這是我第一次在 pandas 中使用多重索引，我需要一些幫助來合并兩個(gè)具有分層列的數(shù)據(jù)幀。這是我的兩個(gè)數(shù)據(jù)框：col_index = pd.MultiIndex.from_product([['a', 'b', 'c'], ['w', 'x']])df1 = pd.DataFrame(np.ones([4,6]),columns=col_index, index=range(4)) a b c w x w x w x0 1.0 1.0 1.0 1.0 1.0 1.01 1.0 1.0 1.0 1.0 1.0 1.02 1.0 1.0 1.0 1.0 1.0 1.03 1.0 1.0 1.0 1.0 1.0 1.0df2 = pd.DataFrame(np.zeros([2,6]),columns=col_index, index=range(2)) a b c w x w x w x0 0.0 0.0 0.0 0.0 0.0 0.01 0.0 0.0 0.0 0.0 0.0 0.0當(dāng)我使用合并方法時(shí)，我得到以下結(jié)果：pd.merge(df1,df2, how='left', suffixes=('', '_2'), left_index = True, right_index= True )) a b c a_2 b_2 c_2 w x w x w x w x w x w x0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.01 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.02 1.0 1.0 1.0 1.0 1.0 1.0 NaN NaN NaN NaN NaN NaN3 1.0 1.0 1.0 1.0 1.0 1.0 NaN NaN NaN NaN NaN NaN但我想在較低級別上合并兩個(gè)數(shù)據(jù)幀，后綴對 ['w', 'x'] 生效，如下所示： a b c w w_2 x x_2 w w_2 x x_2 w w_2 x x_20 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.01 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.02 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN3 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN
1 回答
忽然笑
TA貢獻(xiàn)1806條經(jīng)驗(yàn) 獲得超5個(gè)贊
您可以將joinormerge與swaplevel()or 一起使用reorder_levels。然后使用.sort_index()和 傳遞axis=1來按索引列排序。
.join()當(dāng)您像這樣對索引進(jìn)行合并時(shí)會(huì)更好。.swaplevel()當(dāng)有兩個(gè)級別時(shí)更好（如本例），而當(dāng).reorder_levels()有 3 個(gè)或更多級別時(shí)更好。
以下是這些方法的 4 種組合。對于這個(gè)具體的例子，我認(rèn)為.join()/.swaplevel()是最簡單的（參見最后一個(gè)例子）：
df3 = (df1.reorder_levels([1,0],axis=1)
.join(df2.reorder_levels([1,0],axis=1), rsuffix='_2')
.reorder_levels([1,0],axis=1).sort_index(axis=1, level=[0, 1]))
df3
Out[1]:
a b c
w w_2 x x_2 w w_2 x x_2 w w_2 x x_2
0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0
1 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0
2 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN
3 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN
df3 = (pd.merge(df1.reorder_levels([1,0],axis=1),
df2.reorder_levels([1,0],axis=1),
how='left', left_index=True, right_index=True, suffixes = ('', '_2'))
.reorder_levels([1,0],axis=1).sort_index(axis=1, level=[0, 1]))
df3
Out[2]:
a b c
w w_2 x x_2 w w_2 x x_2 w w_2 x x_2
0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0
1 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0
2 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN
3 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN
df3 = (pd.merge(df1.swaplevel(axis=1),
df2.swaplevel(axis=1),
how='left', left_index=True, right_index=True, suffixes = ('', '_2'))
.swaplevel(axis=1).sort_index(axis=1, level=[0, 1]))
df3
Out[3]:
a b c
w w_2 x x_2 w w_2 x x_2 w w_2 x x_2
0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0
1 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0
2 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN
3 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN
df3 = (df1.swaplevel(i=0,j=1, axis=1)
.join(df2.swaplevel(axis=1), rsuffix='_2')
.swaplevel(axis=1).sort_index(axis=1, level=[0, 1]))
df3
Out[4]:
a b c
w w_2 x x_2 w w_2 x x_2 w w_2 x x_2
0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0
1 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0
2 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN
3 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN
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