使用多个 Python 和 IPython 路径运行 Jupyter

Question

I'd like to work with Jupyter notebooks, but have had difficulty doing basic imports (such as import matplotlib). I think this was because I have several user-managed python installations. For instance:

> which -a python
/usr/bin/python
/usr/local/bin/python

> which -a ipython
/Library/Frameworks/Python.framework/Versions/3.5/bin/ipython
/usr/local/bin/ipython

> which -a jupyter
/Library/Frameworks/Python.framework/Versions/3.5/bin/jupyter
/usr/local/bin/jupyter

I used to have anaconda, but removed if from the ~/anaconda directory. Now, when I start a Jupyter Notebook, I get a Kernel Error:

File "/Library/Frameworks/Python.framework/Versions/3.5/lib/pytho‌n3.5/subprocess.py",
line 947, in init restore_signals, start_new_session)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/pytho‌n3.5/subprocess.py",
line 1551, in _execute_child raise child_exception_type(errno_num, err_msg)
FileNotFoundError: [Errno 2]
No such file or directory: '/Users/npr1/anaconda/envs/py27/bin/python' 

What should I do?!

Answer 1

This is fairly straightforward to fix, but it involves understanding three different concepts:

1. How Unix/Linux/OSX use $PATH to find executables ( %PATH% in Windows)
2. How Python installs and finds packages
3. How Jupyter knows what Python to use

For the sake of completeness, I'll try to do a quick ELI5 on each of these, so you'll know how to solve this issue in the best way for you.

1. Unix/Linux/OSX $PATH

When you type any command at the prompt (say, python ), the system has a well-defined sequence of places that it looks for the executable. This sequence is defined in a system variable called PATH , which the user can specify. To see your PATH , you can type echo $PATH .

The result is a list of directories on your computer, which will be searched in order for the desired executable. From your output above, I assume that it contains this:

$ echo $PATH
/usr/bin/:/Library/Frameworks/Python.framework/Versions/3.5/bin/:/usr/local/bin/

In windows echo %path%

Probably with some other paths interspersed as well. What this means is that when you type python , the system will go to /usr/bin/python . When you type ipython , in this example, the system will go to /Library/Frameworks/Python.framework/Versions/3.5/bin/ipython , because there is no ipython in /usr/bin/ .

It's always important to know what executable you're using, particularly when you have so many installations of the same program on your system. Changing the path is not too complicated; see eg How to permanently set $PATH on Linux? .

Windows - How to set environment variables in Windows 10

2. How Python finds packages

When you run python and do something like import matplotlib , Python has to play a similar game to find the package you have in mind. Similar to $PATH in unix, Python has sys.path that specifies these:

$ python
>>> import sys
>>> sys.path
['',
 '/Users/jakevdp/anaconda/lib/python3.5', 
 '/Users/jakevdp/anaconda/lib/python3.5/site-packages',
 ...]

Some important things: by default, the first entry in sys.path is the current directory. Also, unless you modify this (which you shouldn't do unless you know exactly what you're doing) you'll usually find something called site-packages in the path: this is the default place that Python puts packages when you install them using python setup.py install , or pip , or conda , or a similar means.

The important thing to note is that each python installation has its own site-packages , where packages are installed for that specific Python version . In other words, if you install something for, eg /usr/bin/python , then ~/anaconda/bin/python can't use that package , because it was installed on a different Python! This is why in our twitter exchange I recommended you focus on one Python installation, and fix your $PATH so that you're only using the one you want to use.

There's another component to this: some Python packages come bundled with stand-alone scripts that you can run from the command line (examples are pip , ipython , jupyter , pep8 , etc.) By default, these executables will be put in the same directory path as the Python used to install them, and are designed to work only with that Python installation .

That means that, as your system is set-up, when you run python , you get /usr/bin/python , but when you run ipython , you get /Library/Frameworks/Python.framework/Versions/3.5/bin/ipython which is associated with the Python version at /Library/Frameworks/Python.framework/Versions/3.5/bin/python ! Further, this means that the packages you can import when running python are entirely separate from the packages you can import when running ipython or a Jupyter notebook: you're using two completely independent Python installations.

So how to fix this? Well, first make sure your $PATH variable is doing what you want it to. You likely have a startup script called something like ~/.bash_profile or ~/.bashrc that sets this $PATH variable. On Windows, you can modify the user specific environment variables. You can manually modify that if you want your system to search things in a different order. When you first install anaconda/miniconda, there will be an option to do this automatically (add Python to the PATH): say yes to that, and then python will always point to ~/anaconda/python , which is probably what you want.

3. How Jupyter knows what Python to use

We're not totally out of the water yet. You mentioned that in the Jupyter notebook, you're getting a kernel error: this indicates that Jupyter is looking for a non-existent Python version.

Jupyter is set-up to be able to use a wide range of "kernels", or execution engines for the code. These can be Python 2, Python 3, R, Julia, Ruby... there are dozens of possible kernels to use. But in order for this to happen, Jupyter needs to know where to look for the associated executable: that is, it needs to know which path the python sits in.

