However, with so many resources available to help you utilize Python, how can you know which one will be best for you? One of the main benefits of Python is that its flexible nature enables the data scientist to use one tool every step of the way.Īnother plus is the large community of data scientists, machine learning experts, and programmers who go out of their way not only to make it easy to learn Python and machine learning but also to provide datasets to test a Python student’s mastery of their newfound skills. Whether you are a social scientist who needs Python for advanced data analysis or an experienced developer interested in a growing field, a part of the Python community is ready to help you out. These and other specialized libraries aid in everything from machine learning to data preprocessing to neural networks. While Pandas may be the best-known library, there are hundreds of specialized libraries that serve a similar purpose, such as SymPy (for statistical applications), PyMC (machine learning), matplotlib (plotting and visualization), and PyTables (storage and data formatting). The growth of Python in data science has gone hand in hand with that of Pandas, which opened the use of Python for data analysis to a broader audience by enabling it to deal with row-and-column datasets, import CSV files, and much more. Numbers on Pandas usage are hard to come by, but Quartz notes that Stack Overflow saw 1 million unique visitors viewing 5 million questions on Pandas in October 2017 alone. As a result, Python libraries such as Pandas help individuals clean up data and perform advanced manipulation. This makes Python more than just a programming language it’s one of many tools that data scientists can use to explore and analyze their datasets.īecause the language is multifaceted and flexible and has easy readability, Python is an obvious language of choice in the field. The active community of Python users provides easy-to-follow tutorials that make it simple and quick for machine learning. This means that it can handle website construction, data mining, and much more - all in the same language.įurthermore, Python can be extended via libraries to allow data scientists to tackle machine learning, data analysis, and beyond. It’s the Swiss Army knife of languages, allowing the production environment and researchers to all use the same tools. But how does it work, exactly?įor starters, Python supports multiple paradigms, including functional programming, object-oriented programming, structured programming, and procedural programming. Python is even the most popular introductory coding language taught at top US universities, in part because of its popularity in so many settings.Ī wide range of companies and institutions with very different goals all prefer to use Python, which is a testament to its flexibility. Companies like Industrial Light and Magic, Spotify, Quora, Netflix, Dropbox, and Reddit all rely on Python for everything from moviemaking to social news aggregation. Walt Disney Animation Studios uses Python for many creative tasks. And Python is one of Google’s official languages - meaning it can be deployed to production within the company. For years, Python has been the language of choice for production engineers at Facebook in fact, it is the third-most popular option. The Economist even claimed in 2018 that Python is becoming the world’s most popular coding language.Ĭorporate and research usage supports these numbers. A 2016 O’Reilly Media survey found that 54 percent of data scientists use Python in their work, up from 40 percent in 2013. New Python users can learn enough to work with code quickly, with a large community to support their efforts. Because of this accessibility and portability, it has no shortage of users. Python is an extensible and portable programming language that can be run on Unix, Mac, or Windows.
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