So at the moment, conda ftw. This is incredibly useful if a major feature changes that would break old code, for example. Why should I use conda as another python package manager when I already have pip? This is the most complicated option, but it is also the most flexible, as you can have multiple versions of this particular software installed, and specify at run-time which one to use. The recommended and easiest way to do this is with conda although experts may prefer pip. In short, it's because in Jupyter, the shell environment and the Python executable are disconnected. For this reason, it is safer to use python -m pip install, which explicitly specifies the desired Python version , after all. A Jupyter kernel is a set of files that point Jupyter to some means of executing code within the notebook.
A pip channel for conda? Conda is the package manager of Anaconda, the Python distribution provided by Continuum Analytics, but it can be used outside of Anaconda too. Further Reading: Conda Test Drive: While we provide a number of Python modules, you may need a module we do not provide. Replace some-environment with your environment name. By default, this toggle is switched off and you can manage only the packages available with the selected Python interpreter. With Anaconda, just run in Anaconda Prompt if on Windows conda update anaconda to update the distribution as a whole and conda update spyder to update Spyder specifically.
You can use conda to install and manage any additional third-party dependencies to ensure that the package can be immediately used. In this case pip install will install packages to a path inaccessible to the python executable. 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. We can see this by printing the sys. How your operating system locates executables When you're using the terminal and type a command like python, jupyter, ipython, pip, conda, etc. As such, Conda should be compared to perhaps, another tool that lets you handle both Python and non-Python installation tasks. Each of these installations is referred to as a Python environment.
Recall that the python in your path can be determined using In my current notebook environment, the two differ. Submitting these commands as a job new to writing job scripts- go , just add the above lines to your job script. To specify a custom repository, including or , click Manage Repositories. 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. 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! To install a Python package, click the package name in the Name column, change the version if necessary, and click the Install button.
For many users, the choice between pip and conda can be a confusing one. For instance, pip is built on top of setuptools, whereas conda uses its own format, which has some advantages like being static, and again, Python agnostic. To further extend the versatility and utility of Python packages, multiple installations of Python can coexist on a single workstation, independent of one another. Other package managers exist including platform-specific tools like , , , etc. 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.
There is a tool called conda build that builds packages from source, but conda install itself installs things from already built conda packages. Therefore, we recommend you switch to Anaconda if you encounter installation issues you are unable to solve on your own. Conda installs from binary, meaning that someone e. In short, pip is a general-purpose manager for Python packages; conda is a language-agnostic cross-platform environment manager. This allows multiple versions of Python to be accessible simultaneously.
. You can upgrade several packages at once. In this case, the location was already at the beginning of the path, and the result is that the entry is duplicated. This includes low-level libraries, such as linear algebra, compilers, such as mingw on Windows, editors, version control tools like Hg and Git, or whatever else requires distribution and management. Install your own python modules If the method using conda above is not working or if you prefer, you can consider installing python modules from the source. Updating Spyder If you installed Spyder through Anaconda recommended , WinPython, MacPorts, or your system package manager, update using those same methods.
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! The Python community has designed methods to create projects in multiple versions of Python while simplifying the process of installing nearly all publicly available Python packages. If you break your login environment, you'll have to get our help to fix it. Anaconda is free and easy to install, and it offers. I even used sudo conda to fix a broken pip rather than using sudo pip uninstall pip or sudo pip --upgrade install pip. To install one of these versions, unpack it and run the following from the top-level source directory using the Terminal: pip install. The selected packages are removed from disk. Some packages are pinned to a specific version and cannot be updated; these packages will not appear in the list even when updates are available.