擅长:python、mysql、java
<p>您可以使用:</p>
<pre><code>df.index = df['id'].where(df['Code'].isnull()).ffill()
df = df[(df['Code'] != 'Code') & (df['id'] != df.index)]
df = df.rename_axis('Day').rename_axis('Week', 1)
df = df.set_index(['id','Code'], append=True)
.replace({'n':np.nan})
.stack().reset_index(name='val')
df['Week'] = df['Week'].str.extract('(\d+)', expand=False).astype(int)
cols = ['Code','Day','Week']
df = df.drop(['val','id'], axis=1)[cols].sort_values(['Week','Code']).reset_index(drop=True)
print (df)
Code Day Week
0 100 sunday 1
1 600 Monday 1
2 900 Tuesday 1
3 100 sunday 2
4 200 sunday 2
5 500 Monday 2
6 600 Monday 2
7 800 Tuesday 2
8 300 sunday 3
9 500 Monday 3
10 600 Monday 3
11 800 Tuesday 3
12 900 Tuesday 3
</code></pre>
<p>对于具有所有<code>y</code>和<code>n</code>值的常规输出-<code>id</code>列,请删除<code>replace</code>:</p>
^{pr2}$