Sending Emails in Python — Tutorial with Code Examples

What do you need to send an email with Python? Some basic programming and web knowledge along with the elementary Python skills. I assume you’ve already had a web app built with this language and now you need to extend its functionality with notifications or other emails sending. [...] Sending multiple emails to different recipients and making them personal is the special thing about emails in Python. To add several more recipients, you can just type their addresses in separated by a comma, add Cc and Bcc. But if you work with a bulk email sending, Python will save you with loops. One of the options is to create a database in a CSV format (we assume it is saved to the same folder as your Python script). We often see our names in transactional or even promotional examples. Here is how we can make it with Python.

Binning Data with Pandas qcut and cut

When dealing with continuous numeric data, it is often helpful to bin the data into multiple buckets for further analysis. There are several different terms for binning including bucketing, discrete binning, discretization or quantization. Pandas supports these approaches using the cut and qcut functions. This article will briefly describe why you may want to bin your data and how to use the pandas functions to convert continuous data to a set of discrete buckets. Like many pandas functions, cut and qcut may seem simple but there is a lot of capability packed into those functions. Even for more experience users, I think you will learn a couple of tricks that will be useful for your own analysis. [...] The concept of breaking continuous values into discrete bins is relatively straightforward to understand and is a useful concept in real world analysis. Fortunately, pandas provides the cut and qcut functions to make this as simple or complex as you need it to be. I hope this article proves useful in understanding these pandas functions. Please feel free to comment below if you have any questions.

Analysing music habits with Spotify API and Python

I’m using Spotify since 2013 as the main source of music, and back at that time the app automatically created a playlist for songs that I liked from artists’ radios. By innertion I’m still using the playlist to save songs that I like. As the playlist became a bit big and a bit old (6 years, huh), I’ve decided to try to analyze it.

Python IDEs and Code Editors

A code editor is a tool that is used to write and edit code. They are usually lightweight and can be great for learning. However, once your program gets larger, you need to test and debug your code, that's where IDEs come in. An IDE (Integrated Development Environment) understand your code much better than a text editor. It usually provides features such as build automation, code linting, testing and debugging. This can significantly speed up your work. The downside is that IDEs can be complicated to use.

Announcing Anaconda Distribution 2019.10

As there were some significant changes in the previous Anaconda Distribution 2019.07 installers, this release focuses on polishing up rough edges in that release and bringing all the packages up to date with the latest available in repo.anaconda.com. This means many key packages are updated including Numpy, Scipy, Scikit-Learn, Matplotlib, Pandas, Jupyter Notebook, and many more. As many of the package updates have addressed Common Vulnerabilities and Exposures (CVEs), it is important to update to the latest. Another key change since the last release is that Apple released macOS version 10.15 – Catalina. Unfortunately, this was a breaking release for previous versions of Anaconda that used the pkg installer. The Anaconda Distribution 2019.10 installers address the issues and should install without trouble on macOS Catalina. If you would rather repair your current Anaconda installation, please check out this blog post for tips.

Apple's Numbers and the All-in-One CSV export

The hierarchical form requires a number of generator functions for Sheet-from-CSV, Table-from-CSV, and Row-from-CSV. Each of these works with a single underlying iterator over the source file and a fairly complex hand-off of state. If we only use the sheet iterator, the tables and rows are skipped. If we use the table within a sheet, the first table name comes from the header that started a sheet; the table names come from distinct headers until the sheet name changes. The table-within-sheet iteration is very tricky. The first table is a simple yield of information gathered by the sheet iterator. Any subsequent tables, however, may be based one one of two conditions: either no rows have been consumed, in which case the table iterator consumes (and ignores) rows; or, all the rows of the table have been consumed and the current row is another "sheet: table" header.

Formatting NFL data for doing data science with Python

No matter what medium of content you consume these days (podcasts, articles, tweets, etc.), you'll probably come across some reference to data. Whether it's to back up a talking point or put a meta-view on how data is everywhere, data and its analysis are in high demand. As a programmer, I've found data science to be more comparable to wizardry than an exact science. I've coveted the ability to get ahold of raw data and glean something useful and concrete from it. What a useful talent!

Sony Pushes More AMD Jaguar Optimizations To Upstream LLVM 10 Compiler

Sony engineers working on the PlayStation compiler toolchain continue upstreaming various improvements to the LLVM source tree for helping the AMD APUs powering their latest game console. Several times now we've pointed out Sony engineers contributing AMD "btver2" improvements to upstream LLVM with the company using LLVM/Clang as their default code compiler and the PlayStation 4 relying on a Jaguar APU.

[llvm-dev] GitHub Migration Schedule and Plans

Hi,

We're less than 2 weeks away from the developer meeting, so I wanted to
give an update on the GitHub migration and what's (hopefully) going to
happen during the developer meeting.

Everyone who has added their information to the github-usernames.txt
file in SVN before today should have received an invite to become a collaborator
on the llvm-project repository.  If you did not receive an invite and think
you should have, please contact me off-list.  I will continue to monitor the
file for new updates and periodically send out new batches of invites.

There is still some ongoing work to get the buildbots ready and the mailing lists
ready, but we are optimistic that the work will be done in time.

The team at GitHub has finished implementing the "Require Linear History"
branch protection that we requested.  The feature is in beta and currently
enabled in the llvm-project repository.  This means that we will have the
option to commit directly via git, in addition to using the git-llvm script.
A patch that updates git-llvm to push to git instead of svn can be found here:
https://reviews.llvm.org/D67772.  You should be able to test it out on your
own fork of the llvm-project repository.

