python - 使用每个组的不同列来计算组内的出现次数

Question

在下面的 df 中，变量“group”中有三个组 - 'A'、'AB'、'C'。 df 中的其他列通过后缀分配给特定组 - var1_A 与组 A 相关，依此类推。

data = pd.DataFrame({'group':['A', 'AB', 'A', 'AB', 'AB', 'C', 'C', 'A', 'A', 'AB'],
                     'var1_A':['pass', 'fail', 'pass','fail', 'pass']*2,
                     'var2_A':['pass', 'pass', 'pass','fail', 'pass']*2,
                     'var1_AB':['pass', 'pass', 'pass','fail', 'pass']*2,
                     'var2_AB':['pass', 'pass', 'fail','fail', 'pass']*2,
                     'var1_C':['pass', 'pass', 'pass','fail', 'pass']*2,
                     'var2_C': ['fail', 'fail', 'fail','fail', 'pass']*2
                    })

我想为每一行计算“通过”发生的次数。对于属于 A 组的实例，我只想计算连接到 A 组的变量。我希望将结果放在一个新列中。这几乎可以完成工作。

data['new_col'] = data[data['group']=='A']['var1_A, var2_A].isin(['pass']).sum(1)
data['new_col'] = data[data['group']=='AB']['var1_AB, var2_AB].isin(['pass']).sum(1)
data['new_col'] = data[data['group']=='C']['var1_C, var2_C].isin(['pass']).sum(1)

但是，我希望所有组的结果都在同一列中。这个操作也许可以使用 groupby 和转换来完成？然而，我在弄清楚这一点上遇到了困难。

目标数据框:

pd.DataFrame({'group':['A', 'AB', 'A', 'AB', 'AB', 'C', 'C', 'A', 'A', 'AB'],
                     'var1_A':['pass', 'fail', 'pass','fail', 'pass']*2,
                     'var2_A':['pass', 'pass', 'pass','fail', 'pass']*2,
                     'var1_AB':['pass', 'pass', 'pass','fail', 'pass']*2,
                     'var2_AB':['pass', 'pass', 'fail','fail', 'pass']*2,
                     'var1_C':['pass', 'pass', 'pass','fail', 'pass']*2,
                     'var2_C': ['fail', 'fail', 'fail','fail', 'pass']*2,
                     'result':[2,2,2,0,2,1,1,2,0,2]
                    })

Answer 1

您可以melt ，过滤器和 groupby.count :

data['result'] = (data
  .rename(columns=lambda x: x.split('_')[-1]) # get only part after "_"
  .reset_index().melt(['index', 'group'])
  # keep only identical groups and "pass" values
  .loc[lambda d: d['group'].eq(d['variable']) & d['value'].eq('pass')]
  .groupby('index')['value'].count()
  .reindex(data.index, fill_value=0)
)

print(data)

或者使用矩阵和字符串比较的另一种方法:

df2 = data.set_index('group').eq('pass')
data['result'] = (df2.mul(df2.columns.str.extract('_(\w+)', expand=False))
                     .eq(df2.index, axis=0).sum(axis=1)
                     .to_numpy()
                 )

输出:

  group var1_A var2_A var1_AB var2_AB var1_C var2_C  result
0     A   pass   pass    pass    pass   pass   fail       2
1    AB   fail   pass    pass    pass   pass   fail       2
2     A   pass   pass    pass    fail   pass   fail       2
3    AB   fail   fail    fail    fail   fail   fail       0
4    AB   pass   pass    pass    pass   pass   pass       2
5     C   pass   pass    pass    pass   pass   fail       1
6     C   fail   pass    pass    pass   pass   fail       1
7     A   pass   pass    pass    fail   pass   fail       2
8     A   fail   fail    fail    fail   fail   fail       0
9    AB   pass   pass    pass    pass   pass   pass       2