This Power Point Presentation explains the what are the data types present in Python and each data type has one sample code

2. Mutable Vs Immutable Objects
In general, data types in Python can be distinguished based
on whether objects of the type are mutable or immutable.
The content of objects of immutable types cannot be
changed after they are created.
Mutable Objects Immutable Objects
byte array
list
set
dict
int, float, long,
complex
str
tuple
frozen set

3. Python Data Types
Python´s built-in (or standard) data types can be
grouped into several classes.
Boolean Types
Numeric Types
 Sequences
 Sets
 Mappings

4. Boolean Types:
The type of the built-in values True and False. Useful in
conditional expressions, and anywhere else you want to
represent the truth or falsity of some condition. Mostly
interchangeable with the integers 1 and 0.
Examples:

5. Numeric Types:
Python supports four different numerical types :
1) int (signed integers)
2) long (long integers [can also be represented in octal and
hexadecimal])
3) float (floating point real values)
4) complex (complex numbers)

6. Integers
Examples: 0, 1, 1234, -56
 Integers are implemented as C longs
 Note: dividing an integer by another integer will return only
the integer part of the quotient, e.g. typing 7/2 will yield 3
Long integers
Example: 999999999999999999999L
 Must end in either l or L
 Can be arbitrarily long

7. Floating point numbers
Examples: 0., 1.0, 1e10, 3.14e-2, 6.99E4
 Implemented as C doubles
 Division works normally for floating point numbers: 7./2.
= 3.5
 Operations involving both floats and integers will yield
floats:
6.4 – 2 = 4.4

8. Octal constants
Examples: 0177, -01234
 Must start with a leading ‘0’
Hex constants
Examples: 0x9ff, 0X7AE
 Must start with a leading ‘0x’ or ‘0X’
Complex numbers
Examples: 3+4j, 3.0+4.0j, 2J
 Must end in j or J
 Typing in the imaginary part first will return the complex
number in the order Re+ ImJ

9. Sequences
There are five sequence types
1) Strings
2) Lists
3) Tuple
4) Bytearray
5) Xrange

10. 1. Strings:
 Strings in Python are identified as a contiguous set of
characters in between quotation marks.
 Strings are ordered blocks of text
 Strings are enclosed in single or double quotation marks
 Double quotation marks allow the user to extend strings
over multiple lines without backslashes, which usually
signal the continuation of an expression
Examples: 'abc', “ABC”

11. Concatenation and repetition
Strings are concatenated with the + sign:
>>> 'abc'+'def'
'abcdef'
Strings are repeated with the * sign:
>>> 'abc'*3
'abcabcabc'

12. Indexing and Slicing
 Python starts indexing at 0. A string s will have indexes running from
0 to len(s)-1 (where len(s) is the length of s) in integer quantities.
s[i] fetches the ith element in s
>>> s = 'string'
>>> s[1] # note that Python considers 't' the first element
't' # of our string s
s[i:j] fetches elements i (inclusive) through j (not inclusive)
>>> s[1:4]
'tri'
s[:j] fetches all elements up to, but not including j
>>> s[:3]
'str'
s[i:] fetches all elements from i onward (inclusive)
>>> s[2:]
'ring'

13. s[i:j:k] extracts every kth element starting with index i
(inlcusive) and ending with index j (not inclusive)
>>> s[0:5:2]
'srn'
Python also supports negative indexes. For example, s[-1]
means extract the first element of s from the end (same as
s[len(s)-1])
>>> s[-1]
'g'
>>> s[-2]
'n‘
One of Python's coolest features is the string
format operator %. This operator is unique to strings and
makes up for the pack of having functions from C's printf()
family.

