Why Python is so Powerful for Data Science

In this post we will Discuss about why python is so powerful for Data Science, Why Data Scientists love coding in Python? Python is the language of choice for data scientists because it is easy to learn, scalable, awesome visualization packages and excellent python community where you can find data science libraries.Python's popularity for data science is largely due to the strength of its core libraries (NumPy, SciPy, pandas, matplotlib, IPython), high productivity for prototyping and building small and reusable systems, and its strength as a general purpose programming language. 

Python is a language that builds upon and extends over 50 years of research in numerical methods and scientific computing. It is possible to apply very complex, well developed algorithms to real world data problems quickly and cleanly.Python has great tools, like IPython Notebooks, for Agile programming and collaboration.  It is very easy to sit down with a client and knock out prototype algos for them in a matter of days.  It is easy to iterate, and the handoff is working prototype that can readily be productionalized by other team members.   Compare this to say R, which is comparatively hard to get into production, or Java, which takes 10 times longer to code up.

Since data scientists are also often involved with wiring together network applications, programming for the web, scripting and automating data processing jobs and other processes, and lots of ad hoc data munging (the kind of stuff people loved using Perl for in the 90s), it's very desirable to be able to do all these things, in addition to the actual analysis and modeling, in a single language.By using Python you can build a program very quickly, profile and identify bottlenecks, then optimize by using better array programming techniques.

Data scientists need to use data visualizations to clearly communicate outputs and predictions to stakeholders at any level of a business. This is the real value a great data scientist can provide – without this, it’s a zero-sum game.Hence, Python has become a programing language that links between different units across the business and a direct channel for data sharing and processing language. 

IPython-Notebook:

This is just a GREAT tool. You can run multiple lines/blocks of code in different cells, you can play with them, move them up or down, and you can even get your results appear just underneath the cell. It really is like the magical organizer that Data Scientists (and people who run code and want to iterate) have always dreamed of.  You can also write R, SQL, Scala, and other languages in IPython-Notebook which makes the work-flow much easier and efficient.

Python is easy to learn:

Python's main advantage is that anyone can learn it quickly and easily. The language was designed to be simple.

Scalability:

Relative to other language/Packages for Data Science (as MatLab, Stata, R) Python is much faster. It’s true that Java and Scala are much faster than Python (x3), but with Anaconda (Continuum Analytics) Python has caught up to speed!

As a Data Scientist, I prefer to run my code and get my output in Python relative to the traditional languages that are computationally slower. As always, there are trade-offs. 

Growing Data Analytics Libraries:

With Python, you can find a large variety of Data Analytics/Data Science Libraries (as others have mentioned here: NumPy, SciPy, StatsModels, Scikit-Learn, Pandas, etc.) - which are exponentially growing over time. Constraints (in optimization methods/functions) that were missing a year ago are no longer an issue, and you can find a proper robust solution that works reliably. 

Visualization / Graphics: 

Python is not as good as R (yet), but we’ll see more and more cool APIs (e.g., Plotly) and Data Visualization libraries that make the partial advantage of R insignificant compared to Python. You can do really cool stuff with Python.

Powerful statistical and numerical packages:

1. NumPy and pandas (Python Data Analysis Library) allow you to read/manipulate data efficiently and easily.
2. Matplotlib allows you to create useful and powerful data visualizations. I have also listed more data visualization packages in Python: Yilun (Tom) Zhang's answer to What's some good python data visualization website?
3. Scikit-learn allows you to train and apply machine learning algorithms to your data and make predictions.
4. Cython allows you to convert your code and run them in C environment to largely reduce the runtime and improve your model performance.
5. PyMySQL allows you to easily connect to MySQL database, execute queries and extract data.
6. BeautifulSoup to easily read in XML and HTML type data which is quite common nowadays.
7. iPython notebook for interactive programming as in R.
8. Multi-paradigm. You can write object-oriented and functional code.
9. Interactiveness. Thanks to iPython and Jupyter notebooks. 
10. A strong community
11. Scripting powers. Dealing with thousands of files, scrapping websites, requesting different APIs..

Python’s increase in the share over 2015 rose by 51% demonstrating its influence as a popular data science tool.It is perfect when data analysis tasks involve integration with web apps or when there is a need to incorporate statistical code into the production database. The full-fledged programming nature of Python makes it a perfect fit for implementing algorithms.

This is fantastic, and opens up the door to do very creative and powerful things. That's all about why choose Python So Powerful for Data Science.If any comments on this post let me know..