Python is a prevalent and easy-to-understand object-oriented language that you can use to develop data science projects focusing on machine learning/AI, analytics or visualization, web/GUI development projects, basic programming or even scripting.
It ranked third most popular language in the 2021 StackOverflow Developer Survey and is very popular in the finance, fintech and tech industries. Google promoted Python from the beginning and later developed the open-source ML package TensorFlow. It is being used extensively by engineers at Netflix, Facebook, Spotify, NASA, Uber or Dropbox in their environments.
It integrates with most cloud and platform-as-a-service providers, supports parallel processing enabling large-scale performance in data science projects, and extends with modules written in C/C++. Open-source packages such as scikit-learn, pandas, SciPy, NumPy offer developers algorithms for numerical computations, machine learning or data analysis tasks and are well known in the data science community.
This article will review the main qualities to look for in a Python developer and help you narrow down your search. It will determine the different jobs a Python developer can do, describe how to assess their non-technical and technical skills, and share tips on spotting the best developers in the field.
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How to Find the Best Python Developers for Your Project
As with every programming language, a good developer should have strong fundamentals. For Python, this means that a candidate should understand Python environments, data structures, data types and variables, modules and functions, conditionals and loops, classes, exception handling, and file handling. A good candidate will understand Object-oriented design, object-relational mapping, operations with lists, lambdas, string formatting and be familiar with some of the most used Python packages: Pandas, Requests, NumPy or Matplotlib.
On top of the fundamental knowledge, a good candidate will also be familiar with Python’s limitations, know how to debug applications, write tests, visualize data and break up complex logic into multiple pieces.
Great programmers will write reusable code to share with the team or use in future projects. They are consistent at documenting their code, annotating functions, and writing easy-to-read code for their peers. They also enjoy being up-to-date with the latest developments in the community.
If you are looking for Python developers with experience in data science and machine learning, you should look for developers familiar with a broader range of packages: Scikit-learn, Tensorflow, Keras, PyTorch, Plotly or Seaborn for visualization and be familiar with multi-processing architectures.
Non-Technical Skills to Assess in a Python Developer
Resourcefulness and a solution-oriented
Resourcefulness and a solution-oriented mindset are characteristics of a strong Python developer. Someone who is willing to learn and can put the newly discovered concepts to actual use will make a great candidate. Promising candidates will talk about the last time they tried a new package, books or articles they read, blogs they wrote, if they are part of any online community, or are currently learning any skills as part of a course. It’s a good practice to ask candidates to share how they keep up to date with the latest developments.
A critical skill to look out for is communication. A good Python developer should understand other developers and make oneself understood. Python developers will often collaborate with stakeholders with different backgrounds: business analysts, product managers, DevOps specialists, or other developers specialized in other programming languages, and it will be essential to communicate ideas and find a compromise. A good question to ask is:
“Can you give an example of a time you had to handle disagreements or different points of view and come to a compromise when working as part of a team?”
It is essential to ensure candidates can adapt to different project requirements or priorities. Python developers will frequently clean and manipulate data in data science projects before applying machine learning techniques and visualizing results. Promising candidates will be open to any challenge, even if they arise outside of their comfort zone or if they appear to be mundane tasks. It’s good to ask candidates if they encountered situations when they worked on less exciting tasks and how they approached them.
Great Python developers know how to organize their time, prioritize tasks and deliver results to keep a project progressing forward. You can ask candidates for examples of situations when they had difficulty managing their time at work, how important it is to them, and how they limit distractions.
Lastly, the best Python developers are very analytical and can quickly identify the cause-and-effect relationship when dealing with a problem. It’s a good practice to ask candidates to give examples of times when they had to get to the root of a problem and describe the steps they took.
Technical Skills to Look For in a Python Developer
Core Technical Python Skills
To work on most software development projects in Python, developers should have a minimum set of technical skills and understanding of concepts.
For a Python developer, the foundation should start with universal computer programming concepts like data types, containers, functions, conditions, loops, language syntax, and the runtime environment. An entry-level programmer should also know how to install Python, create separate environments, understand how a program uses memory and storage, and calculate information. Further on, entry-level programmers should be familiar with formatting strings, working with files and creating or manipulating data structures such as lists, dictionaries and tuples. Programmers new to Python will be able to create subprocesses, manage resources, handle exceptions, and control the execution of a program.
