I want to know if there is an option in the Notebook UI or some magic command (for a cell) that prevents its re-execution.
For e.g., if I execute a few cells in sequence, and wanted to go back to re-run the first cell, then I should be prevented from doing that.
For the old Jupyter notebook see: How to freeze a cell alone with its outputs on a jupyter notebook.
For JupyterLab there is no extension yet but there is an agreement that this is a good idea. Everyone can become an extension author and contribute it. A good starting point is the Extension Developer guide.
If the cell has no output that you care about you can use a handy trick of temporarily switching its type to "raw" (as raw cells don't get executed). It can be done with a keyboard sequence of Esc, r; then to restore its type to "code" cell you can use Esc, y.
Related
I'm using Databricks to run my Python notebook, and often use %md to create cells containing section titles and annotations in my code (Markdown cells). Is there some way to create Raw NBConvert cells using a % command? Raw NBConvert is available in JupyterLab in drop down menu:
but not in Databricks.
No, magics can't do that. The fact that a cell is a "raw" cell is independent of the kernel that execute code. There might be some hacks possible, but they will likely not be added in core Jupyter, and will probably break often.
If you are using Databrick, you want to contact support for that.
I have a jupyter notebook that creates a local cache of images. I would like to clear the cache before I run a particular cell. Does anyone know how to do that? I tried:
import gc
gc.collect()
But it had no effect. I also tried clear all the cookies and cache in chrome, but that did work either.
Try the following in a new cell of your Jupyter Notebook:
%reset -f
The above will clear all variables without User Confirmation. If you want explicit user confirmation, just drop the -f.
%reset_selective -f varName
The above will delete particular variable.
In case the Python version you are using is greater than 3.3, you may also try (I haven't used it!):
%store -d someVar # Remove particular variable and its value from storage
%store -z # Remove all variables from storage
For more info, check this doc.
I realise this isn't a great (bordering on terrible) answer - in short, close and re-open the browser.
I've been struggling with this myself today, and even clearing the browser cache did not seem to work. The only way I've found refresh images is to:
Save your notebook
Halt and close the session
Close the browser
When you re-open the notebook - the new images will appear
... I even tried deleting the referenced image file. But the browser still held onto it.
This worked for me, when an old image would not be refreshed in a markdown cell in Jupyter-Lab on Chrome:
Give the image path a bad path in your markdown code
Execute that markdown cell so it errors.
Rename the image path correctly.
Voila! It should refresh.
Python in jupyter is not the same, i think that the automatic garbage collection will work at some point in the code, every step is paused and cached, this makes it very memory intensive to deal with buffers.
The simples solution es to add a destructor to the elements and remove de need for a gc within the requires steps.
This example uses empty buffers as a load, if you have enough ram to open all instances at the same time it will be enough to just use del img_buffs if not handle them in chunks.
n_images = 1024
img_buffs = {}
for i in range(n_images):
img_buffs[i] = (0,) * 150000
for i in range(n_images):
del img_buffs[i]
The best and easiest solution is to first uninstall the Jupyter notebook then re-install it.
Is it possible to execute ONLY the highlighted code in a Jupyter notebook cell? This is possible in Spyder and RStudio.
I find this to be quite useful for trouble-shooting code as you write.
If a cell contains:
a=13
b=17
c=42
a=a*c
I'd like to be able to highlight and run only the desired lines (e.g. variable assignmemnts), but not the final line.
I use this frequently in Spyder and RStudio, would love to do in Jupyter as well. I find I am constantly splitting and re-combining cells in order to troubleshoot a single line of code, where for example, I've indexed into something incorrectly. Highlighting and printing the variable allows me to see what I've actually assigned it to be and is throwing an error, vs. what I had intended.
There is no such thing in Jupyter as 'highligh and run'. At least I am not aware of it.
Run the cell after commenting the other lines out using CTRL + /, split cells and execute only the chosen ones or use a debugger (e.g. pudb, it works in Jupyter) to change variables values on the fly (while debugging).
It seems now it is available in python notebook as well.
https://github.com/jupyterlab/jupyterlab/pull/2191
If I open a python notebook in Kaggle (www.kaggle.com) and select a text, it lets me run only the highlighted part.
