2018年python薪资

我打算发布此收藏集 (My intention with publishing this collection)

Last year I only used Medium for consuming content, and I checked out a ton of Python-related articles. Recently I’ve started to use the community features of the platform, for instance following fellow developers. I also developed the practice of clapping and highlighting the most interesting parts of their articles. My goal is to be an active member of the developer community gathered on Medium.

去年，我只使用Medium来消费内容 ，并且查阅了大量与Python有关的文章。 最近，我开始使用社区 平台的功能 ，例如跟随其他开发人员。 我还开发了拍手并突出显示文章中最有趣的部分的做法。 我的目标是成为活跃在Medium上的开发人员社区的活跃成员 。

I also realized that I would like to give back to the community after reading so many great resources. This was one of my main motivations for writing my first article “Why you need Python environments and how to manage them with Conda”.

我还意识到，在阅读了很多宝贵的资源之后，我想回馈社区 。 这是我撰写第一篇文章“为什么需要Python环境以及如何使用Conda管理它们”的主要动机之一。

In this article, I’d like to share with you the articles I found most interesting and insightful (inspiring) last year and this year (so far). My other goal was to create a comprehensive list of the most valuable pieces for my Python students.

在本文中，我想与大家分享去年和今年(到目前为止)我发现最有趣和最有见地(鼓舞人心)的文章 。 我的另一个目标是为我的Python学生创建一份最有价值的作品的综合清单。

如何浏览本文 (How to navigate this article)

I bookmarked so many great resources that it was not easy to select the best ones. So I divided the article into 10 categories — this, by the way, resonates well with the versatile and multipurpose nature of Python.

我收藏了很多很棒的资源，以至于很难选择最好的资源。 因此，我将文章分为10类 -顺便说一句，这与Python的多功能性和多用途性产生了很好的共鸣。

The categories are:

类别为：

1. General Python programming

1.通用Python编程

2. Python performance optimization

2. Python性能优化

3. Python development environments and DevOps

3. Python开发环境和DevOps

4. Machine learning

4.机器学习

5. Image and video processing

5.图像和视频处理

6. Chatbots and Natural language processing (NLP)

6.聊天机器人和自然语言处理(NLP)

7. Blockchain

7.区块链

8. Web and backend development

8. Web和后端开发

9. Web scraping

9.网页抓取

10. Data visualization

10.数据可视化

Just one more thing before you dive in: how should you use this article? It doesn’t need to be read all at once. Bookmark it, and use it as a starting point or a reference point. With the category list above, you can navigate to the sections that you are interested in most.

在深入之前，还需要做一件事： 您应该如何使用本文 ？ 不需要一次全部读取它。 将其添加为书签 ，并将其用作起点或参考点。 使用上面的类别列表，您可以导航到最感兴趣的部分。

And please let me know in a comment if you feel that I left out an awesome resource so I can update my collection. Thanks in advance!

如果您觉得我遗漏了很棒的资源，以便可以更新自己的收藏集，请在评论中让我知道 。 提前致谢！

1.通用Python编程 (1. General Python programming)

1.1学习TK ：从零到英雄，由TK (1.1 Learning Python: From Zero to Hero, by TK)

A comprehensive introduction to Python, this is a must-read if you are new to this world. It explains the basics: variables, control flow, looping and iteration, collections, arrays, structures and dictionaries. It covers the foundations of object oriented programming, too. So if you’ve just started your Python developer journey, this is a great starting point.

全面介绍Python，如果您是这个世界的新手，那么这是必读的。 它解释了基础知识 ：变量，控制流，循环和迭代，集合，数组，结构和字典。 它还涵盖了面向对象编程的基础。 因此，如果您刚刚开始Python开发人员之旅，那么这是一个很好的起点。

Learning Python: From Zero to HeroFirst of all, what is Python? According to its creator, Guido van Rossum, Python is a:medium.freecodecamp.org

学习Python：从零到英雄 首先，什么是Python？ 根据其创建者Guido van Rossum的说法，Python是： medium.freecodecamp.org

1.2通过mingrammer理解Python的下划线(_) (1.2 Understanding the underscore( _ ) of Python, by mingrammer)

Did you know that the underscore (_) in Python has special meanings? It has five different use cases. Check them out in this article!

您是否知道Python中的下划线 (_)具有特殊含义？ 它有五个不同的用例。 在本文中查看它们！

Understanding the underscore( _ ) of PythonI’m not a native speaker. Sorry for my english. Please understand.hackernoon.com

了解Python的下划线(_) 我不是母语人士。 对不起我的英语不好。 请理解。 hackernoon.com

1.3 Anthony Shaw的Python 3.7数据类简介 (1.3 A brief tour of Python 3.7 data classes, by Anthony Shaw)

Data classes is a brand new feature of Python 3.7. It reduces the boilerplate in case of creating a class with typed data fields. The article provides an easy-to-follow explanation and several examples of this feature.

数据类是Python 3.7的全新功能。 在创建带有类型化数据字段的类的情况下，它减少了样板。 本文提供了易于理解的解释以及此功能的几个示例。

A brief tour of Python 3.7 data classesA Brand-new feature in Python 3.7 is “Data Classes”. Data classes are a way of automating the generation of…hackernoon.com

Python 3.7数据类的简要介绍Python 3.7的 一项新功能是“数据类”。 数据类是自动生成…… hackernoon.com的一种方法。

1.4如何在Python 3.6中使用静态类型检查，作者Adam Geitgey (1.4 How to Use Static Type Checking in Python 3.6, by Adam Geitgey)

As of Python 3.6, there is a syntax for declaring types. However, you need an external tool like mypy or PyCharm to enforce type checking. This article is a good starting point to learn how to implement static types in your code.

从Python 3.6开始，有一种用于声明类型的语法。 但是，您需要使用mypy或PyCharm之类的外部工具来执行类型检查。 本文是学习如何在代码中实现静态类型的一个很好的起点。

How to Use Static Type Checking in Python 3.6Automatically catch many common errors while codingmedium.com

如何在Python 3.6中使用静态类型检查在 编码 medium.com时自动捕获许多常见错误

1.5如何(以及为什么)应该使用Radu Raicea的 Python生成器 (1.5 How — and why — you should use Python Generators, by Radu Raicea)

This tutorial showcases examples of an iterator class and different types of generator functions.

本教程展示了迭代器类和不同类型的生成器函数的示例。

“Generator functions allow you to declare a function that behaves like an iterator. They allow programmers to make an iterator in a fast, easy, and clean way.”

“ 生成器函数使您可以声明行为类似于迭代器的函数。 它们使程序员可以快速，轻松且干净的方式制作迭代器。”

“An iterator is an object that can be iterated (looped) upon. It is used to abstract a container of data to make it behave like an iterable object. You probably already use a few iterable objects every day: strings, lists, and dictionaries to name a few.”

