According to me, I think the choice of language depends upon the compatibility of a particular person and his/her company’s deployment Platform.
Its not easy to prefer one over other.
However, as I am quite comfortable with Python, I would like to throw light on few of the important aspects of Python with respect to Machine Learning.
Python provides highly efficient libraries with in-built modules which allow you to focus more on your goal/ML model rather than hitting your head against complicated coding stuff.
Scikit-learn is the most popular machine learning library for Python. Built on NumPy and SciPy, scikit-learn offers tools for data mining and analysis that bolster Python's already-superlative machine learning usability. NumPy and SciPy impress on their own. They are the core of data analysis in Python and any serious data analyst is likely using them raw, without higher-level packages on top, but scikit-learn pulls them together in a machine learning library with a lower barrier to entry.
When it comes to data analysis, Python receives a welcome boost from several different packages. Pandas, one of its most well-known data analysis packages, gives Python high-performance structures and data analysis tools. As is the case with many of Python's packages, it shortens the time between starting a project and doing meaningful work within that project.
You can refer the below documentation unlocking the capabilities of Pandas: pandas: powerful Python data analysis toolkit