UNIT-3 python and data structure alo.pptxharikahhy
Python works on different platforms (Windows, Mac, Linux, Raspberry Pi, etc).
Python has a simple syntax similar to the English language.
Python has syntax that allows developers to write programs with fewer lines than some other programming languages.
Python runs on an interpreter system, meaning that code can be executed as soon as it is written. This means that prototyping can be very quick.
Python can be treated in a procedural way, an object-oriented way or a functional way.
Good to know
The most recent major version of Python is Python 3, which we shall be using in this tutorial. However, Python 2, although not being updated with anything other than security updates, is still quite popular.
In this tutorial Python will be written in a text editor. It is possible to write Python in an Integrated Development Environment, such as Thonny, Pycharm, Netbeans or Eclipse which are particularly useful when managing larger collections of Python files.
Python Syntax compared to other programming languages
Python was designed for readability, and has some similarities to the English language with influence from mathematics.
Python uses new lines to complete a command, as opposed to other programming languages which often use semicolons or parentheses.
Python relies on indentation, using whitespace, to define scope; such as the scope of loops, functions and classes. Other programming languages often use curly-brackets for this purpose.
Processing data with Python, using standard library modules you (probably) ne...gjcross
Tutorial #2 from PyCon AU 2012
You have data.
You have Python.
You also have a lot of choices about the best way to work with that data...
Ever wondered when you would use a tuple, list, dictionary, set, ordered dictionary, bucket, queue, counter or named tuple? Phew!
Do you know when to use a loop, iterator or generator to work through a data container?
Why are there so many different "containers" to hold data?
What are the best ways to work with these data containers?
This tutorial will give you all the basics to effectively working with data containers and iterators in Python. Along the way we will cover some very useful modules from the standard library that you may not have used before and will end up wondering how you ever did without them.
This tutorial is aimed at Python beginners. Bring along your laptop so you can interactively work through some of the examples in the tutorial. If you can, install ipython (http://ipython.org/) as we will use it for the demonstrations.
This document provides an introduction to using lists in Python. It defines what lists are in Python, how to create, access, update, and delete list elements, and some common list operations. It also provides examples of creating lists, accessing values at different indices, updating and deleting elements, and using basic operators like addition and multiplication. Finally, it proposes three exercises involving lists to practice these concepts.
This document provides information about data structures in Python. It discusses lists, tuples, sets, and dictionaries. For lists and tuples, it covers defining, indexing, slicing, unpacking, methods, built-in functions, and list comprehensions. Lists can contain heterogeneous elements and support methods like append(), pop(), sort(), and reverse(). Tuples are similar to lists but are immutable ordered sequences. They also support indexing, slicing, and unpacking. This document serves as a guide to working with common Python data structures.
The document discusses various Python data structures including lists, tuples, sets, and dictionaries. It provides examples of how to use common list methods like append(), pop(), count(), and sort(). It also covers using lists as stacks and queues. Other topics include the map() and filter() functions, reducing a sequence, deleting items from a list, immutable vs mutable objects, and looping techniques for dictionaries and sequences.
METHODS DESCRIPTION
copy() They copy() method returns a shallow copy of the dictionary.
clear() The clear() method removes all items from the dictionary.
pop() Removes and returns an element from a dictionary having the given key.
popitem() Removes the arbitrary key-value pair from the dictionary and returns it as tuple.
get() It is a conventional method to access a value for a key.
dictionary_name.values() returns a list of all the values available in a given dictionary.
str() Produces a printable string representation of a dictionary.
update() Adds dictionary dict2’s key-values pairs to dict
setdefault() Set dict[key]=default if key is not already in dict
keys() Returns list of dictionary dict’s keys
items() Returns a list of dict’s (key, value) tuple pairs
has_key() Returns true if key in dictionary dict, false otherwise
fromkeys() Create a new dictionary with keys from seq and values set to value.
type() Returns the type of the passed variable.
cmp() Compares elements of both dict.
The document provides information about the course GE3151 Problem Solving and Python Programming. It includes the objectives of the course, which are to understand algorithmic problem solving and learn to solve problems using Python constructs like conditionals, loops, functions, and data structures. It also outlines the 5 units that will be covered in the course, which include computational thinking, Python basics, control flow and functions, lists/tuples/dictionaries, and files/modules. Example problems and programs are provided for different sorting algorithms, quadratic equations, and list operations.
The document discusses lists, tuples, and dictionaries in Python. It provides examples and explanations of these core data types. Lists are ordered and mutable sequences enclosed in brackets. Tuples are ordered and immutable sequences enclosed in parentheses. Dictionaries store data as key-value pairs within curly braces. Common operations on each type like indexing, slicing, length, keys and values are described. Methods for modifying and traversing lists like append, pop, insert and sort are also outlined.
This document discusses Python data structures including lists, tuples, sets, and dictionaries. It provides examples and explanations of operations for each type. For lists, it covers creation, slicing, and common methods like append(), pop(), and sort(). For tuples, it notes they are immutable sequences. Sets are described as unordered collections with no duplicates. Dictionary operations are not discussed.
Python is a popular programming language that is easy to learn and intuitive. It is well-suited for data science tasks. TensorFlow is a library for machine learning. The document provides an introduction and overview of Python basics like variables, data types, operators, and control structures. It also covers TensorFlow and how it can be used for neural network applications.
This document provides an overview of Python data structures including lists, tuples, dictionaries, and sets. It discusses common list methods like append(), insert(), remove(), and sort(). It provides examples of using lists as stacks and queues. It also covers list comprehensions, the del statement, tuple packing and unpacking, set operations, looping through dictionaries, and comparing sequences.
This document provides an overview of Python data structures including lists, tuples, dictionaries, and sets. It discusses common list methods like append(), insert(), remove(), and sort(). It provides examples of using lists as stacks and queues. It also covers list comprehensions, the del statement, tuple packing and unpacking, set operations, looping through dictionaries, and comparing sequences.
This document provides an overview of Python data structures including lists, tuples, dictionaries, and sets. It discusses common list methods like append(), insert(), remove(), and sort(). It provides examples of using lists as stacks and queues. It also covers list comprehensions, the del statement, tuple packing and unpacking, set operations, looping through dictionaries, and comparing sequences.
This document provides an overview of Python data structures including lists, tuples, dictionaries, and sets. It discusses common list methods like append(), insert(), remove(), and sort(). It provides examples of using lists as stacks and queues. It also covers list comprehensions, the del statement, tuple packing and unpacking, set operations, looping through dictionaries, and comparing sequences.
This document provides an overview of Python data structures including lists, tuples, dictionaries, and sets. It discusses common list methods like append(), insert(), remove(), and sort(). It provides examples of using lists as stacks and queues. It also covers list comprehensions, the del statement, tuple packing and unpacking, set operations, looping through dictionaries, and comparing sequences.
This document provides an overview of Python data structures including lists, tuples, dictionaries, and sets. It discusses common list methods like append(), insert(), remove(), and sort(). It provides examples of using lists as stacks and queues. It also covers list comprehensions, the del statement, tuple packing and unpacking, set operations, looping through dictionaries, and comparing sequences.
fundamental of python --- vivek singh shekawatshekhawatasshp
# Fundamentals of Python: A Comprehensive Guide
Python is a versatile and powerful programming language that has gained immense popularity in recent years. Known for its simplicity and readability, Python is an ideal choice for beginners and experienced programmers alike. This comprehensive guide covers the fundamentals of Python, providing a solid foundation for anyone looking to learn this dynamic language.
## Introduction to Python
### What is Python?
Python is a high-level, interpreted programming language designed by Guido van Rossum and first released in 1991. Its design philosophy emphasizes code readability and simplicity, making it an excellent language for beginners. Python supports multiple programming paradigms, including procedural, object-oriented, and functional programming.
### Why Learn Python?
Python's popularity stems from its versatility and ease of use. Here are some key reasons to learn Python:
- **Simplicity**: Python's syntax is straightforward and easy to learn, making it accessible to beginners.
- **Versatility**: Python can be used for web development, data analysis, artificial intelligence, machine learning, automation, and more.
- **Community Support**: Python has a large and active community, providing a wealth of resources, libraries, and frameworks.
- **Job Market**: Python skills are in high demand, making it a valuable language to learn for career opportunities.
## Setting Up Python
### Installation
To start coding in Python, you need to install it on your computer. Python is available for various operating systems, including Windows, macOS, and Linux. Follow these steps to install Python:
1. **Download Python**: Visit the official Python website (https://www.python.org) and download the latest version of Python for your operating system.
2. **Run the Installer**: Follow the installation instructions specific to your operating system. Ensure you select the option to add Python to your system PATH during installation.
3. **Verify Installation**: Open a command prompt or terminal and type `python --version` to verify the installation. You should see the installed Python version displayed.
### Integrated Development Environment (IDE)
An Integrated Development Environment (IDE) enhances your coding experience by providing tools and features to write, debug, and manage code efficiently. Some popular Python IDEs include:
- **PyCharm**: A powerful IDE specifically for Python, offering advanced features for professional developers.
- **Visual Studio Code**: A lightweight, versatile code editor with excellent Python support through extensions.
