Apart from becoming almost a standard in the data world, here are 7 more reasons why you should learn Python if being a data pro is your goal.
Since its release in the 1990s, Python has emerged as one of the leading programming languages. Its versatility makes it the best or one of the best choices for most tasks performed by data professionals.
Anyone can claim that anything is the most popular something. I consider myself a data pro, so let me back up my claim by data.
How Popular Is Python Really?
I’ll use three sources to demonstrate Python’s popularity.
1. Stack Overflow’s 2023 Developer Survey
Stack Overflow is a popular programming community where you can ask or answer others’ questions.
They’ve been conducting surveys among developers for the last 13 years. One of the questions is about the programming languages’ popularity. The popularity is here defined by being used in “extensive development work … over the past year” and the one developers “want to work in over the next year”.
In this year’s survey, Python ranked fourth in the answers from more than 67,000 professional developers. (Developer here is defined as anyone who writes code.)
Since HTML/CSS are not programming languages, we could argue that Python is the third most popular programming language among data pros, behind JavaScript and SQL.
2. TIOBE Index
The TIOBE Index is the programming language’s popularity indicator. This index is “based on the number of skilled engineers worldwide, courses, and third-party vendors.” Popular search engines’ results for each language are also thrown into the mix.
The index is updated monthly. In October 2023, Python claimed the first spot.
If we look at the historical data, we can see that Python was the programming language in three years out of the last five.
3. PYPL Index
The PopularitY of Programming Languages Index is another method that shows the popularity of Python. It’s an index that takes into account “how often language tutorials are searched on Google”.
The October 2023 index shows that Python is again the most popular language.
If you look at the historical data, you can see that Python has been the most popular language in the last five years or so.
Reasons for Python’s Popularity
Now that we’ve established the fact that Python really is popular among data professionals let’s see what the reasons for that are. They should be enough to convince you to learn it, too.
Reason #1: Versatility and Flexibility
Python is a general-purpose programming language. This means you can use it in various contexts required by the diversity of data jobs.
These are the main uses Python excels in:
- Data Analysis
- Data Visualization
- Task Automation
- Machine Learning
- Web Development
- Software Development
It shows Python is useful in any job data that at least dabs into one of these tasks. In short, any data job! If you’re targeting data science, here are all the skills you’ll need in that field.
Reason #2: Python Libraries
What allows Python to be so versatile are not only its intrinsic characteristics but also the richness of available libraries. And what is a library, I hear you ask. A Python library is a collection of codes and functionalities designed for a specific purpose.
For each of the main Python typical uses, there are at least several dedicated libraries that make this task easier to do:
Data Analysis
- pandas – data manipulation and analysis
- NumPy – numerical computing
- SciPy – advanced scientific computing
- math – Python’s built-in module for mathematical operations
Data Visualization
- matplotlib – basic plotting
- seaborn – statistical data visualization
- plotly – interactive plotting/APIs
- bokeh – interactive visualization for web browsers
- Vega-Altair – declarative statistical visualization
- GeoPandas – geospatial data visualization
- HoloViews – interactive visualization
- Pygal – Scalable Vector Graphics (SVG) plots
- folium – geospatial data visualization on interactive maps
- Dash by Plotly – analytical web applications
- plotnine – for statistical visualization
- NetworkX – for network graphs
Task Automation
- Automate – automation
- PyAutoGUI – Graphical User Interface (GUI) automation
- Selenium – web browser automation
- Schedule – job scheduling
- Fabric – streamlining the use of SSH for application deployment
- Celery – distributed task queue
- Invoke – task execution and command-line tooling
Machine Learning
- scikit-learn – general-purpose machine learning
- TensorFlow – deep learning and neural networks
- Keras – high-level deep learning
- PyTorch – deep learning and neural networks
- XGBoost – gradient boosting framework
- LightGBM – gradient boosting framework
- CatBoost – gradient boosting framework
- statsmodels – statistical modeling
- NLTK – natural language processing (NLP)
- spaCy – NLP
- Gensim – topic modeling and document similarity analysis
- fastai – deep learning
- H2O-3 – general-purpose machine learning
- Prophet – time series forecasting
- Neural Structured Learning (NSL) – neural graph learning
Web Development
- Django – a high-level web framework
- Flask – micro web framework
- Pyramid – web framework
- Web2py – web framework
- Bottle – micro web framework
- CherryPy – object-oriented web framework
- Tornado – web framework and asynchronous networking library
- FastAPI – web framework for building RESTful APIs
- Dash by Plotly – web application framework
- Falcon – web API framework
- TurboGears – web framework
- Masonite – web framework
- Sanic – web framework
- AIOHTTP – asynchronous HTTP client/server framework
Software Development
- PyQt – GUI development
- PySide – GUI development
- wxPython – GUI development
- Tkinter – GUI development
- PyGTK – GUI development
- PyGObject – GUI development
- Cython – performance optimization
- PyInstaller – application packaging
- cx_Freeze – application packaging
- pytest – testing
- unittest – testing
- Sphinx – documentation
- SQLAlchemy – database interaction
- pygame – game development
- Pillow – image processing
- Requests – HTTP requests
- asyncio – asynchronous I/O
- Twisted – event-driven networking
If the sheer number of libraries doesn’t make your head spin, I don’t know what will!
