如何使用python中的map reduce函数来确定值？

ukxgm1gy 于 2021-05-27 发布在 Hadoop

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下面是你在杂货店可能找到的食物的数据列表。下面的csv文件表示城市、食物类型、每磅的平均价格，以及加利福尼亚州某个城市的食物消费量。我需要使用python中的map reduce函数来确定哪种食物每磅的总价格最低。

Los Angeles,Vegetables,25.51,Breakfast
San Francisco,Fruits,259.32,Breakfast
Sandiego,Meat,22.94,Lunch
Sacramento,Dairy,53.71,Dinner
San Jose,Fish,44.16,Snack
Fresno,Poultry,393.05,Brunch
Oakland,Vegetables,15.99,Dinner
Bakersfield,Nuts,201.46,Dinner
Long Beach,Poultry,74.6,Snack
Anaheim,Grains,89.6,Breakfast
Riverside,Meat,152.75,Brunch
Irvine,Poultry,88.99,Brunch
Santa Barbara,Dairy,241.26,Lunch
Pasadena,Beans,789.7,Snack

这是我到目前为止的情况。任何帮助都将不胜感激。

from mrjob.job import MRJob

class LowestPrice(MRJob):

    def mapper(self, _, line):
        line_cols = line.split(',')
        yield line_cols[1], 1

if __name__ == '__main__':
    LowestPrice.run()

hadoop mapreduce python mrjob

来源：https://stackoverflow.com/questions/59042518/how-do-i-use-the-map-reduce-function-in-python-to-determine-a-value

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按热度按时间

1rhkuytd1#

你可以用Pandas来做这个

import pandas as pd

data = pd.read_csv('/Users/shrek/Documents/data.csv', header=None)

print(data[2].min())
index = data[data[2]== data[2].min()].index[0]
print(data[1][index]+"in "+data[0][index]+" used in "+data[3][index]+" has lowest price per pound = "+str(data[2].min()))

输出为：

Vegetablesin Oakland used in Dinner has lowest price per pound = 15.99

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如何使用python中的map reduce函数来确定值？

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