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Python is one such tool that has a unique attribute, of being a general purpose programming language as being
easy to use, when it comes to analytical and quantitative computing. Python has been in the industry for quite
a long time and has been used in industries like scientific computing, oil, gas, finance, physics, signal
processing and more. Python has also been used in building applications such as YouTube, has been instrumental
in powering Google’s internal infrastructure and so on. This tool can coordinate massive clusters of computer
graphics servers and aid in the production of movies, this attractive attribute is why companies like Disney,
Sony, Dreamworks and the likes depend on it.
When it comes to data science, Python is a very powerful tool, which is also open sourced and flexible, adding
more to its popularity. It is known to have massive libraries for manipulation of data and is extremely easy to
learn and use for all data analysts. Anyone who is familiar with programming languages such as, Java, Visual
Basic, C++ or C, will find this tool to be very accessible and easy to work with. Apart from being an independent
platform, this tool has the ability to easily integrate with the existing Infrastructure and can also solve the
most difficult of problems. It is said, that this tool is powerful, friendly, easy and plays well with others,
apart from running everywhere. A lot of banks use this tool for the purpose of crunching data, some institutions
use it for analyzing and visualization. This tool offers the great benefit of using one programming language,
across multiple application platforms.
Python: Pros
Python Notebook
The IPython Notebook makes it easier to work with Python and data. You can easily share notebooks with colleagues,
without having them to install anything. This drastically reduces the overhead of organizing code, output and notes
files. This will allow you to spend more time doing real work.
A general purpose language
Python is a general purpose language that is easy and intuitive. This gives it a relatively flat learning curve,
and it increases the speed at which you can write a program. In short, you need less time to code and you have
more time to play around with it!
Furthermore, the Python testing framework is a built-in, low-barrier-to-entry testing framework that encourages
good test coverage. This guarantees your code is reusable and dependable.
A multi purpose language
Python brings people with different backgrounds together. As a common, easy to understand language that is
known by programmers and that can easily be learnt by statisticians, you can build a single tool that integrates
with every part of your workflow.
Python: Cons
Visualizations
Visualizations are an important criteria when choosing data analysis software. Although Python has some nice
visualization libraries, such as Seaborn, Bokeh and Pygal, there are maybe too many options to choose from.
Moreover, compared to R, visualizations are usually more convoluted, and the results are not always so
pleasing to the eye.
Python is a challenger
Python is a challenger to R. It does not offer an alternative to the hundreds of essential R packages.
Although it’s catching up, it’s still unclear if this will make people give up R?
Up to you! As a data scientist it’s your job to pick the language that best fits the needs.
Some questions that can help you while choosing any language in future:
What problems do you want to solve?
What are the net costs for learning a language?
What are the commonly used tools in your field?
What are the other available tools and how do these relate to the commonly used tools?