如何重塑多索引 dataframe？

Question

Here I have a large dataframe (Nx1, it has thousands of stocks, here I just make it to be 4)

ds          code       close
20160101    001         1.5
            002         1.12
            003         1.21
            005         12.4
20160102    001         2.3
            002         3.4
            003         5.4
            005         7.2
```         ```         ```
20201231    001         14.5
            002         2.2
            003         3.3 
            005         24.5

My purpose is to arbitrarily select a time period within the dataset, and calculate the average close of each stock in 30 days. For example, the codes should be able to calculate the average close in 30 days of each stock from 20170101 to 20191231.

So I would like to split the large dataframe by "ds" and reform those splited dataframes to the following form, that it might be easier to reach my goal. But I don't know if there is any easy way to get there?

code      20160101     20160102    ....   20201231  
001          1.5          2.3      ....     14.5
002          1.12         3.4      ....     2.2
003          1.21         5.4      ....     3.3
005          12.4         7.2      ....     24.5

Answer 1

Use Series.unstack with select close for Series ( ds and code are levels of MultiIndex ):

df1 = df['close'].unstack(level=0)