The cache magic cannot save the state of a whole notebook yet. This in combination of hitting the run-all button on top of the notebook is for me a workable solution. This is not exactly answering your question, but it might be enough to when the results of all the lengthy calculations are recovered fast. When rerunning the notebook, the contents of this cell is loaded from the cache. To save the results of individual cells, the caching magic comes in handy.
#Install ipython notebooks how to#
This question is related to: How to cache in IPython Notebook? objects, connections), and it’s hard to keep the notebook organized with so much boilerplate codes. Truth be told, I’d prefer built-in functionality in ipython notebook, too. The next time you want to continue with the notebook: backup = pd.HDFStore('backup.h5') At the beginning of the notebook, declare backup = pd.HDFStore('backup.h5')Īnd then store any new variables as you produce them backup = var1Īt the end, probably a good idea to do backup.close()īefore turning off the server. What I usually do is store all dataframes, arrays, etc. You can store most data-like variables in a systematic way. (I’d rather comment than offer this as an actual answer, but I need more reputation to comment.) Which is fairly close to what I would want, but having to do it manually and being unable to distinguish between different sessions makes it less useful. However you have to store the variables manually like so:
Is it even technically possible? Is there a built-in functionality I overlooked?ĮDIT: based on this answer there is a %store magic which should be “lightweight pickle”. What I would like love to do is pickle or store the whole Jupyter session (all pandas dataframes, np.arrays, variables, …) so I can safely shut down the server knowing I can return to my session in exactly the same state as before. Then, for some reason, I have to shut down the jupyter local server I, but I would like to return to doing the analysis later, without having to go through all the time-consuming computations again.
Let’s say I am doing a larger data analysis in Jupyter/Ipython notebook with lots of time consuming computations done. Question or problem about Python programming:
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