Pandas is a popular Python library used for working with labeled/relational data and time series data. It provides data structures like Series and DataFrames. Series are one-dimensional arrays that can hold data of any type. DataFrames are two-dimensional structures like tables, with labeled rows and columns. DataFrames can be created from lists, dictionaries, or CSV/Excel files. Columns and rows can be accessed, selected, and manipulated. The values of Series can be reshaped into different dimensions.
Pandas is an open source Python library that provides high-performance data manipulation and analysis. It allows users to clean, transform, and analyze data through its powerful and flexible data structures. Pandas has two main data structures: Series, which is a one-dimensional array, and DataFrame, which is a two-dimensional tabular structure. Users can create Series from arrays, lists, and dictionaries, and access elements using indexes or positions. DataFrames can be created from dictionaries or lists and allow selection of columns. Pandas also has tools for handling missing data through functions like isnull() and fillna().
1. NumPy is a fundamental Python library for numerical computing that provides support for arrays and vectorized computations.
2. Pandas is a popular Python library for data manipulation and analysis that provides DataFrame and Series data structures to work with tabular data.
3. When performing arithmetic operations between DataFrames or Series in Pandas, the data is automatically aligned based on index and column labels to maintain data integrity. NumPy also automatically broadcasts arrays during arithmetic to align dimensions element-wise.
Pandas is an open-source Python library that provides high-performance data structures and analysis tools. It contains Series and DataFrame objects for working with one-dimensional and two-dimensional data. Series are one-dimensional arrays that can hold any data type, while DataFrames are similar to tables with rows and columns for arranging multi-dimensional data. Pandas allows for easy data cleaning, manipulation, and analysis.
This document provides an overview of working with DataFrames in Python using the Pandas library. It discusses:
1. What a DataFrame is - a two-dimensional, size-mutable, tabular data structure in Pandas for data manipulation.
2. How to create DataFrames from dictionaries, lists, CSV files and more.
3. Common tasks like viewing data, selecting rows/columns, modifying data, analysis and saving DataFrames.
It also covers indexing and filtering DataFrames using labels or boolean conditions, arithmetic alignment in Pandas and NumPy, and vectorized computation in NumPy.
Lecture on Python Pandas for Decision Makingssuser46aec4
Pandas is a Python library used for data manipulation and analysis. It provides data structures like Series and DataFrames for working with numerical data and time series. Series are one-dimensional arrays like columns in a spreadsheet, while DataFrames are like spreadsheets with rows and columns. DataFrames can be created from CSV files, lists, or dictionaries. Elements can be accessed from Series using integer positions or labels, and rows from DataFrames can be selected using labels or integer positions. Data types of columns in DataFrames can be converted using the astype() method.
Python Pandas is a powerful library for data analysis and manipulation. It provides rich data structures and methods for loading, cleaning, transforming, and modeling data. Pandas allows users to easily work with labeled data and columns in tabular structures called Series and DataFrames. These structures enable fast and flexible operations like slicing, selecting subsets of data, and performing calculations. Descriptive statistics functions in Pandas allow analyzing and summarizing data in DataFrames.
The document provides information about Pandas, a Python library used for data analysis and manipulation. It discusses how to import Pandas, the benefits of using Pandas, basic data structures in Pandas like Series and DataFrames, and examples of creating Series objects from different types of data like lists, dictionaries, and NumPy arrays. It also covers Series object attributes and methods for accessing, slicing, and modifying Series values and indices.
Pandas is a popular Python library used for working with labeled/relational data and time series data. It provides data structures like Series and DataFrames. Series are one-dimensional arrays that can hold data of any type. DataFrames are two-dimensional structures like tables, with labeled rows and columns. DataFrames can be created from lists, dictionaries, or CSV/Excel files. Columns and rows can be accessed, selected, and manipulated. The values of Series can be reshaped into different dimensions.
Pandas is an open source Python library that provides high-performance data manipulation and analysis. It allows users to clean, transform, and analyze data through its powerful and flexible data structures. Pandas has two main data structures: Series, which is a one-dimensional array, and DataFrame, which is a two-dimensional tabular structure. Users can create Series from arrays, lists, and dictionaries, and access elements using indexes or positions. DataFrames can be created from dictionaries or lists and allow selection of columns. Pandas also has tools for handling missing data through functions like isnull() and fillna().
1. NumPy is a fundamental Python library for numerical computing that provides support for arrays and vectorized computations.
2. Pandas is a popular Python library for data manipulation and analysis that provides DataFrame and Series data structures to work with tabular data.
3. When performing arithmetic operations between DataFrames or Series in Pandas, the data is automatically aligned based on index and column labels to maintain data integrity. NumPy also automatically broadcasts arrays during arithmetic to align dimensions element-wise.
Pandas is an open-source Python library that provides high-performance data structures and analysis tools. It contains Series and DataFrame objects for working with one-dimensional and two-dimensional data. Series are one-dimensional arrays that can hold any data type, while DataFrames are similar to tables with rows and columns for arranging multi-dimensional data. Pandas allows for easy data cleaning, manipulation, and analysis.
This document provides an overview of working with DataFrames in Python using the Pandas library. It discusses:
1. What a DataFrame is - a two-dimensional, size-mutable, tabular data structure in Pandas for data manipulation.
2. How to create DataFrames from dictionaries, lists, CSV files and more.
3. Common tasks like viewing data, selecting rows/columns, modifying data, analysis and saving DataFrames.
It also covers indexing and filtering DataFrames using labels or boolean conditions, arithmetic alignment in Pandas and NumPy, and vectorized computation in NumPy.
Lecture on Python Pandas for Decision Makingssuser46aec4
Pandas is a Python library used for data manipulation and analysis. It provides data structures like Series and DataFrames for working with numerical data and time series. Series are one-dimensional arrays like columns in a spreadsheet, while DataFrames are like spreadsheets with rows and columns. DataFrames can be created from CSV files, lists, or dictionaries. Elements can be accessed from Series using integer positions or labels, and rows from DataFrames can be selected using labels or integer positions. Data types of columns in DataFrames can be converted using the astype() method.
Python Pandas is a powerful library for data analysis and manipulation. It provides rich data structures and methods for loading, cleaning, transforming, and modeling data. Pandas allows users to easily work with labeled data and columns in tabular structures called Series and DataFrames. These structures enable fast and flexible operations like slicing, selecting subsets of data, and performing calculations. Descriptive statistics functions in Pandas allow analyzing and summarizing data in DataFrames.
The document provides information about Pandas, a Python library used for data analysis and manipulation. It discusses how to import Pandas, the benefits of using Pandas, basic data structures in Pandas like Series and DataFrames, and examples of creating Series objects from different types of data like lists, dictionaries, and NumPy arrays. It also covers Series object attributes and methods for accessing, slicing, and modifying Series values and indices.
Pandas is an open source Python library that provides data structures and data analysis tools for working with tabular data. It allows users to easily perform operations on different types of data such as tabular, time series, and matrix data. Pandas provides data structures like Series for 1D data and DataFrame for 2D data. It has tools for data cleaning, transformation, manipulation, and visualization of data.
pandasppt with informative topics coverage.pptxvallarasu200364
Pandas is a Python library used for working with structured and unstructured data. It allows users to load, clean, transform, and analyze data. Pandas provides data structures like Series (1D) and DataFrames (2D) that make it easy to manipulate data. DataFrames act like a dictionary of Series and support indexing, slicing, and selection like NumPy arrays. This allows convenient data retrieval and manipulation.
Vectorization refers to performing operations on entire NumPy arrays or sequences of data without using explicit loops. This allows computations to be performed more efficiently by leveraging optimized low-level code. Traditional Python code may use loops to perform operations element-wise, whereas NumPy allows the same operations to be performed vectorized on entire arrays. Broadcasting rules allow operations between arrays of different shapes by automatically expanding dimensions. Vectorization is a key technique for speeding up numerical Python code using NumPy.
