Reverted the stratify input while maintaining numpy functionality #2
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There are two changes that I have made. First, I have altered the functions `estimate_nbins()` and `scsplit()` to be able to take both pandas dataframes and numpy arrays as inputs. The second change I have made is that I have removed the input "stratify" from `scsplit()` and made the inputs "X" and "y" required. The reason I have do this is to make the function behave more like scikit-learn's `train_test_split(X, y)`. This should provide a more seamless integration of the function into other people's projects. Additionally, I have updated the readme to account for my changes. However, I have not changed the version info.
I have kept my alterations to the functions `estimate_nbins()` while reverting and adjusting `scsplit()` to be able to take both pandas dataframes and numpy arrays as inputs while still maintaining the `stratify` input option. However, I have changed `stratify` to be an optional input rather than a required one.
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| X = args[0].drop(stratify.name, axis = 1) | ||
| y = args[0][stratify.name] |
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Are you sure this is going to work?
If the default argument stratify = None, then if the user has not defined this argument - this first X & y definition is going to throw a TypeError.
I think a good practice here would be to catch that exception and print a message that stratify argument is required for single data objects inputs.
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Outline of changes:
I have kept my inital alterations to the function
estimate_nbins().Additionally, I have reverted and adjusted the funtionscsplit()to be able to take both pandas dataframes and numpy arrays as inputs while still maintaining thestratifyinput option. However, I have changedstratifyto be an optional input rather than a required one. This is because if you pass bothXandyas dataframes, I feel that there is no need to specifyyagain as the stratifyer.