Python is one of the leading programming languages in the world. Many of the world’s most popular websites run, at least in part, on Python, including Instagram, Google, Instacart, Uber, Netflix, and Spotify. When the language is used properly, it can be very efficient and requires low processing power in most cases. It’s important for sites to rely on python load testing best practices and security testing services by companies like QAwerk to understand how efficiently their site runs.

Python optimization is the solution to speed performance issues. But, when do you optimize, and what parts of the code should be optimized? This article will help you answer these questions. Developers always want to efficiently write neat code. However, things are quite different when working with a Python-based data science project. There will be situations where you need Python optimization. However, there are cases where optimization yields irrelevant results.

Snowflake and Saturn Cloud are thrilled to announce our partnership to provide the fastest data science and machine learning (ML) platform. Snowflake’s Data Cloud comprises a global network where thousands of organizations mobilize data with near-unlimited scale, concurrency, and performance. Saturn Cloud’s platform provides lightning-fast data science. Combined, our solutions enable customers to maximize their ML and data science initiatives.

The realm of computing is endlessly vast, and programmers can utilize numerous programming languages that facilitate logging. Novice programmers often wonder, why is Python considered the ideal language to learn logging?

Python is not known to be a "fast" programming language. However, according to the 2020 Stack Overflow Developer Survey results, Python is the 2nd most popular programming language behind JavaScript (as you may have guessed). This is largely due to its super friendly syntax and its applicability for just about any purpose.

One of the struggles developers face is how to catch all Python exceptions. Developers often categorize exceptions as coding mistakes that lead to errors when running the program. Some developers still fail to distinguish between errors and exceptions. In the case of Python application development, a python program terminates as soon as it encounters an unhandled error. So, to establish the difference between errors and exceptions, there are two types of errors.

During the course of execution, software programs accumulate several data objects that serve no purpose in the program and need to be let go. If not dealt with, they can keep eating up memory and significantly hamper performance. Garbage collection is the process of cleaning up all these useless data objects that continue to reside in memory. It frees up the corresponding RAM to make room for new program objects.

According to the Stackoverflow survey of 2019, Python programming language garnered 73.1% approval among developers. It ranks second to Rust and continues to dominate in Data Science and Machine Learning(ML). Python is a developers’ favorite. It is a high-level language known for its robustness and its core philosophy―simplicity over complexity. However, Python application’s performance is another story. Just like any other application, it has its share of performance issues.

In this video, we are going to take a look at what memory bloat is, what causes it, and how you can use Scout to eliminate it from your applications. Memory related performance issues have the potential to bring your entire application down, and yet, most APMs completely ignore this fact and fail to provide any useful way of monitoring memory usage at all.

Data scientists today have to choose between a massive toolbox where every item has its pros and cons. We love the simplicity of Python tools like pandas and Scikit-learn, the operation-readiness of Kubernetes, and the scalability of Spark and Hadoop, so we just use all of them. What happens? Data scientists explore data using pandas, then data engineers use Spark to recode the same logic to scale or with live streams or operational databases.