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I'm always frustrated when I want to commit a Jupyter Notebook to a git server because the cell execution count is often a very large number I don't specially want to share to anyone.
I used to use some patches but they are annoying to set everytime.
This prevents me from committing as spontaneously as I would.
Proposed Solution
Remove this field.
Additional context
Actually I absolutely don't see the point of this feature.
Maybe I don't see the use of a Juypter Notebook as it is intended by the developpers ? Actually I think they're absolutely amazing to proof a concept in a repeatable way. In e.g. combining it with pandas surpasses anything else to make a report of data processing. What's the point of having any hidden metadata of the history of how the document was produced ?
The text was updated successfully, but these errors were encountered:
OK, I'm sorry I misunderstood the exact meaning of this execution_count field.
Actually each cell has an unique ID of "execution count" which relates the order in which the cells were executed the last time and not how many times each has been executed. This obviously makes those counts always increasing, but now I can understand its utility.
Now, in VSCode I can just "Clear All Outputs" and they're resetted, which will force me to do it followed by a "Run All" before committing, making it a forced good practice of insuring the outputs are repeatable in their natural orders.
Problem
I'm always frustrated when I want to commit a Jupyter Notebook to a git server because the cell execution count is often a very large number I don't specially want to share to anyone.
I used to use some patches but they are annoying to set everytime.
This prevents me from committing as spontaneously as I would.
Proposed Solution
Remove this field.
Additional context
Actually I absolutely don't see the point of this feature.
Maybe I don't see the use of a Juypter Notebook as it is intended by the developpers ? Actually I think they're absolutely amazing to proof a concept in a repeatable way. In e.g. combining it with
pandas
surpasses anything else to make a report of data processing. What's the point of having any hidden metadata of the history of how the document was produced ?The text was updated successfully, but these errors were encountered: