Functional programming is a paradigm that treats computation as the evaluation of mathematical functions and avoids state and mutable data. It promotes pure functions without side effects. Some advantages of functional programming include cleaner code, referential transparency which enables memoization, parallelization, and easier debugging. While Python is multi-paradigm, functional programming techniques like immutable data, higher-order functions, recursion, and lazy evaluation can be applied for more elegant and maintainable code.
An operator is a symbol designed to operate on data.
They can be a single symbol, di-graphs, tri-graphs or keywords.
Operators can be classified in different ways.
This is similar to function overloading
After the end of lesson you will be able to learn Python basics-What Python is? Its releases. Where we can use Python? Python Features. Tokens, comments variables etc... In out next PPT you will learn how to input and get output in Python
An operator is a symbol designed to operate on data.
They can be a single symbol, di-graphs, tri-graphs or keywords.
Operators can be classified in different ways.
This is similar to function overloading
After the end of lesson you will be able to learn Python basics-What Python is? Its releases. Where we can use Python? Python Features. Tokens, comments variables etc... In out next PPT you will learn how to input and get output in Python
All data values in Python are encapsulated in relevant object classes. Everything in Python is an object and every object has an identity, a type, and a value. Like another object-oriented language such as Java or C++, there are several data types which are built into Python. Extension modules which are written in C, Java, or other languages can define additional types.
To determine a variable's type in Python you can use the type() function. The value of some objects can be changed. Objects whose value can be changed are called mutable and objects whose value is unchangeable (once they are created) are called immutable.
Regular expressions are used to identify whether a pattern exists in a given sequence of characters (string) or not. They help in manipulating textual data, which is often a pre-requisite for data science projects that involve text mining. You must have come across some application of regular expressions: they are used at the server side to validate the format of email addresses or password during registration, used for parsing text data files to find, replace or delete certain string, etc.
More information about the meetup this presentation was created for can be found at https://www.meetup.com/life-michael/events/255429951/ More information about our Python course (in Hebrew) can be found at http://python.course.lifemichael.com More information about our other courses and services can be found at http://www.lifemichael.com.
How to use Map() Filter() and Reduce() functions in Python | EdurekaEdureka!
Youtube Link: https://youtu.be/QxpbE5hDPws
** Python Certification Training: https://www.edureka.co/data-science-python-certification-course**
This Edureka PPT on 'map, filter, and reduce functions in Python' is to educate you about these very important built-in functions in Python. Below are the topics covered in this PPT:
Introduction to map filter reduce
The map() function
The filter() function
The reduce() function
Using map(),filter() and reduce() functions together
filter() within map()
map() within filter()
map() and filter() within reduce()
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All data values in Python are encapsulated in relevant object classes. Everything in Python is an object and every object has an identity, a type, and a value. Like another object-oriented language such as Java or C++, there are several data types which are built into Python. Extension modules which are written in C, Java, or other languages can define additional types.
To determine a variable's type in Python you can use the type() function. The value of some objects can be changed. Objects whose value can be changed are called mutable and objects whose value is unchangeable (once they are created) are called immutable.
Regular expressions are used to identify whether a pattern exists in a given sequence of characters (string) or not. They help in manipulating textual data, which is often a pre-requisite for data science projects that involve text mining. You must have come across some application of regular expressions: they are used at the server side to validate the format of email addresses or password during registration, used for parsing text data files to find, replace or delete certain string, etc.
More information about the meetup this presentation was created for can be found at https://www.meetup.com/life-michael/events/255429951/ More information about our Python course (in Hebrew) can be found at http://python.course.lifemichael.com More information about our other courses and services can be found at http://www.lifemichael.com.
How to use Map() Filter() and Reduce() functions in Python | EdurekaEdureka!
Youtube Link: https://youtu.be/QxpbE5hDPws
** Python Certification Training: https://www.edureka.co/data-science-python-certification-course**
This Edureka PPT on 'map, filter, and reduce functions in Python' is to educate you about these very important built-in functions in Python. Below are the topics covered in this PPT:
Introduction to map filter reduce
The map() function
The filter() function
The reduce() function
Using map(),filter() and reduce() functions together
filter() within map()
map() within filter()
map() and filter() within reduce()
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
The idea of this talk is presenting the Golang functional features, the pros e cons of apply functional paradigm in Golang. Do you want to improve readability and maintainability of your code using more functional paradigms? So, come on and let's have a fun time together!
