Planet SciPy
How to Solve the Model Serving Component of the MLOps Stack
Model serving and deployment is one of the pillars of the MLOps stack. In this article, I’ll dive into it and talk about what a basic, intermediate, and advanced setup for model serving look like. Let’s start by covering some basics. What is ML model serving? Training a machine learning model may seem like a […]
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Introducing Anaconda’s Safari Program
A safari is often characterized as an expedition, a time to explore, a time to observe. And while the word “safari” often conjures up images of wildlife in Africa, it refers to something different at Anaconda; here, the Safari program is a new part of our career development program.Active Learning: Strategies, Tools, and Real-World Use Cases
In this article, you’ll learn: what is active learning, why do we need active learning how it works what techniques are there where it’s used in the real world and what frameworks can help with active learning. Let’s begin! What is active learning? Active learning is a special case of machine learning in which a […]
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Transformer NLP Models (Meena and LaMDA): Are They “Sentient” and What Does It Mean for Open-Domain Chatbots?
First of all, this is not a post about whether Google’s latest Deep Learning Natural Language Processing (NLP) model LaMDA is the real-life version of Hal-9000, the sentient Artificial Intelligence (AI) computer in 2001: A Space Odyssey. This is not to say that it is a pointless question to ask. Quite opposite, it is a […]
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scikit-learn Sprint in Salta, Argentina
Author: Juan Martín LoyolaThere’s No Wrong Way to Become a Software Engineer: Part 2
So you or someone you know is interested in becoming a software engineer or pursuing one of the many adjacent careers (e.g., data scientist, system administrator, tech support, etc.). Well, here's the not-so-secret secret:Setting up MLOps at a Reasonable Scale With Jacopo Tagliabue
This article was originally the second episode of MLOps Live, an interactive Q&A session where ML practitioners answer questions from other ML practitioners. Every episode is focused on one specific ML topic, and during this one, we talked to Jacopo Tagliabue about reasonable scale MLOps. You can watch it on YouTube: Or listen to it […]
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Open-Source Tools for Graph Data Science
About the Author Janit Anjaria is a Senior Software Engineer at Aurora Innovation Inc., where he currently works on building high-definition 3-D maps for self-driving vehicles. Before joining Aurora, Janit worked on the Autonomous Vehicle Maps team at Uber Advanced Technology Group. Prior to Uber, he was at the University of Maryland, College Park Spatial Lab working on spatial data structures and machine learning. He has diverse professional and academic experience, and once worked on building out the Location Intelligence Platform at Flipkart Internet Pvt. Ltd. in India. Outside of professional and academic life, he is an open-source enthusiast and has contributed to Apache Solr and LibreOffice and has been a Linux user since 2011.New 2022 roadmap and grant funding
For the last couple of months, the Spyder team has been working on defining a new roadmap and submitting grant proposals to fund more features and improvements. We are pleased to announce our roadmap for the rest of 2022, and that two proposals were funded!
The roadmapConsidering the importance of sharing a clear perspective of where the Spyder project is going and where we will be focusing our efforts over the coming months, the team has created an initial roadmap for the rest of 2022. We prioritized the highlighted features and enhancements based on input from issues, face-to-face and virtual discussions, Stack Overflow, social media and other feedback, to try to best capture the interests of our users and community.
The proposalsTo help make our roadmap achievable, we wrote and submitted proposals to several different venues and organizations in the last couple of months. While we have yet to hear back from some of them, two have already been funded!
