Introduction to Python For Diploma Students

Python introduction for the Diploma level students

1. Introduction to PythonIntroduction to Python
ByBy
Dr. S N SampatDr. S N Sampat
Venue: Government Polytechnic For Girls, SuratVenue: Government Polytechnic For Girls, Surat
Sep 28, 2024 1

2. What is Python?
A Open source programming language with powerful
typing and object oriented features.
•Commonly used for producing HTML content on websites. Great for
text files.
•Useful for Data Science, AI, NLP, Embedded Hardware development
•Very Good Library Support
•Useful built-in types (lists, dictionaries).
•Clean syntax, dynamic interpreter, powerful extensions,
•Efficient high-level data structures and a simple but effective
approach to object-oriented programming
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3. Why Python?
•Natural Language ToolKit
•Ease of use; interpreter
•AI Processing: Symbolic
•Python’s built-in datatypes for strings, lists, and more.
•Java or C++ require the use of special classes for this.
•AI Processing: Statistical
•Python has strong numeric processing capabilities: matrix
operations, etc.
•Suitable for probability and machine learning code.
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4. Great Applications based on Python by Big Companies
1.Dropbox: client side programs are coded in Python
2.Instagram: high-level Python web framework
3.IBM: Deployment of Web Server- IBM Bluemix, Python SDK
for Watson (Big data and AI)
4.Netflix: Audio and video streaming
 recommendation and
analytics engine based on Python
5.Spotify: For music, Back end analysis based on Python
6.Facebook : 21 % code based on Python
7.Google: Most of Youtube code in Python
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5. Python On line Resources
•https://python.org
•Things to read through
•“Dive into Python” (Chapters 2 to 4)
http://diveintopython.org/
•Things to refer to
•The Official Python Tutorial
https://docs.python.org/3/tutorial/
•The Python Quick Reference
http://rgruet.free.fr/PQR27/PQR2.7.html
•Python Books
https://pythonbooks.org/
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6. Installing Python
•Python for Win/Mac/Unix/Linux is available from
www.python.org.
•Generally an easy install.
•On macs, already part of OS X.
•For NLTK you need Python 2.3 or higher.
•GUI development environment: IDLE.
•Anaconda – an integrated package of Python
•Spyder ( Scientific GUI development environment)
•jupyter lab and notebook (Web based interactive dev. Environment)
• PyQt GUI console (inline figures, multiline input, Graphical calltips)
•,Glueviz ( multidimensional data visualization across files )
•Orange3 ( component based data mining framework)
•R Studio ( For open source R language )
•VS Code (editor for debugging, version control, task management)
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7. IDLE Development Environment
•Shell for interactive evaluation.
•Text editor with color-coding and smart indenting for creating
python files.
•Menu commands for changing system settings and running
files.
•We will use IDLE
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8. Overview
•Names & Assignment
•Data types
•Sequences types: Lists, Tuples, and Strings
•Mutability
•Understanding Reference Semantics in Python
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9. A Code Sample (in IDLE)
x = 34 - 23 # A comment.
y = “Hello” # Another one.
z = 3.45
if z == 3.45 or y == “Hello”:
x = x + 1
y = y + “ World” # String concat.
print x
print y
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10. Enough to Understand the Code
Indentation matters to meaning the code
Block structure indicated by indentation
The first assignment to a variable creates it
Dynamic typing: no declarations, names don’t have types,
objects do
Assignment uses = and comparison uses ==
For numbers + - * / % are as expected.
Use of + for string concatenation.
Use of % for string formatting (like printf in C)
Logical operators are words (and,or,not)
not symbols
The basic printing command is print
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11. Basic Datatypes
Integers (default for numbers)
z = 5 / 2 # Answer 2, integer division
Floats
x = 3.456
Strings
Can use ”…" or ’…’ to specify, "foo" == 'foo’
Unmatched can occur within the string
“John’s” or ‘John said “foo!”.’
Use triple double-quotes for multi-line strings or strings than
contain both ‘ and “ inside of them:
“““a‘b“c”””
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12. Whitespace
Whitespace is meaningful in Python, especially
indentation and placement of newlines
Use a newline to end a line of code
Use \ when must go to next line prematurely
No braces {} to mark blocks of code, use consistent
indentation instead
•First line with less indentation is outside of the block
•First line with more indentation starts a nested block
Colons start of a new block in many constructs, e.g.
function definitions, then clauses
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13. Comments
Start comments with #, rest of line is ignored
Can include a “documentation string” as the first line of
a new function or class you define
Development environments, debugger, and other tools
use it: it’s good style to include one
def fact(n):
“““fact(n) assumes n is a positive integer
and returns facorial of n. ”””
assert(n>0)
return 1 if n==1 else n*fact(n-1)
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14. Assignment
Binding a variable in Python means setting a name to hold
a reference to some object
Assignment creates references, not copies
Names in Python don’t have an intrinsic type, objects have
types
Python determines type of the reference auto-matically
based on what data is assigned to it
You create a name the first time it appears on the left side
of an assignment expression:
x = 3
A reference is deleted via garbage collection after any
names bound to it have passed out of scope
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15. Naming Rules
Names are case sensitive and cannot start with a
number. They can contain letters, numbers, and
underscores.
bob Bob _bob _2_bob_ bob_2 BoB
There are some reserved words:
and, assert, break, class, continue, def,
del, elif, else, except, exec, finally, for,
from, global, if, import, in, is, lambda,
not, or, pass, print, raise, return, try,
while
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16. Variables
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No need to declare
Need to assign (initialize)
use of uninitialized variable raises exception
Dynamically typed
if friendly:
greeting = "hello world"
else:
greeting = 12**2
print greeting
Everything is a "variable":
Even functions, classes, modules

