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')

"""
Explanation: Module 4 - More Control Flow Tools
Besides the while statement just introduced, Python knows the usual control flow statements known from other languages, with some twists.
if Statements
Perhaps the most well-known statement type is the if statement. For example:
End of explanation
"""


# Measure some strings:
words = ['cat', 'window', 'defenestrate']
for w in words:
    print(w, len(w))

"""
Explanation: 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 Statements
The for statement in Python differs a bit from what you may be used to in C or Pascal. 
Rather than 

always iterating over an arithmetic progression of numbers (like in Pascal), 
or giving the user the ability to define both the iteration step and halting condition (as C)

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):
End of explanation
"""


for w in words[:]:  # Loop over a slice copy of the entire list.
    if len(w) > 6:
        words.insert(0, w)

words

"""
Explanation: 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:
End of explanation
"""


list(range(10))

"""
Explanation: The range() Function
If you do need to iterate over a sequence of numbers, the built-in function range() comes in handy. 
It creates generators containing arithmetic progressions:
End of explanation
"""


list(range(5, 10))

list(range(0, 10, 3))

list(range(-10, -100, -30))

"""
Explanation: 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’):
End of explanation
"""


a = ['Mary', 'had', 'a', 'little', 'lamb']
for i in range(len(a)):
    print(i, a[i])

"""
Explanation: To iterate over the indices of a sequence, you can combine range() and len() as follows:
End of explanation
"""


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')

"""
Explanation: In most such cases, however, it is convenient to use the enumerate() function, see Looping Techniques.
break and continue Statements, and else Clauses on Loops
The break statement, like in C, breaks out of the smallest enclosing for or while loop.
Loop statements may have an else clause:
- it is executed when the loop terminates through exhaustion of the list (with for) 
- or when the condition becomes false (with while)
- but not when the loop is terminated by a break statement. 
This is exemplified by the following loop, which searches for prime numbers:
Prime Number Search
End of explanation
"""


for num in range(2, 10):
    if num % 2 == 0:
        print("Found an even number", num)
        continue
    print("Found a number", num)

"""
Explanation: (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: 
- a try statement’s else clause runs when no exception occur
- a loop’s else clause runs when no break occurs. 
For 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:
End of explanation
"""


# while True:
#     pass  # Busy-wait for keyboard interrupt (Ctrl+C)

"""
Explanation: pass Statements
The pass statement does nothing. 
It can be used when a statement is required syntactically but the program requires no action. 
For example:
End of explanation
"""


class MyEmptyClass:
    pass

"""
Explanation: This is commonly used for creating minimal classes:
End of explanation
"""


def initlog(*args):
    pass   # Remember to implement this!

"""
Explanation: 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:
End of explanation
"""


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)

"""
Explanation: Defining Functions
We can create a function that writes the Fibonacci series to an arbitrary boundary:
End of explanation
"""


fib

f = fib
f(100)

"""
Explanation: def
The keyword def introduces a function definition. 
It must be followed by the function name and the parenthesized list of formal parameters. 
The statements that form the body of the function start at the next line, and must be indented.
Document String
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.
Function Scope
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.
Arguments
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:
End of explanation
"""


fib(0) # Nothing prints

print(fib(0))

"""
Explanation: Default Return is None
Coming 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:
End of explanation
"""


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

"""
Explanation: Return a List
It is simple to write a function that returns a list of the numbers of the Fibonacci series, instead of printing it:
End of explanation
"""


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)

"""
Explanation: 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. 

A method is a function that ‘belongs’ to an object and is named 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. 
Different types define different methods. Methods of different types may have the same name without causing ambiguity. (It is possible to define your own object types and methods, using classes, see Classes) 
The method 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.

More on Defining Functions
It is also possible to define functions with a variable number of arguments. There are three forms, which can be combined.
Default Argument Values
The most useful form is to specify a default value for one or more arguments. This creates a function that can be called with fewer arguments than it is defined to allow. For example:
End of explanation
"""


i = 5

def f(arg=i):
    print(arg
)
i = 6
f()

"""
Explanation: This function can be called in several ways:

giving only the mandatory argument: ask_ok('Do you really want to quit?')
giving one of the optional arguments: ask_ok('OK to overwrite the file?', 2)
or even giving all arguments: ask_ok('OK to overwrite the file?', 2, 'Come on, only yes or no!')

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
End of explanation
"""


def f(a, L=[]):
    L.append(a)
    return L

print(f(1))
print(f(2))
print(f(3))

"""
Explanation: Important warning:
The default value is evaluated only once. 
This makes a difference when the default is a mutable object such as a list, dictionary, or instances of most classes. 
For example, the following function accumulates the arguments passed to it on subsequent calls:
End of explanation
"""


def f(a, L=None):
    if L is None:
        L = []
    L.append(a)
    return L

print(f(1))
print(f(2))
print(f(3))

"""
Explanation: If you don’t want the default to be shared between subsequent calls, you can write the function like this instead:
End of explanation
"""


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, "!")

"""
Explanation: Keyword Arguments
Functions can also be called using keyword arguments of the form kwarg=value. For instance, the following function:
End of explanation
"""


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

"""
Explanation: accepts one required argument (voltage) and three optional arguments (state, action, and type). This function can be called in any of the following ways:
End of explanation
"""


def make_incrementor(n):
    return lambda x: x + n

f = make_incrementor(42)
f(0), f(1)


"""
Explanation: Lambda Expressions
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:
End of explanation
"""


pairs = [(1, 'one'), (2, 'two'), (3, 'three'), (4, 'four')]
pairs.sort(key=lambda pair: pair[1])
pairs

"""
Explanation: The above example uses a lambda expression to return a function. Another use is to pass a small function as an argument:
End of explanation
"""


def my_function():
    """Do nothing, but document it.

    No, really, it doesn't do anything.
    """
    pass

print(my_function.__doc__)

"""
Explanation: Document Strings
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.
End of explanation
"""


