Python and data science are almost synonymous with one another these days. For anyone considering a job within data science, knowing how to use Python is almost a prerequisite.
Here, we look at why that is by identifying how Python is used within data science and what it is about the programming language that makes it such an important part of the data science field.
Python and Data Science
While Python can be used to create applications for many different sectors and industries (such as how Python and finance usually go hand in hand), it has been seen to be extremely useful in the field of data science. Data is, increasingly, becoming a big influence over how companies are run and how key decisions are made the world over.
More and more of us are handing over our data each and every time we use the internet, but also, with a growing global population, the amount of data that can be created and sourced is truly incredible. There really is no limit on what or how much data can be collected. In fact, the only limit is a person’s imagination.
But that data needs to be analysed for it to be useful. Drawing conclusions from data is what data science is all about.
So, with the increasing complexity of data sets and their growing sizes, it is essential that there is technology available to help data scientists make sense of it all.
That’s where Python becomes useful; it has been used widely by data scientists the world over for that reason. Below, we look at the pros and cons of Python language and why it is the programming language of choice for those in the data science sector.
Pros of Python in Data Science
First, Python is perfect for developing applications that are complex but capable of handling the scientific and numeric inputs required of them. The result is that Python supports data analysis through improved visualization of data.
Those complex applications that help analyze and sort data do not take any longer to develop than applications that are for use in far simpler end purposes. Many of Python’s visualization libraries make it painless to create charts from data.
One of the reasons that Python is so great for writing applications that are capable of complex functions is that it is one of the simplest programming languages to use. Programmers use a language that is very close to English, which is why it is far more intuitive to use than other programming languages.
Plus, as it interprets what programmers have written, it needs less coding to carry out exactly the same thing that other languages would require more coding to accomplish. And, finally, it is dynamically typed.
That means that data scientists creating an application with Python do not need to denote all the variables and data types within the application. That is because Python automatically deletes the data type during the execution part of the process.
Importantly, Python is open source. In practice, that means that programmers or data scientists can download the code for free, modify it as required for their means and then distribute it as necessary. It can, therefore, help keep company costs down.
So, when coupled with the fact that developing an application to help in the analysis of huge data sets is far quicker using Python’s simple programming language, creating an application with Python becomes even more cost effective.
Finally, Python is compatible with many platforms and operating systems. So for data scientists out there that like to use different operating systems and devices, there is no need to waste time rewriting or modifying code so that it works across all the systems they require.
Again, this is a huge time saver, leaving data scientists more time to examine and analyze data in very granular detail.
Cons of Python in Data Science
Of course, there must be some disadvantages to Python as it has not yet managed to monopolize the programming language market. Despite all the incredible and helpful advantages above, some data scientists may become frustrated with the following Python drawbacks.
First, it is often seen to be slower than other programming languages. That is the trade-off for using language that is simple to write. It can take Python longer to execute code as it uses line by line execution due to being an interpreted language as well as dynamically typed. So there is a big trade-off for being so straightforward.
The slow speed is compounded by the fact that that language requires a huge amount of memory to run. For those that want an end application that is good for memory optimization, Python may not be suitable.
However, some data scientists may find that, on balance, they will happily tolerate a slightly slower speed and more memory for the time savings made when creating an application.
Finally, Python can sometimes be found to be dogged by runtime errors. That may slow down data processing further. Again, it’s down to that dynamically typed language.
Runtime errors can be worked around through extensive and comprehensive testing, which the majority of programmers and data scientists would conduct anyway. However, it does mean that some of those time savings may be lost in the testing phase.
How Python Helps in Data Science
Data science is an incredibly important field of work. Any technology or software available that can help support the results and conclusions drawn from data sets will always be well received by data scientists.
Python is used extensively throughout the industry, which may become a self perpetuating cycle. Data scientists will increasingly be required to understand Python and know how to use it to write code that helps in data analysis—especially if the majority of their peers and colleagues are using it.