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>>> import os, glob

>>> [f for f in glob.glob('*.py') if os.stat(f).st_size > 6000]

①

['pluraltest6.py',

'romantest10.py',

'romantest6.py',

'romantest7.py',

'romantest8.py',

'romantest9.py']

1. To filter a list, you can include an

clauseat the end of thelist comprehension. The expression after the

keyword will be evaluated for each item in the list. If the expression evaluates to

True

,the item will be

included in the output. This list comprehension looks at thelist of all

.py

files in the current directory, and

the

expression filters that list by testing whether thesizeof each fileis greater than

6000

bytes. There

are six such files, so thelist comprehension returns a list of six filenames.

All the examples of list comprehensions so far have featured simple expressions—multiply anumber by a

constant, call a single function, or simply return theoriginal list item (after filtering). But there’s no limit to

how complex a list comprehension can be.

>>> import os, glob

>>> [(os.stat(f).st_size, os.path.realpath(f)) for f in glob.glob('*.xml')]

①

[(3074, 'c:\\Users\\pilgrim\\diveintopython3\\examples\\feed-broken.xml'),

(3386, 'c:\\Users\\pilgrim\\diveintopython3\\examples\\feed-ns0.xml'),

(3070, 'c:\\Users\\pilgrim\\diveintopython3\\examples\\feed.xml')]

>>> import humansize

>>> [(humansize.approximate_size(os.stat(f).st_size), f) for f in glob.glob('*.xml')]

②

[('3.0 KiB', 'feed-broken.xml'),

('3.3 KiB', 'feed-ns0.xml'),

('3.0 KiB', 'feed.xml')]

1. This list comprehension finds all the

.xml

files in the current working directory, gets the size of each file(by

calling the

os.stat()

function), and constructs a tupleof thefile size and the absolutepath of each file (by

calling the

os.path.realpath()

function).

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2. This comprehension builds on the previous one to call the

approximate_size()

function with thefile size of

each

.xml

file.

⁂

3.4. D

ICTIONARY

OMPREHENSIONS

Adictionary comprehension is like a list comprehension, but it constructs a dictionary instead of a list.

>>> import os, glob

>>> metadata = [(f, os.stat(f)) for f in glob.glob('*test*.py')]

①

>>> metadata[0]

②

('alphameticstest.py', nt.stat_result(st_mode=33206, st_ino=0, st_dev=0,

st_nlink=0, st_uid=0, st_gid=0, st_size=2509, st_atime=1247520344,

st_mtime=1247520344, st_ctime=1247520344))

>>> metadata_dict = {f:os.stat(f) for f in glob.glob('*test*.py')}

③

>>> type(metadata_dict)

④

>>> list(metadata_dict.keys())

⑤

['romantest8.py', 'pluraltest1.py', 'pluraltest2.py', 'pluraltest5.py',

'pluraltest6.py', 'romantest7.py', 'romantest10.py', 'romantest4.py',

'romantest9.py', 'pluraltest3.py', 'romantest1.py', 'romantest2.py',

'romantest3.py', 'romantest5.py', 'romantest6.py', 'alphameticstest.py',

'pluraltest4.py']

>>> metadata_dict['alphameticstest.py'].st_size

⑥

2509

1. This is not a dictionary comprehension; it’s a

list comprehension. It finds all

.py

files with

test

in their name,

then constructs a tuple of the filename and the file metadata (from calling the

os.stat()

function).

2. Each item of the resulting list is a tuple.

3. This is a dictionary comprehension. The syntax is similar to a list comprehension, with two differences. First,

it is enclosed in curly braces instead of square brackets. Second, instead of a single expression for each item,

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it contains two expressions separated by a colon. The expression before the colon (

in this example) is the

dictionary key; the expression after thecolon (

os.stat(f)

in this example) is the value.

4. A dictionary comprehension returns adictionary.

5. Thekeys of this particular dictionary are simply the filenames returned from thecall to

glob.glob('*test*.py')

6. Thevalueassociated with each key is the return value from the

os.stat()

function. That means we can

“look up” afile by name in this dictionary toget its file metadata. One of the pieces of metadata is

st_size

the file size. The file

alphameticstest.py

2509

bytes long.

