This document provides an overview and introduction to key Python libraries for data analysis: NumPy, Matplotlib, Pandas, and their main applications. It covers NumPy and its ndarrays, functions, random number generation and linear algebra capabilities. For Matplotlib, it discusses figures, axes, subplots, and plot types. It then outlines Pandas Series and DataFrame basics, data selection, manipulation, cleansing and methods. It also covers Pandas data analysis tools like the Iris data use case, data summarization, reporting, visualization and statistics. Finally, it introduces Pandas time series objects and applications.
The document discusses Python fundamentals including core data types, data structures, core language features, and standard libraries. It covers topics like objects, numbers, strings, datetime, boolean, none, lists, dictionaries, sets, functions, exceptions, statements, files, sys and os modules, csv, json, and math. It also discusses object oriented programming principles such as abstraction, encapsulation, hierarchy, classes, inheritance, composition and dunder methods.
This document discusses biological databases and bioinformatics. It begins by listing various related fields including biology, computer science, bioinformatics, statistics, and machine learning. It then describes different types of searches that can be performed in biological databases, including annotation searches, homology searches, pattern searches, and predictions. Finally, it mentions that databases can be used for comparisons, such as gene families and phylogenetic trees.
ParlBench: a SPARQL-benchmark for electronic publishing applications.Tatiana Tarasova
Slides from the workshop on Benchmarking RDF Systems co-located with the Extended Semantic Web Conference 2013. The presentation is about an on-going work on building the benchmark for electronic publishing applications. The benchmark provides real-world data sets, the Dutch parliamentary proceedings and a set of analytical SPARQL queries that were built on top of these data sets. The queries were grouped into micro-benchmarks according to their analytical aims. This allows one to perform better analysis of RDF stores behaviors with respect to a certain SPARQL feature used in a micro-benchmark/query.
Preliminary results of running the benchmark on the Virtuoso native RDF store are presented, as well as references to the on-line material including the data sets, queries and the scripts that were used to obtain the results.
The document discusses using RDFS and OWL reasoning to integrate heterogeneous linked data by addressing issues like terminology and naming heterogeneity. It presents an approach using a subset of OWL 2 RL rules to reason over a billion triple corpus in a scalable way, handling the TBox separately from the ABox to avoid quadratic inferences. It also describes augmenting the reasoning with annotations to track trustworthiness and using this to filter inferences, detect inconsistencies and perform a light repair of the data. Consolidation is discussed as rewriting URIs to canonical identifiers based on owl:sameAs relations. Performance results show the different techniques taking between 1-20 hours to run over the corpus distributed across 9 machines.
This document provides an overview of the Resource Description Framework (RDF). It begins with background information on RDF including URIs, URLs, IRIs and QNames. It then describes the RDF data model, noting that RDF is a schema-less data model featuring unambiguous identifiers and named relations between pairs of resources. It also explains that RDF graphs are sets of triples consisting of a subject, predicate and object. The document also covers RDF syntax using Turtle and literals, as well as modeling with RDF. It concludes with a brief overview of common RDF tools including Jena.
This document discusses RDFS (Resource Description Framework Schema), which is a standard ontology language for the Semantic Web. RDFS introduces predefined meanings for resources through axioms and allows for basic inferences over RDF data through mechanisms like type propagation between classes and properties. The document provides examples of how RDFS can be used to classify resources in an RDF graph and automatically infer additional types for resources based on their properties and class memberships.
The document discusses Python fundamentals including core data types, data structures, core language features, and standard libraries. It covers topics like objects, numbers, strings, datetime, boolean, none, lists, dictionaries, sets, functions, exceptions, statements, files, sys and os modules, csv, json, and math. It also discusses object oriented programming principles such as abstraction, encapsulation, hierarchy, classes, inheritance, composition and dunder methods.
This document discusses biological databases and bioinformatics. It begins by listing various related fields including biology, computer science, bioinformatics, statistics, and machine learning. It then describes different types of searches that can be performed in biological databases, including annotation searches, homology searches, pattern searches, and predictions. Finally, it mentions that databases can be used for comparisons, such as gene families and phylogenetic trees.
ParlBench: a SPARQL-benchmark for electronic publishing applications.Tatiana Tarasova
Slides from the workshop on Benchmarking RDF Systems co-located with the Extended Semantic Web Conference 2013. The presentation is about an on-going work on building the benchmark for electronic publishing applications. The benchmark provides real-world data sets, the Dutch parliamentary proceedings and a set of analytical SPARQL queries that were built on top of these data sets. The queries were grouped into micro-benchmarks according to their analytical aims. This allows one to perform better analysis of RDF stores behaviors with respect to a certain SPARQL feature used in a micro-benchmark/query.
