The document provides information about strings in Python. Some key points include:
- Strings are immutable sequences of characters that can be accessed using indexes. Common string methods allow operations like uppercase, lowercase, counting characters, etc.
- Strings support slicing to extract substrings, and various string formatting methods allow combining strings with variables or other strings.
- Loops can be used to iterate through strings and perform operations on individual characters. Built-in string methods do not modify the original string.
- Examples demonstrate various string operations like indexing, slicing, checking substrings, string methods, formatting and parsing strings. Loops are used to count characters in examples.
This document discusses Python string data types and operations. Some key points covered include:
- Strings can be defined using single quotes or double quotes and support indexing, slicing, and string operations like concatenation and comparison.
- Common string methods allow searching, modifying, and analyzing string content through functions like find(), lower(), strip(), and replace().
- Strings can be looped through to access individual characters using a for loop or while loop with indexing.
- The len() function returns the length of a string and special string values like -1 are returned when substrings are not found during searches.
A string is a sequence of characters that can be accessed individually using indexes. Strings are immutable, so their characters cannot be changed, only new strings can be reassigned or created. Common string methods include upper()/lower() to convert case, split() to separate a string into a list by a delimiter, join() to combine a list into a string, replace() to substitute characters, and find() to locate a substring.
Slides for Lecture 3 of the course: Introduction to Programming with Python offered at ICCBS.
It covers the following topics:
Strings useful string operations.
• List is a collection, which is ordered and changeable. Allows duplicate members.
• Tuple is a collection, which is ordered and unchangeable. Allows duplicate members.
• Set is a collection, which is unordered and unindexed. No duplicate members.
• Dictionary is a collection, which is unordered, changeable and indexed. No duplicate members.
: String, List, Tuple, Dictionary
• List is a collection, which is ordered and changeable. Allows duplicate members.
• Tuple is a collection, which is ordered and unchangeable. Allows duplicate members.
• Set is a collection, which is unordered and unindexed. No duplicate members.
• Dictionary is a collection, which is unordered, changeable and indexed. No duplicate members.
The document discusses strings and string operations in Python. It defines what a string is, how they are defined and different ways to manipulate strings like concatenation, slicing, formatting, built-in methods etc. It also discusses various string methods like capitalize(), lower(), upper(), count(), find(), format() etc and functions like ord(), chr() to work with strings.
A string in Python is a sequence of characters. It is a derived data type. Strings are immutable. This means that once defined, they cannot be changed. Many Python methods, such as replace , join , or split modify strings.
This document provides an introduction and overview of strings in Python. It discusses that strings are a data type that can contain sequences of characters. The built-in string class is 'str' and strings can be defined using single, double, or triple quotes. Strings support various methods like indexing, slicing, concatenation, formatting and more. Common string methods are also described such as upper(), lower(), split(), join() which allow manipulating strings. The document also discusses comparing and slicing strings in Python.
This document discusses strings and lists in Python. It covers:
- Strings are immutable sequences of characters that can be indexed and sliced.
- Lists are mutable sequences that can store multiple data types and be modified using methods like append(), insert(), and extend().
- Common string methods like isupper(), lower(), upper() are used to manipulate string cases.
Strings in Python can be defined using single quotes, double quotes, or triple quotes for multiline strings. Strings have many built-in methods that allow you to modify, slice, check, count, and format string values. Common string methods include upper(), lower(), strip(), replace(), split(), format(), isX() checker methods, and slice to extract substrings.
This group presentation covers strings in Python. It defines strings as collections of characters that can be manipulated and formatted. The presentation discusses how to create, index, slice, concatenate and format strings. It also covers escape characters and built-in string methods for operations like capitalization, counting characters, and splitting strings. The presentation provides examples to illustrate string concepts in Python.
Python is an interpreted, object-oriented programming language similar to PERL, that has gained popularity because of its clear syntax and readability.
Python is an interpreted, object-oriented, high-level programming language with dynamic semantics.
Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together.
Python is an interpreted, object-oriented programming language similar to PERL, that has gained popularity because of its clear syntax and readability.
Python is an interpreted, object-oriented, high-level programming language with dynamic semantics.
Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together.
This document discusses Python lists and loops. It begins by explaining how lists allow storing multiple values in a single variable, unlike separate variables. Lists can hold any data types and values can be accessed by index like strings. The document then demonstrates how to create and manipulate lists using various methods like append, sort, and join. It also introduces for loops, which are commonly used to iterate through lists. Range functions are described that can generate integer sequences to loop over. In the last section, some additional string methods are covered like startswith, endswith, strip, and find.
