In the latest Python Challenge, researchers bagged over 2,000 pounds of the problematic snakes and discovered some of the species' secrets.

Also: Math for Programmers!; If #Programming languages had honest slogans #humor; 5 Papers on CNNs Every Data Scientist Should Read; Why Understanding CVEs Is Critical for Data Scientists

So in this article, we will interpret, analyze the COVID-19 DNA sequence data and try to get as many insights regarding the proteins that made it up. Later will compare COVID-19 DNA with MERS and SARS and we’ll understand the relationship among them.

Check out these 5 cool Python libraries that the author has come across during an NLP project, and which have made their life easier.

Also: Coronavirus COVID-19 Genome Analysis using Biopython; LSTM for time series prediction; A Concise Course in Statistical Inference: The Free eBook; Exploring the Impact of Geographic Information Systems

5 cool Python libraries for Data Science; NLP Recipes: Best Practices and Examples; Deep Learning: The Free eBook; Demystifying the AI Infrastructure Stack; and more.

Today, we are announcing the results of Project Python, a multilateral interagency operation targeting the Jalisco New Generation Cartel, also known as CJNG.

Lauren and Jonah wrote this case study which shows how to do Mister P in R using Stan. It’s a great case study: it’s not just the code for setting up and fitting the multilevel model, it also discusses the poststratification data, graphical exploration of the inferences, and alternative implementations of the model. Adam Haber […]

Goodbye to Mr. Creosote. Goodbye to the naked organist. Goodbye to Brian's mum, and to all her screeching sisters. Goodbye to Terry Jones, who has consumed his final wafer-thin mint. It's hard to eulogize a Python — for one thing, no one can ever top John Cleese's magnificent sendoff of Graham Chapman in 1989. "Good riddance to him, the freeloading bastard, I hope he fries," Cleese told the assembled mourners. "And the reason I say this is that he would never forgive me if I didn't, if I threw away this glorious opportunity to shock you all on his behalf." If you're watching the YouTube video, the camera at that moment cuts to Jones smiling fondly in the crowd. ( Commenting today on Twitter , Cleese said "Two down, four to go.") And while I'd like to be rude about Terry Jones, circumstances compel me to point out that he was much more than just that naked guy on the organ bench. He was a writer — of Python scripts and children's books alike — a documentarian, a Chaucer scholar and a

Cornell University seeks a Backend Python Developer to join a distributed team building arXiv’s next generation (“NG”) system and maintaining the service’s daily operations. arXiv is the premier open access platform serving scientists in physics, mathematics, computer science, and other disciplines. For over 25 years, arXiv has enabled scientists to rapidly disseminate their papers within their scientific communities. Around the world, arXiv is recognized as an essential resource for the scientists that it serves. As a member of a broader team that is passionate about arXiv’s mission and legacy, the incumbent will also be part of a supportive work culture that places high value on inclusivity, team-work, collegiality and work-life balance.

As a Backend Python Developer, you will be responsible for designing, coding, testing, documenting, and debugging highly complex applications and APIs (mostly implemented in Python/Flask), including but not limited to those that control the infrastructure and configurations that form the backbone of the arXiv platform. You will collaborate closely with team members on the design and implementation of applications, configurations, and workflows to test, deploy, monitor, and scale the arXiv system, and participate in code review, planning, and retrospectives. A strong orientation towards site security and data protection are a big plus.

The oldest known way to process data in Python is building up data in lists, dictionaries and other such data structures. Though such techniques work well in many cases, they cause major problems when dealing with large quantities of data. It's easy to find that your code is running painfully slowly or running out of memory. Generators and iterators help address this problem. These techniques have been around in Python for a while but are not well understood. Used properly, they can bring big data tasks down to size so that they don't require a huge hardware investment to complete.

Python's motto has always been "Batteries included," to highlight its extensive standard library. There are many well-kept secrets among the standard modules, including itertools, which is less well known in part because iterators and generators are less well known. This is a shame because the routines in itertools and related modules such as functools and operators can save developers many hours in developing big data operators. Learn by copious examples how to use itertools to address the most common MapReduce-style data science tasks.

Albany's resident snake catcher shares her passion for chasing reptiles.

A snake catcher believes the 80-kilogram monster is likely to have been imported illegally and may be euthanased due to the threat it poses to native ecosystems.

Snakes have been known to wrap their jaws around crocodiles, kangaroos and goats so why would one give up on a bird?

Professor Thomas Madsen with a dead water python

Stuart Morris initially kept walking when he first laid eyes on a massive snake skin in Cairns, but when he saw it a second time he decided to take it home and straighten it out all 7 metres of it.

Some Monty Python superfans are lightening up lockdown with their "silly" mandate. And John Cleese is here for them.

I was asked by a visitor how he could make a button run a server-side script (such as a PHP, Perl or Python script). This article answers that question.

oscon: Watch our free #opensource webcast series coming in June- #python #linux #raspberrypi #go + more http://t.co/ru0LVl20gq #oscon

oscon: Easily Invoke Common Protocols with Twisted - Spin up Python-friendly services with 0 lines of code http://t.co/29oTkk0isW

DESlib is an open-source python library providing the implementation of several dynamic selection techniques. The library is divided into three modules: (i) dcs, containing the implementation of dynamic classifier selection methods (DCS); (ii) des, containing the implementation of dynamic ensemble selection methods (DES); (iii) static, with the implementation of static ensemble techniques. The library is fully documented (documentation available online on Read the Docs), has a high test coverage (codecov.io) and is part of the scikit-learn-contrib supported projects. Documentation, code and examples can be found on its GitHub page: https://github.com/scikit-learn-contrib/DESlib.

This paper presents a new open source Python framework for causal discovery from observational data and domain background knowledge, aimed at causal graph and causal mechanism modeling. The cdt package implements an end-to-end approach, recovering the direct dependencies (the skeleton of the causal graph) and the causal relationships between variables. It includes algorithms from the `Bnlearn' and `Pcalg' packages, together with algorithms for pairwise causal discovery such as ANM.

pyts is an open-source Python package for time series classification. This versatile toolbox provides implementations of many algorithms published in the literature, preprocessing functionalities, and data set loading utilities. pyts relies on the standard scientific Python packages numpy, scipy, scikit-learn, joblib, and numba, and is distributed under the BSD-3-Clause license. Documentation contains installation instructions, a detailed user guide, a full API description, and concrete self-contained examples.

The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines. Graph kernels have recently emerged as a promising approach to this problem. There are now many kernels, each focusing on different structural aspects of graphs. Here, we present GraKeL, a library that unifies several graph kernels into a common framework. The library is written in Python and adheres to the scikit-learn interface. It is simple to use and can be naturally combined with scikit-learn's modules to build a complete machine learning pipeline for tasks such as graph classification and clustering. The code is BSD licensed and is available at: https://github.com/ysig/GraKeL.

The wavelet scattering transform is an invariant and stable signal representation suitable for many signal processing and machine learning applications. We present the Kymatio software package, an easy-to-use, high-performance Python implementation of the scattering transform in 1D, 2D, and 3D that is compatible with modern deep learning frameworks, including PyTorch and TensorFlow/Keras. The transforms are implemented on both CPUs and GPUs, the latter offering a significant speedup over the former. The package also has a small memory footprint. Source code, documentation, and examples are available under a BSD license at https://www.kymat.io.

The green tree python is a species of python native to New Guinea, some parts of Indonesia and Australia. Juvenile snakes are often yellow in colour. Green tree pythons love to snuggle up branches, coiled up, and ready to ambush prey.

Whether you like it or not, Microsoft Excel is still a big hit in the data analysis world. From small to big customers, we still see fit for daily routines such as filtering, generating plots, calculating items on ad-hoc analysis or even running statistical models. Whenever I talk to customers, [...]

Extending Excel with Python and SAS Viya was published on SAS Users.

Welcome to the first post for the Getting Started with Python Integration to SAS Viya series! With the popularity of the Python programming language for data analysis and SAS Viya's ability to integrate with Python, I thought, why not create tutorials for users integrating the two? To begin the series [...]

Getting Started with Python Integration to SAS® Viya® - Part 1 - Making a Connection was published on SAS Users.

With increasing interest in Continuous Integration/Continuous Delivery (CI/CD), many SAS Users want to know what can be done for Visual Analytics reports. In this article, I will explain how to use Python and SAS Viya REST APIs to extract a report from a SAS Viya environment and import it into another environment.

