How to download jupiter ipython file on github

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Who owns this outage? Building intelligent escalation chains for modern SRE. Featured on Meta. Now live: A fully responsive profile. Reducing the weight of our footer. Intel will soon be sponsoring Data Science. Skip to content. Change Language. Related Articles. Table of Contents. Save Article. Improve Article. Like Article. Last Updated : 05 Aug, This is related to the fact that, even setting Jupyter notebooks aside, it's better to install packages using. Those above solutions should work in all cases In short, it's because in Jupyter, the shell environment and the Python executable are disconnected.

Understanding why that matters depends on a basic understanding of a few different concepts:. For completeness, I'm going to delve briefly into each of these topics this discussion is partly drawn from This StackOverflow answer that I wrote last year. Windows has a slightly different architecture, and so some details will differ. When you're using the terminal and type a command like python , jupyter , ipython , pip , conda , etc. In this case, the location was already at the beginning of the path, and the result is that the entry is duplicated.

Duplicate entries add clutter, but cause no harm. If you want to know what is actually executed when you type python , you can use the type shell command:. You can optionally add the -a tag to see all available versions of the command in your current shell environment; for example:.

Python uses a similar mechanism to locate imported packages. The list of paths searched by Python on import is found in sys. By default, the first place Python looks for a module is an empty path, meaning the current working directory. If the module is not found there, it goes down the list of locations until the module is found.

In most cases, a Python package you install with pip or with conda will be put in a directory called site-packages. 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! We can see this by printing the sys. The full details here are not particularly important, but it is important to emphasize that each Python executable has its own distinct paths , and unless you modify sys.

When you run pip install or conda install , these commands are associated with a particular Python version:. So, for example we see that pip install will install to the conda environment named python3. And conda install will do the same, because python3.

The reason both pip and conda default to the conda python3. I'll say this again for emphasis: the shell environment in Jupyter notebook matches the Python version used to launch the notebook.

The next relevant question is how Jupyter chooses to execute Python code, and this brings us to the concept of a Jupyter Kernel. A Jupyter kernel is a set of files that point Jupyter to some means of executing code within the notebook. To see the kernels you have available on your system, you can run the following command in the shell:. Each of these listed kernels is a directory that contains a file called kernel.

For example:. If you'd like to create a new kernel, you can do so using the jupyter ipykernel command ; for example, I created the above kernels for my primary conda environments using the following as a template:.

Now we have the full background to answer our question: Why don't! The root of the issue is this: the shell environment is determined when the Jupyter notebook is launched, while the Python executable is determined by the kernel, and the two do not necessarily match. In my current notebook environment, the two differ. This is why a simple! As noted above, we can get around this by explicitly identifying where we want packages to be installed. Remember: you need your installation command to match the current python kernel if you want installed packages to be available in the notebook.

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Collectives on Stack Overflow. Learn more. Asked 4 years, 3 months ago. Active 2 months ago. Viewed 36k times. Eswar 1, 17 17 silver badges 39 39 bronze badges. Add a comment. Active Oldest Votes. Open jupyter notebook Go to location where you saved.