From db812297286c5a0ac818e2063fd0c986914e628c Mon Sep 17 00:00:00 2001 From: Max Hoffman Date: Fri, 13 Jan 2023 07:53:42 -0800 Subject: [PATCH 1/2] csv import --- dolt-schema.sql | 4 +- json_conv.py | 21 + oreilly_mod.csv | 11 + oreilly_mod.json | 1506 ++++++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 1540 insertions(+), 2 deletions(-) create mode 100644 json_conv.py create mode 100644 oreilly_mod.csv create mode 100644 oreilly_mod.json diff --git a/dolt-schema.sql b/dolt-schema.sql index cb1d2f3..6672b65 100644 --- a/dolt-schema.sql +++ b/dolt-schema.sql @@ -1,7 +1,7 @@ CREATE TABLE books ( `id` LONGTEXT NOT NULL, `ourn` LONGTEXT, - `isbn` INTEGER, + `isbn` bigint, `last_modified_time` DATETIME, `issued` DATETIME, `format` TEXT, @@ -26,4 +26,4 @@ CREATE TABLE books ( `topics` JSON, `topics_payload` JSON, `minutes_required` INTEGER -) \ No newline at end of file +) diff --git a/json_conv.py b/json_conv.py new file mode 100644 index 0000000..cf53c3f --- /dev/null +++ b/json_conv.py @@ -0,0 +1,21 @@ +import json +import os +import csv +def main(): + j = json.load(open("oreilly.json")) + tostr = ["authors", "publishers", "topics", "topics_payload"] + for o in j: + for f in tostr: + o[f] = json.dumps(o[f]) + with open(os.path.join("oreilly_mod.json"), 'a+', encoding='utf-8', errors="replace") as outfile: + json.dump(j, outfile, indent=2) + field_names = [] + for k in j[0]: + field_names.append(k) + with open('oreilly_mod.csv', 'w') as csvfile: + writer = csv.DictWriter(csvfile, fieldnames = field_names) + writer.writeheader() + writer.writerows(j) + +if __name__ == "__main__": + main() diff --git a/oreilly_mod.csv b/oreilly_mod.csv new file mode 100644 index 0000000..03aa693 --- /dev/null +++ b/oreilly_mod.csv @@ -0,0 +1,11 @@ +id,ourn,isbn,last_modified_time,issued,format,content_format,authors,publishers,language,title,description,url,web_url,content_type,virtual_pages,timestamp,average_rating,number_of_followers,number_of_items,number_of_reviews,popularity,report_score,cover_url,date_added,topics,topics_payload,minutes_required +https://www.safaribooksonline.com/api/v1/book/9781098118723/,urn:orm:book:9781098118723,9781098118730,2023-01-12T16:22:18.945Z,2022-09-20T00:00:00Z,book,book,"[""Tanya Reilly""]","[""O'Reilly Media, Inc.""]",en,The Staff Engineer's Path,"

For years, companies have rewarded their most effective engineers with management positions. But treating management as the default path for an engineer with leadership ability doesn't serve the industry well--or the engineer. The staff engineer's path allows engineers to contribute at a high level as role models, driving big projects, determining technical strategy, and raising everyone's skills.

This in-depth book shows you how to understand your role, manage your time, master strategic thinking, and set the standard for technical work. You'll read about how to be a leader without direct authority, how to plan ahead to make the right technical decisions, and how to make everyone around you better, while still growing as an expert in your domain.

By exploring the three pillars of a staff engineer's job, Tanya Reilly, a veteran of the staff engineer track, shows you how to:

  • Take a broad, strategic view when thinking about your work
  • Dive into practical tactics for making projects succeed
  • Determine what ""good engineering"" means in your organization
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Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.

With this updated third edition, author Aurélien Géron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started.

  • Use Scikit-learn to track an example ML project end to end
  • Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
  • Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
  • Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
  • Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
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Python’s simplicity lets you become productive quickly, but often this means you aren’t using everything it has to offer. With the updated edition of this hands-on guide, you’ll learn how to write effective, modern Python 3 code by leveraging its best ideas.

Don’t waste time bending Python to fit patterns you learned in other languages. Discover and apply idiomatic Python 3 features beyond your past experience. Author Luciano Ramalho guides you through Python’s core language features and libraries and teaches you how to make your code shorter, faster, and more readable.