Continuous Integration/Continuous Delivery – Using Python and REST APIs for SAS Visual Analytics reports was published on SAS Users.

Whether you like it or not, Microsoft Excel is still a big hit in the data analysis world. From small to big customers, we still see fit for daily routines such as filtering, generating plots, calculating items on ad-hoc analysis or even running statistical models. Whenever I talk to customers, [...]

Extending Excel with Python and SAS Viya was published on SAS Users.

Welcome to the first post for the Getting Started with Python Integration to SAS Viya series! With the popularity of the Python programming language for data analysis and SAS Viya's ability to integrate with Python, I thought, why not create tutorials for users integrating the two? To begin the series [...]

Getting Started with Python Integration to SAS® Viya® - Part 1 - Making a Connection was published on SAS Users.

With increasing interest in Continuous Integration/Continuous Delivery (CI/CD), many SAS Users want to know what can be done for Visual Analytics reports. In this article, I will explain how to use Python and SAS Viya REST APIs to extract a report from a SAS Viya environment and import it into another environment.

Continuous Integration/Continuous Delivery – Using Python and REST APIs for SAS Visual Analytics reports was published on SAS Users.

Nick Kearns was shocked by the sight of a huge python in his garden, coiling itself around one of his kittens.

In the former blog about Python and Specman: Specman: Python Is here!, we described the technical information around Specman-Python integration. Since Python provides so many easy to use existing libraries in various fields, it is very tempting to leverage these cool Python apps.

Coverage has always been the center of the verification methodology, however in the last few years it gets even more focus as people develop advanced utilities, usually using Machine Learning aids. Anyhow, any attempt to leverage your coverage usually starts with some analysis of the behavior and trends of some typical tests. Visualizing the data makes it easier to understand, analyze, and communicate. Fortunately, Python has many Visualization libraries.

In this blog, we show an example of how you can use the plotting Python library (matplotlib) to easily display coverage information during a run. In this blog, we use the Specman Coverage API to extract coverage data, and a Python module to display coverage grades interactively during a single run and the way to connect both.

Before we look at the example, if you have read the former blog about Specman and Python and were concerned about the fact that python3 is not supported, we are glad to update that in Specman 19.09, Python3 is now supported (in addition to Python2).

The Testcase
Let’s say I have a stable verification environment and I want to make it more efficient. For example: I want to check whether I can make the tests shorter while hardly harming the coverage. I am not sure exactly how to attack this task, so a good place to start is to visually analyze the behavior of the coverage on some typical test I chose. The first thing we need to do is to extract the coverage information of the interesting entities. This can be done using the old Coverage API. 

Coverage API
Coverage API is a simple interface to extract coverage information at a certain point. It is implemented through a predefined struct type named user_cover_struct. To use it, you need to do the following:

1. Define a child of user_cover_structusing like inheritance (my_cover_struct below).
2. Extend its relevant methods (in our example we extend only the end_group() method) and access the relevant members (you can read about the other available methods and members in cdnshelp).
3. Create an instance of the user_cover_structchild and call the predefined scan_cover() method whenever you want to query the data (even in every cycle). Calling this method will result in calling the methods you extended in step 2.  

 The code example below demonstrates these three steps. We chose to extend the end_group() method and we keep the group grade in some local variable. Note that we divide it by 100,000,000 to get a number between 0 to 1 since the grade in this API is an integer from 0 to 100,000,000. 

Pass the Data to a Python Module
After we have extracted the group grade, we need to pass the grade along with the cycle and the coverage group name (assuming there are a few) to a Python module. We will take a look at the Python module itself later. For now, we will first take a look at how to pass the information from the e code to Python. Note that in addition to passing the grade at certain points (addVal method), we need an initialization method (init_plot) with the number of cycles, so that the X axis can be drawn at the beginning, and end_plot() to mark interesting points on the plot at the end. But to begin with, let’s have empty methods on the Python side and make sure we can just call them from the e code.

 # plot_i.py
def init_plot(numCycles):
    print (numCycles)
def addVal(groupName,cycle,grade):
    print (groupName,cycle,grade)
def end_plot():
    print ("end_plot") 

And add the calls from e code:

 Before running this, note that you need to ensure that Specman finds the Python include and lib directories, and Python finds our Python module. To do this, you need to define a few environment variables: SPECMAN_PYTHON_INCLUDE_DIR, SPECMAN_PYTHON_LIB_DIR, and PYTHONPATH. 

