Question

我为每个ID提供以下数据：

id  ----    Base    AE      Val     LT  RO+ Prem    AM  TN  T3  AR
05  0       34.34   9.42    70.68   0   0   0   0   0   0   0
108 0       43.77   0       28      0   0   0   0   0   0   0
205 0       77.64   0       32.2    0   0   0   0   0   0   0
320 0       66.24   0       59.628  0   0   0   0   0   0   0
313 0       21.66   0       21.442  0   0   0   0   0   0   0
324 0       72.37   0       701.12  0   0   0   0   0   0   0
505 0       76.057  0       43.87   0   0   0   0   0   0   0

现在我想找到所有列的总和，除了我指定的几列以及其他列分别如下所示：

id  Base    Val     Others  Total
05  34.34   70.68   9.42    114.441387
108 43.77   28      0       71.77
205 77.64   32.2    0       109.84
320 66.24   59.628  0       125.868
313 21.66   21.442  0       43.102
324 72.37   701.12  0       773.49
505 76.057  43.87   0       119.927

所以，如果我的列列表要保留：

cols_to_keep = ['Base','Val']

不属于此列表的其他通道将在其他列中汇总，并且每行中的所有值总计为总计。 id是记录的索引。

我可以保留列表中声明的列，但是如何总结其他列，除了在Others列的列表中。有人可以帮我这个吗？这些数据是在pandas df。

中

Answer 1

Drop您不希望总结的列：

df['Others'] = df.drop(cols_to_keep, axis=1).sum(axis=1)
df['Total'] = df.sum(axis=1)

Answer 2

使用assign，过滤列使用Index.difference：

cols_to_keep = ['Base','Val'] 

c = df.columns.difference(cols_to_keep)
df = df[cols_to_keep].assign(Others=df[c].sum(axis=1), Total=df.sum(1))
print (df)
       Base      Val  Others    Total
id                                   
5    34.340   70.680    9.42  114.440
108  43.770   28.000    0.00   71.770
205  77.640   32.200    0.00  109.840
320  66.240   59.628    0.00  125.868
313  21.660   21.442    0.00   43.102
324  72.370  701.120    0.00  773.490
505  76.057   43.870    0.00  119.927

Answer 3

In [47]: !cat b.txt | tr -s ' ' > data.txt
    ...: df = pd.read_csv("data.txt",sep=" ", dtype={'id':str})
    ...: df['Others'] = df['AE']
    ...: df['Total']  = df['Base'] + df['Others'] + df['Val']
    ...:
    ...: cols_to_keep=['id', 'Base', 'Val','Others','Total']
    ...: c = df.columns.difference(cols_to_keep)
    ...: df.drop(c, axis=1)
    ...: newDf = df.drop(c, axis=1)
    ...:

In [48]: newDf
Out[48]:
    id    Base      Val  Others    Total
0   05  34.340   70.680    9.42  114.440
1  108  43.770   28.000    0.00   71.770
2  205  77.640   32.200    0.00  109.840
3  320  66.240   59.628    0.00  125.868
4  313  21.660   21.442    0.00   43.102
5  324  72.370  701.120    0.00  773.490
6  505  76.057   43.870    0.00  119.927

除Python

3 个答案: