Ease of Learning. Python is one of the easiest languages to learn
Faster Development and Processing. While dealing with huge amounts of data, speed is key.
- Powerful Packages.
- Community Support.
- Better Data Visualisation.
- Compatible with Hadoop.
Why Python is best for data science?
Unlike other programming languages, such as R, Python excels when it comes to scalability. It’s also faster than languages like Matlab and Stata. It facilitates scale because it gives data scientists flexibility and multiple ways to approach different problems. As a new Data Scientist, you know that your path begins with programming languages you need to learn. Among all languages that you can select from Python is the most popular language for all Data Scientists.
Simplicity
Python is one of the easiest languages to start your journey. Also, its simplicity does not limit your functional possibilities.
What gives Python such flexibility? There are multiple factors:
- Python is a free and open-source language
- This is a high-level programming
- Python is interpreted
- It has an enormous community
Scalability
Python is a programming language that scales very fast. Among all available languages, Python is a leader in scaling. That means that Python has more and more possibilities. Python flexibility is super useful for any problem in-app development. Any problem can be decided easily with new updates that are coming. Saying that Python provides the best options for newbies because there are many ways to decide the same issue. Even if you have a team of non-Python programmers, who knows C+ +design patterns, Python will be better for them in terms of time needed to develop and verify code correctness. It happens fast because you don`t spend your time to find memory leaks, work for compilation or segmentation faults. Libraries and Frameworks. Due to its popularity, Python has hundreds of different libraries and frameworks which is a great addition to your development process. They save a lot of manual time and can easily replace the whole solution. As a Data Scientist, you will find that many of these libraries will be focused on Data Analytics and Machine Learning. Also, there is a huge support for Big Data. I suppose there should be a strong pro why you need to learn Python as your first language.
Some of these libraries are given below:
It is great for data analysis and data handling. Pandas provides data manipulation control.
NumPy is a free library for numerical computing. It provides high-level math functions along with data manipulations.
This library is related to scientific and technical computing. SciPy can be used for data optimization and modification, algebra, special functions, etc.
Web Development
To make your development process as easy as it is possible only, learn Python. There are a lot of Django and Flask libraries and frameworks that make your coding productive and speed up your work. If you compare PHP and Python, you can find that the same task can be created within a few hours of code via PHP. But with Python, it will take only a few minutes. Just take a look at Reddit website — it was created with Python.
Here are Pythons Full Stack frameworks for web development:
- Django
- Pyramid
- Web2py
- TurboGears
And here are Pythons micro-frameworks for web development:
- Flask
- Bottle
- CherryPy
- Hug
Also, there is an alternative framework you might want to consider:
- Tornado
Huge Community
As I have mentioned before, Python has a powerful community. You might think that it should n`t be one of the main reasons why you need to select Python. But the truth is vice versa. If you don`t get support from other specialists, your learning path can be difficult. That`s why you should know that this won`t happen with your Python learning journey.