Pandas Dataframe MultiIndex将多级索引中的一个转换为另一轴，同时将另一级保留在原始轴中

Question

I have a Pandas Dataframe with MultiIndex in the row indexers like this:

This dataframe is a result of a groupby operation and then slicing from a 3-level MultiIndex .I would like the 'date' row indexer to remain, but shift the 'SlabType' level of row indexers into column indexer with non-available values as NaN .

This is what I would like to get to:

What operations do I need to do to achieve this? Also if the title of the question can be improved, please suggest so.

Answer 1

Use unstack with select column SlabLT :

print (df['SlabLT'].unstack())

But if possible duplicates in MultiIndex is necessary aggregate column, ag by mean :

print (df.groupby(level=[0,1])['SlabLT'].mean().unstack())

Sample :

df = pd.DataFrame({'date':['2017-10-01','2017-10-08','2017-10-08','2017-10-15', '2017-10-15'],
                   'SlabType':['UOM2','AMOUNT','UOM2','AMOUNT','AMOUNT'],
                   'SlabLT':[1,6000,1,6000,5000]}).set_index(['date','SlabType'])

print (df)
                     SlabLT
date       SlabType        
2017-10-01 UOM2           1
2017-10-08 AMOUNT      6000
           UOM2           1
2017-10-15 AMOUNT      6000 <-duplicated MultiIndex '2017-10-15', 'AMOUNT'
           AMOUNT      5000 <-duplicated MultiIndex '2017-10-15', 'AMOUNT'

print (df['SlabLT'].unstack())

ValueError: Index contains duplicate entries, cannot reshape

---

print (df.groupby(level=[0,1])['SlabLT'].mean())
date        SlabType
2017-10-01  UOM2           1
2017-10-08  AMOUNT      6000
            UOM2           1
2017-10-15  AMOUNT      5500
Name: SlabLT, dtype: int64

print (df.groupby(level=[0,1])['SlabLT'].mean().unstack())
SlabType    AMOUNT  UOM2
date                    
2017-10-01     NaN   1.0
2017-10-08  6000.0   1.0
2017-10-15  5500.0   NaN

Answer 2

Since you have NaN values for some entries, you may want to consider pivot table to avoid "duplicate entries" ValueError when unstacking one of the indices.

Suppose you have df DataFrame with column 'SlabLT' with indices date and SlabType , try:

df.reset_index().pivot_table(values = 'SlabLT', index = 'date', columns = 'SlabLT')