Like list comprehensions, you can include an

clause in a dictionary comprehension to filter the input

sequence based on an expression which is evaluated with each item.

>>> import os, glob, humansize

>>> metadata_dict = {f:os.stat(f) for f in glob.glob('*')}

①

>>> humansize_dict = {os.path.splitext(f)[0]:humansize.approximate_size(meta.st_size) \

...

for f, meta in metadata_dict.items() if meta.st_size > 6000}

②

>>> list(humansize_dict.keys())

③

['romantest9', 'romantest8', 'romantest7', 'romantest6', 'romantest10', 'pluraltest6']

>>> humansize_dict['romantest9']

④

'6.5 KiB'

1. This dictionary comprehension constructs a list of all the files in the current working directory

(

glob.glob('*')

), gets the filemetadata for each file (

os.stat(f)

), and constructs adictionary whose keys

are filenames and whose values are the metadata for each file.

2. This dictionary comprehension builds on the previous comprehension, filters out files smaller than

6000

bytes

(

if meta.st_size > 6000

), and uses that filtered list to construct a dictionary whosekeys are the filename

minus the extension (

os.path.splitext(f)[0]

)and whose values aretheapproximate size of each file

(

humansize.approximate_size(meta.st_size)

3. As you saw in a previous example, there are six such files, thus there aresix items in this dictionary.

4. Thevalueof each key is the string returned from the

approximate_size()

function.

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3.4.1. O

THER

TUFF

ITH

ICTIONARY

OMPREHENSIONS

Here’s a trick with dictionary comprehensions that might be useful someday: swapping thekeys and values of

adictionary.

>>> a_dict = {'a': 1, 'b': 2, 'c': 3}

>>> {value:key for key, value in a_dict.items()}

{1: 'a', 2: 'b', 3: 'c'}

⁂

3.5. S

OMPREHENSIONS

Not to be left out, sets have their own comprehension syntax as well. It is remarkably similar to the syntax

for dictionary comprehensions. The only difference is that sets just have values instead of key:valuepairs.

>>> a_set = set(range(10))

>>> a_set

{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}

>>> {x ** 2 for x in a_set}

①

{0, 1, 4, 81, 64, 9, 16, 49, 25, 36}

>>> {x for x in a_set if x % 2 == 0}

②

{0, 8, 2, 4, 6}

>>> {2**x for x in range(10)}

③

{32, 1, 2, 4, 8, 64, 128, 256, 16, 512}

1. Set comprehensions can takea set as input. This set comprehension calculates the squares of the set of

numbers from 0 to

2. Like list comprehensions and dictionary comprehensions, set comprehensions can contain an

clauseto

filter each item beforereturning it in the result set.

3. Set comprehensions do not need to takea set as input; they can takeany sequence.

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⁂

3.6. F

URTHER

EADING

•

module

•

—Portable access to operating system specific features

•

os.path

module

•

os.path

—Platform-independent manipulation of file names

•

glob

module

•

glob

—Filenamepattern matching

•

time

module

•

time

—Functions for manipulating clock time

•

List comprehensions

•

Nested list comprehensions

•

Looping techniques

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HAPTER

4. S

TRINGS

❝

I’m telling you this ’cause you’re one of my friends.

My alphabet startswhere your alphabet ends!

❞

—Dr. Seuss, On Beyond Zebra!

4.1. S

OME

ORING

TUFF

EED

NDERSTAND

EFORE

IVE

id you know that thepeople of

Bougainville have the smallest alphabet in the world? Their

Rotokas

alphabet is composed of only 12 letters: A, E, G, I, K, O, P, R, S, T, U, and V. On the other end of the

spectrum, languages likeChinese, Japanese, and Korean have thousands of characters. English, of course, has

26 letters—52 if you count uppercaseand lowercase separately—plus a handful of

!@#$%&

punctuation

marks.

When people talkabout “text,” they’re thinkingof “characters and symbols on the computer screen.”But

computers don’t deal in characters and symbols; they deal in bits and bytes. Every piece of text you’veever

seen on a computer screen is actually stored in aparticular character encoding. Very roughly speaking, the

character encodingprovides a mapping between thestuff you seeon your screen and thestuff your

computer actually stores in memory and on disk. There aremany different character encodings, some

optimized for particular languages like Russian or Chinese or English, and others that can beused for

multiple languages.

