Optimized solution for REp007 Challenge: Cheapest Cit

submissions/REp007/main.py

@@ -0,0 +1,36 @@
+import pandas as pd
+
+def remove_duplicates(file_name):
+    input_data = pd.read_csv(file_name, header=None, names=["City", "Product", "Price"])
+    cleaned_data = []
+
+    for city in input_data["City"].unique():
+        products = input_data[input_data["City"] == city]["Product"].unique()
+        for product in products:
+            df_filtered = input_data[(input_data["City"] == city) & (input_data["Product"] == product)]
+            min_price_row = df_filtered.loc[df_filtered["Price"].idxmin()]
+            cleaned_data.append(min_price_row.to_dict())
+
+    cleaned_data = pd.DataFrame(cleaned_data)
+    return cleaned_data
+
+def get_cheapest_city(data):
+    city_totals = data.groupby('City')['Price'].sum()
+    return city_totals.idxmin(), city_totals.loc[city_totals.idxmin()]
+
+def get_top_5_cheapest_products(data, cheapest_city):
+    cheapest_city_data = data[data["City"] == cheapest_city]
+    top_5_cheapest_products = cheapest_city_data.sort_values(by=['Price', 'Product']).head(5)
+    return top_5_cheapest_products
+
+if __name__ == '__main__':
+    file_name = "input.txt"
+    input_data = remove_duplicates(file_name)
+    cheapest_city, cheapest_city_sum_product = get_cheapest_city(input_data)
+    top_5_cheapest_products = get_top_5_cheapest_products(input_data, cheapest_city)
+
+    # Write the results to the output file
+    with open("output.txt", "w") as f:
+        f.write(f"{cheapest_city} {cheapest_city_sum_product:.2f}\n")
+        for index, row in top_5_cheapest_products.iterrows():
+            f.write(f"{row['Product']} {row['Price']:.2f}\n")