- **Jupyter Notebook**: An interactive web-based environment, ideal for data analysis and visualization.
## Basic Syntax and Data Types
### Hello, World!
The traditional first program in any language is the "Hello, World!" program. In Python, this is straightforward:
```python
print("Hello, World!")
```
This document provides an overview of Python data structures, focusing on lists and tuples. It discusses how lists and tuples store and organize data, how to define, access, update, and manipulate elements within lists and tuples using various Python functions and methods. Lists are described as mutable sequences that can contain elements of different data types, while tuples are described as immutable sequences. The document provides examples of using lists and tuples for tasks like stacks, queues, and storing records. It also covers list and tuple operations like slicing, filtering, mapping, and reducing.
1. Python provides various built-in container types including lists, tuples, dictionaries, sets, and strings for storing and organizing data.
2. These container types support common operations like indexing, slicing, membership testing, and methods for insertion, deletion, and modification.
3. The document provides examples of using operators and built-in functions to perform tasks like formatting strings, file I/O, conditional logic, loops, functions, and exceptions.
Key AI Technologies Used by Indian Artificial Intelligence CompaniesMypcot Infotech
Indian tech firms are rapidly adopting advanced tools like machine learning, natural language processing, and computer vision to drive innovation. These key AI technologies enable smarter automation, data analysis, and decision-making. Leading developments are shaping the future of digital transformation among top artificial intelligence companies in India.
For more information please visit here https://www.mypcot.com/artificial-intelligence
Maximizing Business Value with AWS Consulting Services.pdfElena Mia
An overview of how AWS consulting services empower organizations to optimize cloud adoption, enhance security, and drive innovation. Read More: https://www.damcogroup.com/aws-cloud-services/aws-consulting.
More Related Content
Similar to Intro Python Data Structures.pptx Intro Python Data Structures.pptx (20)
METHODS DESCRIPTION
copy() They copy() method returns a shallow copy of the dictionary.
clear() The clear() method removes all items from the dictionary.
pop() Removes and returns an element from a dictionary having the given key.
popitem() Removes the arbitrary key-value pair from the dictionary and returns it as tuple.
get() It is a conventional method to access a value for a key.
dictionary_name.values() returns a list of all the values available in a given dictionary.
str() Produces a printable string representation of a dictionary.
update() Adds dictionary dict2’s key-values pairs to dict
setdefault() Set dict[key]=default if key is not already in dict
keys() Returns list of dictionary dict’s keys
items() Returns a list of dict’s (key, value) tuple pairs
has_key() Returns true if key in dictionary dict, false otherwise
fromkeys() Create a new dictionary with keys from seq and values set to value.
type() Returns the type of the passed variable.
cmp() Compares elements of both dict.
The document provides information about the course GE3151 Problem Solving and Python Programming. It includes the objectives of the course, which are to understand algorithmic problem solving and learn to solve problems using Python constructs like conditionals, loops, functions, and data structures. It also outlines the 5 units that will be covered in the course, which include computational thinking, Python basics, control flow and functions, lists/tuples/dictionaries, and files/modules. Example problems and programs are provided for different sorting algorithms, quadratic equations, and list operations.
The document discusses lists, tuples, and dictionaries in Python. It provides examples and explanations of these core data types. Lists are ordered and mutable sequences enclosed in brackets. Tuples are ordered and immutable sequences enclosed in parentheses. Dictionaries store data as key-value pairs within curly braces. Common operations on each type like indexing, slicing, length, keys and values are described. Methods for modifying and traversing lists like append, pop, insert and sort are also outlined.
This document discusses Python data structures including lists, tuples, sets, and dictionaries. It provides examples and explanations of operations for each type. For lists, it covers creation, slicing, and common methods like append(), pop(), and sort(). For tuples, it notes they are immutable sequences. Sets are described as unordered collections with no duplicates. Dictionary operations are not discussed.
Python is a popular programming language that is easy to learn and intuitive. It is well-suited for data science tasks. TensorFlow is a library for machine learning. The document provides an introduction and overview of Python basics like variables, data types, operators, and control structures. It also covers TensorFlow and how it can be used for neural network applications.
This document provides an overview of Python data structures including lists, tuples, dictionaries, and sets. It discusses common list methods like append(), insert(), remove(), and sort(). It provides examples of using lists as stacks and queues. It also covers list comprehensions, the del statement, tuple packing and unpacking, set operations, looping through dictionaries, and comparing sequences.
This document provides an overview of Python data structures including lists, tuples, dictionaries, and sets. It discusses common list methods like append(), insert(), remove(), and sort(). It provides examples of using lists as stacks and queues. It also covers list comprehensions, the del statement, tuple packing and unpacking, set operations, looping through dictionaries, and comparing sequences.
