在分组的熊猫数据框中移动列

Question

I have a dataframe which, after grouping it by country and group looks like this:

                         A       B       C       D
country     group
1           a1           10      20      30      40
            a2           11      21      31      41
            a3           12      22      32      42
            a4           13      23      33      43

                         A       B       C       D
country     group
2           a1           50      60      70      80
            a2           51      61      71      81
            a3           52      62      72      82
            a4           53      63      73      83

My goal is to create another column E that would hold column D values shifted up by 1 row like so:

                         A       B       C       D      E
country     group
1           a1           10      20      30      40     41
            a2           11      21      31      41     42
            a3           12      22      32      42     43
            a4           13      23      33      43     nan

                         A       B       C       D      E
country     group 
2           a1           50      60      70      80     81
            a2           51      61      71      81     82
            a3           52      62      72      82     83
            a4           53      63      73      83     nan

What I've tried:

df.groupby(['country','group']).sum().apply(lambda x['E']: x['D'].shift(-1)) but I get invalid syntax.

Afterwards I am trying to delete those bottom lines in each group where nan is present like so: df = df[~df.isin([np.nan]).any(1)] which works.

How can I add a column E to the df which would hold column D values shifted by -1 ?

Answer 1

Use DataFrameGroupBy.shift by first level:

df = df.groupby(['country','group']).sum()
df['E'] = df.groupby(level=0)['D'].shift(-1)

And then DataFrame.dropna :

df = df.dropna(subset=['E'])

Sample :

print (df)
    country group   A   B   C   D
0         1    a1  10  20  30  40
1         1    a1  11  21  31  41
2         1    a1  12  22  32  42
3         1    a2  13  23  33  43
4         1    a2  11  21  31  41
5         1    a2  12  22  32  42
6         1    a3  13  23  33  43
7         1    a3  11  21  31  41
8         1    a3  12  22  32  42
9         1    a4  13  23  33  43
10        1    a4  11  21  31  41
11        1    a5  12  22  32  42
12        1    a5  13  23  33  43
13        2    a2  50  60  70  80
14        2    a3  51  61  71  81
15        2    a4  52  62  72  82
16        2    a5  53  63  73  83

df = df.groupby(['country','group']).sum()
print (df)
                A   B   C    D
country group                 
1       a1     33  63  93  123
        a2     36  66  96  126
        a3     36  66  96  126
        a4     24  44  64   84
        a5     25  45  65   85
2       a2     50  60  70   80
        a3     51  61  71   81
        a4     52  62  72   82
        a5     53  63  73   83

---

df['E'] = df.groupby(level=0)['D'].shift(-1)
print (df)
                A   B   C    D      E
country group                        
1       a1     33  63  93  123  126.0
        a2     36  66  96  126  126.0
        a3     36  66  96  126   84.0
        a4     24  44  64   84   85.0
        a5     25  45  65   85    NaN
2       a2     50  60  70   80   81.0
        a3     51  61  71   81   82.0
        a4     52  62  72   82   83.0
        a5     53  63  73   83    NaN

df = df.dropna(subset=['E'])
print (df)
                A   B   C    D      E
country group                        
1       a1     33  63  93  123  126.0
        a2     36  66  96  126  126.0
        a3     36  66  96  126   84.0
        a4     24  44  64   84   85.0
2       a2     50  60  70   80   81.0
        a3     51  61  71   81   82.0
        a4     52  62  72   82   83.0