Python-按列保留第一个唯一值

get_dummies()

df[df.filter(like='level').columns] = pd.get_dummies(df.filter(like='level').idxmax(1))

   id  level1  level2  level3
0   1       0       1       0
1   2       0       0       1
2   3       0       1       0
3   4       0       0       1
4   5       1       0       0

使用 numpy.zeros 并通过 numpy.argmax :

out = np.zeros(df.iloc[:, 1:].shape, dtype=int)
out[np.arange(len(out)), np.argmax(df.iloc[:, 1:].values, 1)] = 1
df.iloc[:, 1:] = out

使用 broadcasting 具有 argmax :

a = df.iloc[:, 1:].values
df.iloc[:, 1:] = (a.argmax(axis=1)[:,None] == range(a.shape[1])).astype(int)

两者都产生:

   id  level1  level2  level3
0   1       0       1       0
1   2       0       0       1
2   3       0       1       0
3   4       0       0       1
4   5       1       0       0

int 数据类型。

idx = df.iloc[:, 1:].eq(1).values.argmax(1)
df.iloc[:, 1:] = 0
df.values[np.arange(df.shape[0]), idx+1] = 1

print(df)

   id  level1  level2  level3
0   1       0       1       0
1   2       0       0       1
2   3       0       1       0
3   4       0       0       1
4   5       1       0       0

numpy.eye

v = df.iloc[:, 1:].values
i = np.eye(3, dtype=np.int64)
a = v.argmax(1)
df.iloc[:, 1:] = i[a]
df

   id  level1  level2  level3
0   1       0       1       0
1   2       0       0       1
2   3       0       1       0
3   4       0       0       1
4   5       1       0       0

cumsum mask

df.set_index('id').pipe(
    lambda d: d.mask(d.cumsum(1) > 1, 0)
).reset_index()

   id  level1  level2  level3
0   1       0       1       0
1   2       0       0       1
2   3       0       1       0
3   4       0       0       1
4   5       1       0       0