Pandas pivot table 排列无聚合

Question

我想在不聚合的情况下对 pandas 数据框进行透视，而不是垂直显示透视索引列，我想水平显示它。我试过 pd.pivot_table 但我没有得到我想要的。

data = {'year': [2011, 2011, 2012, 2013, 2013],
        'A': [10, 21, 20, 10, 39],
        'B': [12, 45, 19, 10, 39]}

df = pd.DataFrame(data)
print df
    A   B  year
0  10  12  2011
1  21  45  2011
2  20  19  2012
3  10  10  2013
4  39  39  2013

但我想要：

year      2011     2012      2013
cols     A    B   A    B    A    B
0       10    12  20   19   10   10
1       21    45  NaN  NaN  39   39

Answer 1

您可以先通过 cumcount, then stack with unstack:

为新索引创建列

df['g'] = df.groupby('year')['year'].cumcount()
df1 = df.set_index(['g','year']).stack().unstack([1,2])
print (df1)

year  2011        2012        2013      
         A     B     A     B     A     B
g                                       
0     10.0  12.0  20.0  19.0  10.0  10.0
1     21.0  45.0   NaN   NaN  39.0  39.0

如果需要设置列名，请使用 rename_axis（pandas 0.18.0 中的新功能）：

df['g'] = df.groupby('year')['year'].cumcount()
df1 = df.set_index(['g','year'])
        .stack()
        .unstack([1,2])
        .rename_axis(None)
        .rename_axis(('year','cols'), axis=1)
print (df1)
year  2011        2012        2013      
cols     A     B     A     B     A     B
0     10.0  12.0  20.0  19.0  10.0  10.0
1     21.0  45.0   NaN   NaN  39.0  39.0

另一个解决方案pivot, but you need swap first and second level of Multiindex in columns by swaplevel and then sort it by sort_index：

df['g'] = df.groupby('year')['year'].cumcount()
df1 = df.pivot(index='g', columns='year')
df1 = df1.swaplevel(0,1, axis=1).sort_index(axis=1)
print (df1)
year  2011        2012        2013      
         A     B     A     B     A     B
g                                       
0     10.0  12.0  20.0  19.0  10.0  10.0
1     21.0  45.0   NaN   NaN  39.0  39.0
print (df1)

year  2011        2012        2013      
         A     B     A     B     A     B
g                                       
0     10.0  12.0  20.0  19.0  10.0  10.0
1     21.0  45.0   NaN   NaN  39.0  39.0

Answer 2

groupby('year') 所以我可以 reset_index 得到 0 和 1 的索引值。然后做一堆清理工作。

df.groupby('year')['A', 'B'] \
    .apply(lambda df: df.reset_index(drop=True)) \
    .unstack(0).swaplevel(0, 1, 1).sort_index(1)

Pandas pivot table 排列无聚合

Pandas pivot table arrangement no aggregation

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