This document provides an overview of Python data structures including lists, tuples, dictionaries, and sets. It discusses common list methods like append(), insert(), remove(), and sort(). It provides examples of using lists as stacks and queues. It also covers list comprehensions, the del statement, tuple packing and unpacking, set operations, looping through dictionaries, and comparing sequences.
This document provides an overview of Python data structures including lists, tuples, dictionaries, and sets. It discusses common list methods like append(), insert(), remove(), and sort(). It provides examples of using lists as stacks and queues. It also covers list comprehensions, the del statement, tuple packing and unpacking, set operations, looping through dictionaries, and comparing sequences.
This document provides an overview of Python data structures including lists, tuples, dictionaries, and sets. It discusses common list methods like append(), insert(), remove(), and sort(). It provides examples of using lists as stacks and queues. It also covers list comprehensions, the del statement, tuple packing and unpacking, set operations, looping through dictionaries, and comparing sequences.
This document provides an overview of Python data structures including lists, tuples, dictionaries, and sets. It discusses common list methods like append(), insert(), remove(), and sort(). It provides examples of using lists as stacks and queues. It also covers list comprehensions, the del statement, tuple packing and unpacking, set operations, looping through dictionaries, and comparing sequences.
fundamental of python --- vivek singh shekawatshekhawatasshp
# Fundamentals of Python: A Comprehensive Guide
Python is a versatile and powerful programming language that has gained immense popularity in recent years. Known for its simplicity and readability, Python is an ideal choice for beginners and experienced programmers alike. This comprehensive guide covers the fundamentals of Python, providing a solid foundation for anyone looking to learn this dynamic language.
## Introduction to Python
### What is Python?
Python is a high-level, interpreted programming language designed by Guido van Rossum and first released in 1991. Its design philosophy emphasizes code readability and simplicity, making it an excellent language for beginners. Python supports multiple programming paradigms, including procedural, object-oriented, and functional programming.
### Why Learn Python?
Python's popularity stems from its versatility and ease of use. Here are some key reasons to learn Python:
- **Simplicity**: Python's syntax is straightforward and easy to learn, making it accessible to beginners.
- **Versatility**: Python can be used for web development, data analysis, artificial intelligence, machine learning, automation, and more.
- **Community Support**: Python has a large and active community, providing a wealth of resources, libraries, and frameworks.
- **Job Market**: Python skills are in high demand, making it a valuable language to learn for career opportunities.
## Setting Up Python
### Installation
To start coding in Python, you need to install it on your computer. Python is available for various operating systems, including Windows, macOS, and Linux. Follow these steps to install Python:
1. **Download Python**: Visit the official Python website (https://www.python.org) and download the latest version of Python for your operating system.
2. **Run the Installer**: Follow the installation instructions specific to your operating system. Ensure you select the option to add Python to your system PATH during installation.
3. **Verify Installation**: Open a command prompt or terminal and type `python --version` to verify the installation. You should see the installed Python version displayed.
### Integrated Development Environment (IDE)
An Integrated Development Environment (IDE) enhances your coding experience by providing tools and features to write, debug, and manage code efficiently. Some popular Python IDEs include:
- **PyCharm**: A powerful IDE specifically for Python, offering advanced features for professional developers.
- **Visual Studio Code**: A lightweight, versatile code editor with excellent Python support through extensions.
- **Jupyter Notebook**: An interactive web-based environment, ideal for data analysis and visualization.
## Basic Syntax and Data Types
### Hello, World!
The traditional first program in any language is the "Hello, World!" program. In Python, this is straightforward:
```python
print("Hello, World!")
```
This document provides an overview of Python data structures, focusing on lists and tuples. It discusses how lists and tuples store and organize data, how to define, access, update, and manipulate elements within lists and tuples using various Python functions and methods. Lists are described as mutable sequences that can contain elements of different data types, while tuples are described as immutable sequences. The document provides examples of using lists and tuples for tasks like stacks, queues, and storing records. It also covers list and tuple operations like slicing, filtering, mapping, and reducing.
1. Python provides various built-in container types including lists, tuples, dictionaries, sets, and strings for storing and organizing data.
2. These container types support common operations like indexing, slicing, membership testing, and methods for insertion, deletion, and modification.
3. The document provides examples of using operators and built-in functions to perform tasks like formatting strings, file I/O, conditional logic, loops, functions, and exceptions.
Key AI Technologies Used by Indian Artificial Intelligence CompaniesMypcot Infotech
Indian tech firms are rapidly adopting advanced tools like machine learning, natural language processing, and computer vision to drive innovation. These key AI technologies enable smarter automation, data analysis, and decision-making. Leading developments are shaping the future of digital transformation among top artificial intelligence companies in India.
