防止熊猫数据帧标题行重复for语句

Question

我正在遍历管道，为一个名为safety的类打印20个信息最丰富的特性。

classnum_saf = 3
inds = np.argsort(clf_3.named_steps['clf'].coef_[classnum_saf, :])[-20:]
for i in inds: 
   f = feature_names[i]
   c = clf_3.named_steps['clf'].coef_[classnum_saf, [i]]
   print(f,c)
   output = {'features':f, 'coefficients':c}
   df = pd.DataFrame(output, columns = ['features', 'coefficients'])
   print(df)

我想要一个只有一个头的数据帧，但是我返回这个输出，它似乎一次又一次地重复这个头，因为它是通过i迭代的。

   1800 [-8.73800344]
   features  coefficients
   0     1800     -8.738003
   hr [-8.73656027]
   features  coefficients
   0       hr      -8.73656
   wa [-8.7336777]
   features  coefficients
   0       wa     -8.733678
   1400 [-8.72197545]
   features  coefficients
   0     1400     -8.721975
   hrwa [-8.71952656]
   features  coefficients
   0     hrwa     -8.719527
   perimeter [-8.71173264]
   features  coefficients
   0  perimeter     -8.711733
   response [-8.67388885]
   features  coefficients
   0  response     -8.673889
   analysis [-8.65460329]
   features  coefficients
   0  analysis     -8.654603
   00 [-8.58386785]
   features  coefficients
   0       00     -8.583868
   raw [-8.56148006]
   features  coefficients
   0      raw      -8.56148
   run [-8.51374794]
   features  coefficients
   0      run     -8.513748
   factor [-8.50725691]
   features  coefficients
   0   factor     -8.507257
   200 [-8.50334896]
   features  coefficients
   0      200     -8.503349
   file [-8.39990841]
   features  coefficients
   0     file     -8.399908
   pb [-8.38173753]
   features  coefficients
   0       pb     -8.381738
   mar [-8.21304343]
   features  coefficients
   0      mar     -8.213043
   1998 [-8.21239836]
   features  coefficients
   0     1998     -8.212398
   signal [-8.02426499]
   features  coefficients
   0   signal     -8.024265
   area [-8.01782987]
   features  coefficients
   0     area      -8.01783
   98 [-7.3166918]
   features  coefficients
   0       98     -7.316692

如何返回data frame，如：

          features     coefficients
   0      1800          -8.738003
   ..     ...           ...
   18     area          -8.01783
   19     98            -7.316692

现在，当我返回print(d，f)时，它显示了以下顶级值：

   1800 [-8.73800344]
   hr [-8.73656027]
   wa [-8.7336777]
   1400 [-8.72197545]
   hrwa [-8.71952656]
   perimeter [-8.71173264]
   response [-8.67388885]
   analysis [-8.65460329]
   00 [-8.58386785]
   raw [-8.56148006]
   run [-8.51374794]
   factor [-8.50725691]
   200 [-8.50334896]
   file [-8.39990841]
   pb [-8.38173753]
   mar [-8.21304343]
   1998 [-8.21239836]
   signal [-8.02426499]
   area [-8.01782987]
   98 [-7.3166918]

我研究了几个类似的问题-- here、here和here，但似乎没有直接解决我的问题。

先谢谢你，现在还在这里学习。

Answer 1

我尝试模拟一些数据，您可以在循环的每一步中将list附加到L，最后从L创建df。

L = []
classnum_saf = 3
inds = np.argsort(clf_3.named_steps['clf'].coef_[classnum_saf, :])[-20:]
for i in inds: 
   f = feature_names[i]
   c = clf_3.named_steps['clf'].coef_[classnum_saf, [i]]
   print(f,c)
   #add [0] for removing list of list (it works nice if len of f[i] == 1)
   L.append([c[i], f[i][0]])

df = pd.DataFrame(L, columns = ['features', 'coefficients'])
print(df) 

示例：

import pandas as pd

f = [[1],[2],[3]]
c = ['a','b','c']

L = []
for i in range(3): 
#   print(f[i],c[i])
   #swap c and f
   L.append([c[i], f[i][0]])

print (L)
[['a', 1], ['b', 2], ['c', 3]]

df = pd.DataFrame(L, columns = ['features', 'coefficients'])
print(df)  

  features  coefficients
0        a             1
1        b             2
2        c             3