This document provides an overview of Python fundamentals including basic concepts like data types, operators, flow control, functions and classes. It begins with an introduction to Python versions and environments. The outline covers topics like Hello World, common types and operators for numeric, string and container data types. It also discusses flow control structures like if/else, while loops and for loops. Finally, it briefly mentions functions, classes, exceptions and file I/O.
This presentation covers Python most important data structures like Lists, Dictionaries, Sets and Tuples. Exception Handling and Random number generation using simple python module "random" also covered. Added simple python programs at the end of the presentation
The basics of Python are rather straightforward. In a few minutes you can learn most of the syntax. There are some gotchas along the way that might appear tricky. This talk is meant to bring programmers up to speed with Python. They should be able to read and write Python.
This presentation covers Python most important data structures like Lists, Dictionaries, Sets and Tuples. Exception Handling and Random number generation using simple python module "random" also covered. Added simple python programs at the end of the presentation
The basics of Python are rather straightforward. In a few minutes you can learn most of the syntax. There are some gotchas along the way that might appear tricky. This talk is meant to bring programmers up to speed with Python. They should be able to read and write Python.
Installing and Using Python
Basic I/O
Variables and Expressions
Conditional Code
Functions
Loops and Iteration
Python Data Structures
Errors and Exceptions
Object Oriented with Python
Multithreaded Programming with Python
Install/Create and Using Python Library
Compile Python Script
Resources
===========================
and 7 Quizzes
The presentation from SPb Python Interest Group community meetup.
The presentation tells about the dictionaries in Python, reviews the implementation of dictionary in CPython 2.x, dictionary in CPython 3.x, and also recent changes in CPython 3.6. In addition to CPython the dictionaries in alternative Python implementations such as PyPy, IronPython and Jython are reviewed.
These are the slides of the second part of this multi-part series, from Learn Python Den Haag meetup group. It covers List comprehensions, Dictionary comprehensions and functions.
Python 101++: Let's Get Down to Business!Paige Bailey
You've started the Codecademy and Coursera courses; you've thumbed through Zed Shaw's "Learn Python the Hard Way"; and now you're itching to see what Python can help you do. This is the workshop for you!
Here's the breakdown: we're going to be taking you on a whirlwind tour of Python's capabilities. By the end of the workshop, you should be able to easily follow any of the widely available Python courses on the internet, and have a grasp on some of the more complex aspects of the language.
Please don't forget to bring your personal laptop!
Audience: This course is aimed at those who already have some basic programming experience, either in Python or in another high level programming language (such as C/C++, Fortran, Java, Ruby, Perl, or Visual Basic). If you're an absolute beginner -- new to Python, and new to programming in general -- make sure to check out the "Python 101" workshop!
This presentation was given online in July 2017 and will be given at the NY Java SIG later this year. It progressively builds on Java 8 concepts using puzzles and coding to give students confidence in their Java 8 stream/lambda skills. Handouts and code in https://github.com/boyarsky/java-8-streams-by-puzzles
Analysis of Fatal Utah Avalanches with Python. From Scraping, Analysis, to In...Matt Harrison
I gave this presentation at Code Camp. As a data scientist and backcountry skier, I was interested in looking at fatal avalanche data. This covers scraping the data, analysis with Python, pandas and IPython Notebook. The final result is an infographic
How to Become a Tree Hugger: Random Forests and Predictive Modeling for Devel...Matt Harrison
Python makes data science easy. In this deck we walk through a complete example of creating and evaluating a predictive model using Decision Trees and Random Forests. All of the code is included in the slides.
Presented at 8th Light University London (13th May 2016)
Do this, do that. Coding from assembler to shell scripting, from the mainstream languages of the last century to the mainstream languages now, is dominated by an imperative style. From how we teach variables — they vary, right? — to how we talk about databases, we are constantly looking at state as a thing to be changed and programming languages are structured in terms of the mechanics of change — assignment, loops and how code can be threaded (cautiously) with concurrency.