Reason #3: Community Support
All these libraries I mentioned are just a symptom. A symptom of a massive and vibrant community. Not only does this community provide libraries and elevate Python to a language excellent in everything. The community is also where you can get direct help if you run into a problem. There’s a chance someone has already faced it and found a solution. There are also many users more knowledgeable than you – you can learn from them and try to find the solution yourself.
Reason #4: Relatively Easy to Learn
Python was designed to emphasize code readability. This makes its syntax clear and readable, even for those new to coding. Therefore, its learning curve is less steep, which makes it perfect for beginners. They can concentrate on the substance rather than grappling with the complex syntax of a tool they’re using.
Reason #5: Integration Capabilities
Some of the libraries I mentioned are used for integrating Python with other languages and platforms. Python is highly integrable. In other words, you can learn one programming language (Python) and ‘pretend’ you’re working in other languages. For example, it can be used to fetch data from SQL databases or the web; you can integrate it with C/C++ for performance optimization, and so on.
Reason #6: Scalability
Scalability is the ability to handle an increasing workload regarding users, tasks, or data. With increased workload, concurrency becomes more critical – the ability to simultaneously run multiple tasks.
There are some intrinsic Python characteristics that might make it limited when it comes to scalability.
First, Python is an interpreted language, which means it’s generally slower than compiled languages, e.g., C/C++, Fortran, Go, Java, Scala, and Rust. This can be a bottleneck for CPU-bound tasks. But, in reality, many actual applications are not CPU-bound but I/O-bound or network-bound. That eliminates the potential Python problem, as the raw execution speed is not crucial for these tasks.
Second, there’s Python’s global interpreter lock (GIL). It’s a mechanism that protects access to Python objects, preventing multiple native threads from executing Python bytecodes concurrently in a single process. If multi-threading is applied to CPU-bound tasks, it might not provide a significant performance boost.
However, there are several solutions for that, too. They are in Python itself. You can achieve concurrency and parallelism with Python’s asyncio library, used for I/O-bound operations. And there’s the multiprocessing module for parallelism for CPU-bound tasks.
Python community also comes to the rescue! There are implementations of Python in Java, such as Jython, or in .NET, such as IronPython. Python also boasts other frameworks and tools created with scalability in mind. For example, Django and Flask can be used with Gunicorn (Python WSGI HTTP Server for UNIX), uWSGI, or Nginx (reverse proxy). Or, another example, you can use dask or Vaex for computations on large datasets.
There’s also Python integrability we mentioned earlier. Critical parts of an application that require optimization can be written in a compiled language like C or C++ and then integrated with Python using tools like Cython or the CPython API. That way, you can combine the speed of compiled languages with the ease and expressiveness of Python.
One solution is also designing Python applications to run in distributed environments and use cloud platforms. This can be achieved using tools like Kubernetes and Docker.
You can also use alternative Python implementations, such as PyPy. This can significantly improve the performance of some applications.
With all this, what could be one of Python’s drawbacks suddenly becomes its advantage and reason for popularity!
Reason #7: Reproducibility
Working with data – even for non-scientific purposes – becomes more like science with each day. It’s not surprising, considering that many technical data advances are now used in science. Or, we could argue they actually originated from science.
One of the concepts borrowed from science and applied even in the commercial world is reproducibility. It means the result achieved once should be achieved every time the experiment is repeated. The same is important in statistics and data science: the result you got once in your data science project should be easily reproduced by you and other users using your code.
There are several ways Python improves reproducibility:
- Virtual Environments – environments such as ‘venv’, ‘virtualenv’, or ‘conda’ allow the creation of isolated spaces with specific package versions, ensuring that code runs consistently across different machines and setups.
- Documentation – Jupyter Notebooks are often used with Python as they provide an interactive environment for code, visualizations, and narrative text; this makes it easier to document the data analysis process step-by-step.
- Widespread Libraries – Python’s libraries are widely adopted, so data pros are familiar with them, allowing consistency and improving reproducibility.
- Version Control – Projects done in Python often use Git for version control. Together with GitHub and GitLab, this allows tracking changes, collaborating, and documenting.
- Packaging and Distribution – There are tools like ‘pip’ and ‘setuptools’ in Python; they are used for packaging projects, including dependencies so others can reproduce the environment and results.
- Docker Integration – Python applications and environments can be containerized by creating a Docker image of a data project, encapsulating all dependencies, data, and configurations.
- Open Source – The transparency the open source philosophy provides makes the code for many tools and libraries available for inspection.
- Integration With Data Version Control (DVC) – DVC is a version control system for machine learning projects where you can track datasets, machine learning models, and pipelines.
Conclusion
If you want to become a data pro, Python is a standard today in many or all data jobs. You’d have to try hard to find a tech company that doesn’t use Python. So, if you can’t beat them by rejecting Python, join them by learning it.
You’ll see that there is a reason why Python has become so popular. In fact, there are at least seven good reasons:
- Versatility and Flexibility
- Rich Libraries
- Community Support
- Easy to Learn
- Integration Capabilities
- Scalability
- Reproducibility
All this makes Python a go-to choice for aspiring data pros, especially for data scientists! Python is not the only thing you’ll need when becoming a data scientist from scratch, but it for sure is a crucial one.
