You can also create new environments from within Jupyter Notebook home screen, Conda tab, and then click the plus sign. Note that pip installing conda is not the recommended way for setting up your system. Please note that new packages have to be explicitly installed into the new environments. For this reason, it is safer to use python -m pip install, which explicitly specifies the desired Python version , after all. Could you maybe expand your answer to include how to start jupyter so that I can run python2 and python3 side-by-side? Anaconda conveniently installs Python, the Jupyter Notebook, and other commonly used packages for scientific computing and data science.
Now you should be able to chose between the 2 kernels regardless of whether you use jupyter notebook, ipython notebook or ipython3 notebook the later two are deprecated. GraphLab Create is supported with Anaconda2 v4. In this case, activate py36 For more info, check out this and. If you are using anaconda, please find my for a solution directly tailored to anaconda. If you are not using anaconda, I recently added using pure virtualenvs.
This is why a simple! I already have python 3 installed. Thereafter, all packages you install will be available to you when you activate this environment. And, finally, thanks for all that you do for the open source community. I do not understand why. In the simplest contexts this issue does not arise, but when it does, debugging the problem requires knowledge of the intricacies of the operating system, the intricacies of Python package installation, and the intricacies of Jupyter itself. But that leaves us in an undesireable place, as it increases the learning curve for novice users who may want to do something they rightly presume should be simple: install a package and then use it. The kernel environment can be changed at runtime, while the shell environment is determined when the notebook is launched.
Recall that the python in your path can be determined using In my current notebook environment, the two differ. This post is going to be the first in a series of posts about the Jupyter Notebook and the supercomputing facility at Sheffield University. If a pip magic and conda magic similar to the above were added to Jupyter's default set of magic commands, I think it could go a long way toward solving the common problems that users have when trying to install Python packages for use with Jupyter notebooks. Depending on the system and shell you use, this can be e. The exception is the special case where you run jupyter notebook from the same Python environment to which your kernel points; in that case the simple installation approach should work.
See ipython kernel install --help for more informations. According to it will use Tensorflow by default, but I remember having had to edit at least once by hand. First, ensure that you have the latest pip; older versions may have trouble with some dependencies:. Then I had the option of a new python3 notebook when i launched ipython notebook normally. The section in conda's docs provides further information. Here is a short snippet that should work in general: That bit of extra boiler-plate makes certain that you are running the pip version associated with the current Python kernel, so that the installed packages can be used in the current notebook.
This post will focus on two approaches to installing Python packages: and. But sometimes you might need to specify the default versions of programs that are on della, especially for distributing jobs to other users, etc. The kernel file loads the python file from the folder it is installed in if no specific path is given. First make sure that you have a python2 and a python3 interpreter with pip available. Summary In this post, I tried to answer once and for all the perennial question, how do I install Python packages in the Jupyter notebook. Perhaps: for example, shows an approach to modifying shell variables as part of kernel startup. One final addendum: I have a huge amount of respect and appreciation for the developers of Jupyter, conda, pip, and related tools that form the foundations of the Python data science ecosystem.
Browse other questions tagged or. So what can we as a community do to smooth-out this issue? Explicit invocation For symmetry with pip, it would be nice if python -m conda install could be expected to work in the same way the pip counterpart does. In software, it's said that , and this is true for the Jupyter notebook as it is for any other software. In this case, the location was already at the beginning of the path, and the result is that the entry is duplicated. Under Windows 7 I had anaconda and anaconda3 installed.
Then you are ready to go to call jupyter. A similar approach could work for virtualenvs or other Python environments. Another useful change conda could make would be to add a channel that essentially mirrors the , so that when you do conda install some-package it will automatically draw from packages available to pip as well. You can double check what packages you have by running: conda list After this, the steps for having the notebook serve the notebook to your local browser is identical to the previous post. But you should be set for most use cases. Those above solutions should work in all cases. Even though it's more verbose, I think forcing users to be explicit would be a useful change, particularly as the use of virtualenvs and conda envs becomes more common.
Here's a screenshot of what the default Jupyter insalled with python3 -m install jupyter and opened in the browser with jupyter notebooklooks like: Make sure you have ipykernel installed and use ipython kernel install to drop the kernelspec in the right location for python2. How can I add it to Jupyter? For completeness, I'm going to delve briefly into each of these topics this discussion is partly drawn from that I wrote last year. Here are instructions for anaconda. This post is about a plugin for the Jupyter Notebook I have written to make it easier to work with Jupyter and the python package manager. It was previously available through ipython now through jupyter -. So, could we massage kernel specifications such that they force the two to match? I'm fairly certain those developers have already considered these issues and weighed some of these potential fixes — if any of you are reading this, please feel free to comment and set me straight on anything I've overlooked! I most often see this manifest itself with the following issue: I installed package X and now I can't import it in the notebook. So it's not a full solution to the problem by any means, but if Python kernels could be designed to do this sort of shell initialization by default, it would be far less confusing to users:! The important thing to realize is that each Python executable has its own site-packages: what this means is that when you install a package, it is associated with particular python executable and by default can only be used with that Python installation! Create a virtual environment Specifying the version is optional.
This issue is a perrennial source of StackOverflow questions e. And you can create a notebook in any environment you want. A Jupyter kernel is a set of files that point Jupyter to some means of executing code within the notebook. For Python kernels, this will point to a particular Python version, but Jupyter is designed to be much more general than this: Jupyter has for languages including Python 2, Python 3, Julia, R, Ruby, Haskell, and even C++ and Fortran! In this json you can also edit the kernel name that is displayed in ipython e. So, in summary, the reason that installation of packages in the Jupyter notebook is fraught with difficulty is fundamentally that Jupyter's shell environment and Python kernel are mismatched, and that means that you have to do more than simply pip install or conda install to make things work.