我想在没有聚合的情况下转动pandas数据帧,而不是垂直呈现数据透视索引列,我想水平呈现它。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
但我希望:
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
答案 0 :(得分:3)
您可以先cumcount
为新索引创建列,然后stack
创建unstack
:
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
如果需要设置列名称,请使用rename_axis
(pandas
0.18.0
中的新内容):
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
使用pivot
的另一个解决方案,但您需要在swaplevel
的列中交换Multiindex
的第一级和第二级,然后按sort_index
对其进行排序:
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
答案 1 :(得分:1)