The first was for the
(continued...)Building MLOps Pipeline for Computer Vision: Image Classification Task [Tutorial]
The introduction of Transformers in 2018 by Vaswani and the team brought a significant transformation in the research and development of deep learning models for various tasks. The transformer leverages a self-attention mechanism that was adopted from the attention mechanism by Bahdanau and the team. With this mechanism, one input could interact with other inputs […]
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10 Years of Data Science Innovation: Anaconda’s Commitment to the Open-Source Python Community
For the last decade, Anaconda has helped define the guard rails and infrastructure of Python programming to empower a new generation of data science professionals and enthusiasts. Over the years, we’ve watched as the open-source Python community has burgeoned into a thriving network of creators, contributors, and dedicated maintainers. Because of the human ecology that has formed around Python, it is now the fastest-growing programming language in the world.Building MLOps Pipeline for Time Series Prediction [Tutorial]
In this tutorial, we’ll present a simple example of a time-series-based ML project and build an MLOps pipeline for that. Every step will be executed following the best practices from MLOps, and the whole project will be explained step by step. This time-series project is based on the Binance trading app, but similar logic is […]
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Building Deep Learning-Based OCR Model: Lessons Learned
Deep learning solutions have taken the world by storm, and all kinds of organizations like tech giants, well-grown companies, and startups are now trying to incorporate deep learning (DL) and machine learning (ML) somehow in their current workflow. One of these important solutions that have gained quite a popularity over the past few years is […]
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Pollution in India : Real-time AQI Data
Air pollution has become a serious problem in recent years across the world. Effects of Air Pollution is devastating and its harmful effects are not just limited to Humans but also animals and plants as well. It also leads to global warming which is esentially increasing air and ocean temperatures around the world.
Indian cities have been topping the list of polluted cities. In order to solve the problem of air pollution the most important thing is to track air pollution on real-time basis first which alerts people to avoid outdoor activities during high air Pollution. This post explains how you can fetch real-time Air Quality Index (AQI) of Indian cities using Python and R code. It allows both Python and R programmers to pull pollution data.
You can download the dataset which contains static information about Indian states, cities and AQI stations. Variables stored in this dataset will be used further to fetch real-time data.
My Mayavi story: discovering open source communities
The Mayavi Python software, and my personal history: A thread on Python and scipy ecosystems, building open source codebase, and meeting really cool and friendly people
I am writing today as a goodbye to the project: I used to be one of the core contributors and maintainers but have been …
9 Things That Can Make Your ML Team Meetings More Effective
According to a report by WaveStone, over 90% of the leading companies now have ongoing investments in Artificial Intelligence – a significant yet not-so-surprising result reflecting the blooming of the machine learning era. Its applications rapidly expand due to its data-agnostic nature (e.g., a fully-connected network can be used for weather forecasting just as well as […]
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There’s No Wrong Way to Become a Software Engineer: Part 1
Hi! My name is Bianca Henderson, and I'm a software engineer at Anaconda. When people think about developers, most tend to think about computer science graduates or people who are super good at math; as a single mother and woman of color who has an undergrad degree in English literature, I will challenge those misconceptions and hopefully show people with similarly unconventional backgrounds that they, too, can become software engineers if they want to!Pointwise mutual information (PMI) in NLP
Natural Language Processing (NLP) has secured so much acceptance recently as there are many live projects running and now it's not just limited to academics only. Use cases of NLP can be seen across industries like understanding customers' issues, predicting the next word user is planning to type in the keyboard, automatic text summarization etc. Many researchers across the world trained NLP models in several human languages like English, Spanish, French, Mandarin etc so that benefit of NLP can be seen in every society. In this post we will talk about one of the most useful NLP metric called Pointwise mutual information (PMI) to identify words that can go together along with its implementation in Python and R.
PMI helps us to find related words. In other words, it explains how likely the co-occurrence of two words than we would expect by chance. For example the word "Data Science" has a specific meaning when
Kedro vs ZenML vs Metaflow: Which Pipeline Orchestration Tool Should You Choose?