These paths are specified in jupyter's kernelspec , and it's possible for the user to adjust them to their desires. For example, here's the list of kernels that I have on my system:

$ jupyter kernelspec list
Available kernels:
  python2.7        /Users/jakevdp/.ipython/kernels/python2.7
  python3.3        /Users/jakevdp/.ipython/kernels/python3.3
  python3.4        /Users/jakevdp/.ipython/kernels/python3.4
  python3.5        /Users/jakevdp/.ipython/kernels/python3.5
  python2          /Users/jakevdp/Library/Jupyter/kernels/python2
  python3          /Users/jakevdp/Library/Jupyter/kernels/python3

Each of these is a directory containing some metadata that specifies the kernel name, the path to the executable, and other relevant info.
You can adjust kernels manually, editing the metadata inside the directories listed above.

The command to install a kernel can change depending on the kernel. IPython relies on the ipykernel package which contains a command to install a python kernel: for example

$  python -m ipykernel install

It will create a kernelspec associated with the Python executable you use to run this command. You can then choose this kernel in the Jupyter notebook to run your code with that Python.

You can see other options that ipykernel provides using the help command:

$ python -m ipykernel install --help
usage: ipython-kernel-install [-h] [--user] [--name NAME]
                              [--display-name DISPLAY_NAME] [--prefix PREFIX]
                              [--sys-prefix]

Install the IPython kernel spec.

optional arguments:
  -h, --help            show this help message and exit
  --user                Install for the current user instead of system-wide
  --name NAME           Specify a name for the kernelspec. This is needed to
                        have multiple IPython kernels at the same time.
  --display-name DISPLAY_NAME
                        Specify the display name for the kernelspec. This is
                        helpful when you have multiple IPython kernels.
  --prefix PREFIX       Specify an install prefix for the kernelspec. This is
                        needed to install into a non-default location, such as
                        a conda/virtual-env.
  --sys-prefix          Install to Python's sys.prefix. Shorthand for
                        --prefix='/Users/bussonniermatthias/anaconda'. For use
                        in conda/virtual-envs.

Note: the recent version of anaconda ships with an extension for the notebook that should automatically detect your various conda environments if the ipykernel package is installed in it.

Wrap-up: Fixing your Issue

So with that background, your issue is quite easy to fix:

1. Set your PATH so that the desired Python version is first. For example, you could run export PATH="/path/to/python/bin:$PATH" to specify (one time) which Python you'd like to use. To do this permanently, add that line to your .bash_profile / .bashrc (note that anaconda can do this automatically for you when you install it). I'd recommend using the Python that comes with anaconda or miniconda: this will allow you to conda install all the tools you need.

2. Make sure the packages you want to use are installed for that python. If you're using conda, you can type, eg conda install jupyter matplotlib scikit-learn to install those packages for anaconda/bin/python .

3. Make sure that your Jupyter kernels point to the Python versions you want to use. When you conda install jupyter it should set this up for anaconda/bin/python automatically. Otherwise you can use the jupyter kernelspec command or python -m ipykernel install command to adjust existing kernels or install new ones.

4. For installing modules into other Python Jupyter kernels not managed by Anaconda, you need to copy the path to the Python executable for the kernel and run /path/to/python -m pip install <package>

Hopefully that's clear... good luck!

Answer 2

@jakevdp explained it very well.

When I updated my ubuntu I also had the same problem and I solved it by changing the kernel configuration file(kernel.json). To list the kernel files location. Use

jupyter kernelspec list

It will return

Available kernels:
  python3    /home/user1/.local/share/jupyter/kernels/python3
  python2    /usr/local/share/jupyter/kernels/python2

I was using python3 so I changed the file at

/home/user1/.local/share/jupyter/kernels/python3

by following step

nano /home/user1/.local/share/jupyter/kernels/python3/kernel.json

There inside argv I changed the first parameter(ie python3 directory path) form

"/usr/bin/python3.5"

to

"/usr/bin/python3"

and saved it with ctr+x and restarted jupyter-notebook.

Answer 3

also found not to put your virtual environment inside the git repo as it becomes non-readable to read the python packages. seems different permissions use while reading and writing (writing - installing a package - use pip), how unable to read. Hence, for me the python libraries were getting read from system installation and not virtual environment.

Answer 4

@jakevdp's Answer above & his blog https://jakevdp.github.io/blog/2017/12/05/installing-python-packages-from-jupyter/ gives fairly good idea about what's going wrong, however just updating path from shell was not working for me, there are 2 ways that worked for me

Either update path on notebook using magic commands, run below on cell

originalPath = %env PATH
%env PATH = [local anaconda path]/kernels/[custom_kernel]/bin/:$originalPath

Or you can even update the kernel.json & set the path in env

{
    "argv": [
        "[custom kernel path]/bin/python",
        "-m",
        "ipykernel_launcher",
        "-f",
        "{connection_file}"
    ],
    "env": {
        "PATH": "[custom kernel path]/bin/:[rest of the paths]"
    },

    "display_name": "custom_kerbel",

    "language": "python"
}

Answer 5

If you just want to install a package into the current environment to be able to import it, you can use the %pip and %conda magicks.

As you mention anaconda, you probably should use conda to install:

# Install a conda package in the current Jupyter kernel
%conda install <dependency_name>

Alternatively, if you need to use pip :

# Install a pip package in the current Jupyter kernel
%pip install <python_package_name>