Python Cheat Sheet
Python is ‘strongly typed’ (i.e. types are enforced), dynamically, implicitly typed (i.e. you don’t have to declare variables), case sensitive (i.e. var and VAR are two different variables) and ‘object-oriented’ (i.e. everything is an object)
Getting Help
Help in python is always available right in the interpreter. If you want to know how an object works, all you have to do is call help(object), Also useful are dir(), which shows you all the object’s methods, and object.__doc__, which shows you its documentation string
help(5)
Help on int object;
dir(5)
[‘__abs__’, ‘__add__’,…]
abs.__doc__
‘abs(number) - number
Return the absolute value of the argument
SYNTAX
Python has no mandatory statement termination characters and blocks are specified by indentation. Indent to begin a block, dedent to end one. Statements that expect an indentation level end in a colon(:). Comments start with the pound (#) sign and are single-line, multi-line strings are used for multi-line comments. Values are assigned (In fact, objects are bound to names) with the _equals_sign (“=”), and equality testing is done using two _equals_ signs (“==”). You can increment/decrement values using the += and -+ operators respectively by the right-hand amount. This works on many datatypes, strings included. You can also use multiple variables on one line. For example
myvar = 3
myvar += 2
myvar
5
myvar -= 1
myvar
4
“”” This is a multiline comment. The following lines concatenate the two strings “””
mystring = “Hello”
mystring += “ world”
print mystring
Hello world
#This swaps the variables in one line (!)
# It doesn’t violate strong typing because values aren’t
#actually being assigned, but new objects are bound to the old names
myvar, mystring = mystring, myvar
DATA TYPES
The data structures available in python are lists,tuples and dictionaries. Lists are like one-dimensional arrays (but you can also have lists of other lists), dictionaries are associative arrays (a.k.a hash tables) and tuples are immutable one-dimensional arrays (Python “arrays” can be of any type, so you can mix e.g. integers, strings, etc in lists/dictionaries/tuples). The index of the first item in all array types is 0. Negative numbers count from the end towards the beginning, -1 is the last item. Variables can point to functions. The usage is as follows:
sample = [1, [“another”, “list”], (“a”, “tuple”)]
mylist = [“List item 1”, 2,3,14]
mylist[0] = “List item 1 again”
mylist[-1] = 3.14
mylist = {“key 1”:”value 1”, 2.3,”pi”: 3.14}
mydict[“pi”] = 3.15
mytuple = (1, 2, 3)
myfunction = len
print myfunction(mylist)
3
You can access array ranges using a colon (:). Leaving the start index empty assumes the first item, leaving the end index assumes the last item. Negative indexes count from the last item backwards (thus -1 is the last item)
mylist = [“List item 1”, 2, 3.14]
print mylist[:]
[‘List item 1’, 2, 3.1400]
print mylist[0:2]
[‘List item1’, 2]
print mylist [-3:-1]
[‘List item 1’, 2]
print mylist[1:]
[2, 3.14]
#Adding a third parameter, “step” will have python step in
#N item incements, rather than 1
#E.g. this will return the first item, then go to the third and
#return that (so, items 0 and 2 in 0-indexing)
print mylist[::2]
[‘List item 1’, 3.14]
STRINGS
Its strings can use either single or double quotation marks, and you can have quotation marks of one kind inside a string that uses the other kind (i.e. “He said ‘hello’.” Is valid). Multiline strings are enclosed in _tuple double (or single) quotes_(“””). Python support Unicode out of the box, using the syntax u”This is Unicode string”. To fill a string with values, you use the % (modulo) operator and a tuple. Each %s gets replaced with an item from the tuple, left to right, and you can also use dictionary substitutions, like so:
print “Name: %s\
Number: %s\
String: %s” %(myclass.name, 3, 3* “-“)
Name: Poromenos
String: ---
strString = “””This is a multiline string.”””
#WARNING: Watch out for the trailing s in “%(key)s”
print “This %(verb)s a %(noun)s.” % {“noun”: “test”, “verb”: “is”}
This is a test.
