pandas /Python逐行相乘 [英] Pandas/Python multiply columns by row

问题描述

很抱歉，如果这是一个简单的问题.

Apologies if this is a simple question.

我有两个具有相同列的数据框.我需要将第二个数据帧中的每一行乘以第一行中的唯一行.

I have two dataframes each with the same columns. I need to multiply each row in the second dataframe by the only row in the first.

最终，会有更多不同年龄的列，所以我不想只乘一个标量.

Eventually there will be more columns of different ages so I do not want to just multiply by a scalar.

我使用了df.multiply()并继续为所有值获取NaN，这可能是因为两个df的长度不匹配.

I have used df.multiply() and continue to get NaN for all values presumably because the two df are not matched in length.

是否有一种方法可以将一个数据帧中的每一行与另一数据行中的单数行相乘?

Is there a way to multiply each row in one dataframe by a singular row in another?

age   51200000.0  70000000.0
SFH
0        0.75        0.25

.

age             51200000.0    70000000.0
Lambda                                 
91.0       0.000000e+00  0.000000e+00
94.0       0.000000e+00  0.000000e+00
96.0       0.000000e+00  0.000000e+00
98.0       0.000000e+00  0.000000e+00
100.0      0.000000e+00  0.000000e+00
102.0      0.000000e+00  0.000000e+00
...        ...           ...
1600000.0  1.127428e+22  8.677663e+21

推荐答案

您可以使用 mul ，由df1的第一行. iloc.html"rel =" nofollow> iloc :

You can use mul by first row of df1 selected by iloc:

print (df2.mul(df1.iloc[0]))

示例:

print (df1)
      51200000.0  70000000.0
age                        
0          0.75        0.25

print (df2)
      51200000.0  70000000.0
age                         
91.0         1.0         2.0
94.0         5.0        10.0
96.0         0.0         0.0

print (df2.mul(df1.iloc[0]))
      51200000.0  70000000.0
age                         
91.0        0.75         0.5
94.0        3.75         2.5
96.0        0.00         0.0

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