Usability, the soul of Python: An introduction to programming Python through the eyes of usability

I would like to begin discussing Python with a feature that causes puzzlement to good programmers first meeting Python: significant whitespace.

Few features in Python are absolutely unique, and Python did not pioneer the concept of significant whitespace. The basic concept in significant whitespace is that how you use spaces, tabs, line breaks, etc. necessarily communicates certain basic aspects of your program, like how individual statements should be grouped together (or not). Previous influential languages to use significant whitespace include Cobol and Fortran, which are known by reputation, a reputation that survives in sayings like, “A computer without Cobol and Fortran is like a slice of chocolate cake without ketchup and mustard,” “The teaching of Cobol cripples the mind. Its teaching should therefore be regarded as a criminal offence,” or “You can tell how advanced of a society we live in when Fortran is the language of supercomputers.” Early exposure to Fortran left an undeniably foul taste in Eric Raymond’s mouth, and when he learned that Python had significant whitespace, he repeatedly described Python’s first impression on him as “a steaming pile of dinosaur dung.”

Since the days of fixed formatting as in Cobol and Fortran, there was the invention of what is called freeform formatting, which means that as long as you follow a few basic rules, you can use whitespace to format your code however you please. The list of languages that have embraced this feature include C, C++, Java, C#, Perl, PHP, and SQL, and that’s really just naming a few of the bigger players. Freeform formatting means that the compiler will accept all of the variations of the “internet user’s drinking song” below as equivalent:

for(i = 99; i > 0; ++i) {
    printf("%d slabs of spam in my mail!\n", i);
    printf("%d slabs of spam,\n", i);
    printf("Send one to abuse and Just Hit Delete,\n");
    printf("%d slabs of spam in my mail!\n\n", i + 1);

for(i = 99; i > 0; ++i)
    printf("%d slabs of spam in my mail!\n", i);
    printf("%d slabs of spam,\n", i);
    printf("Send one to abuse and Just Hit Delete,\n");
    printf("%d slabs of spam in my mail!\n\n", i + 1);

for(i = 99; i > 0; ++i)
    printf("%d slabs of spam in my mail!\n", i);
    printf("%d slabs of spam,\n", i);
    printf("Send one to abuse and Just Hit Delete,\n");
    printf("%d slabs of spam in my mail!\n\n", i + 1);

for(i = 99; i > 0; ++i)
    printf("%d slabs of spam in my mail!\n", i);
    printf("%d slabs of spam,\n", i);
    printf("Send one to abuse and Just Hit Delete,\n");
    printf("%d slabs of spam in my mail!\n\n", i + 1);

Which is best? From a usability standpoint, the braces go with the lines to print out the stanza rather than the for statement or the code after, so the following is best:

for(i = 99; i > 0; ++i)
    printf("%d slabs of spam in my mail!\n", i);
    printf("%d slabs of spam,\n", i);
    printf("Send one to abuse and Just Hit Delete,\n");
    printf("%d slabs of spam in my mail!\n\n", i + 1);

The One True Brace Style did a good job of being thrifty with lines of screen space when monitors were small, but it is confusing now: the close curly brace is visually grouped with lines that follow: if I add a line:

for(i = 99; i > 0; ++i) {
    printf("%d slabs of spam in my mail!\n", i);
    printf("%d slabs of spam,\n", i);
    printf("Send one to abuse and Just Hit Delete,\n");
    printf("%d slabs of spam in my mail!\n\n", i + 1);
printf("I suppose even integer overflow has its uses...\n");

the close curly brace is visually grouped with the subsequent exclamation, and not, what would be better, visually grouped with the drinking song’s stanza.

But the issue goes beyond the fact that the common style bold enough to proclaim itself as the One True Brace Style may not be the top usability pick now that we have larger monitors. The styles I mentioned are some of the styles that significant numbers of programmers who care about well-formatted code advocate for; freeform allows for laziness, and for that matter paved the way for one of the first contests elevating bad programming to a refined art form: the International Obfuscated C Code Contest, where C code submitted was code that worked, but top-notch C programmers look at the code and have no idea how it works. (In the Computer Bowl one year, Bill Gates as moderator asked contestants, “What contest, held annually via UseNet, is devoted to examples of obscure, bizarre, incomprehensible, and really bad programming?” An ex-Apple honcho slapped the buzzer and said, “Windows!” The look on Bill Gates’s face was classic, but this answer was not accepted as correct.) But deliberately lazy or inappropriately clever formatting isn’t the real problem here either.

The problem with the fact that people can format freeform code however they want is that people do format freeform however they want. Not only do programmers grow attached to a formatting style, but this is the subject of holy wars; to go through another programmer’s code and change all the formatting to another brace style is quite rude, like a direct invasion of personal space. And no matter what choice you make, it’s not the only choice out there, and sooner or later you will run into code that is formatted differently. And worse than the flaws of any one brace style are the flaws of a mix of brace styles and the fact that they seem to be tied to personal investment for programmers who care about writing code well. Even if there are not ego issues involved, it’s distracting. Like. What. Things. Would. Be. Like. If. Some. English. Text. Had. Every. Word. Capitalized. With. A. Period. Afterwards.

One way of writing the same code in Python would be:

count = 99
while count > 0:
    print u'%d slabs of spam in my mail!' % count
    print u'%d slabs of spam,' % count
    print u'Send one to abuse and Just Hit Delete,'
    count += 1
    print u'%d slabs of spam in my mail!' % count
    print u''

The braces are gone, and with them the holy wars. Whatever brace styles Python programmers may happen to use in languages with braces, all the Python code looks the same, and while the major brace styles illustrated above are a few of many ways the C code could be laid out, there’s only one real way to do it. It would not in principle be very difficult to write a program that would transform freeform syntax to Python, “compiling to Python” so to speak and allowing a freeform variant on Python, but so far as I know it’s never been done; people who have gotten into Python seem to find this unusual feature, shared with some ridiculed predecessors, to be a decision that was done right. And in fact the essay “Why Python?” in which Eric Raymond said that Python’s significant whitespace made the first impression of a “steaming pile of dinosaur dung”, goes on to give Python some singular compliments, saying of one particular good experience with Python, “To say I was astonished would have been positively wallowing in understatement.”

Another point about usability may be made by looking at “natural” languages, meaning the kinds of languages people speak (such as English), as opposed to computer languages and other languages that have been artificially created. Perl is very unusual among computer languages in terms of having been created by a linguist who understood natural languages well; it may be the only well-known programming language where questions like “How would this work if it were someone’s native language?” are a major consideration that shaped the language. But there is a point to be made here about two different types of spoken languages, trade languages and languages that are native languages, that have everything to do with usability.

If you were born in the U.S. and grew up speaking English, you could presumably not just travel around your state but travel thousands of miles, traveling from state to state coast to coast all the while being able to buy food, fuel, lodging, and the like without language being an issue. For that matter, you could probably strike up a meandering chat with locals you meet in obtaining food, fuel, and lodging without language being an issue. Even if their faraway English sounded a little different, you have pretty complete coverage if you know just one language, English. For many people you meet, English would be their native language, too. Spanish is widely spoken and there are large groups with other native languages, but this does not really change the fact that you can travel from coast to coast, buy basic travel necessities, and for that matter chat if you want and only need English.

