python - 根据名称 pandas python 对某些列进行乘法和求和

Question

我有一个小样本数据集:

import pandas as pd
d = {
  'measure1_x': [10,12,20,30,21],
  'measure2_x':[11,12,10,3,3],
  'measure3_x':[10,0,12,1,1],
  'measure1_y': [1,2,2,3,1],
  'measure2_y':[1,1,1,3,3],
  'measure3_y':[1,0,2,1,1]
}
df = pd.DataFrame(d)
df = df.reindex_axis([
    'measure1_x','measure2_x', 'measure3_x','measure1_y','measure2_y','measure3_y'
], axis=1) 

看起来像:

      measure1_x  measure2_x  measure3_x  measure1_y  measure2_y  measure3_y
          10          11          10           1           1           1
          12          12           0           2           1           0
          20          10          12           2           1           2
          30           3           1           3           3           1
          21           3           1           1           3           1

我创建的列名几乎相同，除了“_x”和“_y”以帮助确定应该乘以哪对:我想在忽略“_x”和“_y”时将具有相同列名的对相乘，然后我想对数字求和以获得总数，请记住我的实际数据集很大并且列的顺序不是完美的，因此这种命名是一种识别正确的对乘法的方法:

总计 = measure1_x * measure1_y + measure2_x * measure2_y + measure3_x * measure3_y

如此期望的输出:

measure1_x  measure2_x  measure3_x  measure1_y  measure2_y  measure3_y   total

 10          11          10           1           1           1           31 
 12          12           0           2           1           0           36 
 20          10          12           2           1           2           74
 30           3           1           3           3           1          100
 21           3           1           1           3           1           31

我的尝试和思考过程，但在语法方面无法继续:

#first identify the column names that has '_x' and '_y', then identify if 
#the column names are the same after removing '_x' and '_y', if the pair has 
#the same name then multiply them, do that for all pairs and sum the results 
#up to get the total number

for colname in df.columns:
if "_x".lower() in colname.lower() or "_y".lower() in colname.lower():
    if "_x".lower() in colname.lower():  
        colnamex = colname
    if "_y".lower() in colname.lower():
        colnamey = colname

    #if colnamex[:-2] are the same for colnamex and colnamey then multiply and sum

Answer 1

过滤器 + np.einsum

我想这次我会尝试一些不同的东西——

• 分别获取_x 和_y 列
• 做一个乘积和。这很容易用 einsum(而且快速)指定。

df = df.sort_index(axis=1) # optional, do this if your columns aren't sorted

i = df.filter(like='_x') 
j = df.filter(like='_y')
df['Total'] = np.einsum('ij,ij->i', i, j) # (i.values * j).sum(axis=1)

df
   measure1_x  measure2_x  measure3_x  measure1_y  measure2_y  measure3_y  Total
0          10          11          10           1           1           1     31
1          12          12           0           2           1           0     36
2          20          10          12           2           1           2     74
3          30           3           1           3           3           1    100
4          21           3           1           1           3           1     31

一个稍微更健壮的版本，它过滤掉非数字列并预先执行断言——

df = df.sort_index(axis=1).select_dtypes(exclude=[object])
i = df.filter(regex='.*_x') 
j = df.filter(regex='.*_y')

assert i.shape == j.shape

df['Total'] = np.einsum('ij,ij->i', i, j)

如果断言失败，假设 1) 您的列是数字，以及 2) x 和 y 列的数量相等，正如您的问题所暗示的那样，不适用于您的实际数据集。