

    def __add__(self, other):
        MyListSub.calls += 1                       # Class-wide counter
        self.adds += 1                             # Per-instance counts
        return MyList.__add__(self, other)

    def stats(self):
        return self.calls, self.adds               # All adds, my adds

if __name__ == '__main__':
    x = MyListSub('spam')
    y = MyListSub('foo')
    print(x[2])
    print(x[1:])
    print(x + ['eggs'])
    print(x + ['toast'])
    print(y + ['bar'])
    print(x.stats())

% python mysub.py
a
['p', 'a', 'm']
['s', 'p', 'a', 'm', 'eggs']
['s', 'p', 'a', 'm', 'toast']
['f', 'o', 'o', 'bar']
(3, 2)

Attribute methods. I worked through this exercise as follows. Notice that in Python 2.6, operators try to fetch attributes through __getattr__, too; you need to return a value to make them work. Caveat: as noted in Chapter 30, __getattr__ is not called for built-in operations in Python 3.0, so the following expression won’t work as shown; in 3.0, a class like this must redefine __X__ operator overloading methods explicitly. More on this in Chapters 30, 37, and 38.

>>> class Meta:
...     def __getattr__(self, name):
...         print('get', name)
...     def __setattr__(self, name, value):
...         print('set', name, value)
...
>>> x = Meta()
>>> x.append
get append
>>> x.spam = "pork"
set spam pork
>>>
>>> x + 2
get __coerce__
Traceback (innermost last):
  File "<stdin>", line 1, in ?
TypeError: call of non-function
>>>
>>> x[1]
get __getitem__
Traceback (innermost last):
  File "<stdin>", line 1, in ?
TypeError: call of non-function

>>> x[1:5]
get __len__
Traceback (innermost last):
  File "<stdin>", line 1, in ?
TypeError: call of non-function

Set objects. Here’s the sort of interaction you should get. Comments explain which methods are called. Also, note that sets are a built-in type in Python today, so this is largely just a coding exercise (see Chapter 5 for more on sets).

% python
>>> from setwrapper import Set
>>> x = Set([1, 2, 3, 4])          # Runs __init__
>>> y = Set([3, 4, 5])

>>> x & y                          # __and__, intersect, then __repr__
Set:[3, 4]
>>> x | y                          # __or__, union, then __repr__
Set:[1, 2, 3, 4, 5]

>>> z = Set("hello")               # __init__ removes duplicates
>>> z[0], z[-1]                    # __getitem__
('h', 'o')

>>> for c in z: print(c, end=' ')  # __getitem__
...
h e l o
>>> len(z), z                      # __len__, __repr__
(4, Set:['h', 'e', 'l', 'o'])

>>> z & "mello", z | "mello"
(Set:['e', 'l', 'o'], Set:['h', 'e', 'l', 'o', 'm'])

My solution to the multiple-operand extension subclass looks like the following class (file multiset.py). It only needs to replace two methods in the original set. The class’s documentation string explains how it works:

from setwrapper import Set

class MultiSet(Set):
    """
    Inherits all Set names, but extends intersect
    and union to support multiple operands; note
    that "self" is still the first argument (stored
    in the *args argument now); also note that the
    inherited & and | operators call the new methods
    here with 2 arguments, but processing more than
    2 requires a method call, not an expression:
    """

    def intersect(self, *others):
        res = []
        for x in self:                         # Scan first sequence
            for other in others:               # For all other args
                if x not in other: break       # Item in each one?
            else:                              # No: break out of loop
                res.append(x)                  # Yes: add item to end
        return Set(res)

    def union(*args):                          # self is args[0]
        res = []
        for seq in args:                       # For all args
            for x in seq:                      # For all nodes
                if not x in res:
                    res.append(x)              # Add new items to result
        return Set(res)

Your interaction with the extension will look something like the following. Note that you can intersect by using & or calling intersect, but you must call intersect for three or more operands; & is a binary (two-sided) operator. Also, note that we could have called MultiSet simply Set to make this change more transparent if we used setwrapper.Set to refer to the original within multiset:

