【PyCon China2021】王博-Robust&Ease to use-The Design and Evolution of Python-based Primitive Data Types

Python全栈技术社区 2021-11-02 13:09:03

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Learning Data Mining with Python - Second Edition
earning Data Mining with Python - Second Edition by Robert Layton English | 4 May 2017 | ASIN: B01MRP7VFV | 358 Pages | AZW3 | 2.85 MB Key Features Use a wide variety of Python libraries for practical data mining purposes. Learn how to find, manipulate, analyze, and visualize data using Python. Step-by-step instructions on data mining techniques with Python that have real-world applications. Book Description This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. This book covers a large number of libraries available in Python, including the Jupyter Notebook, pandas, scikit-learn, and NLTK. You will gain hands on experience with complex data types including text, images, and graphs. You will also discover object detection using Deep Neural Networks, which is one of the big, difficult areas of machine learning right now. With restructured examples and code samples updated for the latest edition of Python, each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will have great insights into using Python for data mining and understanding of the algorithms as well as implementations. What you will learn Apply data mining concepts to real-world problems Predict the outcome of sports matches based on past results Determine the author of a document based on their writing style Use APIs to download datasets from social media and other online services Find and extract good features from difficult datasets Create models that solve real-world problems Design and develop data mining applications using a variety of datasets Perform object detection in images using Deep Neural Networks Find meaningful insights from your data through intuitive visualizations Compute on big data, including real-time data from the internet About the Author Robert Layton is a data scientist working mainly on text mining problems for industries including the finance, information security, and transport sectors. He runs dataPipeline to build algorithms for practical use, and Eurekative, helping bringing start-ups to life in regional Australia. He has presented at the last four PyCon AU conferences, at multiple international research conferences, and has been training in some capacity for five years. He has a PhD in cybercrime analytics from the Internet Commerce Security Laboratory at Federation University Australia, where he was the Inaugural Young Alumni of the Year in 2014 and is currently and Honorary Research Fellow. You can find him on LinkedIn at https://www.linkedin.com/in/drrobertlayton and on Twitter at @robertlayton. Robert writes regularly on data mining and cybercrime, in a private, consultancy, and a research capacity. Robert is an Official Member of the Ballarat Hackerspace, where he helps grow the future-tech sector in regional Victoria.
pycon-2017-eda-tutorial, PyCon 2017教程的资源，"Exploratory data analysis in python".zip
pycon-2017-eda-tutorial, PyCon 2017教程的资源，"Exploratory data analysis in python" EDA教程这个 repo 保存了为教程开发的内容，在 python的Exploratory探索性数据分析中，在 2017年05月17日的PyCon 。我们建议设置环境并测试它，然后按照在这里找到的教程的视频 following 。随
vscode远程开发
2019年5月3日，在PyCon 2019 大会上，微软发布了 VS Code Remote，开启了远程开发的新时代！本课程讲述了vscode远程开发的实际使用，和在C++、python、php等领域的开发实战。
Python for data analysis
这本书主要是用 pandas 连接 SciPy 和 NumPy，用pandas做数据处理是Pycon2012上一个很热门的话题。另一个功能强大的东西是Sage，它将很多开源的软件集成到统一的 Python 接口。, Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you’ll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language., Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It’s ideal for analysts new to Python and for Python programmers new to scientific computing., Use the IPython interactive shell as your primary development environment, Learn basic and advanced NumPy (Numerical Python) features, Get started with data analysis tools in the pandas library, Use high-performance tools to load, clean, transform, merge, and reshape data, Create scatter plots and static or interactive visualizations with matplotlib, Apply the pandas groupby facility to slice, dice, and summarize datasets, Measure data by points in time, whether it’s specific instances, fixed periods, or intervals, Learn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples
Python for Data Analysis
这本书主要是用 pandas 连接 SciPy 和 NumPy，用pandas做数据处理是Pycon2012上一个很热门的话题。另一个功能强大的东西是Sage，它将很多开源的软件集成到统一的 Python 接口。, Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you’ll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language., Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It’s ideal for analysts new to Python and for Python programmers new to scientific computing., Use the IPython interactive shell as your primary development environment, Learn basic and advanced NumPy (Numerical Python) features, Get started with data analysis tools in the pandas library, Use high-performance tools to load, clean, transform, merge, and reshape data, Create scatter plots and static or interactive visualizations with matplotlib, Apply the pandas groupby facility to slice, dice, and summarize datasets, Measure data by points in time, whether it’s specific instances, fixed periods, or intervals, Learn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples
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