带有逻辑 pandas 的多重索引和蒙版 [英] Multi Indexing and masks with logic pandas
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问题描述
我有4个索引.门，洛克，地理位置和街区.而且我需要创建遮罩以对其进行操作，以便可以创建遮罩并执行如下所示的操作:
I have 4 indexes. Mun, loc, geo and block. And I need to create masks to operate with them so I can create masks and perform operations that will look like this:
data1 data2
mun loc geo block
0 0 0 0 12 12
1 0 0 0 20 20
1 1 0 0 10 10
1 1 1 0 10 10
1 1 1 1 3 3/4
1 1 1 2 4 4/4
1 1 2 0 30 30
1 1 2 1 1 1/3
1 1 2 2 3 3/3
1 1 0 0 4 4
1 2 1 1 10 10/12
1 2 1 2 12 12/12
2 0 0 0 60 60
2 1 1 1 123 123/123
2 1 1 2 7 7/123
2 1 2 1 6 6/6
2 1 2 2 1 1/6
data1 data2
mun loc geo block
0 0 0 0 12 12
1 0 0 0 20 20
1 1 0 0 10 10
1 1 1 0 10 10/30
1 1 1 1 4 4
1 1 2 0 30 30/30
1 2 1 0 2 2/3
1 2 2 0 3 3/3
1 2 3 0 1 1/3
2 0 0 0 60 60
2 1 1 0 12 12/88
2 1 1 1 1 1
2 1 2 0 88 88/88
2 1 2 1 9 9
data1 data2
mun loc geo block
0 0 0 0 14 14
1 0 0 0 12 12
1 1 0 0 20 20/20
1 1 1 0 10 10
1 1 1 1 31 31
1 2 0 0 15 15/20
1 2 1 1 11 11
2 0 0 0 80 80
2 1 0 0 100 100/100
2 1 1 2 7 7
2 2 0 0 11 11/100
data1 data2
mun loc geo block
0 0 0 0 55 55
1 0 0 0 70 70/70
1 1 0 0 12 12
1 1 1 0 13 13
2 0 0 0 60 60/70
2 1 1 1 12 12
2 1 2 1 6 6
3 0 0 0 12 12/70
也就是说，将最大值放在层次结构内，然后将每个元素除以它.我在有关第一个问题的另一个问题中得到了帮助，但是在掌握多重索引方面我遇到了很多问题.任何帮助我都会感激.
That is, take the max value inside the hierarchy and divide each element by it. I got help in another question regarding the first problem, but I'm having a lot of problems getting grasp of multi index. Any help will me appreciated.
推荐答案
这并不容易.但主要使用 get_level_values 选择条件的值:
It was not easy. But mainly use get_level_values for select values for condition:
级别 阻止 :
Level block:
print (df)
data1 data2
mun loc geo block
0 0 0 0 12 12
1 0 0 0 20 20
1 0 0 10 10
1 0 10 10
1 3 3/4
2 4 4/4
2 0 30 30
1 1 1/3
2 3 3/3
0 0 4 4
2 1 1 10 10/12
2 12 12/12
2 0 0 0 60 60
1 1 1 123 123/123
2 7 7/123
2 1 6 6/6
2 1 1/6
mask3 = (df.index.get_level_values('mun') != 0) & \
(df.index.get_level_values('loc') != 0 ) & \
(df.index.get_level_values('geo') != 0) & \
(df.index.get_level_values('block') != 0 )
print (mask3)
[False False False False True True False True True False True True
False True True True True]
df2 = df.ix[mask3, 'data1'].groupby(level=['mun','loc','geo']).max()
#print (df2)
df2 = df2.reindex(df.reset_index(level=3, drop=True).index).mask(~mask3).fillna(1)
#print (df2)
print (df['data1'].div(df2.values,axis=0))
mun loc geo block
0 0 0 0 12.000000
1 0 0 0 20.000000
1 0 0 10.000000
1 0 10.000000
1 0.750000
2 1.000000
2 0 30.000000
1 0.333333
2 1.000000
0 0 4.000000
2 1 1 0.833333
2 1.000000
2 0 0 0 60.000000
1 1 1 1.000000
2 0.056911
2 1 1.000000
2 0.166667
dtype: float64
级别 geo :
Level geo:
print (df)
data1 data2
mun loc geo block
