pandas建立多重索引的幾種方式

使用pandas建立多重索引的方式有很多，下面舉出常見的幾種：

1、pd.multiindex.from_tuples方法

>>

>
import pandas as pd
>>
>
import numpy as np
>>
> arrays =[[
"bar"
,"bar"
,"baz"
,"baz"
,"foo"
,"foo"
,"qux"
,"qux"],
["one"
,"two"
,"one"
,"two"
,"one"
,"two"
,"one"
,"two"]]
>>
> tuples =
list
(zip
(*arrays)
)>>
> index = pd.multiindex.from_tuples(tuples)
>>
> index
multiindex([(
'bar'
,'one'),
('bar'
,'two'),
('baz'
,'one'),
('baz'
,'two'),
('foo'
,'one'),
('foo'
,'two'),
('qux'
,'one'),
('qux'
,'two')]
,)>>
> s = pd.series(np.random.randn(8)
, index=index)
>>
> s
bar  one    0.612171
two   -
0.615973
baz  one   -
1.611292
two   -
0.708034
foo  one   -
1.588981
two   -
0.998916
qux  one   -
0.320857
two   -
1.623501
dtype: float64

#該方法與from_tuple的是有區別，具體一看例子就懂

>>
> iterables =[[
"bar"
,"baz"
,"foo"
,"qux"],
["one"
,"two"]]
>>
> pd.multiindex.from_product(iterables, names=
["first"
,"second"])
multiindex([(
'bar'
,'one'),
('bar'
,'two'),
('baz'
,'one'),
('baz'
,'two'),
('foo'
,'one'),
('foo'
,'two'),
('qux'
,'one'),
('qux'
,'two')]
,           names=
['first'
,'second'
])

>>

> df = pd.dataframe([[
"bar"
,"one"],
["bar"
,"two"],
["foo"
,"one"],
["foo"
,"two"]]
,columns=
["first"
,"second"])
>>
> df
first second
0   bar    one
1   bar    two
2   foo    one
3   foo    two
>>
> pd.multiindex.from_frame(df)
multiindex([(
'bar'
,'one'),
('bar'
,'two'),
('foo'
,'one'),
('foo'
,'two')]
,           names=
['first'
,'second'
])

>>

> arrays =
[np.array(
["bar"
,"bar"
,"baz"
,"baz"
,"foo"
,"foo"
,"qux"
,"qux"])
,np.array(
["one"
,"two"
,"one"
,"two"
,"one"
,"two"
,"one"
,"two"])
]>>
> s = pd.series(np.random.randn(8)
, index=arrays)
>>
> s
bar  one    0.953744
two    0.069687
baz  one   -
0.205349
two    1.093807
foo  one   -
0.845178
two   -
1.040949
qux  one    1.646463
two   -
0.244347
dtype: float64

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