learnxinyminutes-docs/pythonlegacy.html.markdown at a7af37b804dfd4896bc4f77f888ca9311b8f2cc6

Python was created by Guido Van Rossum in the early 90s. It is now one of the most popular languages in existence. I fell in love with Python for its syntactic clarity. It's basically executable pseudocode.
Feedback would be highly appreciated! You can reach me at @louiedinh or louiedinh [at] [google's email service]
Note: This article applies to Python 2.7 specifically, but should be applicable to Python 2.x. Python 2.7 is reaching end of life and will stop being maintained in 2020, it is though recommended to start learning Python with Python 3. For Python 3.x, take a look at the Python 3 tutorial.
It is also possible to write Python code which is compatible with Python 2.7 and 3.x at the same time, using Python __future__ imports. __future__ imports allow you to write Python 3 code that will run on Python 2, so check out the Python 3 tutorial.
# Single line comments start with a number symbol.

""" Multiline strings can be written
    using three "s, and are often used
    as comments
"""

####################################################
# 1. Primitive Datatypes and Operators
####################################################

# You have numbers
3  # => 3

# Math is what you would expect
1 + 1  # => 2
8 - 1  # => 7
10 * 2  # => 20
35 / 5  # => 7

# Division is a bit tricky. It is integer division and floors the results
# automatically.
5 / 2  # => 2

# To fix division we need to learn about floats.
2.0  # This is a float
11.0 / 4.0  # => 2.75 ahhh...much better

# Result of integer division truncated down both for positive and negative.
5 // 3  # => 1
5.0 // 3.0  # => 1.0 # works on floats too
-5 // 3  # => -2
-5.0 // 3.0  # => -2.0

# Note that we can also import division module(Section 6 Modules)
# to carry out normal division with just one '/'.
from __future__ import division

11 / 4  # => 2.75  ...normal division
11 // 4  # => 2 ...floored division

# Modulo operation
7 % 3  # => 1

# Exponentiation (x to the yth power)
2 ** 4  # => 16

# Enforce precedence with parentheses
(1 + 3) * 2  # => 8

# Boolean Operators
# Note "and" and "or" are case-sensitive
True and False  # => False
False or True  # => True

# Note using Bool operators with ints
0 and 2  # => 0
-5 or 0  # => -5
0 == False  # => True
2 == True  # => False
1 == True  # => True

# negate with not
not True  # => False
not False  # => True

# Equality is ==
1 == 1  # => True
2 == 1  # => False

# Inequality is !=
1 != 1  # => False
2 != 1  # => True

# More comparisons
1 < 10  # => True
1 > 10  # => False
2 <= 2  # => True
2 >= 2  # => True

# Comparisons can be chained!
1 < 2 < 3  # => True
2 < 3 < 2  # => False

# Strings are created with " or '
"This is a string."
'This is also a string.'

# Strings can be added too!
"Hello " + "world!"  # => "Hello world!"
# Strings can be added without using '+'
"Hello " "world!"  # => "Hello world!"

# ... or multiplied
"Hello" * 3  # => "HelloHelloHello"

# A string can be treated like a list of characters
"This is a string"[0]  # => 'T'

# You can find the length of a string
len("This is a string")  # => 16

# String formatting with %
# Even though the % string operator will be deprecated on Python 3.1 and removed
# later at some time, it may still be good to know how it works.
x = 'apple'
y = 'lemon'
z = "The items in the basket are %s and %s" % (x, y)

# A newer way to format strings is the format method.
# This method is the preferred way
"{} is a {}".format("This", "placeholder")
"{0} can be {1}".format("strings", "formatted")
# You can use keywords if you don't want to count.
"{name} wants to eat {food}".format(name="Bob", food="lasagna")

# None is an object
None  # => None

# Don't use the equality "==" symbol to compare objects to None
# Use "is" instead
"etc" is None  # => False
None is None  # => True

# The 'is' operator tests for object identity. This isn't
# very useful when dealing with primitive values, but is
# very useful when dealing with objects.