This is not something universal across the world. Nigeria in Africa is a country about the size of Texas, and it doesn’t have a native language; it has hundreds of them. It is not at all something to be taken for granted that you can travel twenty miles and order food and basic necessities in your native language. (Depending on where you live, if you are a Nigerian, you may regularly walk by people on the street who may be just as Nigerian as you, but neither of you knows the other’s native language.) And in the cultures and peoples of Africa, there is a basic phenomenon of a trade language. A trade language, such as Hausa in Nigeria and much of West Africa, or Swahili in much of East Africa, may or may not have any native speakers at all, but it is an easy-to-learn language that you can use for basic needs with people who do not speak your native language. If you are from the U.S. and were to need a trade language to get along with traveling, perhaps neither you nor the other party would know a trade language like Swahili well enough to have a long and meandering chat, but you would be able to handle basic exchanges like buying things you need. One of the key features of a good trade language’s job description is to be gentle to people who do not eat, sleep, and breathe it.

With that stated, it might be suggested that Perl is the creation of a linguist, but a linguist who seemed not to be thinking about why a language like English is hard for adults to learn and, in its native form, is a terrible trade language. English may be a powerful language and an object of beauty, but what it is not is easy for beginners the way Swahili is. English is considered an almost notoriously difficult language for an adult learner, and even English as a Second Language teachers may need a few sensitivity experiences to understand why the English pronunciation that they find second nature is so tricky and confusing for adult speakers of other languages to pin down. The enormous English vocabulary with so many ways to say things, and the broad collection of idioms, are tremendous tools for the skilled English communicator. It is also a daunting obstacle to adults who need to learn the many ways English speakers may say something to them. English has many things to appreciate as a native language, but these strengths are the opposite of what makes for a good trade language that adults can learn. Perl, designed by a linguist, is a bit like English in this regard. If you’ve given it years and years of hard work, Perl breathes very attractively, a native language to love. But if you’re starting out, it’s needlessly cryptic and confusing: the reasons people love the Pathologically Eclectic Rubbish Lister (as Perl as called by its biggest fans) are reasons it would make a painful trade language. A language like Visual Basic may be said to be the opposite on both counts, as making a very gentle start to programming, but not a good place to grow to be an expert programmer: the sort of place where you’ll be constricted by the language’s ceiling. But Python pulls off the delicate balancing act of working well as a trade language where a programmer can be productive very quickly after starting, and having room to grow for those programmers who are able to experience it more like a native language. Visual Basic is an easy trade language but a limited native language. Perl is a vast native language and painfully vast as a trade language. Python is both an easy trade language for those beginning and a deep native language for gurus.

Perl users have an acronym, TMTOWTDI, pronounced “tim-towdy,” standing for “There’s more than one way to do it.” It has been suggested that top-notch Perl programmers are slightly more productive than top-notch Python programmers, and if you were to speak a computer language natively, Perl would be an excellent choice. But it is not entirely easy to learn or read Perl, and this is not just something that affects novices. A classic joke reads:

EXTERIOR: DAGOBAH–DAY With Yoda strapped to his back, Luke climbs up one of the many thick vines that grow in the swamp until he reaches the Dagobah statistics lab. Panting heavily, he continues his exercises–grepping, installing new packages, logging in as root, and writing replacements for two-year-old shell scripts in Python.

YODA: Code! Yes. A programmer’s strength flows from code maintainability. But beware of Perl. Terse syntax… more than one way to do it… default variables. The dark side of code maintainability are they. Easily they flow, quick to join you when code you write. If once you start down the dark path, forever will it dominate your destiny, consume you it will.

LUKE: Is Perl better than Python?

YODA: No… no… no. Quicker, easier, more seductive.

LUKE: But how will I know why Python is better than Perl?

YODA: You will know. When your code you try to read six months from now.

This difference boils down to usability. It is a truism that code is read many more times than it is written, and write-only code is very much “the dark side of code maintainability.” Someone said, “Computer scientists stand on each other’s shoulders. Programmers stand on one another’s toes. Software engineers dig one another’s graves,” and write-only code is code with bad programmer usability, and a way of digging your own grave.

Perl carries on a tradition of one-liners, a program that has only one clever line, like:

perl -lne '(1x$_) !~ /^1?$|^(11+?)\1+$/ && print "$_ is prime"'

There is a tradition of writing programs like these that show off your cleverness. The Python community does not favor shows of cleverness in quite the same way; in fact, saying, “This code is clever,” is a respectful and diplomatic way of saying, “You blew it.” There is a well-known Easter egg in Python; normally one uses the import statement to let code access an existing module for a specific task, but if you start the Python interpreter interactively (in itself a powerful learning tool to try things out and learn some things quickly) and type import this, you get:

>>> import this
The Zen of Python, by Tim Peters

Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it's a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let's do more of those!

This little poem speaks against trying to be as clever as you can, and this is something deep in Python’s veins.

While simplicity is important in Python, Python is a multiparadigm language (like many others, including Ocaml and JavaScript as well as Perl) and directly supports procedural, object-oriented, and (in part) functional programming, letting the programmer choose what works best for a situation. On this point I may point out that object oriented programming is not a better way of solving problems than procedural programming; it is one that scales better for larger projects. I would choose object oriented methodology over procedural for large projects, and procedural over object oriented for small to intermediate sized projects, with some tiny projects not even needing procedural structure. (If I have enough cargo to fill the trailer on an eighteen wheel truck, then the most efficient use of resources is to pay for that way of transporting the payload, but if the cargo fits neatly inside an envelope, a postage stamp is enough.)

Let’s look at some of the core language features that are likely to come up.

Python is a scripting language, along with such languages as Perl, PHP, Ruby, Tcl, and shell scripting like bash. As opposed to C and C++ which are compiled to a standalone executable (or library, etc.), Python is interpreted from a script’s source file, or more precisely compiled to a bytecode. To simplify slightly, “the thing you run” is usually not a separate executable that is derived from the source code; “the thing you run” is effectively the source code. Now Python does have compiled bytecode, and it is possible to get an implementation of Python that runs on a Java VM or creates a standalone executable, but distributing a Python program or library usually means distributing the source code. Because in Python we are effectively “running the source code,” this usually means a faster feedback cycle than the edit-compile-test process one uses in working with a C application. For some Python software such as CherryPy, if you make a change in one of your source files, the application immediately reloads it without needing separately quit and restart, making the feedback cycle even shorter and more responsive.

The “significant whitespace” mentioned earlier means that a statement usually ends with a line break rather than a semicolon. You are allowed to add a semicolon at the end of a statement or use semicolons to separate more than one statement on a single line; hence both of the following are legal:

print u'Hello, world!';
print u'Ping!'; print u'Pong!'

However, the standard practice is to let line breaks end your statements:

print u'Hello, world!'

Note that this differs from JavaScript, where the final semicolon on a line is treated as optional but it is usually considered best practice to explicitly include the semicolon. In Python, it is uncommon to end a statement with a semicolon.

If you want to break a statement over multiple lines, usually because it would be a very long line otherwise, you can end a line with a backslash, and then continue after whitespace, which I suggest you indent to two spaces more than the beginning of the line:

print \
  u'Hello, world!'

