Python for Data Science: Good Enough Means Good for Data Science

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Python for Data Science: Python is a multi-paradigm programming language: a sort of Swiss Army knife for the coding world. It supports object-oriented programming, structured programming, and functional programming patterns, among others. There’s a joke in the Python community that “Python is generally the second-best language for everything.”

But this is no knock in organizations faced with a confusing proliferation of “best of breed” solutions which quickly render their codebases incompatible and unmaintainable. Python can handle every job from data mining to website construction to running embedded systems, all in one unified language.

Python for Data Science

At ForecastWatch, for example, Python was used to write a parser to harvest forecasts from other websites, an aggregation engine to compile the data, and the website code to display the results. PHP was originally used to build the website until the company realized it was easier to only deal with a single language throughout.

And Facebook, according to a 2014 article in Fast Company magazine, chose to use Python for data analysis because it was already used so widely in other parts of the company.

Python for Data Science: The Meaning of Life in Data Science

The name is appropriated from Monty Python, which creator Guido Van Possum selected to indicate that Python should be fun to use. It’s common to find obscure Monty Python sketches referenced in Python code examples and documentation.

For this reason and others, Python is much beloved by programmers. Data scientists coming from engineering or scientific backgrounds might feel like the barber turned axe-man in The Lumberjack Song the first time they try to use it for data analysis—a little bit out of place.

But Python’s inherent readability and simplicity make it relatively easy to pick up and the number of dedicated analytical libraries available today mean that data scientists in almost every sector will find packages already tailored to their needs freely available for download.

Because of Python’s extensibility and general purpose nature, it was inevitable as its popularity exploded that someone would eventually start using it for data analytics. As a jack of all trades, Python is not especially well-suited to statistical analysis, but in many cases organizations already heavily invested in the language saw advantages to standardizing on it and extending it to that purpose.

The Libraries Make the Language: Free Data Analysis Libraries for Python Abound

As is the case with many other programming languages, it’s the available libraries that lead to Python’s success: some 72,000 of them in the Python Package Index (PyPI) and growing constantly.

With Python explicitly designed to have a lightweight and stripped-down core, the standard library has been built up with tools for every sort of programming task… a “batteries included” philosophy that allows language users to quickly get down to the nuts and bolts of solving problems without having to sift through and choose between competing function libraries.

Who’s Who in the Data Science Zoo: Pythons and Munging Pandas

Python for Data Sience is free, open-source software, and consequently anyone can write a library package to extend its functionality. Data science has been an early beneficiary of these extensions, particularly Pandas, the big daddy of them all.

Pandas is the Python Data Analysis Library, used for everything from importing data from Excel spreadsheets to processing sets for time-series analysis. Pandas puts pretty much every common data munging tool at your fingertips. This means that basic cleanup and some advanced manipulation can be performed with Pandas’ powerful dataframes.

Pandas is built on top of NumPy, one of the earliest libraries behind Python’s data science success story. NumPy’s functions are exposed in Pandas for advanced numeric analysis.

If you need something more specialized, chances are it’s out there:

  • SciPy is the scientific equivalent of NumPy, offering tools and techniques for analysis of scientific data.
  • Statsmodels focuses on tools for statistical analysis.
  • Scilkit-Learn and PyBrain are machine learning libraries that provide modules for building neural networks and data preprocessing.

The other great thing about Python’s broad and diverse base is that there are millions of users who are happy to offer advice or suggestions when you get stuck on something. Chances are, someone else has been stuck there first.

Open-source communities are known for their open discussion policies, but some of them have fierce reputations for not suffering newcomers lightly.

Python, happily, is an exception. Both online and in local meetup groups, many Python experts are happy to help you stumble through the intricacies of learning a new language.

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