This document discusses the string data type in Python. It explains that strings are sequences of characters that can be indexed, sliced, concatenated, and operated on using various string methods. Lists are also introduced as mutable sequences that can contain heterogeneous data types. Common string and list operations like indexing, slicing, length calculation, and concatenation are demonstrated through examples.
• List is a collection, which is ordered and changeable. Allows duplicate members.
• Tuple is a collection, which is ordered and unchangeable. Allows duplicate members.
• Set is a collection, which is unordered and unindexed. No duplicate members.
• Dictionary is a collection, which is unordered, changeable and indexed. No duplicate members.
: String, List, Tuple, Dictionary
• List is a collection, which is ordered and changeable. Allows duplicate members.
• Tuple is a collection, which is ordered and unchangeable. Allows duplicate members.
• Set is a collection, which is unordered and unindexed. No duplicate members.
• Dictionary is a collection, which is unordered, changeable and indexed. No duplicate members.
The document discusses strings and string operations in Python. It defines what a string is, how they are defined and different ways to manipulate strings like concatenation, slicing, formatting, built-in methods etc. It also discusses various string methods like capitalize(), lower(), upper(), count(), find(), format() etc and functions like ord(), chr() to work with strings.
A string in Python is a sequence of characters. It is a derived data type. Strings are immutable. This means that once defined, they cannot be changed. Many Python methods, such as replace , join , or split modify strings.
This document provides an introduction and overview of strings in Python. It discusses that strings are a data type that can contain sequences of characters. The built-in string class is 'str' and strings can be defined using single, double, or triple quotes. Strings support various methods like indexing, slicing, concatenation, formatting and more. Common string methods are also described such as upper(), lower(), split(), join() which allow manipulating strings. The document also discusses comparing and slicing strings in Python.
This document discusses strings and lists in Python. It covers:
- Strings are immutable sequences of characters that can be indexed and sliced.
- Lists are mutable sequences that can store multiple data types and be modified using methods like append(), insert(), and extend().
- Common string methods like isupper(), lower(), upper() are used to manipulate string cases.
Strings in Python can be defined using single quotes, double quotes, or triple quotes for multiline strings. Strings have many built-in methods that allow you to modify, slice, check, count, and format string values. Common string methods include upper(), lower(), strip(), replace(), split(), format(), isX() checker methods, and slice to extract substrings.
This group presentation covers strings in Python. It defines strings as collections of characters that can be manipulated and formatted. The presentation discusses how to create, index, slice, concatenate and format strings. It also covers escape characters and built-in string methods for operations like capitalization, counting characters, and splitting strings. The presentation provides examples to illustrate string concepts in Python.
Python is an interpreted, object-oriented programming language similar to PERL, that has gained popularity because of its clear syntax and readability.
Python is an interpreted, object-oriented, high-level programming language with dynamic semantics.
Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together.
Python is an interpreted, object-oriented programming language similar to PERL, that has gained popularity because of its clear syntax and readability.
Python is an interpreted, object-oriented, high-level programming language with dynamic semantics.
Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together.
This document discusses Python lists and loops. It begins by explaining how lists allow storing multiple values in a single variable, unlike separate variables. Lists can hold any data types and values can be accessed by index like strings. The document then demonstrates how to create and manipulate lists using various methods like append, sort, and join. It also introduces for loops, which are commonly used to iterate through lists. Range functions are described that can generate integer sequences to loop over. In the last section, some additional string methods are covered like startswith, endswith, strip, and find.
This document discusses the string data type in Python. It explains that strings are sequences of characters that can be indexed, sliced, concatenated, and operated on using various string methods. Lists are also introduced as mutable sequences that can contain heterogeneous data types. Common string and list operations like indexing, slicing, length calculation, and concatenation are demonstrated through examples.
