# Kernelerror when matplotlib is installed

**URL:** <https://discourse.jupyter.org/t/kernelerror-when-matplotlib-is-installed/28309>\
**Category:** Notebook\
**Tags:** help-wanted\
**Created:** [September 12, 2024, 9:33am UTC](https://discourse.jupyter.org/t/kernelerror-when-matplotlib-is-installed/28309 "2024-09-12T09:33:58Z")\
**Posts on this page:** 5\
**Page:** 1

<div class="post-metadata">

**Author:** ![MrOnijohn](https://yyz1.discourse-cdn.com/flex031/user_avatar/discourse.jupyter.org/mronijohn/32/14847_2.png) [@MrOnijohn](https://discourse.jupyter.org/u/MrOnijohn)\
**Post date:** [September 12, 2024, 9:33am UTC](https://discourse.jupyter.org/t/kernelerror-when-matplotlib-is-installed/28309/1 "2024-09-12T09:33:59Z")

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I’m having problems using Jupyter notebook in vscode. Everything seems to work fine for a while, but then I always get the error message “Failed to start the kernel” and a log where I find the error “ImportError: /home/~/anaconda3/envs/introduktion/lib/python3.12/lib-dynload/\_sqlite3.cpython-312-x86\_64-linux-gnu.so: undefined symbol: sqlite3\_deserialize…”  
I think I have isolated the error to when I’m using an environment with matplotlib installed, and I’ve noticed that that package downgrades sqlite to version 3.31.1.  
Could this be the source of the problem, and does anyone have a solution?

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**Author:** ![MrOnijohn](https://yyz1.discourse-cdn.com/flex031/user_avatar/discourse.jupyter.org/mronijohn/32/14847_2.png) [@MrOnijohn](https://discourse.jupyter.org/u/MrOnijohn)\
**Post date:** [September 12, 2024, 10:17am UTC](https://discourse.jupyter.org/t/kernelerror-when-matplotlib-is-installed/28309/2 "2024-09-12T10:17:33Z")

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I also just noticed that the path " /home/~/anaconda3/…" looks strange… I’m on linux and the “/home/”-part is redundant when using ~ to point to the users home directory, right?

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**Author:** ![bollwyvl](https://yyz1.discourse-cdn.com/flex031/user_avatar/discourse.jupyter.org/bollwyvl/32/904_2.png) [@bollwyvl](https://discourse.jupyter.org/u/bollwyvl)\
**Post date:** [September 12, 2024, 1:50pm UTC](https://discourse.jupyter.org/t/kernelerror-when-matplotlib-is-installed/28309/3 "2024-09-12T13:50:59Z")

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Interactive package management is fairly nightmarish above a certain level of complexity, especially in the case of mixing more than one of PyPI, `conda-forge` and the ToS-encumbered `defaults`.

As a minimal step, it’s recommended to starting with a fresh `environment.yml` and rebuild the env from the ground up with some “guideposts” for the solver, for example:

```yaml
# environment-introduktion.yml
name: introduktion
channels:
  - conda-forge
  - nodefaults
dependencies:
  - python ==3.11.* # or a suitably-hard pin  
  - numpy ==1.* # or 2 if feeling feisty
  - matplotlib-base # likely fine to leave-unpinned, avoids `qt` from `matplotlib`
  # all the stuff

```

Then:

```auto
mamba env upgrade --name introduktion --file environment-introduktion.yml

```

As a more drastic measure, [discussed](https://discourse.jupyter.org/t/conda-licensing-for-enterprise-use/20124/2) in many other places:

- fully remove anaconda
- start with a brand-new, unencumbered [miniforge](https://conda-forge.org/miniforge/) installer to get a new `base` (e.g. `~/mf`)
  - never install anything in `base` other than e.g. `mamba`, `conda`, etc.

_Then_ start with full-managed, isolated environments.

For an entirely different approach, on can try yet-newer approaches like [`pixi`](https://pixi.sh) which mostly avoid some of the issues caused by heavy global installations. My _personal_ recommendation, however, is to still start with `miniforge`, and manage `pixi` through normal means there instead of the `curl | bash` and `pixi selfupdate` techniques.

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<div class="post-metadata">

**Author:** ![MrOnijohn](https://yyz1.discourse-cdn.com/flex031/user_avatar/discourse.jupyter.org/mronijohn/32/14847_2.png) [@MrOnijohn](https://discourse.jupyter.org/u/MrOnijohn)\
**Post date:** [September 13, 2024, 7:08am UTC](https://discourse.jupyter.org/t/kernelerror-when-matplotlib-is-installed/28309/4 "2024-09-13T07:08:52Z")

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Thank you so much for your answer @bollwyvl !  
Most of it flies above my head right now, but I’ll try to fix that on my own time.

Just to be clear, since you use the phrase “above a certain level of complexity”, all I’ve done is this (from memory, may contain errors):  
Create a new environment using anaconda:

```auto
conda create -n my_python_environment

```

Install python and ipykernel in that environment

```auto
conda activate my_python_environment
conda install python
conda install ipykernel

```

Install matplotlib in the same environment (this step downgrades sqlite)

```auto
conda install conda-forge::matplotlib

```

Create project\_file.ipynb in vscode, and connect it to my\_python\_environment  
Write some code in the cells.  
Everything works fine for a while and then I get the “Failed to start the kernel” message, and I then can’t do anything more with that kernel. Clearing outputs and restarting kernel does nothing. New .ipynb files cannot use that kernel either. I have been able to use pyplot from matplotlib at least a couple of times to do beginner stuff.

As you can probably tell, I don’t really know what I’m doing, but this seems like a really low level of complexity to me…  
What I’m really trying to get to grips with is if I’m the problem, or if I should file a bug report somewhere? Am I maybe looking for support in the wrong forum?

I see I forgot to mention in my original post that I’m on Linux Mint 22 on one computer, and I have the same issue on another running Kubuntu 24.04.1.

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<div class="post-metadata">

**Author:** ![bollwyvl](https://yyz1.discourse-cdn.com/flex031/user_avatar/discourse.jupyter.org/bollwyvl/32/904_2.png) [@bollwyvl](https://discourse.jupyter.org/u/bollwyvl)\
**Post date:** [September 13, 2024, 2:04pm UTC](https://discourse.jupyter.org/t/kernelerror-when-matplotlib-is-installed/28309/5 "2024-09-13T14:04:13Z")

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Well, `matplotlib` _is_ complex… you’re pulling compiled code in C, C++, Fortran, etc.

VSCode makes a lot of things complicated for things it doesn’t manage itself. The most fool-proof thing I’ve found is to follow the steps above, activate the env, and then launch `code` from there:

```bash
mamba env update --name my_python_environment --file environment.yml
mamba activate my_python_environment
code .

```