# Any object can be used in a Boolean context.
# The following values are considered falsey:
#    - None
#    - zero of any numeric type (e.g., 0, 0L, 0.0, 0j)
#    - empty sequences (e.g., '', (), [])
#    - empty containers (e.g., {}, set())
#    - instances of user-defined classes meeting certain conditions
#      see: https://docs.python.org/2/reference/datamodel.html#object.__nonzero__
#
# All other values are truthy (using the bool() function on them returns True).
bool(0)  # => False
bool("")  # => False


####################################################
# 2. Variables and Collections
####################################################

# Python has a print statement
print "I'm Python. Nice to meet you!"  # => I'm Python. Nice to meet you!

# Simple way to get input data from console
input_string_var = raw_input(
    "Enter some data: ")  # Returns the data as a string
input_var = input("Enter some data: ")  # Evaluates the data as python code
# Warning: Caution is recommended for input() method usage
# Note: In python 3, input() is deprecated and raw_input() is renamed to input()

# No need to declare variables before assigning to them.
some_var = 5  # Convention is to use lower_case_with_underscores
some_var  # => 5

# Accessing a previously unassigned variable is an exception.
# See Control Flow to learn more about exception handling.
some_other_var  # Raises a name error

# if can be used as an expression
# Equivalent of C's '?:' ternary operator
"yahoo!" if 3 > 2 else 2  # => "yahoo!"

# Lists store sequences
li = []
# You can start with a prefilled list
other_li = [4, 5, 6]

# Add stuff to the end of a list with append
li.append(1)  # li is now [1]
li.append(2)  # li is now [1, 2]
li.append(4)  # li is now [1, 2, 4]
li.append(3)  # li is now [1, 2, 4, 3]
# Remove from the end with pop
li.pop()  # => 3 and li is now [1, 2, 4]
# Let's put it back
li.append(3)  # li is now [1, 2, 4, 3] again.

# Access a list like you would any array
li[0]  # => 1
# Assign new values to indexes that have already been initialized with =
li[0] = 42
li[0]  # => 42
li[0] = 1  # Note: setting it back to the original value
# Look at the last element
li[-1]  # => 3

# Looking out of bounds is an IndexError
li[4]  # Raises an IndexError

# You can look at ranges with slice syntax.
# (It's a closed/open range for you mathy types.)
li[1:3]  # => [2, 4]
# Omit the beginning
li[2:]  # => [4, 3]
# Omit the end
li[:3]  # => [1, 2, 4]
# Select every second entry
li[::2]  # =>[1, 4]
# Reverse a copy of the list
li[::-1]  # => [3, 4, 2, 1]
# Use any combination of these to make advanced slices
# li[start:end:step]

# Remove arbitrary elements from a list with "del"
del li[2]  # li is now [1, 2, 3]

# You can add lists
li + other_li  # => [1, 2, 3, 4, 5, 6]
# Note: values for li and for other_li are not modified.

# Concatenate lists with "extend()"
li.extend(other_li)  # Now li is [1, 2, 3, 4, 5, 6]

# Remove first occurrence of a value
li.remove(2)  # li is now [1, 3, 4, 5, 6]
li.remove(2)  # Raises a ValueError as 2 is not in the list

# Insert an element at a specific index
li.insert(1, 2)  # li is now [1, 2, 3, 4, 5, 6] again

# Get the index of the first item found
li.index(2)  # => 1
li.index(7)  # Raises a ValueError as 7 is not in the list

# Check for existence in a list with "in"
1 in li  # => True

# Examine the length with "len()"
len(li)  # => 6

# Tuples are like lists but are immutable.
tup = (1, 2, 3)
tup[0]  # => 1
tup[0] = 3  # Raises a TypeError

# You can do all those list thingies on tuples too
len(tup)  # => 3
tup + (4, 5, 6)  # => (1, 2, 3, 4, 5, 6)
tup[:2]  # => (1, 2)
2 in tup  # => True

# You can unpack tuples (or lists) into variables
a, b, c = (1, 2, 3)  # a is now 1, b is now 2 and c is now 3
d, e, f = 4, 5, 6  # you can leave out the parentheses
# Tuples are created by default if you leave out the parentheses
g = 4, 5, 6  # => (4, 5, 6)
# Now look how easy it is to swap two values
e, d = d, e  # d is now 5 and e is now 4

# Dictionaries store mappings
empty_dict = {}
# Here is a prefilled dictionary
filled_dict = {"one": 1, "two": 2, "three": 3}

# Look up values with []
filled_dict["one"]  # => 1

# Get all keys as a list with "keys()"
filled_dict.keys()  # => ["three", "two", "one"]
# Note - Dictionary key ordering is not guaranteed.
# Your results might not match this exactly.

# Get all values as a list with "values()"
filled_dict.values()  # => [3, 2, 1]
# Note - Same as above regarding key ordering.