In reality, it’s more complicated than that. Many characters are common to multipleencodings, but each

encoding may use a different sequence of bytes to actually store those characters in memory or on disk. So

you can thinkof thecharacter encoding as a kind of decryption key. Whenever someone gives you a

sequence of bytes—a file, a web page, whatever—and claims it’s “text,” you need to know what character

encoding they used so you can decodethebytes into characters. If they give you thewrong key or nokey

at all, you’re left with the unenviable task of cracking thecode yourself. Chances are you’ll get it wrong, and

the result will be gibberish.

106

Everything

you thought

you knew

about

strings is

wrong.

Surely you’ve seen web pages like this, with strange

question-mark-likecharacters where apostrophes should

be. That usually means the page author didn’t declare

their character encoding correctly, your browser was

left guessing, and theresult was a mix of expected and

unexpected characters. In English it’s merely annoying;in

other languages, theresult can be completely

unreadable.

Therearecharacter encodings for each major language

in the world. Since each language is different, and

memory and disk space havehistorically been expensive,

each character encoding is optimized for a particular

language. By that, I mean each encoding using thesame

numbers (0–255) to represent that language’s characters.

For instance, you’re probably familiar with the

ASCII

encoding, which stores English characters as numbers

ranging from 0 to 127. (65 is capital “A”, 97 is

lowercase “a”,

c.) English has a very simplealphabet,

so it can be completely expressed in less than 128 numbers. For thoseof you whocan count in base 2,

that’s 7 out of the8 bits in a byte.

Western European languages like French, Spanish, and German have more letters than English. Or, more

precisely, they have letters combined with various diacritical marks, like the

character in Spanish. The most

common encodingfor theselanguages is CP-1252, alsocalled “windows-1252” because it is widely used on

Microsoft Windows. TheCP-1252 encoding shares characters with

ASCII

in the 0–127 range, but then

extends into the 128–255 range for characters like n-with-a-tilde-over-it (241), u-with-two-dots-over-it (252),

c. It’s still a single-byte encoding, though;thehighest possible number, 255, still fits in onebyte.

Then therearelanguages likeChinese, Japanese, and Korean, which haveso many characters that they

require multiple-byte character sets. That is, each “character” is represented by a two-byte number from

0–65535. But different multi-byte encodings still share the same problem as different single-byte encodings,

namely that they each usethesamenumbers to mean different things. It’s just that the rangeof numbers is

broader, because there are many morecharacters to represent.

107

That was mostly OK in a non-networked world, where “text” was something you typed yourself and

occasionally printed. There wasn’t much “plain text”. Sourcecode was

ASCII

,and everyoneelseused word

processors, which defined their own (non-text) formats that tracked character encoding information along

with rich styling,

c. Peopleread these documents with the same word processing program as the original

author, so everything worked, more or less.

Now thinkabout therise of global networks like email and theweb. Lots of “plain text” flying around the

globe, beingauthored on one computer, transmitted through a second computer, and received and displayed

by a third computer. Computers can only see numbers, but the numbers could mean different things. Oh no!

What to do? Well, systems had tobe designed to carry encoding information along with every piece of

“plain text.”Remember, it’s thedecryption key that maps computer-readable numbers to human-readable

characters. A missing decryption key means garbled text, gibberish, or worse.

Now thinkabout trying to store multiple pieces of text in the same place, like in thesamedatabase table

that holds all the email you’ve ever received. You still need to store the character encoding alongside each

piece of text so you can display it properly. Think that’s hard? Try searchingyour email database, which

means converting between multipleencodings on thefly. Doesn’t that sound fun?

Now thinkabout thepossibility of multilingual documents, where characters from several languages are next

to each other in the same document. (Hint: programs that tried to do this typically used escapecodes to

switch “modes.” Poof, you’re in Russian koi8-r mode, so 241 means Я;poof, now you’re in Mac Greek

mode, so 241 means ώ.) And of courseyou’ll want to search those documents, too.

