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---
language: python
contributors:
- ["Louie Dinh", "http://ldinh.ca"]
- ["Amin Bandali", "http://aminbandali.com"]
- ["Andre Polykanine", "https://github.com/Oire"]
filename: learnpython.py
---
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](http://twitter.com/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. For Python 3.x, take a look at the [Python 3 tutorial](http://learnxinyminutes.com/docs/python3/).
```python
# 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
# 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'
# % 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 statement
print "I'm Python. Nice to meet you!"
# 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]
# 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]
r
# 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
####################################################
## 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
"""
"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
####################################################
## 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 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]
####################################################
## 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
# 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
# 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)
####################################################
## 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 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 :(
```
## Ready For More?
### Free Online
* [Learn Python The Hard Way](http://learnpythonthehardway.org/book/)
* [Dive Into Python](http://www.diveintopython.net/)
* [The Official Docs](http://docs.python.org/2.6/)
* [Hitchhiker's Guide to Python](http://docs.python-guide.org/en/latest/)
* [Python Module of the Week](http://pymotw.com/2/)
* [A Crash Course in Python for Scientists](http://nbviewer.ipython.org/5920182)
* [First Steps With Python](https://realpython.com/learn/python-first-steps/)
### Dead Tree
* [Programming Python](http://www.amazon.com/gp/product/0596158106/ref=as_li_qf_sp_asin_tl?ie=UTF8&camp=1789&creative=9325&creativeASIN=0596158106&linkCode=as2&tag=homebits04-20)
* [Dive Into Python](http://www.amazon.com/gp/product/1441413022/ref=as_li_tf_tl?ie=UTF8&camp=1789&creative=9325&creativeASIN=1441413022&linkCode=as2&tag=homebits04-20)
* [Python Essential Reference](http://www.amazon.com/gp/product/0672329786/ref=as_li_tf_tl?ie=UTF8&camp=1789&creative=9325&creativeASIN=0672329786&linkCode=as2&tag=homebits04-20)
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