使用 DataFrame.stack 删除缺失值，然后通过

Use DataFrame.set_index with DataFrame.stack for remove missing values, then create indicators by get_dummies and return 1/0 by max by first level, last convert columns to integers:

df1 = (pd.get_dummies(df.set_index('ID').stack())
         .max(level=0)
         .rename(columns=int)
         .reset_index())
print (df1)
   ID  1  2  3  4  5  10  20
0   1  1  1  0  0  1   1   1
1   2  1  0  1  1  0   1   0

print (df)
   ID   0   1    2   3  4  5
0   1  10  20  5.0   1  2  5
1   2   3   4  NaN  10  1  2

如果使用max，则始终在输出中显示0/1值(选中5列):

If use max then always in output are 0/1 values (check 5 column):

df1 = (pd.get_dummies(df.set_index('ID').stack())
         .max(level=0)
         .rename(columns=int)
         .reset_index())
print (df1)
   ID  1  2  3  4  5  10  20
0   1  1  1  0  0  1   1   1
1   2  1  1  1  1  0   1   0

但是如果使用sum，它会计算值(检查5列):

But if use sum it count values (check 5 column):

df2 = (pd.get_dummies(df.set_index('ID').stack())
         .sum(level=0)
         .rename(columns=int)
         .reset_index())
print (df2)
   ID  1  2  3  4  5  10  20
0   1  1  1  0  0  2   1   1
1   2  1  1  1  1  0   1   0