For more information please visit here https://www.mypcot.com/artificial-intelligence
Maximizing Business Value with AWS Consulting Services.pdfElena Mia
An overview of how AWS consulting services empower organizations to optimize cloud adoption, enhance security, and drive innovation. Read More: https://www.damcogroup.com/aws-cloud-services/aws-consulting.
AI and Deep Learning with NVIDIA TechnologiesSandeepKS52
Artificial intelligence and deep learning are transforming various fields by enabling machines to learn from data and make decisions. Understanding how to prepare data effectively is crucial, as it lays the foundation for training models that can recognize patterns and improve over time. Once models are trained, the focus shifts to deployment, where these intelligent systems are integrated into real-world applications, allowing them to perform tasks and provide insights based on new information. This exploration of AI encompasses the entire process from initial concepts to practical implementation, highlighting the importance of each stage in creating effective and reliable AI solutions.
How the US Navy Approaches DevSecOps with Raise 2.0Anchore
Join us as Anchore's solutions architect reveals how the U.S. Navy successfully approaches the shift left philosophy to DevSecOps with the RAISE 2.0 Implementation Guide to support its Cyber Ready initiative. This session will showcase practical strategies for defense application teams to pivot from a time-intensive compliance checklist and mindset to continuous cyber-readiness with real-time visibility.
Learn how to break down organizational silos through RAISE 2.0 principles and build efficient, secure pipeline automation that produces the critical security artifacts needed for Authorization to Operate (ATO) approval across military environments.
Meet You in the Middle: 1000x Performance for Parquet Queries on PB-Scale Dat...Alluxio, Inc.
Alluxio Webinar
June 10, 2025
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
David Zhu (Engineering Manager @ Alluxio)
Storing data as Parquet files on cloud object storage, such as AWS S3, has become prevalent not only for large-scale data lakes but also as lightweight feature stores for training and inference, or as document stores for Retrieval-Augmented Generation (RAG). However, querying petabyte-to-exabyte-scale data lakes directly from S3 remains notoriously slow, with latencies typically ranging from hundreds of milliseconds to several seconds.
In this webinar, David Zhu, Software Engineering Manager at Alluxio, will present the results of a joint collaboration between Alluxio and a leading SaaS and data infrastructure enterprise that explored leveraging Alluxio as a high-performance caching and acceleration layer atop AWS S3 for ultra-fast querying of Parquet files at PB scale.
David will share:
- How Alluxio delivers sub-millisecond Time-to-First-Byte (TTFB) for Parquet queries, comparable to S3 Express One Zone, without requiring specialized hardware, data format changes, or data migration from your existing data lake.
- The architecture that enables Alluxio’s throughput to scale linearly with cluster size, achieving one million queries per second on a modest 50-node deployment, surpassing S3 Express single-account throughput by 50x without latency degradation.
- Specifics on how Alluxio offloads partial Parquet read operations and reduces overhead, enabling direct, ultra-low-latency point queries in hundreds of microseconds and achieving a 1,000x performance gain over traditional S3 querying methods.
FME for Climate Data: Turning Big Data into Actionable InsightsSafe Software
Regional and local governments aim to provide essential services for stormwater management systems. However, rapid urbanization and the increasing impacts of climate change are putting growing pressure on these governments to identify stormwater needs and develop effective plans. To address these challenges, GHD developed an FME solution to process over 20 years of rainfall data from rain gauges and USGS radar datasets. This solution extracts, organizes, and analyzes Next Generation Weather Radar (NEXRAD) big data, validates it with other data sources, and produces Intensity Duration Frequency (IDF) curves and future climate projections tailored to local needs. This presentation will showcase how FME can be leveraged to manage big data and prioritize infrastructure investments.
Artificial Intelligence Applications Across IndustriesSandeepKS52
Artificial Intelligence is a rapidly growing field that influences many aspects of modern life, including transportation, healthcare, and finance. Understanding the basics of AI provides insight into how machines can learn and make decisions, which is essential for grasping its applications in various industries. In the automotive sector, AI enhances vehicle safety and efficiency through advanced technologies like self-driving systems and predictive maintenance. Similarly, in healthcare, AI plays a crucial role in diagnosing diseases and personalizing treatment plans, while in financial services, it helps in fraud detection and risk management. By exploring these themes, a clearer picture of AI's transformative impact on society emerges, highlighting both its potential benefits and challenges.
Who will create the languages of the future?Jordi Cabot
Will future languages be created by language engineers?
Can you "vibe" a DSL?