# Get all key-value pairs as a list of tuples with "items()"
filled_dict.items()  # => [("one", 1), ("two", 2), ("three", 3)]

# Check for existence of keys in a dictionary with "in"
"one" in filled_dict  # => True
1 in filled_dict  # => False

# Looking up a non-existing key is a KeyError
filled_dict["four"]  # KeyError

# Use "get()" method to avoid the KeyError
filled_dict.get("one")  # => 1
filled_dict.get("four")  # => None
# The get method supports a default argument when the value is missing
filled_dict.get("one", 4)  # => 1
filled_dict.get("four", 4)  # => 4
# note that filled_dict.get("four") is still => None
# (get doesn't set the value in the dictionary)

# set the value of a key with a syntax similar to lists
filled_dict["four"] = 4  # now, filled_dict["four"] => 4

# "setdefault()" inserts into a dictionary only if the given key isn't present
filled_dict.setdefault("five", 5)  # filled_dict["five"] is set to 5
filled_dict.setdefault("five", 6)  # filled_dict["five"] is still 5

# You can declare sets (which are like unordered lists that cannot contain
# duplicate values) using the set object.
empty_set = set()
# Initialize a "set()" with a bunch of values
some_set = set([1, 2, 2, 3, 4])  # some_set is now set([1, 2, 3, 4])

# order is not guaranteed, even though it may sometimes look sorted
another_set = set([4, 3, 2, 2, 1])  # another_set is now set([1, 2, 3, 4])

# Since Python 2.7, {} can be used to declare a set
filled_set = {1, 2, 2, 3, 4}  # => {1, 2, 3, 4}

# Add more items to a set
filled_set.add(5)  # filled_set is now {1, 2, 3, 4, 5}

# Do set intersection with &
other_set = {3, 4, 5, 6}
filled_set & other_set  # => {3, 4, 5}

# Do set union with |
filled_set | other_set  # => {1, 2, 3, 4, 5, 6}

# Do set difference with -
{1, 2, 3, 4} - {2, 3, 5}  # => {1, 4}

# Do set symmetric difference with ^
{1, 2, 3, 4} ^ {2, 3, 5}  # => {1, 4, 5}

# Check if set on the left is a superset of set on the right
{1, 2} >= {1, 2, 3}  # => False

# Check if set on the left is a subset of set on the right
{1, 2} <= {1, 2, 3}  # => True

# Check for existence in a set with in
2 in filled_set  # => True
10 in filled_set  # => False
10 not in filled_set # => True

# Check data type of variable
type(li)   # => list
type(filled_dict)   # => dict
type(5)   # => int


####################################################
#  3. Control Flow
####################################################

# Let's just make a variable
some_var = 5

# Here is an if statement. Indentation is significant in python!
# prints "some_var is smaller than 10"
if some_var > 10:
    print "some_var is totally bigger than 10."
elif some_var < 10:  # This elif clause is optional.
    print "some_var is smaller than 10."
else:  # This is optional too.
    print "some_var is indeed 10."

"""
For loops iterate over lists
prints:
    dog is a mammal
    cat is a mammal
    mouse is a mammal
"""
for animal in ["dog", "cat", "mouse"]:
    # You can use {0} to interpolate formatted strings. (See above.)
    print "{0} is a mammal".format(animal)

"""
"range(number)" returns a list of numbers
from zero to the given number
prints:
    0
    1
    2
    3
"""
for i in range(4):
    print i

"""
"range(lower, upper)" returns a list of numbers
from the lower number to the upper number
prints:
    4
    5
    6
    7
"""
for i in range(4, 8):
    print i

"""
While loops go until a condition is no longer met.
prints:
    0
    1
    2
    3
"""
x = 0
while x < 4:
    print x
    x += 1  # Shorthand for x = x + 1

# Handle exceptions with a try/except block

# Works on Python 2.6 and up:
try:
    # Use "raise" to raise an error
    raise IndexError("This is an index error")
except IndexError as e:
    pass  # Pass is just a no-op. Usually you would do recovery here.
except (TypeError, NameError):
    pass  # Multiple exceptions can be handled together, if required.
else:  # Optional clause to the try/except block. Must follow all except blocks
    print "All good!"  # Runs only if the code in try raises no exceptions
finally:  # Execute under all circumstances
    print "We can clean up resources here"

# Instead of try/finally to cleanup resources you can use a with statement
with open("myfile.txt") as f:
    for line in f:
        print line


####################################################
# 4. Functions
####################################################

