避免Pandas DataFrame循环的有效方法

Question

我正在将Excel电子表格转换为Python，以便自动化并加速完成几项任务。我需要向DataFrame添加几列，并根据前一列中的值向它们添加数据。我已经使用两个嵌套for循环工作，但它确实很慢，我知道Pandas并非专为单元格工作而设计。这里是我的问题的一个样本：

import pandas as pd 

results = pd.DataFrame({'scores':[78.5, 91.0, 103.5], 'outcomes':[1,0,1]}) 

thresholds = [103.5, 98.5, 93.5, 88.5, 83.5, 78.5] 

for threshold in thresholds: 
    results[str(threshold)] = 0 
    for index, row in results.iterrows(): 
     if row['scores'] > threshold: 
      results.set_value(index, str(threshold), row['outcomes']) 

print (results) 

和正确的输出：

outcomes scores 103.5 98.5 93.5 88.5 83.5 78.5 
0   1 78.5  0  0  0  0  0  0 
1   0 91.0  0  0  0  0  0  0 
2   1 103.5  0  1  1  1  1  1 

什么是这样做的更有效的方法？我一直在尝试将DataFrame转换为按列而不是行来工作，但我无法获得任何工作。 感谢您的帮助！

Answer 1

这将做的工作：

import pandas as pd 

results = pd.DataFrame({'scores':[78.5, 91.0, 103.5], 'outcomes':[1,0,1]}) 

thresholds = [103.5, 98.5, 93.5, 88.5, 83.5, 78.5] 

for threshold in thresholds: 
    results[str(threshold)] = results[['scores','outcomes']].apply(lambda x: x['outcomes'] if x['scores']>threshold else 0, axis=1) 

print (results) 

这pronts

outcomes scores 103.5 98.5 93.5 88.5 83.5 78.5 
0   1 78.5  0 0.0 0.0 0.0 0.0 0.0 
1   0 91.0  0 0.0 0.0 0.0 0.0 0.0 
2   1 103.5  0 1.0 1.0 1.0 1.0 1.0 

Answer 2

下面是不使用循环或列表中理解一个完全量化的解决方案。

import pandas as pd 
import numpy as np 
results = pd.DataFrame({'scores':[78.5, 91.0, 103.5], 'outcomes':[1,0,1]}) 
thresholds = [4.7562029077978352, 4.6952820449271861, 4.6343611820565371, 4.5734403191858881, 103.5, 98.5, 93.5, 88.5, 83.5, 78.5] 
thresholds_col = ['{:.16f}'.format(e) for e in thresholds] 
data = results.outcomes[:,np.newaxis] * ((results.scores[:,np.newaxis] - thresholds > 0)) 
results = results.join(pd.DataFrame(data=data, columns=thresholds_col)) 
print results 
print results[thresholds_col] 

Out[79]: 
    4.7562029077978352 4.6952820449271861 4.6343611820565371 \ 
0     1     1     1 
1     0     0     0 
2     1     1     1 

    4.5734403191858881 103.5000000000000000 98.5000000000000000 \ 
0     1      0     0 
1     0      0     0 
2     1      0     1 

    93.5000000000000000 88.5000000000000000 83.5000000000000000 \ 
0     0     0     0 
1     0     0     0 
2     1     1     1 

    78.5000000000000000 
0     0 
1     0 
2     1