You could say a lot of things about AWS, but among the cloud platforms (and I've used quite a few) AWS takes the cake. It is logically structured, you can get through its documentation relatively easily, you have a great variety of tools and services to choose from [from AWS itself and from third-party developers in their marketplace]. There is a learning curve, there is quite a lot of it, but it is still way easier than some other platforms. I've used and abused AWS and EC2 specifically and for me it is the best.
Based on our record, Amazon AWS should be more popular than Pandas. It has been mentiond 364 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
It's also possible for you to give a package an alias by using the as keyword. For instance, you could use the pandas package as pd like this:. - Source: dev.to / 13 days ago
Python is a natural fit for serverless development. It boasts a vast array of libraries, including Powertools for AWS and robust libraries for data engineers. Its versatility and excellent developer experience make it a top choice for serverless projects, offering a seamless and enjoyable development experience. - Source: dev.to / about 1 month ago
In data analysis, managing the structure and layout of data before analyzing them is crucial. Python offers versatile tools to manipulate data, including the often-used Pandas reset_index() method. - Source: dev.to / 24 days ago
Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail.... - Source: dev.to / 3 months ago
Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts. - Source: dev.to / 5 months ago
In 2006, Amazon launched EC2 and S3 which was the foundation of the first major cloud platform, AWS. Amazon decided to essentially provide their users with storage and virtual machines to operate. They had excess servers in their datacenters and saw this as an opportunity to make some extra money. - Source: dev.to / 8 days ago
To start using AWS, you need to create an AWS account. You can sign up for an AWS account at https://aws.amazon.com/. Once you have an account, you can access the AWS Management Console, which is a web-based interface for managing AWS services. - Source: dev.to / 11 days ago
Image credits: All images are sourced from the AWS website (https://aws.amazon.com/). - Source: dev.to / 22 days ago
For this article, you will need: i. A Google account for your app password generation Ii. A Linux terminal. I used the AWS console. You can sign up for a free 1yr tier account here. - Source: dev.to / 23 days ago
If you don’t already have an AWS account, sign up for one at https://aws.amazon.com/. Once you have an account, log in and go to the Elastic Beanstalk service. - Source: dev.to / about 1 month ago
NumPy - NumPy is the fundamental package for scientific computing with Python
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Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Microsoft Azure - Windows Azure and SQL Azure enable you to build, host and scale applications in Microsoft datacenters.
OpenCV - OpenCV is the world's biggest computer vision library
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