Data Ingestion With Python Cookbook
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Data Ingestion With Python Cookbook
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Author : Gláucia Esppenchutz
language : en
Publisher:
Release Date : 2023-05-31
Data Ingestion With Python Cookbook written by Gláucia Esppenchutz and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-31 with categories.
Deploy your data ingestion pipeline, orchestrate, and monitor efficiently to prevent loss of data and quality Purchase of the print or Kindle book includes a free PDF eBook Key Features: Harness best practices to create a Python and PySpark data ingestion pipeline Seamlessly automate and orchestrate your data pipelines using Apache Airflow Build a monitoring framework by integrating the concept of data observability into your pipelines Book Description: Data Ingestion with Python Cookbook offers a practical approach to designing and implementing data ingestion pipelines. It presents real-world examples with the most widely recognized open source tools on the market to answer commonly asked questions and overcome challenges. You'll be introduced to designing and working with or without data schemas, as well as creating monitored pipelines with Airflow and data observability principles, all while following industry best practices. The book also addresses challenges associated with reading different data sources and data formats. As you progress through the book, you'll gain a broader understanding of error logging best practices, troubleshooting techniques, data orchestration, monitoring, and storing logs for further consultation. By the end of the book, you'll have a fully automated set that enables you to start ingesting and monitoring your data pipeline effortlessly, facilitating seamless integration with subsequent stages of the ETL process. What You Will Learn: Implement data observability using monitoring tools Automate your data ingestion pipeline Read analytical and partitioned data, whether schema or non-schema based Debug and prevent data loss through efficient data monitoring and logging Establish data access policies using a data governance framework Construct a data orchestration framework to improve data quality Who this book is for: This book is for data engineers and data enthusiasts seeking a comprehensive understanding of the data ingestion process using popular tools in the open source community. For more advanced learners, this book takes on the theoretical pillars of data governance while providing practical examples of real-world scenarios commonly encountered by data engineers.
Data Ingestion With Python Cookbook
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Author : Glaucia Esppenchutz
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-05-31
Data Ingestion With Python Cookbook written by Glaucia Esppenchutz and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-31 with Computers categories.
Deploy your data ingestion pipeline, orchestrate, and monitor efficiently to prevent loss of data and quality Key Features Harness best practices to create a Python and PySpark data ingestion pipeline Seamlessly automate and orchestrate your data pipelines using Apache Airflow Build a monitoring framework by integrating the concept of data observability into your pipelines Book Description Data Ingestion with Python Cookbook offers a practical approach to designing and implementing data ingestion pipelines. It presents real-world examples with the most widely recognized open source tools on the market to answer commonly asked questions and overcome challenges. You'll be introduced to designing and working with or without data schemas, as well as creating monitored pipelines with Airflow and data observability principles, all while following industry best practices. The book also addresses challenges associated with reading different data sources and data formats. As you progress through the book, you'll gain a broader understanding of error logging best practices, troubleshooting techniques, data orchestration, monitoring, and storing logs for further consultation. By the end of the book, you'll have a fully automated set that enables you to start ingesting and monitoring your data pipeline effortlessly, facilitating seamless integration with subsequent stages of the ETL process. What you will learn Implement data observability using monitoring tools Automate your data ingestion pipeline Read analytical and partitioned data, whether schema or non-schema based Debug and prevent data loss through efficient data monitoring and logging Establish data access policies using a data governance framework Construct a data orchestration framework to improve data quality Who this book is for This book is for data engineers and data enthusiasts seeking a comprehensive understanding of the data ingestion process using popular tools in the open source community. For more advanced learners, this book takes on the theoretical pillars of data governance while providing practical examples of real-world scenarios commonly encountered by data engineers.
Graph Data Modeling In Python
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Author : Gary Hutson
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-06-30
Graph Data Modeling In Python written by Gary Hutson and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-30 with Computers categories.
