python - Pandas :通过groupby进行复杂过滤

Question

test = pd.DataFrame({'injury':['A', 'B', 'B', 'A', 'A', 'C', 'A', 'B', 'A'], 'crash_drinking':[1, 1, 1, 0, 0, 0, 1, 0, 1], 'crash_drugs':[0,0,0,1,1,0,0,1,1], 'driver_drinking':[1,1,0,0,0,0,0,1,0], 'driver_drugged':[0,0,0,0,1,0,0,1,0]})

   crash_drinking  crash_drugs  driver_drinking  driver_drugged injury
0               1            0                1               0      A
1               1            0                1               0      B
2               1            0                0               0      B
3               0            1                0               0      A
4               0            1                0               1      A
5               0            0                0               0      C
6               1            0                0               0      A
7               0            1                1               1      B
8               1            1                0               0      A

我希望我的输出看起来像这样(更改列名以将它们与上面的数据框区分开来):

    drinking crash  drinking driver in crash    drugged crash   drugged driver in crash
A                2                        1                 2                         1
B                2                        1                 1                         0

对于第一行，"injury"= 'A'，以及以下过滤器:

“drinking crash”是指 crash_drinking = 1 和 crash_drugs = 0 的计数；

“drinking driver in crash”是 crash_drinking = 1，crash_drugs = 0，driver_drinking = 1， 和 driver_drugs 0;

“吸毒崩溃”是 crash_drinking = 0 和 crash_drugs = 1;

“车祸中吸毒的司机”是 crash_drinking = 0、crash_drugs = 1、driver_drinking = 0 和 driver_drugs = 1。

B 行也一样，除了那是 "injury"= 'B' 的地方。

现在我只设置了一堆 .loc 过滤器:

test.loc[(test['injury'] == 'A') & (test['crash_drinking'] == 1) & (test['crash_drugs'] == 0)]
test.loc[(test['injury'] == 'A') & (test['crash_drinking'] == 0) & (test['crash_drugs'] == 1)]
test.loc[(test['injury'] == 'A') & (test['crash_drinking'] == 1) & (test['crash_drugs'] == 0) & (test['driver_drinking'] == 1) & (test['driver_drugged'] == 0)]

等等

我宁愿通过 groupby 或 .apply() 执行此操作，因为我认为这比遍历所有这些查询更快。但我不确定这样做的正确语法。也许我应该在“伤害”列上做一个 .groupby()，然后从那里开始......？

Answer 1

result = pd.DataFrame()
result['drinking crash'] = (test['crash_drinking'] == 1) & (test['crash_drugs'] == 0)
result['drinking driver in crash'] = ((test['crash_drinking'] == 1) & (test['crash_drugs'] == 0) 
                                      & (test['driver_drinking'] == 1) & (test['driver_drugs'] == 0))
result['drugged crash'] = (test['crash_drinking'] == 0) & (test['crash_drugs'] == 1)
result['drugged driver in crash'] = ((test['crash_drinking'] == 0) & (test['crash_drugs'] == 1) 
                                     & (test['driver_drinking'] == 0) & (test['driver_drugs'] == 1))
result = result.astype(int)
result['injury'] = test['injury']
result.groupby('injury').sum()