Hire the Top 3% of Freelance Python Development Experts

Generally, any project can be done in any programming language, like Java, C, or Python. However, having a concrete list of technical requirements and business priorities can help you decide which path to take. This guide will outline some of the technical advantages of Python programming, while taking into account what a business might need and signs you might see at different stages of your project.

1. Prototyping and Iterating Quickly

Suppose you have a great idea and you want to make a business out of it. At this stage, your purpose is mostly to understand your customers and get feedback from them as quickly as possible.

Simultaneously, your purpose is to write as little code as possible. Less code means developers having to keep less in mind and being able to refactor more quickly.

Python coding is great exactly because of this: Its terseness allows your developer to move faster and to be more agile in a time when code does not matter as much as ideas and user feedback and engagement.

2. Delivery Mechanism: Web vs. Desktop vs. App

Let’s consider the following scenario: You’re at the inception stage of your application and you want to reach as many clients as possible, so obviously you choose the web as a delivery mechanism.

However, you know that a desktop version would benefit some customers a lot because of some tighter hardware integration your application might need. For instance, it might need to control some hardware appliances.

A similar thought might cross your mind regarding mobile devices or the IoT world, depending on what your application’s domain might be.

What’s for sure is that whether you’ve chosen the web first, or another delivery mechanism, there will be a point further down the line when you will want to offer one or more additional delivery mechanisms.

Python development simplifies this process. A good Python developer will know how to architect the application from the start, so as not to lock it into only one specific delivery mechanism.

3. Cloud Computing

Cloud computing allows businesses to scale up their needs in terms of CPU power, memory, and/or disk space, dynamically and as needed. But it also allows them to scale back down to cut costs when those resources are not needed.

There are many cloud computing providers, each allowing you to meld their infrastructure with your needs at different abstraction levels of your architecture, ranging from the hardware level to the service level. All major cloud computing platforms provide Python libraries for automating scaling up and down of the allocated resources.

If your locally-hosted solution is hitting performance problems, or you need more juice for those long-running cron jobs during the night, you might want to pull in an expert to help you offload and speed up the Python programming you need done.

Cloud computing cannot indefinitely scale up architectures which are by design not scalable—the ones in which scaling up would increase certain requirements exponentially. A Python expert will assess your architecture and suggest using cloud computing where it makes sense, as well as changes in your architecture where up-front scaling could create more damage than improvement.

And if you are already using cloud computing but are still experiencing performance problems, a Python expert with direct experience in distributed systems and architectural refactoring will know how to solve them.

4. DevOps with Python, or Even a “Pythonic” Shell

DevOps is the domain that combines programming with infrastructure. It’s used to put machines in a certain state (installed packages, firewall rules, running processes, configuration files, etc.) and usually operates in the realm of a cloud. The intention is normally making all the relevant machines work in concert to solve a business problem.

We call the action of making a machine be in a specific state provisioning that machine. The go-to solution for provisioning machines in the Python ecosystem is called Ansible, which is itself also developed in Python and, by the law of least resistance, can also be extended and adapted with Python code.

Ansible “playbooks” are easy to write, understand, extend, and run in parallel on multiple machines. They have no special requirements besides a machine being ssh-accessible and having the standard Python interpreter installed. Once your machines finish executing a given playbook successfully, they are guaranteed to be set in the state described by the playbooks.

If this kind of control is not enough, not even by extending Ansible with modules, then a Python programmer can dig even deeper and use libraries like Paramiko for ssh access directly.

If this kind of control is too low-level, not to worry. Python has your back with xonsh (pronounced “conch”), a bash-facing shell written in Python in which you can actually run Python code, beside the regular process-control jobs a shell is meant to be used for. Being able to run Python code means that you can import and call any packages there might be installed on the system, with the added benefit of being able to write well-structured shell scripts that are more robust and predictable.

Provisioning anything from cloud infrastructure to on-premise appliances, a Python developer will know how to automate it effectively.

5. Machine Learning

Machine learning allows your business to analyze and understand a larger amount of data, and Python is here to cover your needs.

From web scraping to sentiment analysis, from search engines to recommendation engines, from speech to image recognition, from constructing mathematical models based on historical data to timeseries forecasting, the Python ecosystem has taken its motto “batteries included” to a new level, by providing libraries and tools for all of these contexts.

If your existing project uses one or more of the following libraries, then you are ready to hire a data scientist with Python knowledge. The same applies if your project doesn’t use any machine learning yet, but the concepts mentioned below catch your attention.

• NumPy is a library for numerical computing. At its core is the concept of an N-dimensional array. It can do linear algebra, compute Fourier transforms, and generate random numbers according to more than 30 probability distribution models.

• Pandas can help load data from many sources, including even Excel tables. It can do data operations that are easy to refactor because its API allows cutting through the data in various ways.

• SciPy is focused more on the scientific aspect of machine learning, allowing the programmer to compute integrals numerically, to solve differential equations, and to use sparse matrices.

• Scikit-learn is built on top of such libraries and provides a homogenous framework for a Python programming project, even if it’s merely exploratory. But it’s also a good fit for intensive calculations in a distributed cluster—e.g., it can integrate with Apache Spark.

• Last but not least, matplotlib can be used to visualize and make sense of your data.

Python packages and libraries cover the whole spectrum of machine learning concepts. This fact has drawn many data scientists and engineers to Python, and this trend continues to grow.

6. Ability to Optimize Later on, as Needed

One aspect which might make you hesitate putting all your bets on Python programming is performance. This, however, can be easily counteracted by rewriting the performance-critical parts of Python modules in C.

This possibility reconciles the need to be able to refactor easily (as mentioned in our first sign above) with the performance requirements you may have. A lot of Python modules employing CPU-intensive algorithms already take this approach. A top Python developer will know how to make such optimizations to your existing code base.

More often than not, you do not need to optimize every last piece of code: It suffices to move some tightly nested loops and the algorithm implemented by them into C modules and leave the outside world in Python. A talented Python developer will notice where to draw a line and which algorithms are worth it. In fact, many machine-learning Python libraries employ this approach by embedding well-known libraries written in C or Fortran.

7. Rich Ecosystem: Libraries, Events, Talented Programmer Pool

Consider the situation in which you have an idea and you need a digital means to let people use your idea. You don’t know what the technical requirements are going to be, but what you do know is that you want to minimize risky scenarios like these:

• Your only programmer leaves you alone in your project and it is difficult to find a trustworthy replacement.

• Your enterprise grows too much for your team and you need more programmers to join forces.

• You start to use a technology, but halfway through the project, you realize some hidden requirements are not supported well by the programming language you had initially chosen.

• The competition gains an advantage by using a library in a different language than your tech stack supports.

Using Python reduces these risks because there is such a large pool of talented Python programmers to be found online and at conferences and other events. With Python coding, it is also easy to embed external libraries and build on top of battle-tested code, instead of reinventing the wheel.

8. PEP Standards and a Strong Community Further Reduce Risk

Speaking of risk, a more technical solution employed by the Python community to reduce risks are the so-called PEP standards. They are a collection of documents outlining how code should be written, documented, tested, and other processes around crafting programs written in Python.

The risk reduction comes from the fact that, by respecting these standards as a community, it is easier for any Python programmer to hit the ground running when joining a new project or a new company like yours. And since the Python community is tightly knit together, most Python programmers respect these standards—to everyone’s benefit.

If your code base gives signs of not being on par with the latest PEP standards, you should hire a top Python developer to get it into better shape.

9. Strange Program Behavior/The Curse of Duck-Typing

The Python programming language implements a type system called duck typing, meaning that, if a class (a concept used to model real-world ideas in code) looks from the outside like another class, then the two classes in question are compatible.

This is in contrast with stronger kinds of typing as supported by languages like C++, Java, and Rust. Duck typing is an advantage because it allows a smoother refactoring process instead of having to respect strict contracts, but this advantage comes with the caveat that inexperienced programmers might get used to sloppy programming.

By following the train of thought behind duck-typing, classes also allow completely foreign classes inside the same Python process to access and modify any of their properties.

As a consequence, if you have a Python program which sometimes behaves in an inexplicable way, it might be that there’s something else inside your code changing it, disregarding principles of data integrity. Such behavior is difficult to debug, but seasoned Python experts are certainly capable of helping you fix such problems.

10. Tests, Code Coverage, and Technical Debt

Technical debt is the amount of technical change which you delay to implement in code in order to maintain development velocity. Having a few such “warts” in your code is usually harmless, but getting into the habit of maintaining velocity at the cost of technical debt can be dangerous for your digital business.

A test suite is a secondary program which you run before putting the system into a production environment. A well-thought-out and extensive test suite will give you confidence that you’re not going to expose customers to bugs—bugs which might drive them away.

A test suite also protects you from technical debt, in a sense. Suppose that debt has reached an unbearable level for your architecture. If the programmer implements a clean solution for a specific requirement, and the test suite starts to fail when testing parts of code which should be unrelated, then it’s a sign that it’s time to reduce the baggage of technical debt you’ve accumulated over time.

Beside the advantage of signaling technical debt, a test suite also documents the code. Let’s face it, programmers don’t naturally like to write documentation for their code. Even if a programmer writes great documentation during the initial implementation, that documentation will deteriorate in time if changes are made to the code but not to the documentation.

But a test suite is meant to be executed at least before each deployment. For this reason, a test suite also serves as a great means of documenting the code in a way that’s always up to date, runnable, and provably correct. It will also help new team members get on board and become more productive more quickly.

When you execute the test suite, you can turn on what is called code coverage, which generates a report telling you which parts of the code are covered by the test suite, which are not covered, and which are covered too much. The information you get from the code coverage is thus twofold: On one side, you get information about the health of the test suite itself, on the other side you get a sense of how trustworthy the results of your test suite are. For instance, a test suite which has 100% pass rate and 1000 tests is not trustworthy if it only covers 0.1% of the code.

If you have any technical debt or shortcomings in your test suite, then before proceeding with further development, it’s time to add some Python expertise to your project to get that cleared up.

The Whole Is Greater than the Sum of Its Parts

Even though each individual sign we mentioned has value by itself, the biggest advantage in hiring Python developers stems from the ability to create a cohesive technical solution for your business idea with only one programming language.

Instead of having separate tools which cannot interact with each other with ease because they’re created by different programmers with different mindsets and in different languages, you can get a Python developer to attack all your problems in a consistent manner across the board.

Besides technical advantages like code reuse, seasoned Python developers can understand the needs of your business better and make decisions that have enterprise-wide benefits.