In software, it's said that, and this is true for the Jupyter notebook as it is for any other software. 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. Cold case season 1. This issue is a perrennial source of StackOverflow questions (e.g.,,,,,,. Fundamentally the problem is usually rooted in the fact that the Jupyter kernels are disconnected from Jupyter's shell; in other words, the installer points to a different Python version than is being used in the notebook. 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. In other words, the Jupyter notebook, like all abstractions, is leaky. Use the following installation steps: Download Anaconda. We recommend downloading Anaconda's latest Python 3 version (currently Python 3.5). Install the version of Anaconda which you downloaded, following the instructions on the download page. Congratulations, you have installed Jupyter Notebook. To run the notebook. In this lesson, we looked at how we can install and start using Jupyter notebook server on Ubuntu 18.04 machine. About the author. Shubham Aggarwal. Free kagan timer tools. Enagic distributors have set all-time US sales records since its launch date of October 31st, 2008. Not only has the Why Kangen Water? DVD received accolades from top professionals in the in the direct sales industry, but it has also proven itself with the distributor force. 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. This post will address a couple things: • First, I'll provide a quick, bare-bones answer to the general question, how can I install a Python package so it works with my jupyter notebook, using pip and/or conda? • Second, I'll dive into some of the background of exactly what the Jupyter notebook abstraction is doing, how it interacts with the complexities of the operating system, and how you can think about where the 'leaks' are, and thus better understand what's happening when things stop working. • Third, I'll talk about some ideas the community might consider to help smooth-over these issues, including some changes that the Jupyter, Pip, and Conda developers might consider to ease the cognitive load on users. This post will focus on two approaches to installing Python packages:. Other package managers exist (including platform-specific tools like,,, etc., as well as cross-platform tools like ), but I'm less familiar with them and won't be remarking on them further. Conda First, a few words on pip vs. For many users, the choice between pip and conda can be a confusing one. I wrote about these in a post last year, but the essential difference between the two is this: • pip installs python packages in any environment. • conda installs any package in conda environments. If you already have a Python installation that you're using, then the choice of which to use is easy: • If you installed Python using Anaconda or Miniconda, then use conda to install Python packages. If conda tells you the package you want doesn't exist, then use pip (or try, which has more packages available than the default conda channel). • If you installed Python any other way (from source, using pyenv, virtualenv, etc.), then use pip to install Python packages Finally, because it often comes up, I should mention that you should never use sudo pip install. ![]() It will always lead to problems in the long term, even if it seems to solve them in the short-term. For example, if pip install gives you a permission error, it likely means you're trying to install/update packages in a system python, such as /usr/bin/python. Doing this can have bad consequences, as often the operating system itself depends on particular versions of packages within that Python installation. 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. The Details: Why is Installation from Jupyter so Messy? Those above solutions should work in all cases. But why is that additional boilerplate necessary? ![]() 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: • how your operating system locates executable programs, • how Python installs and locates packages • how Jupyter decides which Python executable to use. For completeness, I'm going to delve briefly into each of these topics (this discussion is partly drawn from that I wrote last year). Note: the following discussion assumes Linux, Unix, MacOSX and similar operating systems. Windows has a slightly different architecture, and so some details will differ. $PATH lists the directories, in order, that will be searched for any executable: for example, if I type python on my system with the above $PATH, it will first look for /Users/jakevdp/anaconda/envs/python3.6/bin/python, and if that doesn't exist it will look for /Users/jakevdp/anaconda/bin/python, and so on. (Parenthetical note: why is the first entry of $PATH repeated twice here? Because every time you launch jupyter notebook, Jupyter prepends the location of the jupyter executable to the beginning of the $PATH. 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.
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