Pandas 2.2 中文官方教程和指南（十二·二）

发布于：2024-04-26 ⋅ 阅读:(33) ⋅ 点赞:(0)

使用分层索引进行高级索引

在使用.loc进行高级索引时，将MultiIndex在语法上整合在一起有点具有挑战性，但我们已经尽力做到了。一般来说，MultiIndex 键采用元组的形式。例如，以下操作会按您的预期工作：

In [39]: df = df.T

In [40]: df
Out[40]: 
 A         B         C
first second 
bar   one     0.895717  0.410835 -1.413681
 two     0.805244  0.813850  1.607920
baz   one    -1.206412  0.132003  1.024180
 two     2.565646 -0.827317  0.569605
foo   one     1.431256 -0.076467  0.875906
 two     1.340309 -1.187678 -2.211372
qux   one    -1.170299  1.130127  0.974466
 two    -0.226169 -1.436737 -2.006747

In [41]: df.loc[("bar", "two")]
Out[41]: 
A    0.805244
B    0.813850
C    1.607920
Name: (bar, two), dtype: float64

请注意，在这个例子中，df.loc['bar', 'two']也可以工作，但这种简写符号在一般情况下可能会导致歧义。

如果您还想使用.loc索引特定列，必须像这样使用元组：

In [42]: df.loc[("bar", "two"), "A"]
Out[42]: 0.8052440253863785

你不必通过仅传递元组的第一个元素来指定MultiIndex的所有级别。例如，你可以使用“部分”索引来获取所有第一级别中带有bar的元素如下：

In [43]: df.loc["bar"]
Out[43]: 
 A         B         C
second 
one     0.895717  0.410835 -1.413681
two     0.805244  0.813850  1.607920

这是略微更冗长的符号df.loc[('bar',),]的快捷方式（在这个例子中等同于df.loc['bar',]）。

“部分���切片也非常有效。

In [44]: df.loc["baz":"foo"]
Out[44]: 
 A         B         C
first second 
baz   one    -1.206412  0.132003  1.024180
 two     2.565646 -0.827317  0.569605
foo   one     1.431256 -0.076467  0.875906
 two     1.340309 -1.187678 -2.211372

通过提供元组切片来对“范围”值进行切片。

In [45]: df.loc[("baz", "two"):("qux", "one")]
Out[45]: 
 A         B         C
first second 
baz   two     2.565646 -0.827317  0.569605
foo   one     1.431256 -0.076467  0.875906
 two     1.340309 -1.187678 -2.211372
qux   one    -1.170299  1.130127  0.974466

In [46]: df.loc[("baz", "two"):"foo"]
Out[46]: 
 A         B         C
first second 
baz   two     2.565646 -0.827317  0.569605
foo   one     1.431256 -0.076467  0.875906
 two     1.340309 -1.187678 -2.211372

传递标签列表或元组类似于重新索引：

In [47]: df.loc[[("bar", "two"), ("qux", "one")]]
Out[47]: 
 A         B         C
first second 
bar   two     0.805244  0.813850  1.607920
qux   one    -1.170299  1.130127  0.974466

注意

需要注意的是，在 pandas 中，元组和列表在索引时并非被处理相同。元组被解释为一个多级键，而列表用于指定多个键。换句话说，元组水平移动（遍历级别），列表垂直移动（扫描级别）。

重要的是，元组列表索引多个完整的MultiIndex键，而列表元组引用一个级别内的多个值：

In [48]: s = pd.Series(
 ....:    [1, 2, 3, 4, 5, 6],
 ....:    index=pd.MultiIndex.from_product([["A", "B"], ["c", "d", "e"]]),
 ....: )
 ....: 

In [49]: s.loc[[("A", "c"), ("B", "d")]]  # list of tuples
Out[49]: 
A  c    1
B  d    5
dtype: int64

In [50]: s.loc[(["A", "B"], ["c", "d"])]  # tuple of lists
Out[50]: 
A  c    1
 d    2
B  c    4
 d    5
dtype: int64

使用切片器

通过提供多个索引器可以对MultiIndex进行切片。

你可以像通过标签索引一样提供任何选择器，参见按标签选择，包括切片、标签列表、标签和布尔索引器。

你可以使用slice(None)来选择该级别的所有内容。你不需要指定所有更深层次的级别，它们将被隐含为slice(None)。

与标签索引一样，切片器的两侧都包括在内。

警告

你应该在.loc指定器中指定所有轴，即索引和列的索引器。有一些模糊的情况，传递的索引器可能被误解为同时索引两个轴，而不是例如为行的MultiIndex。

你应该这样做：

df.loc[(slice("A1", "A3"), ...), :]  # noqa: E999

你不应该这样做：

df.loc[(slice("A1", "A3"), ...)]  # noqa: E999 

In [51]: def mklbl(prefix, n):
 ....:    return ["%s%s" % (prefix, i) for i in range(n)]
 ....: 

In [52]: miindex = pd.MultiIndex.from_product(
 ....:    [mklbl("A", 4), mklbl("B", 2), mklbl("C", 4), mklbl("D", 2)]
 ....: )
 ....: 

In [53]: micolumns = pd.MultiIndex.from_tuples(
 ....:    [("a", "foo"), ("a", "bar"), ("b", "foo"), ("b", "bah")], names=["lvl0", "lvl1"]
 ....: )
 ....: 

In [54]: dfmi = (
 ....:    pd.DataFrame(
 ....:        np.arange(len(miindex) * len(micolumns)).reshape(
 ....:            (len(miindex), len(micolumns))
 ....:        ),
 ....:        index=miindex,
 ....:        columns=micolumns,
 ....:    )
 ....:    .sort_index()
 ....:    .sort_index(axis=1)
 ....: )
 ....: 

In [55]: dfmi
Out[55]: 
lvl0           a         b 
lvl1         bar  foo  bah  foo
A0 B0 C0 D0    1    0    3    2
 D1    5    4    7    6
 C1 D0    9    8   11   10
 D1   13   12   15   14
 C2 D0   17   16   19   18
...          ...  ...  ...  ...
A3 B1 C1 D1  237  236  239  238
 C2 D0  241  240  243  242
 D1  245  244  247  246
 C3 D0  249  248  251  250
 D1  253  252  255  254

[64 rows x 4 columns]

使用切片、列表和标签进行基本的 MultiIndex 切片。

In [56]: dfmi.loc[(slice("A1", "A3"), slice(None), ["C1", "C3"]), :]
Out[56]: 
lvl0           a         b 
lvl1         bar  foo  bah  foo
A1 B0 C1 D0   73   72   75   74
 D1   77   76   79   78
 C3 D0   89   88   91   90
 D1   93   92   95   94
 B1 C1 D0  105  104  107  106
...          ...  ...  ...  ...
A3 B0 C3 D1  221  220  223  222
 B1 C1 D0  233  232  235  234
 D1  237  236  239  238
 C3 D0  249  248  251  250
 D1  253  252  255  254

[24 rows x 4 columns]

你可以使用pandas.IndexSlice来使用:更自然的语法，而不是使用slice(None)。

In [57]: idx = pd.IndexSlice

In [58]: dfmi.loc[idx[:, :, ["C1", "C3"]], idx[:, "foo"]]
Out[58]: 
lvl0           a    b
lvl1         foo  foo
A0 B0 C1 D0    8   10
 D1   12   14
 C3 D0   24   26
 D1   28   30
 B1 C1 D0   40   42
...          ...  ...
A3 B0 C3 D1  220  222
 B1 C1 D0  232  234
 D1  236  238
 C3 D0  248  250
 D1  252  254

[32 rows x 2 columns]

可以同时在多个轴上使用此方法执行相当复杂的选择。

In [59]: dfmi.loc["A1", (slice(None), "foo")]
Out[59]: 
lvl0        a    b
lvl1      foo  foo
B0 C0 D0   64   66
 D1   68   70
 C1 D0   72   74
 D1   76   78
 C2 D0   80   82
...       ...  ...
B1 C1 D1  108  110
 C2 D0  112  114
 D1  116  118
 C3 D0  120  122
 D1  124  126

[16 rows x 2 columns]

In [60]: dfmi.loc[idx[:, :, ["C1", "C3"]], idx[:, "foo"]]
Out[60]: 
lvl0           a    b
lvl1         foo  foo
A0 B0 C1 D0    8   10
 D1   12   14
 C3 D0   24   26
 D1   28   30
 B1 C1 D0   40   42
...          ...  ...
A3 B0 C3 D1  220  222
 B1 C1 D0  232  234
 D1  236  238
 C3 D0  248  250
 D1  252  254

[32 rows x 2 columns]

使用布尔索引器可以提供与值相关的选择。

In [61]: mask = dfmi[("a", "foo")] > 200

In [62]: dfmi.loc[idx[mask, :, ["C1", "C3"]], idx[:, "foo"]]
Out[62]: 
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

你还可以在.loc中指定axis参数，以在单个轴上解释传递的切片器。

In [63]: dfmi.loc(axis=0)[:, :, ["C1", "C3"]]
Out[63]: 
lvl0           a         b 
lvl1         bar  foo  bah  foo
A0 B0 C1 D0    9    8   11   10
 D1   13   12   15   14
 C3 D0   25   24   27   26
 D1   29   28   31   30
 B1 C1 D0   41   40   43   42
...          ...  ...  ...  ...
A3 B0 C3 D1  221  220  223  222
 B1 C1 D0  233  232  235  234
 D1  237  236  239  238
 C3 D0  249  248  251  250
 D1  253  252  255  254

[32 rows x 4 columns]

此外，你可以使用以下方法设置值。

In [64]: df2 = dfmi.copy()

In [65]: df2.loc(axis=0)[:, :, ["C1", "C3"]] = -10

In [66]: df2
Out[66]: 
lvl0           a         b 
lvl1         bar  foo  bah  foo
A0 B0 C0 D0    1    0    3    2
 D1    5    4    7    6
 C1 D0  -10  -10  -10  -10
 D1  -10  -10  -10  -10
 C2 D0   17   16   19   18
...          ...  ...  ...  ...
A3 B1 C1 D1  -10  -10  -10  -10
 C2 D0  241  240  243  242
 D1  245  244  247  246
 C3 D0  -10  -10  -10  -10
 D1  -10  -10  -10  -10

[64 rows x 4 columns]

你也可以使用可对齐对象的右侧。

In [67]: df2 = dfmi.copy()

In [68]: df2.loc[idx[:, :, ["C1", "C3"]], :] = df2 * 1000

In [69]: df2
Out[69]: 
lvl0              a               b 
lvl1            bar     foo     bah     foo
A0 B0 C0 D0       1       0       3       2
 D1       5       4       7       6
 C1 D0    9000    8000   11000   10000
 D1   13000   12000   15000   14000
 C2 D0      17      16      19      18
...             ...     ...     ...     ...
A3 B1 C1 D1  237000  236000  239000  238000
 C2 D0     241     240     243     242
 D1     245     244     247     246
 C3 D0  249000  248000  251000  250000
 D1  253000  252000  255000  254000

[64 rows x 4 columns] 
```### 横截面

`DataFrame`的`xs()`方法另外接受一个级别参数，以便更轻松地选择`MultiIndex`的特定级别的数据。

```py
In [70]: df
Out[70]: 
 A         B         C
first second 
bar   one     0.895717  0.410835 -1.413681
 two     0.805244  0.813850  1.607920
baz   one    -1.206412  0.132003  1.024180
 two     2.565646 -0.827317  0.569605
foo   one     1.431256 -0.076467  0.875906
 two     1.340309 -1.187678 -2.211372
qux   one    -1.170299  1.130127  0.974466
 two    -0.226169 -1.436737 -2.006747

In [71]: df.xs("one", level="second")
Out[71]: 
 A         B         C
first 
bar    0.895717  0.410835 -1.413681
baz   -1.206412  0.132003  1.024180
foo    1.431256 -0.076467  0.875906
qux   -1.170299  1.130127  0.974466 

# using the slicers
In [72]: df.loc[(slice(None), "one"), :]
Out[72]: 
 A         B         C
first second 
bar   one     0.895717  0.410835 -1.413681
baz   one    -1.206412  0.132003  1.024180
foo   one     1.431256 -0.076467  0.875906
qux   one    -1.170299  1.130127  0.974466

你也可以使用xs在列上进行选择，通过提供轴参数。

In [73]: df = df.T

In [74]: df.xs("one", level="second", axis=1)
Out[74]: 
first       bar       baz       foo       qux
A      0.895717 -1.206412  1.431256 -1.170299
B      0.410835  0.132003 -0.076467  1.130127
C     -1.413681  1.024180  0.875906  0.974466 

# using the slicers
In [75]: df.loc[:, (slice(None), "one")]
Out[75]: 
first        bar       baz       foo       qux
second       one       one       one       one
A       0.895717 -1.206412  1.431256 -1.170299
B       0.410835  0.132003 -0.076467  1.130127
C      -1.413681  1.024180  0.875906  0.974466

xs还允许使用多个键进行选择。

In [76]: df.xs(("one", "bar"), level=("second", "first"), axis=1)
Out[76]: 
first        bar
second       one
A       0.895717
B       0.410835
C      -1.413681 

# using the slicers
In [77]: df.loc[:, ("bar", "one")]
Out[77]: 
A    0.895717
B    0.410835
C   -1.413681
Name: (bar, one), dtype: float64

你可以向xs传递drop_level=False以保留所选的级别。

In [78]: df.xs("one", level="second", axis=1, drop_level=False)
Out[78]: 
first        bar       baz       foo       qux
second       one       one       one       one
A       0.895717 -1.206412  1.431256 -1.170299
B       0.410835  0.132003 -0.076467  1.130127
C      -1.413681  1.024180  0.875906  0.974466

将上述与使用drop_level=True（默认值）的结果进行比较。

In [79]: df.xs("one", level="second", axis=1, drop_level=True)
Out[79]: 
first       bar       baz       foo       qux
A      0.895717 -1.206412  1.431256 -1.170299
B      0.410835  0.132003 -0.076467  1.130127
C     -1.413681  1.024180  0.875906  0.974466 
```### 高级重新索引和对齐

在 pandas 对象的`reindex()`和`align()`方法中使用参数`level`可以实现跨级别广播值。例如：

```py
In [80]: midx = pd.MultiIndex(
 ....:    levels=[["zero", "one"], ["x", "y"]], codes=[[1, 1, 0, 0], [1, 0, 1, 0]]
 ....: )
 ....: 

In [81]: df = pd.DataFrame(np.random.randn(4, 2), index=midx)