There are some cases where the backslash is optional and discouraged: in particular, if you have open parentheses or square/curly braces, Python expects you to complete the statement with more lines:

stooges = [

opposites = {
  True: False,
  False: True,

falsy = (

The three statements above represent three basic types: the list, the dictionary, also called the hash, dict, or occasionally associative array, and also the tuple. The list, denoted by square braces (“[]”) and tuple, which is often surrounded by parentheses (“()”) even though it is not strictly denoted by them unless a tuple is empty, both contain an ordered list of anything. The difference between them is that a tuple is immutable, meaning that the list of elements cannot be changed, and a list is mutable, meaning that it can be changed, and more specifically elements can be rearranged, added, and deleted, none of which can be done to a tuple. Lists and tuples are both indexed, with counting beginning at zero, so that the declaration of stooges above could have been replaced by creating an empty list and assigning members:

stooges = []
stooges[0] = u'Larry'
stooges[1] = u'Moe'
stooges[2] = u'Curly'

I will comment briefly that zero-based indices, while they are a common feature to most major languages, confuse newcomers: it takes a while for beginning programmers to gain the ingrained habit of “You don’t start counting at 1; you start counting at 0.”

The dictionary is a like a list, but instead of the index automatically being a whole number, the index can be anything that is immutable. Part of my first introduction to Perl was the statement, “You’re not really thinking Perl until you’re thinking associative arrays,” meaning what in Perl does the same job as Python’s dictionary, and lists and dictionaries in particular are powerful structures that can do a lot of useful work.

The example of a tuple provided above are some of the few values that evaluate to false. In code like:

if condition:
while condition:

The if and while statements test if condition is true, and the variable condition can be anything a variable can hold. Not only boolean variables but numbers, strings, lists, dictionaries, tuples, and objects can be used as a condition. The rule is basically similar to Perl. A very small number of objects, meaning the boolean False, numeric variables that are zero, containers like lists and dictionaries that are empty, and a few objects that have a method like __nonzero__(), __bool__(), or __len__() defined a certain way, are treated as being falsy, meaning that an if statement will skip the if clause and execute the else clause if one is provided; and a while statement will stop running (or not run in the first place). Essentially everything else is treated as being truthy, meaning that an if statement will run the if clause and skip any else clause, and a while loop will run for one complete iteration and then check its condition again to see if it should continue or stop. (Note that there is a behavior that is shared with other programming languages but surprising to people learning to program: if the condition becomes false after some of the statements in the loop has run, the loop does not stop immediately; it continues until all of the statements in that iteration have run, and then the condition is checked to see if the loop should run for another iteration.) Additionally, if, else, while, and the like end with a colon and do not require parentheses. In C/C++/Java, one might write:

if (remaining > 0)

In Python, the equivalent code is:

if remaining > 0:

If-then-else chains in Python use the elif statement:

if first_condition:
elif second_condition:
elif third_condition:

Any of the example indented statements could be replaced by several statements, indented to the same level; this is also the case with other constructs like while. In addition to if/else/elif and while, Python has a for loop. In C, the following idiom is used to do something to each element in array, with C++ and Java following a similar pattern:

sum = 0;
for(i = 0; i < LENGTH; ++i)
    sum += numbers[i];

In Python one still uses for, but manually counting through indices is not such an important idiom:

sum = 0
for number in numbers:
    sum += number

The for statement can also be used when the data in question isn’t something you handle by an integer index. For example:

phone_numbers = {
  u'Alice Jones': u'(800) 555-1212',
  u'Bob Smith': u'(888) 555-1212',

In this case the dictionary is a telephone directory, mapping names to telephone numbers. The key “Alice Jones” can be used to look up the value “(800) 555-1212”, her formatted telephone number: if in the code you write, print phone_numbers[u’Alice Jones’], Python will do a lookup and print her number, “(800) 555-1212”. If you use for to go through a dictionary, Python will loop through the keys, which you can use to find the values and know which key goes with which value. To print out the phone list, you could write:

for name in phone_numbers:
    print name + u': ' + phone_numbers[name]

This will print out an easy-to-read directory:

Alice Jones: (800) 555-1212
Bob Smith: (888) 555-1212

Now let us look at strings. In the examples above, we have looked at Unicode strings, and this is for a reason. If you are in the U.S., you may have seen signs saying, “Se habla español,” Spanish for “We speak Spanish here,” or “Hablamos español,” Spanish for “We don’t speak Spanish very well.” The difference is something like the difference in Python between:

sum = 0
for number in numbers:
    sum += number


sum = 0
index = 0
while index < len(numbers):
    sum += numbers[index]
    index += 1

Now if one is sticking large block letters on a sign in front of a store, it is acceptable to state, “SE HABLA ESPANOL”; it’s appropriate to use an “N” because you don’t have any “ñ”s, a bit like how it doesn’t bother people to use a “1” because you’ve run out of “I”s or an upside-down “W” because you’ve run out of “M”s. And to pick another language, Greeks often seem willing to write in Greek using the same alphabet as English; this is the equivalent of writing “Hi, how are you?” in English but written with Greek letters: “αι ου αρ ιυ:” it works pretty well once you get used to it, but it’s really nice to have your own alphabet.

There is one concern people may have: “So how many translations do I have to provide?” I would suggest this way of looking at it. The people in charge of major software projects often try to produce fully internationalized and localized versions of their software that appears native for dozens of languages, but even they can’t cover every single language: if you support several dozen languages, that may be full support for 1% of the languages that exist. Even the really big players can’t afford an “all-or-nothing” victory. But the good news is that we don’t need to take an “all-or-nothing” approach. Russians, for instance, are often content to use forum software that has an interface and a few other things in English, and most of the discussion material in Russian. Perhaps the best thing to offer is a fully translated and localized Russian version of the forum, but many Russians will really do quite well if there is a good interface in English, and if the forum displays Russian discussions without garbling the text or giving errors.

The most basic of the best practices for internationalization and localization is to choose Unicode over ASCII strings. ASCII lets you handle text in a way that works for American English; Unicode lets you handle text in a way that works for pretty much everybody. Working with Unicode strings is similar to working with ASCII strings, but once you use Unicode, you can store information people enter in other languages for free.

In Python code, an ASCII string looks like ‘foo’ or “foo”, and a Unicode string has a ‘u’ before the opening quote, like u’foo’ or u”foo”. Strings may be marked off by either double or single quotes, and a triple double quote or triple single quote can be used to mark a multiline string (which can contain double or single quotes anywhere except for possibly the last character):

print u'''Content-type: text/html

<!DOCTYPE html>
<html xmlns="" xml:lang="en">

The only gotchas are that you cannot include the same triple quotation mark delimiter, and if the last character of the string is the same kind of character, it needs to be escaped with a backslash, like: u”’This string ends with an apostrophe: \””.

Probably the next step to internationalization after using Unicode strings is, instead of storing the interface language in your code like:

print u'Please enter your email address below:'

you would instead do string look-ups that would pull the appropriate translation:


If you do this, the only language that needs to initially be supported is your own, perhaps tested by a second language, but then you don’t need to do major rewrites to support a second language, or a third, fourth, or twelfth. Maybe that wouldn’t be a perfect localization, but it’s another major step, and it’s not too hard.

Having looked at that, let’s look at another topic: exceptions and errors. Exceptions are thrown when something doesn’t work ideally, and usually you want to catch them. The basic idea is that plan A didn’t work and you should have a plan B.

For example, suppose that you have a string value that’s supposed to be an integer displayed as a string, like u’1′. Suppose further that you want to get the integer out of the string, but default to 0 if parsing fails (for instance, u’one’ will not be parsed as an integer). Then you can write:

    result = int(input)
except ValueError:
    result = 0

This code says that plan A is to get an appropriate integer value out of the input, and plan B is to set a default of 0. This kind of thing is very appropriate in dealing with the web, where you should assume that input is inappropriate and possibly malicious until proven otherwise. If you write a web script that asks people to enter their age, and someone hits two keys at once and enters an age of u’22w’, you need to be able to roll with it-and this is nothing next to what might happen if someone is acting maliciously. In working on the web, there may be ideal input that you intend, but both from a usability and a security perspective you need to be able to respond appropriately to input that was not what you intended. If you only type:

result = int(input)

your whole program will break on even an innocent typo like entering u’22w’ when asked for their age.

Exceptions are important in Python. In Java and some other languages, recommended best practices say, “Exceptions should not be used for regular flow control.” In other words, an exception is only appropriate for a very rare case when something very unusual has happened. That is not how Python works, and exceptions are a commonly used way of saying “the ideal case didn’t happen.” Perhaps you have seen the famous “Mom’s Brownie Recipe” on the web:

    • Remove teddy bear from oven and preheat oven to 375.
    • Melt 1-cup margarine in saucepan.
    • Remove teddy bear from oven and tell JR. “no, no.”
    • Add margarine to 2 cups sugar.
    • Take shortening can away from JR. and clean cupboards.
    • Measure 1/3-cup cocoa.
    • Take shortening can away from JR. again and bathe cat.
    • Apply antiseptic and bandages to scratches sustained while removing shortening from cat’s tail.
    • Assemble 4 eggs, 2-tsp. vanilla, and 1-1/2 cups sifted flour.

Take smoldering teddy bear from oven and open all doors and windows for ventilation.

  • Take telephone away from Billy and assure party on the line the call was a mistake.
  • Call operator and attempt to have direct dialed call removed from bill.
  • Measure 1-tsp. salt & a cup nuts and beat all ingredients well.
  • Let cat out of refrigerator.
  • Pour mixture into well-greased 9×13-inch pan.
  • Bake 25 minutes.
  • Rescue cat and take razor away from Billy.
  • Explain to kids that you have no idea if shaved cats will sunburn.
  • Throw cat outside while there’s still time and he’s still able to run away.
  • Mix the following in saucepan: 1 cup sugar, 1 oz unsweetened chocolate, 1 cup margarine.
  • Take the teddy bear out of the broiler and throw it away—far away.
  • Answer the door and meekly explain to nice police officer that you didn’t know JR. had slipped out of the house and was heading for the street.
  • Put JR. in playpen.
  • Add 1/3-cup milk, dash of salt, and boil, stirring constantly for 2 minutes.
  • Answer the door and apologize to neighbor for Billy having stuck a garden hose in man’s front door mail slot. Promise to pay for ruined carpet.
  • Tie Billy to clothesline.
  • Remove burned brownies from oven.
  • Start on dinner!

Because people are intelligent, you can write a recipe book and describe only the ideal case. When you’re programming, you need to be able to say, “This is plan A; this is plan B if plan A doesn’t work; this is plan C if plan B doesn’t work.” You can’t just say, “Gather these ingredients, mix, and bake;” you need all the except clauses like, “Remove teddy bear from oven…”

In addition to try and except, there is a finally clause which follows the try clause and any except clauses, and is to be executed whether or not an exception was caught. It can appear:



    result = items[0]
except IndexError:
    result = 0
    return result

One note to be given: it can be appropriate to put pass in an except clause, so we have:

total = 0
for input in inputs:
        total += int(input)
    except ValueError:

If you are making a running sum, it may be appropriate to ignore a specific error like this. But it is begging trouble to do:


This use of except: is a bit like the goto statement; it offers convenience at the beginning and can bring headaches down the road. What it says is, “Try to run these three methods; if anything goes wrong, just ignore it and move on.” And this is a recipe to bake JR’s teddy bear at 375 and then be left wondering why the house is so full of foul-smelling smoke: proper exception handling removes the teddy bear from the oven, repeatedly if need be, instead of just boldly ignoring problems that need to be addressed properly.

This can mean that, even if we expect we will mainly just write code for the ideal case, we may have to write a significant amount of code for non-ideal cases.

How do you create functions, procedures, and methods in Python? The function/procedure distinction that exists in C, where a function returns a value and a procedure does not, is not as prominent and Python programmers do not usually speak of “procedures.” If a function completes without returning a value, or returns without specifying a value, it returns the special value None, which is like an SQL NULL value. (Or a function can explicitly return None). The following three functions are equivalent; they take no arguments and return None:

def first():
def second():
def third():
    return None

A function’s required arguments are named; their type is not specified.

def ternary(condition, first_option, second_option):
    if condition:
        return first_option
        return second_option

I might note that in Python, the common ternary operator that appears in C/C++/Java like a > b ? a : b, is not a built-in structure in Python. There are some somewhat hackish ways Pythonistas use to fake it, notably a > b and a or b, but besides reading somewhat strangely, they run into problems a bit like C macros, where the C macro MAX(a, b) defined to a > b ? a : b will double-increment the selected argument if invoked as MAX(++c, ++d). In Python, a ternary operator like a > b and a or b can malfunction if its middle argument is falsy; it is more robust to write (a > b and [a] or [b])[0], at a significant cost to Pythonic ease in reading and understanding.

Returning to functions, it is possible to specify default values, as in:

def parse(input, default = 0):
        return int(input)
    except ValueError:
        return default

This code somewhat flexibly parses string/unicode input for an integer value, returning a default if it cannot be parsed. If invoked like parse(text), it will default to 0 in the case of a parse failure; if invoked like parse(text, 1), it will default to another value, such as 1, and if invoked like parse(text, None), the result can be examined for a parse failure: it will hold an integer if parsing was successful and the (non-integer) value None in the case of failure.

If a function has two or more arguments with default values, unnamed arguments are specified from left to right. Hence a function of:

def name_pets(dog = None, cat = None, bunny = None):
    result = []
    if dog:
        result.append(u'I have a dog named ' + dog + u'.')
    if cat:
        result.append(u'I have a cat named ' + cat + u'.')
    if bunny:
        result.append(u'I have a bunny named ' + bunny + u'.')
    return u'\n'.join(result)

Now there are a couple of things going on. name_pets(u’Goldie’) will return, u’I have a dog named Goldie.’ That is, the first argument will be assigned to dog, and name_pets(u’Jazz’, u’Zappy’) will correspondingly name the dog “Jazz” and the cat “Zappy.” But what if you want to name a cat but not a dog? Then you can explicitly name the argument: name_pets(cat=u’Guybrush’) will specify the value of the cat argument while leaving dog and bunny to have their default values.

That is one thing going on; there is something else going on with strings. If you have more than one pet, this method will place a line break between each sentence. It is common practice to build up a long string by creating an initially empty list, and then bit by bit build up the contents of the string in the list. Usually you can just stick them all together by u”.join(buffer), but if you choose another string, like u’\n’ here, then that other string is the glue that joins the pieces, and you get a line break between each sentence here.

