我有 2 个要相乘的 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:
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
。我怎样才能避免这种情况?例如,第 1 行应为 -90 8
(-90=-150*0.6; 8=20*0.4)。
例如,Numpy 可能会广播以匹配维度。
最佳答案
编辑:对于任意维度,尝试使用 values
以类似数组的方式操作数据帧的值:
# 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来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
关于python - 如何在 pandas 中将 n*m DataFrame 与 1*m DataFrame 相乘?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/45421723/