Before we get into, why is Python is an excellent tool for data analysis, let us discuss what data analysis is? Data analysis is a process of inspecting, cleansing, transforming, and modeling data to discover valuable information that can support decision making. Data analysis is in use in all industries like banking, hospitality, logistics, media, and more. Data analysts and data scientists use a variety of tools available to find the information they require.
Excel is one of the most used data analysis tools around. However, when it comes to Big data, excel is not the most convenient option. When analysts or data scientists are working with big data, they prefer to use one of the more capable tools like; R programming, Python, Stata, Tableau public, or SAS. There are many more data analysis tools available, but these five are the most used for big data.
Now getting back to the original question, why is Python an excellent tool for data analysis? Various reasons make Python the best option for data analysis; a few are below.
Python is open-source
Python is an open-source platform; this means you do not have to pay anything to use it. Python runs on both Windows and Linux environments, and as Linux is also an open-source platform, you do not need to pay anything to use this as well. It has many open-source libraries that you can also use free of cost. So all you need to do is hire a Python developer to start working on your project without worrying about paying for expensive licensing agreements.
Python is flexible
Software developers can use Python to script applications and websites in the way you want. If you’re going to try something new, something creative, then Python is what you want. Its simplicity and capabilities to develop complex application solutions make it the best bet to pull it off.
Easy to learn
Python is simple to write and read; this gives Python an edge over other programming languages like Java, PHP, Ruby, or others. A Python programmer needs to write less code to perform the same as other programmers. The syntax is easy to read and understand; this has relatively lowered the learning curve and made Python the first choice of code for most computer science classes.
A vast collection of useful libraries
Python has a large group of open-source libraries that support big data analysis; this makes Python and big data a perfect combination. Python has advanced libraries like Pandas, Numpy, SciPy, SymPy, Mlpy, and many more. These libraries are used for all kinds of numeric computing, data analysis, statistical analysis, visualizations, artificial intelligence, and machine learning.
As you are now aware, Python is easy to write, read and maintain; this, along with the fact that Python is a high-level programming language, accelerates the code development, and this helps speed up the data analyzing tasks.
Python is Scalable
Scalability becomes a factor when massive data is involved. Python is not only one of the fastest programming languages is used for data science, but it is also scalable. Meaning Python can handle any amount of data you require and makes it compatible with big data at a grander scale than other scientific programming languages.
Python is compatible with Hadoop
Hadoop is a collection of open-source software utilities that make handling a large amount of data using a network of computers to solve problems possible. Python is compatible with Hadoop to work on big data. It has a Pydoop package that helps in accessing API and writing MapReduce programming.
Python is well supported
As Python is an open-source platform that has been around for almost three decades, it has one of the largest and most loyal communities to its name. Big data analysis is a complex task, and you may come across some issues while working on it, this is where this large Python community comes in. There are thousands of documents to help out Python developers and data analysts, and the best thing is that these are also available for free.
Python has everything that a data analyst needs from his data analysis tool. It is fast, scalable, compatible, free, and has an extensive collection of libraries that support big data analysis. Its loyal community supports it well with detailed materials and tutorials on big data analysis.
Learning to use Python as a data analysis tool is easier than most data analysis tools around. It is one of the reasons several digital marketing experts say that most data scientists and data analysts prefer to use Python over other tools.
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