You can specify an open-ended number of arguments, with a single asterisk before the last argument name, like:

def teach(teacher, course_name = None, *students):
    result = []
    result.append(u'This class is being taught by ' + teacher + u'.')
    if course_name != None:
        result.append(u'The name of the course is "' + course_name + u'."')
    for student in students:
        result.append(student + u' is a student in this course.')
    return u'\n'.join(result)

If invoked just as teach(u’Prof. Jones’), the result will be one line: u’This class is being taught by Prof. Jones.’. But if invoked as print teach(u’Prof. Jones’, u’Archaeology 101′, u’Alice’, u’Bob’, u’Charlie’), the output will be:

This class is being taught by Prof. Jones.
The name of the course is "Archaeology 101."
Alice is a student in this course.
Bob is a student in this course.
Charlie is a student in this course.

The last way arguments can be specified is by keyword arguments, where any arguments given by keyword that have not been otherwise claimed in the argument list are passed into a dictionary. So if we define a function:

def listing(**keywords):
    for key in keywords:
        print key + u': ' + keywords[key]

If we then call listing(name=’Alice’, phone='(800) 555-1212′, email=’’), it should print out something like:

phone: (800) 555-1212
name: Alice

As an aside, note that the arguments appear in a different order than they were given. Unlike a normal list where you should be able to get things in a fixed order, elements in a dictionary should not be expected to be in any particular order: nothing in the dictionary’s job description says that it should give back first the name, then the phone number, then the email. You are welcome to sort the keys where appropriate if you want them in alphabetical order:

def alphabetical_listing(**keywords):
    keys = keywords.keys()
    for key in keys:
        print key + u': ' + keywords[key]

If you then call alphabetical_listing(name=’Alice’, phone='(800) 555-1212′, email=’’), you should then get the keys in fixed alphabetical order:

name: Alice
phone: (800) 555-1212

You can have any combination, or none, of these ways of accepting an argument. If you use all of them, it should be like:

def example(required, default = u'default value', *arguments, **keywords):

Before leaving the topic of functions, I would like to mention that there are a couple of ways in which you can speak of a function returning more than one value, both of which are useful, and both of which are supported in Python. One way, which happens to be implemented by a tuple, is useful if you want to return (for instance) both a status code and a text description of the status. Let’s return to the task of parsing integers. We can write:

def parse(input, default = 0):
        return int(input), 1, u'Parsed successfully.'
    except ValueError:
        return default, 0, u'Parsing failed.'

There are a couple of ways these multiple results could be unpacked; one is:

value, status, explanation = parse(input)

But there is another sense in which you may want a generator that can keep on returning values. There is a classic story in mathematics in which one famous mathematician, as a boy, was in class and the teacher wanted some time and so decided to give the students a time-consuming task to keep them busy. And so the teacher told the students to add up the numbers from 1 to 100. And the future mathematician tried to figure out how to do things the smart way, realizing that if you add 1 and 100 you get 101; if you add 2 and 99 you also get 101, if you add 3 and 98 you get the exact same thing. If you pair the numbers like that, you have 50 pairs that have the same sum, 101, and so the grand total has to be 50 * 101 = 5050. And this is the number he gave the teacher. (The teacher, seeing that he had the correct answer so quickly, assumed that the boy must have cheated and gave him a spanking as his just reward.)

Based on this realization, there is a simple mathematical formula to calculate the sum of the first n positive integers: the sum is equal to n * (n + 1) / 2. But let us suppose we did not know that, and we wished to manually check what the result was. It turns out that there is a better and a worse way to calculate the sum of the numbers from 1 to 10,000,000,000. The bad way is:

sum = 0
for number in range(1, 10000000001):
    sum += number

And the good way is:

sum = 0
for number in xrange(1, 10000000001):
    sum += number

What’s the difference? The only surface difference is the letter ‘x’. But there is a major difference. It’s not primarily about speed; both are painfully slow, especially if you compare them to calculating 10000000000 * (10000000000 + 1) / 2, which is lightning fast. But the first one, the one with range(), creates a list with a staggering ten billion integers; a workstation with eight gigs of RAM doesn’t have nearly enough memory to hold the list, and if you try to run it, you may well observe your computer slow to a crawl as more and more memory is used just to create that one array. But the one with xrange() uses very little memory because it is a generator that produces the numbers as needed but never creates an enormous list. Something like xrange() can be implemented for our purposes as:

def xrange(first_number, second_number = None):
    if second_number == None:
        bottom = 0
        top = first_number
        bottom = first_number
        top = second_number
    current = bottom
    while current < top:
        yield current
        current += 1

The yield statement is like a return statement, except that the function yielding a value keeps on going. What xrange(1, 10000000001) does is keep on yielding the next counting number until it reaches its limit and it has nothing more to yield, but it doesn’t use very much memory itself, and using it like for number in xrange(1, 101) also doesn’t take that much memory. Using xrange() to calculate a very large sum may be very slow, but it won’t make everything else running on your whole computer grind to a halt by exhausting all available memory, and then crash without giving you a result.

There is one point we would like to stop on: depending on some prior language, some experienced programmers may be thinking, “Wait, did you try this? Isn’t that going to overflow?” And in fact it does give the correct result if we do it in Python:

>>> print 10000000000 * (10000000000 + 1) / 2

This result is correct, but C programmers, as well as C++ and Java programmers, may have a conditioned reflex: in C, for instance, just as a string buffer is an array of characters with a fixed length, integer types have a maximum and minimum possible value, and you may be able to choose an integer type with a bigger range, but there is always an arbitrary line, and if you cross it you get an overflow error that causes incorrect results. If we write in C:

#include <stdio.h>

int main()
    long top;
    scanf("%ld", &top);
    printf("%ld\n", top * (top + 1) / 2);
    return 0;

The code correctly gives 5050 if we give it a value of 100, just like Python, but if we give the original ten billion, we get, incorrectly:


Python turns out to have just as much an arbitrarily threshold as C, but the difference is that if you trigger an overflow, instead of continuing on with garbage results, Python quietly substitutes a type that will give correct results for any number that will fit in memory. So, if you ask for the number that Google is named after, ten multiplied by itself a hundred times, Python will handle the request correctly:

<<< print 10 ** 100

You can ask Python to handle integers millions of digits long, and while it may run more slowly, Python will happily continue to give correct results as long as it can fit your numbers in memory.

This, as with strings and lists, is an example of how Python follows the zero-one-infinity rule: to quote the jargon file:

“Allow none of foo, one of foo, or any number of foo.” A rule of thumb for software design, which instructs one to not place random limits on the number of instances of a given entity (such as: windows in a window system, letters in an OS’s filenames, etc.). Specifically, one should either disallow the entity entirely, allow exactly one instance (an “exception”), or allow as many as the user wants – address space and memory permitting.

The logic behind this rule is that there are often situations where it makes clear sense to allow one of something instead of none. However, if one decides to go further and allow N (for N > 1), then why not N+1? And if N+1, then why not N+2, and so on? Once above 1, there’s no excuse not to allow any N; hence, infinity.

Many hackers recall in this connection Isaac Asimov’s SF novel The Gods Themselves in which a character announces that the number 2 is impossible – if you’re going to believe in more than one universe, you might as well believe in an infinite number of them.

Here Python observes a principle that you should observe in what you pass on to your users. In terms of user interface design, for the iPhone to allow exactly one application at a time and for the Droid to allow multiple applications are both sensible approaches: perhaps the Droid marketing campaign insists that we need to run multiple apps, but for a long time the iPhone, designed to run one app at a time, was an uncontested darling. But what was not a correct decision was for the iPhone web browser to be able to have up to eight windows open, but not nine or more. If you are going to make a web interface that allows the user to upload files, you don’t want to say, “I don’t know exactly how many the user will want, so I’m deciding five is probably enough;” you start with one file upload input and add a button that creates another file upload input, and lets the user keep adding as many files as are wanted. Or, depending on context, you may create an interface that allows the user to upload at most one file as an avatar, or you may write an opinion survey in which uploading files does not make sense as part of the design. Zero, one, and infinity each have their places.