# Use "def" to create new functions
def add(x, y):
    print "x is {0} and y is {1}".format(x, y)
    return x + y  # Return values with a return statement


# Calling functions with parameters
add(5, 6)  # => prints out "x is 5 and y is 6" and returns 11

# Another way to call functions is with keyword arguments
add(y=6, x=5)  # Keyword arguments can arrive in any order.


# You can define functions that take a variable number of
# positional args, which will be interpreted as a tuple by using *
def varargs(*args):
    return args


varargs(1, 2, 3)  # => (1, 2, 3)


# You can define functions that take a variable number of
# keyword args, as well, which will be interpreted as a dict by using **
def keyword_args(**kwargs):
    return kwargs


# Let's call it to see what happens
keyword_args(big="foot", loch="ness")  # => {"big": "foot", "loch": "ness"}


# You can do both at once, if you like
def all_the_args(*args, **kwargs):
    print args
    print kwargs


"""
all_the_args(1, 2, a=3, b=4) prints:
    (1, 2)
    {"a": 3, "b": 4}
"""

# When calling functions, you can do the opposite of args/kwargs!
# Use * to expand positional args and use ** to expand keyword args.
args = (1, 2, 3, 4)
kwargs = {"a": 3, "b": 4}
all_the_args(*args)  # equivalent to all_the_args(1, 2, 3, 4)
all_the_args(**kwargs)  # equivalent to all_the_args(a=3, b=4)
all_the_args(*args, **kwargs)  # equivalent to all_the_args(1, 2, 3, 4, a=3, b=4)


# you can pass args and kwargs along to other functions that take args/kwargs
# by expanding them with * and ** respectively
def pass_all_the_args(*args, **kwargs):
    all_the_args(*args, **kwargs)
    print varargs(*args)
    print keyword_args(**kwargs)


# Function Scope
x = 5


def set_x(num):
    # Local var x not the same as global variable x
    x = num  # => 43
    print x  # => 43


def set_global_x(num):
    global x
    print x  # => 5
    x = num  # global var x is now set to 6
    print x  # => 6


set_x(43)
set_global_x(6)


# Python has first class functions
def create_adder(x):
    def adder(y):
        return x + y

    return adder


add_10 = create_adder(10)
add_10(3)  # => 13

# There are also anonymous functions
(lambda x: x > 2)(3)  # => True
(lambda x, y: x ** 2 + y ** 2)(2, 1)  # => 5

# There are built-in higher order functions
map(add_10, [1, 2, 3])  # => [11, 12, 13]
map(max, [1, 2, 3], [4, 2, 1])  # => [4, 2, 3]

filter(lambda x: x > 5, [3, 4, 5, 6, 7])  # => [6, 7]

# We can use list comprehensions for nice maps and filters
[add_10(i) for i in [1, 2, 3]]  # => [11, 12, 13]
[x for x in [3, 4, 5, 6, 7] if x > 5]  # => [6, 7]

# You can construct set and dict comprehensions as well.
{x for x in 'abcddeef' if x in 'abc'}  # => {'a', 'b', 'c'}
{x: x ** 2 for x in range(5)}  # => {0: 0, 1: 1, 2: 4, 3: 9, 4: 16}


####################################################
# 5. Classes
####################################################

# We subclass from object to get a class.
class Human(object):
    # A class attribute. It is shared by all instances of this class
    species = "H. sapiens"

    # Basic initializer, this is called when this class is instantiated.
    # Note that the double leading and trailing underscores denote objects
    # or attributes that are used by python but that live in user-controlled
    # namespaces. You should not invent such names on your own.
    def __init__(self, name):
        # Assign the argument to the instance's name attribute
        self.name = name

        # Initialize property
        self.age = 0

    # An instance method. All methods take "self" as the first argument
    def say(self, msg):
        return "{0}: {1}".format(self.name, msg)

    # A class method is shared among all instances
    # They are called with the calling class as the first argument
    @classmethod
    def get_species(cls):
        return cls.species

    # A static method is called without a class or instance reference
    @staticmethod
    def grunt():
        return "*grunt*"

    # A property is just like a getter.
    # It turns the method age() into an read-only attribute
    # of the same name.
    @property
    def age(self):
        return self._age

    # This allows the property to be set
    @age.setter
    def age(self, age):
        self._age = age

    # This allows the property to be deleted
    @age.deleter
    def age(self):
        del self._age


# Instantiate a class
i = Human(name="Ian")
print i.say("hi")  # prints out "Ian: hi"

j = Human("Joel")
print j.say("hello")  # prints out "Joel: hello"