In this talk, we will explore the changing landscape of language engineering and discuss how Artificial Intelligence and low-code/no-code techniques can play a role in this future by helping in the definition, use, execution, and testing of new languages. Even empowering non-tech users to create their own language infrastructure. Maybe without them even realizing.
Best Inbound Call Tracking Software for Small BusinessesTheTelephony
The best inbound call tracking software for small businesses offers features like call recording, real-time analytics, lead attribution, and CRM integration. It helps track marketing campaign performance, improve customer service, and manage leads efficiently. Look for solutions with user-friendly dashboards, customizable reporting, and scalable pricing plans tailored for small teams. Choosing the right tool can significantly enhance communication and boost overall business growth.
Top 5 Task Management Software to Boost Productivity in 2025Orangescrum
In this blog, you’ll find a curated list of five powerful task management tools to watch in 2025. Each one is designed to help teams stay organized, improve collaboration, and consistently hit deadlines. We’ve included real-world use cases, key features, and data-driven insights to help you choose what fits your team best.
Plooma is a writing platform to plan, write, and shape books your wayPlooma
Plooma is your all in one writing companion, designed to support authors at every twist and turn of the book creation journey. Whether you're sketching out your story's blueprint, breathing life into characters, or crafting chapters, Plooma provides a seamless space to organize all your ideas and materials without the overwhelm. Its intuitive interface makes building rich narratives and immersive worlds feel effortless.
Packed with powerful story and character organization tools, Plooma lets you track character development and manage world building details with ease. When it’s time to write, the distraction-free mode offers a clean, minimal environment to help you dive deep and write consistently. Plus, built-in editing tools catch grammar slips and style quirks in real-time, polishing your story so you don’t have to juggle multiple apps.
What really sets Plooma apart is its smart AI assistant - analyzing chapters for continuity, helping you generate character portraits, and flagging inconsistencies to keep your story tight and cohesive. This clever support saves you time and builds confidence, especially during those complex, detail packed projects.
Getting started is simple: outline your story’s structure and key characters with Plooma’s user-friendly planning tools, then write your chapters in the focused editor, using analytics to shape your words. Throughout your journey, Plooma’s AI offers helpful feedback and suggestions, guiding you toward a polished, well-crafted book ready to share with the world.
With Plooma by your side, you get a powerful toolkit that simplifies the creative process, boosts your productivity, and elevates your writing - making the path from idea to finished book smoother, more fun, and totally doable.
Get Started here: https://www.plooma.ink/
NTRODUCTION TO SOFTWARE TESTING
• Definition:
• Software testing is the process of evaluating and
verifying that a software application or system meets
specified requirements and functions correctly.
• Purpose:
• Identify defects and bugs in the software.
• Ensure the software meets quality standards.
• Validate that the software performs as intended in
various scenarios.
• Importance:
• Reduces risks associated with software failures.
• Improves user satisfaction and trust in the product.
• Enhances the overall reliability and performance of
the software
GDG Douglas - Google AI Agents: Your Next Intern?felipeceotto
Presentation done at the GDG Douglas event for June 2025.
A first look at Google's new Agent Development Kit.
Agent Development Kit is a new open-source framework from Google designed to simplify the full stack end-to-end development of agents and multi-agent systems.
Have you upgraded your application from Qt 5 to Qt 6? If so, your QML modules might still be stuck in the old Qt 5 style—technically compatible, but far from optimal. Qt 6 introduces a modernized approach to QML modules that offers better integration with CMake, enhanced maintainability, and significant productivity gains.
In this webinar, we’ll walk you through the benefits of adopting Qt 6 style QML modules and show you how to make the transition. You'll learn how to leverage the new module system to reduce boilerplate, simplify builds, and modernize your application architecture. Whether you're planning a full migration or just exploring what's new, this session will help you get the most out of your move to Qt 6.
Revolutionize Your Insurance Workflow with Claims Management SoftwareInsurance Tech Services
Claims management software enhances efficiency, accuracy, and satisfaction by automating processes, reducing errors, and speeding up transparent claims handling—building trust and cutting costs. Explore More - https://www.damcogroup.com/insurance/claims-management-software
In a tight labor market and tighter economy, PMOs and resource managers must ensure that every team member is focused on the highest-value work. This session explores how AI reshapes resource planning and empowers organizations to forecast capacity, prevent burnout, and balance workloads more effectively, even with shrinking teams.
