The popularity of Python for data science is understandable since it’s a user-friendly open-source language that’s well-suited to working with large data sets. Additionally, Python users benefit from an engaged community for support. In this article, we’ll explore why Python has become a go-to language for data science and how it's being used in machine learning (ML) and artificial intelligence (AI) applications.
Python in Data Science, AI, and ML
Python is ideal for data science due to its flexibility and rich ecosystem of open-source packages. It’s used to facilitate the cleaning, preparation, and analysis of many data types. Using Python and its open-source ecosystem, data scientists can more efficiently uncover new patterns, relationships, and trends in big data using techniques such as statistical analysis, data visualization, and machine learning algorithms.
Data scientists rely on Python for AI and ML application development. Python AI and ML libraries help data scientists work with complex mathematical functions, image processing, 2D plotting, and more. Popular Python libraries for AI and ML projects include Pandas, Numpy, SciPy, TensorFlow, XGBoost, Pytorch, and Scikit-learn.
What Makes Python Ideal for Data Science Applications?
Python is an all-purpose, object-oriented programming (OOP) language with several characteristics that make it ideal for data science.
Python is a versatile, high-level coding language. In addition to ML and AI applications, Python is commonly used in data analytics, data engineering, and image processing. It has also become a go-to for fast web application development, largely driven by new open-source libraries such as Streamlit, which enables users to build web apps using only Python.
Ease of use
Unlike many coding languages, Python has a gently sloping learning curve. This is especially valuable for professionals working in data science who may not have extensive experience writing code. Python’s simple, highly readable style uses fewer lines of code than many other languages to accomplish the same objectives. Python is written and read much like natural language, making it easy to read and understand.
As an open-source language managed by the Python Software Foundation (PSF), Python benefits from a deep base of enthusiastic, highly skilled users. Extensive online resources are available to help users learn, troubleshoot, and develop new ways to apply this language. The PSF hosts a variety of forums on its website with access to over 1,600 local Python user groups available via the Python Wiki.
Python is platform-independent, allowing it to run on nearly every operating system; its programs use an interpreter built for each platform that enables the language to run virtually anywhere. This capability is especially important for organizations that value flexibility and don’t want to be locked into a proprietary system.
Real-World Applications for Python
Python is a true multi-tool, ideal for deployment in a wide variety of applications. Here are just a few examples of how it’s being used today.
Demand prediction for manufacturing
AI can help manufacturers more accurately understand what future demand will look like, improving inventory management. Python-based AI tools can quickly analyze historical data to predict changes in consumer preferences, upcoming trends, or macroeconomic shifts likely to impact future demand for specific products.
Supply chain and logistics
Python is useful in applications designed to improve the performance of supply chain and logistics networks. Machine learning algorithms can help provide advanced last-mile delivery capabilities for vendors and individual consumers. By optimizing delivery routes and providing customers with more accurate, up-to-the-minute details on the status of their shipments, companies can improve their on-time performance while cutting costs.
Predictive analytics in healthcare
Python is also used in the healthcare industry. Predictive analytics programs train on historical data, using what they’ve learned to make predictions based on patterns and trends uncovered in the data. One exciting use of this technology is analyzing patient data sets to forecast the likelihood of certain patients developing particular diseases or disorders. Using massive numbers of patient records to train, algorithms can help physicians spot disease sooner, providing a valuable opportunity to diagnose and treat medical conditions at an earlier stage.
Deploy Python Directly in Snowflake
The Snowflake Data Cloud makes data science simple. Snowpark allows developers to deploy Python code directly in Snowflake. As part of Snowpark, the integrated Anaconda repository provides the growing Python community of data scientists, data engineers, and developers with effortless access to open-source Python packages. This allows them to build secure and scalable data pipelines and machine learning (ML) workflows directly within Snowflake’s elastic performance engine. Accelerating the pace of innovation enables your teams to collaborate on the same single copy of your data, while natively supporting everyone’s programming language of choice. With fully managed, enterprise-grade governance controls and security features across all workflows, organizations can manage libraries with full governance control while preventing unwanted network access.