Data Science with Python and Dask by Jesse C. Daniel.epub

About the book
Data Science with Python and Dask teaches you to build scalable projects that can handle massive datasets. After meeting the Dask framework, you’ll analyze data in the NYC Parking Ticket database and use DataFrames to streamline your process. Then, you’ll create machine learning models using Dask-ML, build interactive visualizations, and build clusters using AWS and Docker.

What's inside
+ Working with large, structured and unstructured datasets
+ Visualization with Seaborn and Datashader
+ Implementing your own algorithms
+ Building distributed apps with Dask Distributed
+ Packaging and deploying Dask apps

Data Visualization with Python and JavaScript Scrape, Clean, Explore Transform Your Data.epub

+ Learn how to manipulate data with Python
+ Understand the commonalities between Python and JavaScript
+ Extract information from websites by using Python’s web-scraping tools, BeautifulSoup and Scrapy
+ Clean and explore data with Python’s Pandas, Matplotlib, and Numpy libraries
+ Serve data and create RESTful web APIs with Python’s Flask framework
+ Create engaging, interactive web visualizations with JavaScript’s D3 library

Data Wrangling with Python Tips and Tools to Make Your Life Easier by Jacqueline Kazil, Katharine Jarmul.pdf

Through various step-by-step exercises, you’ll learn how to acquire, clean, analyze, and present data efficiently. You’ll also discover how to automate your data process, schedule file- editing and clean-up tasks, process larger datasets, and create compelling stories with data you obtain.
+ Quickly learn basic Python syntax, data types, and language concepts
+ Work with both machine-readable and human-consumable data
+ Scrape websites and APIs to find a bounty of useful information
+ Clean and format data to eliminate duplicates and errors in your datasets
+ Learn when to standardize data and when to test and script data cleanup
+ Explore and analyze your datasets with new Python libraries and techniques
+ Use Python solutions to automate your entire data-wrangling process

Learn Data Analysis with Python Lessons in Coding by A.J. Henley, Dave Wolf.pdf

Each lesson is, as much as possible, self-contained to allow you to dip in and out of the examples as your needs dictate. If you are already using Python for data analysis, you will find a number of things that you wish you knew how to do in Python. You can then take these techniques and apply them directly to your own projects.
If you aren’t using Python for data analysis, this book takes you through the basics at the beginning to give you a solid foundation in the topic. As you work your way through the book you will have a better of idea of how to use Python for data analysis when you are finished.

What You Will Learn
+ Get data into and out of Python code
+ Prepare the data and its format
+ Find the meaning of the data
+ Visualize the data using iPython

Beginning Data Science with Python and Jupyter by Alex Galea.epub

+ Identify potential areas of investigation and perform exploratory data analysis
+ Plan a machine learning classification strategy and train classification models
+ Use validation curves and dimensionality reduction to tune and enhance your models
+ Scrape tabular data from web pages and transform it into Pandas DataFrames
+ Create interactive, web-friendly visualizations to clearly communicate your findings

Data analysis with Python a modern approach by Taieb, David.epub

Key Features
+ Bridge your data analysis with the power of programming, complex algorithms, and AI
+ Use Python and its extensive libraries to power your way to new levels of data insight
+ Work with AI algorithms, TensorFlow, graph algorithms, NLP, and financial time series
+ Explore this modern approach across with key industry case studies and hands-on projects

What you will learn
+ A new toolset that has been carefully crafted to meet for your data analysis challenges
+ Full and detailed case studies of the toolset across several of today's key industry contexts
+ Become super productive with a new toolset across Python and Jupyter Notebook
+ Look into the future of data science and which directions to develop your skills next

Чеклист: на что смотреть, когда затягиваешь в проект новую библиотеку

Зависимости — кошмар любого большого проекта: они приводят к уязвимостям, конфликтуют друг с другом, протухают и блокируют обновление фреймворка. Так получается потому, что добавить в проект зависимость не стоит ничего, а вот поддерживать её (или просто выпилить) — огромный труд.

Кроме очевидного способа минимизировать проблемы от зависимостей (поменьше их притаскивать, кек), есть ещё простая гигиена, которая помогает упростить жизнь. Прежде чем набрать npm install или что там у вас, найдите репозиторий зависимости в Гитхабе и проверьте его:
— Не смотрите на количество лайков.
— Есть ли тесты? Понятно ли написаны?
— Посмотрите 5 минут на код. Удаётся ли понять, как он работает?
— Были ли значимые (не «version bump») коммиты в последние полгода?
— Не смотрите на количество лайков.
— Растёт или падает количество скачиваний (можно найти в npm/pypi).
— Сколько висит неотвеченных пулл-реквестов?
— Какие issues обсуждают?
— Понятно ли написано ридми, много ли документации?

Ну и конечно, не смотрите на количество лайков — люди ставят их за громкие названия и красивые ридми, а не за код, который решает проблемы без геморроя.

Есть что добавить в чек-лист? Напишите на fedor@borshev.com

https://certification.mail.ru - это инструмент проверки знаний программистов. Тесты помогут определить уровень знаний для наиболее популярных сред разработки и языков программирования.

#пятница
Немного английского в ленту 🦠

Перевод текста с помощью Google Translate API в Python
https://dev-gang.ru/article/perevod-teksta-s-pomosczu-google-translate-api-v-python-ahgm88wx1k/

Примеры визуализации
https://miro.com/app/board/o9J_kr3cvUM=/

Send Data Alert with Python SMTP - Towards Data Science
https://towardsdatascience.com/send-data-alert-with-python-smtp-efb9ee08077e

ETL процесс получения данных из электронной почты в Apache Airflow / Хабр
https://habr.com/ru/post/495676/