pandas 如何删除熊猫数据透视表中的多级索引
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How to remove multilevel index in pandas pivot table
提问by Shivpe_R
I have a dataframe as given:
我有一个给定的数据框：
df = {'TYPE' : pd.Series(['Advisory','Advisory1','Advisory2','Advisory3']),
'CNTRY' : pd.Series(['IND','FRN','IND','FRN']),
'VALUE' : pd.Series([1., 2., 3., 4.])}
df = pd.DataFrame(df)
df = pd.pivot_table(df,index=["CNTRY"],columns=["TYPE"]).reset_index()
After pivoting, how can I get the dataframe having columns and dfto be like the below; removing the multilevel index, VALUE
旋转后，如何获得具有列的数据框并df如下所示；删除多级索引，VALUE
Type|CNTRY|Advisory|Advisory1|Advisory2|Advisory3
0 FRN NaN 2.0 NaN 4.0
1 IND 1.0 NaN 3.0 NaN
回答by jezrael
You can add parameter values:
您可以添加参数values：
df = pd.pivot_table(df,index="CNTRY",columns="TYPE", values='VALUE').reset_index()
print (df)
TYPE CNTRY Advisory Advisory1 Advisory2 Advisory3
0 FRN NaN 2.0 NaN 4.0
1 IND 1.0 NaN 3.0 NaN
And for remove columns name rename_axis:
对于删除列名称rename_axis：
df = pd.pivot_table(df,index="CNTRY",columns="TYPE", values='VALUE') \
.reset_index().rename_axis(None, axis=1)
print (df)
CNTRY Advisory Advisory1 Advisory2 Advisory3
0 FRN NaN 2.0 NaN 4.0
1 IND 1.0 NaN 3.0 NaN
But maybe is necessary only pivot:
但也许只是必要的pivot：
df = df.pivot(index="CNTRY",columns="TYPE", values='VALUE') \
.reset_index().rename_axis(None, axis=1)
print (df)
CNTRY Advisory Advisory1 Advisory2 Advisory3
0 FRN NaN 2.0 NaN 4.0
1 IND 1.0 NaN 3.0 NaN
because pivot_tableaggregate duplicates by default aggregate function mean:
因为pivot_table默认聚合函数聚合重复mean：
df = {'TYPE' : pd.Series(['Advisory','Advisory1','Advisory2','Advisory1']),
'CNTRY' : pd.Series(['IND','FRN','IND','FRN']),
'VALUE' : pd.Series([1., 4., 3., 4.])}
df = pd.DataFrame(df)
print (df)
CNTRY TYPE VALUE
0 IND Advisory 1.0
1 FRN Advisory1 1.0 <-same FRN and Advisory1
2 IND Advisory2 3.0
3 FRN Advisory1 4.0 <-same FRN and Advisory1
df = df.pivot_table(index="CNTRY",columns="TYPE", values='VALUE')
.reset_index().rename_axis(None, axis=1)
print (df)
TYPE Advisory Advisory1 Advisory2
CNTRY
FRN 0.0 2.5 0.0
IND 1.0 0.0 3.0
Alternative with groupby, aggregate function and unstack:
替代groupby, 聚合函数 和unstack：
df = df.groupby(["CNTRY","TYPE"])['VALUE'].mean().unstack(fill_value=0)
.reset_index().rename_axis(None, axis=1)
print (df)
CNTRY Advisory Advisory1 Advisory2
0 FRN 0.0 2.5 0.0
1 IND 1.0 0.0 3.0
回答by piRSquared
You can use set_indexwith unstack
你可以用set_index与unstack
df.set_index(['CNTRY', 'TYPE']).VALUE.unstack().reset_index()
TYPE CNTRY Advisory Advisory1 Advisory2 Advisory3
0 FRN NaN 2.0 NaN 4.0
1 IND 1.0 NaN 3.0 NaN
