Top 5 Python Memory Profilers

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.


MLOps for Python: Real-Time Feature Analysis

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.


Sort, Filter, and Remap API Data in Python

Are you taking data from an API in the format the web services gives it to you? You should not dictate the structure of data inside your application based on how an API provider structures their data. Instead, you can take advantage of the power of Python's list manipulation techniques to sort, filter, and reorganize data in ways that best suit your needs.


Python Debugging: More Than Just A (Print) Statement

As most developers will agree, writing code is oftentimes, if not always, easier than debugging. As a simple definition, debugging is the process of understanding what is going on in your code. When speaking in terms of Python, it is a relatively simple process. Every developer has their own personal debugging method or tool they swear by. When it comes to Python, most developers use one (or more) of the following: print statements, traditional logging, a pdb debugger, or an IDE debugger.

Deploy Python Apps Into Production In Seconds!

Getting your Python code into production is the most rewarding thing you can do. It's where users meet your apps, and where you finally get recognition for the time, energy, and skill that you've poured into your code. But without the right platform, getting Python into production can be a real pain in the proverbial. Let Ben Wilcock (@benbravo73) show you how to do it in seconds using open-source tools.

Build Docker Containers For Python Apps Like A Pro

Python apps go great with containers. Docker, Kubernetes, Cloudfoundry, Public Cloud, Private Cloud, they're all awesome places to run your containers. But getting your apps into containers is a tricky business, particularly if you have tens or hundreds of apps to manage, and maintain. Your containers have to be secure, reproducible, and easy to rebuild when vulnerabilities strike or upgrades are required.

Make use of Python bundled with IPHost to create new monitors and alerts

Scripting languages (VBScript, Python, PowerShell etc) are both flexible and convenient to create small scripts, to handle a simple monitoring task (such as poll a device for data or execute custom alert). Python has an advantage of being general purpose cross-platform scripting language for years, with many well-known scripts either already available on the Net, or quick to compose.


Building a Python web application with Elastic App Search

This post is a brief summary of a presentation I gave recently where I deploy Elastic App Search, show off the ease of setup, data indexing, and relevance tuning, and take look at a few of the many refined APIs. It’s also written up in a codelab with step-by-step instructions for building a movies search engine app using Python Flask. The app will work on desktop or mobile and is a fast, simple, and reliable way to query the information.