Pandas provides three fundamental data structures - Series, DataFrame, and Index. A Series is a one-dimensional array of indexed data, where the index associates values with labels rather than integer positions. A DataFrame contains multiple Series objects and allows storing heterogeneous data. Both Series and DataFrame build upon NumPy arrays by adding labeled indexes to associate data values with keys.
Pandas is a Python library used for data analysis and manipulation. It contains data structures like Series and DataFrame.
A Series is a one-dimensional labeled array capable of holding any data type (integers, strings, floating point numbers, etc.). It is like a column in a DataFrame. A DataFrame is a two-dimensional data structure with labeled axes (rows and columns). It is like a spreadsheet or SQL table.
This document discusses how to create Pandas Series objects by specifying data, indices, and datatypes. Methods to access Series attributes and elements are also described.
XII IP New PYTHN Python Pandas 2020-21.pptxlekha572836
This document provides information about the Pandas Python library and how to handle data using Pandas. It discusses Pandas Series and DataFrames, the two main data structures in Pandas. It describes how to create Series and DataFrames from various data types like lists, dictionaries, and CSV files. It also covers accessing and manipulating data in Series and DataFrames through indexing, slicing, selecting rows and columns, adding and deleting rows/columns.
A DataFrame is a two-dimensional data structure that organizes data into rows and columns like a spreadsheet. DataFrames can be created from many different input data types including lists, dictionaries, arrays, and Series. Common methods for creating DataFrames include from a list of lists, dictionary of lists/arrays/Series, or by passing individual Series.
XII - 2022-23 - IP - RAIPUR (CBSE FINAL EXAM).pdfKrishnaJyotish1
The document provides study material and sample papers for Class XII students of Kendriya Vidyalaya Sangathan Regional Office Raipur for the 2022-23 session. It lists the subject coordination by Mrs. Sandhya Lakra, Principal of KV No. 4 Korba and the content team comprising of 7 PGT Computer Science teachers from different KVs. The compilation, review and vetting is done by Mr. Sumit Kumar Choudhary, PGT CS of KV No. 2 Korba NTPC. The document contains introduction and concepts related to data handling using Pandas and Matplotlib libraries in Python.
pandas directories on the python language.pptxSumitMajukar
Pandas is a Python library used for working with datasets and analyzing data. It allows users to clean messy datasets, explore and manipulate data, and draw conclusions from large datasets based on statistical analysis. Pandas provides functions and methods for loading data from files like CSV and JSON files into DataFrames. DataFrames are the primary data structure in Pandas and act like a 2D spreadsheet with rows and columns. Users can view, clean, and analyze data in DataFrames to gain insights.
Pandas Dataframe reading data Kirti final.pptxKirti Verma
Pandas is a Python library used for data manipulation and analysis. It provides data structures like Series and DataFrames that make working with structured data easy. A DataFrame is a two-dimensional data structure that can store data of different types in columns. DataFrames can be created from dictionaries, lists, CSV files, JSON files and other sources. They allow indexing, selecting, adding and deleting of rows and columns. Pandas provides useful methods for data cleaning, manipulation and analysis tasks on DataFrames.
This document provides an overview of Python libraries for data analysis and data science. It discusses popular Python libraries such as NumPy, Pandas, SciPy, Scikit-Learn and visualization libraries like matplotlib and Seaborn. It describes the functionality of these libraries for tasks like reading and manipulating data, descriptive statistics, inferential statistics, machine learning and data visualization. It also provides examples of using these libraries to explore a sample dataset and perform operations like data filtering, aggregation, grouping and missing value handling.
Pandas is an open-source Python library that provides high-performance data manipulation and analysis tools using powerful data structures like DataFrame. It allows users to load, prepare, manipulate, model, and analyze data regardless of its source through these five typical steps of data processing. Pandas contains data structures like Series and DataFrame, and methods for data loading, merging, sorting, filtering and handling missing data.
