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feature to explode join columns #80
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Jasopaum
reviewed
Aug 28, 2020
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| for j in range(i + 1, len(elements)): | ||
| element2 = elements[j] |
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You can write for element2 in elements[i+1:]:
| if degres[j] > 0: | ||
| if isinstance(self.df[cols[j]].iloc[0], list): | ||
| lst_cols.append(cols[j]) | ||
| degres[j] = degres[j] - 1 |
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This line is both in the if and the else so you can put it outside
Also a = a - 1 can be written a -= 1 in Python
| for index, row in df.iterrows(): | ||
| for col in lst_cols: | ||
| for element in row[col]: | ||
| dico[col].append(element) |
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I think the whole function can be written
from collections import defaultdict
def to_list(self, df, lst_cols):
dict = defaultdict(list)
for _, row in df.iterrows():
for col in lst_cols:
dict[col].extend(row[col])
return dict
But aren't you mixing the elements from the different rows here?
| dict_degre = self.compute_degres(element) | ||
| if dict_degre: | ||
| cols = [self.elements.get_element(i).col_name for i in dict_degre.keys()] | ||
| degres = [dict_degre[i] for i in dict_degre.keys()] |
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This is equivalent to degres = [val for i in dict_degre.values()]
| for element in self.elements.get_subset_elements(goal="join"): | ||
| dict_degre = self.compute_degres(element) | ||
| if dict_degre: | ||
| cols = [self.elements.get_element(i).col_name for i in dict_degre.keys()] |
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for i in dict_degre will run through the keys (.keys() is not needed)
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The columns concerned with join need to be in row view (and not in list)
But it is necesary to keep the link between this(or these) column(s) and the others
The idea is to use the trees created to get the link between columns and the join column in the dataframe.