The current plan is to begin the final migration steps on the evening (PDT)
of October 21.  Here is what will happen:

1. Make SVN read-only.
2. Turn-off the SVN->git update process.
3. Commit the new git-llvm script directly to github.
4. Grant all contributors write access to the repository.
5. Email lists announcing that the migration is complete.

Once the migration is complete, if you run into any issues, please file
a bug, and mark it as a blocker for the github metabug PR39393.

If you have any questions or think I am missing something, please
let me know.

Thanks,
Tom

LLVM Plans To Switch From Its SVN To Git Workflow Next Week

On 21 October they plan to make LLVM's SVN repository read-only and finish their git-llvm script to bring all the changes into Git, and then allow developers to begin contributing to the LLVM GitHub project as the new official source repository.

Excellent Free Books to Learn Erlang

Erlang is a general-purpose, concurrent, declarative, functional programming language and runtime environment developed by Ericsson, a Swedish multinational provider of communications technology and services. Erlang is dynamically typed and has a pattern matching syntax. The language solves difficult problems inherent in parallel, concurrent environments. It uses sets of parallel supervised processes, not a single sequential process as found in most programming languages. Erlang was created in 1986 at the Ellemtel Telecommunication Systems Laboratories for telecommunication systems. The objective was to build a simple and efficient programming language resilient large-scale concurrent industrial applications. Besides telecommunication systems and applications and other large industrial real-time systems, Erlang is particularly suitable for servers for internet applications, e-commerce, and networked database applications. The versatility of the language is, in part, due to its extensive collection of libraries.

Configuring Bluetooth on Linux

Configuring the Nginx Error and Access Log

8 Linux Commands: To Find Out Wireless Network Speed, Signal Strength And Other Information

Amazon Linux AMI Install Linux, nginx, MySQL, PHP (LEMP)

Why I use rxvt as my terminal

Hardening Cockpit with systemd (socket activation)

6 Ways to find your internal IP Address on Debian

Eirinix: Writing Extensions for Eirini

At the recent Cloud Foundry Summit EU in the Netherlands, Vlad Iovanov and Ettore Di Giacinto of SUSE presented a talk about Eirini — a project that allows the deployment and management of applications on Kubernetes using the Cloud Foundry Platform. They introduced eirinix — a framework that allows developers to extend Eirini. Eirinix is built from the Quarks codebase, which leverages Kubernetes Mutating Webhooks. With the flexibility of Kubernetes and Eirini’s architecture, developers can now build features around Eirini, like Persi support, access to the application via SSH, ASGs via Network Policies and more. In this talk, they explained how this can be done, and how everyone can start contributing to a rich ecosystem of extensions that will improve Eirini and the developer experience of Cloud Foundry.

Building an open ML platform with Red Hat OpenShift and Open Data Hub Project

Unaddressed, these challenges impact the speed, efficiency and productivity of the highly valuable data science teams. This leads to frustration, lack of job satisfaction and ultimately the promise of AI/ML to the business is not redeemed. IT departments are being challenged to address the above. IT has to deliver a cloud-like experience to data scientists. That means a platform that offers freedom of choice, is easy to access, is fast and agile, scales on-demand and is resilient. The use of open source technologies will prevent lockin, and maintain long term strategic leverage over cost. In many ways, a similar dynamic has played out in the world of application development in the past few years that has led to microservices, the hybrid cloud and automation and agile processes. And IT has addressed this with containers, kubernetes and open hybrid cloud. So how does IT address this challenge in the world of AI – by learning from their own experiences in the world of application development and applying to the world of AI/ML. IT addresses the challenge by building an AI platform that is container based, that helps build AI/ML services with agile process that accelerates innovation and is built with the hybrid cloud in mind.

Launching OpenShift/Kubernetes Support for Solarflare Cloud Onload

This is a guest post co-written by Solarflare, a Xilinx company. Miklos Reiter is Software Development Manager at Solarflare and leads the development of Solarflare’s Cloud Onload Operator. Zvonko Kaiser is Team Lead at Red Hat and leads the development of the Node Feature Discovery operator.

Chemists bitten by Python scripts: How different OSes produced different results during test number-crunching

Chemistry boffins at the University of Hawaii have found, rather disturbingly, that different computer operating systems running a particular set of Python scripts used for their research can produce different results when running the same code. In a research paper published last week in the academic journal Organic Letters, chemists Jayanti Bhandari Neupane, Ram Neupane, Yuheng Luo, Wesley Yoshida, Rui Sun, and Philip Williams describe their efforts to verify an experiment involving cyanobacteria, better known as blue-green algae. Williams, associate chair and professor in the department of chemistry at the University of Hawaii at Manoa, said in a phone interview with The Register on Monday this week that his group was looking at secondary metabolites, like penicillin, that can be used to treat cancer or Alzheimer's.

Chemists discover cross-platform Python scripts not so cross-platform

In a paper published October 8, researchers at the University of Hawaii found that a programming error in a set of Python scripts commonly used for computational analysis of chemistry data returned varying results based on which operating system they were run on—throwing doubt on the results of more than 150 published chemistry studies. While trying to analyze results from an experiment involving cyanobacteria, the researchers—Jayanti Bhandari Neupane, Ram Neupane, Yuheng Luo, Wesley Yoshida, Rui Sun, and Philip Williams—discovered significant variations in results run against the same nuclear magnetic resonance spectroscopy (NMR) data. The scripts, called the "Willoughby-Hoye" scripts after their authors—Patrick Willoughby and Thomas Hoye of the University of Minnesota—were found to return correct results on macOS Mavericks and Windows 10. But on macOS Mojave and Ubuntu, the results were off by nearly a full percent.