Functional programming, mark-up languages, schemas, persistent data structures and more are all based around a more declarative approach to code, where instead of reasoning in terms of who does what to whom and what the consequences are, relationships and uses are described, and the flow of execution follows from how functions, data and other structures are composed. This talk will look at the differences between imperative and declarative approaches, offering lessons, habits and techniques that are applicable from requirements through to code and tests in mainstream languages.
A short talk on what makes Functional Programming - and especially Haskell - different.
We'll take a quick overview of Haskell's features and coding style, and then work through a short but complete example of using it for a Real World problem.
http://lanyrd.com/2011/geekup-liverpool-may/sdykh/
Inspired by Josh Bloch's Java Puzzlers, we put together our own Python Puzzlers. This slide deck brings you a set of 10 python puzzlers, that are fun and educational. Each puzzler will show you a piece of python code. Your task if to figure out what happens when the code is run. Whether you're a python beginner or a passionate python veteran, we hope that there's something to learn for everybody.
This slide deck was first presented at shopkick. Nandan Sawant and Ryan Rueth are engineers at shopkick. Keeping the audience in mind, most of the puzzlers are based on python 2.x.
This presentation is all about various built in
datastructures which we have in python.
List
Dictionary
Tuple
Set
and various methods present in each data structure
Installing and Using Python
Basic I/O
Variables and Expressions
Conditional Code
Functions
Loops and Iteration
Python Data Structures
Errors and Exceptions
Object Oriented with Python
Multithreaded Programming with Python
Install/Create and Using Python Library
Compile Python Script
Resources
===========================
and 7 Quizzes
The presentation from SPb Python Interest Group community meetup.
The presentation tells about the dictionaries in Python, reviews the implementation of dictionary in CPython 2.x, dictionary in CPython 3.x, and also recent changes in CPython 3.6. In addition to CPython the dictionaries in alternative Python implementations such as PyPy, IronPython and Jython are reviewed.
These are the slides of the second part of this multi-part series, from Learn Python Den Haag meetup group. It covers List comprehensions, Dictionary comprehensions and functions.
Python 101++: Let's Get Down to Business!Paige Bailey
You've started the Codecademy and Coursera courses; you've thumbed through Zed Shaw's "Learn Python the Hard Way"; and now you're itching to see what Python can help you do. This is the workshop for you!
Here's the breakdown: we're going to be taking you on a whirlwind tour of Python's capabilities. By the end of the workshop, you should be able to easily follow any of the widely available Python courses on the internet, and have a grasp on some of the more complex aspects of the language.
Please don't forget to bring your personal laptop!
Audience: This course is aimed at those who already have some basic programming experience, either in Python or in another high level programming language (such as C/C++, Fortran, Java, Ruby, Perl, or Visual Basic). If you're an absolute beginner -- new to Python, and new to programming in general -- make sure to check out the "Python 101" workshop!
This presentation was given online in July 2017 and will be given at the NY Java SIG later this year. It progressively builds on Java 8 concepts using puzzles and coding to give students confidence in their Java 8 stream/lambda skills. Handouts and code in https://github.com/boyarsky/java-8-streams-by-puzzles
Analysis of Fatal Utah Avalanches with Python. From Scraping, Analysis, to In...Matt Harrison
I gave this presentation at Code Camp. As a data scientist and backcountry skier, I was interested in looking at fatal avalanche data. This covers scraping the data, analysis with Python, pandas and IPython Notebook. The final result is an infographic
How to Become a Tree Hugger: Random Forests and Predictive Modeling for Devel...Matt Harrison
Python makes data science easy. In this deck we walk through a complete example of creating and evaluating a predictive model using Decision Trees and Random Forests. All of the code is included in the slides.
Presented at 8th Light University London (13th May 2016)
Do this, do that. Coding from assembler to shell scripting, from the mainstream languages of the last century to the mainstream languages now, is dominated by an imperative style. From how we teach variables — they vary, right? — to how we talk about databases, we are constantly looking at state as a thing to be changed and programming languages are structured in terms of the mechanics of change — assignment, loops and how code can be threaded (cautiously) with concurrency.