“ 迭代器是可以对其进行迭代(循环)的对象。 它用于抽象数据容器，使其表现得像可迭代对象。 您可能已经每天使用一些可迭代的对象：字符串，列表和字典等。”

How — and why — you should use Python GeneratorsGenerators have been an important part of Python ever since they were introduced with PEP 255.medium.freecodecamp.org

自从PEP 255引入以来， 生成 器就一直是Python的重要组成部分 。medium.freecodecamp.org

1.6 Brendan Fortuner撰写的 Python线程和进程简介 (1.6 Intro to Threads and Processes in Python, by Brendan Fortuner)

This and the following article (1.7) are about threading and parallel processing in Python. This piece is an introduction to Python’s parallel processing features with processes and threads, and the second article covers more advanced stuff.

本文和以下文章(1.7)有关Python中的线程和并行处理 。 本文是对Python的具有进程和线程的并行处理功能的介绍，第二篇文章介绍了更高级的内容。

Intro to Threads and Processes in PythonBeginner’s guide to parallel programmingmedium.com

Python线程和进程简介 并行编程 medium.com 入门指南

1.7让我们在Python中同步线程，作者： Saurabh Chaturvedi (1.7 Let’s Synchronize Threads in Python, by Saurabh Chaturvedi)

A great overview of multi threading and its most challenging aspect: thread synchronization and communication.

多线程及其最具挑战性的方面概述：线程同步和通信 。

Let’s Synchronize Threads in PythonBecause synchrony is harmonyhackernoon.com

让我们在Python中同步线程， 因为同步是和谐​​的 hackernoon.com

1.8如何在数据科学中编写生产级代码？ 由Venkatesh Pappakrishnan博士 (1.8 How to write a production-level code in Data Science? by Venkatesh Pappakrishnan, Ph.D.)

This article contains suggestions on creating production ready code for data science purposes. It helps you organize and optimize your code, covers the topics of logging, instrumentation and testing, describes the basics of version controlling, and gives directives on code readability. Great advice and best practices!

本文包含有关为数据科学目的创建生产就绪代码的建议。 它可以帮助您组织和优化代码，涵盖日志记录，检测和测试的主题，描述版本控制的基础，并提供有关代码可读性的指令。 好的建议和最佳做法！

How to write a production-level code in Data Science?Ability to write a production-level code is one of the sought-after skills for a data scientist role— either posted…towardsdatascience.com

如何在数据科学中编写生产级代码？ 编写生产级代码的能力是数据科学家角色的抢手技能之一-要么发布，要么对…指向datascience.com

1.9如何在Pandas等中重写SQL查询，作者： Irina Truong (1.9 How to rewrite your SQL queries in Pandas, and more, by Irina Truong)

If you are new to Pandas and DataFrames, and you have a good understanding of SQL, I highly recommend reading this article. It contains a valuable phrasebook with examples. It helps you to translate your SQL query ideas to Pandas’s syntax and to learn this new syntax.

如果您不熟悉Pandas和DataFrames，并且对SQL有很好的了解，我强烈建议您阅读本文。 它包含一个有价值的短语手册和示例。 它可以帮助您将SQL查询思想转换为Pandas的语法，并学习此新语法。

How to rewrite your SQL queries in Pandas, and moreFifteen years ago, there were only a few skills a software developer would need to know well, and he or she would have…codeburst.io

如何在Pandas中重写SQL查询，以及 15年前，软件开发人员仅需要了解一些技能，而他或她将拥有…… Codeburst.io

2. Python性能优化 (2. Python performance optimization)

2.1是的，Python很慢，我不在乎，作者： Nick Humrich (2.1 Yes, Python is Slow, and I Don’t Care, by Nick Humrich)

Python developers achieve high productivity, but we’ve all heard the myth before: Python is slow. I find this article important since it explains Python’s performance optimization features.

Python开发人员实现了高生产率，但是我们之前都听过一个神话：Python很慢。 我发现这篇文章很重要，因为它解释了Python的性能优化功能。

“Run time is no longer your most expensive resource. A company’s most expensive resource is now its employee’s time.”

“运行时间不再是您最昂贵的资源。 现在，公司最昂贵的资源就是员工的时间。”

Yes, Python is Slow, and I Don’t CareA rant on sacrificing performance for productivity.hackernoon.com

是的，Python很慢，我不在乎 为提高性能而牺牲性能。 hackernoon.com

2.2 Sofia Heisler撰写的《优化速度熊猫代码入门指南》 (2.2 A Beginner’s Guide to Optimizing Pandas Code for Speed, by Sofia Heisler)

In case of larger volumes of data processed by Pandas, you should use carefully-chosen coding solutions in order to improve the performance.

如果熊猫处理大量数据，则应使用经过精心选择的编码解决方案，以提高性能。

This and the following article review several methodologies for applying a function to a Pandas DataFrame and compare their running speeds.

本文和以下文章回顾了将函数应用于Pandas DataFrame的几种方法，并比较了它们的运行速度。

A Beginner’s Guide to Optimizing Pandas Code for SpeedIf you’ve done any data analysis in Python, you’ve probably run across Pandas, a fantastic analytics library written by…engineering.upside.com

优化Pandas代码以提高速度的初学者指南 如果您已经用Python完成了任何数据分析，则可能会跨过Pandas，这是一个由... engineering.upside.com 编写的出色的分析库。

2.3 Python中的数据预处理： Ernest Kim撰写的Dask和Numba应用了我对并行化的热爱 (2.3 Data Pre-Processing in Python: How I learned to love parallelized applies with Dask and Numba, by Ernest Kim)

Some more methodologies for applying a function to a Pandas DataFrame. These methods use parallelization to gain more speed.

更多将函数应用于Pandas DataFrame的方法 。 这些方法使用并行化来提高速度。

Data Pre-Processing in Python: How I learned to love parallelized applies with Dask and NumbaTL:DRtowardsdatascience.com

Python中的数据预处理：我如何学习热爱并行化，适用于Dask和Numba TL：DR对 datascience.com

2.4 Python中并行IO操作的内存效率，作者： Jakub Wolf (2.4 Memory efficiency of parallel IO operations in Python, by Jakub Wolf)

This article compares the memory efficiency of three methods for parallelizing IO-bound operations. The newest method is the asyncio module. It is a part of the standard Python library since Python 3.5. It is a good choice if your goal is to have high performance with low memory footprint.

