In [39]:
from __future__ import print_function
x = int(input("Please enter an integer: "))
if x < 0:
x = 0
print('Negative changed to zero')
elif x == 0:
print('Zero')
elif x == 1:
print('Single')
else:
print('More')
There can be zero or more elif parts, and the else part is optional.
The keyword elif is short for else if, and is useful to avoid excessive indentation.
An if ... elif ... elif ... sequence is a substitute for the switch or case statements found in other languages.
for StatementsThe for statement in Python differs a bit from what you may be used to in C or Pascal.
Rather than
Python’s for statement iterates over the items of any sequence (a list or a string), in the order that they appear in the sequence.
For example (no pun intended):
In [4]:
# Measure some strings:
words = ['cat', 'window', 'defenestrate']
for w in words:
print(w, len(w))
If you need to modify the sequence you are iterating over while inside the loop (for example to duplicate selected items), it is recommended that you first make a copy.
Iterating over a sequence does not implicitly make a copy. The slice notation makes this especially convenient:
In [5]:
for w in words[:]: # Loop over a slice copy of the entire list.
if len(w) > 6:
words.insert(0, w)
words
Out[5]:
In [9]:
list(range(10))
Out[9]:
The given end point is never part of the generated list; range(10) generates a list of 10 values, the legal indices for items of a sequence of length 10.
It is possible to let the range start at another number, or to specify a different increment (even negative; sometimes this is called the ‘step’):
In [10]:
list(range(5, 10))
Out[10]:
In [12]:
list(range(0, 10, 3))
Out[12]:
In [13]:
list(range(-10, -100, -30))
Out[13]:
To iterate over the indices of a sequence, you can combine range() and len() as follows:
In [14]:
a = ['Mary', 'had', 'a', 'little', 'lamb']
for i in range(len(a)):
print(i, a[i])
In most such cases, however, it is convenient to use the enumerate() function, see Looping Techniques.
break and continue Statements, and else Clauses on LoopsThe break statement, like in C, breaks out of the smallest enclosing for or while loop.
Loop statements may have an else clause:
for) while)break statement. This is exemplified by the following loop, which searches for prime numbers:
In [15]:
for n in range(2, 10):
for x in range(2, n):
if n % x == 0:
print(n, 'equals', x, '*', n/x)
break
else:
# loop fell through without finding a factor
print(n, 'is a prime number')
(Yes, this is the correct code. Look closely: the else clause belongs to the for loop, not the if statement.)
When used with a loop, the else clause has more in common with the else clause of a try statement than it does that of if statements:
try statement’s else clause runs when no exception occurFor more on the try statement and exceptions, see Handling Exceptions.
The continue statement, also borrowed from C, continues with the next iteration of the loop:
In [16]:
for num in range(2, 10):
if num % 2 == 0:
print("Found an even number", num)
continue
print("Found a number", num)
In [17]:
# while True:
# pass # Busy-wait for keyboard interrupt (Ctrl+C)
This is commonly used for creating minimal classes:
In [19]:
class MyEmptyClass:
pass
Another place pass can be used is as a place-holder for a function or conditional body when you are working on new code, allowing you to keep thinking at a more abstract level.
The pass is silently ignored:
In [20]:
def initlog(*args):
pass # Remember to implement this!
In [22]:
def fib(n): # write Fibonacci series up to n
"""Print a Fibonacci series up to n."""
a, b = 0, 1
while a < n:
print(a,end=' ')
a, b = b, a+b
# Now call the function we just defined:
fib(2000)
The first statement of the function body can optionally be a string literal; this string literal is the function’s documentation string, or docstring. (More about docstrings can be found in the section Documentation Strings.)
There are tools which use docstrings to automatically produce online or printed documentation, or to let the user interactively browse through code.
It’s good practice to include docstrings in code that you write, so make a habit of it.
The execution of a function introduces a new symbol table used for the local variables of the function.
More precisely, all variable assignments in a function store the value in the local symbol table;
whereas variable references first look in the local symbol table, then in the local symbol tables of enclosing functions, then in the global symbol table, and finally in the table of built-in names.
Thus, global variables cannot be directly assigned a value within a function (unless named in a global statement), although they may be referenced.
The actual parameters (arguments) to a function call are introduced in the local symbol table of the called function when it is called;
thus, arguments are passed using call by value (where the value is always an object reference, not the value of the object).
When a function calls another function, a new local symbol table is created for that call.
A function definition introduces the function name in the current symbol table.
The value of the function name has a type that is recognized by the interpreter as a user-defined function.
This value can be assigned to another name which can then also be used as a function. This serves as a general renaming mechanism:
In [23]:
fib
Out[23]:
In [24]:
f = fib
f(100)
NoneComing from other languages, you might object that fib is not a function but a procedure since it doesn’t return a value.
In fact, even functions without a return statement do return a value, albeit a rather boring one.
This value is called None (it’s a built-in name).
Writing the value None is normally suppressed by the interpreter if it would be the only value written.
