python 5 minute quickstart

# Single-line comments

""" 
Multi-line strings can be wrapped in
    three quotes, but this can also be treated as a
    multi-line comment
"""
# print("hello world!") # => output hello world!
print("hello world!") # => output hello world! 

# Number types
4 # => 4

# Simple arithmetic
1 + 2 # => 3
8 - 2 # => 6
10 * 3 # => 30
35 / 7 # => 5

# Division of integers is automatically rounded
5 / 3 # => 1

# To do exact division, we need to introduce floating point numbers
3.0 # Floating point numbers
13.0 / 4.0 # => 3.25 Much more precise.

# Parentheses have the highest priority
(1 + 4) * 2 # => 10

True
False

# Use not to fetch nots
not True # => False
not False # => True

# Equal
2 == 2 # => True
3 == 1 # => False

# Not equal
4 ! 4 ! == 4 # => False
5 ! = 6 # => True

# More comparison operators
1 < 12 # => True
1 > 9 # => False
4 <= 4 # => True
6 >= 6 # => True

# Comparison operations can be written concatenated!
2 < 3 < 4 # => True
3 < 4 < 3 # => False

# The string needs to be enclosed in " or '
"Hello world!"
'Hello world!'

# String is enclosed in a plus sign
"Hello " + "world!" # => "Hello world!"

# String can be treated as a list of characters
"Hello world!"[0] # => 'H'

# % can be used to format strings
"%s can be %s" % ("Hello", "world!")

# Strings can also be formatted using the format method.
# It is recommended to use this method
"{0} can be {1}".format("strings", "formatted")
# You can also replace numbers with variable names
"{name} wants to eat {food}".format(name="Bob", food="lasagna")

# The method now more commonly used with the f argument
name, food = "Bob", "lasagna"
f"{name} wants to eat {food}"

# None is an object
None # => None

# Don't use the equality `==` symbol to compare with None.
# Use `is`.
"etc" is None # => False
None is None # => True

# 'is' can be used to compare objects for equality
# This operator is not very useful for comparing raw data, but is essential for comparing objects.

# None, 0, and the empty string are counted as False
# Everything else is True
0 == False # => True
"" == False # => True

# There is no need to declare a variable before assigning it a value.
some_var = 5 # It is generally recommended to use a combination of lowercase letters and underscores for variable names.
some_var # => 5

# An exception is thrown when accessing an unassigned variable.
# See the section on control flow to see how exceptions are handled.
some_other_var # throws NameError

# The if statement can be used as an expression
"yahoo!" if 3 > 2 else 2 # => "yahoo!"

# A list is used to hold sequences
li = []
# Lists can be initialized directly
other_li = [4, 5, 6]

# Add elements to the end of the list
li.append(1) # li is now [1]
li.append(2) # li now is [1, 2]
li.append(4) # li now is [1, 2, 4]
li.append(3) # li now is [1, 2, 4, 3]
# Remove the end of the list
li.pop() # => 3 li is now [1, 2, 4]
# Add it back in
li.append(3) # li is now [1, 2, 4, 3] again.

# Access the list as you would an array in any other language.
li[0] # => 1
# Access the last element
li[-1] # => 3

# Throw an exception for out-of-bounds.
li[4] # throws an out-of-bounds exception

# Slicing syntax requires indexed access to lists.
# Think of it as left-closed-right-open intervals in math
li[1:3] # => [2, 4]
# Omit the first element
li[2:] # => [4, 3]
# Omit the last element
li[:3] # => [1, 2, 4]

# Delete specific elements
del li[2] # li is now [1, 2, 3]

# Merge the lists
li + other_li # => [1, 2, 3, 4, 5, 6] - doesn't leave the two lists unchanged

# Merge the lists by splicing
li.extend(other_li) # li is [1, 2, 3, 4, 5, 6]

# Use in to return whether an element is in the list or not
1 in li # => True

# Returns the length of the list
len(li) # => 6

# Dictionaries are used to store mapping relationships
empty_dict = {}
# Dictionary initialization
filled_dict = {"one": 1, "two": 2, "three": 3}

# Dictionaries are also accessed with middle brackets
filled_dict["one"] # => 1

# Save all the keys in the list
filled_dict.keys() # => ["three", "two", "one"]
# The order of the keys is not unique, what you get is not necessarily in that order

# Save all the values in the list
filled_dict.values() # => [3, 2, 1]
# Same order as the keys

# Determine if a key exists
"one" in filled_dict # => True
1 in filled_dict # => False

# Querying for a non-existent key throws a KeyError
filled_dict["four"] # KeyError

# Use the get method to avoid KeyError
filled_dict.get("one") # => 1
filled_dict.get("four") # => None
# The get method supports returning a default value if none exists.
filled_dict.get("one", 4) # => 1
filled_dict.get("four", 4) # => 4

# setdefault is a safer way to add a dictionary element
filled_dict.setdefault("five", 5) # filled_dict["five"] has a value of 5
filled_dict.setdefault("five", 6) # filled_dict["five"] still has value 5

