寻找最相关的项目 [英] Finding the most correlated item

问题描述

我有如下餐厅销售明细.

I have a restaurant sales details as below.

+----------+------------+---------+----------+
| Location | Units Sold | Revenue | Footfall |
+----------+------------+---------+----------+
| Loc - 01 |        100 | 1,150   |       85 |
+----------+------------+---------+----------+

我想从下表的餐厅数据中找到与以上餐厅相关性最高的餐厅

I want to find the most correlated restaurant to the above from the below tables restaurant data

+----------+------------+---------+----------+
| Location | Units Sold | Revenue | Footfall |
+----------+------------+---------+----------+
| Loc - 02 |        100 | 1,250   |       60 |
| Loc - 03 |         90 | 990     |       90 |
| Loc - 04 |        120 | 1,200   |       98 |
| Loc - 05 |        115 | 1,035   |       87 |
| Loc - 06 |         89 | 1,157   |       74 |
| Loc - 07 |        110 | 1,265   |       80 |
+----------+------------+---------+----------+

请指导我如何使用python或pandas完成此操作. 注意:-关联性是指根据Units Sold，Revenue& Footfall.

please guide me how this can be done with python or pandas.. Note : - correlation means most matching/similar restaurant in terms of Units Sold, Revenue & Footfall.

推荐答案

如果应将您的相关性描述为最小欧氏距离，则解决方案是:

If your correlation should be described like minimal euclidean distance, solution is:

#convert columns to numeric
df1['Revenue'] = df1['Revenue'].str.replace(',','').astype(int)
df2['Revenue'] = df2['Revenue'].str.replace(',','').astype(int)

#distance of all columns subtracted by first row of first DataFrame
dist = np.sqrt((df2['Units Sold']-df1.loc[0, 'Units Sold'])**2 + 
               (df2['Revenue']- df1.loc[0, 'Revenue'])**2 + 
               (df2['Footfall']- df1.loc[0, 'Footfall'])**2)

print (dist)
0    103.077641
1    160.390149
2     55.398556
3    115.991379
4     17.058722
5    115.542200
dtype: float64

#get index of minimal value and select row of second df
print (df2.loc[[dist.idxmin()]])
   Location  Units Sold  Revenue  Footfall
4  Loc - 06          89     1157        74

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