In [82]: df
Out[82]: 
 0         1
one  y  1.519970 -0.493662
 x  0.600178  0.274230
zero y  0.132885 -0.023688
 x  2.410179  1.450520

In [83]: df2 = df.groupby(level=0).mean()

In [84]: df2
Out[84]: 
 0         1
one   1.060074 -0.109716
zero  1.271532  0.713416

In [85]: df2.reindex(df.index, level=0)
Out[85]: 
 0         1
one  y  1.060074 -0.109716
 x  1.060074 -0.109716
zero y  1.271532  0.713416
 x  1.271532  0.713416

# aligning
In [86]: df_aligned, df2_aligned = df.align(df2, level=0)

In [87]: df_aligned
Out[87]: 
 0         1
one  y  1.519970 -0.493662
 x  0.600178  0.274230
zero y  0.132885 -0.023688
 x  2.410179  1.450520

In [88]: df2_aligned
Out[88]: 
 0         1
one  y  1.060074 -0.109716
 x  1.060074 -0.109716
zero y  1.271532  0.713416
 x  1.271532  0.713416

使用swaplevel交换级别

swaplevel()方法可以交换两个级别的顺序：

In [89]: df[:5]
Out[89]: 
 0         1
one  y  1.519970 -0.493662
 x  0.600178  0.274230
zero y  0.132885 -0.023688
 x  2.410179  1.450520

In [90]: df[:5].swaplevel(0, 1, axis=0)
Out[90]: 
 0         1
y one   1.519970 -0.493662
x one   0.600178  0.274230
y zero  0.132885 -0.023688
x zero  2.410179  1.450520

使用reorder_levels重新排序级别

reorder_levels()方法推广了swaplevel方法，允许您一次性对分层索引级别进行排列：

In [91]: df[:5].reorder_levels([1, 0], axis=0)
Out[91]: 
 0         1
y one   1.519970 -0.493662
x one   0.600178  0.274230
y zero  0.132885 -0.023688
x zero  2.410179  1.450520 
```### 重命名`Index`或`MultiIndex`的名称

`rename()`方法用于重命名`MultiIndex`的标签，通常用于重命名`DataFrame`的列。`rename`的`columns`参数允许指定一个包含您希望重命名的列的字典。

```py
In [92]: df.rename(columns={0: "col0", 1: "col1"})
Out[92]: 
 col0      col1
one  y  1.519970 -0.493662
 x  0.600178  0.274230
zero y  0.132885 -0.023688
 x  2.410179  1.450520

该方法还可用于重命名DataFrame的主索引的特定标签。

In [93]: df.rename(index={"one": "two", "y": "z"})
Out[93]: 
 0         1
two  z  1.519970 -0.493662
 x  0.600178  0.274230
zero z  0.132885 -0.023688
 x  2.410179  1.450520

rename_axis()方法用于重命名Index或MultiIndex的名称。特别是，可以指定MultiIndex级别的名称，如果稍后使用reset_index()将值从MultiIndex移动到列中，则这是有用的。

In [94]: df.rename_axis(index=["abc", "def"])
Out[94]: 
 0         1
abc  def 
one  y    1.519970 -0.493662
 x    0.600178  0.274230
zero y    0.132885 -0.023688
 x    2.410179  1.450520

请注意，DataFrame的列是一个索引，因此使用带有columns参数的rename_axis将更改该索引的名称。

In [95]: df.rename_axis(columns="Cols").columns
Out[95]: RangeIndex(start=0, stop=2, step=1, name='Cols')

rename和rename_axis都支持指定字典、Series或映射函数来将标签/名称映射到新值。

在直接使用Index对象而不是通过DataFrame时，可以使用Index.set_names()来更改名称。

In [96]: mi = pd.MultiIndex.from_product([[1, 2], ["a", "b"]], names=["x", "y"])

In [97]: mi.names
Out[97]: FrozenList(['x', 'y'])

In [98]: mi2 = mi.rename("new name", level=0)

In [99]: mi2
Out[99]: 
MultiIndex([(1, 'a'),
 (1, 'b'),
 (2, 'a'),
 (2, 'b')],
 names=['new name', 'y'])

您无法通过级别设置MultiIndex的名称。

In [100]: mi.levels[0].name = "name via level"
---------------------------------------------------------------------------
RuntimeError  Traceback (most recent call last)
Cell In[100], line 1
----> 1 mi.levels[0].name = "name via level"

File ~/work/pandas/pandas/pandas/core/indexes/base.py:1690, in Index.name(self, value)
  1686 @name.setter
  1687 def name(self, value: Hashable) -> None:
  1688     if self._no_setting_name:
  1689         # Used in MultiIndex.levels to avoid silently ignoring name updates.
-> 1690         raise RuntimeError(
  1691             "Cannot set name on a level of a MultiIndex. Use "
  1692             "'MultiIndex.set_names' instead."
  1693         )
  1694     maybe_extract_name(value, None, type(self))
  1695     self._name = value

RuntimeError: Cannot set name on a level of a MultiIndex. Use 'MultiIndex.set_names' instead.

使用Index.set_names()代替。 ### 使用切片器

通过提供多个索引器，可以对MultiIndex进行切片。

您可以提供任何选择器，就像您正在按标签进行索引一样，请参阅按标签选择，包括切片、标签列表、标签和布尔索引器。

您可以使用slice(None)来选择该级别的所有内容。您不需要指定所有更深层次的级别，它们将被隐含为slice(None)。

通常情况下，切片器的两侧都包含在内，因为这是标签索引。

警告

您应该在.loc指定器中指定所有轴，即索引和列的索引器。有一些模棱两可的情况，传递的索引器可能被误解为对两个轴进行索引，而不是例如对行的MultiIndex进行索引。

你应该这样做：

df.loc[(slice("A1", "A3"), ...), :]  # noqa: E999

你不应该这样做：

df.loc[(slice("A1", "A3"), ...)]  # noqa: E999 

In [51]: def mklbl(prefix, n):
 ....:    return ["%s%s" % (prefix, i) for i in range(n)]
 ....: 

In [52]: miindex = pd.MultiIndex.from_product(
 ....:    [mklbl("A", 4), mklbl("B", 2), mklbl("C", 4), mklbl("D", 2)]
 ....: )
 ....: 

In [53]: micolumns = pd.MultiIndex.from_tuples(
 ....:    [("a", "foo"), ("a", "bar"), ("b", "foo"), ("b", "bah")], names=["lvl0", "lvl1"]
 ....: )
 ....: 

In [54]: dfmi = (
 ....:    pd.DataFrame(
 ....:        np.arange(len(miindex) * len(micolumns)).reshape(
 ....:            (len(miindex), len(micolumns))
 ....:        ),
 ....:        index=miindex,
 ....:        columns=micolumns,
 ....:    )
 ....:    .sort_index()
 ....:    .sort_index(axis=1)
 ....: )
 ....: 

In [55]: dfmi
Out[55]: 
lvl0           a         b 
lvl1         bar  foo  bah  foo
A0 B0 C0 D0    1    0    3    2
 D1    5    4    7    6
 C1 D0    9    8   11   10
 D1   13   12   15   14
 C2 D0   17   16   19   18
...          ...  ...  ...  ...
A3 B1 C1 D1  237  236  239  238
 C2 D0  241  240  243  242
 D1  245  244  247  246
 C3 D0  249  248  251  250
 D1  253  252  255  254

[64 rows x 4 columns]

使用切片、列表和标签进行基本的 MultiIndex 切片。

In [56]: dfmi.loc[(slice("A1", "A3"), slice(None), ["C1", "C3"]), :]
Out[56]: 
lvl0           a         b 
lvl1         bar  foo  bah  foo
A1 B0 C1 D0   73   72   75   74
 D1   77   76   79   78
 C3 D0   89   88   91   90
 D1   93   92   95   94
 B1 C1 D0  105  104  107  106
...          ...  ...  ...  ...
A3 B0 C3 D1  221  220  223  222
 B1 C1 D0  233  232  235  234
 D1  237  236  239  238
 C3 D0  249  248  251  250
 D1  253  252  255  254

[24 rows x 4 columns]

你可以使用pandas.IndexSlice来使用:更自然的语法，而不是使用slice(None)。

In [57]: idx = pd.IndexSlice

In [58]: dfmi.loc[idx[:, :, ["C1", "C3"]], idx[:, "foo"]]
Out[58]: 
lvl0           a    b
lvl1         foo  foo
A0 B0 C1 D0    8   10
 D1   12   14
 C3 D0   24   26
 D1   28   30
 B1 C1 D0   40   42
...          ...  ...
A3 B0 C3 D1  220  222
 B1 C1 D0  232  234
 D1  236  238
 C3 D0  248  250
 D1  252  254

[32 rows x 2 columns]

使用此方法可以在同一时间在多个轴上执行相当复杂的选择。

In [59]: dfmi.loc["A1", (slice(None), "foo")]
Out[59]: 
lvl0        a    b
lvl1      foo  foo
B0 C0 D0   64   66
 D1   68   70
 C1 D0   72   74
 D1   76   78
 C2 D0   80   82
...       ...  ...
B1 C1 D1  108  110
 C2 D0  112  114
 D1  116  118
 C3 D0  120  122
 D1  124  126

[16 rows x 2 columns]

In [60]: dfmi.loc[idx[:, :, ["C1", "C3"]], idx[:, "foo"]]
Out[60]: 
lvl0           a    b
lvl1         foo  foo
A0 B0 C1 D0    8   10
 D1   12   14
 C3 D0   24   26
 D1   28   30
 B1 C1 D0   40   42
...          ...  ...
A3 B0 C3 D1  220  222
 B1 C1 D0  232  234
 D1  236  238
 C3 D0  248  250
 D1  252  254

[32 rows x 2 columns]

使用布尔索引器可以提供与值相关的选择。

In [61]: mask = dfmi[("a", "foo")] > 200

In [62]: dfmi.loc[idx[mask, :, ["C1", "C3"]], idx[:, "foo"]]
Out[62]: 
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

您还可以在.loc中指定axis参数，以在单个轴上解释传递的切片器。

In [63]: dfmi.loc(axis=0)[:, :, ["C1", "C3"]]
Out[63]: 
lvl0           a         b 
lvl1         bar  foo  bah  foo
A0 B0 C1 D0    9    8   11   10
 D1   13   12   15   14
 C3 D0   25   24   27   26
 D1   29   28   31   30
 B1 C1 D0   41   40   43   42
...          ...  ...  ...  ...
A3 B0 C3 D1  221  220  223  222
 B1 C1 D0  233  232  235  234
 D1  237  236  239  238
 C3 D0  249  248  251  250
 D1  253  252  255  254

[32 rows x 4 columns]

此外，您可以使用以下方法设置值。

In [64]: df2 = dfmi.copy()

In [65]: df2.loc(axis=0)[:, :, ["C1", "C3"]] = -10

In [66]: df2
Out[66]: 
lvl0           a         b 
lvl1         bar  foo  bah  foo
A0 B0 C0 D0    1    0    3    2
 D1    5    4    7    6
 C1 D0  -10  -10  -10  -10
 D1  -10  -10  -10  -10
 C2 D0   17   16   19   18
...          ...  ...  ...  ...
A3 B1 C1 D1  -10  -10  -10  -10
 C2 D0  241  240  243  242
 D1  245  244  247  246
 C3 D0  -10  -10  -10  -10
 D1  -10  -10  -10  -10

[64 rows x 4 columns]

你也可以使用可对齐对象的右侧。

In [67]: df2 = dfmi.copy()

In [68]: df2.loc[idx[:, :, ["C1", "C3"]], :] = df2 * 1000

In [69]: df2
Out[69]: 
lvl0              a               b 
lvl1            bar     foo     bah     foo
A0 B0 C0 D0       1       0       3       2
 D1       5       4       7       6
 C1 D0    9000    8000   11000   10000
 D1   13000   12000   15000   14000
 C2 D0      17      16      19      18
...             ...     ...     ...     ...
A3 B1 C1 D1  237000  236000  239000  238000
 C2 D0     241     240     243     242
 D1     245     244     247     246
 C3 D0  249000  248000  251000  250000
 D1  253000  252000  255000  254000

[64 rows x 4 columns]

交叉部分

DataFrame的xs()方法另外接受一个级别参数，使得在MultiIndex的特定级别选择数据更容易。

In [70]: df
Out[70]: 
 A         B         C
first second 
bar   one     0.895717  0.410835 -1.413681
 two     0.805244  0.813850  1.607920
baz   one    -1.206412  0.132003  1.024180
 two     2.565646 -0.827317  0.569605
foo   one     1.431256 -0.076467  0.875906
 two     1.340309 -1.187678 -2.211372
qux   one    -1.170299  1.130127  0.974466
 two    -0.226169 -1.436737 -2.006747

In [71]: df.xs("one", level="second")
Out[71]: 
 A         B         C
first 
bar    0.895717  0.410835 -1.413681
baz   -1.206412  0.132003  1.024180
foo    1.431256 -0.076467  0.875906
qux   -1.170299  1.130127  0.974466 

# using the slicers
In [72]: df.loc[(slice(None), "one"), :]
Out[72]: 
 A         B         C
first second 
bar   one     0.895717  0.410835 -1.413681
baz   one    -1.206412  0.132003  1.024180
foo   one     1.431256 -0.076467  0.875906
qux   one    -1.170299  1.130127  0.974466

你也可以通过提供轴参数在列上使用xs进行选择。

In [73]: df = df.T

In [74]: df.xs("one", level="second", axis=1)
Out[74]: 
first       bar       baz       foo       qux
A      0.895717 -1.206412  1.431256 -1.170299
B      0.410835  0.132003 -0.076467  1.130127
C     -1.413681  1.024180  0.875906  0.974466 

# using the slicers
In [75]: df.loc[:, (slice(None), "one")]
Out[75]: 
first        bar       baz       foo       qux
second       one       one       one       one
A       0.895717 -1.206412  1.431256 -1.170299
B       0.410835  0.132003 -0.076467  1.130127
C      -1.413681  1.024180  0.875906  0.974466

xs还允许使用多个键进行选择。

In [76]: df.xs(("one", "bar"), level=("second", "first"), axis=1)
Out[76]: 
first        bar
second       one
A       0.895717
B       0.410835
C      -1.413681 

# using the slicers
In [77]: df.loc[:, ("bar", "one")]
Out[77]: 
A    0.895717
B    0.410835
C   -1.413681
Name: (bar, one), dtype: float64

你可以传递drop_level=False给xs以保留所选的级别。

In [78]: df.xs("one", level="second", axis=1, drop_level=False)
Out[78]: 
first        bar       baz       foo       qux
second       one       one       one       one
A       0.895717 -1.206412  1.431256 -1.170299
B       0.410835  0.132003 -0.076467  1.130127
C      -1.413681  1.024180  0.875906  0.974466

将上述与使用drop_level=True（默认值）的结果进行比较。

In [79]: df.xs("one", level="second", axis=1, drop_level=True)
Out[79]: 
first       bar       baz       foo       qux
A      0.895717 -1.206412  1.431256 -1.170299
B      0.410835  0.132003 -0.076467  1.130127
C     -1.413681  1.024180  0.875906  0.974466

高级重新索引和对齐

在 pandas 对象的reindex()和align()方法中使用参数level对值进行广播是很有用的。例如：

In [80]: midx = pd.MultiIndex(
 ....:    levels=[["zero", "one"], ["x", "y"]], codes=[[1, 1, 0, 0], [1, 0, 1, 0]]
 ....: )
 ....: 

In [81]: df = pd.DataFrame(np.random.randn(4, 2), index=midx)

In [82]: df
Out[82]: 
 0         1
one  y  1.519970 -0.493662
 x  0.600178  0.274230
zero y  0.132885 -0.023688
 x  2.410179  1.450520

In [83]: df2 = df.groupby(level=0).mean()

In [84]: df2
Out[84]: 
 0         1
one   1.060074 -0.109716
zero  1.271532  0.713416

In [85]: df2.reindex(df.index, level=0)
Out[85]: 
 0         1
one  y  1.060074 -0.109716
 x  1.060074 -0.109716
zero y  1.271532  0.713416
 x  1.271532  0.713416

# aligning
In [86]: df_aligned, df2_aligned = df.align(df2, level=0)

In [87]: df_aligned
Out[87]: 
 0         1
one  y  1.519970 -0.493662
 x  0.600178  0.274230
zero y  0.132885 -0.023688
 x  2.410179  1.450520

In [88]: df2_aligned
Out[88]: 
 0         1
one  y  1.060074 -0.109716
 x  1.060074 -0.109716
zero y  1.271532  0.713416
 x  1.271532  0.713416

使用swaplevel交换级别

swaplevel()方法可以交换两个级别的顺序：

In [89]: df[:5]
Out[89]: 
 0         1
one  y  1.519970 -0.493662
 x  0.600178  0.274230
zero y  0.132885 -0.023688
 x  2.410179  1.450520

In [90]: df[:5].swaplevel(0, 1, axis=0)
Out[90]: 
 0         1
y one   1.519970 -0.493662
x one   0.600178  0.274230
y zero  0.132885 -0.023688
x zero  2.410179  1.450520

使用reorder_levels重新排序级别

reorder_levels()方法推广了swaplevel方法，允许您一次性对分层索引级别进行排列：

In [91]: df[:5].reorder_levels([1, 0], axis=0)
Out[91]: 
 0         1
y one   1.519970 -0.493662
x one   0.600178  0.274230
y zero  0.132885 -0.023688
x zero  2.410179  1.450520

重命名Index或MultiIndex的名称

rename()方法用于重命名MultiIndex的标签，通常用于重命名DataFrame的列。rename的columns参数允许指定一个字典，其中只包含您希望重命名的列。

In [92]: df.rename(columns={0: "col0", 1: "col1"})
Out[92]: 
 col0      col1
one  y  1.519970 -0.493662
 x  0.600178  0.274230
zero y  0.132885 -0.023688
 x  2.410179  1.450520

这种方法也可以用于重命名DataFrame主索引的特定标签。

In [93]: df.rename(index={"one": "two", "y": "z"})
Out[93]: 
 0         1
two  z  1.519970 -0.493662
 x  0.600178  0.274230
zero z  0.132885 -0.023688
 x  2.410179  1.450520

rename_axis()方法用于重命名Index或MultiIndex的名称。特别是，可以指定MultiIndex级别的名称，这在稍后使用reset_index()将值从MultiIndex移动到列时非常有用。

In [94]: df.rename_axis(index=["abc", "def"])
Out[94]: 
 0         1
abc  def 
one  y    1.519970 -0.493662
 x    0.600178  0.274230
zero y    0.132885 -0.023688
 x    2.410179  1.450520

注意，DataFrame 的列是一个索引，因此使用 rename_axis 时使用 columns 参数会更改该索引的名称。

In [95]: df.rename_axis(columns="Cols").columns
Out[95]: RangeIndex(start=0, stop=2, step=1, name='Cols')

rename和rename_axis都支持指定字典、Series或映射函数来将标签/名称映射到新值。

当直接使用Index对象而不是通过DataFrame时，可以使用Index.set_names()来更改名称。

In [96]: mi = pd.MultiIndex.from_product([[1, 2], ["a", "b"]], names=["x", "y"])

In [97]: mi.names
Out[97]: FrozenList(['x', 'y'])

In [98]: mi2 = mi.rename("new name", level=0)

In [99]: mi2
Out[99]: 
MultiIndex([(1, 'a'),
 (1, 'b'),
 (2, 'a'),
 (2, 'b')],
 names=['new name', 'y'])

不能通过级别设置 MultiIndex 的名称。

In [100]: mi.levels[0].name = "name via level"
---------------------------------------------------------------------------
RuntimeError  Traceback (most recent call last)
Cell In[100], line 1
----> 1 mi.levels[0].name = "name via level"

File ~/work/pandas/pandas/pandas/core/indexes/base.py:1690, in Index.name(self, value)
  1686 @name.setter
  1687 def name(self, value: Hashable) -> None:
  1688     if self._no_setting_name:
  1689         # Used in MultiIndex.levels to avoid silently ignoring name updates.
-> 1690         raise RuntimeError(
  1691             "Cannot set name on a level of a MultiIndex. Use "
  1692             "'MultiIndex.set_names' instead."
  1693         )
  1694     maybe_extract_name(value, None, type(self))
  1695     self._name = value

RuntimeError: Cannot set name on a level of a MultiIndex. Use 'MultiIndex.set_names' instead.

而是使用 Index.set_names()。

对MultiIndex进行排序

为了有效地对MultiIndex对象进行索引和切片，它们需要被排序。与任何索引一样，您可以使用sort_index()。

In [101]: import random

In [102]: random.shuffle(tuples)

In [103]: s = pd.Series(np.random.randn(8), index=pd.MultiIndex.from_tuples(tuples))

In [104]: s
Out[104]: 
qux  two    0.206053
bar  one   -0.251905
foo  one   -2.213588
qux  one    1.063327
foo  two    1.266143
baz  two    0.299368
bar  two   -0.863838
baz  one    0.408204
dtype: float64

In [105]: s.sort_index()
Out[105]: 
bar  one   -0.251905
 two   -0.863838
baz  one    0.408204
 two    0.299368
foo  one   -2.213588
 two    1.266143
qux  one    1.063327
 two    0.206053
dtype: float64

In [106]: s.sort_index(level=0)
Out[106]: 
bar  one   -0.251905
 two   -0.863838
baz  one    0.408204
 two    0.299368
foo  one   -2.213588
 two    1.266143
qux  one    1.063327
 two    0.206053
dtype: float64

In [107]: s.sort_index(level=1)
Out[107]: 
bar  one   -0.251905
baz  one    0.408204
foo  one   -2.213588
qux  one    1.063327
bar  two   -0.863838
baz  two    0.299368
foo  two    1.266143
qux  two    0.206053
dtype: float64

如果MultiIndex的级别已命名，还可以将级别名称传递给sort_index。

In [108]: s.index = s.index.set_names(["L1", "L2"])

In [109]: s.sort_index(level="L1")
Out[109]: 
L1   L2 
bar  one   -0.251905
 two   -0.863838
baz  one    0.408204
 two    0.299368
foo  one   -2.213588
 two    1.266143
qux  one    1.063327
 two    0.206053
dtype: float64

In [110]: s.sort_index(level="L2")
Out[110]: 
L1   L2 
bar  one   -0.251905
baz  one    0.408204
foo  one   -2.213588
qux  one    1.063327
bar  two   -0.863838
baz  two    0.299368
foo  two    1.266143
qux  two    0.206053
dtype: float64

在更高维度的对象上，如果它们具有MultiIndex，则可以按级别对任何其他轴进行排序：

In [111]: df.T.sort_index(level=1, axis=1)
Out[111]: 
 one      zero       one      zero
 x         x         y         y
0  0.600178  2.410179  1.519970  0.132885
1  0.274230  1.450520 -0.493662 -0.023688

即使数据未排序，索引也会起作用，但效率会非常低（并显示PerformanceWarning）。它还将返回数据的副本而不是视图：

In [112]: dfm = pd.DataFrame(
 .....:    {"jim": [0, 0, 1, 1], "joe": ["x", "x", "z", "y"], "jolie": np.random.rand(4)}
 .....: )
 .....: 

In [113]: dfm = dfm.set_index(["jim", "joe"])

In [114]: dfm
Out[114]: 
 jolie
jim joe 
0   x    0.490671
 x    0.120248
1   z    0.537020
 y    0.110968

In [115]: dfm.loc[(1, 'z')]
Out[115]: 
 jolie
jim joe 
1   z    0.53702

此外，如果尝试索引未完全按字典排序的内容，可能会引发：

In [116]: dfm.loc[(0, 'y'):(1, 'z')]
---------------------------------------------------------------------------
UnsortedIndexError  Traceback (most recent call last)
Cell In[116], line 1
----> 1 dfm.loc[(0, 'y'):(1, 'z')]

File ~/work/pandas/pandas/pandas/core/indexing.py:1191, in _LocationIndexer.__getitem__(self, key)
  1189 maybe_callable = com.apply_if_callable(key, self.obj)
  1190 maybe_callable = self._check_deprecated_callable_usage(key, maybe_callable)
-> 1191 return self._getitem_axis(maybe_callable, axis=axis)

File ~/work/pandas/pandas/pandas/core/indexing.py:1411, in _LocIndexer._getitem_axis(self, key, axis)
  1409 if isinstance(key, slice):
  1410     self._validate_key(key, axis)
-> 1411     return self._get_slice_axis(key, axis=axis)
  1412 elif com.is_bool_indexer(key):
  1413     return self._getbool_axis(key, axis=axis)

File ~/work/pandas/pandas/pandas/core/indexing.py:1443, in _LocIndexer._get_slice_axis(self, slice_obj, axis)
  1440     return obj.copy(deep=False)
  1442 labels = obj._get_axis(axis)
-> 1443 indexer = labels.slice_indexer(slice_obj.start, slice_obj.stop, slice_obj.step)
  1445 if isinstance(indexer, slice):
  1446     return self.obj._slice(indexer, axis=axis)

File ~/work/pandas/pandas/pandas/core/indexes/base.py:6662, in Index.slice_indexer(self, start, end, step)
  6618 def slice_indexer(
  6619     self,
  6620     start: Hashable | None = None,
  6621     end: Hashable | None = None,
  6622     step: int | None = None,
  6623 ) -> slice:
  6624  """
  6625 Compute the slice indexer for input labels and step.
  6626  
 (...)
  6660 slice(1, 3, None)
  6661 """
-> 6662     start_slice, end_slice = self.slice_locs(start, end, step=step)
  6664     # return a slice
  6665     if not is_scalar(start_slice):

File ~/work/pandas/pandas/pandas/core/indexes/multi.py:2904, in MultiIndex.slice_locs(self, start, end, step)
  2852  """
  2853 For an ordered MultiIndex, compute the slice locations for input
  2854 labels.
 (...)
  2900 sequence of such.
  2901 """
  2902 # This function adds nothing to its parent implementation (the magic
  2903 # happens in get_slice_bound method), but it adds meaningful doc.
-> 2904 return super().slice_locs(start, end, step)

File ~/work/pandas/pandas/pandas/core/indexes/base.py:6879, in Index.slice_locs(self, start, end, step)
  6877 start_slice = None
  6878 if start is not None:
-> 6879     start_slice = self.get_slice_bound(start, "left")
  6880 if start_slice is None:
  6881     start_slice = 0

File ~/work/pandas/pandas/pandas/core/indexes/multi.py:2848, in MultiIndex.get_slice_bound(self, label, side)
  2846 if not isinstance(label, tuple):
  2847     label = (label,)
-> 2848 return self._partial_tup_index(label, side=side)

File ~/work/pandas/pandas/pandas/core/indexes/multi.py:2908, in MultiIndex._partial_tup_index(self, tup, side)
  2906 def _partial_tup_index(self, tup: tuple, side: Literal["left", "right"] = "left"):
  2907     if len(tup) > self._lexsort_depth:
-> 2908         raise UnsortedIndexError(
  2909             f"Key length ({len(tup)}) was greater than MultiIndex lexsort depth "
  2910             f"({self._lexsort_depth})"
  2911         )
  2913     n = len(tup)
  2914     start, end = 0, len(self)

UnsortedIndexError: 'Key length (2) was greater than MultiIndex lexsort depth (1)'

MultiIndex上的is_monotonic_increasing()方法显示索引是否已排序：

In [117]: dfm.index.is_monotonic_increasing
Out[117]: False 

In [118]: dfm = dfm.sort_index()

In [119]: dfm
Out[119]: 
 jolie
jim joe 
0   x    0.490671
 x    0.120248
1   y    0.110968
 z    0.537020

In [120]: dfm.index.is_monotonic_increasing
Out[120]: True

现在选择按预期运行。

In [121]: dfm.loc[(0, "y"):(1, "z")]
Out[121]: 
 jolie
jim joe 
1   y    0.110968
 z    0.537020

采取方法

与 NumPy ndarrays 类似，pandas 的Index、Series和DataFrame还提供了take()方法，该方法在给定轴上以给定索引检索元素。给定的索引必须是整数索引位置的列表或 ndarray。take还将接受负整数作为相对于对象末尾的位置。

In [122]: index = pd.Index(np.random.randint(0, 1000, 10))

In [123]: index
Out[123]: Index([214, 502, 712, 567, 786, 175, 993, 133, 758, 329], dtype='int64')

In [124]: positions = [0, 9, 3]

In [125]: index[positions]
Out[125]: Index([214, 329, 567], dtype='int64')

In [126]: index.take(positions)
Out[126]: Index([214, 329, 567], dtype='int64')

In [127]: ser = pd.Series(np.random.randn(10))

In [128]: ser.iloc[positions]
Out[128]: 
0   -0.179666
9    1.824375
3    0.392149
dtype: float64

In [129]: ser.take(positions)
Out[129]: 
0   -0.179666
9    1.824375
3    0.392149
dtype: float64

对于 DataFrame，给定的索引应该是指定行或列位置的一维列表或 ndarray。

In [130]: frm = pd.DataFrame(np.random.randn(5, 3))

In [131]: frm.take([1, 4, 3])
Out[131]: 
 0         1         2
1 -1.237881  0.106854 -1.276829
4  0.629675 -1.425966  1.857704
3  0.979542 -1.633678  0.615855

In [132]: frm.take([0, 2], axis=1)
Out[132]: 
 0         2
0  0.595974  0.601544
1 -1.237881 -1.276829
2 -0.767101  1.499591
3  0.979542  0.615855
4  0.629675  1.857704

重要的是要注意，pandas 对象上的take方法不适用于布尔索引，可能会返回意外的结果。

In [133]: arr = np.random.randn(10)

In [134]: arr.take([False, False, True, True])
Out[134]: array([-1.1935, -1.1935,  0.6775,  0.6775])

In [135]: arr[[0, 1]]
Out[135]: array([-1.1935,  0.6775])

In [136]: ser = pd.Series(np.random.randn(10))

In [137]: ser.take([False, False, True, True])
Out[137]: 
0    0.233141
0    0.233141
1   -0.223540
1   -0.223540
dtype: float64

In [138]: ser.iloc[[0, 1]]
Out[138]: 
0    0.233141
1   -0.223540
dtype: float64

最后，关于性能的一个小提示，因为take方法处理的输入范围较窄，所以它的性能可能比花式索引快得多。

In [139]: arr = np.random.randn(10000, 5)

In [140]: indexer = np.arange(10000)

In [141]: random.shuffle(indexer)

In [142]: %timeit arr[indexer]
 .....: %timeit arr.take(indexer, axis=0)
 .....: 
257 us +- 4.44 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)
79.7 us +- 1.15 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each) 

In [143]: ser = pd.Series(arr[:, 0])

In [144]: %timeit ser.iloc[indexer]
 .....: %timeit ser.take(indexer)
 .....: 
144 us +- 3.69 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each)
129 us +- 2 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each)

索引类型

我们在前面的章节中已经广泛讨论了 MultiIndex。关于 DatetimeIndex 和 PeriodIndex 的文档在 这里，关于 TimedeltaIndex 的文档在 这里。

在接下来的子节中，我们将突出一些其他索引类型。

CategoricalIndex

CategoricalIndex 是一种支持重复索引的索引类型。这是一个围绕着 Categorical 的容器，允许高效地索引和存储具有大量重复元素的索引。

In [145]: from pandas.api.types import CategoricalDtype

In [146]: df = pd.DataFrame({"A": np.arange(6), "B": list("aabbca")})

In [147]: df["B"] = df["B"].astype(CategoricalDtype(list("cab")))

In [148]: df
Out[148]: 
 A  B
0  0  a
1  1  a
2  2  b
3  3  b
4  4  c
5  5  a

In [149]: df.dtypes
Out[149]: 
A       int64
B    category
dtype: object

In [150]: df["B"].cat.categories
Out[150]: Index(['c', 'a', 'b'], dtype='object')

设置索引将创建一个 CategoricalIndex。

In [151]: df2 = df.set_index("B")

In [152]: df2.index
Out[152]: CategoricalIndex(['a', 'a', 'b', 'b', 'c', 'a'], categories=['c', 'a', 'b'], ordered=False, dtype='category', name='B')

使用 __getitem__/.iloc/.loc 进行索引类似于具有重复项的 Index。索引器必须位于类别中，否则操作将引发 KeyError。

In [153]: df2.loc["a"]
Out[153]: 
 A
B 
a  0
a  1
a  5

索引后，CategoricalIndex 被保留：

In [154]: df2.loc["a"].index
Out[154]: CategoricalIndex(['a', 'a', 'a'], categories=['c', 'a', 'b'], ordered=False, dtype='category', name='B')

对索引进行排序将按照类别的顺序排序（回想一下，我们使用 CategoricalDtype(list('cab')) 创建了索引，因此排序顺序是 cab）。

In [155]: df2.sort_index()
Out[155]: 
 A
B 
c  4
a  0
a  1
a  5
b  2
b  3

对索引的分组操作也将保留索引的特性。

In [156]: df2.groupby(level=0, observed=True).sum()
Out[156]: 
 A
B 
c  4
a  6
b  5

In [157]: df2.groupby(level=0, observed=True).sum().index
Out[157]: CategoricalIndex(['c', 'a', 'b'], categories=['c', 'a', 'b'], ordered=False, dtype='category', name='B')

重新索引操作将返回基于传递的索引器类型的结果索引。传递列表将返回一个普通的 Index；使用 Categorical 进行索引将返回一个 CategoricalIndex，根据传递的 Categorical dtype 的类别进行索引。这允许任意索引这些，即使值不在类别中，类似于如何重新索引任何 pandas 索引。

In [158]: df3 = pd.DataFrame(
 .....:    {"A": np.arange(3), "B": pd.Series(list("abc")).astype("category")}
 .....: )
 .....: 

In [159]: df3 = df3.set_index("B")

In [160]: df3
Out[160]: 
 A
B 
a  0
b  1
c  2 

In [161]: df3.reindex(["a", "e"])
Out[161]: 
 A
B 
a  0.0
e  NaN

In [162]: df3.reindex(["a", "e"]).index
Out[162]: Index(['a', 'e'], dtype='object', name='B')

In [163]: df3.reindex(pd.Categorical(["a", "e"], categories=list("abe")))
Out[163]: 
 A
B 
a  0.0
e  NaN

In [164]: df3.reindex(pd.Categorical(["a", "e"], categories=list("abe"))).index
Out[164]: CategoricalIndex(['a', 'e'], categories=['a', 'b', 'e'], ordered=False, dtype='category', name='B')

警告

对 CategoricalIndex 进行的重塑和比较操作必须具有相同的类别，否则将引发 TypeError。

In [165]: df4 = pd.DataFrame({"A": np.arange(2), "B": list("ba")})

In [166]: df4["B"] = df4["B"].astype(CategoricalDtype(list("ab")))

In [167]: df4 = df4.set_index("B")

In [168]: df4.index
Out[168]: CategoricalIndex(['b', 'a'], categories=['a', 'b'], ordered=False, dtype='category', name='B')

In [169]: df5 = pd.DataFrame({"A": np.arange(2), "B": list("bc")})

In [170]: df5["B"] = df5["B"].astype(CategoricalDtype(list("bc")))

In [171]: df5 = df5.set_index("B")

In [172]: df5.index
Out[172]: CategoricalIndex(['b', 'c'], categories=['b', 'c'], ordered=False, dtype='category', name='B') 

In [173]: pd.concat([df4, df5])
Out[173]: 
 A
B 
b  0
a  1
b  0
c  1 
```### RangeIndex

`RangeIndex` 是 `Index` 的子类，为所有 `DataFrame` 和 `Series` 对象提供默认索引。`RangeIndex` 是 `Index` 的优化版本，可以表示单调有序集合。这类似于 Python 的 [range 类型](https://docs.python.org/3/library/stdtypes.html#typesseq-range)。`RangeIndex` 将始终具有 `int64` dtype。

```py
In [174]: idx = pd.RangeIndex(5)

In [175]: idx
Out[175]: RangeIndex(start=0, stop=5, step=1)

RangeIndex 是所有 DataFrame 和 Series 对象的默认索引：

In [176]: ser = pd.Series([1, 2, 3])

In [177]: ser.index
Out[177]: RangeIndex(start=0, stop=3, step=1)

In [178]: df = pd.DataFrame([[1, 2], [3, 4]])

In [179]: df.index
Out[179]: RangeIndex(start=0, stop=2, step=1)

In [180]: df.columns
Out[180]: RangeIndex(start=0, stop=2, step=1)

一个 RangeIndex 的行为类似于一个具有 int64 数据类型的 Index，对 RangeIndex 进行操作，其结果无法由 RangeIndex 表示，但应具有整数数据类型，并将转换为具有 int64 的 Index。例如：

In [181]: idx[[0, 2]]
Out[181]: Index([0, 2], dtype='int64') 
```### 区间索引

`IntervalIndex` 以及其自身的数据类型 `IntervalDtype` 以及 `Interval` 标量类型，为 pandas 提供了区间表示的一流支持。

`IntervalIndex` 允许一些独特的索引，并且还用作 `cut()` 和 `qcut()` 中的类别的返回类型。

#### 使用 `IntervalIndex` 进行索引

`IntervalIndex` 可以作为索引用于 `Series` 和 `DataFrame`。

```py
In [182]: df = pd.DataFrame(
 .....:    {"A": [1, 2, 3, 4]}, index=pd.IntervalIndex.from_breaks([0, 1, 2, 3, 4])
 .....: )
 .....: 

In [183]: df
Out[183]: 
 A
(0, 1]  1
(1, 2]  2
(2, 3]  3
(3, 4]  4

通过 .loc 进行基于标签的索引，沿着区间的边缘工作方式如您所期望的那样，选择特定的区间。

In [184]: df.loc[2]
Out[184]: 
A    2
Name: (1, 2], dtype: int64

In [185]: df.loc[[2, 3]]
Out[185]: 
 A
(1, 2]  2
(2, 3]  3

如果您选择一个 包含在 区间内的标签，这也将选择该区间。

In [186]: df.loc[2.5]
Out[186]: 
A    3
Name: (2, 3], dtype: int64

In [187]: df.loc[[2.5, 3.5]]
Out[187]: 
 A
(2, 3]  3
(3, 4]  4

使用区间进行选择将仅返回精确匹配。

In [188]: df.loc[pd.Interval(1, 2)]
Out[188]: 
A    2
Name: (1, 2], dtype: int64

尝试选择不完全包含在 IntervalIndex 中的 Interval 将引发 KeyError。

In [189]: df.loc[pd.Interval(0.5, 2.5)]
---------------------------------------------------------------------------
KeyError  Traceback (most recent call last)
Cell In[189], line 1
----> 1 df.loc[pd.Interval(0.5, 2.5)]

File ~/work/pandas/pandas/pandas/core/indexing.py:1191, in _LocationIndexer.__getitem__(self, key)
  1189 maybe_callable = com.apply_if_callable(key, self.obj)
  1190 maybe_callable = self._check_deprecated_callable_usage(key, maybe_callable)
-> 1191 return self._getitem_axis(maybe_callable, axis=axis)

File ~/work/pandas/pandas/pandas/core/indexing.py:1431, in _LocIndexer._getitem_axis(self, key, axis)
  1429 # fall thru to straight lookup
  1430 self._validate_key(key, axis)
-> 1431 return self._get_label(key, axis=axis)

File ~/work/pandas/pandas/pandas/core/indexing.py:1381, in _LocIndexer._get_label(self, label, axis)
  1379 def _get_label(self, label, axis: AxisInt):
  1380     # GH#5567 this will fail if the label is not present in the axis.
-> 1381     return self.obj.xs(label, axis=axis)

File ~/work/pandas/pandas/pandas/core/generic.py:4301, in NDFrame.xs(self, key, axis, level, drop_level)
  4299             new_index = index[loc]
  4300 else:
-> 4301     loc = index.get_loc(key)
  4303     if isinstance(loc, np.ndarray):
  4304         if loc.dtype == np.bool_:

File ~/work/pandas/pandas/pandas/core/indexes/interval.py:678, in IntervalIndex.get_loc(self, key)
  676 matches = mask.sum()
  677 if matches == 0:
--> 678     raise KeyError(key)
  679 if matches == 1:
  680     return mask.argmax()

KeyError: Interval(0.5, 2.5, closed='right')

使用 overlaps() 方法可以选择与给定 Interval 重叠的所有 Intervals，从而创建一个布尔型索引器。

In [190]: idxr = df.index.overlaps(pd.Interval(0.5, 2.5))

In [191]: idxr
Out[191]: array([ True,  True,  True, False])

In [192]: df[idxr]
Out[192]: 
 A
(0, 1]  1
(1, 2]  2
(2, 3]  3
使用 cut 和 qcut 进行数据分箱

cut() 和 qcut() 都返回一个 Categorical 对象，它们创建的区间存储在其 .categories 属性中作为一个 IntervalIndex。

In [193]: c = pd.cut(range(4), bins=2)

In [194]: c
Out[194]: 
[(-0.003, 1.5], (-0.003, 1.5], (1.5, 3.0], (1.5, 3.0]]
Categories (2, interval[float64, right]): [(-0.003, 1.5] < (1.5, 3.0]]

In [195]: c.categories
Out[195]: IntervalIndex([(-0.003, 1.5], (1.5, 3.0]], dtype='interval[float64, right]')

cut() 还接受一个 IntervalIndex 作为其 bins 参数，这使得一种有用的 pandas 习语成为可能。首先，我们调用 cut() 与一些数据和 bins 设置为固定数量，以生成区间。然后，我们将 .categories 的值作为 bins 参数传递给后续调用 cut() 的新数据，供新数据分配到相同的区间中。

In [196]: pd.cut([0, 3, 5, 1], bins=c.categories)
Out[196]: 
[(-0.003, 1.5], (1.5, 3.0], NaN, (-0.003, 1.5]]
Categories (2, interval[float64, right]): [(-0.003, 1.5] < (1.5, 3.0]]

任何落在所有区间之外的值将被分配一个 NaN 值。

生成区间范围

如果我们需要按照常规频率创建区间，则可以使用 interval_range() 函数，使用 start、end 和 periods 的各种组合来创建一个 IntervalIndex。interval_range 的默认频率为数字区间为 1，日期时间区间为日历日：

In [197]: pd.interval_range(start=0, end=5)
Out[197]: IntervalIndex([(0, 1], (1, 2], (2, 3], (3, 4], (4, 5]], dtype='interval[int64, right]')

In [198]: pd.interval_range(start=pd.Timestamp("2017-01-01"), periods=4)
Out[198]: 
IntervalIndex([(2017-01-01 00:00:00, 2017-01-02 00:00:00],
 (2017-01-02 00:00:00, 2017-01-03 00:00:00],
 (2017-01-03 00:00:00, 2017-01-04 00:00:00],
 (2017-01-04 00:00:00, 2017-01-05 00:00:00]],
 dtype='interval[datetime64[ns], right]')