>>> from multiset import *
>>> x = MultiSet([1,2,3,4])
>>> y = MultiSet([3,4,5])
>>> z = MultiSet([0,1,2])

>>> x & y, x | y                               # Two operands
(Set:[3, 4], Set:[1, 2, 3, 4, 5])

>>> x.intersect(y, z)                          # Three operands
Set:[]
>>> x.union(y, z)
Set:[1, 2, 3, 4, 5, 0]
>>> x.intersect([1,2,3], [2,3,4], [1,2,3])     # Four operands
Set:[2, 3]
>>> x.union(range(10))                         # Non-MultiSets work, too
Set:[1, 2, 3, 4, 0, 5, 6, 7, 8, 9]

Class tree links. Here is the way I changed the lister classes, and a rerun of the test to show its format. Do the same for the dir-based version, and also do this when formatting class objects in the tree climber variant:

class ListInstance:
    def __str__(self):
        return '<Instance of %s(%s), address %s:
%s>' % (
                           self.__class__.__name__,       # My class's name
                           self.__supers(),               # My class's own supers
                           id(self),                      # My address
                           self.__attrnames()) )          # name=value list
    def __attrnames(self):
        ...unchanged...
    def __supers(self):
        names = []
        for super in self.__class__.__bases__:            # One level up from class
            names.append(super.__name__)                  # name, not str(super)
        return ', '.join(names)

C:misc> python testmixin.py
<Instance of Sub(Super, ListInstance), address 7841200:
        name data1=spam
        name data2=eggs
        name data3=42
>

Composition. My solution is below (file lunch.py), with comments from the description mixed in with the code. This is one case where it’s probably easier to express a problem in Python than it is in English:

class Lunch:
    def __init__(self):                          # Make/embed Customer, Employee
        self.cust = Customer()
        self.empl = Employee()
    def order(self, foodName):                   # Start Customer order simulation
        self.cust.placeOrder(foodName, self.empl)
    def result(self):                            # Ask the Customer about its Food
        self.cust.printFood()

class Customer:
    def __init__(self):                          # Initialize my food to None
        self.food = None
    def placeOrder(self, foodName, employee):    # Place order with Employee
        self.food = employee.takeOrder(foodName)
    def printFood(self):                         # Print the name of my food
        print(self.food.name)

class Employee:
    def takeOrder(self, foodName):               # Return Food, with desired name
        return Food(foodName)

class Food:
    def __init__(self, name):                    # Store food name
        self.name = name

if __name__ == '__main__':
    x = Lunch()                                  # Self-test code
    x.order('burritos')                          # If run, not imported
    x.result()
    x.order('pizza')
    x.result()

% python lunch.py
burritos
pizza

Zoo animal hierarchy. Here is the way I coded the taxonomy in Python (file zoo.py); it’s artificial, but the general coding pattern applies to many real structures, from GUIs to employee databases. Notice that the self.speak reference in Animal triggers an independent inheritance search, which finds speak in a subclass. Test this interactively per the exercise description. Try extending this hierarchy with new classes, and making instances of various classes in the tree:

class Animal:
    def reply(self):   self.speak()              # Back to subclass
    def speak(self):   print('spam')             # Custom message

class Mammal(Animal):
    def speak(self):   print('huh?')

class Cat(Mammal):
    def speak(self):   print('meow')

class Dog(Mammal):
    def speak(self):   print('bark')

class Primate(Mammal):
    def speak(self):   print('Hello world!')

class Hacker(Primate): pass                      # Inherit from Primate

The Dead Parrot Sketch. Here’s how I implemented this one (file parrot.py). Notice how the line method in the Actor superclass works: by accessing self attributes twice, it sends Python back to the instance twice, and hence invokes two inheritance searches—self.name and self.says() find information in the specific subclasses:

class Actor:
    def line(self): print(self.name + ':', repr(self.says()))

class Customer(Actor):
    name = 'customer'
    def says(self): return "that's one ex-bird!"