In this article, I’m going to compare Kedro, Metaflow, and ZenML, but before that, I think it’s worth taking a few steps back. Why even bother using ML orchestration tools such as these three? It is not that hard to start a Machine Learning project. You install some python libraries, initiate the model, train it, […]
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8 Levels of Reproducibility: Future-Proofing Your Python Projects
Anaconda is amplifying the voices of some of its most active and cherished community members in a monthly blog series. If you’re a Maker who has been looking for a chance to tell your story, elaborate on a favorite project, educate your peers, and build your personal brand, consider submitting an abstract. For more details and to access a wealth of educational data science resources and discussion threads, visit Anaconda Nucleus.Real-World MLOps Examples: Model Development in Hypefactors
In this first installment of the series “Real-world MLOps Examples,” Jules Belveze, an MLOps Engineer, will walk you through the model development process at Hypefactors, including the types of models they build, how they design their training pipeline, and other details you may find valuable. Enjoy the chat! Company profile Hypefactors provides an all-in-one media […]
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How to import your data into Acoular
Acoular is a Python library that processes multichannel data (up to a few hundred channels) from acoustic measurements with a microphone array which is stored in an HDF5 file. This blog post explains how to convert data available in other formats into this file format. As examples for other file formats we will use both .csv (comma separated text files) and .mat (Matlab files).Anaconda Acquires PythonAnywhere to Increase Python Accessibility and Adoption
At Anaconda, we are always seeking new ways to empower people with data literacy. With that in mind, we built the Anaconda Distribution to include an easy-to-use package and environment manager, packages that contain their cross-language dependencies, installers for all major operating systems and architectures, and a desktop console with direct access to all the tools and artifacts a developer needs for their data science and machine learning projects.A Case for R & R: My Women Who Code CONNECT Recharge 2022 Keynote
When the phone battery drains, what do we do? When the laptop gets overheated, what do we do? We are forced to recharge, shut down, or buy a replacement. In this era, the blinking red battery icons on our smartphones, tablets, and laptops send us scrambling for our chargers.The True Value of Community
With more than 25 years years of executive-level financial development experience for a wide range of businesses, Angela now provides financial stewardship and executive leadership at Anaconda. Angela is no stranger to crafting financial management plans for technology leaders. In her tenure as the CFO for AirStrip Technologies, a med-tech software company backed by Sequoia Capital, she supported the successful scale of the business and raised more than $100 million in equity and debt financing. Previously, as CFO of Trillion Partners, she spearheaded the effort to raise $60 million in debt and private equity for the business. As VP of Finance at Broadwing, Angela led the raising of more than $200 million and managed the company's M&A activities and investor relations functions.Checking for accessibility: thoughts and a checklist!
On the Link Between Optimization and Polynomials, Part 5
Six: All of this has happened before.
Baltar: But the question remains, does all of this have to happen again?
Six: This time I bet no.
Baltar: You know, I've never known you to play the optimist. Why the change of heart?
Six: Mathematics. Law of averages. Let a complex …
5 Routes for Going from Zero to Viz in Data Science
About the Author Kathryn Hurchla is a data developer and designer at home shaping human experiences as an Analytics Lead with F Λ N T Λ S Y, a design agency like no other. She has a master's degree in data analytics and visualization and enjoys building end-to-end analytic applications and writing about visual data science. You can find her lost in exploratory data analysis. She contributes to open-source technology communities as a Plotly Dash Ambassador, by leading hands-on learning, and by publishing content independently and with Data Visualization Society’s Nightingale Editorial Committee. Her own enterprise Data Design Dimension may one day be just what her daughters need to make the world as they see it too. Her words are not a reflection of her employer.Announcing ribbity - a hacky project to build Web sites from GitHub issue trackers
Munging GitHub issue trackers for fun!
Interview with Norbert Preining, scikit-learn Team Member
Author: Reshama Shaikh , Norbert PreiningThe Value of Open Source Sprints, the scikit-learn Experience
Author: Reshama Shaikh5 Years, 10 Sprints, A scikit-learn Open Source Journey
Author: Reshama ShaikhOnly size-1 arrays can be converted to Python scalars
Numpy is one of the most used module in Python and it is used in a variety of tasks ranging from creating array to mathematical and statistical calculations. Numpy also bring efficiency in Python programming. While using numpy you may encounter this errorTypeError: only size-1 arrays can be converted to Python scalars It is one of the frequently appearing error and sometimes it becomes a daunting challenge to solve it. 
Interview with Lucy Liu, scikit-learn Team Member
Author: Reshama Shaikh , Lucy LiuThe second Common Fund Data Ecosystem hackathon - May 9-13, 2022!
We're running another hackathon!
Storing 64-bit unsigned integers in SQLite databases, for fun and profit
Storing unsigned longs in SQLite is possible, and can be fast.