17. Reference Semantics
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Assignment manipulates references
x = y does not make a copy of y
x = y makes x reference the object y references
Very useful; but beware!
Example:
>>> a = [1, 2, 3]
>>> b = a
>>> a.append(4)
>>> print b
[1, 2, 3, 4]

18. Control Structures
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if condition:
statements
[elif condition:
statements] ...
[else:
statements]
while condition:
statements
for var in sequence:
statements
break
continue

19. Grouping Indentation
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In Python:
for i in range(20):
if i%3 == 0:
print i
if i%5 == 0:
print "Bingo!"
print "---"
In C:
for (i = 0; i < 20; i++)
{
if (i%3 == 0) {
printf("%d\n", i);
if (i%5 == 0) {
printf("Bingo!\n"); }
}
printf("---\n");
}
0
Bingo!
---
---
---
3
---
---
---
6
---
---
---
9
---
---
---
12
---
---
---
15
Bingo!
---
---
---
18
---
---

20. Naming conventions
The Python community has these recommended
naming conventions
joined_lower for functions, methods and, attributes
joined_lower or ALL_CAPS for constants
StudlyCaps for classes
camelCase only to conform to pre-existing
conventions
Attributes: interface, _internal, __private
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21. Python PEPs
Where do such conventions come from?
The community of users
Codified in PEPs
Python's development is done via the Python
Enhancement Proposal (PEP) process
PEP: a standardized design document, e.g. proposals,
descriptions, design rationales, and explanations for
language features
Similar to IETF RFCs
See the PEP index
PEP 8: Style Guide for Python Code
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22. Accessing Non-Existent Name
Accessing a name before it’s been properly created
(by placing it on the left side of an assignment), raises
an error
>>> y
Traceback (most recent call last):
File "<pyshell#16>", line 1, in -toplevel-
y
NameError: name ‘y' is not defined
>>> y = 3
>>> y
3
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23. Numbers
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C-style shifting & masking
1 << 16, x & 0xff, x | 1, ~x, x^y
Integer division truncates
1/2 -> 0# 1./2 -> 0.5, float(1)/2 -> 0.5
Long (arbitrary precision), complex
2**100 -> 1267650600228229401496703205376L
1j**2 -> (-1+0j)

24. String
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"hello"+"world""helloworld"# concatenation
"hello"*3 "hellohellohello" # repetition
"hello"[0] "h" # indexing
"hello"[-1] "o" # (from end)
"hello"[1:4] "ell" # slicing
"hello"[::2] "hlo" # more slicing
len("hello") 5 # size
"hello" < "jello”True # comparison
"e" in "hello"True # search
'single quotes' """triple quotes""" r"raw strings"

25. String continued
Sep 28, 2024 25
Formatted Strings
>>> "Pi: %.5f - Pi/2: %.5f" % (math.pi, math.pi/2)
'Pi: 3.14159 - Pi/2: 1.57080'
Splitting
>>> "The quick brown fox jumps".split()
['The', 'quick', 'brown', 'fox', 'jumps']
Joining
>>> '?'.join(['The', 'quick', 'brown', 'fox', 'jumps'])
'The?quick?brown?fox?jumps'