The document provides an overview of pandas series including:
- Creation of series from arrays, dictionaries, scalar values
- Mathematical operations on series like addition, subtraction
- Functions to access series data like head(), tail(), indexing, slicing
- Examples of arithmetic operations on series using operators and methods
Pandas is a Python library used for data manipulation and analysis. It has powerful data structures like Series, DataFrames, and Panels. A Series is a one-dimensional array, DataFrame is a two-dimensional array that allows columns of different types, and Panel is a three-dimensional array. DataFrames can be created from lists, dictionaries, and other DataFrames. Columns can be added, deleted, sliced and concatenated. Categorical data types can be used to handle repetitive string values.
The document provides a cheat sheet on the pandas DataFrame object. It discusses importing pandas, creating DataFrames from various data sources like CSVs, Excel, and dictionaries. It covers common operations on DataFrames like selecting, filtering, and transforming columns; handling indexes; and saving DataFrames. The DataFrame is a two-dimensional data structure with labeled columns that can be manipulated using various methods.
Unit 4_Working with Graphs _python (2).pptxprakashvs7
The document discusses various techniques for string manipulation in Python. It covers common string operations like concatenation, slicing, searching, replacing, formatting, splitting, stripping whitespace, and case conversion. Specific methods and functions are provided for each technique using Python's built-in string methods. Examples are given to demonstrate how to use string manipulation methods like find(), replace(), split(), strip(), lower(), upper(), etc. to perform various string operations in Python.
The document discusses various data manipulation techniques in pandas such as creating, filtering, joining and merging DataFrames. Some key points:
- Pandas DataFrames can be created from lists, dictionaries or other DataFrames and allow storing and manipulating tabular data.
- Common operations include filtering rows based on conditions, aggregating using functions like mean(), sorting values, and joining/merging DataFrames on indexes.
- DataFrames support different types of joins like inner, outer, left and right joins to combine data from multiple tables.
The document provides an overview of the Pandas library in Python for working with data structures like Series and DataFrames. It covers common operations for selecting, filtering, sorting, applying functions, handling missing data, and reading/writing data to files and databases. These operations allow for easy manipulation and analysis of data in Pandas.
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
Pandas is an open source Python library that provides data structures and data analysis tools for working with tabular data. It allows users to easily perform operations on different types of data such as tabular, time series, and matrix data. Pandas provides data structures like Series for 1D data and DataFrame for 2D data. It has tools for data cleaning, transformation, manipulation, and visualization of data.
pandasppt with informative topics coverage.pptxvallarasu200364
Pandas is a Python library used for working with structured and unstructured data. It allows users to load, clean, transform, and analyze data. Pandas provides data structures like Series (1D) and DataFrames (2D) that make it easy to manipulate data. DataFrames act like a dictionary of Series and support indexing, slicing, and selection like NumPy arrays. This allows convenient data retrieval and manipulation.
Vectorization refers to performing operations on entire NumPy arrays or sequences of data without using explicit loops. This allows computations to be performed more efficiently by leveraging optimized low-level code. Traditional Python code may use loops to perform operations element-wise, whereas NumPy allows the same operations to be performed vectorized on entire arrays. Broadcasting rules allow operations between arrays of different shapes by automatically expanding dimensions. Vectorization is a key technique for speeding up numerical Python code using NumPy.
Pandas provides three fundamental data structures - Series, DataFrame, and Index. A Series is a one-dimensional array of indexed data, where the index associates values with labels rather than integer positions. A DataFrame contains multiple Series objects and allows storing heterogeneous data. Both Series and DataFrame build upon NumPy arrays by adding labeled indexes to associate data values with keys.
Pandas is a Python library used for data analysis and manipulation. It contains data structures like Series and DataFrame.
A Series is a one-dimensional labeled array capable of holding any data type (integers, strings, floating point numbers, etc.). It is like a column in a DataFrame. A DataFrame is a two-dimensional data structure with labeled axes (rows and columns). It is like a spreadsheet or SQL table.
This document discusses how to create Pandas Series objects by specifying data, indices, and datatypes. Methods to access Series attributes and elements are also described.
XII IP New PYTHN Python Pandas 2020-21.pptxlekha572836
This document provides information about the Pandas Python library and how to handle data using Pandas. It discusses Pandas Series and DataFrames, the two main data structures in Pandas. It describes how to create Series and DataFrames from various data types like lists, dictionaries, and CSV files. It also covers accessing and manipulating data in Series and DataFrames through indexing, slicing, selecting rows and columns, adding and deleting rows/columns.
