Python applications leverage one of the most popular programming languages in the world to deliver exceptional speed of development and performance. With its versatility, ease of use, and extensive collection of libraries and frameworks, Python powers a wide range of today’s data-centric applications. In this article, we’ll explore the primary reasons driving Python’s rapid adoption and share specific examples of how it’s being used in application development. In conclusion, we’ll share how Snowflake enables developers to efficiently build powerful Python applications.
Why Is Python So Popular for Application Development?
Python has many characteristics that make it an ideal choice for data-driven application development. Here are six reasons why data scientists prefer Python.
Python is an open-source language. Without any of the use restrictions that accompany proprietary languages, it’s free to use and distribute, even for commercial purposes. Python’s open-source nature is a significant factor in its widespread adoption for application development.
Extensive number of libraries and frameworks
Python benefits from an enormous collection of libraries and frameworks, many with direct application to app development. These prewritten snippets of code allow developers to work faster and more efficiently. Python’s libraries and frameworks help developers solve common issues, optimize performance, and add new functionalities without needing to write new code from scratch.
Active developer community
Python has a large, dedicated community of users who contribute to user education. The community has also created a wealth of resources for both new and advanced Python users, including online communities, forums, user groups, conferences, and events. In addition, the Python Software Foundation (PSF), a nonprofit organization that supports the development of Python, offers many resources and initiatives to support the development community, such as funding for projects and support for conferences.
Python is an interpreted language, with code executed line-by-line. This characteristic makes it easier to identify and fix errors. Python provides actionable error messages that can be used to quickly identify the root cause, including the source lines that contain the error. Robust debugging tools such as Sentry and integrated development environments (IDEs) and software development kits (SDKs) provide advanced monitoring and debugging capabilities.
Compatibility with other languages and tools
Python works well with a variety of other programming languages, including Java and C/C++, making it possible to build applications with a combination of languages. It’s also compatible with several popular full-stack and micro frameworks including Django, Flask, and Pyramid.
Python programs work on any platform, including Windows, Mac, Linux/UNIX, and the Android and iOS mobile operating systems. This cross-platform capability allows the same Python code to be run on different platforms with little to no modification.
Python Application Development Use Cases
Python is a robust and flexible programming language that underpins many modern technologies. Here are three examples of how it's being used to develop data-centric applications.
Python is frequently used in web application development, helping developers quickly build prototypes and minimum viable products (MVPs). Its ease of use, flexibility, and large collection of libraries and frameworks streamline development tasks such as database integration and writing and running tests that ensure the app is bug-free and working as intended. Many APIs are also Python-based.
Python is the workhorse of the data science community, with libraries and tools purpose-built for data science workflows. Python plays an important role in nearly every stage of the data science process, including data cleaning and preprocessing, data visualization, and statistical analysis, as well as data storage and management.
Python also shines in machine learning applications. It’s used in a number of machine learning processes, including data manipulation and visualization. Python’s extensive collection of ML libraries provide algorithms and tools needed for tasks such as regression, classification, clustering, and deep learning. These libraries are used to create ML models for many different applications, including image and speech recognition, natural language processing, and predictive analytics.
How Snowflake Is Advancing Python Application Development
With fast data access and elastic, scalable data processing, Snowflake is purpose-built for data science and machine learning applications, including Python development. Snowflake offers a unique set of tools and capabilities, enabling developers to write code in familiar programming languages and execute it directly within Snowflake. Here are two ways Snowflake is helping developers do more with their data.
Snowpark for Python
By utilizing Snowpark, developers can enjoy the same performance, ease of use, and security advantages of the Snowflake engine using Snowflake's developer framework. This framework enables data scientists, data engineers, and application developers to collaborate more effectively and streamline their data architecture by bringing everyone onto the same platform.
Snowpark enables developers to work on data in their preferred coding languages, including Python, while taking advantage of Snowflake's performance, governance, and security benefits. Moreover, Snowflake now provides support for Python UDF batch API (vectorized UDFs), table functions (UDTFs), and stored procedures, in addition to the Snowpark Python API and Python user defined scalar functions (UDFs). By combining these offerings with the Anaconda integration, the Python community of data scientists, data engineers, and developers can now use a range of flexible programming contracts and have effortless access to open-source Python packages to construct secure and scalable data pipelines and ML workflows within Snowflake directly.
Streamlit + Snowflake
Streamlit is an open-source app framework that is entirely based on Python, enabling data script conversion into shareable web apps quickly and easily. Streamlit’s compatibility with several major Python libraries—such as PyTorch, scikit-learn, SymPy(latex), Keras, NumPy, pandas, and Matplotlib,—is incredibly useful for developing web apps for data science and ML applications. Following its acquisition by Snowflake, Streamlit has become even more versatile and is now supported by a thriving open-source community. When combined with Snowflake's scope of data, scalability, and governance, Streamlit provides the means to develop an entirely new category of data apps.
Snowflake Is the Future of Python Development
Snowflake is the leading infrastructure for Python application development. With fast data access and elastically scalable data processing for Python, it accelerates data science and ML workflows, including building data models, pipelines, and ML models. The Snowflake Data Cloud provides a single, convenient location to instantly access all of your data. Snowflake's processing engine and scalable compute and storage resources unify teams and tools around data quickly and flexibly.