You can see it if you really want to using print:
In [25]:
fib(0) # Nothing prints
In [26]:
print(fib(0))
In [27]:
def fib2(n): # return Fibonacci series up to n
"""Return a list containing the Fibonacci series up to n."""
result = []
a, b = 0, 1
while a < n:
result.append(a) # see below
a, b = b, a+b
return result
f100 = fib2(100) # call it
f100 # write the result
Out[27]:
This example, as usual, demonstrates some new Python features:
The return statement returns with a value from a function. return without an expression argument returns None. Falling off the end of a function also returns None.
The statement result.append(a) calls a method of the list object result.
obj.methodname, where obj is some object (this may be an expression), and methodname is the name of a method that is defined by the object’s type. append() shown in the example is defined for list objects; it adds a new element at the end of the list. In this example it is equivalent to result = result + [a], but more efficient.
In [28]:
def ask_ok(prompt, retries=4, complaint='Yes or no, please!'):
while True:
ok = raw_input(prompt)
if ok in ('y', 'ye', 'yes'):
return True
if ok in ('n', 'no', 'nop', 'nope'):
return False
retries = retries - 1
if retries < 0:
raise IOError('refusenik user')
print(complaint)
This function can be called in several ways:
This example also introduces the in keyword. This tests whether or not a sequence contains a certain value.
The default values are evaluated at the point of function definition in the defining scope, so that
In [29]:
i = 5
def f(arg=i):
print(arg
)
i = 6
f()
In [31]:
def f(a, L=[]):
L.append(a)
return L
print(f(1))
print(f(2))
print(f(3))
If you don’t want the default to be shared between subsequent calls, you can write the function like this instead:
In [33]:
def f(a, L=None):
if L is None:
L = []
L.append(a)
return L
print(f(1))
print(f(2))
print(f(3))
In [34]:
def parrot(voltage, state='a stiff', action='voom', type='Norwegian Blue'):
print("-- This parrot wouldn't", action,)
print("if you put", voltage, "volts through it.")
print("-- Lovely plumage, the", type)
print("-- It's", state, "!")
accepts one required argument (voltage) and three optional arguments (state, action, and type). This function can be called in any of the following ways:
In [35]:
parrot(1000) # 1 positional argument
parrot(voltage=1000) # 1 keyword argument
parrot(voltage=1000000, action='VOOOOOM') # 2 keyword arguments
parrot(action='VOOOOOM', voltage=1000000) # 2 keyword arguments
parrot('a million', 'bereft of life', 'jump') # 3 positional arguments
parrot('a thousand', state='pushing up the daisies') # 1 positional, 1 keyword
Small anonymous functions can be created with the lambda keyword.
This function returns the sum of its two arguments: lambda a, b: a+b.
Lambda functions can be used wherever function objects are required.
They are syntactically restricted to a single expression.
Semantically, they are just syntactic sugar for a normal function definition.
Like nested function definitions, lambda functions can reference variables from the containing scope:
In [36]:
def make_incrementor(n):
return lambda x: x + n
f = make_incrementor(42)
f(0), f(1)
Out[36]:
The above example uses a lambda expression to return a function. Another use is to pass a small function as an argument:
In [37]:
pairs = [(1, 'one'), (2, 'two'), (3, 'three'), (4, 'four')]
pairs.sort(key=lambda pair: pair[1])
pairs
Out[37]:
There are emerging conventions about the content and formatting of documentation strings.
The first line should always be a short, concise summary of the object’s purpose.
For brevity, it should not explicitly state the object’s name or type, since these are available by other means (except if the name happens to be a verb describing a function’s operation).
This line should begin with a capital letter and end with a period.
If there are more lines in the documentation string, the second line should be blank, visually separating the summary from the rest of the description.
The following lines should be one or more paragraphs describing the object’s calling conventions, its side effects, etc.
In [38]:
def my_function():
"""Do nothing, but document it.
No, really, it doesn't do anything.
"""
pass
print(my_function.__doc__)
Now that you are about to write longer, more complex pieces of Python, it is a good time to talk about coding style.
Most languages can be written (or more concise, formatted) in different styles; some are more readable than others.
Making it easy for others to read your code is always a good idea, and adopting a nice coding style helps tremendously for that.
For Python, PEP 8 has emerged as the style guide that most projects adhere to; it promotes a very readable and eye-pleasing coding style. Every Python developer should read it at some point; here are the most important points extracted for you:
Use 4-space indentation, and no tabs.
4 spaces are a good compromise between small indentation (allows greater nesting depth) and large indentation (easier to read). Tabs introduce confusion, and are best left out.
Wrap lines so that they don’t exceed 79 characters.
This helps users with small displays and makes it possible to have several code files side-by-side on larger displays.
Use blank lines to separate functions and classes, and larger blocks of code inside functions.
When possible, put comments on a line of their own.
Use docstrings.
Use spaces around operators and after commas, but not directly inside bracketing constructs: a = f(1, 2) + g(3, 4).
Name your classes and functions consistently; the convention is to use CamelCase for classes and lower_case_with_underscores for functions and methods. Always use self as the name for the first method argument (see A First Look at Classes for more on classes and methods).
Don’t use fancy encodings if your code is meant to be used in international environments. Plain ASCII works best in any case.