Learn how to transform, store, evolve, refactor, model, and create graph projections using the Python programming language Purchase of the print or Kindle book includes a free PDF eBook Key Features Transform relational data models into graph data model while learning key applications along the way Discover common challenges in graph modeling and analysis, and learn how to overcome them Practice real-world use cases of community detection, knowledge graph, and recommendation network Book Description Graphs have become increasingly integral to powering the products and services we use in our daily lives, driving social media, online shopping recommendations, and even fraud detection. With this book, you'll see how a good graph data model can help enhance efficiency and unlock hidden insights through complex network analysis. Graph Data Modeling in Python will guide you through designing, implementing, and harnessing a variety of graph data models using the popular open source Python libraries NetworkX and igraph. Following practical use cases and examples, you'll find out how to design optimal graph models capable of supporting a wide range of queries and features. Moreover, you'll seamlessly transition from traditional relational databases and tabular data to the dynamic world of graph data structures that allow powerful, path-based analyses. As well as learning how to manage a persistent graph database using Neo4j, you'll also get to grips with adapting your network model to evolving data requirements. By the end of this book, you'll be able to transform tabular data into powerful graph data models. In essence, you'll build your knowledge from beginner to advanced-level practitioner in no time. What you will learn Design graph data models and master schema design best practices Work with the NetworkX and igraph frameworks in Python Store, query, ingest, and refactor graph data Store your graphs in memory with Neo4j Build and work with projections and put them into practice Refactor schemas and learn tactics for managing an evolved graph data model Who this book is for If you are a data analyst or database developer interested in learning graph databases and how to curate and extract data from them, this is the book for you. It is also beneficial for data scientists and Python developers looking to get started with graph data modeling. Although knowledge of Python is assumed, no prior experience in graph data modeling theory and techniques is required.
Modern Python Cookbook
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Author : Steven F. Lott
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-07-31
Modern Python Cookbook written by Steven F. Lott and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-31 with Computers categories.
Enhance your Python skills with the third edition of Modern Python Cookbook with 130+ new and updated recipes covering Python 3.12, including new coverage on graphics, visualizations, dependencies, virtual environments, and more. Purchase of the print or Kindle book includes a free eBook in PDF format Key Features New chapters on type matching, data visualization, dependency management, and more Comprehensive coverage of Python 3.12 with updated recipes and techniques Provides practical examples and detailed explanations to solve real-world problems efficiently Book DescriptionPython is the go-to language for developers, engineers, data scientists, and hobbyists worldwide. Known for its versatility, Python can efficiently power applications, offering remarkable speed, safety, and scalability. This book distills Python into a collection of straightforward recipes, providing insights into specific language features within various contexts, making it an indispensable resource for mastering Python and using it to handle real-world use cases. The third edition of Modern Python Cookbook provides an in-depth look into Python 3.12, offering more than 140 new and updated recipes that cater to both beginners and experienced developers. This edition introduces new chapters on documentation and style, data visualization with Matplotlib and Pyplot, and advanced dependency management techniques using tools like Poetry and Anaconda. With practical examples and detailed explanations, this cookbook helps developers solve real-world problems, optimize their code, and get up to date with the latest Python features.What you will learn Master core Python data structures, algorithms, and design patterns Implement object-oriented designs and functional programming features Use type matching and annotations to make more expressive programs Create useful data visualizations with Matplotlib and Pyplot Manage project dependencies and virtual environments effectively Follow best practices for code style and testing Create clear and trustworthy documentation for your projects Who this book is for This Python book is for web developers, programmers, enterprise programmers, engineers, and big data scientists. If you are a beginner, this book offers helpful details and design patterns for learning Python. If you are experienced, it will expand your knowledge base. Fundamental knowledge of Python programming and basic programming principles will be helpful
Python Data Cleaning And Preparation Best Practices
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Author : Maria Zervou
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-09-27
Python Data Cleaning And Preparation Best Practices written by Maria Zervou and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-27 with Computers categories.
Take your data preparation skills to the next level by converting any type of data asset into a structured, formatted, and readily usable dataset Key Features Maximize the value of your data through effective data cleaning methods Enhance your data skills using strategies for handling structured and unstructured data Elevate the quality of your data products by testing and validating your data pipelines Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionProfessionals face several challenges in effectively leveraging data in today's data-driven world. One of the main challenges is the low quality of data products, often caused by inaccurate, incomplete, or inconsistent data. Another significant challenge is the lack of skills among data professionals to analyze unstructured data, leading to valuable insights being missed that are difficult or impossible to obtain from structured data alone. To help you tackle these challenges, this book will take you on a journey through the upstream data pipeline, which includes the ingestion of data from various sources, the validation and profiling of data for high-quality end tables, and writing data to different sinks. You’ll focus on structured data by performing essential tasks, such as cleaning and encoding datasets and handling missing values and outliers, before learning how to manipulate unstructured data with simple techniques. You’ll also be introduced to a variety of natural language processing techniques, from tokenization to vector models, as well as techniques to structure images, videos, and audio. By the end of this book, you’ll be proficient in data cleaning and preparation techniques for both structured and unstructured data.What you will learn Ingest data from different sources and write it to the required sinks Profile and validate data pipelines for better quality control Get up to speed with grouping, merging, and joining structured data Handle missing values and outliers in structured datasets Implement techniques to manipulate and transform time series data Apply structure to text, image, voice, and other unstructured data Who this book is for Whether you're a data analyst, data engineer, data scientist, or a data professional responsible for data preparation and cleaning, this book is for you. Working knowledge of Python programming is needed to get the most out of this book.