FLOW CONTROL STATEMENTS
Flow control statements are if, for and while. The is no select, instead, use if. Use for to enumerate through members of a list. To obtain a list of numbers, use range(number). These statements’ syntax
rangelist = range(10)
print rangelist
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
for number in rangelist:
#check if number is one of the numbers in the tuple
if number in (3, 4, 7, 9):
#”Break” terminates a for without executing the “else” clause
Break
else:
# “Continue” starts the next iteration of the loop. It’s rather useless here, as it’s the last statement of the loop
Continue
else:
#The “else” clause is optional and is executed only if the loop didn’t “break”.
pass # Do nothing
if rangelist[1] == 2:
print “The second item (lists are 0-based) is 2”
elif rangelist[1] == 3:
print “The second item (lists are 0-based) is 3”
else:
print “Dunno”
while rangelist[1] == 1:
pass
FUNCTIONS
Functions are declared with the “def” keyword. Optional arguments are set in the function declaration after the mandatory arguments by being assigned a default value. For named arguments, the name of the argument is assigned a value. Functions can return a tuple ( and using tuple unpacking you can effectively return multiple values). Lambda functions are ad hoc functions that are comprised of a single statement. Parameters are passed by reference, but immutable types (tuples, ints, strings etc) “Cannot be changed”. This is because only the memory location of the item is passed and binding another object to a variable discards the old one, so immutable types are replaced. For example
#Same as def f(x): return x + 1
functionvar = lambda x: x + 1
print functionvar(1)
2
#an_int and a_string are optional, they have default values
# if one is not passed (2 and “A default string”, respectively).
def passing_example(a_list, an_int=2, a_string=”A default string”):
a_list.append(“A new item”)
an_int = 4
return a_list, an_int, a_string
my_list = [1, 2, 3]
my_int = 10
print passing_example(my_list, my_int)
([1, 2, 3, ‘A new item’], 4, “A default string”)
my_list
[1, 2, 3, ‘A new item’]
my_int
10
CLASSES
Python supports a limited form of multiple inheritance in classes. Private variables and methods can be declared (by convention, this is not enforced by the language) by adding at least two leading underscores and at most one trailing one (e.g. “__spam”). We can also bind arbitrary names to class instances
class MyClass(object):
common = 10
def __init__(self):
self.myvariable = 3
def myfunction(self, arg1, arg2):
return self.myvariable
# This is the class instantiation
classinstance = MyClass()
classinstance.myfunction(1, 2)
3
# This variable is shared by all classes.
classinstance2 = MyClass()
classinstance.common
10
classinstance2.common
10
# Note how we use the class name
# instead of the instance.
MyClass.common = 30
classinstance.common
30
classinstance2.common
30
# This will not update the variable on the class,
# instead it will bind a new object to the old
# variable name.
classinstance.common = 10
classinstance.common
10
classinstance2.common
30
MyClass.common = 50
# This has not changed, because "common" is
# now an instance variable.
classinstance.common
10
classinstance2.common
50
# This class inherits from MyClass. The example
# class above inherits from "object", which makes
# it what's called a "new-style class".
# Multiple inheritance is declared as:
# class OtherClass(MyClass1, MyClass2, MyClassN)
class OtherClass(MyClass):
# The "self" argument is passed automatically
# and refers to the class instance, so you can set
# instance variables as above, but from inside the class.
def __init__(self, arg1):
self.myvariable = 3
print arg1
classinstance = OtherClass("hello")
hello
classinstance.myfunction(1, 2)
3
# This class doesn't have a .test member, but
# we can add one to the instance anyway. Note
# that this will only be a member of classinstance.
classinstance.test = 10
classinstance.test
10
EXCEPTIONS
Exceptions in Python are handled with try-except [ exceptionname] blocks:
def some_function():
try:
# Division by zero raises an exception
10 / 0
except ZeroDivisionError:
print "Oops, invalid."
else:
# Exception didn't occur, we're good.
pass
finally:
# This is executed after the code block is run
# and all exceptions have been handled, even
# if a new exception is raised while handling.
print "We're done with that."
some_function()
Oops, invalid.
We're done with that.
