Single line comments start with a number symbol.
""" Multiline strings can be written
using three "s, and are often used
as comments
"""
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1. Primitive Datatypes and Operators
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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
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!"
... or multiplied
"Hello" * 3 # => "HelloHelloHello"
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
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2. Variables and Collections
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Python has a print statement, in all 2.x versions but removed from 3.
print "I'm Python. Nice to meet you!"
Python also has a print function, available in versions 2.7 and 3...
but for 2.7 you need to add the import (uncommented):
from future import print_function
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
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]
Revert 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]
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
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}
Check for existence in a set with in
2 in filled_set # => True
10 in filled_set # => False
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3. Control Flow
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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.
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
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4. Functions
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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 args, which will be interpreted as a tuple if you do not use the *
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 map if you do not use **
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 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)
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 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
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]
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5. Classes
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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
# 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"
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6. Modules
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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)
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7. Advanced
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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 xrange is a generator that does the same thing range does.
Creating a list 1-900000000 would take lot of time and space to be made.
xrange creates an xrange generator object instead of creating the entire list
like range does.
We use a trailing underscore in variable names when we want to use a name that
would normally collide with a python keyword
xrange_ = xrange(1, 900000000)
will double all numbers until a result >=30 found
for i in double_numbers(xrange_):
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 :(