Python is often used to build websites, and software apps, automate various tasks, and perform data analysis. While many other programming languages can achieve the same tasks, Python always comes as everyone’s favorite choice when working with data. Everyone agrees that Python is a great choice for data analysis, and today we will see why.
We will try to break down Python into its best features and functionalities and show you how to master Python in the right way.
What type of language is Python?
For starters, let us talk about Python as a type of language. Python is an object-oriented language, and that is one of the reasons why it is so helpful for data analysis. That is a concept of procedural programming, where you can create these objects that act as containers for data. That makes data manipulation very simple.
Another thing to understand is that Python is a dynamic language. Let’s try to explain the meaning of dynamic versus static in programming. The easiest way to describe this without going too deep into the core of a programming language is to say that something static never changes. For example, if you use a calculator on your smartphone, numbers will always have the same value. They never change. However, the result of various calculations will always be dynamic because it depends on the input.
In that manner, Python is a great choice for data analysis because the data will continually change. With Python, you can build dynamic applications that take some data in and return a result depending on the data analyzed.
Understanding data and data analysis
To work in Python, you must also understand data structure and algorithms. The entire programming process will depend on this. Furthermore, this knowledge is also a requirement for future data analysis.
Many people mix up data analysis and data science. These two terms have different meanings.
A data scientist works on hypothetical situations and various what-ifs that may come from analyzing some data. They are interested in future outcomes and potential situations that may arise.
Conversely, a data analyst takes known data and creates meaningful, accurate insights. They are interested in what is happening here and now and how to use data to get precise answers to various questions.
Because of that, it is imperative to use a programming language such as Python. Data analysis needs to be correct and quick.
Benefits of using Python for programming
There are a couple of essential benefits that make Python such a good programming language, especially for beginners:
- It is a very flexible language;
- Python is easy to learn;
- It is an open-source programming language;
- Python is well-supported;
Why is Python flexible?
Various programming languages are created with a specific need in mind. For example, CSS is only used to style HTML, and HTML is used only for creating websites. Because of that, you can only use those languages for that particular purpose.
Python is not built in that way, and you can use it to create whatever you want. There are almostt no limitations to programming in Python. That is a significant benefit.
Per Digital Dot marketing experts, data analysis requires a lot of flexibility. There is a need to go beyond the usual strategies to accommodate the needs of new clients.
Furthermore, other sectors like accounting and financing depend on data analysis and a flexible approach to understanding data.
As we can see, flexibility gives us the option to experiment with new ideas, which is essential in programming.
Why is Python easy to learn?
To understand why Python is easy to learn, let’s compare it with C++. Some would say this is not a fair comparison because C++ is more complex than many languages. Nevertheless, it is the easiest way to explain this difference.
C++ starts with a slower learning curve, but it becomes more complex and challenging as you become more advanced. It heavily relies on coding and syntax and is challenging to write.
Furthermore, C++ has a vast library of various functions, and you need more lines of code to accomplish something.
Another thing to understand is that C++ is a compiled language, which means you need an executable file to run the program after it compiles. On the other hand, Python is an interpreted language. You can directly open a Python file through a Python interpreter and generate the output.
Also, let us mention garbage collectors for a moment. Any time you run a code in C++, a part of the memory is reserved for running that code. After the program is compiled, you must tell the system to free up that memory. In the case of Python, there is a systematic functionality that automatically clears this allocated memory.
Python is built in such a way that it makes your life easier. You don’t have to code a lot to get the desired result.
It is open-source
That means that Python is a free programming language. You can use it on Windows and Linux and port it to various platforms.
Python is well-supported
Even though it is free, Python has a solid online community. Since it is used for machine learning and in academic and industrial circles, it is easy to find answers to any question. That is very important because programming involves making mistakes and finding ways to fix the issues.
Why Python is a great choice for data analysis – explained!
There is no doubt that Python is a great choice for data analysis. You should give it a chance and learn Python for a couple of months. There is a slow learning curve, so you will quickly understand the language and see why it is a good solution for data analysis.