- #INSTALL JUPYTER NOTEBOOK INSTALL#
- #INSTALL JUPYTER NOTEBOOK UPGRADE#
- #INSTALL JUPYTER NOTEBOOK FULL#
- #INSTALL JUPYTER NOTEBOOK SOFTWARE#
- #INSTALL JUPYTER NOTEBOOK PC#
To run just the official Jupyter Notebook image in your Docker engine, type the following into the Docker Quickstart Terminal: $ docker run -rm -it -p 8888:8888 -v "$(pwd):/notebooks" jupyter/notebookĪfter all the image's "layers" are downloaded, it will start up.
#INSTALL JUPYTER NOTEBOOK FULL#
There are many images on Docker Hub that offer Jupyter, including the official Jupyter Notebook image, and Anaconda itself if you want the full SciPy stack.
#INSTALL JUPYTER NOTEBOOK SOFTWARE#
Once you see a prompt in the terminal, you can use the docker run command to run Docker "images", which you can think of as pre-packaged bundles of software that will be automatically downloaded from the Docker Hub when you run them. Double click that shortcut and it will create your first Docker engine for you and set up everything you need automatically. Once installed, you'll have a "Docker Quickstart Terminal" shortcut in your Start Menu.
#INSTALL JUPYTER NOTEBOOK INSTALL#
Run the installer and make sure the checkbox to install Virtualbox is checked if you don't already have Virtualbox or another virtualization platform ( like VMWare Workstation) installed. To get started on Windows, download the Docker Toolbox, which contains the tools you need to get up and running.
#INSTALL JUPYTER NOTEBOOK PC#
Don't let the flexibility and power and confusing terminology put you off though - Docker can be easy to get up and running on your PC and has some advantages of its own with respect to Python and Jupyter. While it may sound similar in concept to python virtual environments, Docker containers are an entirely different kind of technology offering vast flexibility and power. Who is it for? People who want the quickest, easiest way to get Jupyter notebook up and running (IE, most people).ĭocker is a platform for running software in " containers", or self-contained, isolated processes. Relies on a commercial 3rd party for support. And anything it doesn't ship with you can still instalAl via its built in conda package manager.ĭisadvantages: No virtualenv support, although the conda package manager provides very similar functionality with the conda create command.
It doesn't get any easier than that.Īdvantages: Simplest, fastest way to get started and it comes with probably everything you need for your scientific computing projects.
Click to start it up and it'll launch in the background and open up your browser to the notebook console. Download their Python 3.5 installer for Windows, run it, and in your Start menu you'll have a bunch of neat new tools, including an entry for Jupyter Notebook. Who is it for? Python users comfortable with the command line and the tools that ship with Python itself.īecause of the difficulty mentioned above in getting packages like SciPy installed on Windows, a few commercial entities have put together pre-packaged Python "distributions" that contain most, if not all, of the commonly used libraries for data analysis and/or scientific computing.Īnaconda is an excellent option for this. Well supported, with plenty of online documentation and support communities.ĭisadvantages: While many popular data analysis or scientific python libraries can be installed by pip on windows (including Pandas and Matplotlib), some (for example SciPy) require a C compiler and the presence of 3rd party C libraries on the system which are difficult to install on Windows.
#INSTALL JUPYTER NOTEBOOK UPGRADE#
Incidentally, if you get a warning about upgrading pip, make sure to use the following incantation to upgrade (to prevent an issue on windows where pip is unable to upgrade its own executable in-place): PS C:\> python -m pip install -upgrade pipĪdvantages: Uses "pure" python, official tools, and no external dependencies. Then, let's install jupyter and start up a notebook: PS C:\> pip install jupyter To do this, open up a Powershell window, and enter the following commands (where " myenv" is the name of the virtualenv we're going to create, you can use any name you like for this): PS C:\> python -m venv myenv Once that's installed, you'll want to create a virtual environment, a lightweight, disposable, isolated python installation where you can experiment and install 3rd party libraries without affecting your "main" installation. C:\Python35, so my PATH contains that directory. I like to install it in the root of my C: drive, e.g. Make your way over to, download and install the latest version (3.5.1 as of this writing) and make sure that wherever you install it, the directory containing python.exe is in your system PATH environment variable.
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