Today, Python is one of the most popular programming languages. Although it is a general-purpose language, it is used in various areas of applications such as Machine Learning, Artificial Intelligence, web development, IoT, and more.
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Discussing Design Patterns and OOP popularity,
Multithreading and OOP,
Functional Design for Multithreaded programming
and how Multithreading does not mean always concurency but multicore paralelism.
Functional Programming от Валеры Розувана
* Tech Hangout – мероприятие, организованное разработчиками для разработчиков с целью обмена знаниями и опытом. Подобные встречи проводятся еженедельно по средам с 12:00 до 13:00 и охватывают исключительно инженерные темы. Формат данного ивента подразумевает под собой 30 минутный доклад на ранее определенную тему, и такую же по продолжительности дискуссию в формате круглого стола.
Если у вас есть неутомимое рвение к новым знаниям, профессиональному росту, или же вы хотите поделиться своим опытом - добро пожаловать в Hangout Club!
Присоединяйтесь к обсуждению - https://www.facebook.com/groups/techhangout/
Читайте нас на - http://hangout.innovecs.com/
Workshop slides which give an overview of python programming. The slides are accompanied by DIY (do it yourself) programs which can be found as in GitHub (https://github.com/bhalajin/blueprints)
Respondendo as principais dúvidas sobre essa tecnologia que promete permitir o compartilhamento da lógica de negócios para seus aplicativos iOS, Android, Web e Desktop.
REST (Representational State Transfer) is an architectural style, and an approach to communications that is often used in the development of Web services and nowadays with Mobile world and Internet of Things.
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NetSage is an open privacy-aware network measurement, analysis, and visualization service designed to help end-users visualize and reason about large data transfers. NetSage traditionally has used a combination of passive measurements, including SNMP and flow data, as well as active measurements, mainly perfSONAR, to provide longitudinal network performance data visualization. It has been deployed by dozens of networks world wide, and is supported domestically by the Engagement and Performance Operations Center (EPOC), NSF #2328479. We have recently expanded the NetSage data sources to include logs for Globus data transfers, following the same privacy-preserving approach as for Flow data. Using the logs for the Texas Advanced Computing Center (TACC) as an example, this talk will walk through several different example use cases that NetSage can answer, including: Who is using Globus to share data with my institution, and what kind of performance are they able to achieve? How many transfers has Globus supported for us? Which sites are we sharing the most data with, and how is that changing over time? How is my site using Globus to move data internally, and what kind of performance do we see for those transfers? What percentage of data transfers at my institution used Globus, and how did the overall data transfer performance compare to the Globus users?
Accelerate Enterprise Software Engineering with PlatformlessWSO2
Key takeaways:
Challenges of building platforms and the benefits of platformless.
Key principles of platformless, including API-first, cloud-native middleware, platform engineering, and developer experience.
How Choreo enables the platformless experience.
How key concepts like application architecture, domain-driven design, zero trust, and cell-based architecture are inherently a part of Choreo.
Demo of an end-to-end app built and deployed on Choreo.
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Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
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top nidhi software solution freedownloadvrstrong314
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Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
Keep tabs on your field staff effortlessly with Informap Technology Centre LLC. Real-time tracking, task assignment, and smart features for efficient management. Request a live demo today!
For more details, visit us : https://informapuae.com/field-staff-tracking/
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
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A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
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The alleged breach affected Europol agencies CCSE, EC3, Europol Platform for Experts, Law Enforcement Forum, and SIRIUS. Infiltration of these entities can disrupt ongoing investigations and compromise sensitive intelligence shared among international law enforcement agencies.
However, this is neither the first nor the last activity of IntekBroker. We have compiled for you what happened in the last few days. To track such hacker activities on dark web sources like hacker forums, private Telegram channels, and other hidden platforms where cyber threats often originate, you can check SOCRadar’s Dark Web News.
Stay Informed on Threat Actors’ Activity on the Dark Web with SOCRadar!
