Question

我想要繁殖的2个pandas DataFrame：

frame_score: 
   Score1  Score2
0     100      80
1    -150      20
2    -110      70
3     180      99
4     125      20

frame_weights: 
   Score1  Score2
0     0.6     0.4

我试过了：

import pandas as pd
import numpy as np

frame_score = pd.DataFrame({'Score1'  : [100, -150, -110, 180, 125], 
                      'Score2'  : [80,  20, 70, 99, 20]})

frame_weights = pd.DataFrame({'Score1': [0.6], 'Score2' : [0.4]})

print('frame_score: \n{0}'.format(frame_score))
print('\nframe_weights: \n{0}'.format(frame_weights))

# Each of the following alternatives yields the same results
frame_score_weighted = frame_score.mul(frame_weights, axis=0)
frame_score_weighted = frame_score * frame_weights
frame_score_weighted = frame_score.multiply(frame_weights, axis=1)

print('\nframe_score_weighted: \n{0}'.format(frame_score_weighted))

返回：

frame_score_weighted:   
    Score1  Score2
0    60.0    32.0
1     NaN     NaN
2     NaN     NaN
3     NaN     NaN
4     NaN     NaN

第1行到第4行是NaN。我怎么能避免这种情况？例如，第1行应为-90 8（-90 = -150 * 0.6; 8 = 20 * 0.4）。

例如，Numpy可以广播以匹配尺寸。

Answer 1

编辑：对于任意维度，请尝试使用values以类似数组的方式处理数据框的值：

# element-wise multiplication
frame_score_weighted = frame_score.values*frame_weights.values

# change to pandas dataframe and rename columns
frame_score_weighted = pd.DataFrame(data=frame_score_weighted, columns=['Score1','Score2'])

#Out: 
   Score1  Score2
0    60.0    32.0
1   -90.0     8.0
2   -66.0    28.0
3   108.0    39.6
4    75.0     8.0

只需使用一些额外的索引，以确保在进行乘法时将所需的权重提取为标量。

frame_score['Score1'] = frame_score['Score1']*frame_weights['Score1'][0]
frame_score['Score2'] = frame_score['Score2']*frame_weights['Score2'][0]

frame_score
#Out: 
   Score1  Score2
0    60.0    32.0
1   -90.0     8.0
2   -66.0    28.0
3   108.0    39.6
4    75.0     8.0

Answer 2

默认情况下，当pd.DataFrame乘以pd.Series时，pandas会将pd.Series的索引与pd.DataFrame的列对齐。因此，我们只需访问第一行即可从pd.Series获得相关的frame_weights。

frame_score * frame_weights.loc[0]

   Score1  Score2
0    60.0    32.0
1   -90.0     8.0
2   -66.0    28.0
3   108.0    39.6
4    75.0     8.0

您可以使用

编辑frame_score

frame_score *= frame_weights.loc[0]

如何在pandas中将n * m DataFrame与1 * m DataFrame相乘？

2 个答案: