Can you guess which is the most widely used language in the Data Science universe? Well, judging by the title of this article, you must already know what it is, and if you’re still wondering – it is Python.
According to a StackOverflow analysis,
“The fastest-growing use of Python is for data science, machine learning, and academic research.”
Behind this massive fan-following of Python lie numerous reasons. The primary reason being that Python is super easy to learn. When it comes to Data Science, Python is a nifty tool with a whole range of benefits. Since it is open-source, it is flexible and continuously improving. Plus, Python has an array of useful libraries and not to forget that it can be integrated with other languages (like Java) as well as existing structures. Long story short – Python is an excellent Data Science tool.
We’ll give you 6 strong reasons to support our claim!
When talking about Python’s popularity in both the programming and Data Science community, the first thing that comes to mind is its simplicity. One of the best features of Python is its inherent simplicity and readability that makes it a beginner-friendly language. Its has a neat and lucid syntax, thereby offering a shorter learning curve than most other languages. In fact, you could write a program much faster in Python that you probably could with other languages such as C++ or Java.
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Python is time-savvy as it allows you to get straight to the research part without having to spend hours reading the documentation. Today, Python is extensively used for data analysis, statistical analysis, web development, text processing, and so much more.
- Libraries – there’s one for every need!
While Python’s simplicity makes it the first choice for many, its assortment of fantastic libraries makes it all the more appealing to Data Science professionals. Over the years, Python has been made richer with the inclusion of libraries that enhance its functionality even further. There are so many libraries that you are sure to find one tailor-made to fit your Data Science needs.
Let’s take a look at some of the most popular Python libraries –
NumPy is one of the earliest libraries to find a use case in Data Science. It incorporates high-level mathematical functions that operate on multi-dimensional arrays and matrices and is excellent for scientific computing.
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Pandas was built on top of NumPy. It is Python’s data analysis library and can be used for everything – from importing data from Excel sheets to processing datasets for time-series analysis.
SciPy is the scientific equivalent of NumPy. It has all the tools required for numerical integration and effective analysis of scientific data. Matplotlib is a 2D-plotting library that comes equipped with all the tools necessary for offers data visualization. Scikit-Learn and PyBrain are ML libraries equipped with modules for developing neural networks.
Apart from these libraries, there are also other libraries like SymPy (statistical applications); Shogun, PyLearn2 and PyMC (machine learning); Bokeh, ggplot, Plotly, prettyplotlib, and seaborn (data visualization and plotting), and csvkit, PyTables, SQLite3 (data formatting and storage), to name a few.
- Multi-paradigm approach.
A great thing about Python is that unlike OOP languages, it isn’t limited in approach – it is a multi-paradigm programming language. So, for instance, while in Java, you’d be required to create a separate OO class for printing ‘Hello World,’ you do not have to do so in Python. Having a multi-paradigm approach, Python supports functional, procedural, and both object-oriented programming and aspect-oriented programming styles.
- Enterprise Application Integration (EAI).
Python is an excellent tool for Enterprise Application Integration (EAI). As we mentioned earlier, Python is highly embeddable in applications, even those written in other programming languages. Thus, it allows for easy integration with other languages, thereby making the web development process easier. For instance, it can invoke CORBA/COM components and also directly calling from and to Java, C++ or C code. Python’s strong integration bonding with Java, C, and C++ makes it a great choice for application scripting.
Furthermore, Python is also a useful tool for software testing owing to robust text processing and integration capabilities. It comes with its unique unit testing framework and can be used for developing sophisticated GUI desktop applications as well.
- The Jupyter Notebook.
Working with Python, every programmer is familiar with The Jupyter Notebook. It is an open-source web application that allows coders to write expressive code. The Jupyter Notebook is a handy tool for Data Science and ML. It enables you to exhibit your findings and embed the results (visualizations) in the same document as your code.
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Among the many services revolving around The Jupyter Notebook is the Google Colaboratory that grants you free cloud computing perks along with access to high-performance GPUs to run the Jupyter Notebook. Since Google Colab is synced directly with Google Drive apps, you can store your data and notebooks on your Google Drive.
- Community – there’s always someone to rely on!
What could be more awesome about Python than the things we’ve already mentioned so far?
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The Python Community.
For better or for worse, the Python community will always be there for you. There’s no issue, no problem, or no question, that won’t be solved or answered by Python enthusiasts and volunteers. All you need to do is ask. This is one of the most commendable features of open-source communities – they are always open to discussions.
If you are stuck somewhere in your code or on something, you can be sure that someone somewhere has faced such a problem before. So, there’s always a solution. You can connect with Python experts and community members on online platforms like Reddit and StackOverflow, or you can attend meetups/conferences and other gatherings.
To sum up, Python has proven to be a game-changer for Data Science. It is packed with such useful tools and features that make it the first choice of many Data Scientists and Data Analysts everywhere.
While we’re convinced that the above reasons are enough to show you the advantages of Python for Data Science, you’ve got to test it for yourself to believe it!
Why should we use Pandas and not NumPy?
Pandas, like NumPy, is one of the most popular Python libraries for data science. It provides high-performance structures and easy-to-use data analysis tools. Pandas provides an in-memory 2d table object named Dataframe, unlike the NumPy library, which provides objects for multi-dimensional arrays. When the number of rows is 500K or more, Pandas perform better. When it comes to cleaning, converting, manipulating, and analyzing data, Pandas is a game changer. Pandas, to put it simply, assist in the cleanup of the mess.
What are the cons of using Python?
Python is a high-level language, thus it isn't as near to hardware as C or C++. It's only used infrequently for mobile development. Python is not a suitable choice for any memory-intensive activities. As a result, it isn't employed for that purpose. Python consumes a lot of RAM due to the data types' flexibility. Python's database access layer is discovered to be immature and unsophisticated. When large corporations are looking for a language that assures the seamless interaction of complicated legacy data, it functions as a huge roadblock. Python programmers encounter a number of challenges due to the language's architecture. Since the language is dynamically typed, it necessitates additional testing and also contains faults that only appear at runtime.
When is the use of Jupyter Notebook most preferred?
The Jupyter Notebook is an open-source web tool that lets data scientists create and share documents with live code, equations, computational output, visualizations, and other multimedia elements, as well as explanatory text. The Jupyter Notebook has grown widespread among data scientists due to the growing popularity of open-source software in business as well as the rapid expansion of data science and machine learning. Data cleansing and transformation, numerical simulation, exploratory data analysis, data visualization, statistical modelling, machine learning, and deep learning are all possible with Jupyter Notebooks.