Third Review PPT that consists of the project d etails like abstract.Sowndarya6
CyberShieldX is an AI-driven cybersecurity SaaS web application designed to provide automated security analysis and proactive threat mitigation for business websites. As cyber threats continue to evolve, traditional security tools like OpenVAS and Nessus require manual configurations and lack real-time automation. CyberShieldX addresses these limitations by integrating AI-powered vulnerability assessment, intrusion detection, and security maintenance services. Users can analyze their websites by simply submitting a URL, after which CyberShieldX conducts an in-depth vulnerability scan using advanced security tools such as OpenVAS, Nessus, and Metasploit. The system then generates a detailed report highlighting security risks, potential exploits, and recommended fixes. Premium users receive continuous security monitoring, automatic patching, and expert assistance to fortify their digital infrastructure against emerging threats. Built on a robust cloud infrastructure using AWS, Docker, and Kubernetes, CyberShieldX ensures scalability, high availability, and efficient security enforcement. Its AI-driven approach enhances detection accuracy, minimizes false positives, and provides real-time security insights. This project will cover the system's architecture, implementation, and its advantages over existing security solutions, demonstrating how CyberShieldX revolutionizes cybersecurity by offering businesses a smarter, automated, and proactive defense mechanism against ever-evolving cyber threats.
First Review PPT gfinal gyft ftu liu yrfut goSowndarya6
CyberShieldX provides end-to-end security solutions, including vulnerability assessment, penetration testing, and real-time threat detection for business websites. It ensures that organizations can identify and mitigate security risks before exploitation.
Unlike traditional security tools, CyberShieldX integrates AI models to automate vulnerability detection, minimize false positives, and enhance threat intelligence. This reduces manual effort and improves security accuracy.
Many small and medium businesses lack dedicated cybersecurity teams. CyberShieldX provides an easy-to-use platform with AI-powered insights to assist non-experts in securing their websites.
Traditional enterprise security solutions are often expensive. CyberShieldX, as a SaaS platform, offers cost-effective security solutions with flexible pricing for businesses of all sizes.
Businesses must comply with security regulations, and failure to do so can result in fines or data breaches. CyberShieldX helps organizations meet compliance requirements efficiently.
WIRELESS COMMUNICATION SECURITY AND IT’S PROTECTION METHODSsamueljackson3773
In this paper, the author discusses the concerns of using various wireless communications and how to use
them safely. The author also discusses the future of the wireless industry, wireless communication
security, protection methods, and techniques that could help organizations establish a secure wireless
connection with their employees. The author also discusses other essential factors to learn and note when
manufacturing, selling, or using wireless networks and wireless communication systems.
本資料「To CoT or not to CoT?」では、大規模言語モデルにおけるChain of Thought(CoT)プロンプトの効果について詳しく解説しています。
CoTはあらゆるタスクに効く万能な手法ではなく、特に数学的・論理的・アルゴリズム的な推論を伴う課題で高い効果を発揮することが実験から示されています。
一方で、常識や一般知識を問う問題に対しては効果が限定的であることも明らかになりました。
複雑な問題を段階的に分解・実行する「計画と実行」のプロセスにおいて、CoTの強みが活かされる点も注目ポイントです。
This presentation explores when Chain of Thought (CoT) prompting is truly effective in large language models.
The findings show that CoT significantly improves performance on tasks involving mathematical or logical reasoning, while its impact is limited on general knowledge or commonsense tasks.
Rigor, ethics, wellbeing and resilience in the ICT doctoral journeyYannis
The doctoral thesis trajectory has been often characterized as a “long and windy road” or a journey to “Ithaka”, suggesting the promises and challenges of this journey of initiation to research. The doctoral candidates need to complete such journey (i) preserving and even enhancing their wellbeing, (ii) overcoming the many challenges through resilience, while keeping (iii) high standards of ethics and (iv) scientific rigor. This talk will provide a personal account of lessons learnt and recommendations from a senior researcher over his 30+ years of doctoral supervision and care for doctoral students. Specific attention will be paid on the special features of the (i) interdisciplinary doctoral research that involves Information and Communications Technologies (ICT) and other scientific traditions, and (ii) the challenges faced in the complex technological and research landscape dominated by Artificial Intelligence.
This document provides information about the Fifth edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
This study will provide the audience with an understanding of the capabilities of soft tools such as Artificial Neural Networks (ANN), Support Vector Regression (SVR), Model Trees (MT), and Multi-Gene Genetic Programming (MGGP) as a statistical downscaling tool. Many projects are underway around the world to downscale the data from Global Climate Models (GCM). The majority of the statistical tools have a lengthy downscaling pipeline to follow. To improve its accuracy, the GCM data is re-gridded according to the grid points of the observed data, standardized, and, sometimes, bias-removal is required. The current work suggests that future precipitation can be predicted by using precipitation data from the nearest four grid points as input to soft tools and observed precipitation as output. This research aims to estimate precipitation trends in the near future (2021-2050), using 5 GCMs, for Pune, in the state of Maharashtra, India. The findings indicate that each one of the soft tools can model the precipitation with excellent accuracy as compared to the traditional method of Distribution Based Scaling (DBS). The results show that ANN models appear to give the best results, followed by MT, then MGGP, and finally SVR. This work is one of a kind in that it provides insights into the changing monsoon season in Pune. The anticipated average precipitation levels depict a rise of 300–500% in January, along with increases of 200-300% in February and March, and a 100-150% increase for April and December. In contrast, rainfall appears to be decreasing by 20-30% between June and September.
