Polars

Transitioning from Pandas to Polars the easy way — by taking a pit stop at SQL.Continue reading on Towards Data Science » Transitioning from Pandas to Polars the easy way — by taking a pit stop at SQL.Continue reading on Towards Data Science » FOLLOW US ON GOOGLE NEWS Read original article here Denial of responsibility! Techno Blender is an automatic aggregator of the all world’s media. In each content, the hyperlink to the primary source is specified. All trademarks…

PolarAfter releasing the Vantage V3 in October 2023, Polar is back today to announce the Polar Grit X2 Pro in Night Black or Stone Gray. The Titan version in Autumn Leather and Black Silicone is priced at $799.90. A bundle with the Polar H10 heart rate chest strap sells for $869.90. The Polar Grit X2 Pro will be available starting on April 3. !function(f,b,e,v,n,t,s) {if(f.fbq)return;n=f.fbq=function(){n.callMethod? n.callMethod.apply(n,arguments):n.queue.push(arguments)};…

Both Pandas and Polars code are includedContinue reading on Towards Data Science » Both Pandas and Polars code are includedContinue reading on Towards Data Science » FOLLOW US ON GOOGLE NEWS Read original article here Denial of responsibility! Techno Blender is an automatic aggregator of the all world’s media. In each content, the hyperlink to the primary source is specified. All trademarks belong to their rightful owners, all materials to their authors. If you are the owner of the content and do not want us…

Polar wants to help you become a better you with its new Ignite 3 Titanium edition fitness watch, which the company is launching today. Polar says this is an “enhanced version” of last year’s Ignite 3. Sporting new features for an even more capable fitness watch to help you reach your goals. It all starts with a brand-new Titanium bezel that surrounds all the internals for a more premium look. And dare we say it, an honestly better style. The use of Titanium is also more lightweight and yet it’s still strong and…

Core ObjectsPolars dataframe and series representations (Image by author)In this section, we will explore the fundamental concepts of Polars. As always, to enhance your learning experience, a Jupyter Notebook was utilized for executing code snippets in this article. As many are aware, Jupyter is an interactive computing platform accessible through any web browser, enabling us to create and share documents containing live codes along with visualizations and explanatory text, making learning more engaging than ever…

Basic recommenders that are easy to understand and implement, as well as fast to trainCreated by me on dreamstudio.ai.Recommender systems are algorithms designed to provide user recommendations based on their past behavior, preferences, and interactions. Becoming integral to various industries, including e-commerce, entertainment, and advertising, recommender systems improve user experience, increase customer retention, and drive sales.While various advanced recommender systems exist, today I want to show you one of the…

You may need to make the transition soonPhoto by Daphné Be Frenchie on UnsplashI admire Pandas. I have been using it since the first day I started learning data science. Pandas has been more than enough for most of my tasks in data cleaning, preprocessing, and analysis.The only issue I have with pandas is when working with large datasets. Pandas does in-memory analytics so its performance starts to go down when the data size becomes very large.Another downside associated with data size is that some operations make…

Learn how to use SQL to query your Polars DataFramesPhoto by Hans-Jurgen Mager on UnsplashIn my previous few articles on data analytics, I talk about two important up-and-coming libraries that are currently gaining a lot of tractions in the industry:DuckDB — where you can query your dataset in-memory using SQL statements.Polars — a much more efficient DataFrame library compared to the venerable Pandas library.What about combining the power of these two libraries?In fact, you can directly query a Polars dataframe through…

A cheat sheet of the most common Pandas operations translated into PolarsImage by author via MidjourneyTime for goodbyes!Pandas can do anything. Virtually anything. But (and this is an I-wish-a-million-times-it-was-any-other-way but) it lacks speed. Pandas just can't keep up with the pace at which the size and complexity of today's datasets are growing.Pandas author, Wes McKinney, states that when he wrote Pandas, he had this rule of thumb in mind for his library:Have 5 to 10 times as much RAM as the size of your…

I came for the speed, but I stayed for the syntaxPhoto by Hans-Jurgen Mager on UnsplashAt the time of writing this post, it’s been six years since I landed my first job in data science. And, for those entire six years spent doing data science, Pandas has been the foundation of all my work: exploratory data analyses, impact analyses, data validations, model experimentation, you name it. My career was built on top of Pandas!Needless to say, I had some serious Pandas lock-in.That is, until I discovered Polars, the new…