Featuring major updates throughout the book, Fluent Python, second edition, covers:

  • Special methods: The key to the consistent behavior of Python objects
  • Data structures: Sequences, dicts, sets, Unicode, and data classes
  • Functions as objects: First-class functions, related design patterns, and type hints in function declarations
  • Object-oriented idioms: Composition, inheritance, mixins, interfaces, operator overloading, static typing and protocols
  • Control flow: Context managers, generators, coroutines, async/await, and thread/process pools
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Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords?

In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications.

  • Peer under the hood of the systems you already use, and learn how to use and operate them more effectively
  • Make informed decisions by identifying the strengths and weaknesses of different tools
  • Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity
  • Understand the distributed systems research upon which modern databases are built
  • Peek behind the scenes of major online services, and learn from their architectures
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Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle.

Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers. You'll understand how to apply the concepts of data generation, ingestion, orchestration, transformation, storage, and governance that are critical in any data environment regardless of the underlying technology.

This book will help you:

  • Get a concise overview of the entire data engineering landscape
  • Assess data engineering problems using an end-to-end framework of best practices
  • Cut through marketing hype when choosing data technologies, architecture, and processes
  • Use the data engineering lifecycle to design and build a robust architecture
  • Incorporate data governance and security across the data engineering lifecycle
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For years, companies have rewarded their most effective engineers with management positions. But treating management as the default path for an engineer with leadership ability doesn't serve the industry well--or the engineer. The staff engineer's path allows engineers to contribute at a high level as role models, driving big projects, determining technical strategy, and raising everyone's skills.

This in-depth book shows you how to understand your role, manage your time, master strategic thinking, and set the standard for technical work. You'll read about how to be a leader without direct authority, how to plan ahead to make the right technical decisions, and how to make everyone around you better, while still growing as an expert in your domain.

By exploring the three pillars of a staff engineer's job, Tanya Reilly, a veteran of the staff engineer track, shows you how to:

  • Take a broad, strategic view when thinking about your work
  • Dive into practical tactics for making projects succeed
  • Determine what ""good engineering"" means in your organization
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Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.

With this updated third edition, author Aurélien Géron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started.

  • Use Scikit-learn to track an example ML project end to end
  • Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
  • Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
  • Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
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Python’s simplicity lets you become productive quickly, but often this means you aren’t using everything it has to offer. With the updated edition of this hands-on guide, you’ll learn how to write effective, modern Python 3 code by leveraging its best ideas.

Don’t waste time bending Python to fit patterns you learned in other languages. Discover and apply idiomatic Python 3 features beyond your past experience. Author Luciano Ramalho guides you through Python’s core language features and libraries and teaches you how to make your code shorter, faster, and more readable.

Featuring major updates throughout the book, Fluent Python, second edition, covers:

  • Special methods: The key to the consistent behavior of Python objects
  • Data structures: Sequences, dicts, sets, Unicode, and data classes
  • Functions as objects: First-class functions, related design patterns, and type hints in function declarations
  • Object-oriented idioms: Composition, inheritance, mixins, interfaces, operator overloading, static typing and protocols
  • Control flow: Context managers, generators, coroutines, async/await, and thread/process pools
  • Metaprogramming: Properties, attribute descriptors, class decorators, and new class metaprogramming hooks that are simpler than metaclasses
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In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications.

  • Peer under the hood of the systems you already use, and learn how to use and operate them more effectively
  • Make informed decisions by identifying the strengths and weaknesses of different tools
  • Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity
  • Understand the distributed systems research upon which modern databases are built
  • Peek behind the scenes of major online services, and learn from their architectures
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Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers. You'll understand how to apply the concepts of data generation, ingestion, orchestration, transformation, storage, and governance that are critical in any data environment regardless of the underlying technology.

This book will help you:

  • Get a concise overview of the entire data engineering landscape
  • Assess data engineering problems using an end-to-end framework of best practices
  • Cut through marketing hype when choosing data technologies, architecture, and processes
  • Use the data engineering lifecycle to design and build a robust architecture
  • Incorporate data governance and security across the data engineering lifecycle
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For years, companies have rewarded their most effective engineers with management positions. But treating management as the default path for an engineer with leadership ability doesn't serve the industry well--or the engineer. The staff engineer's path allows engineers to contribute at a high level as role models, driving big projects, determining technical strategy, and raising everyone's skills.

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By exploring the three pillars of a staff engineer's job, Tanya Reilly, a veteran of the staff engineer track, shows you how to:

  • Take a broad, strategic view when thinking about your work
  • Dive into practical tactics for making projects succeed
  • Determine what \"good engineering\" means in your organization
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Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.

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  • Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
  • Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
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