In [199]: pd.interval_range(end=pd.Timedelta("3 days"), periods=3)
Out[199]: 
IntervalIndex([(0 days 00:00:00, 1 days 00:00:00],
 (1 days 00:00:00, 2 days 00:00:00],
 (2 days 00:00:00, 3 days 00:00:00]],
 dtype='interval[timedelta64[ns], right]')

freq参数可用于指定非默认频率，并且可以利用各种频率别名与类似于日期时间的间隔：

In [200]: pd.interval_range(start=0, periods=5, freq=1.5)
Out[200]: IntervalIndex([(0.0, 1.5], (1.5, 3.0], (3.0, 4.5], (4.5, 6.0], (6.0, 7.5]], dtype='interval[float64, right]')

In [201]: pd.interval_range(start=pd.Timestamp("2017-01-01"), periods=4, freq="W")
Out[201]: 
IntervalIndex([(2017-01-01 00:00:00, 2017-01-08 00:00:00],
 (2017-01-08 00:00:00, 2017-01-15 00:00:00],
 (2017-01-15 00:00:00, 2017-01-22 00:00:00],
 (2017-01-22 00:00:00, 2017-01-29 00:00:00]],
 dtype='interval[datetime64[ns], right]')

In [202]: pd.interval_range(start=pd.Timedelta("0 days"), periods=3, freq="9h")
Out[202]: 
IntervalIndex([(0 days 00:00:00, 0 days 09:00:00],
 (0 days 09:00:00, 0 days 18:00:00],
 (0 days 18:00:00, 1 days 03:00:00]],
 dtype='interval[timedelta64[ns], right]')

此外，closed参数可用于指定间隔关闭的哪一侧。默认情况下，间隔在右侧关闭。

In [203]: pd.interval_range(start=0, end=4, closed="both")
Out[203]: IntervalIndex([[0, 1], [1, 2], [2, 3], [3, 4]], dtype='interval[int64, both]')

In [204]: pd.interval_range(start=0, end=4, closed="neither")
Out[204]: IntervalIndex([(0, 1), (1, 2), (2, 3), (3, 4)], dtype='interval[int64, neither]')

指定start、end和periods将生成从start到end的一系列均匀间隔，包括periods个元素在生成的IntervalIndex中：

In [205]: pd.interval_range(start=0, end=6, periods=4)
Out[205]: IntervalIndex([(0.0, 1.5], (1.5, 3.0], (3.0, 4.5], (4.5, 6.0]], dtype='interval[float64, right]')

In [206]: pd.interval_range(pd.Timestamp("2018-01-01"), pd.Timestamp("2018-02-28"), periods=3)
Out[206]: 
IntervalIndex([(2018-01-01 00:00:00, 2018-01-20 08:00:00],
 (2018-01-20 08:00:00, 2018-02-08 16:00:00],
 (2018-02-08 16:00:00, 2018-02-28 00:00:00]],
 dtype='interval[datetime64[ns], right]') 
```### CategoricalIndex

`CategoricalIndex`是一种支持具有重复项索引的索引类型。这是围绕`Categorical`的容器，允许高效地索引和存储具有大量重复元素的索引。

```py
In [145]: from pandas.api.types import CategoricalDtype

In [146]: df = pd.DataFrame({"A": np.arange(6), "B": list("aabbca")})

In [147]: df["B"] = df["B"].astype(CategoricalDtype(list("cab")))

In [148]: df
Out[148]: 
 A  B
0  0  a
1  1  a
2  2  b
3  3  b
4  4  c
5  5  a

In [149]: df.dtypes
Out[149]: 
A       int64
B    category
dtype: object

In [150]: df["B"].cat.categories
Out[150]: Index(['c', 'a', 'b'], dtype='object')

设置索引将创建一个CategoricalIndex。

In [151]: df2 = df.set_index("B")

In [152]: df2.index
Out[152]: CategoricalIndex(['a', 'a', 'b', 'b', 'c', 'a'], categories=['c', 'a', 'b'], ordered=False, dtype='category', name='B')

使用__getitem__/.iloc/.loc进行索引操作与具有重复项的Index类似。索引器必须在类别中，否则操作将引发KeyError。

In [153]: df2.loc["a"]
Out[153]: 
 A
B 
a  0
a  1
a  5

在索引后，CategoricalIndex被保留：

In [154]: df2.loc["a"].index
Out[154]: CategoricalIndex(['a', 'a', 'a'], categories=['c', 'a', 'b'], ordered=False, dtype='category', name='B')

对索引进行排序将按照类别的顺序进行排序（回想我们使用CategoricalDtype(list('cab'))创建了索引，所以排序顺序是cab）。

In [155]: df2.sort_index()
Out[155]: 
 A
B 
c  4
a  0
a  1
a  5
b  2
b  3

对索引进行分组操作将保留索引的性质。

In [156]: df2.groupby(level=0, observed=True).sum()
Out[156]: 
 A
B 
c  4
a  6
b  5

In [157]: df2.groupby(level=0, observed=True).sum().index
Out[157]: CategoricalIndex(['c', 'a', 'b'], categories=['c', 'a', 'b'], ordered=False, dtype='category', name='B')

重新索引操作将根据传递的索引器类型返回结果索引。传递列表将返回普通的Index；使用Categorical进行索引将返回一个CategoricalIndex，根据传递的Categorical数据类型的类别进行索引。这允许任意索引这些值，即使值不在类别中，类似于如何重新索引任何pandas 索引。

In [158]: df3 = pd.DataFrame(
 .....:    {"A": np.arange(3), "B": pd.Series(list("abc")).astype("category")}
 .....: )
 .....: 

In [159]: df3 = df3.set_index("B")

In [160]: df3
Out[160]: 
 A
B 
a  0
b  1
c  2 

In [161]: df3.reindex(["a", "e"])
Out[161]: 
 A
B 
a  0.0
e  NaN

In [162]: df3.reindex(["a", "e"]).index
Out[162]: Index(['a', 'e'], dtype='object', name='B')

In [163]: df3.reindex(pd.Categorical(["a", "e"], categories=list("abe")))
Out[163]: 
 A
B 
a  0.0
e  NaN

In [164]: df3.reindex(pd.Categorical(["a", "e"], categories=list("abe"))).index
Out[164]: CategoricalIndex(['a', 'e'], categories=['a', 'b', 'e'], ordered=False, dtype='category', name='B')

警告

对CategoricalIndex进行重塑和比较操作必须具有相同的类别，否则将引发TypeError。

In [165]: df4 = pd.DataFrame({"A": np.arange(2), "B": list("ba")})

In [166]: df4["B"] = df4["B"].astype(CategoricalDtype(list("ab")))

In [167]: df4 = df4.set_index("B")

In [168]: df4.index
Out[168]: CategoricalIndex(['b', 'a'], categories=['a', 'b'], ordered=False, dtype='category', name='B')

In [169]: df5 = pd.DataFrame({"A": np.arange(2), "B": list("bc")})

In [170]: df5["B"] = df5["B"].astype(CategoricalDtype(list("bc")))

In [171]: df5 = df5.set_index("B")

In [172]: df5.index
Out[172]: CategoricalIndex(['b', 'c'], categories=['b', 'c'], ordered=False, dtype='category', name='B') 

In [173]: pd.concat([df4, df5])
Out[173]: 
 A
B 
b  0
a  1
b  0
c  1

RangeIndex

RangeIndex是DataFrame和Series对象的默认索引的子类。RangeIndex是Index的优化版本，可以表示单调有序集合。这类似于 Python 的range 类型。RangeIndex始终具有int64数据类型。

In [174]: idx = pd.RangeIndex(5)

In [175]: idx
Out[175]: RangeIndex(start=0, stop=5, step=1)

RangeIndex是所有DataFrame和Series对象的默认索引：

In [176]: ser = pd.Series([1, 2, 3])

In [177]: ser.index
Out[177]: RangeIndex(start=0, stop=3, step=1)

In [178]: df = pd.DataFrame([[1, 2], [3, 4]])

In [179]: df.index
Out[179]: RangeIndex(start=0, stop=2, step=1)

In [180]: df.columns
Out[180]: RangeIndex(start=0, stop=2, step=1)

一个 RangeIndex 的行为类似于具有 int64 dtype 的 Index，对 RangeIndex 的操作，其结果不能由 RangeIndex 表示，但应该具有整数 dtype，将转换为具有 int64 的 Index。例如：

In [181]: idx[[0, 2]]
Out[181]: Index([0, 2], dtype='int64')

区间索引

IntervalIndex 以及它自己的 dtype，IntervalDtype 以及 Interval 标量类型，允许 pandas 对区间表示法提供一流支持。

IntervalIndex 允许一些独特的索引，并且也被用作 cut() 和 qcut() 中的类别的返回类型。

使用 IntervalIndex 进行索引

IntervalIndex 可以在 Series 和 DataFrame 中作为索引使用。

In [182]: df = pd.DataFrame(
 .....:    {"A": [1, 2, 3, 4]}, index=pd.IntervalIndex.from_breaks([0, 1, 2, 3, 4])
 .....: )
 .....: 

In [183]: df
Out[183]: 
 A
(0, 1]  1
(1, 2]  2
(2, 3]  3
(3, 4]  4

通过 .loc 进行基于标签的索引，沿着区间的边缘工作如你所期望的那样，选择特定的区间。

In [184]: df.loc[2]
Out[184]: 
A    2
Name: (1, 2], dtype: int64

In [185]: df.loc[[2, 3]]
Out[185]: 
 A
(1, 2]  2
(2, 3]  3

如果选择一个 包含 在区间内的标签，这也将选择该区间。

In [186]: df.loc[2.5]
Out[186]: 
A    3
Name: (2, 3], dtype: int64

In [187]: df.loc[[2.5, 3.5]]
Out[187]: 
 A
(2, 3]  3
(3, 4]  4

使用 Interval 进行选择将只返回精确匹配。

In [188]: df.loc[pd.Interval(1, 2)]
Out[188]: 
A    2
Name: (1, 2], dtype: int64

尝试选择一个不完全包含在 IntervalIndex 中的 Interval 将引发一个 KeyError。

In [189]: df.loc[pd.Interval(0.5, 2.5)]
---------------------------------------------------------------------------
KeyError  Traceback (most recent call last)
Cell In[189], line 1
----> 1 df.loc[pd.Interval(0.5, 2.5)]

File ~/work/pandas/pandas/pandas/core/indexing.py:1191, in _LocationIndexer.__getitem__(self, key)
  1189 maybe_callable = com.apply_if_callable(key, self.obj)
  1190 maybe_callable = self._check_deprecated_callable_usage(key, maybe_callable)
-> 1191 return self._getitem_axis(maybe_callable, axis=axis)

File ~/work/pandas/pandas/pandas/core/indexing.py:1431, in _LocIndexer._getitem_axis(self, key, axis)
  1429 # fall thru to straight lookup
  1430 self._validate_key(key, axis)
-> 1431 return self._get_label(key, axis=axis)

File ~/work/pandas/pandas/pandas/core/indexing.py:1381, in _LocIndexer._get_label(self, label, axis)
  1379 def _get_label(self, label, axis: AxisInt):
  1380     # GH#5567 this will fail if the label is not present in the axis.
-> 1381     return self.obj.xs(label, axis=axis)

File ~/work/pandas/pandas/pandas/core/generic.py:4301, in NDFrame.xs(self, key, axis, level, drop_level)
  4299             new_index = index[loc]
  4300 else:
-> 4301     loc = index.get_loc(key)
  4303     if isinstance(loc, np.ndarray):
  4304         if loc.dtype == np.bool_:

File ~/work/pandas/pandas/pandas/core/indexes/interval.py:678, in IntervalIndex.get_loc(self, key)
  676 matches = mask.sum()
  677 if matches == 0:
--> 678     raise KeyError(key)
  679 if matches == 1:
  680     return mask.argmax()

KeyError: Interval(0.5, 2.5, closed='right')

可以使用 overlaps() 方法创建一个布尔索引器，选择与给定 Interval 重叠的所有 Intervals。

In [190]: idxr = df.index.overlaps(pd.Interval(0.5, 2.5))

In [191]: idxr
Out[191]: array([ True,  True,  True, False])

In [192]: df[idxr]
Out[192]: 
 A
(0, 1]  1
(1, 2]  2
(2, 3]  3
使用 cut 和 qcut 对数据进行分箱

cut() 和 qcut() 都返回一个 Categorical 对象，它们创建的区间存储在其 .categories 属性中作为一个 IntervalIndex。

In [193]: c = pd.cut(range(4), bins=2)

In [194]: c
Out[194]: 
[(-0.003, 1.5], (-0.003, 1.5], (1.5, 3.0], (1.5, 3.0]]
Categories (2, interval[float64, right]): [(-0.003, 1.5] < (1.5, 3.0]]

In [195]: c.categories
Out[195]: IntervalIndex([(-0.003, 1.5], (1.5, 3.0]], dtype='interval[float64, right]')

cut() 还接受 IntervalIndex 作为其 bins 参数，这使得 pandas 中有一个有用的习惯用法。首先，我们使用一些数据和将 bins 设置为一个固定数字来调用 cut()，以生成区间。然后，我们将.categories 的值作为后续对 cut() 的调用中的 bins 参数传递，提供新的数据，这些数据将被分配到相同的区间中。

In [196]: pd.cut([0, 3, 5, 1], bins=c.categories)
Out[196]: 
[(-0.003, 1.5], (1.5, 3.0], NaN, (-0.003, 1.5]]
Categories (2, interval[float64, right]): [(-0.003, 1.5] < (1.5, 3.0]]

所有落在所有区间之外的值将被分配一个 NaN 值。

生成区间范围

如果我们需要定期频率的区间，我们可以使用 interval_range() 函数，使用各种组合的 start、end 和 periods 来创建一个 IntervalIndex。对于 interval_range 的默认频率是数值区间为 1，日期时间区间为日历日：

In [197]: pd.interval_range(start=0, end=5)
Out[197]: IntervalIndex([(0, 1], (1, 2], (2, 3], (3, 4], (4, 5]], dtype='interval[int64, right]')

In [198]: pd.interval_range(start=pd.Timestamp("2017-01-01"), periods=4)
Out[198]: 
IntervalIndex([(2017-01-01 00:00:00, 2017-01-02 00:00:00],
 (2017-01-02 00:00:00, 2017-01-03 00:00:00],
 (2017-01-03 00:00:00, 2017-01-04 00:00:00],
 (2017-01-04 00:00:00, 2017-01-05 00:00:00]],
 dtype='interval[datetime64[ns], right]')

