learnxinyminutes-docs/python.html.markdown

language	contributors	filename
python	Louie Dinh http://ldinh.ca Amin Bandali http://aminbandali.com	learnpython.py

Python was created by Guido Van Rossum in the early 90's. 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. Look for another tour of Python 3 soon!

# Single line comments start with a hash.
""" 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

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

# Boolean values are primitives
True
False

# 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!"

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

# % can be used to format strings, like this:
"%s can be %s" % ("strings", "interpolated")

# A newer way to format strings is the format method.
# This method is the preferred way
"{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.

# None, 0, and empty strings/lists all evaluate to False.
# All other values are True
bool(0)  #=> False
bool("") #=> False


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

# Python has a print function, available in versions 2.7 and 3...
print("I'm Python. Nice to meet you!")
# and an older print statement, in all 2.x versions but removed from 3.
print "I'm also Python!"


# 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
"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
# 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]
# Revert the list
li[::-1] #=> [3, 4, 2, 1]

# 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: li and other_li is left alone

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

# 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
# Tuples are created by default if you leave out the parentheses
d, e, f = 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.

# 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

# "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


# Sets store ... well sets
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])

# 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}

# Check for existence in a set with in
2 in filled_set #=> True
10 in filled_set #=> False


####################################################
## 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 % to interpolate formatted strings
    print("%s is a mammal" % 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)

"""
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.


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

# Use "def" to create new functions
def add(x, y):
    print("x is %s and y is %s" % (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 arguments
def varargs(*args):
    return args

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


# You can define functions that take a variable number of
# keyword arguments, as well
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 tuples and use ** to expand kwargs.
args = (1, 2, 3, 4)
kwargs = {"a": 3, "b": 4}
all_the_args(*args) # equivalent to foo(1, 2, 3, 4)
all_the_args(**kwargs) # equivalent to foo(a=3, b=4)
all_the_args(*args, **kwargs) # equivalent to foo(1, 2, 3, 4, a=3, b=4)

# 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

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

####################################################
## 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
    def __init__(self, name):
        # Assign the argument to the instance's name attribute
        self.name = name

    # An instance method. All methods take "self" as the first argument
    def say(self, msg):
       return "%s: %s" % (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*"


# 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*"


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

# You can import modules
import math
print(math.sqrt(16) )#=> 4

# 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

# 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)


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

# Generators help you make lazy code
def double_numbers(iterable):
    for i in iterable:
        yield i + i

# generator creates the value on the fly
# instead of generating and returning all values at once it creates one in each iteration
# this means values bigger than 15 wont be processed in double_numbers
# note range is a generator too, creating a list 1-900000000 would take lot of time to be made
_range = range(1, 900000000)
# will double all numbers until a result >=30 found
for i in double_numbers(_range):
    print(i)
    if i >= 30:
        break


# Decorators
# 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(_say):
    @wraps(_say)
    def wrapper(*args, **kwargs):
        msg, say_please = _say(*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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