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'''
Python 2.7x Reference Guide
https://github.com/justmarkham/python-reference
By Kevin Markham (kevin@dataschool.io)
http://dataschool.io
Adapted from:
https://docs.python.org/2/
http://www.codecademy.com/tracks/python
https://developers.google.com/edu/python/
Book: Python for Data Analysis (Appendix)
and many other sources...
Table of Contents:
Imports
Data Types
Math
Comparisons and Boolean Operations
Conditional Statements
Lists
Tuples
Strings
Dictionaries
Sets
Defining Functions
Anonymous (Lambda) Functions
For Loops and While Loops
Comprehensions
Map, Reduce, Filter
Future additions:
Zip
Try/Except
*args
Assertions
Reading/writing files
And more...
'''
### IMPORTS ###
# 'generic import' of math module
import math
math.sqrt(25)
# import a function
from math import sqrt
sqrt(25) # no longer have to reference the module
# import multiple functions at once
from math import cos, floor
# import all functions in a module (generally discouraged)
from os import *
# define an alias
import numpy as np
# show all functions in math module
dir(math)
### DATA TYPES ###
# determine the type of an object
type(2) # returns 'int'
type(2.0) # returns 'float'
type('two') # returns 'str'
type(True) # returns 'bool'
type(None) # returns 'NoneType'
# check if an object is of a given type
isinstance(2.0, int) # returns False
isinstance(2.0, (int, float)) # returns True
# convert an object to a given type
float(2)
int(2.9)
str(2.9)
# zero, None, and empty containers are converted to False
bool(0)
bool(None)
bool('') # empty string
bool([]) # empty list
bool({}) # empty dictionary
# non-empty containers and non-zeros are converted to True
bool(2)
bool('two')
bool([2])
### MATH ###
# basic operations
10 + 4 # add (returns 14)
10 - 4 # subtract (returns 6)
10 * 4 # multiply (returns 40)
10 ** 4 # exponent (returns 10000)
10 / 4 # divide (returns 2 because both types are 'int')
10 / float(4) # divide (returns 2.5)
5 % 4 # modulo (returns 1) - also known as the remainder
# force '/' in Python 2.x to perform 'true division' (unnecessary in Python 3.x)
from __future__ import division
10 / 4 # true division (returns 2.5)
10 // 4 # floor division (returns 2)
### COMPARISONS AND BOOLEAN OPERATIONS ###
# comparisons (these return True)
5 > 3
5 >= 3
5 != 3
5 == 5
# boolean operations (these return True)
5 > 3 and 6 > 3
5 > 3 or 5 < 3
not False
False or not False and True # evaluation order: not, and, or
### CONDITIONAL STATEMENTS ###
# if statement
if x > 0:
print 'positive'
# if/else statement
if x > 0:
print 'positive'
else:
print 'zero or negative'
# if/elif/else statement
if x > 0:
print 'positive'
elif x == 0:
print 'zero'
else:
print 'negative'
# single-line if statement (sometimes discouraged)
if x > 0: print 'positive'
# single-line if/else statement (sometimes discouraged)
# known as a 'ternary operator'
'positive' if x > 0 else 'zero or negative'
### LISTS ###
## properties: ordered, iterable, mutable, can contain multiple data types
# create an empty list (two ways)
empty_list = []
empty_list = list()
# create a list
simpsons = ['homer', 'marge', 'bart']
# examine a list
simpsons[0] # print element 0 ('homer')
len(simpsons) # returns the length (3)
# modify a list (does not return the list)
simpsons.append('lisa') # append element to end
simpsons.extend(['itchy', 'scratchy']) # append multiple elements to end
simpsons.insert(0, 'maggie') # insert element at index 0 (shifts everything right)
simpsons.remove('bart') # searches for first instance and removes it
simpsons.pop(0) # removes element 0 and returns it
del simpsons[0] # removes element 0 (does not return it)
simpsons[0] = 'krusty' # replace element 0
# concatenate lists (slower than 'extend' method)
neighbors = simpsons + ['ned','rod','todd']
# find elements in a list
simpsons.count('lisa') # counts the number of instances
simpsons.index('itchy') # returns index of first instance
# list slicing [start:end:stride]
weekdays = ['mon','tues','wed','thurs','fri']
