Jupiter notebook ошибка при запуске

Это очень частая проблема, которая появляется на некоторых ОС.
Дело в том, что среда разработки запустилась, но браузер не открылся автоматически.

Как решить эту проблему?

В первую очередь, попробуйте запустить Anaconda Navigator с правами администратора (правой кнопкой мыши нажать на иконку Anaconda Navigator, во всплывающем меню выбрать «Запуск от имени администратора»).
Теперь, запустите Jupyter Notebook.

Если все равно не открывается окно браузера, выполните инструкции описанные далее.

ОС WINDOWS:

1. Запустите программу, которая называется CMD.exe Prompt (может также называться Anaconda Prompt), нажав на Launch.
   Эта программа находится тут же, в Anaconda Navigator, рядом с программой Jupyter Notebook. После нажатия на Launch, должна открыться командная строка.
   Если этой программы нет в Anaconda Navigator, можно найти программу «Anaconda Prompt» на компьютере с помощью обычного поиска по программам.

2. В этой командной строке мы должны выполнить команду jupyter notebook list
   (если команда не сработала, попробуйте сначала выполнить команду jupyter notebook list -V, а потом уже команду jupyter notebook list)

3. Вышеупомянутая команда показывает тот адрес, по которому мы сможем получить доступ к нашей среде разработки.
   Адрес имеет вид: http://localhost:8888/?token=СЛУЧАЙНАЯ_ПОСЛЕДОВАТЕЛЬНОСТЬ_БУКВ_И_ЦИФР

Вам необходимо скопировать этот адрес, вставить его в адресную строку вашего браузера и перейти на эту страницу.
После этого откроется среда разработки Jupyter Notebook. Можно работать.

Чтобы скопировать адрес из командной строки Windows, необходимо кликнуть правой кнопкой мыши в любом месте командной строки.
В выпадающем меню надо выбрать пункт «пометить». После этого, можно будет выделить курсором интересующий нас адрес.
После того, как адрес будет выделен, надо нажать на клавишу Enter на вашей клавиатуре. Готово — адрес скопирован в буфер обмена. Можно его вставлять в адресную строку браузера.

ОС Linux или Mac OS:
Надо просто открыть терминал и там написать jupyter notebook
Полученный адрес надо скопировать в адресную строку браузера.

P.S.
Если Jupyter Notebook так и не запустился, можно использовать среду разработки PyCharm.
Эта среда разработки ничуть не хуже, чем Jupyter Notebook, и тоже отлично нам подойдет.

What to do when things go wrong

First, have a look at the common problems listed below. If you can figure it out
from these notes, it will be quicker than asking for help.

Check that you have the latest version of any packages that look relevant.
Unfortunately it’s not always easy to figure out what packages are relevant,
but if there was a bug that’s already been fixed,
it’s easy to upgrade and get on with what you wanted to do.

Jupyter fails to start

• Have you installed it?
• If you’re using a menu shortcut or Anaconda launcher to start it, try
  opening a terminal or command prompt and running the command jupyter notebook.
• If it can’t find jupyter,
  you may need to configure your PATH environment variable.
  If you don’t know what that means, and don’t want to find out,
  just (re)install Anaconda with the default settings,
  and it should set up PATH correctly.
• If Jupyter gives an error that it can’t find notebook,
  check with pip or conda that the notebook package is installed.
• Try running jupyter-notebook (with a hyphen). This should normally be the
  same as jupyter notebook (with a space), but if there’s any difference,
  the version with the hyphen is the ‘real’ launcher, and the other one wraps
  that.

Jupyter doesn’t load or doesn’t work in the browser

• Try in another browser (e.g. if you normally use Firefox, try with Chrome).
  This helps pin down where the problem is.
• Try disabling any browser extensions and/or any Jupyter extensions you have
  installed.
• Some internet security software can interfere with Jupyter.
  If you have security software, try turning it off temporarily,
  and look in the settings for a more long-term solution.
• In the address bar, try changing between localhost and 127.0.0.1.
  They should be the same, but in some cases it makes a difference.

Jupyter can’t start a kernel

Files called kernel specs tell Jupyter how to start different kinds of kernels.
To see where these are on your system, run jupyter kernelspec list:

$ jupyter kernelspec list
Available kernels:
  python3      /home/takluyver/.local/lib/python3.6/site-packages/ipykernel/resources
  bash         /home/takluyver/.local/share/jupyter/kernels/bash
  ir           /home/takluyver/.local/share/jupyter/kernels/ir

There’s a special fallback for the Python kernel:
if it doesn’t find a real kernelspec, but it can import the ipykernel package,
it provides a kernel which will run in the same Python environment as the notebook server.
A path ending in ipykernel/resources, like in the example above,
is this default kernel.
The default often does what you want,
so if the python3 kernelspec points somewhere else
and you can’t start a Python kernel,
try deleting or renaming that kernelspec folder to expose the default.

If your problem is with another kernel, not the Python one we maintain,
you may need to look for support about that kernel.

Python Environments

Multiple python environments, whether based on Anaconda or Python Virtual environments,
are often the source of reported issues. In many cases, these issues stem from the
Notebook server running in one environment, while the kernel and/or its resources,
derive from another environment. Indicators of this scenario include:

• import statements within code cells producing ImportError or ModuleNotFound exceptions.
• General kernel startup failures exhibited by nothing happening when attempting
  to execute a cell.

In these situations, take a close look at your environment structure and ensure all
packages required by your notebook’s code are installed in the correct environment.
If you need to run the kernel from different environments than your Notebook
server, check out IPython’s documentation
for using kernels from different environments as this is the recommended approach.
Anaconda’s nb_conda_kernels
package might also be an option for you in these scenarios.