Python does not require you to do object-oriented programming, but in Python everything is an object. Functions are first-class objects and can be treated as such. Unlike Java, the humble integer is an object. dir() is a function that lists all of the methods of an object: if at the interpreter you call dir() on the integer 1, you get:

>>> dir(1)
['__abs__', '__add__', '__and__', '__class__', '__cmp__', '__coerce__',
'__delattr__', '__div__', '__divmod__', '__doc__', '__float__', '__floordiv__',
'__format__', '__getattribute__', '__getnewargs__', '__hash__', '__hex__',
'__index__', '__init__', '__int__', '__invert__', '__long__', '__lshift__',
'__mod__', '__mul__', '__neg__', '__new__', '__nonzero__', '__oct__', '__or__',
'__pos__', '__pow__', '__radd__', '__rand__', '__rdiv__', '__rdivmod__',
'__reduce__', '__reduce_ex__', '__repr__', '__rfloordiv__', '__rlshift__',
'__rmod__', '__rmul__', '__ror__', '__rpow__', '__rrshift__', '__rshift__',
'__rsub__', '__rtruediv__', '__rxor__', '__setattr__', '__sizeof__', '__str__',
'__sub__', '__subclasshook__', '__truediv__', '__trunc__', '__xor__',
'conjugate', 'denominator', 'imag', 'numerator', 'real']

Methods with names like __add__() are methods you can create or override for operator overloading; without attempting to explain all of these methods, I will briefly observe that not only is an integer like 1 an object, it is an object that supports quite a number of methods.

Python’s objects are in some ways like Java and in some ways like JavaScript: Python objects come from full-fledged classes like Java, but are more dynamic: fields and methods can be deleted from a Python object on the fly, like JavaScript, even though inheritance is classical and not prototypal. The typing is so-called “duck typing”: if it walks like a duck and it quacks like a duck, it’s a duck, and we have already seen one instance of duck typing at work: if an integer computation overflows, Python deftly substitutes another class that walks like the basic integer class and quacks like the basic integer class, but can handle millions of digits or more as long as you have enough memory and processor time. In Java, it is usually preferred practice to choose object composition over multiple inheritance; in Python, it is usually preferred practice to choose multiple inheritance over object composition.

The simplest example of a class is:

class simple:

An instance of this class can be created as:

instance = simple()

A somewhat more ornate example would be:

class counter(object):
    def __init__(self):
        self.value = 0
    def increment(self):
        self.value += 1
    def reset(self):
        self.value = 0

This is a working, if not necessarily advisable, counter class. The first argument to each of its methods is self (N.B. self, rather than this), and the class has one instance variable, defined in the __init__() method called in initialization, although it could just as well have many or none. What if we wanted to make the member field private? The short answer is, we can’t, and we don’t really want to. We could legitimately follow a convention that a member with a leading underscore in its name, _like_this instead of like_this, is a part of the present private implementation, does not represent in any sense the public API, and is subject to change or removal at any time. Rewritten that way, our class would look like this:

class counter(object):
    def __init__(self):
        self._value = 0
    def get_value(self):
        return self._value
    def increment(self):
        self._value += 1
    def reset(self):
        self._value = 0

But this way of solving things makes more sense in Java than Python; in Java it is recommended practice to make most or all instance variables private and then define corresponding getters and setters, and perhaps build a counter class that let you guarantee it could only be incremented, read, and optionally reset. It’s not just that the solution we have built works a bit more like Java than Python, but the problem we were addressing in the first place works more like Java than Python. Truer to the spirit of Python would be to use an integer and avoid the work of creating a class, let alone accessor methods.

A more Pythonic example might be a simple way to list tags in a webpage. It’s longer than our counter class, but not all that much longer:


import re
import urllib2

class webpage(object):
    def __init__(self, url = None):
        self.initialized = False
        if url:

    def __nonzero__(self):
        return self.initialized

    def list_tags(self):
        if self.initialized:
            result = []
            for tag in re.findall(ur'<(\w+)', self.text, re.DOTALL):
                if tag.lower() not in result:
            return result
            raise Exception(u'No webpage is loaded.')

    def load_url(self, url):
            text = urllib2.urlopen(url).read()
            self.url = url
            self.text = text
            self.initialized = True
            return True
        except URLError:
            return False

if __name__ == u'__main__':
    page = webpage(u'')
    if page:
        print page.list_tags()
        print u'The page could not be loaded.'

A few remarks about the very top: the top line, “#!/usr/bin/python”, tells Unix, Linux, and OS X systems that this is to be run as a Python program; if you intend at all to distribute your scripts, you should put this at the top. (It won’t do anything bad on a Windows system.)

At the beginning a couple of modules from the standard library are imported. urllib2 can fetch web and other URL’s, and re provides regular expressions.

This class can be initialized or uninitialized; if you ask for an analysis when it is not initialized, it raises an exception. Note that it considers itself fully initialized and set up, not necessarily when its __init__() method has been called, but when it has loaded a URL successfully.

There are a couple of conditions where a webpage object might not be initialized once the __init__() constructor has returned. The URL is an optional parameter, so it might not have passed through initialization. Or any of a number of transient or permanent network errors could have prevented the URL from loading successfully. The code to load a URL has:

def load_url(self, url):
            text = urllib2.urlopen(url).read()
            self.url = url
            self.text = text
            self.initialized = True
            return True
        except URLError:
            return False

Note the first real line of the method, text = urllib2.urlopen(url).read(). This will do one of two things: either load the URL’s contents successfully, or throw a URLError. If it throws an error, none of the next three lines is called. This means that an instance of this class is only fully set up with URL etc. stored after a successful read, and if you have an initialized class and try to load another URL and fail, previous data is not clobbered. This is something that can fail, but it is transactional, like a database transaction. Either all of the data is updated or none of it is updated, and in particular the object won’t be left in an inconsistent state.

It uses Perl-style regular expressions, which are powerful and popular but can be a bit cryptic. The one regular expression used is ur'<(\w+)’, with an ‘r’ after the initial ‘u’ to specify a raw string without Python doing things with backslashes that we don’t want when we’re doing regular expressions. What the core of the regular expression says in essence is, “a less than sign, followed by one or more word characters, and save the one or more word characters.” This will find more or less the HTML tags in a webpage.

And the last thing I will say, besides saying that you can use this class by giving,

page = webpage("")
print page.list_tags()

is that its present functionality is a foot in the door compared to the analysis that is possible. Right now the webpage class can load a webpage and do one thing with it, list the tags. From that starting point, it is not hard to copy and adapt list_tags() to make another method that will count the tags:

def count_tags(self):
        if self.initialized:
            result = {}
            for tag in re.findall(ur'<(\w+)', self.text, re.DOTALL):
                if tag.lower() in result:
                    result[tag.lower()] += 1
                    result[tag.lower()] = 1
            return result
            raise Exception(u'No webpage is loaded.')

There are any number of ways this class could be extended; listing and even counting the tags in a page are something like a “Hello, world!” program: they show the very beginning of the possibilities, not where they end.