I would like to access the textual contents of another cell in the notebook from Python so that I can feed it to the unit testing script, regardless of whether it has been executed or not. It seems like this should be possible, but I can't find the right API to do it in the IPython kernel. Thoughts?
This question asks the same question, but I don't really want to use magics unless I have to. Ideally the workflow would be "select widget choice" followed by "click widget" to cause the tests to run.
Background
I'm working with some students to teach them Python, and in the past when I've done this I've set up a bunch of unit tests and then provided instructions on how to run the tests via a shell script. However, I'm working with some students that don't have access to computers at home, so I decided to try and use an Jupyter notebook environment (via mybinder.org) to allow them to do the same thing. I've got most of it working already via some ipywidgets and a helper script that runs the unit tests on some arbitrary set of code.
As far as the cells that have been run, the input and output caching system described at https://ipython.readthedocs.io/en/stable/interactive/reference.html#input-caching-system might be useful. (Examples of its use at https://stackoverflow.com/a/27952661/8508004 ). It works in Jupyter notebooks as shown below. (The corresponding notebook can be viewed/accessed here.)
Because #A. Donda raised the issue of markdown in the comments below, I'll add here that nbformat provides related abilities that works with saved notebook files. Reading a saved notebook file with nbformat allowa getting cells and content, no matter if it is code or markdown, and sorting whether the cells are markdown or code, etc.. I have posted a number of examples of using nbformat on the Jupyter Discourse forum that can be seen listed via this search here. It offers more utility than the related json.load(open('test.ipynb','r')) command, highlighted in a comment here to read a notebook file because of the additional notebook context automatically included.
If you want to capture the contents of a specific cell to access it from another one, one workaround seems to bee to write the contents of the first cell to file when executing it and later loading the resulting file (i.e. the text giving the former cell's content) inside the latter cell, where the content of the former cell is required.
Cell's contents can be saved to file (when executing the respective cell) via the command
%%writefile foo.py
which has to be placed at the beginning of a cell. This results in the cell's content (in which the upper command is executed) being saved to the file foo.py and it's just a matter of later reading it in, again.
The output of a cell can be made available more easily:
Just place %%capture output in the first line of a cell. Then, the output of the cell (after execution) is going to be saved as a string to the variable output and can be used like any standard python string-variable.
References:
Programmatically get current Ipython notebook cell output? and
https://nbviewer.jupyter.org/github/ipython/ipython/blob/1.x/examples/notebooks/Cell%20Magics.ipynb
I found a solution to this that can get the output of any cell. It requires running some Javascript.
Here is the code that you can put in a cell and run it an it will generate a Python variable called cell_outputs which will be an array of the cell outputs in the same order as they appear on the page. The output of the cell executing this code, will have an empty string.
%%js
{
let outputs=[...document.querySelectorAll(".cell")].map(
cell=> {
let output=cell.querySelector(".output_text")
if(output) return output.innerText
output=cell.querySelector(".rendered_html")
if(output) return output.innerHTML
return ""
}
)
IPython.notebook.kernel.execute("cell_outputs="+JSON.stringify(outputs))
}
If you need the output of a specific cell, just use its index, eg: cell_outputs[2] in your Python code to access the output of cell #3.
I tested this on my Jupyter notebook 6.0.3 (recent install via the Anaconda community edition) on Google Chrome. The above could should work fine on any modern browser (Chrome, Firefox or Edge).
I am trying to write an IPython notebook extension that will run analysis on the code in each cell and will then output messages via the logger (this could be for example a PEP8 analysis, to take a simple example). The catch is that I want to be able to load the extension once at the top of the notebook and have it apply to all cells.
It is possible to define custom line and cell magics (see here) but one has to put the magic command in each cell whereas I would like it to apply to all code cells.
I also found that it's possible to register a class that pre-filters code input, using ip.prefilter_manager.registre_transformer but that operates on a line by line basis which is not good enough.
Does anyone have any suggestions on how to register a function that can analyze the content of all cells, when they are executed, and add logger messages to the output of that given cell?
I know there is a PEP8 extension for IPython notebook but this requies that the magic %%pep8 command be put inside each cell to be checked, and I want to change this to be more of a global setting in the notebook.