learnxinyminutes-docs/python3.html.markdown at fe525731183779ef5b76cc714c47bd9033953e46

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 3 specifically. Check out here if you want to learn the old Python 2.7


# 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

# Except division which returns floats, real numbers, by default
35 / 5  # => 7.0

# 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

# When you use a float, results are floats
3 * 2.0 # => 6.0

# Modulo operation
7 % 3 # => 1

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

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

# Boolean values are primitives (Note: the capitalization)
True
False

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

# 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

# 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

# (is vs. ==) is checks if two variable refer to the same object, but == checks 
# if the objects pointed to have the same values.
a = [1, 2, 3, 4] # Point a at a new list, [1, 2, 3, 4]
b = a # Point b at what a is pointing to
b is a # => True, a and b refer to the same object
b == a # => True, a's and b's objects are equal
b = [1, 2, 3, 4] # Point a at a new list, [1, 2, 3, 4]
b is a # => False, a and b do not refer to the same object
b == a # => True, a's and b's objects are equal

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

# Strings can be added too! But try not to do this.
"Hello " + "world!"  # => "Hello world!"
# Strings can be added without using '+'
"Hello " "world!"  # => "Hello world!"

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

# .format can be used to format strings, like this:
"{} can be {}".format("strings", "interpolated")

# You can repeat the formatting arguments to save some typing.
"{0} be nimble, {0} be quick, {0} jump over the {1}".format("Jack", "candle stick")
#=> "Jack be nimble, Jack be quick, Jack jump over the candle stick"

# You can use keywords if you don't want to count.
"{name} wants to eat {food}".format(name="Bob", food="lasagna") #=> "Bob wants to eat lasagna"

# If your Python 3 code also needs to run on Python 2.5 and below, you can also
# still use the old style of formatting:
"%s can be %s the %s way" % ("strings", "interpolated", "old")


# None is an object
None  # => None

# Don't use the equality "==" symbol to compare objects to None
# Use "is" instead. This checks for equality of object identity.
"etc" is None  # => False
None is None  # => True

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


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

# Python has a print function
print("I'm Python. Nice to meet you!")

# By default the print function also prints out a newline at the end.
# Use the optional argument end to change the end character.
print("Hello, World", end="!") # => Hello, World!

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

# Accessing a previously unassigned variable is an exception.
# See Control Flow to learn more about exception handling.
some_unknown_var  # Raises a NameError

# 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]
# Select every second entry
li[::2]   # =>[1, 4]
# Return a reversed copy of the list
li[::-1]   # => [3, 4, 2, 1]
# Use any combination of these to make advanced slices
# li[start:end:step]

# Make a one layer deep copy using slices
li2 = li[:] # => li2 = [1, 2, 4, 3] but (li2 is li) will result in false.

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

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

# 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

# Note that a tuple of length one has to have a comma after the last element but
# tuples of other lengths, even zero, do not.
type((1))  # => <class 'int'>
type((1,)) # => <class 'tuple'>
type(())   # => <class 'tuple'>

# You can do most of the list operations 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}

# Note keys for dictionaries have to be immutable types. This is to ensure that 
# the key can be converted to a constant hash value for quick look-ups. 
# Immutable types include ints, floats, strings, tuples.
invalid_dict = {[1,2,3]: "123"} # => Raises a TypeError: unhashable type: 'list'
valid_dict = {(1,2,3):[1,2,3]}  # Values can be of any type, however.

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

# Get all keys as an iterable with "keys()". We need to wrap the call in list()
# to turn it into a list. We'll talk about those later.  Note - Dictionary key
# ordering is not guaranteed. Your results might not match this exactly.
list(filled_dict.keys())   # => ["three", "two", "one"]


# Get all values as an iterable with "values()". Once again we need to wrap it
# in list() to get it out of the iterable. Note - Same as above regarding key
# ordering.
list(filled_dict.values())   # => [3, 2, 1]


# 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

# Adding to a dictionary
filled_dict.update({"four":4}) #=> {"one": 1, "two": 2, "three": 3, "four": 4}
#filled_dict["four"] = 4  #another way to add to dict

# Remove keys from a dictionary with del
del filled_dict["one"]  # Removes the key "one" from filled dict


# Sets store ... well sets
empty_set = set()
# Initialize a set with a bunch of values. Yeah, it looks a bit like a dict. Sorry.
some_set = {1, 1, 2, 2, 3, 4}   # some_set is now {1, 2, 3, 4}

# Similar to keys of a dictionary, elements of a set have to be immutable.
invalid_set = {[1], 1} # => Raises a TypeError: unhashable type: 'list'
valid_set = {(1,), 1}

# Can set new variables to a set
filled_set = some_set

# Add one more item to the 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 and Iterables
####################################################

# 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 format() to interpolate formatted strings
    print("{} is a mammal".format(animal))

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

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

"""
"range(lower, upper, step)" returns an iterable of numbers
from the lower number to the upper number, while incrementing
by step. If step is not indicated, the default value is 1.
prints:
    4
    6
    8
"""
for i in range(4, 8, 2):
    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
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)

# Python offers a fundamental abstraction called the Iterable.
# An iterable is an object that can be treated as a sequence.
# The object returned the range function, is an iterable.

filled_dict = {"one": 1, "two": 2, "three": 3}
our_iterable = filled_dict.keys()
print(our_iterable) #=> range(1,10). This is an object that implements our Iterable interface

# We can loop over it.
for i in our_iterable:
    print(i)    # Prints one, two, three

# However we cannot address elements by index.
our_iterable[1]  # Raises a TypeError

# An iterable is an object that knows how to create an iterator.
our_iterator = iter(our_iterable)

# Our iterator is an object that can remember the state as we traverse through it.
# We get the next object with "next()".
next(our_iterator)  #=> "one"

# It maintains state as we iterate.
next(our_iterator)  #=> "two"
next(our_iterator)  #=> "three"

# After the iterator has returned all of its data, it gives you a StopIterator Exception
next(our_iterator) # Raises StopIteration

# You can grab all the elements of an iterator by calling list() on it.
list(filled_dict.keys())  #=> Returns ["one", "two", "three"]


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

# Use "def" to create new functions
def add(x, y):
    print("x is {} and y is {}".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 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)

# Returning multiple values (with tuple assignments)
def swap(x, y):
    return y, x # Return multiple values as a tuple without the parenthesis.
                # (Note: parenthesis have been excluded but can be included)

x = 1
y = 2
x, y = swap(x, y) # => x = 2, y = 1
# (x, y) = swap(x,y) # Again parenthesis have been excluded but can be included.

# Function Scope
x = 5

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

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

setX(43)
setGlobalX(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

# TODO - Fix for iterables
# 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
# List comprehension stores the output as a list which can itself be a nested list
[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, 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. Methods(or objects or attributes) like: __init__, __str__,
    # __repr__ etc. are called magic methods (or sometimes called dunder methods)
    # 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

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

# A generator creates values 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
# We use a trailing underscore in variable names when we want to use a name that
# would normally collide with a python keyword
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(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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