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In fact, according to a report by Statista, the number of React Native developers has been steadily increasing over the years, reaching an estimated 1.9 million by the end of 2024. This means that the demand for this framework in the job market has been growing making it a valuable skill.
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2. Python Functional Programming
Functional Programming by Wikipidia:
“Functional programming is a programming paradigm that treats
computation as the evaluation of mathematical functions and avoids
state and mutable data". In other words, functional programming
promotes code with no side effects, no change of value in
variables. It oposes to imperative programming, which enfatizes
change of state”.
3. Python Functional Programming
What this means?
● No mutable data (no side effect).
● No state (no implicit, hidden state).
Once assigned (value binding), a variable (a symbol) does not change its value.
All state is bad? No, hidden, implicit state is bad.
Functional programming do not eliminate state, it just make it visible and explicit
(at least when programmers want it to be).
● Functions are pure functions in the mathematical sense: their output depend only
in their inputs, there is not “environment”.
● Same result returned by functions called with the same inputs.
4. Python Functional Programming
What are the advantages?
● Cleaner code: "variables" are not modified once defined, so we don't have to
follow the change of state to comprehend what a function, a, method, a class, a
whole project works.
● Referential transparency: Expressions can be replaced by its values. If we call a
function with the same parameters, we know for sure the output will be the same
(there is no state anywhere that would change it).
There is a reason for which Einstein defined insanity as "doing the same thing over
and over again and expecting different results".
5. Python Functional Programming
Advantages enabled by referential transparence
● Memoization
○ Cache results for previous function calls.
● Idempotence
○ Same results regardless how many times you call a function.
● Modularization
○ We have no state that pervades the whole code, so we build our project with
small, black boxes that we tie together, so it promotes bottom-up
programming.
● Ease of debugging
○ Functions are isolated, they only depend on their input and their output, so
they are very easy to debug.
6. Python Functional Programming
Advantages enabled by referential transparence
● Parallelization
○ Functions calls are independent.
○ We can parallelize in different process/CPUs/computers/…
We can execute func1 and func2 in paralell because a won’t be modified.
result = func1(a, b) + func2(a, c)
7. Python Functional Programming
Advantages enabled by referential transparence
● Concurrence
a. With no shared data, concurrence gets a lot simpler:
i. No semaphores.
ii. No monitors.
iii. No locks.
iv. No race-conditions.
v. No dead-locks.
8. Python Functional Programming
Python is a multi paradigm programming language. As a Python
programmer why uses functional programming in Python?
Python is not a functional language but have a lot of features that enables us to
applies functional principles in the development, turning our code more elegant,
concise, maintanable, easier to understand and test.
9. Python Functional Programming
Don’t Update, Create - String
name = 'Geison'
name = '{0} Flores'.format(name)
FIRSTNAME = 'Geison'
LASTNAME = '{0} Flores'.format(FIRSTNAME)
NAME = '{0} {1}'.format(FIRSTNAME, LASTNAME)
10. Python Functional Programming
Don’t Update, Create - Lists
years = [2001, 2002]
years.append(2003)
years += [2004, 2005]
years # [2001, 2002, 2003, 2004, 2005]
YEARS = [2001, 2001]
ALL_YEARS = YEARS + [2003] + [2004, 2005]
12. Python Functional Programming
Higher Order Functions
Functions and methods are first-class objects in Python, so if you want to pass a
function to another function, you can just treat it as any other object.
def caller(f):
f()
def say_hello(name):
return 'Hello {0}'.format(name)
caller(say_hello)
13. Python Functional Programming
Higher Order Functions - Map
map(lambda word: word.upper(), ["milu", "rantanplan"])
# result ["MILU", "RANTANPLAN"]
def add_2(n):
n + 2
map(add_2, [1, 2, 3]) # result [3, 4, 5]
14. Python Functional Programming
Higher Order Functions - Filter
filter(lambda word: len(word) == 4, ["milu", "rantanplan"]) # result ["MILU"]
def greater_than_10(num):
return num > 10
filter(greater_than_10, range(15)) # result [11, 12, 13, 14, 15]
16. Python Functional Programming
Higher Order Functions - Reduce
from itertools import izip
list1 = [1, 2, 3]
list2 = ["a", "b", "c"]
[list(x) for x in izip(list1, list2)] # result [[1, "a"], [2, "b"], [3, "c"]]
17. Python Functional Programming
Higher Order Functions - Closure
def add_x(x):
def adder(y):
return x + y
return adder
add_5 = add_x(5)
add_7 = add_x(7)
add_5(10) # result 15
add_7(10) # result 17
18. Python Functional Programming
Currying and Partial Functions
Higher-order functions enable Currying, which the ability to take a function that accepts n
parameters and turns it into a composition of n functions each of them take 1 parameter. A direct
use of currying is the Partial Functions where if you have a function that accepts n parameters then
you can generate from it one of more functions with some parameter values already filled in.
