将Pandas DataFrame乘成级数，并保留所有不同的列

Question

我有这样的熊猫数据：

import pandas as pd
pdata = (
    {'id': [26, 26, 26, 26, 26, 12, 12, 12, 12, 12],
     'cat':  [18, 18, 18, 18, 18, 18, 18, 18, 18, 18],
     's43': [7.7, 4.0, 5.8, 5.6, 6.2, 10, 10, 10, 10, 10],
     's44': [6.9, 2.4, 5.3, 4.3, 2.0, 9.9, 9.9, 10, 8.2, 10],
     's45': [7.7, 3.7, 5.1, 5.5, 5.7, 10, 10, 10, 10, 10],
     's46': [7.8, 3.6, 4.9, 5.3, 5.0, 9.9, 9.9, 9.9, 10, 9.9],
     's47': [6.7, 1.4, 3.4, 1.9, 1.1, 10, 10, 10, 5.0, 10],
     's40': [7.7, 4.0, 5.6, 5.7, 5.0, 10, 10, 10, 10, 10]
    }
)

pdata = pd.DataFrame(pdata)

像这样的熊猫系列：

pseries = ({'pen': 0.450000,
  'rs':  0.693650,
  's43': 0.142731,
  's44': 0.170520,
  's45': 0.061518,
  's46': 0.014668,
  's47': 0.129210,
  's40': 0.409219}
)

pseries = pd.Series(pseries)

我想要做的是将数据乘以序列，并获得一个具有新值的数据，该值与显示NaN的列中的原始数据一起被乘以。

我使用了pseries.mul(pdata, axis=index, fill_value=1)，但它似乎没有做我想做的事情。

    cat id  pen rs  s40         s43         s44         s45         s46         s47
0   NaN NaN NaN NaN 3.150986    1.099029    1.176588    0.473689    0.114410    0.865707
1   NaN NaN NaN NaN 1.636876    0.570924    0.409248    0.227617    0.052805    0.180894
2   NaN NaN NaN NaN 2.291626    0.827840    0.903756    0.313742    0.071873    0.439314
3   NaN NaN NaN NaN 2.332548    0.799294    0.733236    0.338349    0.077740    0.245499
4   NaN NaN NaN NaN 2.046095    0.884932    0.341040    0.350653    0.073340    0.142131
5   NaN NaN NaN NaN 4.092190    1.427310    1.688148    0.615180    0.145213    1.292100
6   NaN NaN NaN NaN 4.092190    1.427310    1.688148    0.615180    0.145213    1.292100
7   NaN NaN NaN NaN 4.092190    1.427310    1.705200    0.615180    0.145213    1.292100
8   NaN NaN NaN NaN 4.092190    1.427310    1.398264    0.615180    0.146680    0.646050
9   NaN NaN NaN NaN 4.092190    1.427310    1.705200    0.615180    0.145213    1.292100

当我用

col = ['s40', 's43', 's44', 's45', 's46', 's47']

pdata[col] = pdata[col].mul(pseries[col], axis=1)

下面的输出没有pseries中的“pen”和“rs”值：

    id  cat s43         s44         s45         s46         s47         s40
0   26  18  1.099029    1.176588    0.473689    0.114410    0.865707    3.150986
1   26  18  0.570924    0.409248    0.227617    0.052805    0.180894    1.636876
2   26  18  0.827840    0.903756    0.313742    0.071873    0.439314    2.291626
3   26  18  0.799294    0.733236    0.338349    0.077740    0.245499    2.332548
4   26  18  0.884932    0.341040    0.350653    0.073340    0.142131    2.046095
5   12  18  1.427310    1.688148    0.615180    0.145213    1.292100    4.092190
6   12  18  1.427310    1.688148    0.615180    0.145213    1.292100    4.092190
7   12  18  1.427310    1.705200    0.615180    0.145213    1.292100    4.092190
8   12  18  1.427310    1.398264    0.615180    0.146680    0.646050    4.092190
9   12  18  1.427310    1.705200    0.615180    0.145213    1.292100    4.092190

增编:对不起，预期产出如下：

   cat    id   pen  rs       s40       s43       s44       s45       s46        s47
0  18.0  26.0  0.45  0.693650  3.150986  1.099029  1.176588  0.473689  0.114410   0.865707 
1  18.0  26.0  0.450 0.693650  1.636876  0.570924  0.409248  0.227617  0.052805   0.180894  
2  18.0  26.0  0.450 0.693650  2.291626  0.827840  0.903756  0.313742  0.071873   0.439314  
3  18.0  26.0  0.450 0.693650  2.332548  0.799294  0.733236  0.338349  0.077740   0.245499  
4  18.0  26.0  0.450 0.693650  2.046095  0.884932  0.341040  0.350653  0.073340   0.142131  
5  18.0  12.0  0.450 0.693650  4.092190  1.427310  1.688148  0.615180  0.145213   1.292100   
6  18.0  12.0  0.450 0.693650  4.092190  1.427310  1.688148  0.615180  0.145213   1.292100  
7  18.0  12.0  0.450 0.693650  4.092190  1.427310  1.705200  0.615180  0.145213   1.292100  
8  18.0  12.0  0.450 0.693650  4.092190  1.427310  1.398264  0.615180  0.146680   0.646050  
9  18.0  12.0  0.450 0.693650  4.092190  1.427310  1.705200  0.615180  0.145213   1.292100

Answer 1

#子集，转换为数组，乘并再转换为df

pdata.update(pd.DataFrame(pdata.iloc[:,2:].to_numpy()*pseries.iloc[2:].to_numpy(), columns=pdata.iloc[:,2:].columns))

指定pdseries的缺失部分。

pdata=pdata.assign(**pseries.iloc[:2])

结果

id  cat       s43       s44       s45       s46       s47       s40   pen  \
0  26   18  1.099029  1.176588  0.473689  0.114410  0.865707  3.150986  0.45   
1  26   18  0.570924  0.409248  0.227617  0.052805  0.180894  1.636876  0.45   
2  26   18  0.827840  0.903756  0.313742  0.071873  0.439314  2.291626  0.45   
3  26   18  0.799294  0.733236  0.338349  0.077740  0.245499  2.332548  0.45   
4  26   18  0.884932  0.341040  0.350653  0.073340  0.142131  2.046095  0.45   
5  12   18  1.427310  1.688148  0.615180  0.145213  1.292100  4.092190  0.45   
6  12   18  1.427310  1.688148  0.615180  0.145213  1.292100  4.092190  0.45   
7  12   18  1.427310  1.705200  0.615180  0.145213  1.292100  4.092190  0.45   
8  12   18  1.427310  1.398264  0.615180  0.146680  0.646050  4.092190  0.45   
9  12   18  1.427310  1.705200  0.615180  0.145213  1.292100  4.092190  0.45   

        rs  
0  0.69365  
1  0.69365  
2  0.69365  
3  0.69365  
4  0.69365  
5  0.69365  
6  0.69365  
7  0.69365  
8  0.69365  
9  0.69365