‘How to develop Pythonic coding rather than Python coding – Logic Perspective’
1. Online Workshop on ‘How to develop
Pythonic coding rather than Python
coding – Logic Perspective’
21.7.20 Day 1 session 2
Dr. S.Mohideen Badhusha
Professor/ CSE department
Alva’s Institute Engineering and
Technology
Mijar, Moodbidri, Mangalore
1
3. 3
To acquire knowledge of iteration and string in Python
To comprehend the concept of looping statements in
Python
To practice the simple problems in iteration and string
Objectives of the Day 1 session 2
4. 4
Iteration
for loop
for var in <collection>:
<statements>
where collection is iterable obj like list, tuple,dictionary,string
and range
a = [’i’, ’say’, ’hello’] # a list of string elements
for i in range(len(a)):
print (i,end=’ ’)
print (a[i])
Output
0 I
1 say
2 hello
8. 8
for loop using range()
for x in range(5):
print x
o/p
0
1
2
3
4
9. 9
Python program but not Pythonic
def traverse(string):
index = 0
while index < len(string):
letter = string[index]
print(letter)
index += 1
traverse('Monty Python')
Output ?
11. 11
in operator
in is a boolean operator that checks
membership within a sequence
It is also called membership operator.
'a' in 'banana'
True
'o' in 'banana'
False
13. 13
String handling functions
s=’HELLO’
s.lower() # hello
s1=’hello’
s1.upper() # HELLO
s.strip() -- returns a string with whitespace
removed from the start and end
s=’ I am happy ‘
s.strip() # I am happy ( no front and back spaces)
s.isalpha()# True
s.isdigit() # False
s.isspace() #False
14. 14
split() and join() functions
s.split('delim') - returns a string into a list of
words separated by delim
Ex:
'aaa,bbb,ccc'.split(',') -> ['aaa', 'bbb', 'ccc'].
Defalut delim is space
s.join(list) -- opposite of split(), joins the
elements in the given list together using the
string as the delimiter.
Ex:
'---'.join(['aaa', 'bbb', 'ccc']) -> ‘aaa---bbb---ccc’
14
15. 15
String Formatting Operator: %
The operator % allows strings to be built out of many
data items in a “fill in the blanks” fashion.
x = “Ram”
y = 34
print(“%s’s age is %d” % (x, y))
o/p
Ram’s age is 34
The tuple following the % operator is used to fill in
the blanks in the original string marked with %s %g
%d to represent string, float and integer values
16. 16
Converting anything to a String
The built-in str() function can convert an
instance of any data type into a string
Ex:
print(“Hello ” + str(2))
o/p
Hello 2
17. 17
Slicing strings
A segment of a string is called a slice.
Selecting a slice is similar to selecting a
character:
Ex:
s = 'Monty Python'
print(s[0:5])
Monty
print(s[6:12])
Python
19. 19
String Indexing
print(s[-1])
o/p
'o' #last char (1st from the end)
print(s[-4])
o/p
'e' # 4th from the end
print(s[:-3])
o/p
'He' – from starting, going up to but not
including the last 3 chars.
s[-3:] is 'llo' -- starting with the 3rd char from
the end and extending to the end of the string.
20. 20
s[1:4] is 'ell' -- chars starting at index 1 and
extending up to but not including index 4
s[1:] is 'ello' -- omitting either index defaults
to the start or end of the string
s[:] is 'Hello' -- omitting both always gives
us a copy of the whole thing
s[1:100] is 'ello' -- an index that is too big is
truncated down to the string length
21. 21
Concluding Tips
for loop in Python is entirely different from
other programming languages[ for var in
collection]
while loop in Python is similar to the
programming constructs in other languages
There is no do… while loop existing in Python
To be Pythonic, we should use data
structures and PEP 8 rules in Python
In slicing of string, st[i:n], we have to consider
from i th index to n-1 .
st[:n] from 0 th index to n-1.
st[i:] from i th index to end of the string