A DataFrame is a two-dimensional data structure that organizes data into rows and columns like a spreadsheet. DataFrames can be created from many different input data types including lists, dictionaries, arrays, and Series. Common methods for creating DataFrames include from a list of lists, dictionary of lists/arrays/Series, or by passing individual Series.
XII - 2022-23 - IP - RAIPUR (CBSE FINAL EXAM).pdfKrishnaJyotish1
The document provides study material and sample papers for Class XII students of Kendriya Vidyalaya Sangathan Regional Office Raipur for the 2022-23 session. It lists the subject coordination by Mrs. Sandhya Lakra, Principal of KV No. 4 Korba and the content team comprising of 7 PGT Computer Science teachers from different KVs. The compilation, review and vetting is done by Mr. Sumit Kumar Choudhary, PGT CS of KV No. 2 Korba NTPC. The document contains introduction and concepts related to data handling using Pandas and Matplotlib libraries in Python.
pandas directories on the python language.pptxSumitMajukar
Pandas is a Python library used for working with datasets and analyzing data. It allows users to clean messy datasets, explore and manipulate data, and draw conclusions from large datasets based on statistical analysis. Pandas provides functions and methods for loading data from files like CSV and JSON files into DataFrames. DataFrames are the primary data structure in Pandas and act like a 2D spreadsheet with rows and columns. Users can view, clean, and analyze data in DataFrames to gain insights.
Pandas Dataframe reading data Kirti final.pptxKirti Verma
Pandas is a Python library used for data manipulation and analysis. It provides data structures like Series and DataFrames that make working with structured data easy. A DataFrame is a two-dimensional data structure that can store data of different types in columns. DataFrames can be created from dictionaries, lists, CSV files, JSON files and other sources. They allow indexing, selecting, adding and deleting of rows and columns. Pandas provides useful methods for data cleaning, manipulation and analysis tasks on DataFrames.
This document provides an overview of Python libraries for data analysis and data science. It discusses popular Python libraries such as NumPy, Pandas, SciPy, Scikit-Learn and visualization libraries like matplotlib and Seaborn. It describes the functionality of these libraries for tasks like reading and manipulating data, descriptive statistics, inferential statistics, machine learning and data visualization. It also provides examples of using these libraries to explore a sample dataset and perform operations like data filtering, aggregation, grouping and missing value handling.
Pandas is an open-source Python library that provides high-performance data manipulation and analysis tools using powerful data structures like DataFrame. It allows users to load, prepare, manipulate, model, and analyze data regardless of its source through these five typical steps of data processing. Pandas contains data structures like Series and DataFrame, and methods for data loading, merging, sorting, filtering and handling missing data.
The document provides an overview of pandas series including:
- Creation of series from arrays, dictionaries, scalar values
- Mathematical operations on series like addition, subtraction
- Functions to access series data like head(), tail(), indexing, slicing
- Examples of arithmetic operations on series using operators and methods
Pandas is a Python library used for data manipulation and analysis. It has powerful data structures like Series, DataFrames, and Panels. A Series is a one-dimensional array, DataFrame is a two-dimensional array that allows columns of different types, and Panel is a three-dimensional array. DataFrames can be created from lists, dictionaries, and other DataFrames. Columns can be added, deleted, sliced and concatenated. Categorical data types can be used to handle repetitive string values.
The document provides a cheat sheet on the pandas DataFrame object. It discusses importing pandas, creating DataFrames from various data sources like CSVs, Excel, and dictionaries. It covers common operations on DataFrames like selecting, filtering, and transforming columns; handling indexes; and saving DataFrames. The DataFrame is a two-dimensional data structure with labeled columns that can be manipulated using various methods.
Unit 4_Working with Graphs _python (2).pptxprakashvs7
The document discusses various techniques for string manipulation in Python. It covers common string operations like concatenation, slicing, searching, replacing, formatting, splitting, stripping whitespace, and case conversion. Specific methods and functions are provided for each technique using Python's built-in string methods. Examples are given to demonstrate how to use string manipulation methods like find(), replace(), split(), strip(), lower(), upper(), etc. to perform various string operations in Python.
