Why Businesses Choose Python for Prototyping and Production

Authors: Will Serene and Adam Gao

For engineering solutions that deliver value, it doesn’t get much better than Python. This open-source programming language is accessible, versatile, flexible, and expressive — advantages that have driven its tremendous popularity. Data professionals throughout the world use Python as their tool of choice as it can easily handle a wide range of tasks from simple data processing to application development and advanced analytics.  

Key advantages include: 

• Intuitive syntax with robust functionality as a multipurpose language 
• Platform independence 
• An extensive number of libraries and frameworks supported by a large and growing community of developers 
• The 137,000+ libraries existing today, many of which are free! (a.k.a. open source) 

RevGen’s team of data experts prefer Python for its intuitive syntax, flexibility as general-purpose programming language, and robust functionality. Additionally, Python is an interpreted language, which eliminates the need for it to be compiled prior to execution, simplifying code debugging and modification. These advantages are especially valued in prototyping and production environments where speed, cost, and reliability must be carefully managed.  

Many Python libraries are written in performant languages under the hood for optimal compute times, addressing concerns for Python’s efficiency. For example, while Pandas is a library accessed through Python, it employs highly efficient indexing algorithms written in Python, Cython, and C. 

Furthermore, Python’s libraries often support intuitive syntax designed for users specific to a domain. For example, Pandas support an intuition like Excel, but in a more “programmable” fashion. You can open a table from a csv file, pivot the columns (or reverse-pivot it using “melt”), and get aggregate values (sum, average, etc.) in as little as three lines of code (four, if you include importing the library). 

The above is an example of one specific subset of business needs, but different Python libraries exist (both open source and paid) for different needs:  

• Need to work with higher-dimensional data as opposed to rows and columns?
• Pytorch (tensors) 
• Numpy (multidimensional arrays) 
• Need ad-hoc dashboarding capabilities? 
• Plotly 
• Visualize plots for business reporting or for ad-hoc purposes 
• Matplotlib and libraries which templatize it like seaborn 
• Connect to SQL databases
• SQL alchemy 
• MySQL 

[Read More: When Omnichannel Data Science Leads to Better Customer Experience]

Examples of Python success stories are abundant across industries and businesses both large and small. Here are just a couple of use cases that illustrate its value: 

Python Use Case 1 

RevGen’s team leveraged Python’s flexibility to overcome data complexity and non-trivial computing requirements, creating a systematic yet iterative data science solution through rapid prototyping for a national telecommunications company.

Problem:  

Enormous amounts of disparate data, complicated security protocols that obscured the data’s location and accessibility, and non-trivial computing requirements. Moreover, a lack of formal processes around data science initiatives added to the complexity and challenged the company’s goal of understanding the optimal allocation of resources across several customer interaction channels.  

Solution: 

RevGen was able to quickly consolidate disparate data sources into one master file and perform Principal Component Analysis (PCA) with Python and Jupyter Notebook to prioritize the various types of customer data. This paved the way for efficient and effective analytics, revealing a fourth and previously undiscovered customer segment as well as the creation of personas across all four segments.  

Among its advantages, Python’s clean syntax and robust functionality helped RevGen provide the company with documentation for analysis code, specifications, and build modularity in their data science processes, aiding the company’s in-house data science team in future projects. 

Python Use Case 2

A global software developer integrated their core technology with existing customer applications through a Python API, building the foundation for what has evolved into a growing developer community within the maritime industry.  

Problem:  

Complex engineering specifications unique to customer requirements challenged the efficiency of shipbuilding among the company’s clientele.  

Solution:  

A Python API was built after easily integrating the programming language with the company’s platform, allowing shipyards, designers, and equipment suppliers to develop their own functionality while leveraging the expertise of software company’s core technology. This has allowed their customers to “reduce design time of certain complex ship structures from four weeks to two days, while improving overall quality.”   

This impressive efficiency gain would not have been possible without key advantages embedded in their Python API, which include platform independence, relative ease of use, and the robust capabilities of a modern, multipurpose programming language. 

Conclusion

As evidenced by these success stories, Python is a proven tool for delivering valuable solutions. Looking forward, RevGen stands behind Python as a critical programming language for businesses, especially those engaged with data science, machine learning, and artificial intelligence. The discovery of new insights and competitive advantages gained with Python make us confident it will continue to be one of the most popular programming languages for years to come. 

To learn more about RevGen Partner’s data and analytics practice, visit our Analytics & Insights page. 

 Will Serene is a senior consultant at RevGen. He is focused on and motivated by optimizing our client’s use and management of their data, from analytics and architecture to security and privacy.

 Adam Gao is a data scientist at RevGen, passionate about executing quantitative solutions to empower real-world decision making.