Data Wrangling With Sql
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Author : Raghav Kandarpa
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-07-31
Data Wrangling With Sql written by Raghav Kandarpa and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-31 with Computers categories.
Become a data wrangling expert and make well-informed decisions by effectively utilizing and analyzing raw unstructured data in a systematic manner Purchase of the print or Kindle book includes a free PDF eBook Key Features Implement query optimization during data wrangling using the SQL language with practical use cases Master data cleaning, handle the date function and null value, and write subqueries and window functions Practice self-assessment questions for SQL-based interviews and real-world case study rounds Book DescriptionThe amount of data generated continues to grow rapidly, making it increasingly important for businesses to be able to wrangle this data and understand it quickly and efficiently. Although data wrangling can be challenging, with the right tools and techniques you can efficiently handle enormous amounts of unstructured data. The book starts by introducing you to the basics of SQL, focusing on the core principles and techniques of data wrangling. You’ll then explore advanced SQL concepts like aggregate functions, window functions, CTEs, and subqueries that are very popular in the business world. The next set of chapters will walk you through different functions within SQL query that cause delays in data transformation and help you figure out the difference between a good query and bad one. You’ll also learn how data wrangling and data science go hand in hand. The book is filled with datasets and practical examples to help you understand the concepts thoroughly, along with best practices to guide you at every stage of data wrangling. By the end of this book, you’ll be equipped with essential techniques and best practices for data wrangling, and will predominantly learn how to use clean and standardized data models to make informed decisions, helping businesses avoid costly mistakes.What you will learn Build time series models using data wrangling Discover data wrangling best practices as well as tips and tricks Find out how to use subqueries, window functions, CTEs, and aggregate functions Handle missing data, data types, date formats, and redundant data Build clean and efficient data models using data wrangling techniques Remove outliers and calculate standard deviation to gauge the skewness of data Who this book is forThis book is for data analysts looking for effective hands-on methods to manage and analyze large volumes of data using SQL. The book will also benefit data scientists, product managers, and basically any role wherein you are expected to gather data insights and develop business strategies using SQL as a language. If you are new to or have basic knowledge of SQL and databases and an understanding of data cleaning practices, this book will give you further insights into how you can apply SQL concepts to build clean, standardized data models for accurate analysis.
Python Data Science Cookbook
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Author : Taryn Voska
language : en
Publisher: GitforGits
Release Date : 2025-02-10
Python Data Science Cookbook written by Taryn Voska and has been published by GitforGits this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-10 with Computers categories.
