![]() The good news is that the distribution is available for all major platforms. Everything else can be installed through a GUI. The best way around this is to go with the Anaconda Python distribution, which will install most of what you’ll need. There are thousands of modules in Python, so it can take days to manually install a PyData stack if you don’t know what tools you’ll need to engage in data analytics. To learn more, the Pythonic Data Cleaning With NumPy and Pandas tutorial is an excellent place to start.įor those of you who are interested in data science, blindly installing Python will be the wrong approach, as it can quickly become overwhelming. This can be achieved easily by leveraging NumPy and Pandas. So before you can engage in data analytics, you have to break the data down to a form that you can work with. This means handling data that’s missing values or has nonsensical outliers or even inconsistent formatting. When you’re dealing with large sets of data, you’ll also have to spend a considerable amount of time “cleaning” unstructured data. Tuples are enclosed in parentheses: t = (1, 2, "a")Īll of the above have their own sets of advantages and disadvantages, so you have to know where to use them to get the best results. The primary difference between a dictionary and a set is the fact that it holds key-value pairs instead of single values.ĭictionaries are enclosed in curly brackets: d = In fact, a set is a lot like a mathematical set because it doesn’t hold duplicate values.Ī dictionary in Python holds key-value pairs, but you’re not allowed to use an unhashable item as a key. Sets, on the other hand, are mutable, unordered sequences of unique elements. Tuples are also considerably faster and demand less memory. This means that you can delete or reassign an entire tuple, but you can’t make any changes to a single item or slice. Tuples, like strings, are immutable, so that’s a significant difference when compared to lists. For example, the Python Data Science Handbook: Essential Tools for Working With Data is a popular book you can read for free online. Whenever you get stuck, it’s also relatively easy to solve Python-related problems because of the sheer amount of documentation that’s freely available. With the help of data-focused libraries (like NumPy and Pandas), anyone familiar with Python’s rules and syntax can quickly deploy it as a robust tool to process, manipulate, and visualize data. Even if you don’t work in AI, ML, or data analytics, Python is still vital to web development and the development of graphical user interfaces (GUIs).Ī major reason why Python is used for data science is the fact that it has proven time and again to be capable of solving complex problems efficiently. If you learn Python for data science or another career, it can open a lot of doors for you and improve your career opportunities. ![]() However, while other programming languages like R and SQL are also highly efficient to use in the field of data science, Python has become the go-to language for data scientists. Since its early days as a utility language, Python has grown to become a major force in artificial intelligence (AI), machine learning (ML), and big data and analytics. Why Should You Learn Python for Data Science? ![]() Thus, it can be easily leveraged for a variety of purposes from scientific modeling to advanced gaming. As alluded to above, Python is also highly versatile and supports multiple systems and platforms. The clean code, along with extensive documentation, also makes it easy to create and customize web assets. So it’s not surprising that most programmers are familiar with it. It’s a relatively simple programming language to learn and utilize because the code is clean and easy to comprehend. The primary aim here was to automate repetitive tasks, to rapidly prototype applications, and to implement them in other languages. The foundation for Python was laid in the late 1980s, but the code was only published in 1991.
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