设定值多索引 pandas [英] Set value multiindex Pandas

问题描述

我是Python和Pandas的新手.

I'm a newbie to both Python and Pandas.

我正在尝试构造一个数据框，然后再用值填充它.

I am trying to construct a dataframe, and then later populate it with values.

我已经构建了数据框

from pandas import *

ageMin = 21
ageMax = 31
ageStep = 2

bins_sumins = [0, 10000, 20000]
bins_age = list(range(ageMin, ageMax, ageStep))
indeks_sex = ['M', 'F']
indeks_age  =  ['[{0}-{1})'.format(bins_age[i-1], bins_age[i]) for i in range(1, len(bins_age))]
indeks_sumins = ['[{0}-{1})'.format(bins_sumins[i-1], bins_sumins[i]) for i in range(1, len(bins_sumins))]
indeks = MultiIndex.from_product([indeks_age, indeks_sex, indeks_sumins], names=['Age', 'Sex', 'Sumins'])

cols = ['A', 'B', 'C', 'D']

df = DataFrame(data = 0, index = indeks, columns = cols)

到目前为止，一切都很好.我可以将值分配给整个值

So far all is well. I am able to assign value to a whole set of values

>>> df['A']['[21-23)']['M'] = 1
>>> df
                           A  B  C  D
Age     Sex Sumins                   
[21-23) M   [0-10000)      1  0  0  0
            [10000-20000)  1  0  0  0
        F   [0-10000)      0  0  0  0
            [10000-20000)  0  0  0  0
[23-25) M   [0-10000)      0  0  0  0
            [10000-20000)  0  0  0  0
        F   [0-10000)      0  0  0  0
            [10000-20000)  0  0  0  0
[25-27) M   [0-10000)      0  0  0  0
            [10000-20000)  0  0  0  0
        F   [0-10000)      0  0  0  0
            [10000-20000)  0  0  0  0
[27-29) M   [0-10000)      0  0  0  0
            [10000-20000)  0  0  0  0
        F   [0-10000)      0  0  0  0
            [10000-20000)  0  0  0  0

但是，仅设置一个位置的值是不可行的...

however, setting the value of one position only is a no go...

>>> df['B']['[21-23)']['M']['[10000-20000)'] = 2
>>> df
                           A  B  C  D
Age     Sex Sumins                   
[21-23) M   [0-10000)      1  0  0  0
            [10000-20000)  1  0  0  0
        F   [0-10000)      0  0  0  0
            [10000-20000)  0  0  0  0
[23-25) M   [0-10000)      0  0  0  0
            [10000-20000)  0  0  0  0
        F   [0-10000)      0  0  0  0
            [10000-20000)  0  0  0  0
[25-27) M   [0-10000)      0  0  0  0
            [10000-20000)  0  0  0  0
        F   [0-10000)      0  0  0  0
            [10000-20000)  0  0  0  0
[27-29) M   [0-10000)      0  0  0  0
            [10000-20000)  0  0  0  0
        F   [0-10000)      0  0  0  0
            [10000-20000)  0  0  0  0
[16 rows x 4 columns]

这是怎么回事?我对我完全误解了多索引的工作方式持开放态度.有人吗?

What is going on here? I am open to the idea that i have completely misunderstood how multiindexing works. Anyone?

推荐答案

首先，看看第二，阅读有关这将使您获得此解决方案:

That will get you to this solution:

In [46]: df = df.sort_index()

In [47]: df.loc['[21-23)', 'M', '[10000-20000)'] = 2

In [48]: df
Out[48]: 
                           A  B  C  D
Age     Sex Sumins                   
[21-23) F   [0-10000)      0  0  0  0
            [10000-20000)  0  0  0  0
        M   [0-10000)      0  0  0  0
            [10000-20000)  2  2  2  2
[23-25) F   [0-10000)      0  0  0  0
            [10000-20000)  0  0  0  0
        M   [0-10000)      0  0  0  0
            [10000-20000)  0  0  0  0
[25-27) F   [0-10000)      0  0  0  0
            [10000-20000)  0  0  0  0
        M   [0-10000)      0  0  0  0
            [10000-20000)  0  0  0  0
[27-29) F   [0-10000)      0  0  0  0
            [10000-20000)  0  0  0  0
        M   [0-10000)      0  0  0  0
            [10000-20000)  0  0  0  0

[16 rows x 4 columns]

pandas .14将有一些其他切片MultiIndex的方法.

pandas .14 will have some additional ways for slicing a MultiIndex.

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