好的，这有两个步骤 - 首先是编写一个执行您想要的翻译的函数 - 我已经根据您的伪代码将示例放在一起:

OK, two steps to this - first is to write a function that does the translation you want - I've put an example together based on your pseudo-code:

def label_race (row):
   if row['eri_hispanic'] == 1 :
      return 'Hispanic'
   if row['eri_afr_amer'] + row['eri_asian'] + row['eri_hawaiian'] + row['eri_nat_amer'] + row['eri_white'] > 1 :
      return 'Two Or More'
   if row['eri_nat_amer'] == 1 :
      return 'A/I AK Native'
   if row['eri_asian'] == 1:
      return 'Asian'
   if row['eri_afr_amer']  == 1:
      return 'Black/AA'
   if row['eri_hawaiian'] == 1:
      return 'Haw/Pac Isl.'
   if row['eri_white'] == 1:
      return 'White'
   return 'Other'

您可能想仔细研究一下，但它似乎可以解决问题 - 请注意，进入函数的参数被认为是标记为行"的 Series 对象.

You may want to go over this, but it seems to do the trick - notice that the parameter going into the function is considered to be a Series object labelled "row".

接下来，使用pandas中的apply函数来应用函数——例如

Next, use the apply function in pandas to apply the function - e.g.

df.apply (lambda row: label_race(row), axis=1)

注意 axis=1 说明符，这意味着应用程序是在行级而不是列级完成的.结果在这里:

Note the axis=1 specifier, that means that the application is done at a row, rather than a column level. The results are here:

0           White
1        Hispanic
2           White
3           White
4           Other
5           White
6     Two Or More
7           White
8    Haw/Pac Isl.
9           White

如果您对这些结果感到满意，请再次运行，将结果保存到原始数据框中的新列中.

If you're happy with those results, then run it again, saving the results into a new column in your original dataframe.

df['race_label'] = df.apply (lambda row: label_race(row), axis=1)

结果数据框如下所示(向右滚动以查看新列):

The resultant dataframe looks like this (scroll to the right to see the new column):

      lname   fname rno_cd  eri_afr_amer  eri_asian  eri_hawaiian   eri_hispanic  eri_nat_amer  eri_white rno_defined    race_label
0      MOST    JEFF      E             0          0             0              0             0          1       White         White
1    CRUISE     TOM      E             0          0             0              1             0          0       White      Hispanic
2      DEPP  JOHNNY    NaN             0          0             0              0             0          1     Unknown         White
3     DICAP     LEO    NaN             0          0             0              0             0          1     Unknown         White
4    BRANDO  MARLON      E             0          0             0              0             0          0       White         Other
5     HANKS     TOM    NaN             0          0             0              0             0          1     Unknown         White
6    DENIRO  ROBERT      E             0          1             0              0             0          1       White   Two Or More
7    PACINO      AL      E             0          0             0              0             0          1       White         White
8  WILLIAMS   ROBIN      E             0          0             1              0             0          0       White  Haw/Pac Isl.
9  EASTWOOD   CLINT      E             0          0             0              0             0          1       White         White