Stepping up, advanced Python programmers are familiar with scraping, parsing, and accessing data from web APIs. These developers know how to create and distribute packages, apply design patterns, create an object-relational mapping and handle database connections. They are familiar with popular database packages such as pyodbc, SQL-Alchemy, PyMySQL, PyMongo or SQLite.
Skills Required for Data Science Projects in Python
In addition to foundational skills and familiarity with commonly used packages, Python developers with experience in data science projects will know how to apply machine learning techniques and algorithms using at least one of the popular packages such as TensorFlow, SciPy, Keras, Scikit-learn, PyTorch, XGBoost or Statsmodels.
For data visualization, strong candidates should be familiar with Plotly, Seaborn, Matplotlib, Bokeh, Folium or Altair. If you are looking for someone to build analytics dashboards, candidates with experience in Dash by Plotly or Bokeh should be a good match.
Top candidates can demonstrate a good understanding of multi-processing architectures using packages such as Dask, Vaex or datatable. They will also be able to estimate the complexity of code in terms of space and time and work on improving it before asking for more hardware resources. You can ask questions that require the candidates to give examples of times when they improved the performance of the code and reduced the time needed to run or the memory used.
Skills Required for Web or Desktop Application Development in Python
Django or Flask are the most popular Python web development frameworks on the back-end side, so if you are looking for a Python developer for this purpose, make sure he is familiar with one of those.
For desktop development, developers should know one of the frequently used packages: PyQT, Tkinter, Kivy or PyGUI, or the wxPython toolkit.
Skills and Frameworks Desirable for Both Web and Data Science Python Programmers
There are technical skills that are transferable between web development and data science projects.
Every Python programmer must be familiar with at least one IDE. The most popular IDE for data science projects is JupyterLab, and successful candidates should be familiar with creating Jupyter Notebooks, setting up code environments and installing 3rd party packages. Other popular alternatives are Spyder, Atom, PyCharm or Visual Studio Code, with the last two more suitable for web development projects.
Python programmers working on either data science or web development projects will require reading and writing data from and to databases most of the time. They must know how to manage database connections from Python code, commit database transactions, create and run SQL queries and create object-relationship mappings. Several Python packages such as Pyodbc, PyMySql, SqlAlchemy, PyMongo and many others allow developers to interact with databases. If your project relies on extracting data from databases, make sure candidates have experience using at least one.
Data can also be available over API endpoints or sitting in flat files on a locally accessible path or in a cloud service. A good Python programmer with a data science background will be familiar with accessing data from various sources using packages such as Requests, BeautifulSoup or Scrapy. At a minimum, candidates should be familiar with web APIs, different file formats such as JSON or XML, security authentication via API tokens, and reading and writing flat files.
Technical Interview Questions for Python Developers
If you’re interviewing a candidate for a Python development role, it’s important to assess their knowledge of the language’s core features and best practices. These five questions cover topics ranging from Python’s thread safety mechanisms to its functional programming features, and can help you gauge the candidate’s understanding of the language’s data structures, syntax, and performance optimization techniques. By asking these questions, you can get a sense of the candidate’s experience with Python and their ability to write efficient, readable, and maintainable code.
Question: What is the role of Python GIL?
Answer: Python GIL allows only one thread to hold control of the Python interpreter at a time, preventing multiple threads from executing Python bytecode in parallel.
Question: What is the difference between a metaclass and a class in Python?
Answer: A metaclass is a class that defines the behavior of other classes, including how they are created and initialized.
Question: What represents a problem that can be solved using a dictionary in Python?
Answer: The problem of mapping unique keys to corresponding values in an unordered collection of data.
Question: What represents a limitation in Python?
Answer: While Python supports multithreading and multiprocessing, the Global Interpreter Lock (GIL) can limit the performance gains of running multiple threads or processes on a single CPU-bound task.
Question: What is a lambda function?
Answer: A lambda function is an anonymous function in Python that can be defined inline and has a concise syntax for declaring inputs and outputs.
Based on what we covered, you should have a better overall understanding of the Python ecosystem as a whole and how to differentiate between Python developers with experience and background in data science, machine learning, web development, scripting, or general programming.
Python has a huge community of developers, and the online medium is one of the best places to look for one. Contributions to open-source frameworks and participation in data science competitions or hackathons are excellent indicators when looking to hire a Python developer.
Now that you have a better understanding of the core technical skills to look for, a sample of technical interview questions and a range of industry-practices methods, you should be able to thoroughly assess candidates and find the right Python developer to join your team.