In [199]: pd.interval_range(end=pd.Timedelta("3 days"), periods=3)
Out[199]: 
IntervalIndex([(0 days 00:00:00, 1 days 00:00:00],
 (1 days 00:00:00, 2 days 00:00:00],
 (2 days 00:00:00, 3 days 00:00:00]],
 dtype='interval[timedelta64[ns], right]')

freq 参数可以用于指定非默认频率，并且可以利用各种频率别名，以及类似于日期时间的间隔：

In [200]: pd.interval_range(start=0, periods=5, freq=1.5)
Out[200]: IntervalIndex([(0.0, 1.5], (1.5, 3.0], (3.0, 4.5], (4.5, 6.0], (6.0, 7.5]], dtype='interval[float64, right]')

In [201]: pd.interval_range(start=pd.Timestamp("2017-01-01"), periods=4, freq="W")
Out[201]: 
IntervalIndex([(2017-01-01 00:00:00, 2017-01-08 00:00:00],
 (2017-01-08 00:00:00, 2017-01-15 00:00:00],
 (2017-01-15 00:00:00, 2017-01-22 00:00:00],
 (2017-01-22 00:00:00, 2017-01-29 00:00:00]],
 dtype='interval[datetime64[ns], right]')

In [202]: pd.interval_range(start=pd.Timedelta("0 days"), periods=3, freq="9h")
Out[202]: 
IntervalIndex([(0 days 00:00:00, 0 days 09:00:00],
 (0 days 09:00:00, 0 days 18:00:00],
 (0 days 18:00:00, 1 days 03:00:00]],
 dtype='interval[timedelta64[ns], right]')

此外，closed 参数可用于指定区间关闭的哪一侧（或哪些侧）。区间默认在右侧关闭。

In [203]: pd.interval_range(start=0, end=4, closed="both")
Out[203]: IntervalIndex([[0, 1], [1, 2], [2, 3], [3, 4]], dtype='interval[int64, both]')

In [204]: pd.interval_range(start=0, end=4, closed="neither")
Out[204]: IntervalIndex([(0, 1), (1, 2), (2, 3), (3, 4)], dtype='interval[int64, neither]')

指定 start、end 和 periods 将从 start 到 end（包括 end）生成一系列等间隔的区间，结果为 IntervalIndex 中的 periods 个元素：

In [205]: pd.interval_range(start=0, end=6, periods=4)
Out[205]: IntervalIndex([(0.0, 1.5], (1.5, 3.0], (3.0, 4.5], (4.5, 6.0]], dtype='interval[float64, right]')

In [206]: pd.interval_range(pd.Timestamp("2018-01-01"), pd.Timestamp("2018-02-28"), periods=3)
Out[206]: 
IntervalIndex([(2018-01-01 00:00:00, 2018-01-20 08:00:00],
 (2018-01-20 08:00:00, 2018-02-08 16:00:00],
 (2018-02-08 16:00:00, 2018-02-28 00:00:00]],
 dtype='interval[datetime64[ns], right]')
使用 IntervalIndex 进行索引

IntervalIndex 可以在 Series 和 DataFrame 中用作索引。

In [182]: df = pd.DataFrame(
 .....:    {"A": [1, 2, 3, 4]}, index=pd.IntervalIndex.from_breaks([0, 1, 2, 3, 4])
 .....: )
 .....: 

In [183]: df
Out[183]: 
 A
(0, 1]  1
(1, 2]  2
(2, 3]  3
(3, 4]  4

基于标签的索引通过 .loc 在区间的边缘按预期工作，选择特定的区间。

In [184]: df.loc[2]
Out[184]: 
A    2
Name: (1, 2], dtype: int64

In [185]: df.loc[[2, 3]]
Out[185]: 
 A
(1, 2]  2
(2, 3]  3

如果选择的标签包含在区间内，这也将选择该区间。

In [186]: df.loc[2.5]
Out[186]: 
A    3
Name: (2, 3], dtype: int64

In [187]: df.loc[[2.5, 3.5]]
Out[187]: 
 A
(2, 3]  3
(3, 4]  4

使用 Interval 进行选择将只返回精确匹配。

In [188]: df.loc[pd.Interval(1, 2)]
Out[188]: 
A    2
Name: (1, 2], dtype: int64

尝试选择不完全包含在 IntervalIndex 中的 Interval 将引发 KeyError。

In [189]: df.loc[pd.Interval(0.5, 2.5)]
---------------------------------------------------------------------------
KeyError  Traceback (most recent call last)
Cell In[189], line 1
----> 1 df.loc[pd.Interval(0.5, 2.5)]

File ~/work/pandas/pandas/pandas/core/indexing.py:1191, in _LocationIndexer.__getitem__(self, key)
  1189 maybe_callable = com.apply_if_callable(key, self.obj)
  1190 maybe_callable = self._check_deprecated_callable_usage(key, maybe_callable)
-> 1191 return self._getitem_axis(maybe_callable, axis=axis)

File ~/work/pandas/pandas/pandas/core/indexing.py:1431, in _LocIndexer._getitem_axis(self, key, axis)
  1429 # fall thru to straight lookup
  1430 self._validate_key(key, axis)
-> 1431 return self._get_label(key, axis=axis)

File ~/work/pandas/pandas/pandas/core/indexing.py:1381, in _LocIndexer._get_label(self, label, axis)
  1379 def _get_label(self, label, axis: AxisInt):
  1380     # GH#5567 this will fail if the label is not present in the axis.
-> 1381     return self.obj.xs(label, axis=axis)

File ~/work/pandas/pandas/pandas/core/generic.py:4301, in NDFrame.xs(self, key, axis, level, drop_level)
  4299             new_index = index[loc]
  4300 else:
-> 4301     loc = index.get_loc(key)
  4303     if isinstance(loc, np.ndarray):
  4304         if loc.dtype == np.bool_:

File ~/work/pandas/pandas/pandas/core/indexes/interval.py:678, in IntervalIndex.get_loc(self, key)
  676 matches = mask.sum()
  677 if matches == 0:
--> 678     raise KeyError(key)
  679 if matches == 1:
  680     return mask.argmax()

KeyError: Interval(0.5, 2.5, closed='right')

选择与给定 Interval 重叠的所有 Intervals 可以使用 overlaps() 方法创建布尔索引器。

In [190]: idxr = df.index.overlaps(pd.Interval(0.5, 2.5))

In [191]: idxr
Out[191]: array([ True,  True,  True, False])

In [192]: df[idxr]
Out[192]: 
 A
(0, 1]  1
(1, 2]  2
(2, 3]  3
使用 cut 和 qcut 对数据进行分箱

cut() 和 qcut() 都返回一个 Categorical 对象，并且它们创建的区间被存储为其 .categories 属性中的 IntervalIndex。

In [193]: c = pd.cut(range(4), bins=2)

In [194]: c
Out[194]: 
[(-0.003, 1.5], (-0.003, 1.5], (1.5, 3.0], (1.5, 3.0]]
Categories (2, interval[float64, right]): [(-0.003, 1.5] < (1.5, 3.0]]

In [195]: c.categories
Out[195]: IntervalIndex([(-0.003, 1.5], (1.5, 3.0]], dtype='interval[float64, right]')

cut() 还接受一个 IntervalIndex 作为其 bins 参数，这使得一个有用的 pandas 习惯用法成为可能。首先，我们用一些数据和 bins 设置为一个固定数目来调用 cut()，以生成区间。然后，我们将 .categories 的值作为 bins 参数传递给后续调用 cut()，提供将被分箱到相同区间的新数据。

In [196]: pd.cut([0, 3, 5, 1], bins=c.categories)
Out[196]: 
[(-0.003, 1.5], (1.5, 3.0], NaN, (-0.003, 1.5]]
Categories (2, interval[float64, right]): [(-0.003, 1.5] < (1.5, 3.0]]

所有落在所有区间之外的值都将被分配一个 NaN 值。

生成区间范围

如果我们需要定期的间隔，我们可以使用 interval_range() 函数使用各种组合的 start、end 和 periods 创建 IntervalIndex。对于数值区间，默认频率为 1，对于类似于日期时间的区间，默认为日历日：

In [197]: pd.interval_range(start=0, end=5)
Out[197]: IntervalIndex([(0, 1], (1, 2], (2, 3], (3, 4], (4, 5]], dtype='interval[int64, right]')

In [198]: pd.interval_range(start=pd.Timestamp("2017-01-01"), periods=4)
Out[198]: 
IntervalIndex([(2017-01-01 00:00:00, 2017-01-02 00:00:00],
 (2017-01-02 00:00:00, 2017-01-03 00:00:00],
 (2017-01-03 00:00:00, 2017-01-04 00:00:00],
 (2017-01-04 00:00:00, 2017-01-05 00:00:00]],
 dtype='interval[datetime64[ns], right]')

In [199]: pd.interval_range(end=pd.Timedelta("3 days"), periods=3)
Out[199]: 
IntervalIndex([(0 days 00:00:00, 1 days 00:00:00],
 (1 days 00:00:00, 2 days 00:00:00],
 (2 days 00:00:00, 3 days 00:00:00]],
 dtype='interval[timedelta64[ns], right]')

freq 参数可以用于指定非默认频率，并且可以利用各种频率别名，以及类似于日期时间的间隔：

In [200]: pd.interval_range(start=0, periods=5, freq=1.5)
Out[200]: IntervalIndex([(0.0, 1.5], (1.5, 3.0], (3.0, 4.5], (4.5, 6.0], (6.0, 7.5]], dtype='interval[float64, right]')

In [201]: pd.interval_range(start=pd.Timestamp("2017-01-01"), periods=4, freq="W")
Out[201]: 
IntervalIndex([(2017-01-01 00:00:00, 2017-01-08 00:00:00],
 (2017-01-08 00:00:00, 2017-01-15 00:00:00],
 (2017-01-15 00:00:00, 2017-01-22 00:00:00],
 (2017-01-22 00:00:00, 2017-01-29 00:00:00]],
 dtype='interval[datetime64[ns], right]')

In [202]: pd.interval_range(start=pd.Timedelta("0 days"), periods=3, freq="9h")
Out[202]: 
IntervalIndex([(0 days 00:00:00, 0 days 09:00:00],
 (0 days 09:00:00, 0 days 18:00:00],
 (0 days 18:00:00, 1 days 03:00:00]],
 dtype='interval[timedelta64[ns], right]')

此外，closed 参数可用于指定区间关闭的哪一侧（或哪些侧）。区间默认在右侧关闭。

In [203]: pd.interval_range(start=0, end=4, closed="both")
Out[203]: IntervalIndex([[0, 1], [1, 2], [2, 3], [3, 4]], dtype='interval[int64, both]')

In [204]: pd.interval_range(start=0, end=4, closed="neither")
Out[204]: IntervalIndex([(0, 1), (1, 2), (2, 3), (3, 4)], dtype='interval[int64, neither]')

指定 start、end 和 periods 将从 start 到 end 生成一系列均匀间隔的区间，结果将包含 IntervalIndex 中的 periods 个元素：

In [205]: pd.interval_range(start=0, end=6, periods=4)
Out[205]: IntervalIndex([(0.0, 1.5], (1.5, 3.0], (3.0, 4.5], (4.5, 6.0]], dtype='interval[float64, right]')

In [206]: pd.interval_range(pd.Timestamp("2018-01-01"), pd.Timestamp("2018-02-28"), periods=3)
Out[206]: 
IntervalIndex([(2018-01-01 00:00:00, 2018-01-20 08:00:00],
 (2018-01-20 08:00:00, 2018-02-08 16:00:00],
 (2018-02-08 16:00:00, 2018-02-28 00:00:00]],
 dtype='interval[datetime64[ns], right]')

杂项索引 FAQ

整数索引

使用整数轴标签进行基于标签的索引是一个棘手的问题。它在邮件列表和科学 Python 社区的各个成员之间被广泛讨论。在 pandas 中，我们的一般观点是标签比整数位置更重要。因此，只有使用标准工具如 .loc 进行基于标签的索引。以下代码将生成异常：

In [207]: s = pd.Series(range(5))

In [208]: s[-1]
---------------------------------------------------------------------------
ValueError  Traceback (most recent call last)
File ~/work/pandas/pandas/pandas/core/indexes/range.py:413, in RangeIndex.get_loc(self, key)
  412 try:
--> 413     return self._range.index(new_key)
  414 except ValueError as err:

ValueError: -1 is not in range

The above exception was the direct cause of the following exception:

KeyError  Traceback (most recent call last)
Cell In[208], line 1
----> 1 s[-1]

File ~/work/pandas/pandas/pandas/core/series.py:1121, in Series.__getitem__(self, key)
  1118     return self._values[key]
  1120 elif key_is_scalar:
-> 1121     return self._get_value(key)
  1123 # Convert generator to list before going through hashable part
  1124 # (We will iterate through the generator there to check for slices)
  1125 if is_iterator(key):

File ~/work/pandas/pandas/pandas/core/series.py:1237, in Series._get_value(self, label, takeable)
  1234     return self._values[label]
  1236 # Similar to Index.get_value, but we do not fall back to positional
-> 1237 loc = self.index.get_loc(label)
  1239 if is_integer(loc):
  1240     return self._values[loc]

File ~/work/pandas/pandas/pandas/core/indexes/range.py:415, in RangeIndex.get_loc(self, key)
  413         return self._range.index(new_key)
  414     except ValueError as err:
--> 415         raise KeyError(key) from err
  416 if isinstance(key, Hashable):
  417     raise KeyError(key)

KeyError: -1

In [209]: df = pd.DataFrame(np.random.randn(5, 4))

In [210]: df
Out[210]: 
 0         1         2         3
0 -0.435772 -1.188928 -0.808286 -0.284634
1 -1.815703  1.347213 -0.243487  0.514704
2  1.162969 -0.287725 -0.179734  0.993962
3 -0.212673  0.909872 -0.733333 -0.349893
4  0.456434 -0.306735  0.553396  0.166221

In [211]: df.loc[-2:]
Out[211]: 
 0         1         2         3
0 -0.435772 -1.188928 -0.808286 -0.284634
1 -1.815703  1.347213 -0.243487  0.514704
2  1.162969 -0.287725 -0.179734  0.993962
3 -0.212673  0.909872 -0.733333 -0.349893
4  0.456434 -0.306735  0.553396  0.166221