The document discusses various data manipulation techniques in pandas such as creating, filtering, joining and merging DataFrames. Some key points:
- Pandas DataFrames can be created from lists, dictionaries or other DataFrames and allow storing and manipulating tabular data.
- Common operations include filtering rows based on conditions, aggregating using functions like mean(), sorting values, and joining/merging DataFrames on indexes.
- DataFrames support different types of joins like inner, outer, left and right joins to combine data from multiple tables.
The document provides an overview of the Pandas library in Python for working with data structures like Series and DataFrames. It covers common operations for selecting, filtering, sorting, applying functions, handling missing data, and reading/writing data to files and databases. These operations allow for easy manipulation and analysis of data in Pandas.
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Nunit vs XUnit vs MSTest Differences Between These Unit Testing Frameworks.pdfflufftailshop
When it comes to unit testing in the .NET ecosystem, developers have a wide range of options available. Among the most popular choices are NUnit, XUnit, and MSTest. These unit testing frameworks provide essential tools and features to help ensure the quality and reliability of code. However, understanding the differences between these frameworks is crucial for selecting the most suitable one for your projects.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on automated letter generation for Bonterra Impact Management using Google Workspace or Microsoft 365.
Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...Tatiana Kojar
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Python Library-Series.pptx
1. Python Library – Pandas
It is a most famous Python package for data science, which offers
powerful and flexible data structures that make data analysis and
manipulation easy. Pandas make data importing and data analyzing
much easier. Pandas build on packages like NumPy and matplotlib
to give us a single & convenient place for data analysis and
visualization work. Pandas is a high level data manipulation tool
developed by Wes McKinney.
3. Basic Features of Panda
1. With a pandas dataframe, we can have different data types (float, int, string,
datetime, etc) all in one place
2. Columns from a Panda data structure can be deleted or inserted
3. Good IO capabilities; Easily pull data from a MySQL database directly into a
data frame
4. Itsupportsgroupbyoperationfordataaggregationandtransformationandalowshigh
performancemergingandjoiningofdata.
5. It can easily select subsets of data from bulky datasets and even combine
multiple data sets together.
6. It has the functionality to find and fill missing data.
7. Reshaping and pivoting of data sets into different forms.
8. Label-based slicing, indexing and subsetting of large data sets.
9. It allows us to apply operations to independent groups within the data.
10. It supports advanced time-series functionality( which is the use of a model
to predict future values based on previously observed values.
11. It supports visualization by integrating libraries such as matplotlib, ans
seaborn etc. Pandas is best at handling hugs tabular datasets comprising
different data formats.
4. Data Structures in Pandas
A data structure is a way of storing and organizing data in a computer so that it can be
accessed and worked with in an appropriate way.
Important data structures of pandas are–Series,DataFrame, Panel
1. Series
Series is like a one-dimensional array like structure with homogeneous data. For example,
the following series is a collection of integers.
Basic feature of series are
Homogeneous data
Size Immutable
Values of Data Mutable
5. Pandas Series
It is like one-dimensional array capable of holding data of any type (integer, string, float, python objects,
etc.). Series can be created using Series() method. Any list or dictionary data can be converted into series
using this method.
A series can be described as an ordered dictionary with mapping of index values to data values.
Create an Empty Series
s1--------
series variable
pd-------
alternate name given to Pandas module
import pandas as pd
s1 = pd.Series()
print(s1)
Output
Series([], dtype: float64)
Creating Series using Series() with arguments
Syntax :- pandas.Series( data, index, dtype, copy)
Data supplied to Series() can be
A sequence( list)
An ndarray
A scalar value
A python dictionary
A mathematical expression or function
6. Creating Series using List
Like array, a list is also a one-dimensional datatype. But the difference lies in the fact that an array contains elements of
same datatype, while a list may contain elements of same or different data types.
