To run statistical tests on my data, I use SPSS and call it from a Jupyter Notebook. The integration works fine. I wondered, if there is a better way to display the results in a jupyter notebook. The tables seem to be messed up and figures wont show. Is it possible to output in a SPSS window from a Jupyter Notebook?
You can use the SPSS Model Visualizations for Jupyter [1], but if that is what you are already doing, then open and issue on the project? How are you calling SPSS from your Notebook?
[1] https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/spss-visualization.html?linkInPage=true
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Let's say that you have two windows open in VS Code, one is a Jupyter notebook, and the other is the Python interactive window. Both are running the same kernel. Is there a way to link these two windows, so when cells are run in the notebook, the interactive window will know about the resulting variables?
I'm aware that VS Code has a Python code format that can be linked to the interactive window, and that Jupyter notebooks can be converted into this format. Is it possible to directly link a Jupyter notebook to the interactive window without first doing this conversion?
With JupyterLab the is accomplished with the New Console for Notebook command.
I'm trying to use the terminal to run a jupyter notebook (kernel: Julia v1.6.2), which contains generated using Plots.jl, before uploading the notebook to github for viewing on nbviewer.com.
Following this question:
How to run an .ipynb Jupyter Notebook from terminal?
I have been using nbconvert as follows:
jupyter nbconvert --execute --to notebook --inplace
This runs the notebook (if you tweak the timeout limits), however, it does not display plots when using Plots.jl, even when I explicitly call display(plot()) at the end of a cell.
Does anyone have any idea how notebooks can be run remotely in such a manner that plots will be generated and displayed, particularly when using Julia?
I managed to generate Plots.jl plots by getting from IJulia the same configuration it uses to run notebooks (this is probably the most sure way when you have many Pyhtons etc.).
using Conda, IJulia
Conda.add("nbconvert") # I made sure nbconvert is installed
mycmd = IJulia.find_jupyter_subcommand("nbconvert")
append!(mycmd.exec, ["--ExecutePreprocessor.timeout=600","--to", "notebook" ,"--execute", "note1.ipynb"])
Now mycmd has exactly the same environment as seen by IJulia so we can do run(mycmd):
julia> run(mycmd)
[NbConvertApp] Converting notebook note1.ipynb to notebook
Starting kernel event loops.
[NbConvertApp] Writing 23722 bytes to note1.nbconvert.ipynb
The outcome got saved to note1.nbconvert.ipynb, I open it with nteract to show that graphs actually got generated:
Launch notebook with using IJulia and notebook() in the REPL
Good morning,
I am using Jupyter Notebook but my file has already 9MB and it requires too much time to run it. Given this, I want to use R studio from now on.
I tried to download my notebook as a R file, but when I open it in R studio it does not show any code...
Is there any way of converting the notebook into a script readable in R studio? Or is my procedure correct and the file is only loading (I uploaded it to R studio and I am waiting for more than 45 minutes)?
Thank you for the help.
Maybe you might want to use convert_ipynb function from rmarkdown package.
You use it as:
convert_ipynb(input, output), where input is the .ipynb file path.
It converts your notebook to a as a .Rmd file, which is much easier to deal with.
For more info, check the documentation here: https://www.rdocumentation.org/packages/rmarkdown/versions/2.6/topics/convert_ipynb
As my RAM is scarce, I'd like to not replicate data and use objects created in an RStudio session inside my Jupyter notebook (running w/ R kernel).
Any idea how to do it?
Basically I'd like to use the same workspace in both, the RStudio and the Jupyter notebook session.
Thanks for help!
One problem I encountered with an R notebook in Jupyter, though, was saving my workspace. In a normal R session I’m used to saving my workspace at the end of the session and coming back to it later to pick up where I left off. However, with the Jupyter notebook I found that I had to rerun all the code to regenerate all the objects again! This appears to be an issue for Python notebook users too.
There’s a very simple fix for this: Just run the standard R command
save.image()
Your workspace will then be saved to the usual hidden .RData file in the same folder as the Jupyter notebook. If you want to share the code and the workspace, you’ll have to make sure that you copy both the notebook file and the .RData file that goes along with it.
Likewise, if you start a notebook in a folder that already has an .RData file, you’ll find that you can access that workspace from the Jupyter notebook – just run ls() to see what’s there.
I am new to IPython Notebook. I am using the Anaconda distribution on CrunchBang (Waldorf). My development cycle is as follows:
1. Open Spyder.
2. Open the .py file if not already loaded
3. Start IPython Notebook
4. Open the specific notebook from the main IPython screen
5. Select Cell/Run All
6. Note errors. If none goto step 11.
7. Save and close the notebook
8. Shutdown the notebook from main IPython screen
9. Correct errors in Spyder and save
10. go to step 4
11. Move on to the next part of the project and start the process over.
Is there a better approach for a noob? This really gets monotonous although I am learning quite a bit.
Thanks in advance
Forget Spyder for the time being just use the IPython notebook.
1, write code in notebook
2. test it
3. when done if needed make a py file...
You really will only need Spyder later for starting out it just complicates things for no gain
Use Spyder and .py files for writing big functions, classes, modules, tests, etc.
Use IPython notebooks for interactive work where you want to keep the output together with the code (e.g. data processing and analysis, demos, etc.).
To add to Ian's answer, another useful tool is the autoreload extension, which reloads modules automatically when they are changed.
To use, type into your IPython console or notebook:
%load_ext autoreload
%autoreload 2
For example:
This way you can work on a Python file and an IPython notebook at the same time, without having to reload the Python file after each change.
In addition to #dartdog's answer about developing directly in the notebook, if you must edit .py files used by the notebook then note the reload function which allows you to re-import already imported modules without having to shutdown and reopen the notebook.