* learn.py
# 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
# Sets store ... well sets (which are like lists but can contain no duplicates)
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
# 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 :(
