我有一个多索引数据框,我希望根据索引值和 bool 标准提取一个子集。我希望使用多索引键和 bool 索引器覆盖特定新值的值来选择要修改的记录。
import pandas as pd
import numpy as np
years = [1994,1995,1996]
householdIDs = [ id for id in range(1,100) ]
midx = pd.MultiIndex.from_product( [years, householdIDs], names = ['Year', 'HouseholdID'] )
householdIncomes = np.random.randint( 10000,100000, size = len(years)*len(householdIDs) )
householdSize = np.random.randint( 1,5, size = len(years)*len(householdIDs) )
df = pd.DataFrame( {'HouseholdIncome':householdIncomes, 'HouseholdSize':householdSize}, index = midx )
df.sort_index(inplace = True)
示例数据如下所示...
df.head()
=> HouseholdIncome HouseholdSize
Year HouseholdID
1994 1 23866 3
2 57956 3
3 21644 3
4 71912 4
5 83663 3
我能够使用索引和列标签成功查询数据框。
此示例为我提供了 1996 年家庭 3 的 HouseholdSize
df.loc[ (1996,3 ) , 'HouseholdSize' ]
=> 1
但是,我无法将 bool 选择与多索引查询相结合...
pandas docs on Multi-indexing说有一种方法可以将 bool 索引与多索引结合起来,并给出了一个例子......
In [52]: idx = pd.IndexSlice
In [56]: mask = dfmi[('a','foo')]>200
In [57]: dfmi.loc[idx[mask,:,['C1','C3']],idx[:,'foo']]
Out[57]:
lvl0 a b
lvl1 foo foo
A3 B0 C1 D1 204 206
C3 D0 216 218
D1 220 222
B1 C1 D0 232 234
D1 236 238
C3 D0 248 250
D1 252 254
...我似乎无法在我的数据框上复制它
idx = pd.IndexSlice
housholdSizeAbove2 = ( df.HouseholdSize > 2 )
df.loc[ idx[ housholdSizeAbove2, 1996, :] , 'HouseholdSize' ]
Traceback (most recent call last):
File "python", line 1, in <module>
KeyError: 'MultiIndex Slicing requires the index to be fully lexsorted tuple len (3), lexsort depth (2)'
在此示例中,我想查看 1996 年家庭规模大于 2 的所有家庭
最佳答案
Pandas.query()在这种情况下应该有效:
df.query("Year == 1996 and HouseholdID > 2")
演示:
In [326]: with pd.option_context('display.max_rows',20):
...: print(df.query("Year == 1996 and HouseholdID > 2"))
...:
HouseholdIncome HouseholdSize
Year HouseholdID
1996 3 28664 4
4 11057 1
5 36321 2
6 89469 4
7 35711 2
8 85741 1
9 34758 3
10 56085 2
11 32275 4
12 77096 4
... ... ...
90 40276 4
91 10594 2
92 61080 4
93 65334 2
94 21477 4
95 83112 4
96 25627 2
97 24830 4
98 85693 1
99 84653 4
[97 rows x 2 columns]
更新:
Is there a way to select a specific column?
In [333]: df.loc[df.eval("Year == 1996 and HouseholdID > 2"), 'HouseholdIncome']
Out[333]:
Year HouseholdID
1996 3 28664
4 11057
5 36321
6 89469
7 35711
8 85741
9 34758
10 56085
11 32275
12 77096
...
90 40276
91 10594
92 61080
93 65334
94 21477
95 83112
96 25627
97 24830
98 85693
99 84653
Name: HouseholdIncome, dtype: int32
and ultimately I want to overwrite the data on the dataframe.
In [331]: df.loc[df.eval("Year == 1996 and HouseholdID > 2"), 'HouseholdSize'] *= 10
In [332]: df.loc[df.eval("Year == 1996 and HouseholdID > 2")]
Out[332]:
HouseholdIncome HouseholdSize
Year HouseholdID
1996 3 28664 40
4 11057 10
5 36321 20
6 89469 40
7 35711 20
8 85741 10
9 34758 30
10 56085 20
11 32275 40
12 77096 40
... ... ...
90 40276 40
91 10594 20
92 61080 40
93 65334 20
94 21477 40
95 83112 40
96 25627 20
97 24830 40
98 85693 10
99 84653 40
[97 rows x 2 columns]
更新 2:
I want to pass a variable
year
instead of a specific value. Is there a cleaner way to do it thanYear == " + str(year) + " and HouseholdID > " + str(householdSize)
?
In [5]: year = 1996
In [6]: household_ids = [1, 2, 98, 99]
In [7]: df.loc[df.eval("Year == @year and HouseholdID in @household_ids")]
Out[7]:
HouseholdIncome HouseholdSize
Year HouseholdID
1996 1 42217 1
2 66009 3
98 33121 4
99 45489 3
关于python - 如何在 Pandas 中将 bool 索引器与多索引结合起来?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/42193544/