This book's got a bunch of handy recipes for data science pros to get them through the most common challenges they face when using Python tools and libraries. Each recipe shows you exactly how to do something step-by-step. You can load CSVs directly from a URL, flatten nested JSON, query SQL and NoSQL databases, import Excel sheets, or stream large files in memory-safe batches. Once the data's loaded, you'll find simple ways to spot and fill in missing values, standardize categories that are off, clip outliers, normalize features, get rid of duplicates, and extract the year, month, or weekday from timestamps. You'll learn how to run quick analyses, like generating descriptive statistics, plotting histograms and correlation heatmaps, building pivot tables, creating scatter-matrix plots, and drawing time-series line charts to spot trends. You'll learn how to build polynomial features, compare MinMax, Standard, and Robust scaling, smooth data with rolling averages, apply PCA to reduce dimensions, and encode high-cardinality fields with sparse one-hot encoding using feature engineering recipes. As for machine learning, you'll learn to put together end-to-end pipelines that handle imputation, scaling, feature selection, and modeling in one object, create custom transformers, automate hyperparameter searches with GridSearchCV, save and load your pipelines, and let SelectKBest pick the top features automatically. You'll learn how to test hypotheses with t-tests and chi-square tests, build linear and Ridge regressions, work with decision trees and random forests, segment countries using clustering, and evaluate models using MSE, classification reports, and ROC curves. And you'll finally get a handle on debugging and integration: fixing pandas merge errors, correcting NumPy broadcasting mismatches, and making sure your plots are consistent. Key Learnings You can load remote CSVs directly into pandas using read_csv, so you don't have to deal with manual downloads and file clutter. Use json_normalize to convert nested JSON responses into simple tables, making it a breeze to analyze. You can query relational and NoSQL databases directly from Python, and the results will merge seamlessly into Pandas. Find and fill in missing values using IGNSA(), forward-fill, and median strategies for all of your data over time. You can free up a lot of memory by turning string columns into Pandas' Categorical dtype. You can speed up computations with NumPy vectorization and chunked CSV reading to prevent RAM exhaustion. You can build feature pipelines using custom transformers, scaling, and automated hyperparameter tuning with GridSearchCV. Use regression, tree-based, and clustering algorithms to show linear, nonlinear, and group-specific vaccination patterns. Evaluate models using MSE, R², precision, recall, and ROC curves to assess their performance. Set up automated data retrieval with scheduled API pulls, cloud storage, Kafka streams, and GraphQL queries. Table of Content Data Ingestion from Multiple Sources Preprocessing and Cleaning Complex Datasets Performing Quick Exploratory Analysis Optimizing Data Structures and Performance Feature Engineering and Transformation Building Machine Learning Pipelines Implementing Statistical and Machine Learning Techniques Debugging and Troubleshooting Advanced Data Retrieval and Integration
Python Gpt Cookbook
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Author : Dr. Neil Williams
language : en
Publisher: BPB Publications
Release Date : 2025-03-19
Python Gpt Cookbook written by Dr. Neil Williams and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-19 with Computers categories.
DESCRIPTION GPT has redefined the landscape of AI, enabling the creation of powerful language models capable of diverse applications. The objective of the Python GPT Cookbook is to equip readers with practical recipes and foundational knowledge to build business solutions using GPT and Python. The book is divided into four parts. The first covers the basics, the second teaches the fundamentals of NLP, the third delves into applying GPT in various fields, and the fourth provides a conclusion. Each chapter includes recipes and practical insights to help readers deepen their understanding and apply the concepts presented. This cookbook approach delivers 78 practical recipes, including creating OpenAI accounts, utilizing playgrounds and API keys. You will learn text preprocessing, embeddings, fine-tuning, and GPT integration with Hugging Face. Learn to implement GPT using PyTorch and TensorFlow, convert models, and build authenticated actions. Applications include chatbots, email summarization, DBA copilots, and use cases in marketing, sales, IP, and manufacturing. By the end of the book, readers will have a robust understanding of GPT models and how to use them for real-world NLP tasks, along with the skills to continue exploring this powerful technology independently. WHAT YOU WILL LEARN ● Learn Python, OpenAI, TensorFlow, Hugging Face, and vector databases. ● Master Python for NLP applications and data manipulation. ● Understand and implement GPT models for various tasks. ● Integrate GPT with various architectural components, such as databases, third-party APIs, servers, and data pipelines ● Utilise NLTK, PyTorch, and TensorFlow for advanced NLP projects. ● Use Jupyter for interactive coding and data analysis. WHO THIS BOOK IS FOR The Python GPT Cookbook is for IT professionals and business innovators who already have basic Python skills. Data scientists, ML engineers, NLP engineers, and ML researchers will also find it useful. TABLE OF CONTENTS 1. Introduction to GPT 2. Crafting Your GPT Workspace 3. Pre-processing 4. Embeddings 5. Classifying Intent 6. Hugging Face and GPT 7. Vector Databases 8. GPT, PyTorch, and TensorFlow 9. Custom GPT Actions 10. Integrating GPT with the Enterprise 11. Marketing and Sales with GPT 12. Intellectual Property Management with GPT 13. GPT in Manufacturing 14. Scaling up 15. Emerging Trends and Future Directions
The Secrets Of Ai Value Creation
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Author : Michael Proksch
language : en
Publisher: John Wiley & Sons
Release Date : 2024-03-04
The Secrets Of Ai Value Creation written by Michael Proksch and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-04 with Computers categories.