Syntax :- pandas.Series( data, index = idx)
import pandas as pd
s1=pd.Series([10,20,30,40,50])
print(s1)
*Pandas create a default index and automatically assigns the index value from 0 to 4, which is length of the list-1
import pandas as pd
s1=pd.Series([10,20,30,40])
s1.index=['a', 'b', 'c', 'd']
print(s1)
(or)
import pandas as pd
s1=pd.Series([10,20,30,40], index= ['a', 'b', 'c', 'd'])
print(s1)
11. import pandas as pd
s1=pd.Series([1,2,3.3,4,7])
print(s1)
* One of the element in the list, is a float value, it will convert the rest of the integer values into float and displays a
float series.
range() method
import pandas as pd
s1=pd.Series(range(4))
print(s1)
Access single and multiple values based on index.
import pandas as pd
s1=pd.Series([2,3,5.3,7,9], index=['first','sec','third','fourth','fifth'])
print(s1['sec'])
Output
3.0
import pandas as pd
s1=pd.Series([2,3,5.3,7,9], index=['first','sec','third','fourth','fifth'])
print(s1)
print(s1[['sec','third','fifth']])
12. Values and index
import pandas as pd
s1=pd.Series([10,20,30,40,50],index=['First', 'sec', 'third', 'forth', 'fifth'])
print(s1.values)
import pandas as pd
s1=pd.Series([10,20,30,40,50],index=['First', 'sec', 'third', 'forth','fifth'])
print(s1.index)
Accessing data from a Series with Position
Indexing, slicing and accessing data from a series
import pandas as pd
s1=pd.Series([1,2,3,4,5], index=['a', 'b', 'c', 'd', 'e'])
print(s1[0])
print(s1[:3])
print(s1[-3:])
13. iloc and loc
iloc – used for indexing or selecting based on position ie, by row number and column number. It refers to position-
based indexing.
Syntax
iloc = [<row number range>,<column number range>]
It refers to name-based
loc - used for indexing or selecting based on name ie, by row name and column name.
indexing.
Syntax
iloc = [<list of row names >,<list of column names>]
import pandas as pd
s1=pd.Series([1,2,3,4,5], index=['a', 'b', 'c', 'd', 'e'])
print(s1.iloc[1:4])
print(s1.loc['b':'e'])
14. Retrieving values from Series using head()and tail () functions
Series.head() function in a series fetches first ‘n’ values from a pandas object. By default, it gives us the top 5 rows of data
in the series. Series.tail() function displays the last 5 elements by default.
import pandas as pd
s1=pd.Series([10,20,30,40,50,60,70,80,90])
print(s1.head())
import pandas as pd
s1=pd.Series([10,20,30,40,50,60,70,80,90])
print(s1.head(3))
import pandas as pd
s1=pd.Series([10,20,30,40,50,60,70,80,90])
print(s1.tail(2))
Creating a Series from Scalar or Constant Values
A data is a scalar value for which it is a must to provide an index. This constant value shall be repeated to match the
length of the index.
import pandas as pd
s1=pd.Series(55, index=['a', 'b', 'c', 'd', 'e'])
print(s1)
Note :- here 55 is repeated for 5 times (as per no of index)
15. import pandas as pd
s1=pd.Series(55, index=[1,2,3,4,5])
print(s1)
Using range() method
import pandas as pd
s1=pd.Series(40, index=range(0,4))
print(s1)
import pandas as pd
s1=pd.Series(40, index=range(1,6,2))
print(s1)
Creating a Series with index of String (text) type
String or text can be used as an index to the elements of a series.
import pandas as pd
s1=pd.Series('Stay Home', index=['Madhav', 'Smitha', 'Vivek'])
print(s1)
16. Creating a Series with range() and for loop
import pandas as pd
s1=pd.Series(range(1,15,3), index=[x for x in 'abcde'])
print(s1)
Creating a Series using two different lists
* Two lists are passed as arguments to Series()method
import pandas as pd
months=['jan', 'feb', 'mar', 'apr', 'may']
no_days=[31,28,31,30,31]
s1=pd.Series(no_days,index=months)
print(s1)
Creating a Series using missing values [NaN]
We may need to create a series object for which size is defined but some element or data are missing. This is handled
by defining NaN [Not a Number], which is an attribute of Numpy library, defining a missing value using np.NaN.