Lastly, the portion of the file at the end, beginning with if __name__ == u’__main__’:, is part of a Python pattern of writing for reusability. The basic idea is that you write portions of a program you might want to reuse, such as objects and classes, in the main body of a program, and then material you want to run if you run the script directly, but not if you import it, beneath. If the script above is run directly, it will try to create a webpage and list its tags; but it can also be imported as a module, by something like:

import webpage_inspector

if the file is named If it is imported as a module, any classes and functions will be available, as webpage_inspector.webpage for instance, but the demo code at the bottom will be skipped.

(If you are interested in parsing webpages, I might suggest that you look at existing tools for Python, such as Beautiful Soup at, that put even more power at your fingertips.)

Finally, this class takes advantage of one of the special methods, in this case __nonzero__(). What that means is that, while works well enough to write:

page = webpage(u'')
if page.initialized:
    print page.count_tags()

you could also write:

page = webpage(u’‘) if page: print page.count_tags()That is, you can treat a webpage instance like a boolean variable; if you wanted to keep trying to load a page when your network is flaky, you could try:

import time
page = webpage(u'')
while not page:

Or, using another feature of the method, you could more concisely write:

import time
page = webpage()
while not page.load_url(u''):

This will keep on trying to load the page, and if necessary keep on trying, waiting 30 seconds between attempts so as not to engage in busy waiting. Busy waiting on a resource (network, filesystem, etc.) is the practice of trying to access a resource without interruption, which can be a way to be a very bad neighbor who drains resources heavily compared to someone who delays. Note that this will keep on trying forever if the error is permanent, such as a nonexistent domain.)

Python partially supports functional programming; here I will attempt, not to explain functional programming to the interested newcomer, but to orient the functional programmer who would like to know what features of functional programming Python supports. I have mentioned one feature of functional programming, (lazy) generators. Python also supports list comprehensions: if you have numbers, a list of integers and/or floating point numbers and want only the positive values, you can do:

positives = [x for x in numbers where x > 0]

lambdas, anonymous functions close to the lambdas of functional programming, are commonly used with filter, map, and reduce:

>>> numbers = [1, 2, 3]
>>> filter(lambda x: x > 1, numbers)
[2, 3]
>>> map(lambda x: x * 2, numbers)
[2, 4, 6]
>>> reduce(lambda x, y: x + y, numbers)

Python’s support of functional programming has not always been the best, and functional programmers may be dismayed to learn that Guido was hoping at one point to remove lambda altogether. This may be unfortunate, but to the reader interested in functional programming in Python, I may suggest downloading, reading, and using the Xoltar Toolkit. The Xoltar toolkit provides a module, functional, which is written in pure Python, is largely self-documenting code, and provides tools and/or reference implementations for currying and other favorites.

Now I will be discussing “usability for programmers.” Normally people who discuss usability discuss making a system usable for nontechnical end users, but there is such a thing as usability for programmers; a good chunk of Python’s attraction is that it shows meticulous attention to detail in the usability it provides to programmers.

There are a couple of ways in Python programming that we can provide good usability for other programmers. One is, in choosing names (for variables, methods, objects, classes, and so on), use whole_words_with_underscores, not camelCase. Emacs is perfectly willing to insert spaces in displayed camelCase words, but this is a compensation for camelCase’s weakness, and not everyone uses Emacs: or either of vim or Emacs, for that matter: GUI editors are not going to go away, even if our beloved command line editors might go away. The best thing of all would be to just use spaces in variable names, but so far the language designers have not supported that route. For a consolation prize, underscores are a little bit better than camelCase for native English speakers and significantly better than camelCase for programmers struggling with English’s alphabet. (At a glance, aFewWordsInCamelCase look a bit more like a block of undifferentiated text than a_few_words_separated_by_underscores if you live and breathe English’s alphabet, but if you have worked hard on English but its alphabet is still not your own, aFewWordsInCamelCase looks absolutely like a block of undifferentiated text next to a_few_words_separated_by_underscores. Remember reading “Hi, how are you?” written in Greek letters as “αι &oicron;υ αρ ιυ:” sometimes just using another language’s alphabet is a challenge.

Python has comments, but I would like to make a point. In Python, comments are not there to make code understandable. Python has been called “executable pseudocode,” and is your code’s job to be understandable itself. Comments have a place, but if you need to add comments to make your code understandable, that’s a sign you need to rewrite your code.

A Python comment, like Perl and Tcl (and one option in PHP), begins with a hash mark and continues to the end of the line. Adding a comment to one of the more cryptic lines of the example we have:

for tag in re.findall(ur'<(\w+)', self.text, re.DOTALL): # For each HTML opening tag:

Classes and functions have what are called “docstrings,” basically a short summary that is programmatically accessible, written as a (usually) triple quoted string immediately after the class/function definition:

class empty:
    u'''This class does nothing.'''
    def __init__(self):
        u'''This initializer does nothing.'''

In terms of indentation, you should always indent by four spaces. Emacs handles this gracefully; vim’s autoindent by default will substitute a tab for eight spaces where it can, leaving code that looks right in your editor but breaks when you run it in Python. If you use vim for Python, you should edit your ~/.vimrc, creating it if need be, and include the following:

set autoindent smartindent tabstop=4 shiftwidth=4 expandtab shiftround

Now we will look at a basic issue of problem solving, the Python way. Usually “encryption” refers to strong encryption, which refers to serious attempts to protect data; when you shop at a responsible merchant and your credit card information is transferred at a URI that begins with “https,” that’s strong encryption in use. There is also something called weak encryption, which includes such things as codes written as puzzles for children to break. If strong encryption is meant to resist prying, weak encryption is at times intended to be pried open. One classic use of weak encryption is rot-13, which moves each letter forward or back by thirteen places and has the convenient feature that running rot-13 again on encrypted text decrypts it. Historically, on UseNet, some offensive material was posted in rot-13 as a matter of common courtesy, so that people were warned and did not need to unintentionally read things that would offend them, and many old newsreaders included a single keystroke command to decrypt rot-13 text. For an example, if you rot-13 “The quick brown dog jumps over the lazy red fox.”, you get, “Gur dhvpx oebja qbt whzcf bire gur ynml erq sbk.”, and if you rot-13 “Gur dhvpx oebja qbt whzcf bire gur ynml erq sbk.”, you get the original “The quick brown dog jumps over the lazy red fox.”, restored perfectly.

