Building a dataset of Python versions with regular expressions
Learn how to use pandas, requests, and regular expressions (“regex”) to create a dataset of every Python version and its release date!continue reading.
Learn how to use pandas, requests, and regular expressions (“regex”) to create a dataset of every Python version and its release date!continue reading.
Jesus. I just spent about an hour playing with GPT, asking it to write some Pandas code for me, and I want to set my computer on fire. Do you...continue reading.
I won’t wax long and poetic here since I’ve already posted the experiment that has all the details. TL;DR: there are still only ~90-ish 📦 in the WebR WASM “CRAN”,...continue reading.
The adoption of Shiny, the R package for building interactive web applications, is growing. As a leader in R Shiny development for enterprises, we’re asking you, fellow users of R...continue reading.
Are you working with datetime data in pandas? Learn how to become “timezone-aware” so that your dataset cooperates with Daylight Saving Time!continue reading.
While it sounds like the title of a science-fiction catastrophe novel or of a (of course) convoluted nouveau roman, this book by Nick Huntington-Klein is a massive initiation to econometrics...continue reading.
Recently, I wrote a blog post stating that I’m starting to use Python for most of my data science work. And I’m starting to recommend Python as the data science...continue reading.
Recently, I wrote a blog post stating that I’m starting to use Python for most of my data science work. And I’m starting to recommend Python as the data science...continue reading.
There are multiple data science languages to choose from. The most popular being R and Python. Many people will tell you to learn both. Learn R AND Python. I think...continue reading.
Learn how to use Python’s f-strings for substitution and formatting, and then combine those features to solve a real-world pandas problem!continue reading.
The first question that aspiring data scientists ask me is “what is the best data science language … which should I learn.” And almost always, this is framed as a...continue reading.
In this tutorial, I’ll show you how to use the Numpy transpose function. I’ll explain what the function does, how the syntax works, and I’ll show you step-by-step examples of...continue reading.
In this tutorial, I’ll explain the Numpy copy function. I’ll explain the syntax of np.copy and show you a clear example of how to use it. If you need something...continue reading.
In the series of Azure Machine Learning posts: Yesterday we have looked into how to start the MLflow configurations and today, let’s put this to the test. We will create...continue reading.
In the series of Azure Machine Learning posts: MLFlow is an open-source framework for registering, managing and tracking machine learning models. It is multiplatform, bringing consistent model training and model...continue reading.
In the series of Azure Machine Learning posts: Important asset is the “Models” in navigation bar. This feature allows you to work with different model types -> custom, MLflow, and...continue reading.
In the series of Azure Machine Learning posts: Automated ML is a no-code automated machine learning task. It iterates over many combinations of algorithms and hyperparameters in order to find...continue reading.
In the series of Azure Machine Learning posts: An Azure ML job executes a task against a specified compute target. This is also how the job is created. By configuring...continue reading.
In the series of Azure Machine Learning posts: A pipeline is set of instructions (or a workflow) for executing particular work of a machine learning task. The idea behind pipelines...continue reading.
In the series of Azure Machine Learning posts: Let’s continue to explore the power of SDK and the namespaces. And we will look into namespace that will help you connect...continue reading.