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list.index(argument) would return the index of the item in the list that first matched the argument It will not return the value stored at the index of the argument as it was prior. Added some more clarity to the subject as well.
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| language | contributors | filename | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| python3 |
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learnpython3.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 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") # => "Strings can be 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") # => "Strings can be interpolated the old way"
# 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!") # => 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!
# Simple way to get input data from console
input_string_var = input("Enter some data: ") # Returns the data as a string
# Note: In earlier versions of Python, input() method was named as raw_input()
# 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]
# Remove first occurrence of a value
li.remove(2) # li is now [1, 3]
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] again
# Get the index of the first item found matching the argument
li.index(2) # => 1
li.index(4) # Raises a ValueError as 4 is not in the list
# 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
# You can also do extended unpacking
a, *b, c = (1, 2, 3, 4) # a is now 1, b is now [2, 3] and c is now 4
# 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
# From Python 3.5 you can also use the additional unpacking options
{'a': 1, **{'b': 2}} # => {'a': 1, 'b': 2}
{'a': 1, **{'a': 2}} # => {'a': 2}
# 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}
# 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
####################################################
## 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
"""
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) # => dict_keys(['one', 'two', 'three']). 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 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
# TODO - Fix for iterables
# 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
# 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 use the "class" operator to get a class
class Human:
# 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
# Initialize property
self.age = 0
# 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*"
# 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)) # => 4.0
# 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.
# 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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