14. Some of the string methods are listed in below:
str.capitalize ( )
str.center(width[, fillchar])
str.count(sub[, start[, end]])
str.encode([encoding[, errors]])
str.decode([decoding[, errors]])
str.endswith(suffix[, start[, end]])
str.find(sub[, start[, end]])
str.isalnum()
str.isalpha()
str.isdigit()
str.islower()
str.isspace()

15. Sample Program for String Methods
var1='Hello World!'
var2='Python Programming'
print 'var1[0]: ',var1[0]
print 'var2[0:6]:',var2[0:6]
print 'Updatestring:-',var1[:6]+'Python'
print var1*2
print "My name is %s and dob is
%d"%('Python',1990)
# first character capitalized in string
str1='guido van rossum'
print str1.capitalize()
print str1.center(30,'*‘)
sub='s';
print 'str1.count(sub,0):-',str1.count(sub,0)
sub='van'
print 'str1.count(sub):-',str1.count(sub)
str1=str1.encode('base64','strict')
print 'Encoding string :'+str1
print 'Decode string :'+str1.decode('base64','strict')
str2='Guido van Rossum'
suffix='Rossum'
print str2.endswith(suffix)
print str2.endswith(suffix,1,17)
# find string in a existed string or not
str4='Van'
print str2.find(str4)
print str2.find(str4,17)
str5='sectokphb10k'
print str5.isalnum()
str6='kumar pumps'
print str6.isalnum()
print str4.isalpha()
str7='123456789'
print str7.isdigit()
print str6.islower()
print str2.islower()
str8=' '
print str8.isspace()

16. Output for the string methods sample code

17. 2) Lists
Lists are positionally ordered collections of arbitrarily typed
objects, and they have no fixed size and they are mutable.
 Lists are contained in square brackets []
 Lists can contain numbers, strings, nested sublists, or
nothing
Examples:
L1 = [0,1,2,3],
L2 = ['zero', 'one'],
L3 = [0,1,[2,3],'three',['four,one']],
L4 = []

18.  List indexing works just like string indexing
 Lists are mutable: individual elements can be reassigned in
place. Moreover, they can grow and shrink in place
Example:
>>> L1 = [0,1,2,3]
>>> L1[0] = 4
>>> L1[0]
4

19. Basic List Operations
Lists respond to the + and * operators much like
strings; they mean concatenation and repetition here
too, except that the result is a new list, not a string.
Python Expression Results Description
len([1, 2, 3]) 3 Length
[1, 2, 3] + [4, 5, 6] [1, 2, 3, 4, 5, 6] Concatenation
['Hi!'] * 4 ['Hi!', 'Hi!', 'Hi!', 'Hi!'] Repetition
3 in [1, 2, 3] True Membership
for x in [1, 2, 3]: print x, 1 2 3 Iteration

20. Some of the List methods are listed in below
list.append(obj)
list.count(obj)
list.extend(seq)
list.index(obj)
list.insert(index, obj)
list.pop(obj=list[-1])
list.remove(obj)
listlist.reverse()
list.sort([func])

21. Sample Code for List Methods
list1=['python','cython','jython']
list1.append('java')
print list1
list1.insert(2,'c++')
print list1
list2=['bash','perl','shell','ruby','perl']
list1.extend(list2)
print list1
if 'python' in list1:
print 'it is in list1'
if 'perl' in list2:
print 'it is in list2'
# reversing the list
list1.reverse()
print list1
# sorting the list
list2.sort()
print list2
# count the strings in list
value=list2.count('perl')
print value
# Locate string
index=list1.index("jython")
print index,list1[index]

22. Output for the list methods sample code

23. 3) Tuple:
 A tuple is a sequence of immutable Python objects. Tuples are
sequences, just like lists.
 The only difference is that tuples can't be changed i.e., tuples are
immutable and tuples use parentheses and lists use square brackets.
 Tuples are contained in parentheses ()
 Tuples can contain numbers, strings, nested sub-tuples, or nothing
Examples:
t1 = (0,1,2,3)
t2 = ('zero', 'one')
t3 = (0,1,(2,3),'three',('four,one'))
t4 = ()

24. Basic Tuple Operations
Tuples respond to the + and * operators much like strings;
they mean concatenation and repetition here too, except that
the result is a new tuple, not a string.
Python Expression Results Description
len((1, 2, 3)) 3 Length
(1, 2, 3) + (4, 5, 6) (1, 2, 3, 4, 5, 6) Concatenation
['Hi!'] * 4 ('Hi!', 'Hi!', 'Hi!', 'Hi!') Repetition
3 in (1, 2, 3) True Membership
for x in (1, 2, 3): print x, 1 2 3 Iteration