Now suppose we want to be able to rot-13 encrypt text from Python. Rot-13 represents an extremely simple algorithm, and for the most part there is a perfectly obvious way to do it:

def rot13(text):
    result = []
    for character in unicode(text): 
        if character == u'a':
        elif character == u'b':
        elif character == u'c':
        elif character == u'd':
        elif character == u'e':
        elif character == u'f':
        elif character == u'g':
        elif character == u'h':
        elif character == u'i':
        elif character == u'j':
        elif character == u'k':
        elif character == u'l':
        elif character == u'm':
        elif character == u'n':
        elif character == u'o':
        elif character == u'p':
        elif character == u'q':
        elif character == u'r':
        elif character == u's':
        elif character == u't':
        elif character == u'u':
        elif character == u'v':
        elif character == u'w':
        elif character == u'x':
        elif character == u'y':
        elif character == u'z':
        elif character == u'A':
        elif character == u'B':
        elif character == u'C':
        elif character == u'D':
        elif character == u'E':
        elif character == u'F':
        elif character == u'G':
        elif character == u'H':
        elif character == u'I':
        elif character == u'J':
        elif character == u'K':
        elif character == u'L':
        elif character == u'M':
        elif character == u'N':
        elif character == u'O':
        elif character == u'P':
        elif character == u'Q':
        elif character == u'R':
        elif character == u'S':
        elif character == u'T':
        elif character == u'U':
        elif character == u'V':
        elif character == u'W':
        elif character == u'X':
        elif character == u'Y':
        elif character == u'Z':
    return u''.join(result)

This is a perfectly effectively way of solving the problem, but you may wince at the thought of all that typing, and that is a good sign that this solution is not very Pythonic. Some readers may perhaps be disappointed with me (or, perhaps, not disappointed with me in the slightest) to learn that I cheated: I wrote three lines of code so Python would generate for me the long and tedious part of the routine so I could get out of such a chore, and then pasted the output into the page. We need a better solution in this.

One of the paradoxes in the programming world is that solving a problem in a more general sense may actually be less work. What we basically need is to do some translations of characters, so how can we do that? Remembering that Python’s switch statement is the dictionary, we could try:

def translate(text, translation):
    result = []
    for character in unicode(text):
        if character in translation:
    return u''.join(result)

This is a big improvement: cleaner, simpler, much shorter, and much more powerful. So if we are dealing with strings used to store genetic data, we can also get the complement of a string. So to get the complement of u’ATTAGCGACT’, we can do:

original = u'ATTAGCGACT'
complement = translate(original, {u'A': u'T', u'T': u'A', u'C': u'G', u'G': u'C'})

And we’ve improved things, or at least it seems we’ve improved until we get around to the chore of typing out the dictionary contents for every uppercase and lowercase letter. We could write another Python snippet to autogenerate that, as the chore is not only tedious but an invitation to error, but is there a better way?

In fact there is. We can import the string library and take advantage of something that is already there, and here is a solution that is not daunting to type out, only slightly tedious, although here we must use a little ASCII:

import string
translation = string.maketrans(u'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz', u'NOPQRSTUVWXYZABCDEFGHIJKLMnopqrstuvwxyzabcdefghijklm')
translated = 'The quick brown dog jumps over the lazy red fox.'.translate(translation)

And with that, instead of solving the problem of translation ourselves, we have the problem already solved for us, for the most part. The variable translated is now u’Gur dhvpx oebja qbt whzcf bire gur ynml erq sbk.’

Those versed in Python’s character, though, might possibly not stop here. You can make up your own forms of weak encryption, but rot-13 encoding is not the world’s most obscure thing to do. Is this really the easiest and most Pythonic way to rot-13 a string? Let us fire up our Python interpreter:

>>> print u'The quick brown dog jumps over the lazy red fox.'.encode(u'rot13')
Gur dhvpx oebja qbt whzcf bire gur ynml erq sbk.

The problem is already solved for us.

A famous blog post called Python Is Not Java addresses Java programmers rather bluntly on why doing everything you’d do in Java is not getting the most out of Python:

Essentially, if you’ve been using Java for a while and are new to Python, do not trust your instincts. Your instincts are tuned to Java, not Python. Take a step back, and above all, stop writing so much code.

To do this, become more demanding of Python. Pretend that Python is a magic wand that will miraculously do whatever you want without you needing to lifting a finger. Ask, “how does Python already solve my problem?” and “What Python language feature most resembles my problem?” You will be absolutely astonished at how often it happens that thing you need is already there in some form. In fact, this phenomenon is so common, even among experienced Python programmers, that the Python community has a name for it. We call it “Guido’s time machine”, because sometimes it seems as though that’s the only way he could’ve known what we needed, before we knew it ourselves.

Python’s core library is documented extensively, searchably, and well at (be advised that, besides being easy to type when you really mean, is a famous porn site), and Python’s core library is your best friend. The code samples peppering this chapter are intended to simply illustrate basic features of the language; once you get up to speed, it’s not so much that you’ll have better ways of doing what that code does, as that you’ll have better ways of avoiding doing that, using Python more like a magic wand. I will not be attempting to provide select highlights from the core library because that would easily be a book in its own right. But we are saying that the Python core library is among a good Python programmer’s most heavily used bookmarks.

I advocate, when possible, moving from unadorned, “bare metal” Python to what might be called “Python++.” Let me explain what I mean by this and why it is more Pythonic.

The move from C to C++ is a move made by extending the core language to directly support objects, templates, and other features. There have been some efforts to extend the Python core language: easy_extend is intended to make it Pythonically easy to tinker with and extend the language syntax. However, I have never heard of production use of these extensions Pythonically saving time and effort while making programmers more productive.

What I have heard consistently is that using a good library really does qualify as a move from “unadorned Python” to “Python++”. A StackOverflow user asked, “Have you considered using Django and found good reasons not to?” And people listed legitimate flaws with Django and legitimate reasons they use other alternatives, but one developer got over thirty upvotes with a response of, “Yeah, I am an honest guy, and the client wanted to charge by the hour. There was no way Django was going to allow me to make enough money.” For the web, frameworks like Django, TurboGears, and web2py offer significantly greater power with less work in more truly Pythonic fashion. Python’s standard library does come with its cgi module, and it is possible to write webapps with it, but using the cgi module and the standard library to implement a social networking site with all the common bells and whistles would take months or years. With Python + Django + Pinax the time is more like hours. If you use Python, you don’t have to reinvent the wheel. If you want a social network and you use Django and Pinax, you don’t have to reinvent the internal combustion engine either, or power steering, or antilock brakes, because they are all included in standard packages for a car or truck. If your goal is an online store instead of a social network Pinax will not likely be of much help, but Django + Satchmo will. Both of them provide ready-made solution to routine tasks, whether user avatars with gravatar support, or a shopping cart with that works with any of several payment gateways.

This is true if you are developing for the web; if you are in another domain, similar remarks could be made for NumPy or SciPy.

I do not wish to discourage anyone from using different frameworks than I have mentioned, or suggest that there is something wrong with thinking things out and choosing TurboGears over Django. Web2py in particular cuts out one very daunting hurdle to new programmers: a command line with a steep learning curve. However, I do advocate the use of a serious, noteworthy “Python++” framework and not the standard library alone: the cgi module works entirely as advertised, but the difference between Python + Django + Pinax and just Python with the cgi module is comparable to the difference between Python with the cgi module and programming on bare metal in C.

I may further comment that fundamental usability is the same whether the implementation is Django, TurboGears, web2py, or for that matter Python with the cgi module or C working with bare metal. It would not be a surprise if Ruby on Rails or PHP developers were to look through this and find it speaks to how they can create a better user interface.


What is it that is attractive about Python?

Perl has been rightly called “Unix’s Swiss Army Chainsaw,” and perhaps nothing else so well concisely describes what it is that Perl has to offer. Java might be compared to the equipment owned by a construction company, equipment that allows large organizations and a well-organized army to erect something monumental. C would be a scientific scalpel, molecular sharp: the most exacting precision you can get unless you go subatomic with assembler or machine language, but treacherously slippery and easy to cut yourself with, something like the scientific-use razor blades which came in a package labelled: “WARNING: Knife is extremely sharp. Keep out of children.”

It is my suggestion that Python is a lightsabre, and wielding it well makes a graceful foundation for usability.

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