Another thing to check is the kernel.json file that will be located in the
aforementioned kernel specs directory identified by running jupyter kernelspec list.
This file will contain an argv stanza that includes the actual command to run
when launching the kernel. Oftentimes, when reinstalling python environments, a previous
kernel.json will reference an python executable from an old or non-existent location.
As a result, it’s always a good idea when encountering kernel startup issues to validate
the argv stanza to ensure all file references exist and are appropriate.

Windows Systems

Although Jupyter Notebook is primarily developed on the various flavors of the Unix
operating system it also supports Microsoft
Windows — which introduces its own set of commonly encountered issues,
particularly in the areas of security, process management and lower-level libraries.

pywin32 Issues

The primary package for interacting with Windows’ primitives is pywin32.

• Issues surrounding the creation of the kernel’s communication file utilize
  jupyter_core‘s secure_write() function. This function ensures a file is
  created in which only the owner of the file has access. If libraries like pywin32
  are not properly installed, issues can arise when it’s necessary to use the native
  Windows libraries.

  Here’s a portion of such a traceback:

  File "c:usersjovyanpythonmyenv.venvlibsite-packagesjupyter_corepaths.py", line 424, in secure_write
  win32_restrict_file_to_user(fname)
  File "c:usersjovyanpythonmyenv.venvlibsite-packagesjupyter_corepaths.py", line 359, in win32_restrict_file_to_user
  import win32api
  ImportError: DLL load failed: The specified module could not be found.
  

• As noted earlier, the installation of pywin32 can be problematic on Windows
  configurations. When such an issue occurs, you may need to revisit how the environment
  was setup. Pay careful attention to whether you’re running the 32 or 64 bit versions
  of Windows and be sure to install appropriate packages for that environment.

  Here’s a portion of such a traceback:

  File "C:UsersjovyanAppDataRoamingPythonPython37site-packagesjupyter_corepaths.py", line 435, in secure_write
  win32_restrict_file_to_user(fname)
  File "C:UsersjovyanAppDataRoamingPythonPython37site-packagesjupyter_corepaths.py", line 361, in win32_restrict_file_to_user
  import win32api
  ImportError: DLL load failed: %1 is not a valid Win32 application
  

Resolving pywin32 Issues

In this case, your pywin32 module may not be installed correctly and the following
should be attempted:

pip install --upgrade pywin32

or:

conda install --force-reinstall pywin32

followed by:

python.exe Scripts/pywin32_postinstall.py -install

where Scripts is located in the active Python’s installation location.

• Another common failure specific to Windows environments is the location of various
  python commands. On *nix systems, these typically reside in the bin directory
  of the active Python environment. However, on Windows, these tend to reside in the
  Scripts folder — which is a sibling to bin. As a result, when encountering
  kernel startup issues, again, check the argv stanza and verify it’s pointing to a
  valid file. You may find that it’s pointing in bin when Scripts is correct, or
  the referenced file does not include its .exe extension — typically resulting in
  FileNotFoundError exceptions.

This Worked An Hour Ago

The Jupyter stack is very complex and rightfully so, there’s a lot going on. On occasion
you might find the system working perfectly well, then, suddenly, you can’t get past a
certain cell due to import failures. In these situations, it’s best to ask yourself
if any new python files were added to your notebook development area.

These issues are usually evident by carefully analyzing the traceback produced in
the notebook error or the Notebook server’s command window. In these cases, you’ll typically
find the Python kernel code (from IPython and ipykernel) performing its imports
and notice a file from your Notebook development error included in that traceback followed
by an AttributeError:

File "C:Usersjovyananaconda3libsite-packagesipykernelconnect.py", line 13, in
from IPython.core.profiledir import ProfileDir
File "C:Usersjovyananaconda3libsite-packagesIPython_init.py", line 55, in
from .core.application import Application
...
File "C:Usersjovyananaconda3libsite-packagesipython_genutilspath.py", line 13, in
import random
File "C:UsersjovyanDesktopNotebooksrandom.py", line 4, in
rand_set = random.sample(english_words_lower_set, 12)
AttributeError: module 'random' has no attribute 'sample'

What has happened is that you have named a file that conflicts with an installed package
that is used by the kernel software and now introduces a conflict preventing the
kernel’s startup.

Resolution: You’ll need to rename your file. A best practice would be to prefix or
namespace your files so as not to conflict with any python package.

Asking for help

As with any problem, try searching to see if someone has already found an answer.
If you can’t find an existing answer, you can ask questions at:

• The Jupyter Discourse Forum

• The jupyter-notebook tag on Stackoverflow

• Peruse the jupyter/help repository on Github (read-only)

• Or in an issue on another repository, if it’s clear which component is
  responsible. Typical repositories include:

  • jupyter_core — secure_write()
    and file path issues
  • jupyter_client — kernel management
    issues found in Notebook server’s command window.
  • IPython and
    ipykernel — kernel runtime issues
    typically found in Notebook server’s command window and/or Notebook cell execution.

Gathering Information

Should you find that your problem warrants that an issue be opened in
notebook please don’t forget to provide details
like the following:

• What error messages do you see (within your notebook and, more importantly, in
  the Notebook server’s command window)?
• What platform are you on?
• How did you install Jupyter?
• What have you tried already?

The jupyter troubleshoot command collects a lot of information
about your installation, which can also be useful.

When providing textual information, it’s most helpful if you can scrape the contents
into the issue rather than providing a screenshot. This enables others to select
pieces of that content so they can search more efficiently and try to help.

Remember that it’s not anyone’s job to help you.
We want Jupyter to work for you,
but we can’t always help everyone individually.