import pandas as pd
import numpy as np
s1=pd.Series([31,28,31,np.NaN,31])
print(s1)
17. Creating Series from Dictionary
Using dictionary for creating a series gives us the advantage of built-in keys used as index. We do not require declaring
an index as a separate list: instead, built-in keys will be treated as the index
import pandas as pd
data = {'a' : 0., 'b' : 1., 'c' : 2.}
s = pd.Series(data)
print(s)
* A dictionary can be passed as input and if no index is specified, the dictionary keys are taken in the
sorted order to construct index
import pandas as pd1
data = {'a' : 0., 'b' : 1., 'c' : 2.}
s = pd1.Series(data,index=['b','c','d','a'])
print(s)
import pandas as pd
s1=pd.Series({'Jan':31,'Feb':28,'Mar':31,'Apr':30})
print(s1)
18. Naming a series
We can give a name to the two columns, index and values of a series using ‘name’ property.
import pandas as pd
s=pd.Series({'Jan':31,'Feb':28,'Mar':31,'Apr':30})
#naming the series and index
s.name='Days'
s.index.name='Month'
print(s)
* The index column is assigned the name ‘Month’ and data is assigned the name ‘Days’
Creating a Series using a mathematical expression/function
import pandas as pd
import numpy as np
s1=np.arange(5,10)
print(s1)
s2=pd.Series(index=s1,data=s1*4)
print(s2)
19. import pandas as pd
import numpy as np
s1=np.arange(5,10)
print(s1)
s2=pd.Series(index=s1,data=s1**4)
print(s2)
Mathematical operation on series
import pandas as pd
s1=pd.Series([10,20,30,40,50], index=[1,2,3,4,5])
print(s1)
s2=pd.Series([15,25,35,45,55], index=[1,2,3,4,5])
print(s2)
s3=pd.Series([11,22,33,44,55], index=[10,20,30,40,50])
print(s3)
print(s1+s2)
print(s1*s2)
print(s2/s1)
print(s1+s3)
20. Vector operations on series
Series supports vectors operations. Any operation to be performed on a series
gets performed on every single element of it.
import pandas as pd
s1=pd.Series([10,20,30,40,50], index=[1,2,3,4,5])
print(s1>25) # returns booleanoutput
import pandas as pd
s1=pd.Series([10,20,30,40,50], index=[1,2,3,4,5])
print(s1[s1>25]) # print s1 only if the value of s1 is greater than 25
Modifying Elements of a Series Object
import pandas as pd
s1=pd.Series([10,20,30,40,50], index=[1,2,3,4,5])
s2=pd.Series([15,25,35,45,55], index=[1,2,3,4,5])
s1[2]=222
s2[1:4]=[1000,2000,3000]
print(s1)
print(s2)
21. Deleting elements from a Series
We can delete an element from a series using drop() method by passing
the index of the element to be deleted as the argument to it.
import pandas as pd
s1=pd.Series([10,20,30,40,50], index=[1,2,3,4,5])
print(s1.drop(3))
import pandas as pd
s1=pd.Series([10,20,30,40,50], index=[1,2,3,4,5])
s2=pd.Series([[15,25,34],[35,45,55]])
print(s1)
print(s2)
print(s1.dtype)
print(s2.dtype)
print(type(s1))
print(type(s2))
print(s1.shape)
print(s2.shape)
print(s1.ndim, ' ', s2.ndim)
print(s1.size,'; ',s2.size)
print(s1.empty)
print(s2.hasnans)
print(s2.count())
print(s1.nbytes,';',s2.nbytes)
22. Series Object Attributes
Attributes Description
Series.index Returns index of the series
Series.values Returns ndarrays
Series.dtype Returns dtype object of the underlying data
Series.shape Returns tuple of the shape of underlying data
Series.size Returns the size of the element
Series.itemsize Returns the size of the dtype
Series.hasnans Returns true if there are any NaN
Series.empty Returns true if series object is empty