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访问大熊猫的一级多索引

multi-index pandas python

Josh Friedlander · 技术社区 · 7 年前

我有一个数据框架,看起来像一个多索引的简单用例:我有一个作为索引的ISO周数和日期,我想按特定的周过滤。遵循中的说明 the docs ,看起来我应该能够通过传递一系列周数来索引。但是,这给了我一个关键错误。

MCVE:

data = {'foo': {('2016_32', '2016-08-07'): 0.14285714285714285,
  ('2016_32', '2016-08-08'): 0.14285714285714285,
  ('2016_32', '2016-08-09'): 0.14285714285714285,
  ('2016_32', '2016-08-10'): 0.14285714285714285,
  ('2016_32', '2016-08-11'): 0.14285714285714285,
  ('2016_32', '2016-08-12'): 0.14285714285714285,
  ('2016_32', '2016-08-13'): 0.14285714285714285,
  ('2016_36', '2016-09-04'): 0.14285714285714285,
  ('2016_36', '2016-09-05'): 0.14285714285714285,
  ('2016_36', '2016-09-06'): 0.14285714285714285,
  ('2016_36', '2016-09-07'): 0.14285714285714285,
  ('2016_36', '2016-09-08'): 0.14285714285714285,
  ('2016_36', '2016-09-09'): 0.14285714285714285},
 'bar': {('2016_32', '2016-08-07'): np.nan,
  ('2016_32', '2016-08-08'): np.nan,
  ('2016_32', '2016-08-09'): np.nan,
  ('2016_32', '2016-08-10'): np.nan,
  ('2016_32', '2016-08-11'): np.nan,
  ('2016_32', '2016-08-12'): np.nan,
  ('2016_32', '2016-08-13'): np.nan,
  ('2016_36', '2016-09-04'): 0.0,
  ('2016_36', '2016-09-05'): 0.0,
  ('2016_36', '2016-09-06'): 0.0,
  ('2016_36', '2016-09-07'): 0.0,
  ('2016_36', '2016-09-08'): 0.0,
  ('2016_36', '2016-09-09'): 0.0}}

df = pd.DataFrame(data)
df['2016_32']

KeyError: '2016_32'

2 回复 | 直到 7 年前

jezrael 7 年前

一般用于选择 Multiindex 使用 DataFrame.xs :

#default first level should be omit
print (df.xs('2016_32'))
#select by second level
#print (df.xs('2016-09-07', level=1))
                 foo  bar
2016-08-07  0.142857  NaN
2016-08-08  0.142857  NaN
2016-08-09  0.142857  NaN
2016-08-10  0.142857  NaN
2016-08-11  0.142857  NaN
2016-08-12  0.142857  NaN
2016-08-13  0.142857  NaN

或 loc :

#no parameter if select first level
print (df.loc['2016_32'])
#if want select second level axis=0 and : for select all values of first level
print (df.loc(axis=0)[:, '2016-09-07'])

列和行中多索引中的选择差异:

np.random.seed(235)
a = [np.array(['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux']),
          np.array(['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two'])]
a1 = pd.MultiIndex.from_product([['A', 'B', 'C'], ['E','F']])
df = pd.DataFrame(np.random.randint(10, size=(6, 8)), index=a1, columns=a)
print (df)
    bar     baz     foo     qux    
    one two one two one two one two
A E   8   1   5   8   3   5   3   3
  F   3   1   3   6   6   1   0   2
B E   0   3   1   7   0   0   8   2
  F   6   7   7   4   2   7   7   5
C E   7   3   1   7   3   9   7   3
  F   8   2   0   8   5   2   2   0

#select by column bar level  
print (df['bar'])
     one  two
A E    8    1
  F    3    1
B E    0    3
  F    6    7
C E    7    3
  F    8    2

#select by column bar and then by `one`
print (df['bar']['one'])
A  E    8
   F    3
B  E    0
   F    6
C  E    7
   F    8
Name: one, dtype: int32

#select by tuples for columns select
print (df[('bar', 'one')])
A  E    8
   F    3
B  E    0
   F    6
C  E    7
   F    8
Name: (bar, one), dtype: int32

对于“按行选择”(索引中的多索引),请使用 洛克 :

print (df.loc['A'])
  bar     baz     foo     qux    
  one two one two one two one two
E   8   1   5   8   3   5   3   3
F   3   1   3   6   6   1   0   2

print (df.loc['A'].loc['F'])
bar  one    3
     two    1
baz  one    3
     two    6
foo  one    6
     two    1
qux  one    0
     two    2
Name: F, dtype: int32

print (df.loc[('A', 'F')])
bar  one    3
     two    1
baz  one    3
     two    6
foo  one    6
     two    1
qux  one    0
     two    2
Name: (A, F), dtype: int32

Karn Kumar 7 年前

或者,您可以使用交换级别 swaplevel 不更改顺序:

>>> df[:7].swaplevel(0, 0, axis=0)
                         foo  bar
2016_32 2016-08-07  0.142857  NaN
        2016-08-08  0.142857  NaN
        2016-08-09  0.142857  NaN
        2016-08-10  0.142857  NaN
        2016-08-11  0.142857  NaN
        2016-08-12  0.142857  NaN
        2016-08-13  0.142857  NaN

或者简单地说:

>>> df[1:7]
                         foo  bar
2016_32 2016-08-08  0.142857  NaN
        2016-08-09  0.142857  NaN
        2016-08-10  0.142857  NaN
        2016-08-11  0.142857  NaN
        2016-08-12  0.142857  NaN
        2016-08-13  0.142857  NaN