0 0 0 0 12 12
1 0 0 0 20 20
1 0 0 10 10
1 0 10 10/30
1 4 4
2 0 30 30/30
2 1 0 2 2/3
2 0 3 3/3
3 0 1 1/3
2 0 0 0 60 60
1 1 0 12 12/88
1 1 1
2 0 88 88/88
1 9 9
df1 = df.reset_index(drop=True, level='block')
mask3 = (df.index.get_level_values('mun') != 0) & \
(df.index.get_level_values('loc') != 0 ) & \
(df.index.get_level_values('geo') != 0) & \
(df.index.get_level_values('block') == 0 )
print (mask3)
[False False False True False True True True True False True False
True False]
df2 = df1.ix[mask3, 'data1'].groupby(level=['mun','loc']).max()
df2=df2.reindex(df.reset_index(level=['geo','block'], drop=True).index).mask(~mask3).fillna(1)
print (df2)
df['new'] = df['data1'].div(df2.values,axis=0)
print (df)
data1 data2 new
mun loc geo block
0 0 0 0 12 12 12.000000
1 0 0 0 20 20 20.000000
1 0 0 10 10 10.000000
1 0 10 10/30 0.333333
1 4 4 4.000000
2 0 30 30/30 1.000000
2 1 0 2 2/3 0.666667
2 0 3 3/3 1.000000
3 0 1 1/3 0.333333
2 0 0 0 60 60 60.000000
1 1 0 12 12/88 0.136364
1 1 1 1.000000
2 0 88 88/88 1.000000
1 9 9 9.000000
级别 loc :
Level loc:
print (df)
data1 data2
mun loc geo block
0 0 0 0 14 14
1 0 0 0 12 12
1 0 0 20 20/20
1 0 10 10
1 31 31
2 0 0 15 15/20
1 1 11 11
2 0 0 0 80 80
1 0 0 100 100/100
1 2 7 7
2 0 0 11 11/100
df1 = df.reset_index(drop=True, level=['block', 'geo'])
mask3 = (df.index.get_level_values('mun') != 0) & \
(df.index.get_level_values('loc') != 0 ) & \
(df.index.get_level_values('geo') == 0) & \
(df.index.get_level_values('block') == 0 )
print (mask3)
[False False True False False True False False True False True]
df2 = df1.ix[mask3, 'data1'].groupby(level=['mun']).max()
#print (df2)
df2 =df2.reindex(df.reset_index(level=['geo','block', 'loc'], drop=True).index).mask(~mask3).fillna(1)
#print (df2)
print (df['data1'].div(df2.values,axis=0))
mun loc geo block
0 0 0 0 14.00
1 0 0 0 12.00
1 0 0 1.00
1 0 10.00
1 31.00
2 0 0 0.75
1 1 11.00
2 0 0 0 80.00
1 0 0 1.00
1 2 7.00
2 0 0 0.11
dtype: float64
级别 mun :
Level mun:
print (df)
data1 data2
mun loc geo block
0 0 0 0 55 55
1 0 0 0 70 70/70
1 0 0 12 12
1 0 13 13
2 0 0 0 60 60/70
1 1 1 12 12
2 1 6 6
3 0 0 0 12 12/70
mask3 = (df.index.get_level_values('mun') != 0) & \
(df.index.get_level_values('loc') == 0 ) & \
(df.index.get_level_values('geo') == 0) & \
(df.index.get_level_values('block') == 0 )
print (mask3)
[False True False False True False False True]
df2 = df.ix[mask3, 'data1'].max()
#print (df2)
df2 = pd.Series(df2, index=df.index).mask(~mask3).fillna(1)
#print (df2)
print (df['data1'].div(df2.values,axis=0))
mun loc geo block
0 0 0 0 55.000000
1 0 0 0 1.000000
1 0 0 12.000000
1 0 13.000000
2 0 0 0 0.857143
1 1 1 12.000000
2 1 6.000000
3 0 0 0 0.171429
dtype: float64
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