IMPORTING
External libraries are used with the ‘import’ [libname] keyword. You can also use from [libname] import [funcname] for individual functions. Here is an example
import random
from time import clock
randomint = random.randint(1, 100)
print randomint
64
FILE I/O
Python has a wide array of libraries built in. As an example, here is how serializing (converting data structures to strings using the pickle library) with file I/O is used:
import pickle
mylist = ["This", "is", 4, 13327]
# Open the file C:\\binary.dat for writing. The letter r before the
# filename string is used to prevent backslash escaping.
myfile = open(r"C:\\binary.dat", "w")
pickle.dump(mylist, myfile)
myfile.close()
myfile = open(r"C:\\text.txt", "w")
myfile.write("This is a sample string")
myfile.close()
myfile = open(r"C:\\text.txt")
print myfile.read()
'This is a sample string'
myfile.close()
# Open the file for reading.
myfile = open(r"C:\\binary.dat")
loadedlist = pickle.load(myfile)
myfile.close()
print loadedlist
['This', 'is', 4, 13327]
MISCELLANEOUS
Conditions can be chained, 1 a 3 checks that a is both less than 3 and greater than 1
You can use del to delete variables or items in arrays
List comprehensions provide a powerful way to create and manipulate lists. They consist of an expression followed by a for clause followed by zero or more if or for clauses, like so
lst1 = [1, 2, 3]
lst2 = [3, 4, 5]
print [x * y for x in lst1 for y in lst2]
[3, 4, 5, 6, 8, 10, 9, 12, 15]
print [x for x in lst1 if 4 x 1]
[2, 3]
# Check if an item has a specific property.
# "any" returns true if any item in the list is true.
any([i % 3 for i in [3, 3, 4, 4, 3]])
True
# This is because 4 % 3 = 1, and 1 is true, so any()
# returns True.
# Check how many items have this property.
sum(1 for i in [3, 3, 4, 4, 3] if i == 4)
2
del lst1[0]
print lst1
[2, 3]
del lst1
Global variables are declared outside of functions and can be read without any special declarations, but if you want to write to them you must declare them at the beginning of the function with the "global" keyword, otherwise Python will bind that object to a new local variable (be careful of that, it's a small catch that can get you if you don't know it)
number = 5
def myfunc():
# This will print 5.
print number
def anotherfunc():
# This raises an exception because the variable has not
# been bound before printing. Python knows that it an
# object will be bound to it later and creates a new, local
# object instead of accessing the global one.
print number
number = 3
def yetanotherfunc():
global number
# This will correctly change the global.
number = 3
References:
http://trypython.org
Python is ‘strongly typed’ (i.e. types are enforced), dynamically, implicitly typed (i.e. you don’t have to declare variables), case sensitive (i.e. var and VAR are two different variables) and ‘object-oriented’ (i.e. everything is an object)
Getting Help
Help in python is always available right in the interpreter. If you want to know how an object works, all you have to do is call help(object), Also useful are dir(), which shows you all the object’s methods, and object.__doc__, which shows you its documentation string
help(5)
Help on int object;
dir(5)
[‘__abs__’, ‘__add__’,…]
abs.__doc__
‘abs(number) - number
Return the absolute value of the argument
SYNTAX
Python has no mandatory statement termination characters and blocks are specified by indentation. Indent to begin a block, dedent to end one. Statements that expect an indentation level end in a colon(:). Comments start with the pound (#) sign and are single-line, multi-line strings are used for multi-line comments. Values are assigned (In fact, objects are bound to names) with the _equals_sign (“=”), and equality testing is done using two _equals_ signs (“==”). You can increment/decrement values using the += and -+ operators respectively by the right-hand amount. This works on many datatypes, strings included. You can also use multiple variables on one line. For example
myvar = 3
myvar += 2
myvar
5
myvar -= 1
myvar
4
“”” This is a multiline comment. The following lines concatenate the two strings “””
mystring = “Hello”
mystring += “ world”
print mystring
Hello world
#This swaps the variables in one line (!)