Interview with Maren Westermann: Extending the Impact of the scikit-learn Sprints to the Community
Author: Reshama Shaikh , Maren WestermannBehind the Scenes of Data Umbrella scikit-learn Open Source Sprints
Author: Reshama Shaikh , Angela OkuneWomen in Machine Learning - A WiMLDS Paris sprint and contribution workshop
Author: François GoupilThe First Common Fund Data Ecosystem Hackathon
We ran a successful pilot hackathon, and we will run a second one soon!
Three Components for Reviewing a Pull Request
Author: Thomas J. FanPerformances and scikit-learn
Author: Julien JerphanionMestrado em Ciência da Computação 2022: Metaheurísticas
Estamos ainda com algumas vagas abertas para o Mestrado em Ciência da Computação na UFPA, Belém. Os interessados, favor olhar as instruções para submissão na página de seleção do programa. Desde meu ingresso no programa venho orientando alunos em diferentes pesquisas sobre inteligência computacional aplicados a problemas de smart grids. Já tivemos trabalhos sobre sistemas multiagentes… Continue a ler »Mestrado em Ciência da Computação 2022: MetaheurísticasDiffCast: Hands-free Python Screencast Creator — Create reproducible programming screencasts without typos or edits
Programming screencasts are a popular way to teach programming and demo tools. Typically people will open up their favorite editor and record themselves tapping away. But this has a few problems. A good setup for coding isn't necessarily a good setup for video -- with text too small, a window too …
On minimum metagenome covers, and calculating them for your own data.
You, too, can run our software!
Optimization Nuggets: Implicit Bias of Gradient-based Methods
When an optimization problem has multiple global minima, different algorithms can find different solutions, a phenomenon often referred to as the implicit bias of optimization algorithms. In this post we'll characterize the implicit bias of gradient-based methods on a class of regression problems that includes linear least squares and Huber …
Optimization Nuggets: Exponential Convergence of SGD
This is the first of a series of blog posts on short and beautiful proofs in optimization (let me know what you think in the comments!). For this first post in the series I'll show that stochastic gradient descent (SGD) converges exponentially fast to a neighborhood of the solution.
A bioinformatics training career panel in the DIB Lab
Careers in training!
Hiring an engineer and post-doc to simplify data science on dirty data
Note
Join us to work on reinventing data-science practices and tools to produce robust analysis with less data curation.
It is well known that data cleaning and preparation are a heavy burden to the data scientist.
In the dirty data project, we have been conducting machine-learning research …
TorchVision Datasets: Getting Started
The TorchVision datasets subpackage is a convenient utility for accessing well-known public image and video datasets. You can use these tools to start training new computer vision models very quickly. TorchVision Datasets Example To get started, all you have to do is import one of the Dataset classes. Then, instantiate it and access one of ... Read more
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NumPy Any: Understanding np.any()
The np.any() function tests whether any element in a NumPy array evaluates to true: The input can have any shape and the data type does not have to be boolean (as long as it’s truthy). If none of the elements evaluate to true, the function returns false: Passing in a value for the axis argument ... Read more
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PyTorch DataLoader Quick Start
PyTorch comes with powerful data loading capabilities out of the box. But with great power comes great responsibility and that makes data loading in PyTorch a fairly advanced topic. One of the best ways to learn advanced topics is to start with the happy path. Then add complexity when you find out you need it. ... Read more
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How the NumPy append operation works
Understanding the np.append() operation and when you might want to use it.
The post How the NumPy append operation works appeared first on Sparrow Computing.
Hiring someone to develop scikit-learn community and industry partners
Note
With the growth of scikit-learn and the wider PyData ecosystem, we want to recruit in the Inria scikit-learn team for a new role. Departing from our usual focus on excellence in algorithms, statistics, or code, we want to add to the team someone with some technical understanding, but an …
Using snakemake to do simple wildcard operations on many, many, many files
snakemake is awesome
A paper on the Lees-Edwards method
A few years ago1, Sebastian contacted me to help with simulations. Great, I like simulation studies, so we start discussing the details. The idea: use an established method, the Lees-Edwards boundary condition, to study colloids under shear.
