Why Data Scientists Choose Python for Machine Learning and Artificial Intelligence

Due to the broad ecosystem of packages that streamlined the data preparation and model development process, and because Python is so flexible and easy to use, it has become a go-to for a wide variety of data science applications. Many data scientists rely on Python for machine learning (ML), artificial intelligence (AI), and data modeling projects. In this article, we’ll dig into what makes Python ideal for data science and how it’s being used to help businesses extract the most value from their data. We’ll also explore how Snowflake is expanding options for Python developers to work with their data directly in Snowflake.

Python for ML, AI, and Data Modeling

What began as a hobby project in 1991 has morphed into one of data science’s most widely used programming languages. Here’s why.

Simplicity

Whether you choose to use Python for machine learning, artificial intelligence, data modeling, or other data science applications, its simplicity is one of its greatest strengths. The syntax reads like natural language and doesn’t follow the complex coding conventions that make many other languages so challenging to pick up.

Versatility

Python is an incredibly versatile programming language. It's often referred to as the Swiss Army knife of computer languages. Python has found a place in everything from e-commerce applications to complex IoT networks to deep learning projects. 

Open-source/platform-agnostic

Python is an open-source project that’s platform-independent. This characteristic allows Python code to be run on virtually all operating systems and platforms, making it ideal for projects that involve collaboration.

Large selection of libraries

Python users enjoy a wide variety of ML and AI libraries. A library is a collection of prewritten code that can be customized to meet a specific use case or used as is. Libraries save developers a significant amount of time, speeding up workflows,including data modeling or building data pipelines using Python.

Robust community of developers

Python has a large and avid base of users and contributors. With a vibrant network of millions of developers, it’s easy to get answers, find opportunities to grow your Python coding skills, and plug into an online or offline Python development group. 

Python in Big Data Projects

Python is a natural fit for big data applications, allowing developers to write code for a wide array of projects with clarity and flexibility. Python has become the default programming language for building data pipelines, data models, ML models, and AI software. 

Python data modeling

Data modeling is the process of mapping out and visualizing where data is stored and how each data source fits together into the larger whole. A well-designed data model enables the creation of a simplified, logical database that eliminates redundancy, reduces storage requirements, and enables efficient retrieval of the data it contains. Python’s syntax and environment make it easy to organize different elements of data and standardize the way that they fit together and relate to each other.

Python data pipelines

Data pipelines are a means of moving data from its source, such as transactional data, to a destination, often a data lake. Along the way, data is transformed and optimized, arriving in a state that can be analyzed and used to develop business insights. Python-based data pipelines are flexible and easy to scale. They are well-suited for ingesting and processing large amounts of data before it can be used for machine learning.

Python for data science

Data scientists use various methods, processes, algorithms, and systems to extract insights from data. Python’s simple syntax makes it one of the easiest languages to learn, which is a benefit to data scientists who don’t come from an engineering background or haven’t had extensive programming experience. Data scientists want to spend their time working with data, not getting bogged down with complicated programming requirements. In addition, Python has a large number of libraries and resources designed to simplify and streamline data science workflows, such as rolling out programs and getting prototypes up and running quickly. 

Python for ML and AI

Python offers simplicity, stability, consistency, and ready access to a wealth of libraries and frameworks to speed development, all of which are important in ML and AI projects. Python is also easy to integrate with other languages and provides a well-structured environment for testing and debugging. 

Accelerating Python Development with Snowflake 

Snowflake is bringing enterprise-level Python innovation to life. Here’s why Python developers enjoy working directly in Snowflake.

Snowpark for Python

Using Snowpark for Python, developers can experience the same ease of use, performance, and security benefits of the Snowflake engine by accessing Snowflake’s Snowpark developer framework. This framework empowers data scientists, data engineers, and application developers to collaborate more easily and streamline their data architecture by bringing everyone onto the same platform. Snowpark lets developers collaborate on data in the coding languages and construct familiar to them, while taking advantage of Snowflake’s security, governance, and performance benefits. 

In addition to the Snowpark Python API and Python Scalar User Defined Functions (UDFs), Snowflake now offers support for Python UDF Batch API (Vectorized UDFs), Table Functions (UDTFs), and Stored Procedures. Combined with the Anaconda integration, the Python community of data scientists, data engineers, and developers can now use a variety of flexible programming contracts and effortless access to open-source Python packages to build secure and scalable data pipelines and ML workflows directly within Snowflake.

Streamlit + Snowflake

Streamlit is a pure Python, open-source app framework that turns data scripts into shareable web apps quickly and easily. Compatibility with major Python libraries such as scikit-learn, Keras, PyTorch, SymPy(latex), NumPy, pandas, and Matplotlib makes it incredibly useful for creating web apps for data science and ML applications. Recently acquired by Snowflake, Streamlit is easy to use, flexible, and has a highly active open-source community. When combined with Snowflake’s scalability, scope of data, and governance, Streamlit enables an entirely new class of data apps to be built. Learn more about Streamlit in Snowflake.

Snowflake is the future of data science

Snowflake provides the data infrastructure Python developers rely on to build their data models, pipelines, and ML models. Accelerate your data science and ML workflows with fast data access and elastically scalable data processing for Python and SQL. The Snowflake Data Cloud provides a single place to instantly access all of your data. Snowflake’s fast processing engine and rapidly scalable compute and storage resources provide the speed and flexibility required to unify teams and tools around data.

Learn more: Using Snowflake and Generative AI to Rapidly Build Features