Learn Python (programming language)


Python (programming language)

Python is a widely used high-level programming language used for general-purpose programming, created by Guido van Rossum and first released in 1991. An interpreted language, Python has a design philosophy which emphasizes code readability (notably using whitespace indentation to delimit code blocks rather than curly braces or keywords), and a syntax which allows programmers to express concepts in fewer lines of code than possible in languages such as C++ or Java. The language provides constructs intended to enable writing clear programs on both a small and large scale.


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:

(etc etc)


>>> dir(5)

[‘__abs__’, ‘__add__’, …]


>>> abs.__doc__

‘abs(number) -> number


Return the absolute value of the argument.’



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


>>> myvar -= 1

>>> myvar


“””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. Sets are available in the sets library (but are built-in in Python 2.5 and later). 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” # We’re changing the item.

>>> mylist[-1] = 3.21 # Here, we refer to the last item.

>>> mydict = {“Key 1”: “Value 1”, 2: 3, “pi”: 3.14}

>>> mydict[“pi”] = 3.15 # This is how you change dictionary values.

>>> mytuple = (1, 2, 3)

>>> myfunction = len

>>> print(myfunction(mylist))


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. Indexing is inclusive-exclusive, so specifying [2:10] will return items [2] (the third item, because of 0-indexing) to [9] (the tenth item), inclusive (8 items). Negative indexes count from the last item backwards (thus -1 is the last item) like so:

>>> mylist = [“List item 1”, 2, 3.14]

>>> print(mylist[:])

[‘List item 1’, 2, 3.1400000000000001]

>>> print(mylist[0:2])

[‘List item 1’, 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 increments, 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]


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 triple double (or single) quotes (“””). Python supports Unicode out of the box, using the syntax u”This is a 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

Number: 3

String: —


strString = “””This is

a multiline



# 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. There is no switch; instead, use if. Use for to enumerate through members of a list. To obtain a list of numbers, use range(<number>). These statements’ syntax is thus:

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.



# “Continue” starts the next iteration

# of the loop. It’s rather useless here,

# as it’s the last statement of the loop.



# 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”)




while rangelist[1] == 1:



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 in the caller by the callee*. 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 funcvar(x): return x + 1

funcvar = lambda x: x + 1

>>> print(funcvar(1))



# 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



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. An example follows:

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)


# This variable is shared by all instances.

>>> classinstance2 = MyClass()

>>> classinstance.common


>>> classinstance2.common


# Note how we use the class name

# instead of the instance.

>>> MyClass.common = 30

>>> classinstance.common


>>> classinstance2.common


# 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


>>> classinstance2.common


>>> MyClass.common = 50

# This has not changed, because “common” is

# now an instance variable.

>>> classinstance.common


>>> classinstance2.common



# 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



>>> classinstance = OtherClass(“hello”)


>>> classinstance.myfunction(1, 2)


# 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



Exceptions in Python are handled with try-except [exceptionname] blocks:

def some_function():


# Division by zero raises an exception

10 / 0

except ZeroDivisionError:

print(“Oops, invalid.”)


# Exception didn’t occur, we’re good.



# 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.


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)


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 = open(r”C:\\text.txt”, “w”)

myfile.write(“This is a sample string”)



myfile = open(r”C:\\text.txt”)

>>> print(myfile.read())

‘This is a sample string’



# Open the file for reading.

myfile = open(r”C:\\binary.dat”)

loadedlist = pickle.load(myfile)


>>> print(loadedlist)

[‘This’, ‘is’, 4, 13327]


  • 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 a condition is true for any items.

# “any” returns true if any item in the list is true.

>>> any([i % 3 for i in [3, 3, 4, 4, 3]])


# This is because 4 % 3 = 1, and 1 is true, so any()

# returns True.


# Check for how many items a condition is true.

>>> sum(1 for i in [3, 3, 4, 4, 3] if i == 4)


>>> 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). For example:

number = 5


def myfunc():

# This will print 5.



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.


number = 3


def yetanotherfunc():

global number

# This will correctly change the global.

number = 3



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