# A tuple is similar to a list, but it is immutable
tup = (1, 2, 3)
tup[0] # => 1
tup[0] = 3 # type error

# For most list operations, this also applies to tuples
len(tup) # => 3
tup + (4, 5, 6) # => (1, 2, 3, 4, 5, 6)
tup[:2] # => (1, 2)
2 in tup # => True

# You can unwrap a tuple and assign it to multiple variables
a, b, c = (1, 2, 3) # a is 1, b is 2, c is 3
# Without parentheses, they are automatically treated as tuples
d, e, f = 4, 5, 6
# Now we can see how easy it is to exchange two numbers
e, d = d, e # d is 5, e is 4

# Sets store unordered elements
empty_set = set()
# Initialize a set
some_set = set([1, 2, 2, 3, 4]) # some_set is now set([1, 2, 3, 4])

# After Python 2.7, curly braces can be used to represent sets
filled_set = {1, 2, 2, 3, 4} # => {1 2 3 4}

# Add elements to the set
filled_set.add(5) # filled_set is now {1, 2, 3, 4, 5}

# Use & to compute the intersection of sets
other_set = {3, 4, 5, 6}
filled_set & other_set # => {3, 4, 5}

# Use | to compute the union of sets
filled_set | other_set # => {1, 2, 3, 4, 5, 6}

# Use - to compute the difference of sets
{1, 2, 3, 4} - {2, 3, 5} # => {1, 4}

# Use in to determine if an element exists in the set
2 in filled_set # => True
10 in filled_set # => False

# Create a new variable
some_var = 5

# This is an if statement, indentation is important in python.
# The following code snippet will output "some var is smaller than 10"
if some_var > 10.
    print "some_var is totally bigger than 10."
elif some_var < 10: # This elif statement is not required.
    print "some_var is smaller than 10." else: # This else is also not required.
else: # This else is also not required
    print "some_var is indeed 10."

"""
Iterate through the list with a for loop
Output.
    dog is a mammal
    cat is a mammal
    mouse is a mammal
"""
for animal in ["dog", "cat", "mouse"].
    # You can format strings with %
    print "%s is a mammal" % animal
    
"""
`range(number)` Returns a list of numbers from 0 to the given number.
Output.
    0
    1
    2
    3
for i in range(4):
    print i

"""
while loop
Output: 0
    0
    1
    2
    3
"""
x = 0
while x < 4.
    print x
    x += 1 # shorthand for x = x + 1

# Handling exceptions with try/except blocks
try.
    # Throw an exception with raise
    raise IndexError("This is an index error")
except IndexError as e: # pass
    pass # pass means do nothing, but usually some recovery work is done here

# Use def to create a new function
def add(x, y).
    print "x is %s and y is %s" % (x, y)
    return x + y # Return values by return

# Call a function with parameters
add(5, 6) # => output "x is 5 and y is 6" return 11

# Call a function by keyword assignment
add(y=6, x=5) # The order doesn't matter.

# We can also define functions that accept multiple variables that are in order
def varargs(*args).
    return args

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


# We can also define functions that accept multiple variables, ordered by keyword
def keyword_args(**kwargs):
    return kwargs

# The actual effect:
keyword_args(big="foot", loch="ness") # => {"big": "foot", "loch": "ness"}

# You can also define a function in both forms at the same time
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 a function, we can also do the opposite, expanding tuples and dictionaries into arguments
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)

# Functions are first class citizens in python
def create_adder(x).
    def adder(y).
        return x + y
    return adder

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

# anonymous function
(lambda x: x > 2)(3)  # => True

# 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]

# You can use list methods to make more subtle references to higher-order functions
[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]

# Our new class inherits from the object class.
class Human(object).

     # Class attribute, shared by all objects of the class
    species = "H. sapiens"

    # Basic constructor
    def __init__(self, name).
        # Assign parameters to object member attributes
        self.name = name

    # Member method with self as argument
    def say(self, msg): # Return "%s: %s" % (self.name).
        return "%s: %s" % (self.name, msg)

    # Class methods are shared by all objects of the class
    # This class method passes the class itself as the first parameter when it is called
    @classmethod
    def get_species(cls):: return cls.species(cls).
        return cls.species

    # Static methods are methods that don't require a reference to a class or an object to be called.
    @staticmethod
    def grunt(): return "*grunt*
        return "*grunt*"

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

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

# Access the methods of the class
i.get_species() # => "H. sapiens"

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

# Access static variables
human.grunt() # => "*grunt*"

# We can import other modules
import math
print math.sqrt(25) # => 5

# We can also import specific functions from a module
from math import ceil, floor
print ceil(4.7) # => 5.0
print floor(4.7) # => 4.0

# Import all functions from a module
# Warning: not recommended
from math import *

# Shorten the module name
import math as m
math.sqrt(16) == m.sqrt(16) # => True

# Python modules are really just normal python files.
# You can also create your own modules and import them.
# The module name is the same as the file name.

# You can also see what properties and methods are in a module by doing the following
import math
dir(math)