![]() ![]() Jupyter Notebook Tutorial: The Definitive Guide.But you can find lots of beginners guides on the internet: How exactly the Jupyter notebooks can be used is not central to this post. Activate the environment you want to useĪ new tab is generated in the specified default browser from which you can launch Jupyter Notebooks.Determine the path where the notebook should be opened.jupyter contrib nbextension install -user.pip install jupyter_contrib_nbextensions.conda install -c conda-forge matplotlib.This is really very simple and fast, just use:Īs I said, super easy but as we can see it is unfortunately still completely empty.įirst of all I activate the new environment and install pip from anaconda as a package.Īfter that, all sorts of other libraries can be added without hesitation.įor example, here is a list of the packages I used to create the example environment (found here in my GitHub Repository): yml file with a base environment and adding all the necessary packages manually or by creating a completely new environment directly from Anaconda Powershell Prompt. Make a backup copy of your environment so that you can fall back to the old stateĪs I mentioned before, it is advisable to create a separate environment for each new project.Make sure that pip does not run in the “root” environment.If there is no way around pip, install the pip version of Anaconda (conda install -c anaconda pip) in your virtual environment first, so that the PyPI package is only installed in this environment and not across all environments.Install as many libraries as possible with conda.Create a separate (virtual) environment for each project.Therefore, there are some recommendations that should be followed: Thus, sometimes it is simply unavoidable to use both pip and conda in the same environment at the same time, as some libraries are exclusively available to PyPI. However, pip is the central package pool with far more available libraries than is the case in conda. Likewise, a library installed by pip may inadvertently update or remove packages that are needed by other conda libraries and thus become unusable.Ĭonda is often used for data science and machine learning applications and is therefore the more interesting package manager for me. If conda is run after pip, packages installed via pip can be overwritten. Most of these problems originate from the fact that conda has limited ability to control packages that it did not install itself. Unfortunately, problems can occur if conda and pip are used together when creating an environment. It is supplied by default with the installation of Anaconda. In contrast, conda is a language-independent and cross-platform package manager that manages Python packages, among other things (C libraries or executables like C compilers). It gets its packages from the official software repository PyPI (Python Package Index) and is delivered by default with the installation of Python. Pip is the official package manager of the Python Software Foundation. Pip and Conda are both package managers that assist in the installation and management (update or removal) of packages. Python supports third-party frameworks and libraries that already contain various features and elements that can be used by programmers. How this comes about I explain in the following chapter. See the entries ‘pypi’ or ‘anaconda’ in the column ‘Channel’. You can also use the list command to see which library was installed with which package manager. And already we have a new environment installed: How to add them you will learn in a moment. Of course, some cool libraries will be added over time. Please do not forget to uncheck ‘disable configuration for nbextensions without explicit compatibility’ in Jupyter Notebook so you can use the nbextensions. Jupyter contrib nbextension install -user Please execute the following command to make the nbextensions work: In our demo environment I installed the following basic libraries: Navigate again with cd to the respective place where the. ![]() If you want to have the created sample environment feel free to download it from my GitHub Repository. We can also import predefined environments using the Anaconda Powershell prompt. ![]()
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