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import pandas as pd
class DemandAssessment:
def __init__(self):
self.raw_score = pd.read_csv("data/demand_raw.csv")
def print_data(self):
return self.raw_score
def get_demand_score(self, region, city, district):
def calculate_normalized_score(score: float, flag: str ) -> str:
map = dict(city=[0, 2.70238], region=[0, 1.234568], country=[0, 0.289575])
threshold = map[flag]
if score >= threshold[1]:
return "High"
elif threshold[0] < score < threshold[1]:
return "Moderate"
else:
return "Low"
if region:
temp = self.raw_score[
(self.raw_score["Region"] == region) &
(self.raw_score["City"] == city) &
(self.raw_score["District"] == district)
]
else:
temp = self.raw_score[
(self.raw_score["City"] == city) &
(self.raw_score["District"] == district)
]
temp = temp.iloc[0]
return {
"City_Normalized_Score": temp["City Scaled Score"],
"Region_Normalized_Score": temp["Region Scaled Score"],
"Country_Normalized_Score": temp["Region Scaled Score"],
"City_Demand_Label": calculate_normalized_score(temp["City Scaled Score"], "city"),
"Region_Demand_Label": calculate_normalized_score(temp["Region Scaled Score"], "region"),
"Country_Demand_Label": calculate_normalized_score(temp["Region Scaled Score"], "country"),
}
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