Preliminary results of running the benchmark on the Virtuoso native RDF store are presented, as well as references to the on-line material including the data sets, queries and the scripts that were used to obtain the results.
The document discusses using RDFS and OWL reasoning to integrate heterogeneous linked data by addressing issues like terminology and naming heterogeneity. It presents an approach using a subset of OWL 2 RL rules to reason over a billion triple corpus in a scalable way, handling the TBox separately from the ABox to avoid quadratic inferences. It also describes augmenting the reasoning with annotations to track trustworthiness and using this to filter inferences, detect inconsistencies and perform a light repair of the data. Consolidation is discussed as rewriting URIs to canonical identifiers based on owl:sameAs relations. Performance results show the different techniques taking between 1-20 hours to run over the corpus distributed across 9 machines.
This document provides an overview of the Resource Description Framework (RDF). It begins with background information on RDF including URIs, URLs, IRIs and QNames. It then describes the RDF data model, noting that RDF is a schema-less data model featuring unambiguous identifiers and named relations between pairs of resources. It also explains that RDF graphs are sets of triples consisting of a subject, predicate and object. The document also covers RDF syntax using Turtle and literals, as well as modeling with RDF. It concludes with a brief overview of common RDF tools including Jena.
This document discusses RDFS (Resource Description Framework Schema), which is a standard ontology language for the Semantic Web. RDFS introduces predefined meanings for resources through axioms and allows for basic inferences over RDF data through mechanisms like type propagation between classes and properties. The document provides examples of how RDFS can be used to classify resources in an RDF graph and automatically infer additional types for resources based on their properties and class memberships.
The document summarizes topics to be covered in a Python workshop on February 12, 2024 about data science. The topics are Pandas, Numpy, and Matplotlib. Pandas is a library for working with and analyzing datasets. Numpy is a library for working with arrays and linear algebra. Matplotlib is a visualization library for creating 2D plots from array data.
The document discusses the return of an old enemy to the PHP community who has reformed and is now well-organized to improve. It then provides an extensive list of PHP libraries, frameworks, and tools categorized by function to help with improving PHP skills and code quality. The list is maintained by the Awesome PHP project and covers areas like dependency management, frameworks, testing, security, and more.
Which library should you choose for data-science? That's the question!Anastasia Bobyreva
This talk presents you the data-science ecosystem in two languages : Python and Scala. It demonstrates the use of their libraries on real dataset to solve binary classification problem with decision tree algorithm.
This document introduces pharo-ai v0.8, a modular library for shallow machine learning in Pharo. It provides an overview of machine learning techniques and positions pharo-ai relative to Python and R libraries. pharo-ai includes implementations of common algorithms like linear regression, k-means clustering, and decision trees. Benchmarks show pharo-ai with LAPACK integration outperforms Scikit-learn for large datasets. The document encourages users to visit the pharo-ai wiki for help getting started.
The document discusses converting data into information using NumPy and Pandas Python libraries. It covers topics like arrays and matrices, different data formats, NumPy operations for linear algebra and math, and Pandas for working with labeled data and performing analyses like sorting, filtering, and correlations. The goal is to understand how to structure and analyze data using these Python tools.
Python is an interpreted programming language created by Guido van Rossum in 1991. It has an elegant syntax, large standard library, and is used widely for data science, machine learning, web development, and more. Key Python libraries for data analysis include NumPy, pandas, and matplotlib. Pandas allows importing and cleaning data from files like CSVs, and matplotlib can be used to visualize and present analyzed data. For example, a program can use pandas to read baby name data from a CSV, find the most popular name with the highest birth count, and plot the results to clearly present the findings.
Python standard library & list of important librariesgrinu
We know that a module is a file with some Python code, and a package is a directory for sub packages and modules. But the line between a package and a Python library is quite blurred.
A Python library is a reusable chunk of code that you may want to include in your programs/ projects. Compared to languages like C++ or C, a Python libraries do not pertain to any specific context in Python. Here, a ‘library’ loosely describes a collection of core modules. Essentially, then, a library is a collection of modules. A package is a library that can be installed using a package manager like rubygems or npm.
Learn Python Programming | Python Programming - Step by Step | Python for Beg...Edureka!
