I'd like to return the index name
and cost
in the following code. I think I'm on the right track, but not sure how to push this forward, I've attached my code and output
EDIT: I'm open to design changes in the way I'm doing this.
for example a
in test_data
, it should return 'a': 'rep_asp_art', '1000000'
import pandas as pd
treatments = {'min_asp_art': {'treat': 'min', 'mat': 'asp', 'class': 'art',
'hci': 85, 'cost': 100000},
'maj_asp_art': {'treat': 'maj', 'mat': 'asp', 'class': 'art',
'hci': 60, 'cost': 350000},
'rep_asp_art': {'treat': 'maj', 'mat': 'asp', 'class': 'art',
'hci': 0, 'cost': 1000000},
'min_asp_col': {'treat': 'min', 'mat': 'asp', 'class': 'col',
'hci': 85, 'cost': 100000},
'maj_asp_col': {'treat': 'maj', 'mat': 'asp', 'class': 'col',
'hci': 60, 'cost': 350000},
'rep_asp_col': {'treat': 'maj', 'mat': 'asp', 'class': 'col',
'hci': 0, 'cost': 1000000},
'min_chip_col': {'treat': 'min', 'mat': 'chip', 'class': 'col',
'hci': 70, 'cost': 30000},
'maj_chip_col': {'treat': 'maj', 'mat': 'chip', 'class': 'col',
'hci': 50, 'cost': 80000},
'rep_chip_col': {'treat': 'maj', 'mat': 'chip', 'class': 'col',
'hci': 0, 'cost': 100000},
'min_chip_loc': {'treat': 'min', 'mat': 'chip', 'class': 'loc',
'hci': 70, 'cost': 30000},
'maj_chip_loc': {'treat': 'maj', 'mat': 'chip', 'class': 'loc',
'hci': 45, 'cost': 80000},
'rep_chip_loc': {'treat': 'maj', 'mat': 'chip', 'class': 'loc',
'hci': 0, 'cost': 100000},
}
ndf = pd.DataFrame(treatments)
ndf = ndf.round(3)
dft = ndf.T
def get_treatment(material, func_class, hci):
return dft[(dft['mat'] == material) &
(dft['class'] == func_class) &
(dft['hci'].astype(int) >= hci)].index
test_data = {'a': {'mat': 'asp', 'class': 'art', 'hci': 35},
'b': {'mat': 'asp', 'class': 'art', 'hci': 90},
'c': {'mat': 'asp', 'class': 'col', 'hci': 68},
'd': {'mat': 'asp', 'class': 'art', 'hci': 5},
'e': {'mat': 'chip', 'class': 'col', 'hci': 55},
'f': {'mat': 'chip', 'class': 'loc', 'hci': 95},
'g': {'mat': 'asp', 'class': 'art', 'hci': 15},
'h': {'mat': 'asp', 'class': 'art', 'hci': 70},
'i': {'mat': 'asp', 'class': 'art', 'hci': 3}
}
for k, v in test_data.items():
res = get_treatment(v['mat'], v['class'], v['hci'])
print (k, res)
# Output:
# a Index(['min_asp_art', 'maj_asp_art'], dtype='object')
# b Index([], dtype='object')
# c Index(['min_asp_col'], dtype='object')
# d Index(['min_asp_art', 'maj_asp_art'], dtype='object')
# e Index(['min_chip_col'], dtype='object')
# f Index([], dtype='object')
# g Index(['min_asp_art', 'maj_asp_art'], dtype='object')
# h Index(['min_asp_art'], dtype='object')
# i Index(['min_asp_art', 'maj_asp_art'], dtype='object')
from Pandas: interpolate between column values to return index name
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