A biotech career panel in the DIB Lab
Careers outside of universities!
Scaling sourmash to millions of samples
Bigger and better!
Poetry for Package Management in Machine Learning Projects
When you’re building a production machine learning system, reproducibility is a proxy for the effectiveness of your development process. But without locking all your Python dependencies, your builds are not actually repeatable. If you work in a Python project without locking long enough, you will eventually get a broken build because of a transitive dependency ... Read more
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Development containers in VS Code: a quick start guide
If you’re building production ML systems, dev containers are the killer feature of VS Code. Dev containers give you full VS Code functionality inside a Docker container. This lets you unify your dev and production environments if production is a Docker container. But even if you’re not targeting a Docker deployment, running your code in ... Read more
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New sourmash databases are available!
Databases are now available for GTDB!
Colunando no O Estado do Piauí
O Estado do Piauí é um novo jornal que surgiu recentemente pelas bandas de lá. Com um foco maior em reportagens longas e densas, misturando jornalismo investigativo e literário, o projeto pretende discutir em profundidade os temas de interesse do estado, descobrir histórias piauienses únicas, repercutir situações problemáticas, apontar alternativas e muito mais. Não se… Continue a ler »Colunando no O Estado do PiauíCiclo de Entrevistas sobre as Pesquisas no PPGCC da UFPA – Inteligência Computacional
A Faculdade de Computação e o Programa de Pós-Graduação em Ciência da Computação da UFPA estão desenvolvendo um projeto que pretende atingir dois objetivos: o primeiro, fazer uma melhor divulgação para o público externo à universidade do que produzimos em nossas pesquisas; o segundo, uma melhor divulgação INTERNA da mesma coisa – o que desenvolvemos… Continue a ler »Ciclo de Entrevistas sobre as Pesquisas no PPGCC da UFPA – Inteligência ComputacionalMoving sourmash towards more community engagement - a funding application
CZI EOSS4 application for sourmash support
Searching all public metagenomes with sourmash
Searching all the things!
Is your software ready for the Journal of Open Source Software?
For the unaware reader, the Journal of Open Source Software (JOSS) is an open-access scientific journal founded in 2016 and aimed at publishing scientific software. A JOSS article in itself is short and its publication contributes to recognize the work on the software. I share here my point of view on what makes some software tools more ready to be published in JOSS. I do not comment on the size or the relevance for research which are both documented on JOSS' website.
Basic Counting in Python
I love fancy machine learning algorithms as much as anyone. But sometimes, you just need to count things. And Python’s built-in data structures make this really easy. Let’s say we have a list of strings: With a list like this, you might care about a few different counts. What’s the count of all items? What’s ... Read more
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How to Use the PyTorch Sigmoid Operation
The PyTorch sigmoid function is an element-wise operation that squishes any real number into a range between 0 and 1. This is a very common activation function to use as the last layer of binary classifiers (including logistic regression) because it lets you treat model predictions like probabilities that their outputs are true, i.e. p(y ... Read more
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On the Link Between Optimization and Polynomials, Part 4
While the most common accelerated methods like Polyak and Nesterov incorporate a momentum term, a little known fact is that simple gradient descent –no momentum– can achieve the same rate through only a well-chosen sequence of step-sizes. In this post we'll derive this method and through simulations discuss its practical …
NumFOCUS Welcomes Tesco Technology to Corporate Sponsors
NumFOCUS is pleased to announce our new partnership with Tesco Technology. A long-time PyData event sponsor, Tesco Technology joined NumFOCUS as a Silver Corporate Sponsor in December 2020. “We are very excited to formalize our partnership with Tesco Technology,” said Leah Silen, NumFOCUS Executive Director. “Tesco Technology has partnered with NumFOCUS for the past several […]
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Job Posting | Communications and Marketing Manager
Job Title: Communications and Marketing Manager Position Overview The primary role of the Communications & Marketing Manager is to manage the NumFOCUS brand by overseeing all outgoing communications between NumFOCUS and our stakeholders. You will serve the project communities by playing a key role in their event marketing management and assist with project promotional and […]
