The significant factor giving the push for Python is the variety of data science/data analytics libraries made available for the aspirants. Pandas, StatsModels, NumPy, SciPy, and Scikit-Learn, are some of the libraries well known in the data science community. Python does not stop with that as libraries have been growing over time. What you thought was a constraint a year ago would be addressed well by Python with a robust solution addressing problems of specific nature.
One of the reasons for the phenomenal rise of Python is attributed to its ecosystem. As Python extends its reach to the data science community, more and more volunteers are creating data science libraries. This, in turn, has led the way for creating the most modern tools and processing in Python.
The widespread and involved community promotes easy access for aspirants who want to find solutions to their coding problems. Whatever queries you need, it is a click or a Google search away. Enthusiasts can also find access to professionals on Codementor and Stack Overflow to find the right answers for their queries.
Graphics and visualization
Python comes with varied visualization options. Matplotlib provides the solid foundation around which other libraries like Seaborn, pandas plotting, and ggplot have been built. The visualization packages help you get a good sense of data, create charts, graphical plot and create web-ready interactive plots.
Is Python ‘the’ tool for machine learning?
When it comes to data science, machine learning is one of the significant elements used to maximize value from data. With Python as the data science tool, exploring the basics of machine learning becomes easy and effective. In a nutshell, machine learning is more about statistics, mathematical optimization, and probability. It has become the most preferred machine learning tool in the way it allows aspirants to ‘do math’ easily.
Name any math function, and you have a Python package meeting the requirement. There is Numpy for numerical linear algebra, CVXOPT for convex optimization, Scipy for general scientific computing, SymPy for symbolic algebra, PYMC3, and Statsmodel for statistical modeling.
With the grip on the basics of machine learning algorithm including logistic regression and linear regression, it makes it easy to implement machine learning systems for predictions by way of its scikit-learn library. It’s easy to customize for neutral networks and deep learning with libraries including Keras, Theano, and TensorFlow.
Data science landscape is changing rapidly, and tools used for extracting value from data science have also grown in numbers. The two most popular languages that fight for the top spot are R and Python. Both are revered by enthusiasts, and both come with their strengths and weaknesses. But with the tech giants like Google showing the way to use Python and with the learning curve made short and easy, it inches ahead to become the most popular language in the data science world.