Using Python for Big Data Analysis

It’s no surprise that big data?is becoming an integral part of any business conversation. Desktop and mobile search are providing data to marketers and companies around the world on an unprecedented scale, and with the advent of the Internet of Things, the already large amount of data on consumers will expand exponentially. This consumer data is a goldmine for businesses looking to better target an audience, understand how people use their product or service, and collect more information on how to increase their profit margin.

The role of sifting through this data and finding conclusions that businesses can actually act on falls to software developers, data scientists, and statisticians. Now, there are numerous tools to aid in big data analysis, but one of the most popular is the programming language Python.

Why Python?

The biggest strength of Python is that it is simple and easy to use. The language utilizes intuitive syntax and is a very capable general-purpose language. This is important in the context of big data analysis because many businesses already use Python internally, such as Google, YouTube, Disney, and Sony DreamWorks. Plus, the language is open source and has numerous libraries dedicated to data science. As a result, Python developers are high in demand for big data jobs, and professionals who aren’t Python developers can learn the language relatively quickly to maximize the time spent in analysis of data and minimizing the time spent learning how to use the language for those ends.

To use Python for big data analysis, you’ll first need to download Anaconda from It is a package of just about everything you could need when it comes to data science in Python. The one downside is that Anaconda downloads and updates as a unit, so it can be a time-consuming process to update individual libraries, but it’s worth it as it gives you access to all the tools you’ll need, and you won’t have to think twice about it.

Now, if you’re serious about using Python for big data analysis, it goes without saying that you need to be a Python developer. This doesn’t mean you need to be a master of the language, but you do need to understand Python’s syntax, have a grasp of regular expressions, and know what tuples, strings, dictionaries, dictionary comprehensions, lists, and list comprehensions are???and that’s just to start.


Once you grasp the basics of Python, you’ll need to understand how its data science libraries work and which you’ll need. The essentials include NumPy, a good foundation that provides advanced math functionality, SciPy, a go-to library for tools and algorithms, Sci-kit-learn, which targets machine learning, and Pandas, tools that provide DataFrame functionality.

Outside of libraries, it’s worth noting that Python doesn’t have a clear winner for the best integrated development environment (IDE) to use, as R does. Instead, you’ll have to check out several and find what best suits your needs. Good places to start are IPython Notebook, Rodeo, and Spyder. Similar to the multiple IDEs, Python also offers various data visualization libraries, such as Pygal, Bokeh, and Seaborn. The most essential of these data visualization tools is Matplotlib, which is a simple yet effective numerical plotting library.

All of these tools are included in Anaconda, so once you download it, you can explore and see which combination of tools best fits your needs. There are plenty of mistakes you can make while using Python for data analysis, so be careful with your approach. Once you get familiar with the setup and each of the tools, you’ll find that Python is one of the best platforms for big data analysis currently on the market.

About the Author

Ellie Martin?is co-founder of Startup Change group. Her works have been featured on Yahoo!, Wisebread, AOL, among others. She currently splits her time between her home office in New York and Israel. You may connect with her on Twitter.

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