Unlock unprecedented levels of value at your firm by implementing artificial intelligence In The Secrets of AI Value Creation: Practical Guide to Business Value Creation with Artificial Intelligence from Strategy to Execution, a team of renowned artificial intelligence leaders and experts delivers an insightful blueprint for unlocking the value of AI in your company. This book presents a comprehensive framework that can be applied to your organisation, exploring the value drivers and challenges you might face throughout your AI journey. You will uncover effective strategies and tactics utilised by successful artificial intelligence (AI) achievers to propel business growth. In the book, you’ll explore critical value drivers and key capabilities that will determine the success or failure of your company’s AI initiatives. The authors examine the subject from multiple perspectives, including business, technology, data, algorithmics, and psychology. Organized into four parts and fourteen insightful chapters, the book includes: Concrete examples and real-world case studies illustrating the practical impact of the ideas discussed within Best practices used and common challenges encountered when first incorporating AI into your company’s operations A comprehensive framework you can use to navigate the complexities of AI implementation and value creation An indispensable blueprint for artificial intelligence implementation at your organisation, The Secrets of AI Value Creation is a can’t-miss resource for managers, executives, directors, entrepreneurs, founders, data analysts, and business- and tech-side professionals looking for ways to unlock new forms of value in their company. The authors, who are industry leaders, assemble the puzzle pieces into a comprehensive framework for AI value creation: Michael Proksch is an expert on the subject of AI strategy and value creation. He worked with various Fortune 2000 organisations and focuses on optimising business operations building customised AI solutions, and driving organisational adoption of AI through the creation of value and trust. Nisha Paliwal is a senior technology executive. She is known for her expertise in various technology services, focusing on the importance of bringing AI technology, computing resources, data, and talent together in a synchronous and organic way. Wilhelm Bielert is a seasoned senior executive with an extensive of experience in digital transformation, program and project management, and corporate restructuring. With a proven track record, he has successfully led transformative initiatives in multinational corporations, specialising in harnessing the power of AI and other cutting-edge technologies to drive substantial value creation.
Python For Algorithmic Trading Cookbook
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Author : Jason Strimpel
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-08-16
Python For Algorithmic Trading Cookbook written by Jason Strimpel and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-16 with Business & Economics categories.
Harness the power of Python libraries to transform freely available financial market data into algorithmic trading strategies and deploy them into a live trading environment Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free Key Features Follow practical Python recipes to acquire, visualize, and store market data for market research Design, backtest, and evaluate the performance of trading strategies using professional techniques Deploy trading strategies built in Python to a live trading environment with API connectivity Book DescriptionDiscover how Python has made algorithmic trading accessible to non-professionals with unparalleled expertise and practical insights from Jason Strimpel, founder of PyQuant News and a seasoned professional with global experience in trading and risk management. This book guides you through from the basics of quantitative finance and data acquisition to advanced stages of backtesting and live trading. Detailed recipes will help you leverage the cutting-edge OpenBB SDK to gather freely available data for stocks, options, and futures, and build your own research environment using lightning-fast storage techniques like SQLite, HDF5, and ArcticDB. This book shows you how to use SciPy and statsmodels to identify alpha factors and hedge risk, and construct momentum and mean-reversion factors. You’ll optimize strategy parameters with walk-forward optimization using VectorBT and construct a production-ready backtest using Zipline Reloaded. Implementing all that you’ve learned, you’ll set up and deploy your algorithmic trading strategies in a live trading environment using the Interactive Brokers API, allowing you to stream tick-level data, submit orders, and retrieve portfolio details. By the end of this algorithmic trading book, you'll not only have grasped the essential concepts but also the practical skills needed to implement and execute sophisticated trading strategies using Python.What you will learn Acquire and process freely available market data with the OpenBB Platform Build a research environment and populate it with financial market data Use machine learning to identify alpha factors and engineer them into signals Use VectorBT to find strategy parameters using walk-forward optimization Build production-ready backtests with Zipline Reloaded and evaluate factor performance Set up the code framework to connect and send an order to Interactive Brokers Who this book is for Python for Algorithmic Trading Cookbook equips traders, investors, and Python developers with code to design, backtest, and deploy algorithmic trading strategies. You should have experience investing in the stock market, knowledge of Python data structures, and a basic understanding of using Python libraries like pandas. This book is also ideal for individuals with Python experience who are already active in the market or are aspiring to be.