这个刻意的决定是为了防止歧义和微妙的错误（许多用户报告在 API 更改为停止“回退”到基于位置的索引时发现错误）。

非单调索引需要精确匹配

如果 Series 或 DataFrame 的索引单调递增或递减，则标签的边界可以超出索引的范围，就像对普通 Python list 进行切片索引一样。可以使用 is_monotonic_increasing() 和 is_monotonic_decreasing() 属性测试索引的单调性。

In [212]: df = pd.DataFrame(index=[2, 3, 3, 4, 5], columns=["data"], data=list(range(5)))

In [213]: df.index.is_monotonic_increasing
Out[213]: True

# no rows 0 or 1, but still returns rows 2, 3 (both of them), and 4:
In [214]: df.loc[0:4, :]
Out[214]: 
 data
2     0
3     1
3     2
4     3

# slice is are outside the index, so empty DataFrame is returned
In [215]: df.loc[13:15, :]
Out[215]: 
Empty DataFrame
Columns: [data]
Index: []

另一方面，如果索引不是单调的，那么切片的两个边界都必须是索引的唯一成员。

In [216]: df = pd.DataFrame(index=[2, 3, 1, 4, 3, 5], columns=["data"], data=list(range(6)))

In [217]: df.index.is_monotonic_increasing
Out[217]: False

# OK because 2 and 4 are in the index
In [218]: df.loc[2:4, :]
Out[218]: 
 data
2     0
3     1
1     2
4     3 

 # 0 is not in the index
In [219]: df.loc[0:4, :]
---------------------------------------------------------------------------
KeyError  Traceback (most recent call last)
File ~/work/pandas/pandas/pandas/core/indexes/base.py:3805, in Index.get_loc(self, key)
  3804 try:
-> 3805     return self._engine.get_loc(casted_key)
  3806 except KeyError as err:

File index.pyx:167, in pandas._libs.index.IndexEngine.get_loc()

File index.pyx:191, in pandas._libs.index.IndexEngine.get_loc()

File index.pyx:234, in pandas._libs.index.IndexEngine._get_loc_duplicates()

File index.pyx:242, in pandas._libs.index.IndexEngine._maybe_get_bool_indexer()

File index.pyx:134, in pandas._libs.index._unpack_bool_indexer()

KeyError: 0

The above exception was the direct cause of the following exception:

KeyError  Traceback (most recent call last)
Cell In[219], line 1
----> 1 df.loc[0:4, :]

File ~/work/pandas/pandas/pandas/core/indexing.py:1184, in _LocationIndexer.__getitem__(self, key)
  1182     if self._is_scalar_access(key):
  1183         return self.obj._get_value(*key, takeable=self._takeable)
-> 1184     return self._getitem_tuple(key)
  1185 else:
  1186     # we by definition only have the 0th axis
  1187     axis = self.axis or 0

File ~/work/pandas/pandas/pandas/core/indexing.py:1377, in _LocIndexer._getitem_tuple(self, tup)
  1374 if self._multi_take_opportunity(tup):
  1375     return self._multi_take(tup)
-> 1377 return self._getitem_tuple_same_dim(tup)

File ~/work/pandas/pandas/pandas/core/indexing.py:1020, in _LocationIndexer._getitem_tuple_same_dim(self, tup)
  1017 if com.is_null_slice(key):
  1018     continue
-> 1020 retval = getattr(retval, self.name)._getitem_axis(key, axis=i)
  1021 # We should never have retval.ndim < self.ndim, as that should
  1022 #  be handled by the _getitem_lowerdim call above.
  1023 assert retval.ndim == self.ndim

File ~/work/pandas/pandas/pandas/core/indexing.py:1411, in _LocIndexer._getitem_axis(self, key, axis)
  1409 if isinstance(key, slice):
  1410     self._validate_key(key, axis)
-> 1411     return self._get_slice_axis(key, axis=axis)
  1412 elif com.is_bool_indexer(key):
  1413     return self._getbool_axis(key, axis=axis)

File ~/work/pandas/pandas/pandas/core/indexing.py:1443, in _LocIndexer._get_slice_axis(self, slice_obj, axis)
  1440     return obj.copy(deep=False)
  1442 labels = obj._get_axis(axis)
-> 1443 indexer = labels.slice_indexer(slice_obj.start, slice_obj.stop, slice_obj.step)
  1445 if isinstance(indexer, slice):
  1446     return self.obj._slice(indexer, axis=axis)

File ~/work/pandas/pandas/pandas/core/indexes/base.py:6662, in Index.slice_indexer(self, start, end, step)
  6618 def slice_indexer(
  6619     self,
  6620     start: Hashable | None = None,
  6621     end: Hashable | None = None,
  6622     step: int | None = None,
  6623 ) -> slice:
  6624  """
  6625 Compute the slice indexer for input labels and step.
  6626  
 (...)
  6660 slice(1, 3, None)
  6661 """
-> 6662     start_slice, end_slice = self.slice_locs(start, end, step=step)
  6664     # return a slice
  6665     if not is_scalar(start_slice):

File ~/work/pandas/pandas/pandas/core/indexes/base.py:6879, in Index.slice_locs(self, start, end, step)
  6877 start_slice = None
  6878 if start is not None:
-> 6879     start_slice = self.get_slice_bound(start, "left")
  6880 if start_slice is None:
  6881     start_slice = 0

File ~/work/pandas/pandas/pandas/core/indexes/base.py:6804, in Index.get_slice_bound(self, label, side)
  6801         return self._searchsorted_monotonic(label, side)
  6802     except ValueError:
  6803         # raise the original KeyError
-> 6804         raise err
  6806 if isinstance(slc, np.ndarray):
  6807     # get_loc may return a boolean array, which
  6808     # is OK as long as they are representable by a slice.
  6809     assert is_bool_dtype(slc.dtype)

File ~/work/pandas/pandas/pandas/core/indexes/base.py:6798, in Index.get_slice_bound(self, label, side)
  6796 # we need to look up the label
  6797 try:
-> 6798     slc = self.get_loc(label)
  6799 except KeyError as err:
  6800     try:

File ~/work/pandas/pandas/pandas/core/indexes/base.py:3812, in Index.get_loc(self, key)
  3807     if isinstance(casted_key, slice) or (
  3808         isinstance(casted_key, abc.Iterable)
  3809         and any(isinstance(x, slice) for x in casted_key)
  3810     ):
  3811         raise InvalidIndexError(key)
-> 3812     raise KeyError(key) from err
  3813 except TypeError:
  3814     # If we have a listlike key, _check_indexing_error will raise
  3815     #  InvalidIndexError. Otherwise we fall through and re-raise
  3816     #  the TypeError.
  3817     self._check_indexing_error(key)

KeyError: 0

 # 3 is not a unique label
In [220]: df.loc[2:3, :]
---------------------------------------------------------------------------
KeyError  Traceback (most recent call last)
Cell In[220], line 1
----> 1 df.loc[2:3, :]

File ~/work/pandas/pandas/pandas/core/indexing.py:1184, in _LocationIndexer.__getitem__(self, key)
  1182     if self._is_scalar_access(key):
  1183         return self.obj._get_value(*key, takeable=self._takeable)
-> 1184     return self._getitem_tuple(key)
  1185 else:
  1186     # we by definition only have the 0th axis
  1187     axis = self.axis or 0

File ~/work/pandas/pandas/pandas/core/indexing.py:1377, in _LocIndexer._getitem_tuple(self, tup)
  1374 if self._multi_take_opportunity(tup):
  1375     return self._multi_take(tup)
-> 1377 return self._getitem_tuple_same_dim(tup)

File ~/work/pandas/pandas/pandas/core/indexing.py:1020, in _LocationIndexer._getitem_tuple_same_dim(self, tup)
  1017 if com.is_null_slice(key):
  1018     continue
-> 1020 retval = getattr(retval, self.name)._getitem_axis(key, axis=i)
  1021 # We should never have retval.ndim < self.ndim, as that should
  1022 #  be handled by the _getitem_lowerdim call above.
  1023 assert retval.ndim == self.ndim

File ~/work/pandas/pandas/pandas/core/indexing.py:1411, in _LocIndexer._getitem_axis(self, key, axis)
  1409 if isinstance(key, slice):
  1410     self._validate_key(key, axis)
-> 1411     return self._get_slice_axis(key, axis=axis)
  1412 elif com.is_bool_indexer(key):
  1413     return self._getbool_axis(key, axis=axis)

File ~/work/pandas/pandas/pandas/core/indexing.py:1443, in _LocIndexer._get_slice_axis(self, slice_obj, axis)
  1440     return obj.copy(deep=False)
  1442 labels = obj._get_axis(axis)
-> 1443 indexer = labels.slice_indexer(slice_obj.start, slice_obj.stop, slice_obj.step)
  1445 if isinstance(indexer, slice):
  1446     return self.obj._slice(indexer, axis=axis)

File ~/work/pandas/pandas/pandas/core/indexes/base.py:6662, in Index.slice_indexer(self, start, end, step)
  6618 def slice_indexer(
  6619     self,
  6620     start: Hashable | None = None,
  6621     end: Hashable | None = None,
  6622     step: int | None = None,
  6623 ) -> slice:
  6624  """
  6625 Compute the slice indexer for input labels and step.
  6626  
 (...)
  6660 slice(1, 3, None)
  6661 """
-> 6662     start_slice, end_slice = self.slice_locs(start, end, step=step)
  6664     # return a slice
  6665     if not is_scalar(start_slice):

File ~/work/pandas/pandas/pandas/core/indexes/base.py:6885, in Index.slice_locs(self, start, end, step)
  6883 end_slice = None
  6884 if end is not None:
-> 6885     end_slice = self.get_slice_bound(end, "right")
  6886 if end_slice is None:
  6887     end_slice = len(self)

File ~/work/pandas/pandas/pandas/core/indexes/base.py:6812, in Index.get_slice_bound(self, label, side)
  6810     slc = lib.maybe_booleans_to_slice(slc.view("u1"))
  6811     if isinstance(slc, np.ndarray):
-> 6812         raise KeyError(
  6813             f"Cannot get {side} slice bound for non-unique "
  6814             f"label: {repr(original_label)}"
  6815         )
  6817 if isinstance(slc, slice):
  6818     if side == "left":

KeyError: 'Cannot get right slice bound for non-unique label: 3'

Index.is_monotonic_increasing 和 Index.is_monotonic_decreasing 只检查索引是否弱单调。要检查严格单调性，可以将其中一个与 is_unique() 属性结合使用。

In [221]: weakly_monotonic = pd.Index(["a", "b", "c", "c"])

In [222]: weakly_monotonic
Out[222]: Index(['a', 'b', 'c', 'c'], dtype='object')

In [223]: weakly_monotonic.is_monotonic_increasing
Out[223]: True

In [224]: weakly_monotonic.is_monotonic_increasing & weakly_monotonic.is_unique
Out[224]: False

端点是包含的

与标准 Python 序列切片相比，在 pandas 中基于标签的切片是包含的。这样做的主要原因是通常不可能轻松确定索引中特定标签后的“后继”或下一个元素。例如，请考虑以下 Series：

In [225]: s = pd.Series(np.random.randn(6), index=list("abcdef"))

In [226]: s
Out[226]: 
a   -0.101684
b   -0.734907
c   -0.130121
d   -0.476046
e    0.759104
f    0.213379
dtype: float64

假设我们希望从 c 到 e 进行切片，使用整数可以这样完成：

In [227]: s[2:5]
Out[227]: 
c   -0.130121
d   -0.476046
e    0.759104
dtype: float64

然而，如果你只有 c 和 e，确定索引中的下一个元素可能有些复杂。例如，以下方法不起作用：

In [228]: s.loc['c':'e' + 1]
---------------------------------------------------------------------------
TypeError  Traceback (most recent call last)
Cell In[228], line 1
----> 1 s.loc['c':'e' + 1]

TypeError: can only concatenate str (not "int") to str

一个非常常见的用例是将时间序列限制在两个特定日期开始和结束。为了实现这个目标，我们做出了设计选择，使基于标签的切片包含两个端点：

In [229]: s.loc["c":"e"]
Out[229]: 
c   -0.130121
d   -0.476046
e    0.759104
dtype: float64

这绝对是一个“实用性胜于纯粹性”的事情，但如果你期望基于标签的切片的行为与标准 Python 整数切片的行为完全相同，这是需要注意的事情。

索引可能会改变底层 Series 的 dtype

不同的索引操作可能会更改Series的dtype。

In [230]: series1 = pd.Series([1, 2, 3])

In [231]: series1.dtype
Out[231]: dtype('int64')

In [232]: res = series1.reindex([0, 4])

In [233]: res.dtype
Out[233]: dtype('float64')

In [234]: res
Out[234]: 
0    1.0
4    NaN
dtype: float64 

In [235]: series2 = pd.Series([True])

In [236]: series2.dtype
Out[236]: dtype('bool')

In [237]: res = series2.reindex_like(series1)

In [238]: res.dtype
Out[238]: dtype('O')

In [239]: res
Out[239]: 
0    True
1     NaN
2     NaN
dtype: object

这是因为上述重新索引操作会静默地插入NaNs，并相应地更改dtype。这在使用numpy的ufuncs（如numpy.logical_and）时可能会导致一些问题。

参见GH 2388以获取更详细的讨论。

整数索引

具有整数轴标签的基于标签的索引是一个棘手的问题。在邮件列表和科学 Python 社区的各个成员中已经进行了大量讨论。在 pandas 中，我们的一般观点是标签比整数位置更重要。因此，只有具有整数轴索引的情况下，才可以使用标准工具（如.loc）进行基于标签的索引。以下代码将生成异常：

In [207]: s = pd.Series(range(5))

In [208]: s[-1]
---------------------------------------------------------------------------
ValueError  Traceback (most recent call last)
File ~/work/pandas/pandas/pandas/core/indexes/range.py:413, in RangeIndex.get_loc(self, key)
  412 try:
--> 413     return self._range.index(new_key)
  414 except ValueError as err:

ValueError: -1 is not in range

The above exception was the direct cause of the following exception:

KeyError  Traceback (most recent call last)
Cell In[208], line 1
----> 1 s[-1]

File ~/work/pandas/pandas/pandas/core/series.py:1121, in Series.__getitem__(self, key)
  1118     return self._values[key]
  1120 elif key_is_scalar:
-> 1121     return self._get_value(key)
  1123 # Convert generator to list before going through hashable part
  1124 # (We will iterate through the generator there to check for slices)
  1125 if is_iterator(key):

File ~/work/pandas/pandas/pandas/core/series.py:1237, in Series._get_value(self, label, takeable)
  1234     return self._values[label]
  1236 # Similar to Index.get_value, but we do not fall back to positional
-> 1237 loc = self.index.get_loc(label)
  1239 if is_integer(loc):
  1240     return self._values[loc]

