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
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
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.
Array
Introduction
One-dimensional array
Multidimensional array
Advantage of Array
Write a C program using arrays that produces the multiplication of two matrices.
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.
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.
Array
Introduction
One-dimensional array
Multidimensional array
Advantage of Array
Write a C program using arrays that produces the multiplication of two matrices.
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.
this document consists of the introduction to python, how to install and run it, arithmetic operations, values and types (dictionaries, lists, tuples, strings, numbers, etc.) and the formal language and natural language
This was the fourth presentation used in pySIG 2015 @ BMS College of Engineering, Bangalore. The code and assignments can be found at https://github.com/pranavsb
Code Like Pythonista
Beautifully made PPT.
Ref. http://python.net/~goodger/projects/pycon/2007/idiomatic/handout.html
Image ref : https://pixabay.com/ko/ and https://morguefile.com/
licensed under a Creative Commons Attribution/Share-Alike (BY-SA) license.
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
Enhancing Project Management Efficiency_ Leveraging AI Tools like ChatGPT.pdfJay Das
With the advent of artificial intelligence or AI tools, project management processes are undergoing a transformative shift. By using tools like ChatGPT, and Bard organizations can empower their leaders and managers to plan, execute, and monitor projects more effectively.
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...Mind IT Systems
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. It’s here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...Anthony Dahanne
Les Buildpacks existent depuis plus de 10 ans ! D’abord, ils étaient utilisés pour détecter et construire une application avant de la déployer sur certains PaaS. Ensuite, nous avons pu créer des images Docker (OCI) avec leur dernière génération, les Cloud Native Buildpacks (CNCF en incubation). Sont-ils une bonne alternative au Dockerfile ? Que sont les buildpacks Paketo ? Quelles communautés les soutiennent et comment ?
Venez le découvrir lors de cette session ignite
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Developing Distributed High-performance Computing Capabilities of an Open Sci...Globus
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Globus
The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.
Understanding Globus Data Transfers with NetSageGlobus
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?
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I didn't get rich from it but it did have 63K downloads (powered possible tens of thousands of websites).
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
Enhancing Research Orchestration Capabilities at ORNL.pdfGlobus
Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.
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/
2. Add a Slide Title - 4
• A dictionary is mutable and is another container
type that can store any number of Python objects
• Dictionaries consist of pairs (called items) of keys
and their corresponding values.
• Python dictionaries are also known as associative
arrays or hash tables.
• The general syntax of a dictionary is as follows:
Dog = {‘name': ‘Marley', ‘color': ‘Gold', ‘legs': ‘4'}
or
Dog=dict(name=“Marley", color=“Gold”,legs=4);
Python Dictionary:
3. Python Dictionary Features:
• Each key is separated from its value by a colon (:).
• The items are separated by commas, and the whole thing is enclosed
in curly braces.
• An empty dictionary without any items is written with just two curly
braces, like this: {}.
• Keys are unique within a dictionary while values may not be
5. Accessing Values in Dictionary:
• To access dictionary elements, you can use the familiar square
brackets along with the key to obtain its value.
• For example:
student = {'Name': 'Zara', 'Age': 7, 'Class': 'First'};
print (“student['Name']: ", student['Name'])
● Output: student['Name']: Zara
6. Updating Dictionary:
• You can update a dictionary by adding a new entry or item (i.e., a key-value
pair)
• modifying an existing entry, or deleting an existing entry as shown below in
the simple example
• student = {'Name': 'Zara', 'Age': 7, 'Class': 'First'};
student['Age'] = 8; # update existing entry
student['School'] = "DPS School"; # Add new entry
print (“student['Age']: ", student['Age'])
print (“student['School']: ", student['School'])
• Output: student['Age']: 8 student['School']: DPS School
8. Delete Dictionary Elements:
• You can either remove individual dictionary elements or clear the entire
contents of a dictionary
• To explicitly remove an entire dictionary, just use the del statement. i.e:
student = {'Name': 'Zara', 'Age': 7, 'Class': 'First'};
• del student['Name']; # remove entry with key 'Name'
• student.clear(); # remove all entries in student del student ; # delete
entire dictionary
• print (“student['Age']: ", student['Age'])
• print (“student['School']: ", student['School'])
9. Properties of Dictionary Keys:
There are two important points to remember about dictionary keys:
(a) More than one entry per key not allowed. Which means no duplicate key is allowed.
When duplicate keys encountered during assignment, the last assignment wins.
• student= {'Name': 'Zara', 'Age': 7, 'Name': 'Manni'};
• print (“student['Name']: ", student['Name']) //Output: student['Name']: Manni
(b) Keys must be immutable. Which means you can use strings or numbers as
dictionary keys but something like ['key'] is not allowed. Following is a simple example:
• student = {['Name']: 'Zara', 'Age': 7};
• print (student['Name']: ", student['Name']) //TypeError
10. Let’s Practice .. !
Problem I
• Write a Python program to check if a given
key already exists in a dictionary.
Problem II
. Write a Python program to remove duplicates
from Dictionary