# Call our class method
i.get_species()  # => "H. sapiens"

# Change the shared attribute
Human.species = "H. neanderthalensis"
i.get_species()  # => "H. neanderthalensis"
j.get_species()  # => "H. neanderthalensis"

# Call the static method
Human.grunt()  # => "*grunt*"

# Update the property
i.age = 42

# Get the property
i.age  # => 42

# Delete the property
del i.age
i.age  # => raises an AttributeError

####################################################
# 6. Modules
####################################################

# You can import modules
import math

print math.sqrt(16)  # => 4.0

# You can get specific functions from a module
from math import ceil, floor

print ceil(3.7)  # => 4.0
print floor(3.7)  # => 3.0

# You can import all functions from a module.
# Warning: this is not recommended
from math import *

# You can shorten module names
import math as m

math.sqrt(16) == m.sqrt(16)  # => True
# you can also test that the functions are equivalent
from math import sqrt

math.sqrt == m.sqrt == sqrt  # => True

# Python modules are just ordinary python files. You
# can write your own, and import them. The name of the
# module is the same as the name of the file.

# You can find out which functions and attributes
# defines a module.
import math

dir(math)


# If you have a Python script named math.py in the same
# folder as your current script, the file math.py will
# be loaded instead of the built-in Python module.
# This happens because the local folder has priority
# over Python's built-in libraries.


####################################################
# 7. Advanced
####################################################

# Generators
# A generator "generates" values as they are requested instead of storing
# everything up front

# The following method (*NOT* a generator) will double all values and store it
# in `double_arr`. For large size of iterables, that might get huge!
def double_numbers(iterable):
    double_arr = []
    for i in iterable:
        double_arr.append(i + i)
    return double_arr


# Running the following would mean we'll double all values first and return all
# of them back to be checked by our condition
for value in double_numbers(range(1000000)):  # `test_non_generator`
    print value
    if value > 5:
        break


# We could instead use a generator to "generate" the doubled value as the item
# is being requested
def double_numbers_generator(iterable):
    for i in iterable:
        yield i + i


# Running the same code as before, but with a generator, now allows us to iterate
# over the values and doubling them one by one as they are being consumed by
# our logic. Hence as soon as we see a value > 5, we break out of the
# loop and don't need to double most of the values sent in (MUCH FASTER!)
for value in double_numbers_generator(xrange(1000000)):  # `test_generator`
    print value
    if value > 5:
        break

# BTW: did you notice the use of `range` in `test_non_generator` and `xrange` in `test_generator`?
# Just as `double_numbers_generator` is the generator version of `double_numbers`
# We have `xrange` as the generator version of `range`
# `range` would return back and array with 1000000 values for us to use
# `xrange` would generate 1000000 values for us as we request / iterate over those items

# Just as you can create a list comprehension, you can create generator
# comprehensions as well.
values = (-x for x in [1, 2, 3, 4, 5])
for x in values:
    print(x)  # prints -1 -2 -3 -4 -5 to console/terminal

# You can also cast a generator comprehension directly to a list.
values = (-x for x in [1, 2, 3, 4, 5])
gen_to_list = list(values)
print(gen_to_list)  # => [-1, -2, -3, -4, -5]

# Decorators
# A decorator is a higher order function, which accepts and returns a function.
# Simple usage example – add_apples decorator will add 'Apple' element into
# fruits list returned by get_fruits target function.
def add_apples(func):
    def get_fruits():
        fruits = func()
        fruits.append('Apple')
        return fruits
    return get_fruits

@add_apples
def get_fruits():
    return ['Banana', 'Mango', 'Orange']

# Prints out the list of fruits with 'Apple' element in it:
# Banana, Mango, Orange, Apple
print ', '.join(get_fruits())

# in this example beg wraps say
# Beg will call say. If say_please is True then it will change the returned
# message
from functools import wraps


def beg(target_function):
    @wraps(target_function)
    def wrapper(*args, **kwargs):
        msg, say_please = target_function(*args, **kwargs)
        if say_please:
            return "{} {}".format(msg, "Please! I am poor :(")
        return msg

    return wrapper


@beg
def say(say_please=False):
    msg = "Can you buy me a beer?"
    return msg, say_please


print say()  # Can you buy me a beer?
print say(say_please=True)  # Can you buy me a beer? Please! I am poor :(
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