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Programming languages that build the apps, programs and environments you use are sophisticated and, according to the TIOBE Index, there are more than 250 programming languages currently in existence. External link  One of the most popular of these is Python, an open-source language that’s been around since February of 1991. External link  Data scientists have been using Python regularly for years, but let’s take a closer look at what Python is and why it’s popular among data scientists.

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Introducing Python
Python is an extensible and portable programming language that can be run on Unix, Mac, or Windows. Because of this accessibility and portability, it has no shortage of users. New Python users can learn enough to work with code quickly, with a large community to support their efforts. A 2016 O’Reilly Media survey found that 54 percent of data scientists use Python in their work, External link  up from 40 percent in 2013. The Economist even claimed in 2018 that Python is becoming the world’s most popular coding language. External link

Corporate and research usage supports these numbers. For years, Python has been the language of choice for production engineers at Facebook; External link  in fact, it is the third-most popular option. And Python is one of Google’s official languages External link  — meaning it can be deployed to production within the company. Walt Disney Animation Studios External link  uses Python for many creative tasks. Companies like Industrial Light and Magic, Spotify, Quora, Netflix, Dropbox, and Reddit all rely on Python External link  for everything from moviemaking to social news aggregation. Python is even the most popular introductory coding language taught External link  at top US universities, in part because of its popularity in so many settings.

A wide range of companies and institutions with very different goals all prefer to use Python, which is a testament to its flexibility. But how does it work, exactly?

For starters, Python supports multiple paradigms, External link  including functional programming, object-oriented programming, structured programming, and procedural programming. It’s the Swiss Army knife of languages, allowing the production environment and researchers to all use the same tools External link . This means that it can handle website construction, data mining, and much more — all in the same language.

Furthermore, Python can be extended via libraries to allow data scientists to tackle machine learning, data analysis, and beyond.The active community of Python users provides easy-to-follow tutorials External link  that make it simple and quick for machine learning. 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.

Why is data science using Python?
Because the language is multifaceted and flexible and has easy readability, Python is an obvious language of choice in the field. However, Python usage is relatively new. As a result, Python libraries such as Pandas External link  help individuals clean up data and perform advanced manipulation. External link

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. External link

The growth of Python in data science has gone hand in hand with that of Pandas, External link  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.

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). These and other specialized libraries aid in everything from machine learning to data preprocessing to neural networks. 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.

Another 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. External link  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.

However, with so many resources available to help you utilize Python, how can you know which one will be best for you?

Learning from a trusted source like UC Berkeley can ensure that you are able to use the programming language with confidence. Through datascience@berkeley, UC Berkeley’s online Master of Information and Data Science you can take an entire course on Python for data science. Students are introduced to a range of Python objects and control structures; the course then has you build on this knowledge with classes and object-oriented programming before delving into Python’s system of packages for data analysis.
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Using Python for Data Science

Python for Data Science?

Python is a general-use high-level programming language that bills itself as powerful, fast, friendly, open, and easy to learn. Python “plays well with others” and “runs everywhere”.

Conceived in the late 1980s, Python didn’t make inroads into data science until recently. For a long time, as Tal Yarkoni of UT Austin says, “you couldn’t really do statistics in Python unless you wanted to spend most of your time pulling your hair out.”

Now, however, tools for almost every aspect of scientific computing are readily available in Python. (Thanks in part, no doubt, to the $3 million the Defense Advanced Research Projects Agency (DARPA) put toward the development of data analytics and data processing libraries for Python in late 2012.)

Bank of America uses Python to crunch financial data. Facebook turns to the Python library Pandas for its data analysis because it sees the benefit of using one programming language across multiple applications.

“One of the reasons we like to use Pandas is because we like to stay in the Python ecosystem,” Burc Arpat, a quantitative engineering manager at Facebook, told Fast Company in May 2014.

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