File ~/work/pandas/pandas/pandas/core/indexes/range.py:415, in RangeIndex.get_loc(self, key)
  413         return self._range.index(new_key)
  414     except ValueError as err:
--> 415         raise KeyError(key) from err
  416 if isinstance(key, Hashable):
  417     raise KeyError(key)

KeyError: -1

In [209]: df = pd.DataFrame(np.random.randn(5, 4))

In [210]: df
Out[210]: 
 0         1         2         3
0 -0.435772 -1.188928 -0.808286 -0.284634
1 -1.815703  1.347213 -0.243487  0.514704
2  1.162969 -0.287725 -0.179734  0.993962
3 -0.212673  0.909872 -0.733333 -0.349893
4  0.456434 -0.306735  0.553396  0.166221

In [211]: df.loc[-2:]
Out[211]: 
 0         1         2         3
0 -0.435772 -1.188928 -0.808286 -0.284634
1 -1.815703  1.347213 -0.243487  0.514704
2  1.162969 -0.287725 -0.179734  0.993962
3 -0.212673  0.909872 -0.733333 -0.349893
4  0.456434 -0.306735  0.553396  0.166221

这个故意的决定是为了防止歧义和细微的错误（许多用户报告在停止“回退”到基于位置的索引时进行 API 更改时发现错误）。

非单调索引需要精确匹配

如果Series或DataFrame的索引是单调递增或递减的，则基于标签的切片的边界可以超出索引的范围，就像切片索引正常的 Python list一样。可以使用is_monotonic_increasing()和is_monotonic_decreasing()属性来测试索引的单调性。

In [212]: df = pd.DataFrame(index=[2, 3, 3, 4, 5], columns=["data"], data=list(range(5)))

In [213]: df.index.is_monotonic_increasing
Out[213]: True

# no rows 0 or 1, but still returns rows 2, 3 (both of them), and 4:
In [214]: df.loc[0:4, :]
Out[214]: 
 data
2     0
3     1
3     2
4     3

# slice is are outside the index, so empty DataFrame is returned
In [215]: df.loc[13:15, :]
Out[215]: 
Empty DataFrame
Columns: [data]
Index: []

另一方面，如果索引不是单调的，则切片边界必须是索引的唯一成员。

In [216]: df = pd.DataFrame(index=[2, 3, 1, 4, 3, 5], columns=["data"], data=list(range(6)))

In [217]: df.index.is_monotonic_increasing
Out[217]: False

# OK because 2 and 4 are in the index
In [218]: df.loc[2:4, :]
Out[218]: 
 data
2     0
3     1
1     2
4     3 

 # 0 is not in the index
In [219]: df.loc[0:4, :]
---------------------------------------------------------------------------
KeyError  Traceback (most recent call last)
File ~/work/pandas/pandas/pandas/core/indexes/base.py:3805, in Index.get_loc(self, key)
  3804 try:
-> 3805     return self._engine.get_loc(casted_key)
  3806 except KeyError as err:

File index.pyx:167, in pandas._libs.index.IndexEngine.get_loc()

File index.pyx:191, in pandas._libs.index.IndexEngine.get_loc()

File index.pyx:234, in pandas._libs.index.IndexEngine._get_loc_duplicates()

File index.pyx:242, in pandas._libs.index.IndexEngine._maybe_get_bool_indexer()

File index.pyx:134, in pandas._libs.index._unpack_bool_indexer()

KeyError: 0

The above exception was the direct cause of the following exception:

KeyError  Traceback (most recent call last)
Cell In[219], line 1
----> 1 df.loc[0:4, :]

File ~/work/pandas/pandas/pandas/core/indexing.py:1184, in _LocationIndexer.__getitem__(self, key)
  1182     if self._is_scalar_access(key):
  1183         return self.obj._get_value(*key, takeable=self._takeable)
-> 1184     return self._getitem_tuple(key)
  1185 else:
  1186     # we by definition only have the 0th axis
  1187     axis = self.axis or 0

File ~/work/pandas/pandas/pandas/core/indexing.py:1377, in _LocIndexer._getitem_tuple(self, tup)
  1374 if self._multi_take_opportunity(tup):
  1375     return self._multi_take(tup)
-> 1377 return self._getitem_tuple_same_dim(tup)

File ~/work/pandas/pandas/pandas/core/indexing.py:1020, in _LocationIndexer._getitem_tuple_same_dim(self, tup)
  1017 if com.is_null_slice(key):
  1018     continue
-> 1020 retval = getattr(retval, self.name)._getitem_axis(key, axis=i)
  1021 # We should never have retval.ndim < self.ndim, as that should
  1022 #  be handled by the _getitem_lowerdim call above.
  1023 assert retval.ndim == self.ndim

File ~/work/pandas/pandas/pandas/core/indexing.py:1411, in _LocIndexer._getitem_axis(self, key, axis)
  1409 if isinstance(key, slice):
  1410     self._validate_key(key, axis)
-> 1411     return self._get_slice_axis(key, axis=axis)
  1412 elif com.is_bool_indexer(key):
  1413     return self._getbool_axis(key, axis=axis)

File ~/work/pandas/pandas/pandas/core/indexing.py:1443, in _LocIndexer._get_slice_axis(self, slice_obj, axis)
  1440     return obj.copy(deep=False)
  1442 labels = obj._get_axis(axis)
-> 1443 indexer = labels.slice_indexer(slice_obj.start, slice_obj.stop, slice_obj.step)
  1445 if isinstance(indexer, slice):
  1446     return self.obj._slice(indexer, axis=axis)

File ~/work/pandas/pandas/pandas/core/indexes/base.py:6662, in Index.slice_indexer(self, start, end, step)
  6618 def slice_indexer(
  6619     self,
  6620     start: Hashable | None = None,
  6621     end: Hashable | None = None,
  6622     step: int | None = None,
  6623 ) -> slice:
  6624  """
  6625 Compute the slice indexer for input labels and step.
  6626  
 (...)
  6660 slice(1, 3, None)
  6661 """
-> 6662     start_slice, end_slice = self.slice_locs(start, end, step=step)
  6664     # return a slice
  6665     if not is_scalar(start_slice):

File ~/work/pandas/pandas/pandas/core/indexes/base.py:6879, in Index.slice_locs(self, start, end, step)
  6877 start_slice = None
  6878 if start is not None:
-> 6879     start_slice = self.get_slice_bound(start, "left")
  6880 if start_slice is None:
  6881     start_slice = 0

File ~/work/pandas/pandas/pandas/core/indexes/base.py:6804, in Index.get_slice_bound(self, label, side)
  6801         return self._searchsorted_monotonic(label, side)
  6802     except ValueError:
  6803         # raise the original KeyError
-> 6804         raise err
  6806 if isinstance(slc, np.ndarray):
  6807     # get_loc may return a boolean array, which
  6808     # is OK as long as they are representable by a slice.
  6809     assert is_bool_dtype(slc.dtype)

File ~/work/pandas/pandas/pandas/core/indexes/base.py:6798, in Index.get_slice_bound(self, label, side)
  6796 # we need to look up the label
  6797 try:
-> 6798     slc = self.get_loc(label)
  6799 except KeyError as err:
  6800     try:

File ~/work/pandas/pandas/pandas/core/indexes/base.py:3812, in Index.get_loc(self, key)
  3807     if isinstance(casted_key, slice) or (
  3808         isinstance(casted_key, abc.Iterable)
  3809         and any(isinstance(x, slice) for x in casted_key)
  3810     ):
  3811         raise InvalidIndexError(key)
-> 3812     raise KeyError(key) from err
  3813 except TypeError:
  3814     # If we have a listlike key, _check_indexing_error will raise
  3815     #  InvalidIndexError. Otherwise we fall through and re-raise
  3816     #  the TypeError.
  3817     self._check_indexing_error(key)

KeyError: 0

 # 3 is not a unique label
In [220]: df.loc[2:3, :]
---------------------------------------------------------------------------
KeyError  Traceback (most recent call last)
Cell In[220], line 1
----> 1 df.loc[2:3, :]

File ~/work/pandas/pandas/pandas/core/indexing.py:1184, in _LocationIndexer.__getitem__(self, key)
  1182     if self._is_scalar_access(key):
  1183         return self.obj._get_value(*key, takeable=self._takeable)
-> 1184     return self._getitem_tuple(key)
  1185 else:
  1186     # we by definition only have the 0th axis
  1187     axis = self.axis or 0

File ~/work/pandas/pandas/pandas/core/indexing.py:1377, in _LocIndexer._getitem_tuple(self, tup)
  1374 if self._multi_take_opportunity(tup):
  1375     return self._multi_take(tup)
-> 1377 return self._getitem_tuple_same_dim(tup)

File ~/work/pandas/pandas/pandas/core/indexing.py:1020, in _LocationIndexer._getitem_tuple_same_dim(self, tup)
  1017 if com.is_null_slice(key):
  1018     continue
-> 1020 retval = getattr(retval, self.name)._getitem_axis(key, axis=i)
  1021 # We should never have retval.ndim < self.ndim, as that should
  1022 #  be handled by the _getitem_lowerdim call above.
  1023 assert retval.ndim == self.ndim

File ~/work/pandas/pandas/pandas/core/indexing.py:1411, in _LocIndexer._getitem_axis(self, key, axis)
  1409 if isinstance(key, slice):
  1410     self._validate_key(key, axis)
-> 1411     return self._get_slice_axis(key, axis=axis)
  1412 elif com.is_bool_indexer(key):
  1413     return self._getbool_axis(key, axis=axis)

File ~/work/pandas/pandas/pandas/core/indexing.py:1443, in _LocIndexer._get_slice_axis(self, slice_obj, axis)
  1440     return obj.copy(deep=False)
  1442 labels = obj._get_axis(axis)
-> 1443 indexer = labels.slice_indexer(slice_obj.start, slice_obj.stop, slice_obj.step)
  1445 if isinstance(indexer, slice):
  1446     return self.obj._slice(indexer, axis=axis)

File ~/work/pandas/pandas/pandas/core/indexes/base.py:6662, in Index.slice_indexer(self, start, end, step)
  6618 def slice_indexer(
  6619     self,
  6620     start: Hashable | None = None,
  6621     end: Hashable | None = None,
  6622     step: int | None = None,
  6623 ) -> slice:
  6624  """
  6625 Compute the slice indexer for input labels and step.
  6626  
 (...)
  6660 slice(1, 3, None)
  6661 """
-> 6662     start_slice, end_slice = self.slice_locs(start, end, step=step)
  6664     # return a slice
  6665     if not is_scalar(start_slice):

File ~/work/pandas/pandas/pandas/core/indexes/base.py:6885, in Index.slice_locs(self, start, end, step)
  6883 end_slice = None
  6884 if end is not None:
-> 6885     end_slice = self.get_slice_bound(end, "right")
  6886 if end_slice is None:
  6887     end_slice = len(self)

File ~/work/pandas/pandas/pandas/core/indexes/base.py:6812, in Index.get_slice_bound(self, label, side)
  6810     slc = lib.maybe_booleans_to_slice(slc.view("u1"))
  6811     if isinstance(slc, np.ndarray):
-> 6812         raise KeyError(
  6813             f"Cannot get {side} slice bound for non-unique "
  6814             f"label: {repr(original_label)}"
  6815         )
  6817 if isinstance(slc, slice):
  6818     if side == "left":

KeyError: 'Cannot get right slice bound for non-unique label: 3'

Index.is_monotonic_increasing和Index.is_monotonic_decreasing仅检查索引是否是弱单调的。要检查严格的单调性，可以将其中一个与is_unique()属性结合使用。

In [221]: weakly_monotonic = pd.Index(["a", "b", "c", "c"])

In [222]: weakly_monotonic
Out[222]: Index(['a', 'b', 'c', 'c'], dtype='object')

In [223]: weakly_monotonic.is_monotonic_increasing
Out[223]: True

In [224]: weakly_monotonic.is_monotonic_increasing & weakly_monotonic.is_unique
Out[224]: False

端点是包含的

与标准的 Python 序列切片相比，在 pandas 中，基于标签的切片是包含的。主要原因是通常很难确定索引中特定标签后面的“后继”或下一个元素。例如，考虑以下 Series：

In [225]: s = pd.Series(np.random.randn(6), index=list("abcdef"))

In [226]: s
Out[226]: 
a   -0.101684
b   -0.734907
c   -0.130121
d   -0.476046
e    0.759104
f    0.213379
dtype: float64

假设我们希望从c到e进行切片，使用整数可以这样完成：

In [227]: s[2:5]
Out[227]: 
c   -0.130121
d   -0.476046
e    0.759104
dtype: float64

但是，如果你只有c和e，确定索引中的下一个元素可能会有些复杂。例如，以下内容不起作用：

In [228]: s.loc['c':'e' + 1]
---------------------------------------------------------------------------
TypeError  Traceback (most recent call last)
Cell In[228], line 1
----> 1 s.loc['c':'e' + 1]

TypeError: can only concatenate str (not "int") to str

一个非常常见的用例是将时间序列限制在两个特定日期开始和结束。为了实现这一点，我们做出了设计选择，使基于标签的切片包括两个端点：

In [229]: s.loc["c":"e"]
Out[229]: 
c   -0.130121
d   -0.476046
e    0.759104
dtype: float64

这绝对是“实用性胜过纯粹性”的一种情况，但如果你期望基于标签的切片行为与标准的 Python 整数切片完全相同，则需要注意这一点。

索引可能会改变底层 Series 的数据类型

不同的索引操作可能会改变Series的数据类型。

In [230]: series1 = pd.Series([1, 2, 3])

In [231]: series1.dtype
Out[231]: dtype('int64')

In [232]: res = series1.reindex([0, 4])

In [233]: res.dtype
Out[233]: dtype('float64')

In [234]: res
Out[234]: 
0    1.0
4    NaN
dtype: float64 

In [235]: series2 = pd.Series([True])

In [236]: series2.dtype
Out[236]: dtype('bool')

In [237]: res = series2.reindex_like(series1)

In [238]: res.dtype
Out[238]: dtype('O')

In [239]: res
Out[239]: 
0    True
1     NaN
2     NaN
dtype: object

这是因为上述（重新）索引操作会默默地插入NaNs，并相应地更改dtype。这可能会在使用numpy的ufuncs（如numpy.logical_and）时造成一些问题。

参见GH 2388以获取更详细的讨论。

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