class Clerk(Actor):
    name = 'clerk'
    def says(self): return "no it isn't..."

class Parrot(Actor):
    name = 'parrot'
    def says(self): return None

class Scene:
    def __init__(self):
        self.clerk    = Clerk()                  # Embed some instances
        self.customer = Customer()               # Scene is a composite
        self.subject  = Parrot()

    def action(self):
        self.customer.line()                     # Delegate to embedded
        self.clerk.line()
        self.subject.line()

try/except. My version of the oops function (file oops.py) follows. As for the noncoding questions, changing oops to raise a KeyError instead of an IndexError means that the try handler won’t catch the exception (it “percolates” to the top level and triggers Python’s default error message). The names KeyError and IndexError come from the outermost built-in names scope. Import builtins (__builtin__ in Python 2.6) and pass it as an argument to the dir function to see for yourself:

def oops():
    raise IndexError()

def doomed():
    try:
        oops()
    except IndexError:
        print('caught an index error!')
    else:
        print('no error caught...')

if __name__ == '__main__': doomed()

% python oops.py
caught an index error!

Exception objects and lists. Here’s the way I extended this module for an exception of my own:

class MyError(Exception): pass

def oops():
    raise MyError('Spam!')

def doomed():
    try:
        oops()
    except IndexError:
        print('caught an index error!')
    except MyError as data:
        print('caught error:', MyError, data)
    else:
        print('no error caught...')

if __name__ == '__main__':
    doomed()

% python oops.py
caught error: <class '__main__.MyError'> Spam!

Like all class exceptions, the instance comes back as the extra data; the error message shows both the class (<...>) and its instance (Spam!). The instance must be inheriting both an __init__ and a __repr__ or __str__ from Python’s Exception class, or it would print like the class does. See Chapter 34 for details on how this works in built-in exception classes.

Error handling. Here’s one way to solve this one (file safe2.py). I did my tests in a file, rather than interactively, but the results are about the same.

import sys, traceback

def safe(entry, *args):
    try:
        entry(*args)                       # Catch everything else
    except:
        traceback.print_exc()
        print('Got', sys.exc_info()[0], sys.exc_info()[1])

import oops
safe(oops.oops)

% python safe2.py
Traceback (innermost last):
  File "safe2.py", line 5, in safe
    entry(*args)                           # Catch everything else
  File "oops.py", line 4, in oops
    raise MyError('Spam!')
oops.MyError: Spam!
Got Spam!

Here are a few examples for you to study as time allows; for more, see follow-up books and the Web:

# Find the largest Python source file in a single directory

import os, glob
dirname = r'C:Python30Lib'

allsizes = []
allpy = glob.glob(dirname + os.sep + '*.py')
for filename in allpy:
    filesize = os.path.getsize(filename)
    allsizes.append((filesize, filename))

allsizes.sort()
print(allsizes[:2])
print(allsizes[-2:])


# Find the largest Python source file in an entire directory tree

import sys, os, pprint
if sys.platform[:3] == 'win':
    dirname = r'C:Python30Lib'
else:
    dirname = '/usr/lib/python'

allsizes = []
for (thisDir, subsHere, filesHere) in os.walk(dirname):
    for filename in filesHere:
        if filename.endswith('.py'):
            fullname = os.path.join(thisDir, filename)
            fullsize = os.path.getsize(fullname)
            allsizes.append((fullsize, fullname))

allsizes.sort()
pprint.pprint(allsizes[:2])
pprint.pprint(allsizes[-2:])


# Find the largest Python source file on the module import search path

import sys, os, pprint
visited  = {}
allsizes = []
for srcdir in sys.path:
    for (thisDir, subsHere, filesHere) in os.walk(srcdir):
        thisDir = os.path.normpath(thisDir)
        if thisDir.upper() in visited:
            continue
        else:
            visited[thisDir.upper()] = True
        for filename in filesHere:
            if filename.endswith('.py'):
                pypath  = os.path.join(thisDir, filename)
                try:
                    pysize = os.path.getsize(pypath)
                except:
                    print('skipping', pypath)
                allsizes.append((pysize, pypath))

allsizes.sort()
pprint.pprint(allsizes[:3])
pprint.pprint(allsizes[-3:])


