pandas 如何使用python pandas通过多索引获取价值？
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How to get value by multi-index with python pandas?
提问by xirururu
How can I get the value from a dataframe by its multi-index?
如何通过多索引从数据框中获取值？
For example I have a dataframe mm:
例如我有一个数据框mm：
np.random.seed(1)
mm = pd.DataFrame(np.random.randn(5,2))
mm['A'] = np.arange(5)
mm['B'] = np.arange(5,10)
mm.set_index(['A','B'], inplace=True)
print mm
0 1
A B
0 5 1.624345 -0.611756
1 6 -0.528172 -1.072969
2 7 0.865408 -2.301539
3 8 1.744812 -0.761207
4 9 0.319039 -0.249370
I want to get the value where A = 2, B = 7, how can I do that?
我想获得 A = 2, B = 7 的值，我该怎么做？
Is it possible to write a function like get_value(mm, (2,7)), then I can get the following result:
是否可以写一个类似的函数get_value(mm, (2,7))，然后我可以得到以下结果：
2 7 0.865408 -2.301539
回答by unutbu
Use mm.locto select rows by label:
用于mm.loc按标签选择行：
In [28]: row = mm.loc[2,7]; row
Out[28]:
0 0.865408
1 -2.301539
Name: (2, 7), dtype: float64
In [40]: np.concatenate([row.name, row])
Out[40]: array([ 2. , 7. , 0.86540763, -2.3015387 ])
Since mmhas a MultiIndex, each row label is expressed as a tuple (e.g. (2,7)). When there is no ambiguity, such as inside brackets, the parentheses can be dropped: mm.loc[2, 7]is equivalent to mm.loc[(2, 7)].
由于mm有一个 MultiIndex，每个行标签都表示为一个元组（例如(2,7)）。当没有歧义时，例如在括号内，可以去掉括号：mm.loc[2, 7]相当于mm.loc[(2, 7)]。
To get all rows where B=7, you could
要获取所有行 where B=7，您可以
use
pd.IndexSlice:xs = pd.IndexSlice mm.loc[xs[:, 7], :]or the
mm.querymethod:mm.query('B==7')or
mm.index.get_loc_levelwithmm.loc:mask, idx = index.get_loc_level(7, level='B') mm.loc[mask]or
mm.index.get_loc_levelwithmm.iloc:mask, idx = index.get_loc_level(7, level='B') mm.iloc[idx]
使用
pd.IndexSlice：xs = pd.IndexSlice mm.loc[xs[:, 7], :]mm.query('B==7')或
mm.index.get_loc_level与mm.loc：mask, idx = index.get_loc_level(7, level='B') mm.loc[mask]或
mm.index.get_loc_level与mm.iloc：mask, idx = index.get_loc_level(7, level='B') mm.iloc[idx]
Each of the expressions above return the DataFrame
上面的每个表达式都返回 DataFrame
0 1
A B
2 7 0.865408 -2.301539
回答by Alexander
This returns your selection as a dataframe:
这将您的选择作为数据框返回：
>>> mm.loc[[(2, 7)]]
0 1
A B
2 7 0.865408 -2.301539
To get the index and values:
获取索引和值：
>>> mm.loc[[(2, 7)]].reset_index().values.tolist()[0]
[2.0, 7.0, 0.8654076293246785, -2.3015386968802827]
To get all values where the second item is 7:
要获取第二项为 7 的所有值：
idx = pd.IndexSlice
>>> mm.loc[idx[:, 7], :]
0 1
A B
2 7 0.865408 -2.301539
