As noted above, we can get around this by explicitly identifying where we want packages to be installed. I tried your steps and also ran into problems. 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. Any libraries can then be installed within R using the install. For instance, if you want to install rbokeh, you will need to use conda install r-rbokeh or for rJava, type conda install r-rjava. Installing Jupyter notebook The easiest way to install Jupyter notebook is to install Anaconda.
It was previously available through ipython now through jupyter -. The kernel environment can be changed at runtime, while the shell environment is determined when the notebook is launched. I don't have details on your build; make sure you remove any previous kernels from Jupyter before trying this. We need to add the path, i. Cling is evolving, so check out the doc for the latest syntax.
For completeness, I'm going to delve briefly into each of these topics this discussion is partly drawn from that I wrote last year. First comes the obvious stuff. Note that we use --yes to automatically answer y if and when conda asks for user confirmation For various reasons that I'll outline more fully below, this will not generally work if you want to use these installed packages from the current notebook, though it may work in the simplest cases. This makes it easy to run one notebook instance and access kernels with access to different versions of Python or different modules seamlessly. Kerry for ggplot, i cant use your tutorial but I use conda install -c conda-forge ggplot also I cant use this in windows Install r-matrix, r-nlme, and some other useful libraries. I use gcc to compile my c++ code usually. And, finally, thanks for all that you do for the open source community.
Finally register for the kernelspec: jupyter-kernelspec install --user cling-cpp11. To start a jupyter notebook with C++ kernel, just type the following in the terminal: jupyter notebook A webpage named Home will open. But if they are implemented carefully, I think it would lead to a much nicer overall user experience. Those above solutions should work in all cases. The fact that a full explanation took so many words and touched so many concepts, I think, indicates a real usability issue for the Jupyter ecosystem, and so I proposed a few possible avenues that the community might adopt to try to streamline the experience for users. New Jupyter Magic Functions Even if the above changes to the stack are not possible or desirable, we could simplify the user experience somewhat by introducing %pip and %conda magic functions within the Jupyter notebook that detect the current kernel and make certain packages are installed in the correct location. The kernel file loads the python file from the folder it is installed in if no specific path is given.
You only need to install Altair in the Python environment that runs ipykernel. We will start with loading the necessary header files. In this case pip install will install packages to a path inaccessible to the python executable. Unless you change the R interpreter, conda will continue to use the default interpreter in each environment. This approach is not without its own dangers, though: these magics are yet another layer of abstraction that, like all abstractions, will inevitably leak. Congratulations, you have installed Jupyter Notebook! So, open up a terminal and type the following. Nevertheless, if I run the notebook with the hello world example or any code , the kernel does not return any output, it just runs but it does not return anything.
Once things are up and running, you may wish to go through the tutorials at and , read through the User Guide indexed in the left panel, or check out the for more ideas. Anaconda Navigator, the Anaconda graphical package manager and application launcher, creates R environments by default. Basically, in your kernel directory, you can add a script kernel-startup. This might look something like this: The plugin that enables this is catchy name I know. If the module is not found there, it goes down the list of locations until the module is found.
When searching for a resource, the code will search the search path starting at the first directory until it finds where the resource is contained. R is the default interpreter installed into new environments. In the wake of several discussions on this topic with colleagues, some online , and some off, I decided to treat this issue in depth here. 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. That said, such a symmetry would certainly be a help to users.
For this reason, it is safer to use python -m pip install, which explicitly specifies the desired Python version , after all. If you have not done it already, install a C++ compiler such as g++ from terminal or software center. Normally I would use Python for this kind of task but, since there was already a considerable amount of code in R, it made sense for me to do some work in R. 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. 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. On the top right corner, we will have a button titled new, click on it and select C++.
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! 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! For day-to-day Python usage, you should isolate your packages from the system Python, using either or — I personally prefer conda for this, but I know many colleagues who prefer virtualenv. Have a question about this project? We can see this by printing the sys. This is one reason that pip install no longer appears in , and experienced Python educators like David Beazley. A similar approach could work for virtualenvs or other Python environments. Note: I am assuming that you are using Linux probably works on Mac too but I make no guarantees whatsoever that following this will get you a working environment! Installing C++ kernel for Jupyter notebook: Cling Cling is an interactive C++ interpreter; it allows us to type and execute C++ code dynamically, like Python or Julia.
In other words, the Jupyter notebook, like all abstractions, is leaky. If the plot does not render, ensure you have installed the most recent versions of the above packages, and if it still does not work see for help. For example, kernel specs are in kernels subdirectories. A Jupyter kernel is a set of files that point Jupyter to some means of executing code within the notebook. In the notebook that opens, you can run the following code to ensure everything is properly set up: alt. Doing this can have bad consequences, as often the operating system itself depends on particular versions of packages within that Python installation. Then I had the option of a new python3 notebook when i launched ipython notebook normally.