25. 4) Bytearray:
bytearray([source[, encoding[, errors]]])
Return a new array of bytes. The bytearray type is a mutable
sequence of integers in the range 0 <= x < 256. The
optional source parameter can be used to initialize the array in a few
different ways:
If it is a string, you must also give the encoding (and
optionally, errors) parameters; bytearray() then converts the string to
bytes using str.encode()
If it is an integer, the array will have that size and will be initialized
with null bytes.
If it is an object conforming to the buffer interface, a read-only buffer
of the object will be used to initialize the bytes array.
If it is an iterable, it must be an iterable of integers in the
range 0 <= x < 256, which are used as the initial contents of the array.
Without an argument, an array of size 0 is created.

26. 5) Xrange:
The xrange type is an immutable sequence which is
commonly used for looping. The advantage of the xrange type is
that an xrange object will always take the same amount of
memory, no matter the size of the range it represents. There are no
consistent performance advantages.
XRange objects have very little behavior: they only support
indexing, iteration, and the len( ) function.

27. Sets
The sets module provides classes for
constructing and manipulating unordered collections of
unique elements.
Common uses include membership testing,
removing duplicates from a sequence, and computing
standard math operations on sets such as intersection, union,
difference, and symmetric difference.
Curly braces or the set() function can be used to
create sets.

28. Sets Operations
Operation Equivalent Result
len(s) cardinality of set s
x in s test x for membership in s
x not in s test x for non-membership in s
s.issubset(t) s <= t test whether every element in s is in t
s.issuperset(t) s >= t test whether every element in t is in s
s.union(t) s | t
new set with elements from
both s and t
s.intersection(t) s & t
new set with elements common
to s and t
s.difference(t) s - t
new set with elements in s but not
in t
s.symmetric_difference(t) s ^ t
new set with elements in
either s or t but not both
s.copy() new set with a shallow copy of s

29. Operation Equivalent Result
s.update(t) s |= t
return set s with elements added
from t
s.intersection_update(t) s &= t
return set s keeping only elements
also found in t
s.difference_update(t) s -= t
return set s after removing
elements found in t
s.symmetric_difference_update(t) s ^= t
return set s with elements
from s or t but not both
s.add(x) add element x to set s
s.remove(x)
remove x from set s;
raises KeyError if not present
s.discard(x) removes x from set s if present
s.pop()
remove and return an arbitrary
element from s; raises KeyError if
empty
s.clear() remove all elements from set s

30. Mapping Type
 A mapping object maps hashable values to arbitrary objects.
Mappings are mutable objects. There is currently only one standard
mapping type, the dictionary.
 Dictionaries consist of pairs (called items) of keys and
their corresponding values.
Dictionaries can be created by placing a comma-separated list
of key: value pairs within curly braces
Keys are unique within a dictionary while values may not be. The
values of a dictionary can be of any type, but the keys must be of an
immutable data type such as strings, numbers, or tuples.
Example:
d={‘python’:1990,’cython’:1995,’jython’:2000}

31. Some of the dictionary methods listed in below
len( dict )
dict.copy( )
dict.items( )
dict.keys( )
dict.values( )
dict.has_key(‘key’)
viewitems( )
viewkeys( )
viewvalues( )

32. Sample code for dictionary methods
dict = {'Language': 'Python', 'Founder': 'Guido Van Rossum'}
print dict
print "Length of dictionary : %d" % len(dict)
copydict = dict.copy()
print "New Dictionary : %s" % str(copydict)
print "Items in dictionary: %s" % dict.items()
print "Keys in dictionary: %s" % dict.keys()
print "Vales in Dictionary: %s" % dict.values()
print "Key in dictionary or not: %s" % dict.has_key('Language')
print "Key in dictionary or not: %s" % dict.has_key('Year')

33. Output for the dictionary sample code