 The Python Module to Draw the Plot
After we extracted the coverage information and ensured that we can pass it to a Python module, we need to display this data in the Python module. There are many code examples out there for drawing a graph with Python, especially with matplotlib. You can either accumulate the data and draw a graph at the end of the run or draw a graph interactively during the run itself- which is very useful especially for long runs.

Below is a code that draws the coverage grade of multiple groups interactively during the run and at the end of the run it prints circles around the maximum point and adds some text to it. I am new to Python so there might be better or simpler ways to do so, but it does the work. The cool thing is that there are so many examples to rely on that you can produce this kind of code very fast.

 In the example we used, we had two interesting entities: packet and state_machine, thus we had two equivalent coverage groups. When running our example connecting to the Python module, we get the following graph which is displayed interactively during the run.

When analyzing this specific example, we can see two things. First, packet gets to a high coverage quite fast and significant part of the run does not contribute to its coverage. On the other hand, something interesting happens relating to state_machine around cycle 700 which suddenly boosts its coverage. The next step would be to try to dump graphic information relating to other entities and see if something noticeable happens around cycle 700.

To run a complete example, you can download the files from: https://github.com/okirsh/Specman-Python/

Do you feel like analyzing the coverage behavior in your environment? We will be happy to hear about your outcomes and other usages of the Python interface.

Orit Kirshenberg
Specman team

Slackware Security Advisory - New python packages are available for Slackware 14.0, 14.1, 14.2, and -current to fix security issues.

Proof of concept exploit for a Microsoft VSCode python extension code execution vulnerability.

This Metasploit module exploits a vulnerability found in Windows Object Linking and Embedding (OLE) allowing arbitrary code execution, bypassing the patch MS14-060, for the vulnerability publicly known as "Sandworm", on systems with Python for Windows installed. Windows Vista SP2 all the way to Windows 8, Windows Server 2008 and 2012 are known to be vulnerable. However, based on our testing, the most reliable setup is on Windows platforms running Office 2013 and Office 2010 SP2. Please keep in mind that some other setups such as those using Office 2010 SP1 may be less stable, and may end up with a crash due to a failure in the CPackage::CreateTempFileName function.

An analysis of a classic Monty Python sketch suggests the Minister of Silly Walks has a walking style 6.7 times more variable, or silly, than normal walking

Sea cucumber poo may be the key to saving the world's great coral reefs from devastation. Invasive pythons are doing damage in the Everglades, eight sea lions were found shot to death in Washington and more.

Организация Python Software Foundation, курирующая разработку эталонной реализации языка программирования Python, представила план перевода инфраструктуры отслеживания ошибок CPython с bugs.python.org на GitHub. Репозитории с кодом были переведены на GitHub в качестве первичной платформы ещё в 2017 году. В качестве варианта также рассматривался GitLab, но решение в пользу GitHub было мотивировано тем, что данный сервис более привычен для основных разработчиков, новичков и сторонних участников.

На это неделе был анонсирован выход Python 2.7.18. Анонс этой версии был запланирован на конференцию PyCon 2020, но её пришлось отменить из-за эпидемии COVID-19. Помимо того, что 2.718 — максимальное приближение к числу e из всех вышедших версий, 2.7.18 ещё и последняя версия Python 2.7 и вообще всего Python 2.
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Company: 2COMS Consulting Private Limited
Experience: 3 to 8
location: Hyderabad / Secunderabad
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Summary: Job Title - Business Intelligence Engineer Location - Hyderabad As a BIE, you will be play a key role inClient's Social Media customer service by partnering with forecasters, supply planners, finance,....

Company: MNR Solutions Private Limited
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A Filipino man gets himself a little tied up as he plays with Albino Burmese Pythons, some of the largest pythons in the world.

The daughter of Lionel Richie, 21, was seen outside the beachfront restaurant wearing a stylish python print coat and black shades.

Matt Damon revealed on Monday's Ellen DeGeneres show that when he was in Australia with Chris Hemsworth in March, he stepped on an eight-foot python.

Chris Hemsworth had the shock of his life when he discovered a green python in his motorcycle helmet while cleaning out his garage this week.