2. List
General purpose
Most widely used data structure
Grow and shrink size as needed
Sequence type
Sortable
Tuple
Immutable (can’t add/change)
Useful for fixed data
Faster than Lists
Sequence type
Set
Store non-duplicate items
Very fast access vs Lists
Math Set ops (union, intersect)
Unordered
Dict
Key/Value pairs
Associative array, like Java HashMap
Unordered
4. • indexing
– Access any item in the sequence using its index
x = 'frog'
print (x[3]) # prints 'g'
String
x = ['pig', 'cow', 'horse']
print (x[1]) # prints 'cow'
List
SEQUENCES
String List Tuple
5. • slicing
– Slice out substrings, sublists, subtuples using indexes
[start : end+1 : step]
Code Result Explanation
x[1:4] 'omp' Items 1 to 3
x[1:6:2] 'opt' Items 1, 3, 5
x[3:] 'puter' Items 3 to end
x[:5] 'compu' Items 0 to 4
x[-1] 'r' Last item
x[-3:] 'ter' Last 3 items
x[:-2] 'comput' All except last 2 items
x = 'computer'
SEQUENCES
String List Tuple
6. • adding / concatenating
– Combine 2 sequences of the same type using +
x = 'horse' + 'shoe'
print (x) # prints 'horseshoe'
String
x = ['pig', 'cow'] + ['horse']
print (x) # prints ['pig', 'cow', 'horse']
List
SEQUENCES
String List Tuple
7. • multiplying
– Multiply a sequence using *
x = ‘bug' * 3
print (x) # prints ‘bugbugbug'
String
x = [8, 5] * 3
print (x) # prints [8, 5, 8, 5, 8, 5]
List
SEQUENCES
String List Tuple
8. • checking membership
– Test whether an item is in or not in a sequence
x = 'bug'
print ('u' in x) # prints True
String
x = ['pig', 'cow', 'horse']
print ('cow' not in x) # prints False
List
SEQUENCES
String List Tuple
9. • iterating
– Iterate through the items in a sequence
x = [7, 8, 3]
for item in x:
print (item * 2) # prints 14, 16, 6
Item
x = [7, 8, 3]
for index, item in enumerate(x):
print (index, item) # prints 0 7, 1 8, 2 3
Index & Item
SEQUENCES
String List Tuple
10. • number of items
– Count the number of items in a sequence
x = 'bug'
print (len(x)) # prints 3
String
x = ['pig', 'cow', 'horse']
print (len(x)) # prints 3
List
SEQUENCES
String List Tuple
11. • minimum
– Find the minimum item in a sequence lexicographically
– alpha or numeric types, but cannot mix types
x = 'bug'
print (min(x)) # prints 'b'
String
x = ['pig', 'cow', 'horse']
print (min(x)) # prints 'cow'
List
SEQUENCES
String List Tuple
12. • maximum
– Find the maximum item in a sequence
– alpha or numeric types, but cannot mix types
x = 'bug'
print (max(x)) # prints 'u'
String
x = ['pig', 'cow', 'horse']
print (max(x)) # prints 'pig'
List
SEQUENCES
String List Tuple
13. • sum
– Find the sum of items in a sequence
– entire sequence must be numeric type
x = [5, 7, 'bug‘]
print (sum(x)) # error!
String -> Error
x = [2, 5, 8, 12]
print (sum(x)) # prints 27
print (sum(x[-2:])) # prints 20
List
SEQUENCES
String List Tuple
14. • sorting
– Returns a new list of items in sorted order
– Does not change the original list
x = 'bug'
print (sorted(x))# prints ['b', 'g', 'u']
String
x = ['pig', 'cow', 'horse']
print (sorted(x)) # prints ['cow', 'horse', 'pig']
List
SEQUENCES
String List Tuple
15. • count (item)
– Returns count of an item
x = 'hippo'
print (x.count('p')) # prints 2
String
x = ['pig', 'cow', 'horse', 'cow']
print (x.count('cow')) # prints 2
List
SEQUENCES
String List Tuple
16. • index (item)
– Returns the index of the first occurrence of an item
x = 'hippo'
print (x.index('p')) # prints 2
String
x = ['pig', 'cow', 'horse', 'cow']
print (x.index('cow')) # prints 1
List
SEQUENCES
String List Tuple
17. • unpacking
– Unpack the n items of a sequence into n variables
x = ['pig', 'cow', 'horse']
a, b, c = x # now a is 'pig'
# b is 'cow',
# c is 'horse'
Note:
The number of variables must exactly match the length of the list.
