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I am working on a flat resale price dataset (In case you are interested, I am using the Jan 2015 onward data).

First, I group the data by using data.groupby('flat_type').sum(), Then the data becomes: enter image description here

My attempt to swap rows

Now, I try to swap the last two rows by using the answer from this post:

b,c = data_by_type.iloc[-2], data_by_type.iloc[-1]
temp = data_by_type.iloc[-2].copy()
data_by_type.iloc[-2] = c
data_by_type.iloc[-1] = b

enter image description here

The content for the last two rows changed, but the index for the last two rows remains the same.

So my question is: How to swap the entire row, including the index?

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1 Answer 1

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2

Use np.r_:

df = df.iloc[np.r_[0:len(df) - 2, -1, -2]]

Alternative:

df = df.loc[df.index[:-2].tolist() + df.index[-1:].tolist() + df.index[-2:-1].tolist()]

print (df)
                  floor_area_sqm  lease_commence_date  remaining_lease  \
flat_type                                                                
1 ROOM                    1085.0                69125             1977   
2 ROOM                   40898.0              1788955            62356   
3 ROOM                 1430010.1             41448329          1344374   
4 ROOM                 3226160.4             67177320          2604002   
5 ROOM                 2349460.0             39663269          1555300   
MULTI-GENERATION          4105.0                49677             1708   
EXECUTIVE               943961.0             13059434           495628   

                  resale_price  
flat_type                       
1 ROOM            6.938000e+06  
2 ROOM            2.168112e+08  
3 ROOM            6.628663e+09  
4 ROOM            1.460442e+10  
5 ROOM            1.043298e+10  
MULTI-GENERATION  1.969189e+07  
EXECUTIVE         4.101716e+09
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