26. Everything is an object
Python data is represented by objects or by relations
between objects
Every object has an identity, a type and a value
Identity never changes once created Location or
address in memory
Type (e.g., integer, list) is unchangeable and
determines the possible values it could have and
operations that can be applied
Value of some objects is fixed (e.g., an integer) and
can change for others (e.g., list)
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27. Python’s built-in type hierarchy
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28. Sequence Types
Sequences are containers that hold objects
Finite, ordered, indexed by integers
Tuple: (1, “a”, [100], “foo”)
Tuple is An immutable ordered sequence of items
Strings: “foo bar”
Strings can be of mixed types, including collection types
An immutable ordered sequence of chars
Conceptually very much like a tuple
List: [“one”, “two”, 3]
A Mutable ordered sequence of items of mixed types
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29. Similar SyntaxSimilar Syntax
All three sequence types (tuples, strings, and
lists) share much of the same syntax and
functionality.
Key difference:
Tuples and strings are immutable
Lists are mutable
The operations shown in this section can be
applied to all sequence types
most examples will just show the operation
performed on one
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30. Sequence Types 1
Define tuples using parentheses and commas
>>> tu = (23, ‘abc’, 4.56, (2,3), ‘def’)
Define lists are using square brackets and commas
>>> li = [“abc”, 34, 4.34, 23]
Define strings using quotes (“, ‘, or “””).
>>> st = “Hello World”
>>> st = ‘Hello World’
>>> st = “””This is a multi-line
string that uses triple quotes.”””
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31. Sequence Types 2Sequence Types 2
Access individual members of a tuple, list, or string
using square bracket “array” notation
Note that all are 0 based…
>>> tu = (23, ‘abc’, 4.56, (2,3), ‘def’)
>>> tu[1] # Second item in the tuple.
‘abc’
>>> li = [“abc”, 34, 4.34, 23]
>>> li[1] # Second item in the list.
34
>>> st = “Hello World”
>>> st[1] # Second character in string.
‘e’
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32. Positive and negative indices
>>> t = (23, ‘abc’, 4.56, (2,3), ‘def’)
Positive index: count from the left, starting with 0
>>> t[1]
‘abc’
Negative index: count from right, starting with –1
>>> t[-3]
4.56
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33. Slicing: Return Copy of a Subset
>>> t = (23, ‘abc’, 4.56, (2,3), ‘def’)
Returns copy of container with subset of original
members. Start copying at first index, and stop
copying before the second index
>>> t[1:4]
(‘abc’, 4.56, (2,3))
You can also use negative indices
>>> t[1:-1]
(‘abc’, 4.56, (2,3))
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34. Slicing: Return Copy of a Subset
>>> t = (23, ‘abc’, 4.56, (2,3), ‘def’)
Omit first index to make a copy starting from the
beginning of container
>>> t[:2]
(23, ‘abc’)
Omit second index to make a copy starting at 1st index
and going to end of the container
>>> t[2:]
(4.56, (2,3), ‘def’)
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35. Copying the Whole Sequence
[ : ] makes a copy of an entire sequence
>>> t[:]
(23, ‘abc’, 4.56, (2,3), ‘def’)
Note the difference between these two lines for
mutable sequences
>>> l2 = l1 # Both refer to same ref,
# changing one affects both
>>> l2 = l1[:] # Independent copies, two refs

Sep 28, 2024 35

36. The ‘in’ Operator
Boolean test whether a value is inside a container:
>>> t = [1, 2, 4, 5]
>>> 3 in t
False
>>> 4 in t
True
>>> 4 not in t
False
For strings, tests for substrings
>>> a = 'abcde'
>>> 'c' in a
True
>>> 'cd' in a
True
>>> 'ac' in a
False
Careful: the in keyword is also used in the syntax of for loops
and list comprehensionsSep 28, 2024 36