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Getting started with Acoular - Part 2
This is the second in a series of three blog posts about the basic use of Acoular. It assumes that you already have read the first post and continues by explaining some more concepts and additional methods. Acoular is a Python library that processes multichannel data (up to a few hundred channels) from acoustic measurements with a microphone array. The focus of the processing is on the construction of a map of acoustic sources. This is somewhat similar to taking an acoustic photograph of some sound sources.Getting started with Acoular - Part 3
This is the third and final in a series of three blog posts about the basic use of Acoular. It assumes that you already have read the first two posts and continues by explaining additional concepts to be used with time domain methods. Acoular is a Python library that processes multichannel data (up to a few hundred channels) from acoustic measurements with a microphone array. The focus of the processing is on the construction of a map of acoustic sources. This is somewhat similar to taking an acoustic photograph of some sound sources. To continue, we do the same set up as in Part 1. However, as we are setting out to do some signal processing in time domain, we define only TimeSamples, MicGeom, RectGrid and SteeringVector objects but no PowerSpectra or BeamformerBase. import acoular ts = acoular.TimeSamples( name="three_sources.h5" ) mg = acoular.MicGeom( from_file="array_64.xml" ) rg = acoular.RectGrid( x_min=-0.2, x_max=0.2, y_min=-0.2, y_max=0.2, z=0.3, increment=0.01 ) st = acoular.SteeringVector( grid=rg, mics=mg (continued...)Getting started with Acoular - Part 1
This is the first in a series of three blog posts about the basic use of Acoular. It explains some fundamental concepts and walks through a simple example. Acoular is a Python library that processes multichannel data (up to a few hundred channels) from acoustic measurements with a microphone array. The focus of the processing is on the construction of a map of acoustic sources. This is somewhat similar to taking an acoustic photograph of some sound sources.PyTorch Tensor to NumPy Array and Back
You can easily convert a NumPy array to a PyTorch tensor and a PyTorch tensor to a NumPy array. This post explains how it works.
The post PyTorch Tensor to NumPy Array and Back appeared first on Sparrow Computing.
TorchVision Transforms: Image Preprocessing in PyTorch
TorchVision, a PyTorch computer vision package, has a great API for image pre-processing in its torchvision.transforms module. This post gives some basic usage examples, describes the API and shows you how to create and use custom image transforms.
The post TorchVision Transforms: Image Preprocessing in PyTorch appeared first on Sparrow Computing.
sourmash 4.0 is now available! Low low cost if you buy now!
sourmash v4.0.0 is here!
On the Link Between Optimization and Polynomials, Part 3
I've seen things you people wouldn't believe.
Valleys sculpted by trigonometric functions.
Rates on fire off the shoulder of divergence.
Beams glitter in the dark near the Polyak gate.
All those landscapes will be lost in time, like tears in rain.
Time to halt.
A momentum optimizer *
Introducing PyMC Labs: Saving the World with Bayesian Modeling
After I left Quantopian in 2020, something interesting happened: various companies contacted me inquiring about consulting to help them with their PyMC3 models.
Usually, I don't hear how people are using PyMC3 -- they mostly show up on GitHub or Discourse when something isn't working right. So, hearing about all these …
Using MicroPython and uploading libraries on Raspberry Pi Pico — Using rshell to upload custom code
MicroPython is an implementation of the Python 3 programming language, optimized to run microcontrollers. It's one of the options available for programming your Raspberry Pi Pico and a nice friendly way to get started with microcontrollers.
MicroPython can be installed easily on your Pico, by following the instructions on the …
sourmash v4.0.0 release candidate 1 is now available for comment!
sourmash v4.0.0 is coming!
Job Posting | Events and Digital Marketing Coordinator
Job Title: Events and Digital Marketing Coordinator Position Overview The primary role of the Events and Digital Marketing Coordinator is to support and assist the Events Manager and the Community Communications and Marketing Manager to advance one of NumFOCUS’s primary missions of educating and building the community of users and developers of open source scientific […]
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Transition your Python project to use pyproject.toml and setup.cfg! (An example.)
Updating old Python packages, in this year of the PSF 2021!
