Mac Os X come with Pythong 2.7 pre-installed but many Machine Learning packages are progressing to Python 3.x. Therefore, it's recommended you start using Python 3 and the best way to do that is to first install pyenv version manager. This will allow you to install any version of Python you'd like.
Although it takes a little bit of extra work to get R working with Jupyter, it's totally worth it. Otherwise, if you want to import data, clean it, structure it and process it, you'll have to have to learn all of these other Python libraries to do what R does natively.
NumPy supports scientific computing and linear algebra support.
Pandas provide data frames which make it easy to access and analyze data. This is a data manipulation tool.
Mat plotlib is a 2D publication library that produces high quality graphics.
Scikit-learn's purpose is to support machine learning and therefore it's used for many of the tasks performed routinely in machine learning. A few key features are:
It works well with the libraries stated above.
It helps integrate the algorithms we will use for predictive models.
Methods that will help us pre-process data.
Methods for helping us measure the performance of our models.
Methods for splitting data into test sets
Methods for pre-processing data before training.
Methods for creating trained models, tuning models and identifying which features within the models are important.