8000 Remove `patch_method_to_DataFrame` and use piping for functions in `computation.operations` · Issue #166 · OSOceanAcoustics/echopop · GitHub
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Remove patch_method_to_DataFrame and use piping for functions in computation.operations #166

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leewujung opened this issue Jan 25, 2024 · 1 comment
Open

Remove patch_method_to_DataFrame and use piping for functions in computation.operations #166

leewujung opened this issue Jan 25, 2024 · 1 comment
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low_priority refactor
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Refactoring and g...

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@leewujung
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See #164 (review) -- to keep code easily traceable seems better to just use .pipe and not a specialized decorator for these operations.

This is low priority since all functions are working fine now. We can do this after v0.4.2 is released.
@leewujung leewujung mentioned this issue Jan 25, 2024
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@brandynlucca
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Just to provide additional context so I can reference this later to discern the logic/rationale for why I implemented these functions using a sort of monkey patch rather than just using the native pandas.DataFrame.pipe method.

There is certainly overlap between patch_method_to_DataFrame decorator and the associated functions (that will be renamed in via #164, but like discretize_variable) and the flexibility/extension of pandas.DataFrame that .pipe provides. My brain definitely prefers moving through pipes with chained method, e.g. object.function(*args), rather than as independent functions/mutations, e.g. function(object, *args). As you've pointed out, .pipe accomplishes the same task (it's almost as if pipe was intentionally designed that way or something...). So the (kind of) monkey patch feature in patch_method_to_DataFrame is (almost) entirely an aesthetic choice such that the difference for how discretize_variable is written out would be:

# the current (sort of) monkey patched pandas.DataFrame extension approach
specimen_grouped = (
    specimen_df_copy
    .assign( arbitrary_value = 1 )
    .discretize_variable( bin_values = length_intervals , bin_variable = 'length' )
    .assign( group = lambda x: np.where( x['sex'] == int(1) , 'male' , 'female' ) )
    .pipe( lambda df: pd.concat( [ df.loc[ df[ 'sex' ] != 3 ] , df.assign( group = 'all' ) ] ) )
    .assign( station = 2 )
)

# the 'let's not be beholden to Brandyn's mostly trivial and idiosyncratic code aesthetic preferences" approach
specimen_grouped = (
    specimen_df_copy
    .assign( arbitrary_value = 1 )
    .pipe( lambda df: discretize_variable( df , bin_values = length_intervals , bin_variable = 'length' ) )
    .assign( group = lambda x: np.where( x['sex'] == int(1) , 'male' , 'female' ) )
    .pipe( lambda df: pd.concat( [ df.loc[ df[ 'sex' ] != 3 ] , df.assign( group = 'all' ) ] ) )
    .assign( station = 2 )
)

@brandynlucca brandynlucca mentioned this issue Mar 28, 2024
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low_priority refactor
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Refactoring and generalization
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