weekdays[0] # element 0
weekdays[0:3] # elements 0, 1, 2
weekdays[:3] # elements 0, 1, 2
weekdays[3:] # elements 3, 4
weekdays[-1] # last element (element 4)
weekdays[::2] # every 2nd element (0, 2, 4)
weekdays[::-1] # backwards (4, 3, 2, 1, 0)
# alternative method for returning the list backwards
list(reversed(weekdays))
# sort a list in place (modifies but does not return the list)
simpsons.sort()
simpsons.sort(reverse=True) # sort in reverse
simpsons.sort(key=len) # sort by a key
# return a sorted list (but does not modify the original list)
sorted(simpsons)
sorted(simpsons, reverse=True)
sorted(simpsons, key=len)
# insert into an already sorted list, and keep it sorted
num = [10, 20, 40, 50]
from bisect import insort
insort(num, 30)
# create a second reference to the same list
same_num = num
same_num[0] = 0 # modifies both 'num' and 'same_num'
# copy a list (two ways)
new_num = num[:]
new_num = list(num)
# examine objects
id(num) == id(same_num) # returns True
id(num) == id(new_num) # returns False
num is same_num # returns True
num is new_num # returns False
num == same_num # returns True
num == new_num # returns True (their contents are equivalent)
### TUPLES ###
## like lists, but they don't change size
## properties: ordered, iterable, immutable, can contain multiple data types
# create a tuple
digits = (0, 1, 'two') # create a tuple directly
digits = tuple([0, 1, 'two']) # create a tuple from a list
zero = (0,) # trailing comma is required to indicate it's a tuple
# examine a tuple
digits[2] # returns 'two'
len(digits) # returns 3
digits.count(0) # counts the number of instances of that value (1)
digits.index(1) # returns the index of the first instance of that value (1)
# elements of a tuple cannot be modified
digits[2] = 2 # throws an error
# concatenate tuples
digits = digits + (3, 4)
# create a single tuple with elements repeated (also works with lists)
(3, 4) * 2 # returns (3, 4, 3, 4)
# sort a list of tuples
tens = [(20, 60), (10, 40), (20, 30)]
sorted(tens) # sorts by first element in tuple, then second element
# returns [(10, 40), (20, 30), (20, 60)]
# tuple unpacking
bart = ('male', 10, 'simpson') # create a tuple
(sex, age, surname) = bart # assign three values at once
### STRINGS ###
## properties: iterable, immutable
# create a string
s = str(42) # convert another data type into a string
s = 'I like you'
# examine a string
s[0] # returns 'I'
len(s) # returns 10
# string slicing like lists
s[:6] # returns 'I like'
s[7:] # returns 'you'
s[-1] # returns 'u'
# basic string methods (does not modify the original string)
s.lower() # returns 'i like you'
s.upper() # returns 'I LIKE YOU'
s.startswith('I') # returns True
s.endswith('you') # returns True
s.isdigit() # returns False (returns True if every character in the string is a digit)
s.find('like') # returns index of first occurrence (2), but doesn't support regex
s.find('hate') # returns -1 since not found
s.replace('like','love') # replaces all instances of 'like' with 'love'
# split a string into a list of substrings separated by a delimiter
s.split(' ') # returns ['I','like','you']
s.split() # same thing
s2 = 'a, an, the'
s2.split(',') # returns ['a',' an',' the']
# join a list of strings into one string using a delimiter
stooges = ['larry','curly','moe']
' '.join(stooges) # returns 'larry curly moe'
# concatenate strings
s3 = 'The meaning of life is'
s4 = '42'
s3 + ' ' + s4 # returns 'The meaning of life is 42'
s3 + ' ' + str(42) # same thing
# remove whitespace from start and end of a string
s5 = ' ham and cheese '
s5.strip() # returns 'ham and cheese'
# string substitutions: all of these return 'raining cats and dogs'
'raining %s and %s' % ('cats','dogs') # old way
'raining {} and {}'.format('cats','dogs') # new way
'raining {arg1} and {arg2}'.format(arg1='cats',arg2='dogs') # named arguments
# string formatting
# more examples: http://mkaz.com/2012/10/10/python-string-format/
'pi is {:.2f}'.format(3.14159) # returns 'pi is 3.14'
# normal strings versus raw strings
print 'first line\nsecond line' # normal strings allow for escaped characters
print r'first line\nfirst line' # raw strings treat backslashes as literal characters
### DICTIONARIES ###
## properties: unordered, iterable, mutable, can contain multiple data types
## made up of key-value pairs
## keys must be unique, and can be strings, numbers, or tuples
## values can be any type
# create an empty dictionary (two ways)
empty_dict = {}
empty_dict = dict()
# create a dictionary (two ways)
family = {'dad':'homer', 'mom':'marge', 'size':6}
family = dict(dad='homer', mom='marge', size=6)
# convert a list of tuples into a dictionary
list_of_tuples = [('dad','homer'), ('mom','marge'), ('size', 6)]
family = dict(list_of_tuples)
# examine a dictionary
family['dad'] # returns 'homer'
len(family) # returns 3
family.keys() # returns list: ['dad', 'mom', 'size']
family.values() # returns list: ['homer', 'marge', 6]
family.items() # returns list of tuples:
# [('dad', 'homer'), ('mom', 'marge'), ('size', 6)]
'mom' in family # returns True
'marge' in family # returns False (only checks keys)
# modify a dictionary (does not return the dictionary)
family['cat'] = 'snowball' # add a new entry
family['cat'] = 'snowball ii' # edit an existing entry
del family['cat'] # delete an entry
family['kids'] = ['bart', 'lisa'] # value can be a list
family.pop('dad') # removes an entry and returns the value ('homer')
family.update({'baby':'maggie', 'grandpa':'abe'}) # add multiple entries
# accessing values more safely with 'get'
family['mom'] # returns 'marge'
family.get('mom') # same thing
family['grandma'] # throws an error
family.get('grandma') # returns None
family.get('grandma', 'not found') # returns 'not found' (the default)
# accessing a list element within a dictionary
family['kids'][0] # returns 'bart'
family['kids'].remove('lisa') # removes 'lisa'
# string substitution using a dictionary
'youngest child is %(baby)s' % family # returns 'youngest child is maggie'
### SETS ###
## like dictionaries, but with keys only (no values)
## properties: unordered, iterable, mutable, can contain multiple data types
## made up of unique elements (strings, numbers, or tuples)
# create an empty set
empty_set = set()
# create a set
languages = {'python', 'r', 'java'} # create a set directly
snakes = set(['cobra', 'viper', 'python']) # create a set from a list
# examine a set
len(languages) # returns 3
'python' in languages # returns True
# set operations
languages & snakes # returns intersection: {'python'}
languages | snakes # returns union: {'cobra', 'r', 'java', 'viper', 'python'}
languages - snakes # returns set difference: {'r', 'java'}
snakes - languages # returns set difference: {'cobra', 'viper'}
# modify a set (does not return the set)
languages.add('sql') # add a new element
languages.add('r') # try to add an existing element (ignored, no error)
languages.remove('java') # remove an element
languages.remove('c') # try to remove a non-existing element (throws an error)
languages.discard('c') # removes an element if present, but ignored otherwise
languages.pop() # removes and returns an arbitrary element
languages.clear() # removes all elements
languages.update('go', 'spark') # add multiple elements (can also pass a list or set)
# get a sorted list of unique elements from a list
sorted(set([9, 0, 2, 1, 0])) # returns [0, 1, 2, 9]
### DEFINING FUNCTIONS ###
# define a function with no arguments and no return values
def print_text():
print 'this is text'
# call the function
print_text()
# define a function with one argument and no return values
def print_this(x):
print x
# call the function
print_this(3) # prints 3
n = print_this(3) # prints 3, but doesn't assign 3 to n
# because the function has no return statement
# define a function with one argument and one return value
def square_this(x):
return x**2
# include an optional docstring to describe the effect of a function
def square_this(x):
"""Return the square of a number."""