Writing a SAM Coupé SCREEN$ Converter in Python — Interrupt optimizing image converter
The SAM Coupé was a British 8 bit home computer that was pitched as a successor to the ZX Spectrum, featuring improved graphics and sound and higher processor speed.
The SAM Coupé's high-color MODE4 could manage 256x192 resolution graphics, with 16 colors from a choice of 128. Each pixel can …
A snakemake hack for checkpoints
snakemake checkpoints r awesome
Squeezing Space Invaders onto the BBC micro:bit's 25 pixels — MicroPython retro game in just 25 pixels
How much game can you fit into 25 pixels? Quite a bit it turns out.
This is a mini clone of arcade classic Space Invaders for the BBC micro:bit microcomputer. Using the accelerometer and two buttons for input, to can beat off wave after wave of aliens that advance …
Run SAS in Python without Installation
Do you wish
(continued...)Disnatia X/Potências de X
Nenhuma equipe de heróis me é tão querida quanto X-Men. Lá pelo final dos anos 90 comecei a colecionar por alguns anos, mas em seguida veio o fatídico aumento de preço com as Super-Heróis Premium, o que me acabou desmotivando a comprar. De lá para cá, acompanho esporadicamente, lendo notícias sobre, comprando uma ou outra… Continue a ler »Disnatia X/Potências de XWish Christmas with Python and R
This post is dedicated to all the Python and R Programming Lovers...Flaunt your knowledge in your peer group with the following programs. As a data science professional, you want your wish to be special on eve of christmas. If you observe the code, you may also learn 1-2 tricks which you can use later in your daily tasks.
Method 1 : Run the following program and see what I mean
R Code
paste(intToUtf8(acos(log(1))*180/pi-13),
toupper(substr(month.name[2],2,2)),
paste(rep(intToUtf8(acos(exp(0)/2)*180/pi+2^4+3*2),2), collapse = intToUtf8(0)),
LETTERS[5^(3-1)], intToUtf8(atan(1/sqrt(3))*180/pi+2),
toupper(substr(month.abb[10],2,2)),
intToUtf8(acos(log(1))*180/pi-(2*3^2)),
toupper(substr(month.name[4],3,4)),
intToUtf8(acos(exp(0)/2)*180/pi+2^4+3*2+1),
intToUtf8(acos(exp(0)/2)*180/pi+2^4+2*4),
intToUtf8(acos(log(1))*180/pi-13),
LETTERS[median(0:2)],
intToUtf8(atan(1/sqrt(3))*180/pi*3-7),
sep = intToUtf8(0)
)
Python Code
import math
import datetime
(chr(int(math.acos(math.log(1))*180/math.pi-13)) \
+ datetime.date(1900, 2, 1).strftime('%B')[1] \
+ 2 * datetime.date(1900, 2, 1).strftime('%B')[3] \
+ datetime.date(1900, 2, 1).strftime('%B')[7] \
+ chr(int(math.atan(1/math.sqrt(3))*180/math.pi+2)) \
+ datetime.date(1900, 10, 1).strftime('%B')[1] \
+ chr(int(math.acos(math.log(1))*180/math.pi-18)) \
+ datetime.date(1900, 4, 1).strftime('%B')[2:4] \
+ chr(int(math.acos(math.exp(0)/2)*180/math.pi+2**4+3*2+1)) \
+ chr(int(math.acos(math.exp(0)/2)*180/math.pi+2**4+2*4)) \
+ chr(int(math.acos(math.log(1))*180/math.pi-13)) \
+ "{:c}".format(97) \
+ chr(int(math.atan(1/math.sqrt(3))*180/math.pi*3-7))).upper()
Turn on computer speakers before running the code.
R Code
install.packages("audio")
library(audio)
christmas_file <- tempfile()
download.file("https://github.com/deepanshu88/Datasets/raw/master/UploadedFiles/merrychristmas1.wav", christmas_file, mode = "wb")
xmas
On the Link Between Optimization and Polynomials, Part 2
We can tighten the analysis of gradient descent with momentum through a cobination of Chebyshev polynomials of the first and second kind. Following this connection, we'll derive one of the most iconic methods in optimization: Polyak momentum.
