Just a month after announcing our partnership with Continuum Analytics, we are proud to introduce the integration of Anaconda with Jupyter Notebooks in IBM Data Science Experience. What does that mean for you? More options and more power!
We started supporting Python 2.7 because it supports all the packages that data scientists need for data analysis, including SciPy, Numpy, Matplotlib, scikit-learn, PySpark, and more. While most companies who use Python prefer Python 2.7 right now, Python 3 offers several nice improvements over earlier versions. As Python 3 gains additional features and packages, it will become the natural choice for data scientists. With support for both Python 2 and Python 3.5 IBM Data Science Experience gives you the option to choose your favorite.
Anaconda is the leading Open Data Science platform powered by Python. It uses over 720 packages for data preparation, data analysis, data visualization, machine learning and interactive data science applications that deliver results—everything from discovering gravitational waves to creating new revenue channels.
Getting started with Python 3 in Data Science Experience is easy. Create a new notebook and you are prompted with four language choices: Scala, R, Python 2 and Python 3.
From the Notebook interface you can see on which Spark instance this Notebook is being executed, which Spark version (now we support Spark 1.6 and 2.0), and the language and the libraries installed on that instance.
Anaconda 4.2.0 includes an easy installation of Python (2.7.12, 3.4.5, and/or 3.5.2) and updates of over 100 pre-built and tested scientific and analytic Python packages, including Numpy, Pandas, SciPy, Matplotlib, and IPython, with over 620 more packages available via a simple
conda install <packagename> command. You can find the full list of packages available here: https://docs.continuum.io/anaconda/pkg-docs