Functional programming, mark-up languages, schemas, persistent data structures and more are all based around a more declarative approach to code, where instead of reasoning in terms of who does what to whom and what the consequences are, relationships and uses are described, and the flow of execution follows from how functions, data and other structures are composed. This talk will look at the differences between imperative and declarative approaches, offering lessons, habits and techniques that are applicable from requirements through to code and tests in mainstream languages.
A short talk on what makes Functional Programming - and especially Haskell - different.
We'll take a quick overview of Haskell's features and coding style, and then work through a short but complete example of using it for a Real World problem.
http://lanyrd.com/2011/geekup-liverpool-may/sdykh/
Inspired by Josh Bloch's Java Puzzlers, we put together our own Python Puzzlers. This slide deck brings you a set of 10 python puzzlers, that are fun and educational. Each puzzler will show you a piece of python code. Your task if to figure out what happens when the code is run. Whether you're a python beginner or a passionate python veteran, we hope that there's something to learn for everybody.
This slide deck was first presented at shopkick. Nandan Sawant and Ryan Rueth are engineers at shopkick. Keeping the audience in mind, most of the puzzlers are based on python 2.x.
This presentation is all about various built in
datastructures which we have in python.
List
Dictionary
Tuple
Set
and various methods present in each data structure
Python's "batteries included" philosophy means that it comes with an astonishing amount of great stuff. On top of that, there's a vibrant world of third-party libraries that help make Python even more wonderful. We'll go on a breezy, example-filled tour through some of my favorites, from treasures in the standard library to great third-party packages that I don't think I could live without, and we'll touch on some of the fuzzier aspects of the Python culture that make it such a joy to be part of.
Introduction to Python 01-08-2023.pon by everyone else. . Hence, they must be...DRVaibhavmeshram1
Python
Language
is uesd in engineeringStory adapted from Stephen Covey (2004) “The Seven Habits of Highly Effective People” Simon & Schuster).
“Management is doing things right, leadership is doing the right things”
(Warren Bennis and Peter Drucker)
Story adapted from Stephen Covey (2004) “The Seven Habits of Highly Effective People” Simon & Schuster).
“Management is doing things right, leadership is doing the right things”
(Warren Bennis and Peter Drucker)
Story adapted from Stephen Covey (2004) “The Seven Habits of Highly Effective People” Simon & Schuster).
“Management is doing things right, leadership is doing the right things”
(Warren Bennis and Peter Drucker)
The Sponsor:
Champion and advocates for the change at their level in the organization.
A Sponsor is the person who won’t let the change initiative die from lack of attention, and is willing to use their political capital to make the change happen
The Role model:
Behaviors and attitudes demonstrated by them are looked upon by everyone else. . Hence, they must be willing to go first.
Employees watch leaders for consistency between words and actions to see if they should believe the change is really going to happen.
The decision maker:
Leaders usually control resources such as people, budgets, and equipment, and thus have the authority to make decisions (as per their span of control) that affect the initiative.
During change, leaders must leverage their decision-making authority and choose the options that will support the initiative.
The Decision-Maker is decisive and sets priorities that support change.
The Sponsor:
Champion and advocates for the change at their level in the organization.
A Sponsor is the person who won’t let the change initiative die from lack of attention, and is willing to use their political capital to make the change happen
The Role model:
Behaviors and attitudes demonstrated by them are looked upon by everyone else. . Hence, they must be willing to go first.
Employees watch leaders for consistency between words and actions to see if they should believe the change is really going to happen.
The decision maker:
Leaders usually control resources such as people, budgets, and equipment, and thus have the authority to make decisions (as per their span of control) that affect the initiative.
During change, leaders must leverage their decision-making authority and choose the options that will support the initiative.
The Decision-Maker is decisive and sets priorities that support change.
The Sponsor:
Champion and advocates for the change at their level in the organization.
A Sponsor is the person who won’t let the change initiative die from lack of attention, and is willing to use their political capital to make the change happen
The Role model:
Behaviors and attitudes demonstrated by them are looked upon by everyone else. . Hence, they must be willing to go first.