Now cry a lot, because everything you thought you knew about strings is wrong, and thereain’t nosuch

thing as “plain text.”

⁂

4.2. U

NICODE

Enter Unicode.

108

Unicode is a system designed to represent every character from every language. Unicode represents each

letter, character, or ideograph as a 4-byte number. Each number represents a uniquecharacter used in at

least one of the world’s languages. (Not all the numbers areused, but more than 65535 of them are, so2

bytes wouldn’t be sufficient.) Characters that are used in multiplelanguages generally have the same number,

unless there is a good etymological reason not to. Regardless, thereis exactly 1 number per character, and

exactly 1 character per number. Every number always means just one thing; there are no “modes” to keep

track of.

U+0041

is always

'A'

,even if your languagedoesn’t have an

'A'

in it.

On theface of it, this seems like agreat idea. One encoding to rule them all. Multiple languages per

document. No more“modeswitching” to switch between encodings mid-stream. But right away, theobvious

question should leap out at you. Four bytes? For every singlecharacter‽ That seems awfully wasteful,

especially for languages like English and Spanish, which need less than onebyte (256 numbers) to express

every possible character. In fact, it’s wasteful even for ideograph-based languages (like Chinese), which never

need more than two bytes per character.

Thereis a Unicodeencoding that uses four bytes per character. It’s called UTF-32, because 32 bits = 4

bytes. UTF-32 is a straightforward encoding; it takes each Unicode character (a 4-bytenumber) and

represents thecharacter with that same number. This has some advantages, themost important being that

you can find the

Nth

character of a string in constant time, because the

Nth

character starts at the

4×Nth

byte. It also has several disadvantages, the most obvious being that it takes four freaking bytes to store every

freaking character.

Even though there are a lot of Unicode characters, it turns out that most people will never useanything

beyond the first 65535. Thus, there is another Unicodeencoding, called UTF-16 (because 16 bits = 2 bytes).

UTF-16 encodes every character from 0–65535 as two bytes, then uses somedirty hacks if you actually need

to represent therarely-used “astral plane” Unicode characters beyond 65535. Most obvious advantage:

UTF-16 is twiceas space-efficient as UTF-32, because every character requires only two bytes to store

instead of four bytes (except for the ones that don’t). And you can still easily find the

Nth

character of a

string in constant time, if you assumethat the string doesn’t include any astral plane characters, which is a

good assumption right up until themoment that it’s not.

But therearealso non-obvious disadvantages to both UTF-32 and UTF-16. Different computer systems store

individual bytes in different ways. That means that the character

U+4E2D

could be stored in UTF-16 as either

4E 2D

2D 4E

,depending on whether the system is big-endian or little-endian. (For UTF-32, there are even

109

more possible byte orderings.) As long as your documents never leave your computer, you’re

safe—different applications on the same computer will all use the same byte order. But the minuteyou

want to transfer documents between systems, perhaps on a world wideweb of some sort, you’re going to

need away to indicate which order your bytes are stored. Otherwise, the receiving system has no way of

knowing whether the two-byte sequence

4E 2D

means

U+4E2D

U+2D4E

To solve thisproblem, the multi-byte Unicode encodings definea “Byte Order Mark,” which is a special non-

printable character that you can include at the beginningof your document to indicate what order your

bytes are in. For UTF-16, the Byte Order Mark is

U+FEFF

.If you receivea UTF-16 document that starts with

the bytes

FF FE

,you know thebyteordering is oneway;if it starts with

FE FF

,you know the byte ordering

is reversed.

Still, UTF-16 isn’t exactly ideal, especially if you’redealing with a lot of

ASCII

characters. If you thinkabout

it, even a Chinese web page is going to contain a lot of

ASCII

characters—all the elements and attributes

surrounding theprintableChinese characters. Being able to find the

Nth

character in constant timeis nice,

but there’s still the nagging problem of those astral plane characters, which mean that you can’t guarantee

that every character is exactly twobytes, so you can’t really find the

Nth

character in constant timeunless

you maintain a separate index. And boy, there sureis a lot of

ASCII

text in theworld…

Other peoplepondered these questions, and they came up with asolution:

UTF-8

110

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