本文比较了三种并行化IO绑定操作的内存效率。 最新的方法是asyncio模块。 自Python 3.5以来，它是标准Python库的一部分。 如果您的目标是在低内存占用的情况下获得高性能，那么这是一个不错的选择。

Memory efficiency of parallel IO operations in PythonPython allows for several different approaches to parallel processing. The main issue with parallelism is knowing its…code.kiwi.com

Python中并行IO操作的内存效率 Python允许使用几种不同的并行处理方法。 并行性的主要问题是了解并行性 。code.kiwi.com

2.5正则表达式需要5天才能运行。 所以我建立了一个可以在15分钟内完成的工具，作者是Vikash Singh (2.5 Regex was taking 5 days to run. So I built a tool that did it in 15 minutes, by Vikash Singh)

This piece explains how to search and replace keywords in high volumes of data using the Aho-Corasick algorithm and the Trie Data Structure approach. I was amazed by this clever optimization.

本文介绍了如何使用Aho-Corasick算法和Trie数据结构方法搜索和替换大量数据中的关键字 。 如此巧妙的优化让我感到惊讶。

Regex was taking 5 days to run. So I built a tool that did it in 15 minutes.FlashText is 28x faster than Complied Regexmedium.freecodecamp.org

正则表达式需要5天才能运行。 因此，我构建了一个可以在15分钟内完成操作的工具。 FlashText比 Compreged Regex medium 快 28倍。freecodecamp.org

2.6在Instagram上取消Python垃圾收集，作者是Instagram Engineering (2.6 Dismissing Python Garbage Collection at Instagram, by Instagram Engineering)

This article shows an advanced optimization technique for multi-process Python applications.

本文介绍了用于多进程Python应用程序的高级优化技术 。

Dismissing Python Garbage Collection at InstagramBy dismissing the Python garbage collection (GC) mechanism, which reclaims memory by collecting and freeing unused…engineering.instagram.com

在Instagram上取消Python垃圾收集 通过关闭Python垃圾收集(GC)机制，该机制通过收集和释放未使用的内存来回收内存... engineering.instagram.com

PawełPiotr Przeradowski使用Python每秒处理2.7百万个请求 (2.7 A million requests per second with Python, by Paweł Piotr Przeradowski)

Introductory article to an amazingly fast, new framework for micro-services: Japronto.

Japronto ，一个关于微服务的快速，新的框架的介绍性文章。

A million requests per second with PythonIs it possible to hit a million requests per second with Python? Probably not until recently.medium.freecodecamp.org

使用Python每秒处理一百万个请求 是否可以使用Python每秒处理一百万个请求？ 大概要等到最近。 medium.freecodecamp.org

3. Python开发环境和DevOps (3. Python development environments and DevOps)

3.1安装PyCharm和Anaconda(Windows / Mac / Ubuntu)，作者Michael Galarnyk (3.1 Install PyCharm and Anaconda (Windows /Mac/Ubuntu), by Michael Galarnyk)

This tutorial is a great starting point for beginners. It summarizes the installation process and also contains a ten-minute-long video, that walks you through each step.

对于初学者来说，本教程是一个很好的起点 。 它概述了安装过程，还包含一个十分钟长的视频，引导您完成每个步骤。

Install PyCharm and Anaconda (Windows /Mac/Ubuntu)This tutorial is split into three sections. The first part is installing PyCharm. The second part is testing your…medium.com

安装PyCharm和Anaconda(Windows / Mac / Ubuntu) 本教程分为三个部分。 第一部分是安装PyCharm。 第二部分正在测试您的… medium.com

3.2 Docker教程— Python，Redis和Nginx入门，作者： Roman Gaponov (3.2 Docker Tutorial — Getting Started with Python, Redis, and Nginx, by Roman Gaponov)

This article explains how you can benefit from Docker in your software development process. Great piece for beginners!

本文介绍了如何在软件开发过程中从Docker中受益。 对于初学者来说很棒！

Docker is an open source tool that automates the deployment of the application inside software container.

Docker是一个开源工具，可自动在软件容器内部署应用程序。

Docker Tutorial — Getting Started with Python, Redis, and Nginx.Hey!hackernoon.com

Docker教程— Python，Redis和Nginx入门。 嘿! hackernoon.com

3.3如何为Python Web Apps编写Dockerfile，作者Praveen Durairaj (3.3 How to write Dockerfiles for Python Web Apps, by Praveen Durairaj)

An excellent guide with sample Dockerfiles — check it out if you are building Python web apps using Docker!

优秀的示例Dockerfile指南-如果您正在使用Docker构建Python Web应用程序，请查看！

How to Write Dockerfiles for Python Web AppsStep by step guide to writing highly optimized Dockerfiles for Python web app development and deployment.blog.hasura.io

如何为Python Web App编写Dockerfiles 的逐步指南，为Python Web App开发和部署编写高度优化的Dockerfiles。 blog.hasura.io

3.4在3分钟内开始使用PySpark和Jupyter Notebook，作者： Charles Bochet (3.4 Get Started with PySpark and Jupyter Notebook in 3 Minutes, by Charles Bochet)

Apache Spark is a big data processing engine that can be used from Python with the PySpark library. This library is great for creating prototypes on the big data and machine learning field. This guide kickstarts you on the path of using Spark from Python.

Apache Spark是一个大数据处理引擎，可以将Python与PySpark库一起使用。 该库非常适合在大数据和机器学习领域创建原型。 本指南将引导您开始使用Python中的Spark。

Get Started with PySpark and Jupyter Notebook in 3 MinutesApache Spark is a must for Big data’s lovers. In a few words, Spark is a fast and powerful framework that provides an…blog.sicara.com

在3分钟内开始使用PySpark和Jupyter Notebook， 对于大数据爱好者来说，Apache Spark是必须的。 简而言之，Spark是一个快速而强大的框架，它提供了一个... blog.sicara.com

3.5 JupyterLab的第一印象， Brian Ray (3.5 JupyterLab first impressions, by Brian Ray)

Jupyter is such an essential tool for Python programmers. Here is a nice article about its newest version.

Jupyter对于Python程序员来说是一个必不可少的工具。 这是一篇有关其最新版本的不错的文章。

JupyterLab first impressionsI’m not new to the Python evolution from the c Python shell, to IPython, to IPython notebooks, to Jupyter, and now…medium.com

JupyterLab给人的第一印象是， 我对从c Python外壳，IPython，IPython笔记本，Jupyter以及现在的Python演变并不陌生。

4.机器学习 (4. Machine learning)

4.1 Conor Dewey撰写的《 Python机器学习旅行者指南》 (4.1 The Hitchhiker’s Guide to Machine Learning in Python, by Conor Dewey)

This article showcases sample codes and explanatory videos for eight machine learning algorithms: Linear Regression, Logistic Regression, Decision Trees, Support Vector Machines, K-Nearest Neighbors, Random Forests, K-Means Clustering, and Principal Components Analysis. One of my favorite articles, a must-read for beginners.