Employees watch leaders for consistency between words and actions to see if they s
Six Feet Up's senior developer Clayton Parker will lead you on a journey to become a Python zen master. Your code should be as fashionable as it is functional. To quote the Zen of Python, "Beautiful is better than ugly". This talk will teach you about the Python style guide and why it is important. The talk will show you examples of well written Python and how to analyze your current code to make Guido proud.
Six Feet Up's senior developer Clayton Parker will lead you on a journey to become a Python zen master. Your code should be as fashionable as it is functional. To quote the Zen of Python, "Beautiful is better than ugly". This talk will teach you about the Python style guide and why it is important. The talk will show you examples of well written Python and how to analyze your current code to make Guido proud.
Presented at BJUG, 6/12/2012 by Roger Brinkley
This talk is on 55 new features in Java 7 you (probably) didn't hear about in an ignite format of one per minute. No stopping, no going back....Questions, sure but only if time remains (otherwise save for later).
This presentation about Python Interview Questions will help you crack your next Python interview with ease. The video includes interview questions on Numbers, lists, tuples, arrays, functions, regular expressions, strings, and files. We also look into concepts such as multithreading, deep copy, and shallow copy, pickling and unpickling. This video also covers Python libraries such as matplotlib, pandas, numpy,scikit and the programming paradigms followed by Python. It also covers Python library interview questions, libraries such as matplotlib, pandas, numpy and scikit. This video is ideal for both beginners as well as experienced professionals who are appearing for Python programming job interviews. Learn what are the most important Python interview questions and answers and know what will set you apart in the interview process.
Simplilearn’s Python Training Course is an all-inclusive program that will introduce you to the Python development language and expose you to the essentials of object-oriented programming, web development with Django and game development. Python has surpassed Java as the top language used to introduce U.S. students to programming and computer science. This course will give you hands-on development experience and prepare you for a career as a professional Python programmer.
What is this course about?
The All-in-One Python course enables you to become a professional Python programmer. Any aspiring programmer can learn Python from the basics and go on to master web development & game development in Python. Gain hands on experience creating a flappy bird game clone & website functionalities in Python.
What are the course objectives?
By the end of this online Python training course, you will be able to:
1. Internalize the concepts & constructs of Python
2. Learn to create your own Python programs
3. Master Python Django & advanced web development in Python
4. Master PyGame & game development in Python
5. Create a flappy bird game clone
The Python training course is recommended for:
1. Any aspiring programmer can take up this bundle to master Python
2. Any aspiring web developer or game developer can take up this bundle to meet their training needs
Learn more at https://www.simplilearn.com/mobile-and-software-development/python-development-training
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
15. Common Types & Operator
§ Numeric type
• int, float, bool, complex
• expression
§ String type
§ Container type
• list, tuple, dict, set
15
1
2
3
4
5
6
7
8
9
w = 49
h = 163
bmi = 49 / (163/100)**2
print(bmi) # 49
w = 49.0
h = 163.0
bmi = 49 / (163/100)**2
print(bmi) # 18.4425...
16. Try it!
§ #練習:Set the following variables to the corresponding
values:
1. my_int to the value 7
2. my_float to the value 1.23
3. my_bool to the value True
16
18. Try it!
§ #練習:Add two Numbers
18
1
2
3
4
5
6
7
8
9
1
0
1
1
a = input()
b = input()
…
print('The sum of {0} and {1} is {2} '.format(a, b, sum))
19. Try it!
§ #練習:Find the Square Root
19
1
2
3
4
5
6
7
8
9
1
0
1
1
num = 8
…
print('The square root of %0.3f is %0.3f'%(num ,num_sqrt))
20. Common Types & Operator
§ Numeric type
• int, float, bool, complex
§ String type
• len, lower, upper, split
§ Container type
• list, tuple, dict, set
20
1
2
3
4
5
6
7
8
a = '12345'
b = 'hello world'
c = 'n'
d = r'n'
print(b + str(a) + c + d)
print(b + str(a) + str(c) + str(d))
print(b + str(a) + repr(c) + repr(d))
21. Try it!
§ #練習:Set the following variables to their respective phrases:
1. Set caesar to Graham
2. Set sentence to There's a snake in my boot!
3. Set paragraph to Dear Mr. Chen,
I’m Jacky, nice to meet you.
21
22. Common Types & Operator
§ Numeric type
• int, float, bool, complex
§ String type
§ Container type
• list [] => mutable sequence
• tuple (,) => immutable sequence
• dict {:} => mutable unordered collection of key-value mapped element
• set {} => mutable unordered collections of unique elements
22
23. list
23
1
2
3
4
5
6
L = [1, 2, 3, 4]
G = [3, 4, 5, 6]
L + G # [1, 2, 3, 4, 3, 4, 5, 6]
L – G
L * 2 # [1, 2, 3, 4, 1, 2, 3, 4]
L / 2
33. Try it!
§ #練習:Illustrate Different Set Operations
33
1
2
3
4
5
6
7
8
9
10
# define three sets
E, N = {0, 2, 4, 6, 8}, {1, 2, 3, 4, 5}
print("Union of E and N is",E | N) #
print("Intersection of E and N is",E & N)
print("Difference of E and N is",E - N)
print("Symmetric difference of E and N is",E ^ N)
34. 34
§ Mutable objects
• list, dict, set
§ Immutable objects
• int, float, complex, string, tuple
Reference: https://www.linkedin.com/pulse/mutable-vs-immutable-objects-python-megha-mohan
Im vs Mutable ?
36. tuple vs list?
§ slower but more powerful than tuples.
§ Lists can be modified, and they have lots of handy operations
we can perform on them.
§ Tuples are immutable and have fewer features.
§ To convert between tuples and lists use the list() and tuple()
functions:
• li = list(tu)
• tu = tuple(li)
36
51. Flow Control
§ if - elif - else
§ while
§ for in
§ break, continue, pass
§ range(), zip(), enumerate()
51
1
2
while condition:
....
52. Flow Control
§ if - elif - else
§ while
§ for in
§ break, continue, pass
§ range(), zip(), enumerate()
52
1
2
3
4
5
for i in [...]:
...
a = [i for i in [...]] # list
b = (i for i in [...]) # generator
58. Flow Control
§ if - elif - else
§ while
§ for in
§ break, continue, pass
§ range(), zip(), enumerate()
58
59. Flow Control
§ if - elif - else
§ while
§ for in
§ break, continue, pass
§ range(), zip(), enumerate()
59
1
2
3
4
5
6
7
8
for i in range(1, 3):
print(i)
for i, j in zip([a, b, c], [1, 2, 3]):
print(i, j)
for i,j in enumerate([a, b, c]):
print(i, j)
69. Function
69
1
2
3
4
5
6
7
8
9
max1 = a if a > b else b...
max2 = x if x > y else y...
def max(a, b):
return a if a > b else b
maximum = max
maximum(10, 20)
# 20
70. Basic Method for Call Function
70
1
2
3
4
5
6
7
def f(x, y):
return x, y
f(1, 2)
f(y=2, x=1)
f(*(1, 2))
f(**{y=2, x=1})
91. Error Exception
91
1
2
3
4
5
6
7
8
9
10
11
12
13
14
10 * (1/0)
# Traceback (most recent call last):
# File "<stdin>", line 1, in
<module>ZeroDivisionError: division by zero
4 + spam*3
# Traceback (most recent call last):
# File "<stdin>", line 1, in <module>NameError:
name 'spam' is not defined
'2' + 2
# Traceback (most recent call last):
# File "<stdin>", line 1, in <module>TypeError:
Can't convert 'int' object to str implicitly
92. try-except
92
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
try:
x = input("the first number:")
y = input("the second number:")
r = float(x)/float(y)
print(r)
except Exception as e:
print(e)
else:
pass
the first number: 2
the second number: 0
# float division by zero
the first number: 2
the second number: a
# could not convert string to float: a
the first number: 4
the second number: 2
# 2.0