How to use variable in a query in pandas
Suppose you want to reference a variable in a query in pandas package in Python. This seems to be a straightforward task but it becomes daunting sometimes. Let's discuss it with examples in the article below.Let's create a sample dataframe having 3 columns and 4 rows. This dataframe is used for demonstration purpose.
import pandas as pd
df = pd.DataFrame({"col1" : range(1,5),
"col2" : ['A A','B B','A A','B B'],
"col3" : ['A A','A A','B B','B B']
})
A A in column col2@. NumFOCUS hires Open Source Developer Advocate!
NumFOCUS is pleased to announce that Arliss Collins has been hired as our organization’s first Open Source Developer Advocate. Founded in 2012, NumFOCUS has finally grown beyond just providing non-technical needs for our 40+ sponsored projects! As our first technical hire, Arliss will work to help understand our projects from a technical perspective and […]
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A Pivotal Time in NumFOCUS’s Project Aimed DEI Efforts
NumFOCUS is pleased to announce the launch of our Contributor Diversification & Retention Research Project funded by a grant from the Gordon and Betty Moore Foundation. “We were eager to support NumFOCUS’s diversity initiative because it aims to get to the heart of what is preventing greater participation in data science. We are hopeful that […]
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Anaconda Announces Multi-Year Partnership with NumFOCUS
A key stakeholder in the open source scientific computing ecosystem has further formalized their long-standing partnership with NumFOCUS. Anaconda, the Austin, Texas-based software development and consulting company which provides global distribution of Python and R software packages, last month introduced their Anaconda Dividend Program. Through this initiative, Anaconda plans to direct a portion of their […]
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What's in a model
During the coronavirus epidemic, the belgian federal group of scientific experts came up regularly in the official communication of the government. How can scientists understand the spread of an epidemic? By using a model: a mathematical description of a phenomenon. By varying the parameters of the model, one can test …
NumFOCUS Receives Support from Heising-Simons
NumFOCUS is grateful to announce that we received a grant award of $50,000 in October from the Heising-Simons Foundation. This generous grant funding will provide general support resources to NumFOCUS and will benefit all of our Sponsored and Affiliated Projects as well as our organization’s several programs and initiatives. “This grant award from Heising-Simons will […]
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Bate-papo com Vivi Reis sobre tecnologia e política
Hoje à noite (5 de novembro) às 20h conversarei com Vivi Reis, candidata a vereadora pelo PSOL em Belém. No bate-papo vamos focar bastante sobre temas que entrelaçam tecnologia e política. Entre os pontos, teremos o Escritório de Dados, dados e políticas públicas, software livre na administração pública, conectividade em Belém, inclusão digital, aplicativos cidadãos,… Continue a ler »Bate-papo com Vivi Reis sobre tecnologia e políticaNew features in Spyder 4's new debugger!
IPython is a great improvement over the standard Python interpreter, bringing many enhancements such as autocompletion and "magic" commands. When debugging, however, many of these features become inaccessible. With Spyder, we aim to bring back these capabilities and more for a truly premium debugging experience! (And believe me, I use this debugger a lot, and not only because I write code that might contain bugs :p).
In this post, I will describe the debugger improvements we've already made in Spyder 4, as well as those that are already implemented or under review for Spyder 4.2 and beyond.
Make the debugger more like IPythonIPython improves on the stock Python interpreter by adding syntax highlighting, completion, and history. We have done the same for the debugger!
The output is prettier (and easier to read) than plain black text, as it was in Spyder 3!
Code completion and history for the debugger use the same functionality as the IPython console, so you should not notice any difference in behaviour. Just press
(continued...)JupyterCon 2020: Code of Conduct Reports
Following the reports to the NumFOCUS Code-of-Conduct committee on Jeremy Howard’s keynote at JupyterCon 2020, and the controversy that followed, the NumFOCUS Code of Conduct Committee issued a public apology to Jeremy Howard and escalated the case to the board of directors. The context In his keynote at JupyterCon 2020, Jeremy Howard gave a point-by-point rebuttal of […]
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