本文展示了8种机器学习算法的示例代码和说明性视频：线性回归，逻辑回归，决策树，支持向量机，K最近邻，随机森林，K均值聚类和主成分分析。 我最喜欢的文章之一，是初学者必读的文章。

The Hitchhiker’s Guide to Machine Learning in PythonFeaturing implementation code, instructional videos, and moremedium.freecodecamp.org

《 Hitchhiker的Python机器学习指南》，其中包含 实现代码，教学视频以及更多 medium.freecodecamp.org

4.2如果学习数学系列，学习人工智能，作者丹尼尔·杰弗里斯 ( Daniel Jeffries) (4.2 Learning AI if You Suck at Math series, by Daniel Jeffries)

An excellent series of 7 articles (thanks Daniel Jeffries for your effort!). The title is quite self-evident: not being great at math does not mean that you can’t understand how artificial intelligence works! Give it a go — it’s really worth it!

精彩的7篇文章系列 (感谢Daniel Jeffries的努力！)。 这个标题是不言而喻的：不擅长数学并不意味着您无法理解人工智能的工作原理！ 试试吧-真的值得！

You suck at math.

你真讨厌数学。

That’s all right! I share your dirty little secret and I have some books and websites that will really help you get rolling fast.

没关系！ 我分享了您的肮脏小秘密，我有一些书和网站可以真正帮助您快速上手。

Learning AI if You Suck at Math — Part 1This article guides you through the essential books to read if you were never a math fan but you’re learning it as adult.hackernoon.comLearning AI if You Suck at Math — Part 2 — Practical ProjectsThis article guides you through getting started with your first projects.hackernoon.comLearning AI if You Suck at Math — Part 3 — Building an AI Dream Machine or Budget Friendly SpecialThis article guides you through getting a powerful deep learning machine setup and installed with all the latest and greatest frameworks.hackernoon.comLearning AI if You Suck at Math — Part 4 — Tensors Illustrated (with Cats!)This one answers the ancient mystery: What the hell is a tensor?hackernoon.comLearning AI if You Suck at Math — Part 5— Deep Learning and Convolutional Neural Nets in Plain…Here we create our first Python program and explore the inner workings of neural networks!hackernoon.comLearning AI If You Suck at Math — Part 6 — Math Notation Made Easy!Still struggling to understand those funny little symbols? Let’s change that now!hackernoon.comLearning AI if You Suck at Math — Part 7 — The Magic of Natural Language ProcessingUnderstand how Google and Siri understand what you’re mumbling.hackernoon.com

学习数学，学习人工智能—第1部分 如果您从不曾是数学迷，但您正在成年学习，本文将指导您阅读基本书籍。 hackernoon.com 学习数学的人工智能-第2部分-实际项目 本文将指导您开始第一个项目。 hackernoon.com 学习数学，如果 想 学习人工智能-第3部分-构建AI Dream Machine或预算友好的特殊 文章本文将指导您完成功能强大的深度学习机器的设置并安装所有最新和最出色的框架。 hackernoon.com 学习数学，如果 爱上 数学-第4部分-张量图(与猫！) 这回答了一个古老的奥秘：张量到底是什么？ hackernoon.com 学习数学 就算 学习AI —第5部分—平原的深度学习和卷积神经网络… 在这里，我们创建了第一个Python程序，并探索了神经网络的内部工作原理！ hackernoon.com 学习数学，如果您喜欢数学-第6部分-简化数学符号！ 还在努力理解那些有趣的小符号吗？ 让我们现在改变它！ hackernoon.com 学习数学 就算 学习AI —第7部分—自然语言处理的魔力 理解Google和Siri如何理解您所抱怨的内容。 hackernoon.com

4.3机器学习从零开始：逐步进行Kaggle竞争所需的一切！ 由Oren Dar (4.3 Machine Learning Zero-to-Hero: Everything you need in order to compete on Kaggle for the first time, step-by-step! by Oren Dar)

Participating in Kaggle competitions is a great way to improve your machine learning skills. This guide shows you how to solve one of Kaggle’s machine learning problems and submit your results to the competition.

参加Kaggle比赛是提高机器学习技能的好方法。 本指南向您展示如何解决Kaggle的机器学习问题之一，以及如何将结果提交给竞赛。

Machine Learning Zero-to-Hero: Everything you need in order to compete on Kaggle for the first…If you’ve always wanted to learn more about Machine Learning and see how easy it is to compete on Kaggle, then this…towardsdatascience.com

机器学习零到零：您需要的一切，才能首次在Kaggle上竞争… 如果您一直想了解有关机器学习的更多信息，并了解在Kaggle上竞争的难易程度，那么……向datascience.com

4.4 TensorFlow中的高级API，作者Peter Roelants (4.4 Higher-Level APIs in TensorFlow, by Peter Roelants)

TensorFlow 1.3 introduces three new higher-level frameworks: Estimator, Experiment, and Dataset. This post explains them and shows an example usage.

TensorFlow 1.3引入了三个新的高级框架： Estimator ， Experiment和Dataset 。 这篇文章解释了它们，并显示了用法示例。

Higher-Level APIs in TensorFlowHow to use Estimator, Experiment and Dataset to train modelsmedium.com

TensorFlow中的高级API 如何使用估算器，实验和数据集来训练模型 medium.com

4.5部署Frances Zuppichini的 TensorFlow模型 (4.5 Deploy TensorFlow models, by Francesco Zuppichini)

This tutorial shows you how to make your TensorFlow models accessible from the web with Flask and TensorFlow serving.

本教程将向您展示如何通过Flask和TensorFlow服务从网络上访问TensorFlow模型。

Okay, you have a model and you want to make it accessible from the web. There are several ways you can do that, but the faster and the most robust is TensorFlow serving.

好的，您有一个模型，并且想要使其可以从Web上访问。 您可以通过多种方法来执行此操作，但是更快，最可靠的是TensorFlow服务。

Deploy TensorFlow modelsSuper fast and concise tutorialtowardsdatascience.com

部署TensorFlow模型 超级快速，简洁的教程

4.6 Python中的简单多元线性回归，作者： Adi Bronshtein (4.6 Simple and Multiple Linear Regression in Python, by Adi Bronshtein)

Linear regression is an approach for modelling the linear relationship between a dependent variable and one or more independent variables. It is one of the most widely used algorithms of machine learning.

线性回归是一种建模因变量和一个或多个自变量之间的线性关系的方法。 它是最广泛使用的机器学习算法之一 。

This article explains the mathematical basics of linear regression, then provides examples using Python’s Statsmodels and Scikit-Learn libraries. If you consider yourself a beginner, I highly recommend reading this article.