return x**2
# call the function
square_this(3) # prints 9
var = square_this(3) # assigns 9 to var, but does not print 9
# define a function with two 'positional arguments' (no default values) and
# one 'keyword argument' (has a default value)
def calc(a, b, op='add'):
if op == 'add':
return a + b
elif op == 'sub':
return a - b
else:
print 'valid operations are add and sub'
# call the function
calc(10, 4, op='add') # returns 14
calc(10, 4, 'add') # also returns 14: unnamed arguments are inferred by position
calc(10, 4) # also returns 14: default for 'op' is 'add'
calc(10, 4, 'sub') # returns 6
calc(10, 4, 'div') # prints 'valid operations are add and sub'
# use 'pass' as a placeholder if you haven't written the function body
def stub():
pass
# return two values from a single function
def min_max(nums):
return min(nums), max(nums)
# return values can be assigned to a single variable as a tuple
nums = [1, 2, 3]
min_max_num = min_max(nums) # min_max_num = (1, 3)
# return values can be assigned into multiple variables using tuple unpacking
min_num, max_num = min_max(nums) # min_num = 1, max_num = 3
### ANONYMOUS (LAMBDA) FUNCTIONS ###
## primarily used to temporarily define a function for use by another function
# define a function the "usual" way
def squared(x):
return x**2
# define an identical function using lambda
squared = lambda x: x**2
# sort a list of strings by the last letter (without using lambda)
simpsons = ['homer', 'marge', 'bart']
def last_letter(word):
return word[-1]
sorted(simpsons, key=last_letter)
# sort a list of strings by the last letter (using lambda)
sorted(simpsons, key=lambda word: word[-1])
### FOR LOOPS AND WHILE LOOPS ###
# range returns a list of integers
range(0, 3) # returns [0, 1, 2]: includes first value but excludes second value
range(3) # same thing: starting at zero is the default
range(0, 5, 2) # returns [0, 2, 4]: third argument specifies the 'stride'
# for loop (not recommended)
fruits = ['apple', 'banana', 'cherry']
for i in range(len(fruits)):
print fruits[i].upper()
# alternative for loop (recommended style)
for fruit in fruits:
print fruit.upper()
# use xrange when iterating over a large sequence to avoid actually creating the integer list in memory
for i in xrange(10**6):
pass
# iterate through two things at once (using tuple unpacking)
family = {'dad':'homer', 'mom':'marge', 'size':6}
for key, value in family.items():
print key, value
# use enumerate if you need to access the index value within the loop
for index, fruit in enumerate(fruits):
print index, fruit
# for/else loop
for fruit in fruits:
if fruit == 'banana':
print "Found the banana!"
break # exit the loop and skip the 'else' block
else:
# this block executes ONLY if the for loop completes without hitting 'break'
print "Can't find the banana"
# while loop
count = 0
while count < 5:
print "This will print 5 times"
count += 1 # equivalent to 'count = count + 1'
### COMPREHENSIONS ###
# for loop to create a list of cubes
nums = [1, 2, 3, 4, 5]
cubes = []
for num in nums:
cubes.append(num**3)
# equivalent list comprehension
cubes = [num**3 for num in nums] # [1, 8, 27, 64, 125]
# for loop to create a list of cubes of even numbers
cubes_of_even = []
for num in nums:
if num % 2 == 0:
cubes_of_even.append(num**3)
# equivalent list comprehension
# syntax: [expression for variable in iterable if condition]
cubes_of_even = [num**3 for num in nums if num % 2 == 0] # [8, 64]
# for loop to cube even numbers and square odd numbers
cubes_and_squares = []
for num in nums:
if num % 2 == 0:
cubes_and_squares.append(num**3)
else:
cubes_and_squares.append(num**2)
# equivalent list comprehension (using a ternary expression)
# syntax: [true_condition if condition else false_condition for variable in iterable]
cubes_and_squares = [num**3 if num % 2 == 0 else num**2 for num in nums] # [1, 8, 9, 64, 25]
# for loop to flatten a 2d-matrix
matrix = [[1, 2], [3, 4]]
items = []
for row in matrix:
for item in row:
items.append(item)
# equivalent list comprehension
items = [item for row in matrix
for item in row] # [1, 2, 3, 4]
# set comprehension
fruits = ['apple', 'banana', 'cherry']
unique_lengths = {len(fruit) for fruit in fruits} # {5, 6}
# dictionary comprehension
fruit_lengths = {fruit:len(fruit) for fruit in fruits} # {'apple': 5, 'banana': 6, 'cherry': 6}
fruit_indices = {fruit:index for index, fruit in enumerate(fruits)} # {'apple': 0, 'banana': 1, 'cherry': 2}
### MAP, REDUCE, FILTER ###
# 'map' applies a function to every element of a sequence and returns a list
simpsons = ['homer', 'marge', 'bart']
map(len, simpsons) # returns [5, 5, 4]
map(lambda word: word[-1], simpsons) # returns ['r', 'e', 't']
# equivalent list comprehensions
[len(word) for word in simpsons]
[word[-1] for word in simpsons]
# 'reduce' applies a binary function to the first two elements of a sequence,
# then repeats with the result and the next element, through the end of the sequence
reduce(lambda x, y: x + y, range(4)) # (((0+1)+2)+3) = 6
# 'filter' returns a sequence containing the items from the original sequence
# for which the condition is True
filter(lambda x: x % 2 == 0, range(5)) # returns [0, 2, 4]