Python has been around since the early 1990s. Many programming languages have come and gone, but Python has been growing, developing, and improving with time.

It is one of the fastest-growing programming languages in the world. Python is widely used as a high-level programming language in mobile app development, web development, software development, numerical and scientific data analysis, and calculation.

Popular websites such as Dropbox, Google, Instagram, Spotify, and YouTube are built with this powerful programming language.

The large open source community that evolved around Python advances the language with many tools that help coders use it efficiently. In addition, more tools have been developed specifically for data science in recent years, making it easier than ever to apply Python to data science.

Is Python good for Data Science? 100%! The rest of this article will cover using Python in Data Science, learning Python for Data Science, and more.

What is Python?

The origin of Python was developed in the late 1980s, but the code was not available until 1991. The main objective of the language is to automate repetitive tasks, quickly prototyping programs, and implement them in other languages.

Python is a relatively simple programming language to learn and use because the code is clean and straightforward. It is therefore not surprising that it’s a prevalent language among developers.

Clean code and thorough documentation also make it easy to create and customize web properties. As mentioned above, Python is very versatile and supports many platforms and systems. Therefore, it is easy to use for various purposes, from mathematical modeling to advanced games.

Python for Data Science

Since its introduction as a utility language, Python has evolved into an essential force in the fields of artificial intelligence (AI), machine learning (ML), as well as big data and analytics. However, while other programming languages such as R and SQL are also very effective for use in data science, Python has become available as the language of computer scientists.

Learning Python for computer science or another career can open many doors for you and improve your career prospects. Even if you don’t work with artificial intelligence, machine learning, or data analytics, Python is still essential for web development and graphical user interface (GUI) development.

The main reason Python is used in data science is that it proves over and over again its ability to solve complex problems effectively. Furthermore, with data-driven libraries (such as NumPy and Pandas), anyone familiar with Python’s rules and syntax can quickly use them as a powerful tool for processing, manipulation, and data representations.

Python’s appeal has spread beyond software engineering to those working in non-technical fields. For example, ita vital makes data analysis achievable for those who come from backgrounds such as business and marketing.

Most data scientists will never have to deal with things like cryptography or memory leaks, so as long as you can write clean and logical code on Python, you’ll be doing some data analysis pretty quickly.

Python is very suitable for beginners because it is expressive, concise and easy to read. This makes it easier for beginners to start fast coding quickly, and the language-supporting community will provide enough resources to solve problems whenever they occur.

It also pays to be a developer for Python. According to Glassdoor, the developers of Python earn an average salary of $76,526 a year. Those with significant experience in coding can earn as much as 107,000 US dollars a year.

Basic Data Structures

Without examining some of the basic data structures available, I can’t talk about learning Python for data science. These can be described as ways to organize and store data to be easily accessed and modified.

Some of the data structures already embedded include the following:

• dictionaries
• lists
• sets
• strings
• tuple

Lists, arrays, and sets are ordered sequences of objects. Both lists and sets are similar to strings (in C++) and can contain objects of any type, and strings can contain only characters. Thus, lists are heterogeneous containers of objects, but lists are mutable and can be collapsed or expanded as needed.

As with strings, tuples are immutable, so there’s a big difference when compared to lists. This means that you can delete or reassign an entire tuple, but you cannot make changes to a single item or slice.

Tuples are also pretty fast and require less memory. A set, on the other hand, is a mutable unordered sequence of unique elements. Sets are very similar to mathematical sets because they do not hold duplicate values.

Python dictionaries hold key-value pairs, but they are not allowed to use non-hashable items like keys. Thus, dictionaries and sets’ main difference is that dictionaries hold key-value pairs rather than a single value.

Dictionaries are enclosed in curly brackets: d = {“a”:1, “b”:2}

Lists are enclosed in brackets: l = [1, 2, “a”]

Sets are also enclosed in curly brackets: s = {1, 2, 3}

Tuples are enclosed in parentheses: t = (1, 2, “a”)

All of the above has its pros and cons, so you should know where to use them for the best results.

When dealing with large data sets, you’ll also need to spend a lot of time ‘cleaning’ unstructured data. This means dealing with data that has missing values or inconsistent formatting.

Therefore, before you get involved in data analysis, you should split the data into a form that you can work with. Then, cleaning up the data can be done quickly by leveraging NumPy and Pandas. 

For those interested in data science, the blind installation of Python will be the wrong approach, as it can quickly become overwhelming. There are thousands of Python modules, so it can take days to manually install a PyData stack if you don’t know what tools you’ll need to get involved in data analysis.

The best way to get around this is to use the Anaconda Python distribution, which will install most of what you need. Everything else can be installed via a GUI. The good news is that distribution is available for all major platforms.