本文介绍了线性回归的数学基础，然后提供了使用Python的Statsmodels和Scikit-Learn库的示例。 如果您认为自己是初学者，我强烈建议您阅读本文。

Simple and Multiple Linear Regression in PythonQuick introduction to linear regression in Pythontowardsdatascience.com

Python中的简单和多元线性回归Python中线性回归的 快速入门对 datascience.com

4.7通过降维技术减少降维，作者： Elior Cohen (4.7 Reducing Dimensionality from Dimensionality Reduction Techniques, by Elior Cohen)

In this post, Elior demystifies three dimensionality reduction techniques: PCA, t-SNE, and Auto Encoders.

在本文中，Elior揭开了降维技术的神秘面纱： PCA，t-SNE和自动编码器 。

“The need to reduce dimensionality is often associated with visualizations (reducing to 2–3 dimensions so we can plot it) but that is not always the case. Sometimes we might value performance over precision so we could reduce 1,000 dimensional data to 10 dimensions so we can manipulate it faster (eg. calculate distances).”

“降低尺寸的需求通常与可视化相关(缩小到2–3尺寸，以便我们可以对其进行绘制)，但并非总是如此。 有时我们可能会重视性能而不是精度，因此我们可以将1,000维数据缩减为10维，以便我们可以更快地进行操作(例如，计算距离)。”

Reducing Dimensionality from Dimensionality Reduction Techniquestowardsdatascience.com

从 降 维技术到 datascience.com 降低维 数

4.8 Python中的随机森林，作者： William Koehrsen (4.8 Random Forest in Python, by William Koehrsen)

This step by step guide shows how to solve a machine learning problem with random forests. It is very detailed, explains how to prepare and clean the data, then creates and improves a model, finally visualizes the results. Awesome stuff.

本循序渐进的指南介绍了如何解决随机森林中的机器学习问题。 它非常详细，说明了如何准备和清除数据，然后创建和改进模型，最后将结果可视化 。 很棒的东西。

Random Forest in PythonA Practical End-to-End Machine Learning Exampletowardsdatascience.com

Python中 的 随机森林： 一个实用的端到端机器学习示例，朝向datadata.com

4.9使用Python逐步构建Logistic回归，作者Susan Li (4.9 Building A Logistic Regression in Python, Step by Step, by Susan Li)

There are many concepts in the field of machine learning to master. One of these is logistic regression. This article provides a great starting point, and I’ve recommended it several times to my students.

机器学习领域有许多概念需要掌握。 其中之一就是逻辑回归 。 本文提供了一个很好的起点，并且我已经向我的学生推荐了几次。

“Logistic Regression is a Machine Learning classification algorithm that is used to predict the probability of a categorical dependent variable. In logistic regression, the dependent variable is a binary variable that contains data coded as 1 (yes, success, etc.) or 0 (no, failure, etc.).”

Logistic回归是一种机器学习分类算法，用于预测分类因变量的概率。 在逻辑回归中，因变量是一个二进制变量，其中包含编码为1(是，成功等)或0(否，失败等)的数据。”

Building A Logistic Regression in Python, Step by Step( This article first appeared on Datascience+)towardsdatascience.com

使用Python逐步构建Logistic回归 (本文首次出现在Datascience +上)朝向datascience.com

4.10了解特征工程，作者Dipanjan Sarkar (4.10 Understanding Feature Engineering, by Dipanjan Sarkar)

This series introduces you to feature engineering, one of the most important tasks of a data scientist. What is the essence of feature engineering? Here is a great quote:

本系列向您介绍功能工程 ，这是数据科学家最重要的任务之一 。 特征工程的本质是什么？ 这是一个很好的报价：

“Coming up with features is difficult, time-consuming, requires expert knowledge. ‘Applied machine learning’ is basically feature engineering.”

“提出功能非常困难，耗时，需要专业知识。 “应用机器学习”基本上是功能工程。”

— Prof. Andrew Ng.

—吴安德教授

Understanding Feature Engineering (Part 1) — Continuous Numeric DataStrategies for working with continuous, numerical datatowardsdatascience.comUnderstanding Feature Engineering (Part 2) — Categorical DataStrategies for working with discrete, categorical datatowardsdatascience.comUnderstanding Feature Engineering (Part 3) — Traditional Methods for Text DataTraditional strategies for taming unstructured, textual datatowardsdatascience.comUnderstanding Feature Engineering (Part 4) — Deep Learning Methods for Text DataNewer, advanced strategies for taming unstructured, textual datatowardsdatascience.com

理解特征工程(第1部分)- 用于处理 连续的数值数据的连续数值数据 策略，朝向datascience.com 理解特征工程(第2部分)- 用于处理离散，分类数据的 分类数据 策略，朝向datascience.com 理解特征工程(第3部分) —文本数据的传统方法面向 非结构化 文本数据的 传统策略，趋向datascience.com 理解特征工程(第4部分)—文本数据的深度学习方法面向 非数据结构的 文本数据的 新型高级策略

4.11成为数据科学家应该知道的十种机器学习算法，作者Shashank Gupta (4.11 Ten Machine Learning Algorithms You Should Know to Become a Data Scientist, by Shashank Gupta)

This article showcases ten useful machine learning algorithms, explains the basic concepts of them, and suggests Python libraries and introductory tutorials to start with.

本文展示了十种有用的机器学习算法，解释了它们的基本概念，并提出了Python库和入门教程 。

Ten Machine Learning Algorithms You Should Know to Become a Data ScientistMachine Learning Practitioners have different personalities. While some of them are “I am an expert in X and X can…towardsdatascience.com

成为数据科学家时应该知道的十种机器学习算法 机器学习从业者具有不同的个性。 虽然其中一些人是“我是X方面的专家，但X可以……朝向datascience.com

4.12开放机器学习课程。 主题1. Yury Kashnitskiy的“ Pandas探索性数据分析” (4.12 Open Machine Learning Course. Topic 1. Exploratory Data Analysis with Pandas, by Yury Kashnitskiy)

I believe in knowledge sharing, and The Machine Learning Course by OpenDataScience is an awesome resource. If you are interested in the series, you can find the topics at the beginning of this article.

我相信知识共享，并且OpenDataScience的机器学习课程是很棒的资源。 如果您对该系列感兴趣，可以在本文开头找到主题。

Open Machine Learning Course. Topic 1. Exploratory Data Analysis with PandasWith this article, we, OpenDataScience, launch an open Machine Learning course. This is not aimed at developing another…medium.com

打开机器学习课程。 主题1.使用Pandas进行探索性数据分析 在本文中，我们OpenDataScience启动了开放式机器学习课程。 这不是为了开发另一个… media.com

4.13 Python中的时间序列分析：简介，作者： William Koehrsen (4.13 Time Series Analysis in Python: An Introduction, by William Koehrsen)

This article is a good introduction to time series analysis. It contains an example evaluation of the Tesla and GM stock prices, and builds a forecast model with the Facebook Prophet package.