SEQUENCES
String List Tuple
18. LISTS
All operations from Sequences, plus:
• constructors:
• del list1[2] delete item from list1
• list1.append(item) appends an item to list1
• list1.extend(sequence1) appends a sequence to list1
• list1.insert(index, item) inserts item at index
• list1.pop() pops last item
• list1.remove(item) removes first instance of
item
• list1.reverse() reverses list order
• list1.sort() sorts list in place
LISTS
19. • constructors – creating a new list
LISTS
x = list()
x = ['a', 25, 'dog', 8.43]
x = list(tuple1)
List Comprehension:
x = [m for m in range(8)]
resulting list: [0, 1, 2, 3, 4, 5, 6, 7]
x = [z**2 for z in range(10) if z>4]
resulting list: [25, 36, 49, 64, 81]
20. • delete
– Delete a list or an item from a list
x = [5, 3, 8, 6]
del(x[1]) # [5, 8, 6]
del(x) # deletes list x
LISTS
21. • append
– Append an item to a list
x = [5, 3, 8, 6]
x.append(7) # [5, 3, 8, 6, 7]
LISTS
22. • extend
– Append an sequence to a list
x = [5, 3, 8, 6]
y = [12, 13]
x.extend(y) # [5, 3, 8, 6, 7, 12, 13]
LISTS
23. • insert
– Insert an item at given index x.insert(index, item)
x = [5, 3, 8, 6]
x.insert(1, 7) # [5, 7, 3, 8, 6]
x.insert(1,['a','m']) # [5, ['a', 'm'], 7, 3, 8,
6]
LISTS
24. • pop
– Pops last item off the list, and returns item
x = [5, 3, 8, 6]
x.pop() # [5, 3, 8]
# and returns the 6
print(x.pop()) # prints 8
# x is now [5, 3]
LISTS
25. • remove
– Remove first instance of an item
x = [5, 3, 8, 6, 3]
x.remove(3) # [5, 8, 6, 3]
LISTS
26. • reverse
– Reverse the order of the list
x = [5, 3, 8, 6]
x.reverse() # [6, 8, 3, 5]
LISTS
27. • sort
– Sort the list in place
x = [5, 3, 8, 6]
x.sort() # [3, 5, 6, 8]
Note:
sorted(x) returns a new sorted list without changing the original list x.
x.sort() puts the items of x in sorted order (sorts in place).
LISTS
28. TUPLES
• Support all operations for Sequences
• Immutable, but member objects may be mutable
• If the contents of a list shouldn’t change, use a tuple
to prevent items from accidently being added,
changed or deleted
• Tuples are more efficient than lists due to Python’s
implementation
TUPLES
29. • constructors – creating a new tuple
TUPLES
x = () # no-item tuple
x = (1,2,3)
x = 1, 2, 3 # parenthesis are optional
x = 2, # single-item tuple
x = tuple(list1) # tuple from list
30. • immutable
– But member objects may be mutable
x = (1, 2, 3)
del(x[1]) # error!
x[1] = 8 # error!
x = ([1,2], 3) # 2-item tuple: list and int
del(x[0][1]) # ([1], 3)
TUPLES
31. • constructors – creating a new set
SETS
x = {3,5,3,5} # {5, 3}
x = set() # empty set
x = set(list1) # new set from list
# strips duplicates
Set Comprehension:
x = {3*x for x in range(10) if x>5}
resulting set: {18, 21, 24, 27} but in random order
32. SETS
Description Code
Add item to set x x.add(item)
Remove item from set x x.remove(item)
Get length of set x len(x)
Check membership in x
item in x
item not in x
Pop random item from set x x.pop()
Delete all items from set x x.clear()
• basic set operations
33. • standard mathematical set operations
SETS
Set Function Description Code
Intersection AND set1 & set2
Union OR set1 | set2
Symmetric Difference XOR set1 ^ set2
Difference In set1 but not in set2 set1 – set2
Subset set2 contains set1 set1 <= set2
Superset set1 contains set2 set1 >= set2
34. • constructors – creating a new dict
DICTIONARIES
x = {'pork':25.3, 'beef':33.8, 'chicken':22.7}
x = dict([('pork', 25.3),('beef', 33.8),('chicken', 22.7)])
x = dict(pork=25.3, beef=33.8, chicken=22.7)
35. Description Code
Add or change item in dict x x['beef'] = 25.2
Remove item from dict x del x['beef']
Get length of dict x len(x)
Check membership in x
(only looks in keys, not values)
item in x
item not in x
Delete all items from dict x x.clear()
Delete dict x del x
• basic dict operations
DICTIONARIES
36. • accessing keys and values in a dict
DICTIONARIES
x.keys() # returns list of keys in x
x.values() # returns list of values in x
x.items() # returns list of key-value tuple pairs in x
item in x.values() # tests membership in x: returns boolean
37. • iterating a dict
DICTIONARIES
for key in x: # iterate keys
print(key, x[key]) # print all key/value pairs
for k, v in x.items(): # iterate key/value pairs
print(k, v) # print all key/value pairs
Note:
Entries in a dict are in random order.