# Sum columns in a text file separated by commas

filename = 'data.txt'
sums = {}

for line in open(filename):
    cols = line.split(',')
    nums = [int(col) for col in cols]
    for (ix, num) in enumerate(nums):
        sums[ix] = sums.get(ix, 0) + num

for key in sorted(sums):
    print(key, '=', sums[key])


# Similar to prior, but using lists instead of dictionaries for sums

import sys
filename = sys.argv[1]
numcols  = int(sys.argv[2])
totals   = [0] * numcols

for line in open(filename):
    cols = line.split(',')
    nums = [int(x) for x in cols]
    totals = [(x + y) for (x, y) in zip(totals, nums)]

print(totals)


# Test for regressions in the output of a set of scripts

import os
testscripts = [dict(script='test1.py', args=''),       # Or glob script/args dir
               dict(script='test2.py', args='spam')]

for testcase in testscripts:
    commandline = '%(script)s %(args)s' % testcase
    output = os.popen(commandline).read()
    result = testcase['script'] + '.result'
    if not os.path.exists(result):
        open(result, 'w').write(output)
        print('Created:', result)
    else:
        priorresult = open(result).read()
        if output != priorresult:
            print('FAILED:', testcase['script'])
            print(output)
        else:
            print('Passed:', testcase['script'])


# Build GUI with tkinter (Tkinter in 2.6) with buttons that change color and grow

from tkinter import *                                  # Use Tkinter in 2.6
import random
fontsize = 25
colors = ['red', 'green', 'blue', 'yellow', 'orange', 'white', 'cyan', 'purple']

def reply(text):
    print(text)
    popup = Toplevel()
    color = random.choice(colors)
    Label(popup, text='Popup', bg='black', fg=color).pack()
    L.config(fg=color)

def timer():
    L.config(fg=random.choice(colors))
    win.after(250, timer)

def grow():
    global fontsize
    fontsize += 5
    L.config(font=('arial', fontsize, 'italic'))
    win.after(100, grow)

win = Tk()
L = Label(win, text='Spam',
          font=('arial', fontsize, 'italic'), fg='yellow', bg='navy',
          relief=RAISED)
L.pack(side=TOP, expand=YES, fill=BOTH)
Button(win, text='press', command=(lambda: reply('red'))).pack(side=BOTTOM,fill=X)
Button(win, text='timer', command=timer).pack(side=BOTTOM, fill=X)
Button(win, text='grow', command=grow).pack(side=BOTTOM, fill=X)
win.mainloop()


# Similar to prior, but use classes so each window has own state information

from tkinter import *
import random

class MyGui:
    """
    A GUI with buttons that change color and make the label grow
    """
    colors = ['blue', 'green', 'orange', 'red', 'brown', 'yellow']

    def __init__(self, parent, title='popup'):
        parent.title(title)
        self.growing = False
        self.fontsize = 10
        self.lab = Label(parent, text='Gui1', fg='white', bg='navy')
        self.lab.pack(expand=YES, fill=BOTH)
        Button(parent, text='Spam', command=self.reply).pack(side=LEFT)
        Button(parent, text='Grow', command=self.grow).pack(side=LEFT)
        Button(parent, text='Stop', command=self.stop).pack(side=LEFT)

    def reply(self):
        "change the button's color at random on Spam presses"
        self.fontsize += 5
        color = random.choice(self.colors)
        self.lab.config(bg=color,
                font=('courier', self.fontsize, 'bold italic'))

    def grow(self):
        "start making the label grow on Grow presses"
        self.growing = True
        self.grower()

    def grower(self):
        if self.growing:
            self.fontsize += 5
            self.lab.config(font=('courier', self.fontsize, 'bold'))
            self.lab.after(500, self.grower)

    def stop(self):
        "stop the button growing on Stop presses"
        self.growing = False

class MySubGui(MyGui):
    colors = ['black', 'purple']           # Customize to change color choices