本文是时间序列分析的很好的介绍。 它包含对特斯拉和通用汽车股价的评估示例，并使用Facebook Prophet软件包构建了预测模型。

Time Series Analysis in Python: An IntroductionAdditive models for time series modelingtowardsdatascience.com

Python中的时间序列分析：简介 用于时间序列建模的加性模型对 datascience.com

5.图像和视频处理 (5. Image and video processing)

5.1我如何使用Python中的深度学习实现iPhone X的FaceID，作者Norman Di Palo (5.1 How I implemented iPhone X’s FaceID using Deep Learning in Python, by Norman Di Palo)

The algorithm behind Apple’s FaceID is proprietary. This article analyzes how this feature might work and shows a proof-of-concept implementation of FaceID using siamese convolutional networks.

苹果公司的FaceID背后的算法是专有的。 本文分析了此功能的工作方式，并展示了使用暹罗卷积网络的FaceID的概念验证实现。

How I implemented iPhone X’s FaceID using Deep Learning in Python.Reverse engineering iPhone X’s new unlocking mechanism.towardsdatascience.com

我如何使用Python深度学习实现iPhone X的FaceID。 反向工程iPhone X的新解锁机制。 endingdatascience.com

5.2使用TensorFlow跟踪千年猎鹰( Nick Bourdakos) (5.2 Tracking the Millennium Falcon with TensorFlow, by Nick Bourdakos)

By following this tutorial, you’ll be able to learn how to build you own TensorFlow-based custom object detector building on the COCO dataset.

通过遵循本教程，您将能够学习如何在COCO数据集上构建自己的基于TensorFlow的自定义对象检测器 。

Tracking the Millennium Falcon with TensorFlowAt the time of writing this post, most of the big tech companies (such as IBM, Google, Microsoft, and Amazon) have…medium.freecodecamp.org

使用TensorFlow跟踪千禧猎鹰 在撰写本文时，大多数大型科技公司(例如IBM，Google，Microsoft和Amazon)都具有… medium.freecodecamp.org

5.3使用深度学习改善FIFA 18图形， Chintan Trivedi (5.3 Using Deep Learning to improve FIFA 18 graphics, by Chintan Trivedi)

The concept of face-swapping done by deep neural networks has been developed recently. One of the most well-known algorithms is Deepfakes, and its results has even been featured in the mainstream media.

由深度神经网络完成的面部交换概念最近得到了发展。 Deepfakes是最著名的算法之一 ，其结果甚至已经在主流媒体中得到了报道。

This article shows how it could be used in the game industry, and explains the basics of the algorithm. I believe that the key takeaway is that deep learning can be applied in almost any industry / in any field.

本文介绍了如何在游戏行业中使用它，并解释了该算法的基础。 我认为，关键的收获是深度学习可以应用于几乎任何行业/任何领域 。

Using Deep Learning to improve FIFA 18 graphicsGame Studios spend millions of dollars and thousands of development hours designing game graphics in trying to make…towardsdatascience.com

利用深度学习来改善FIFA 18图形， 游戏工作室花费数百万美元和数千个开发小时来设计游戏图形，以试图使...

5.4使用Keras在Google Compute Engine上进行深度学习，作者： Cole Murray (5.4 Deep Learning with Keras on Google Compute Engine, by Cole Murray)

I’m fascinated by the idea of image recognition. This article takes the learning-by-doing approach, since it explains setting up

我对图像识别的想法着迷。 本文采用边做边学的方法，因为它解释了如何设置

• a Flask-based web application connected to a Keras-supported image recognition and

  基于Flask的Web应用程序，连接到Keras支持的图像识别和

• an image store using Google Cloud Storage使用Google云端存储的图像存储

in a step-by-step manner.

以逐步的方式。

Deep Learning with Keras on Google Compute EngineInception, a model developed by Google is a deep CNN. Against the ImageNet dataset (a common dataset for measuring…medium.com

Google在Google Compute Engine Inception 上使用Keras进行深度学习 ，这是一种由Google开发的深层CNN。 针对ImageNet数据集(用于测量的常见数据集… medium.com

5.5如何在Keras和Tensorflow中使用转移学习和微调来构建图像识别系统并分类(几乎)任何对象，作者Greg Chu (5.5 How to use transfer learning and fine-tuning in Keras and Tensorflow to build an image recognition system and classify (almost) any object, by Greg Chu)

By using a pre-trained network built for a similar task, you can make your convolutional neural network’s training speed faster.

通过使用为类似任务而构建的预训练网络 ，您可以提高卷积神经网络的训练速度。

“It’s well known that convolutional networks require significant amounts of data and resources to train. For example, the ImageNet ILSVRC model was trained on 1.2 million images over the period of 2–3 weeks across multiple GPUs.”

“众所周知，卷积网络需要大量的数据和资源进行训练。 例如，ImageNet ILSVRC模型在2-3个星期的时间内使用多个GPU训练了120万张图像。”

How to use transfer learning and fine-tuning in Keras and Tensorflow to build an image recognition…Go straight to the code on GitHub here!deeplearningsandbox.com

如何在Keras和Tensorflow中使用转移学习和微调来建立图像识别… 在此处直接转到GitHub上的代码！ deeplearningsandbox.com

6.聊天机器人和自然语言处理(NLP) (6. Chatbots and Natural language processing (NLP))

6.1机器学习，NLP： Javed Shaikh使用scikit-learn，python和NLTK进行文本分类 (6.1 Machine Learning, NLP: Text Classification using scikit-learn, python and NLTK, by Javed Shaikh)

Text classification is one of the basic concepts of natural language processing. The tutorial follows these steps:

文本分类是自然语言处理的基本概念之一。 本教程遵循以下步骤：

1. Prerequisite and setting up the environment.

1.前提条件和设置环境。

2. Loading the data set in jupyter.

2.在jupyter中加载数据集。

3. Extracting features from text files.

3.从文本文件中提取特征。

4. Running ML algorithms.

4.运行ML算法。

5. Grid Search for parameter tuning.

5.网格搜索以进行参数调整。

Machine Learning, NLP: Text Classification using scikit-learn, python and NLTK.Latest Update:I have uploaded the complete code (Python and Jupyter notebook) on GitHub…towardsdatascience.com

机器学习，NLP：使用scikit-learn，python和NLTK进行文本分类。 最新更新： 我已经在GitHub上上传了完整的代码(Python和Jupyter笔记本)…朝向datascience.com

6.2使用神经网络的文本分类，由gk_ (6.2 Text Classification using Neural Networks, by gk_)

This article helps you understand how text classification works and demonstrates it using a two layer neural network.

