我有一个多索引DataFrame,df
:
arrays = [['bar', 'bar', 'baz', 'baz', 'baz', 'baz', 'foo', 'foo'],
['one', 'two', 'one', 'two', 'three', 'four', 'one', 'two']]
df = pd.DataFrame(np.ones([8, 4]), index=arrays)
看起来像:
0 1 2 3
bar one 1.0 1.0 1.0 1.0
two 1.0 1.0 1.0 1.0
baz one 1.0 1.0 1.0 1.0
two 1.0 1.0 1.0 1.0
three 1.0 1.0 1.0 1.0
four 1.0 1.0 1.0 1.0
foo one 1.0 1.0 1.0 1.0
two 1.0 1.0 1.0 1.0
我现在需要将“baz
”子级别排序为新订单,以创建类似df_end
的内容:
arrays_end = [['bar', 'bar', 'baz', 'baz', 'baz', 'baz', 'foo', 'foo'],
['one', 'two', 'two', 'four', 'three', 'one', 'one', 'two']]
df_end = pd.DataFrame(np.ones([8, 4]), index=arrays_end)
看起来像:
0 1 2 3
bar one 1.0 1.0 1.0 1.0
two 1.0 1.0 1.0 1.0
baz two 1.0 1.0 1.0 1.0
four 1.0 1.0 1.0 1.0
three 1.0 1.0 1.0 1.0
one 1.0 1.0 1.0 1.0
foo one 1.0 1.0 1.0 1.0
two 1.0 1.0 1.0 1.0
我认为我可以重新索引baz
行:
new_index = ['two','four','three','one']
df.loc['baz'].reindex(new_index)
给出了:
0 1 2 3
two 1.0 1.0 1.0 1.0
four 1.0 1.0 1.0 1.0
three 1.0 1.0 1.0 1.0
one 1.0 1.0 1.0 1.0
...并将这些值插回到原始DataFrame:
df.loc['baz'] = df.loc['baz'].reindex(new_index)
但结果是:
0 1 2 3
bar one 1.0 1.0 1.0 1.0
two 1.0 1.0 1.0 1.0
baz one NaN NaN NaN NaN
two NaN NaN NaN NaN
three NaN NaN NaN NaN
four NaN NaN NaN NaN
foo one 1.0 1.0 1.0 1.0
two 1.0 1.0 1.0 1.0
这不是我想要的!所以我的问题是如何使用new_index
重新排序baz
索引中的行。任何建议都将不胜感激。
答案 0 :(得分:2)
编辑:(以适合所需的布局)
arrays = [['bar', 'bar', 'baz', 'baz', 'baz', 'baz', 'foo', 'foo'],
['one', 'two', 'one', 'two', 'three', 'four', 'one', 'two']]
df = pd.DataFrame(np.arange(32).reshape([8, 4]), index=arrays)
new_baz_index = [('baz', i) for i in ['two','four','three','one']]
index = df.index.values.copy()
index[df.index.get_loc('baz')] = new_baz_index
df.reindex(index)
df.index.get_loc('baz')
会将baz
部分的位置作为切片对象,我们只替换那里的部分。
答案 1 :(得分:1)
更新: - )
pd.concat([df[df.index.get_level_values(level=0)!='baz'],df.reindex(list(zip(['baz']*4,['two','four','three','one'])))])
Out[1156]:
0 1 2 3
bar one 1.0 1.0 1.0 1.0
two 1.0 1.0 1.0 1.0
foo one 1.0 1.0 1.0 1.0
two 1.0 1.0 1.0 1.0
baz two 1.0 1.0 1.0 1.0
four 1.0 1.0 1.0 1.0
three 1.0 1.0 1.0 1.0
one 1.0 1.0 1.0 1.0