Pivot a pandas df 以特定方式

Question

我有以下数据集，我想进行一些重大调整（以某种方式旋转数据）。 （这一天一直到 12 月只是为了方便没有包括它）

输入：

Series ID       View Description          Jan-10   Feb-10   Mar-10   Apr-10 
Food            12-Month Percent Change   219.98   210.98   205      202  
Drinks          Original Data Value       215.46   205.04   206      203   
Food at Home    Original Data Value       202.88   203      207      199.98 

所需的 output：

Series ID     View Description          Month    Year   Value
Food          12-Month Percent Change   January  2010   219.98
Food          12-Month Percent Change   February 2010   210.98
Food          12-Month Percent Change   March    2010   205
Food          12-Month Percent Change   April    2010   202
Drinks        Original Data Value       January  2010   215.45
Drinks        Original Data Value       February 2010   205.04
Drinks        Original Data Value       March    2010   206
Drinks        Original Data Value       April    2010   203
Food at Home  Original Data Value       January  2010   202.88
Food at Home  Original Data Value       February 2010   203
Food at Home  Original Data Value       March    2010   207
Food at Home  Original Data Value       April    2010   199.98

注意：实际上数据包括所有 12 个月，并且 Year 列应该能够采用任何值。 任何建议都非常感谢！

Answer 1

这是一种方法。 使用melt来堆叠DF，排序然后将日期拆分为年月

df2=df.melt(id_vars=['Series ID','View Description' ],
            var_name='date', 
            value_name='value'
           ).sort_values(['View Description','Series ID'])

df2['year']=pd.to_datetime(df2['date'],format='%b-%y').dt.year
df2['month']=pd.to_datetime(df2['date'],format='%b-%y').dt.month_name()
df2.drop(columns='date', inplace=True)
df2

    Series ID       View Description             value  year    month
0   Food            12-Month Percent Change     219.98  2010    January
3   Food            12-Month Percent Change     210.98  2010    February
6   Food            12-Month Percent Change     205.00  2010    March
9   Food            12-Month Percent Change     202.00  2010    April
1   Drinks          Original Data Value         215.46  2010    January
4   Drinks          Original Data Value         205.04  2010    February
7   Drinks          Original Data Value         206.00  2010    March
10  Drinks          Original Data Value         203.00  2010    April
2   Food at Home    Original Data Value         202.88  2010    January
5   Food at Home    Original Data Value         203.00  2010    February
8   Food at Home    Original Data Value         207.00  2010    March
11  Food at Home    Original Data Value         199.98  2010    April