本文可帮助您了解文本分类的工作原理，并使用两层神经网络对其进行演示。

Text Classification using Neural NetworksUnderstanding how chatbots work is important. A fundamental piece of machinery inside a chat-bot is the text…machinelearnings.co

使用神经网络进行文本分类 了解聊天机器人的工作方式非常重要。 聊天机器人内部的基本机械是文本… machinelearnings.co

6.3使用Tensorflow的上下文聊天机器人，作者： gk_ (6.3 Contextual Chatbots with Tensorflow, by gk_)

This post shows how to transform conversational intent definitions to a TensorFlow model and how to build a chatbot framework around it.

这篇文章展示了如何将会话意图定义转换为TensorFlow模型以及如何围绕它构建一个聊天机器人框架。

Contextual Chatbots with TensorflowIn conversations, context is king! We’ll build a chatbot framework using Tensorflow and add some context handling to…chatbotsmagazine.com

具有Tensorflow的上下文聊天机器人 在对话中，上下文为王！ 我们将使用Tensorflow构建一个聊天机器人框架，并向…chatbotsmagazine.com添加一些上下文处理。

6.4如何创建和部署电报机器人 罗马·加蓬诺夫 ( Roman Gaponov) (6.4 How to Create and Deploy a Telegram Bot? by Roman Gaponov)

By following this tutorial, you’ll be able to build a simple Telegram-based chatbot and deploy it on Heroku.

通过遵循本教程，您将能够构建一个简单的基于Telegram的聊天机器人并将其部署在Heroku上 。

How to Create and Deploy a Telegram Bot?Hey!hackernoon.com

如何创建和部署电报机器人？ 嘿! hackernoon.com

6.5我在周末构建了一个无服务器的Telegram机器人。 这是我从摩西·苏 ( Moses Soh)那里学到的 (6.5 I built a serverless Telegram bot over the weekend. Here’s what I learned, by Moses Soh)

Another Telegram-based chatbot example, it shows how to deploy the bot on AWS Lambda with the help of a tool called Zappa. Building something over a weekend sounds fun, doesn’t it? :)

另一个基于Telegram的聊天机器人示例，该示例展示了如何借助名为Zappa的工具在AWS Lambda上部署该机器人。 在一个周末建造东西听起来很有趣，不是吗？ :)

I built a serverless Telegram bot over the weekend. Here’s what I learned.It sends out a SOS to rescuers when someone is stranded in the rain ☔ It’s written in Python using AWS Lambda…medium.freecodecamp.org

我在周末构建了一个无服务器的Telegram机器人。 这是我学到的。 当有人被困在雨中时，它会向救援人员发送SOS☔这是使用AWS Lambda用Python编写的… medium.freecodecamp.org

7.区块链 (7. Blockchain)

7.1让我们构建最细的区块链，作者Gerald Nash⚡️ (7.1 Let’s Build the Tiniest Blockchain, by Gerald Nash ⚡️)

This series and the following article(6.2) help you understand how a blockchain works by building one! I always prefer learning by doing, and that’s why I like these articles so much.

本系列和以下文章(6.2) 通过构建一个区块链来帮助您了解区块链的工作原理 ！ 我一直喜欢边做边学，这就是为什么我如此喜欢这些文章。

Let’s Build the Tiniest BlockchainIn Less Than 50 Lines of Pythonmedium.com

让我们 用少于50行的Python medium.com 构建最细微的区块链

Second part of the article:

文章的第二部分：

Let’s Make the Tiniest Blockchain BiggerPart 2: With More Lines of Pythonmedium.com

让我们使最细的区块链更大： 第2部分：使用更多行的Python medium.com

7.2通过第一层学习区块链， Daniel van Flymen (7.2 Learn Blockchains by Building One, by Daniel van Flymen)

Learn Blockchains by Building OneThe fastest way to learn how Blockchains work is to build onehackernoon.com

通过构建一个区块链 来学习区块链学习区块链如何工作的最快方法是建立一个 hackernoon.com

8. Web和后端开发 (8. Web and backend development)

8.1如何使用Python和Flask构建Web应用程序—深入的教程，作者Abhinav Suri (8.1 How to use Python and Flask to build a web app — an in-depth tutorial, by Abhinav Suri)

A great tutorial for creating a full-stack Python application based on Flask, a Python microframework.

出色的教程，可基于Flask(Python微框架)创建全栈Python应用程序 。

How to use Python and Flask to build a web app — an in-depth tutorialPython is an incredibly versatile language. It’s considered to be a staple of modern development. It’s used for the…medium.freecodecamp.org

如何使用Python和Flask构建Web应用程序—深入的教程 Python是一种用途广泛的语言。 它被认为是现代发展的主要内容。 它用于... medium.freecodecamp.org

8.2使用Python构建微服务，作者Sergio Sola (8.2 Building Microservices with Python, by Sergio Sola)

This piece is divided into 3 parts. The author is a Software Engineer building microservices for a personal project. Sidenote: having personal projects is a great way of developing your skills! This is what the tutorial is about:

这部分分为3部分。 作者是为个人项目构建微服务的软件工程师。 旁注：拥有个人项目是发展技能的好方法！ 这是本教程的内容：

1. Building the skeleton of the microservice:

   构建微服务的框架 ：

Building Microservices with Python , Part ICurrently I am working in my current job as a Software Engineer at HelloFresh on the DataWarehouse Team. I am working…medium.com

使用Python构建微服务，第一部分 目前，我在DataWarehouse团队的HelloFresh担任软件工程师。 我正在工作… medium.com

2. Creating the microservice’s infrastructure in Docker:

2.在Docker中创建微服务的基础架构：

Building Microservices with Python, Part 2In this second article, I am going to explain how to create your development environment for your Python Microservice…medium.com

使用Python构建微服务，第2部分 在第二篇文章中，我将解释如何为Python微服务创建开发环境。

3. Last, but not least, building the microservice’s business logic:

3.最后但并非最不重要的一点是，构建微服务的业务逻辑 ：

Building Microservices with Python, Part 3Building the first endpoint defining a more complex OpenAPI Spec and Elasticsearch, on top of Flask packages.medium.com

使用Python构建微服务，第3部分 在Flask软件包之上构建定义更复杂的OpenAPI Spec和Elasticsearch的第一个端点。 medium.com

8.3使用Django的简单方法ElasticSearch，作者Adam Wattis (8.3 ElasticSearch with Django the easy way, by Adam Wattis)

ElasticSearch is a great tool for implementing free text search. By following this tutorial, you’ll set up an ElasticSearch server, load data into it, and connect it with a Django-based application.