# It doesn’t violate strong typing because values aren’t
#actually being assigned, but new objects are bound to the old names
myvar, mystring = mystring, myvar
DATA TYPES
The data structures available in python are lists,tuples and dictionaries. Lists are like one-dimensional arrays (but you can also have lists of other lists), dictionaries are associative arrays (a.k.a hash tables) and tuples are immutable one-dimensional arrays (Python “arrays” can be of any type, so you can mix e.g. integers, strings, etc in lists/dictionaries/tuples). The index of the first item in all array types is 0. Negative numbers count from the end towards the beginning, -1 is the last item. Variables can point to functions. The usage is as follows:
sample = [1, [“another”, “list”], (“a”, “tuple”)]
mylist = [“List item 1”, 2,3,14]
mylist[0] = “List item 1 again”
mylist[-1] = 3.14
mylist = {“key 1”:”value 1”, 2.3,”pi”: 3.14}
mydict[“pi”] = 3.15
mytuple = (1, 2, 3)
myfunction = len
print myfunction(mylist)
3
You can access array ranges using a colon (:). Leaving the start index empty assumes the first item, leaving the end index assumes the last item. Negative indexes count from the last item backwards (thus -1 is the last item)
mylist = [“List item 1”, 2, 3.14]
print mylist[:]
[‘List item 1’, 2, 3.1400]
print mylist[0:2]
[‘List item1’, 2]
print mylist [-3:-1]
[‘List item 1’, 2]
print mylist[1:]
[2, 3.14]
#Adding a third parameter, “step” will have python step in
#N item incements, rather than 1
#E.g. this will return the first item, then go to the third and
#return that (so, items 0 and 2 in 0-indexing)
print mylist[::2]
[‘List item 1’, 3.14]
STRINGS
Its strings can use either single or double quotation marks, and you can have quotation marks of one kind inside a string that uses the other kind (i.e. “He said ‘hello’.” Is valid). Multiline strings are enclosed in _tuple double (or single) quotes_(“””). Python support Unicode out of the box, using the syntax u”This is Unicode string”. To fill a string with values, you use the % (modulo) operator and a tuple. Each %s gets replaced with an item from the tuple, left to right, and you can also use dictionary substitutions, like so:
print “Name: %s\
Number: %s\
String: %s” %(myclass.name, 3, 3* “-“)
Name: Poromenos
String: ---
strString = “””This is a multiline string.”””
#WARNING: Watch out for the trailing s in “%(key)s”
print “This %(verb)s a %(noun)s.” % {“noun”: “test”, “verb”: “is”}
This is a test.
FLOW CONTROL STATEMENTS
Flow control statements are if, for and while. The is no select, instead, use if. Use for to enumerate through members of a list. To obtain a list of numbers, use range(number). These statements’ syntax
rangelist = range(10)
print rangelist
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
for number in rangelist:
#check if number is one of the numbers in the tuple
if number in (3, 4, 7, 9):
#”Break” terminates a for without executing the “else” clause
Break
else:
# “Continue” starts the next iteration of the loop. It’s rather useless here, as it’s the last statement of the loop
Continue
else:
#The “else” clause is optional and is executed only if the loop didn’t “break”.
pass # Do nothing
if rangelist[1] == 2:
print “The second item (lists are 0-based) is 2”
elif rangelist[1] == 3:
print “The second item (lists are 0-based) is 3”
else:
print “Dunno”
while rangelist[1] == 1:
pass
FUNCTIONS
Functions are declared with the “def” keyword. Optional arguments are set in the function declaration after the mandatory arguments by being assigned a default value. For named arguments, the name of the argument is assigned a value. Functions can return a tuple ( and using tuple unpacking you can effectively return multiple values). Lambda functions are ad hoc functions that are comprised of a single statement. Parameters are passed by reference, but immutable types (tuples, ints, strings etc) “Cannot be changed”. This is because only the memory location of the item is passed and binding another object to a variable discards the old one, so immutable types are replaced. For example
#Same as def f(x): return x + 1
functionvar = lambda x: x + 1
print functionvar(1)
2
#an_int and a_string are optional, they have default values
# if one is not passed (2 and “A default string”, respectively).
def passing_example(a_list, an_int=2, a_string=”A default string”):
a_list.append(“A new item”)
an_int = 4
return a_list, an_int, a_string
my_list = [1, 2, 3]
my_int = 10
print passing_example(my_list, my_int)
([1, 2, 3, ‘A new item’], 4, “A default string”)
my_list
[1, 2, 3, ‘A new item’]
my_int
10
CLASSES
Python supports a limited form of multiple inheritance in classes. Private variables and methods can be declared (by convention, this is not enforced by the language) by adding at least two leading underscores and at most one trailing one (e.g. “__spam”). We can also bind arbitrary names to class instances
class MyClass(object):
common = 10
def __init__(self):
self.myvariable = 3
def myfunction(self, arg1, arg2):
return self.myvariable
# This is the class instantiation
classinstance = MyClass()
classinstance.myfunction(1, 2)
3
# This variable is shared by all classes.
classinstance2 = MyClass()
classinstance.common
10
classinstance2.common
10
# Note how we use the class name
# instead of the instance.
MyClass.common = 30
classinstance.common
30
classinstance2.common
30
# This will not update the variable on the class,
# instead it will bind a new object to the old
# variable name.
classinstance.common = 10
classinstance.common
10
classinstance2.common
30
MyClass.common = 50
# This has not changed, because "common" is
# now an instance variable.
classinstance.common
10
classinstance2.common
50
# This class inherits from MyClass. The example
# class above inherits from "object", which makes
# it what's called a "new-style class".
# Multiple inheritance is declared as:
# class OtherClass(MyClass1, MyClass2, MyClassN)
class OtherClass(MyClass):
# The "self" argument is passed automatically
# and refers to the class instance, so you can set
# instance variables as above, but from inside the class.
def __init__(self, arg1):
self.myvariable = 3
print arg1
classinstance = OtherClass("hello")
hello
classinstance.myfunction(1, 2)
3
# This class doesn't have a .test member, but
# we can add one to the instance anyway. Note
# that this will only be a member of classinstance.
classinstance.test = 10
classinstance.test
10
EXCEPTIONS
Exceptions in Python are handled with try-except [ exceptionname] blocks:
def some_function():
try:
# Division by zero raises an exception
10 / 0
except ZeroDivisionError:
print "Oops, invalid."
else:
# Exception didn't occur, we're good.
pass
finally:
# This is executed after the code block is run
# and all exceptions have been handled, even
# if a new exception is raised while handling.
print "We're done with that."
some_function()
Oops, invalid.
We're done with that.
IMPORTING
External libraries are used with the ‘import’ [libname] keyword. You can also use from [libname] import [funcname] for individual functions. Here is an example
import random
from time import clock
randomint = random.randint(1, 100)
print randomint
64
FILE I/O
Python has a wide array of libraries built in. As an example, here is how serializing (converting data structures to strings using the pickle library) with file I/O is used:
import pickle
mylist = ["This", "is", 4, 13327]
# Open the file C:\\binary.dat for writing. The letter r before the
# filename string is used to prevent backslash escaping.
myfile = open(r"C:\\binary.dat", "w")
pickle.dump(mylist, myfile)
myfile.close()
myfile = open(r"C:\\text.txt", "w")
myfile.write("This is a sample string")
myfile.close()
myfile = open(r"C:\\text.txt")
print myfile.read()
'This is a sample string'
myfile.close()
# Open the file for reading.
myfile = open(r"C:\\binary.dat")
loadedlist = pickle.load(myfile)
myfile.close()
print loadedlist
['This', 'is', 4, 13327]
MISCELLANEOUS
Conditions can be chained, 1 a 3 checks that a is both less than 3 and greater than 1
You can use del to delete variables or items in arrays
List comprehensions provide a powerful way to create and manipulate lists. They consist of an expression followed by a for clause followed by zero or more if or for clauses, like so
lst1 = [1, 2, 3]
lst2 = [3, 4, 5]
print [x * y for x in lst1 for y in lst2]
[3, 4, 5, 6, 8, 10, 9, 12, 15]
print [x for x in lst1 if 4 x 1]
[2, 3]
# Check if an item has a specific property.
# "any" returns true if any item in the list is true.
any([i % 3 for i in [3, 3, 4, 4, 3]])
True
# This is because 4 % 3 = 1, and 1 is true, so any()
# returns True.
# Check how many items have this property.
sum(1 for i in [3, 3, 4, 4, 3] if i == 4)
2
del lst1[0]
print lst1
[2, 3]
del lst1
Global variables are declared outside of functions and can be read without any special declarations, but if you want to write to them you must declare them at the beginning of the function with the "global" keyword, otherwise Python will bind that object to a new local variable (be careful of that, it's a small catch that can get you if you don't know it)
number = 5
def myfunc():
# This will print 5.
print number
def anotherfunc():
# This raises an exception because the variable has not
# been bound before printing. Python knows that it an
# object will be bound to it later and creates a new, local
# object instead of accessing the global one.
print number
number = 3
def yetanotherfunc():
global number
# This will correctly change the global.
number = 3
References:
http://trypython.org
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