( Python Training : https://www.edureka.co/python )
This Edureka “Python Programming" introduces you to Python by giving you enough reasons to learn it. It will then take you to its various fundamentals along with a practical demonstrating the various libraries such as Numpy, Pandas, Matplotlib and Seaborn. This video helps you to learn the below topics:
1. Why should you go for Python?
2. Introduction to Python Programming Language
3. How to work with Jupyter?
4. Python Programming Fundamentals: Operators & Data Types
5. Libraries: Numpy, Pandas, Matplotlib, Seaborn
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
The document summarizes topics to be covered in a Python workshop on February 12, 2024 about data science. The topics are Pandas, Numpy, and Matplotlib. Pandas is a library for working with and analyzing datasets. Numpy is a library for working with arrays and linear algebra. Matplotlib is a visualization library for creating 2D plots from array data.
The document discusses the return of an old enemy to the PHP community who has reformed and is now well-organized to improve. It then provides an extensive list of PHP libraries, frameworks, and tools categorized by function to help with improving PHP skills and code quality. The list is maintained by the Awesome PHP project and covers areas like dependency management, frameworks, testing, security, and more.
Which library should you choose for data-science? That's the question!Anastasia Bobyreva
This talk presents you the data-science ecosystem in two languages : Python and Scala. It demonstrates the use of their libraries on real dataset to solve binary classification problem with decision tree algorithm.
This document introduces pharo-ai v0.8, a modular library for shallow machine learning in Pharo. It provides an overview of machine learning techniques and positions pharo-ai relative to Python and R libraries. pharo-ai includes implementations of common algorithms like linear regression, k-means clustering, and decision trees. Benchmarks show pharo-ai with LAPACK integration outperforms Scikit-learn for large datasets. The document encourages users to visit the pharo-ai wiki for help getting started.
The document discusses converting data into information using NumPy and Pandas Python libraries. It covers topics like arrays and matrices, different data formats, NumPy operations for linear algebra and math, and Pandas for working with labeled data and performing analyses like sorting, filtering, and correlations. The goal is to understand how to structure and analyze data using these Python tools.
Python is an interpreted programming language created by Guido van Rossum in 1991. It has an elegant syntax, large standard library, and is used widely for data science, machine learning, web development, and more. Key Python libraries for data analysis include NumPy, pandas, and matplotlib. Pandas allows importing and cleaning data from files like CSVs, and matplotlib can be used to visualize and present analyzed data. For example, a program can use pandas to read baby name data from a CSV, find the most popular name with the highest birth count, and plot the results to clearly present the findings.
Python standard library & list of important librariesgrinu
We know that a module is a file with some Python code, and a package is a directory for sub packages and modules. But the line between a package and a Python library is quite blurred.
A Python library is a reusable chunk of code that you may want to include in your programs/ projects. Compared to languages like C++ or C, a Python libraries do not pertain to any specific context in Python. Here, a ‘library’ loosely describes a collection of core modules. Essentially, then, a library is a collection of modules. A package is a library that can be installed using a package manager like rubygems or npm.
Learn Python Programming | Python Programming - Step by Step | Python for Beg...Edureka!
( Python Training : https://www.edureka.co/python )
This Edureka “Python Programming" introduces you to Python by giving you enough reasons to learn it. It will then take you to its various fundamentals along with a practical demonstrating the various libraries such as Numpy, Pandas, Matplotlib and Seaborn. This video helps you to learn the below topics:
1. Why should you go for Python?
2. Introduction to Python Programming Language
3. How to work with Jupyter?
4. Python Programming Fundamentals: Operators & Data Types
5. Libraries: Numpy, Pandas, Matplotlib, Seaborn
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
Python for Data Analysis
1. Python for Data Analysis
Sherif Rasmy
• Numpy
• Matplotlib
• Pandas
Python Snippets Series
2. Contents
Sherif Rasmy 2Python for Data Analysis - Overview
• Numpy
• Matplotlib
• Pandas
o Series: basics, methods
o Data Frames: basics, data selection, Data manipulation, data cleansing, methods
o Data Analysis: data life cycle, Iris data analysis use case, data summarization, reporting, visualization, statistics
o Time Series: Files, sys and os, csv, json, math, statistics
o Software Architecture, Object Orientated Design Principles, Classes,
Inheritance, Composition, Dunder Methods
o ndarrays, functions, random, linear algebra
92. Sherif Rasmy
Python Libraries
Pandas Data Analysis
Data Life Cycle, Iris Data Analysis Use Case, Data Summarization,
Reporting, Visualization, Statistics