ElasticSearch是实现自由文本搜索的好工具。 通过学习本教程，您将设置一个ElasticSearch服务器，将数据加载到其中，并将其与基于Django的应用程序连接 。

ElasticSearch with Django the easy wayA while back I was working on a Django project and wanted to implement fast free text search. Instead of using a…medium.freecodecamp.org

ElasticSearch与Django的简单方法不久前， 我在Django项目上工作，想要实现快速的自由文本搜索。 而不是使用... medium.freecodecamp.org

9.网页抓取 (9. Web scraping)

9.1如何使用Python和BeautifulSoup抓取网站， Justin Yek (9.1 How to scrape websites with Python and BeautifulSoup, by Justin Yek)

BeautifoulSoup is a helpful utility for extracting data from a HTML page. This article shows how it works.

BeautifoulSoup是用于从HTML页面提取数据的有用实用程序。 本文介绍了它是如何工作的。

How to scrape websites with Python and BeautifulSoupThere is more information on the Internet than any human can absorb in a lifetime. What you need is not access to that…medium.freecodecamp.org

如何使用Python和BeautifulSoup抓取网站 互联网上的信息比任何人一生所吸收的信息还多。 您需要的是无需访问该文件… medium.freecodecamp.org

9.2使用Scrapy构建自己的数据集，作者Michael Galarnyk (9.2 Using Scrapy to Build your Own Dataset, by Michael Galarnyk)

By utilizing Scrapy, you can download websites and extract data from the HTML pages using CSS selectors. It is a fully-fledged solution for web scraping. This article demonstrates how it works by showing an example of scraping data from a real website.

通过使用Scrapy ，您可以使用CSS选择器下载网站并从HTML页面提取数据。 它是用于刮纸的成熟解决方案 。 本文通过显示从真实网站抓取数据的示例来演示其工作原理。

Using Scrapy to Build your Own DatasetWhen I first started working in industry, one of the things I quickly realized is sometimes you have to gather…towardsdatascience.com

使用Scrapy构建您自己的数据集 当我刚开始从事行业工作时，我很快意识到的一件事就是有时您必须聚在一起……朝向datascience.com

9.3 30分钟的Python Web 抓取工具 ，作者： Angelos Chalaris (9.3 30-minute Python Web Scraper, by Angelos Chalaris)

This post explains how to use Selenium webdriver with Geckodriver to open a browser window and control it from Python.

这篇文章解释了如何将Selenium webdriver和Geckodriver一起使用来打开浏览器窗口并从Python控制它。

30-minute Python Web ScraperI’ve been meaning to create a web scraper using Python and Selenium for a while now, but never gotten around to it. A…hackernoon.com

30分钟的Python Web 抓取工具我一直想使用Python和Selenium创建一个Web抓取工具已有一段时间了，但从未尝试过。 A… hackernoon.com

9.4我是如何用Python找到有趣的人跟随Radu Raicea进行研究的 (9.4 How I used Python to find interesting people to follow on Medium, by Radu Raicea)

This article provides a great example for using APIs from Python. I believe that the author addresses an important issue here: it is hard to select the most relevant and useful content from the overwhelming information tsunami — this is, by the way, one of the reasons why I created this article collection.

本文提供了一个使用Python API的绝佳示例。 我相信作者在这里解决了一个重要问题： 很难从压倒性的信息海啸中选择最相关和最有用的内容 -顺便说一下，这就是我创建此文章集的原因之一。

Medium has a large amount of content, a large number of users, and an almost overwhelming number of posts. When you try to find interesting users to interact with, you’re flooded with visual noise.

中型网站包含大量内容，大量用户，并且帖子数量几乎是压倒性的。 当您尝试寻找有趣的用户进行交互时，您会被视觉噪音所淹没。

How I used Python to find interesting people to follow on MediumMedium has a large amount of content, a large number of users, and an almost overwhelming number of posts. When you try…medium.freecodecamp.org

我是如何使用Python查找有趣的人来关注 Medium的，它具有大量的内容，大量的用户，并且帖子数量几乎是巨大的。 当您尝试时… medium.freecodecamp.org

10.数据可视化 (10. Data visualization)

10.1-5个带代码的Python中的快速便捷的数据可视化， George Seif (10.1 - 5 Quick and Easy Data Visualizations in Python with Code, by George Seif)

This article contains a great chart that helps you select the proper data visualization technique for a given situation. Then it takes a closer look at six data visualization types and shows examples of using Python’s Matplotlib for creating these assets: two kinds of scatter plots, line plots, histograms, bar plots and box plots.

本文包含一个不错的图表，可帮助您为给定情况选择适当的数据可视化技术 。 然后，它仔细研究了六种数据可视化类型，并显示了使用Python的Matplotlib创建这些资产的示例 ：两种散点图，线图，直方图，条形图和箱形图。

5 Quick and Easy Data Visualizations in Python with CodeData Visualization is a big part of a data scientist’s jobs. In the early stages of a project, you’ll often be doing an…towardsdatascience.com

5使用代码实现Python的快速简便的数据可视化 数据可视化是数据科学家工作的重要组成部分。 在项目的早期阶段，您通常会做的是…朝向datascience.com

10.2用Python中的Bokeh进行数据可视化，作者： William Koehrsen (10.2 Data Visualization with Bokeh in Python, by William Koehrsen)

The series answers the following question: “How can we add more interactivity to our visualizations?” This tutorial walks you through fully-interactive examples using Python, the Bokeh interactive visualization library, and publicly available datasets.

该系列回答了以下问题：“如何为可视化添加更多交互性？” 本教程将引导您使用Python， 完全互动的例子 背景虚化的交互式可视化库 ，和公开的数据集。

Data Visualization with Bokeh in Python, Part I: Getting StartedElevate your visualization gametowardsdatascience.comData Visualization with Bokeh in Python, Part II: InteractionsMoving beyond static plotstowardsdatascience.comData Visualization with Bokeh in Python, Part III: Making a Complete DashboardCreating an interactive visualization application in Bokehtowardsdatascience.com

数据可视化与背景虚化在Python中，第一部分：入门 提升您的可视化游戏 towardsdatascience.com 数据可视化与背景虚化在Python中，第二部分：互动 超越静态地块 towardsdatascience.com 数据可视化与背景虚化在Python中，第三部分：制作一个完整的仪表板 在Bokeh中创建一个交互式可视化应用程序对 datadata.com

So that is my collection. I’m really grateful for these excellent resources, and many of them have already helped my Python students. I’ve also learned a lot of new tricks along the way.

这就是我的收藏。 我非常感谢这些优秀的资源 ，其中许多资源已经帮助了我的Python学生。 在此过程中，我还学到了许多新技巧。

Respond ? — please let me know in the response section if you have any suggestions or questions!

回应？ —如果您有任何建议或问题，请在回复部分告诉我！

Thanks for reading! ?

谢谢阅读！ ？

And thanks to my wife, Krisztina Szerovay, for creating the cover!

多亏了我的妻子Krisztina Szerovay创造了封面！

翻译自: https://www.freecodecamp.org/news/python-collection-of-my-favorite-articles-8469b8455939/

2018年python薪资