3 回答

TA贡献1788条经验 获得超4个赞
您可以重命名列,pd.melt
然后sort_values
:
df.columns = [i if not i.startswith('Week') else int(i[-1]) for i in df]
res = pd.melt(df, id_vars='Shop', var_name='Week', value_name='Hour')\
.sort_values('Shop').reset_index(drop=True)
print(res)
Shop Week Hour
0 1 1 15
1 1 2 5
2 2 1 25
3 2 2 44
...
16 9 2 81
17 9 1 76
18 10 1 62
19 10 2 46

TA贡献2003条经验 获得超2个赞
使用 wide_to_long
pd.wide_to_long(df,'Week ',i='Shop',j='week')
Out[770]:
Week
Shop week
1 1 15
2 1 25
3 1 11
4 1 22
5 1 0
6 1 -1
7 1 15
8 1 11
9 1 76
10 1 62
1 2 5
2 2 44
3 2 55
4 2 21
5 2 12
6 2 51
7 2 -10
8 2 25
9 2 81
10 2 46
#pd.wide_to_long(df,'Week ',i='Shop',j='week').sort_index(level=0).reset_index().rename(columns={'Week ':'Hour'})

TA贡献1876条经验 获得超5个赞
我会使用这样的东西,尽管所有的重命名有点混乱:
# Rename columns with dict comprehension so it can extend to more than week 1 and week 2
df2 = (df.rename(columns={i: int(i.split()[-1]) for i in df.columns[1:]})
.set_index('Shop')
.stack()
.reset_index()
.rename(columns={'level_1':'Week', 0:'Hour'}))
>>> df2
Shop Week Hour
0 1 1 15
1 1 2 5
2 2 1 25
3 2 2 44
4 3 1 11
5 3 2 55
6 4 1 22
7 4 2 21
8 5 1 0
9 5 2 12
10 6 1 -1
11 6 2 51
12 7 1 15
13 7 2 -10
14 8 1 11
15 8 2 25
16 9 1 76
17 9 2 81
18 10 1 62
19 10 2 46
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