from functools import partial
plus = lambda a, b: a + b # defining a function that sums 2 numbers
plus(3, 5) # result 8
# curring calling partial function by supplying the first parameters with value 1
plus_one = partial(plus, 1)
# I can use the new function as normal
plus_one(5) # result 6
19. Python Functional Programming
Eager vs Lazy Evaluation
● Eager evaluation: expressions are calculated at the moment that variables is
assined, function called...
● Lazy evaluation: delays the evaluation of the expression until it is needed.
○ Memory efficient: no memory used to store complete structures.
○ CPU efficient: no need to calculate the complete result before returning.
○ Laziness is not a requisite for FP, but it is a strategy that fits nicely on
the paradigm(Haskell).
Python uses eager evaluation (but short-circuits && or ||).
Python generators are a mechanism for lazy evaluation.
Python arrays are not lazy, use enumarators when necessary.
20. Python Functional Programming
Recursion
Looping by calling a function from within itself. When you don’t have access to mutable
data, recursion is used to build up and chain data construction. This is because looping is
not a functional concept, as it requires variables to be passed around to store the state of
the loop at a given time.
● Purely functional languages have no imperative for-loops, so they use recursion a lot.
● If every recursion created an stack, it would blow up very soon.
● Tail-call optimization (TCO) avoids creating a new stack when the last call in a
recursion is the function itself.
● TCO is not implemented in Python.
● Unfortunarely following recursion style in Python has it’s own tax: Performance.
21. Python Functional Programming
Solving Python Lack of TCO(Tail Call Optimization)
# The functional solution have problens with big values
fib = lambda n: n if n < 2 else fib(n-1) + fib(n-2)
# The iterative solution using generators works perfectly with large values
def fibs():
a = 0
b = 1
while True:
yield a
a, b = b, a + b
22. Python Functional Programming
FP in OOP?
It is possible do FP in OOP? Yes it is!
● OOP is orthogonal to FP.
● Well, at least in theory, because:
○ Typical OOP tends to emphasize change of state in objects.
○ Typical OOP mixes the concepts of identity and state.
○ Mixture of data and code raises both conceptual and practical problems.
● OOP functional languages: Scala, F#, ...
23. Python Functional Programming
A Pratical Example
Exercise: "What's the sum of the first 10 natural number whose square value is
divisible by 5?"
Imperative:
Functional:
n, num_elements, s = 1, 0, 0
while num_elements < 10:
if n**2 % 5 == 0:
s += n
num_elements += 1
n += 1
n #275
sum(filter(lambda x: x**2 % 5 == 0, range(1, 100))[:10])
24. Python Functional Programming
The last advice
Learn at least one functional language, it will open your mind to a new paradigm
becoming you a better programmer.
Some Functional Languages:
● Haskell
● ML (Standard ML, Objective Caml, ...)
● Scheme
● Erlang
● Scala
● Closure
● F#
25. Python Functional Programming
Conclusion
● As you can tell, Python helps you write in functional style but it doesn’t force
you to it.
● Writing in functional style enhances your code and makes it more self documented.
Actually it will make it more thread-safe also.
● The main support for FP in Python comes from the use of list conprehension,
lambdas, closures, iterators and generators, also from the modules functools and
itertools.
● Python still lack an important aspect of FP: Pattern Matching and Tails
Recursion.
● There should be more work on tail recursion optimization, to encourage developers
to use recursion.
● Any other thoughts?