MyGui(Tk(), 'main')
MyGui(Toplevel())
MySubGui(Toplevel())
mainloop()


# Email inbox scanning and maintenance utility

"""
scan pop email box, fetching just headers, allowing
deletions without downloading the complete message
"""

import poplib, getpass, sys

mailserver = 'your pop email server name here'                 # pop.rmi.net
mailuser   = 'your pop email user name here'                   # brian
mailpasswd = getpass.getpass('Password for %s?' % mailserver)

print('Connecting...')
server = poplib.POP3(mailserver)
server.user(mailuser)
server.pass_(mailpasswd)

try:
    print(server.getwelcome())
    msgCount, mboxSize = server.stat()
    print('There are', msgCount, 'mail messages, size ', mboxSize)
    msginfo = server.list()
    print(msginfo)
    for i in range(msgCount):
        msgnum  = i+1
        msgsize = msginfo[1][i].split()[1]
        resp, hdrlines, octets = server.top(msgnum, 0)         # Get hdrs only
        print('-'*80)
        print('[%d: octets=%d, size=%s]' % (msgnum, octets, msgsize))
        for line in hdrlines: print(line)

        if input('Print?') in ['y', 'Y']:
            for line in server.retr(msgnum)[1]: print(line)    # Get whole msg
        if input('Delete?') in ['y', 'Y']:
            print('deleting')
            server.dele(msgnum)                                # Delete on srvr
        else:
            print('skipping')
finally:
    server.quit()                                  # Make sure we unlock mbox
input('Bye.')                                      # Keep window up on Windows


# CGI server-side script to interact with a web browser

#!/usr/bin/python
import cgi
form = cgi.FieldStorage()                          # Parse form data
print("Content-type: text/html
")                 # hdr plus blank line
print("<HTML>")
print("<title>Reply Page</title>")                 # HTML reply page
print("<BODY>")
if not 'user' in form:
    print("<h1>Who are you?</h1>")
else:
    print("<h1>Hello <i>%s</i>!</h1>" % cgi.escape(form['user'].value))
print("</BODY></HTML>")


# Database script to populate and query a MySql database

from MySQLdb import Connect
conn = Connect(host='localhost', user='root', passwd='darling')
curs = conn.cursor()
try:
    curs.execute('drop database testpeopledb')
except:
    pass                                           # Did not exist

curs.execute('create database testpeopledb')
curs.execute('use testpeopledb')
curs.execute('create table people (name char(30), job char(10), pay int(4))')

curs.execute('insert people values (%s, %s, %s)', ('Bob', 'dev', 50000))
curs.execute('insert people values (%s, %s, %s)', ('Sue', 'dev', 60000))
curs.execute('insert people values (%s, %s, %s)', ('Ann', 'mgr', 40000))

curs.execute('select * from people')
for row in curs.fetchall():
    print(row)

curs.execute('select * from people where name = %s', ('Bob',))
print(curs.description)
colnames = [desc[0] for desc in curs.description]
while True:
    print('-' * 30)
    row = curs.fetchone()
    if not row: break
    for (name, value) in zip(colnames, row):
        print('%s => %s' % (name, value))

conn.commit()                                      # Save inserted records


# Database script to populate a shelve with Python objects

# see also Chapter 27 shelve and Chapter 30 pickle examples

rec1 = {'name': {'first': 'Bob', 'last': 'Smith'},
        'job':  ['dev', 'mgr'],
        'age':  40.5}

rec2 = {'name': {'first': 'Sue', 'last': 'Jones'},
        'job':  ['mgr'],
        'age':  35.0}

import shelve
db = shelve.open('dbfile')
db['bob'] = rec1
db['sue'] = rec2
db.close()


# Database script to print and update shelve created in prior script

import shelve
db = shelve.open('dbfile')
for key in db:
    print(key, '=>', db[key])

bob = db['bob']
bob['age'] += 1
db['bob'] = bob
db.close()