37. + Operator is Concatenation
The + operator produces a new tuple, list, or string
whose value is the concatenation of its arguments.
>>> (1, 2, 3) + (4, 5, 6)
(1, 2, 3, 4, 5, 6)
>>> [1, 2, 3] + [4, 5, 6]
[1, 2, 3, 4, 5, 6]
>>> “Hello” + “ “ + “World”
‘Hello World’
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38. Lists are mutable
>>> li = [‘abc’, 23, 4.34, 23]
>>> li[1] = 45
>>> li
[‘abc’, 45, 4.34, 23]
We can change lists in place.
Name li still points to the same memory
reference when we’re done.
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39. Tuples are immutable
>>> t = (23, ‘abc’, 4.56, (2,3), ‘def’)
>>> t[2] = 3.14
Traceback (most recent call last):
File "<pyshell#75>", line 1, in -toplevel-
tu[2] = 3.14
TypeError: object doesn't support item assignment
You can’t change a tuple.
You can make a fresh tuple and assign its reference to
a previously used name.
>>> t = (23, ‘abc’, 3.14, (2,3), ‘def’)
The immutability of tuples means they are faster than
lists
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40. Functions vs. methods
Some operations are functions and others methods
Remember that (almost) everything is an object
You just have to learn (and remember or lookup) which operations are
functions and which are methods
len() is a function on collec-
tions that returns the num-
ber of things they contain
>>> len(['a', 'b', 'c'])
3
>>> len(('a','b','c'))
3
>>> len("abc")
3
index() is a method on col-
lections that returns the
index of the 1st occurrence
of its arg
>>> ['a’,'b’,'c'].index('a')
0
>>> ('a','b','c').index('b')
1
>>> "abc".index('c')
2
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41. Lists methods
Lists have many methods, including index, count,
append, remove, reverse, sort, etc.
Many of these modify the list
>>> l = [1,3,4]
>>> l.append(0) # adds a new element to the end of the list
>>> l
[1, 3, 4, 0]
>>> l.insert(1,200) # insert 200 just before index position 1
>>> l
[1, 200, 3, 4, 0]
>>> l.reverse() # reverse the list in place
>>> l
[0, 4, 3, 200, 1]
>>> l.sort() # sort the elements. Optional arguments can
give
>>> l # the sorting function and direction
[0, 1, 3, 4, 200]
>>> l.remove(3) # remove first occurence of element from
list
>>> l
[0, 1, 4, 200]
Sep 28, 2024 41

42. Tuple details
The comma is the tuple creation operator, not parens
>>> 1,
(1,)
Python shows parens for clarity (best practice)
>>> (1,)
(1,)
Don't forget the comma!
>>> (1)
1
Trailing comma only required for singletons others
Empty tuples have a special syntactic form
>>> ()
()
>>> tuple()
()
Sep 28, 2024 42

43. Tuples vs. Lists
Lists slower but more powerful than tuples
Lists can be modified and they have many handy operations
and methods
Tuples are immutable & have fewer features
Sometimes an immutable collection is required (e.g., as a
hash key)
Tuples used for multiple return values and parallel
assignments
x,y,z = 100,200,300
old,new = new,old
Convert tuples and lists using list() and tuple():
mylst = list(mytup); mytup = tuple(mylst)
Sep 28, 2024 43

44. List discussionList discussion
Sep 28, 2024 44
Flexible arrays, not linked lists
a = [99, "bottles of beer", ["on", "the", "wall"]]
Same operators as for strings
a+b, a*3, a[0], a[-1], a[1:], len(a)
Item and slice assignment
a[0] = 98
a[1:2] = ["bottles", "of", "beer"]
-> [98, "bottles", "of", "beer", ["on", "the", "wall"]]
del a[-1]# -> [98, "bottles", "of", "beer"]

45. List Discussion continue
Sep 28, 2024 45
>>> a = range(5) # [0,1,2,3,4]
>>> a.append(5) # [0,1,2,3,4,5]
>>> a.pop() # [0,1,2,3,4]
5
>>> a.insert(0, 42)# [42,0,1,2,3,4]
>>> a.pop(0) # [0,1,2,3,4]
42
>>> a.reverse() # [4,3,2,1,0]
>>> a.sort() # [0,1,2,3,4]

46. Dictionaries
Sep 28, 2024 46
Hash tables, "associative arrays"
d = {"duck": "eend", "water": "water"}
Lookup:
d["duck"] -> "eend"
d["back"] # raises KeyError exception
Insert, delete, overwrite:
d["back"] = "rug" # {"duck": "eend", "water": "water",
"back": "rug"}
del d["water"] # {"duck": "eend", "back": "rug"}
d["duck"] = "duik" # {"duck": "duik", "back": "rug"}

47. More Dictionary Operations
Sep 28, 2024 47
Keys, values, items:
d.keys() -> ["duck", "back"]
d.values() -> ["duik", "rug"]
d.items() -> [("duck","duik"), ("back","rug")]
Presence check:
d.has_key("duck") -> True
d.has_key("spam") -> False
"duck" in d -> True; "spam" in d -> False
Values of any type; keys almost any
{"name":"Bryce", "age":22, ("hello","world"):1,
42:"yes", "flag": ["red","white","blue"]}

48. Dictionary Detail
Sep 28, 2024 48
Keys must be immutable:
numbers, strings, tuples of immutables
these cannot be changed after creation
reason is hashing (fast lookup technique)
not lists or other dictionaries
these types of objects can be changed "in
place"
no restrictions on values
Keys will be listed in arbitrary order
again, because of hashing

49. Thank You All
Questions Please
Sep 28, 2024 49