Question

我的DataFrame看起来像这样：

class    passed       failed           extra_teaching
A11      1            2                0.5
A12      2            1                0.7

我想解开＆＃39; DataFrame，并且丢失了关于班级的信息，但保留了extra_teaching的信息，所以我最后为每个通过的学生排了一行。

所以DataFrame应该看起来像这样：

pass           extra_teaching
1              0.5
0              0.5 
0              0.5
1              0.7
1              0.7
0              0.7

我不知道如何在pandas中执行此操作，除非使用iterrows()并手动将行添加到新的DataFrame - 有人有更简洁的方法吗？

更新：

我试过这个，似乎工作虽然不是很优雅：

temp = []
df = df.set_index('class')
for idx in df.index:
    row = df.loc[idx]
    t = {'class': idx, 'extra_teaching': row['extra_teaching']}
    for i in range(0, int(row['passed'])):
        t['pass'] = 1
        temp.append(t)
    for i in range(0, int(row['failed'])):
        t['pass'] = 0
        temp.append(t)
df_exploded = pd.DataFrame(temp)

Answer 1

尝试：

def teaching_results(x):
    num_rows = x.passed.iloc[0] + x.failed.iloc[0]
    passed = x.passed.iloc[0] * [1] + x.failed.iloc[0] * [0]
    extra_teaching = num_rows * [x.extra_teaching.iloc[0]]
    class_code = x['class'].iloc[0]
    return pd.DataFrame({'pass': passed, 'extra_teaching': extra_teaching, 'class': class_code})

df.groupby('class', as_index=False).apply(lambda x: teaching_results(x))

得到：

    class  extra_teaching  pass
0 0   A11             0.5     1
  1   A11             0.5     0
  2   A11             0.5     0
1 0   A12             0.7     1
  1   A12             0.7     1
  2   A12             0.7     0

如何解开＆＃39;使用行值的数据框？

1 个答案: