Pandas索引排序详解

索引排序-sort_index

针对Pandas中索引的排序功能介绍，详细内容参考官网：

https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sort_index.html

参数介绍

代码语言：javascript复制

DataFrame.sort_index(axis=0,
                     level=None,
                     ascending=True,
                     inplace=False,
                     kind='quicksort',
                     na_position='last',
                     sort_remaining=True,
                     ignore_index=False,
                     key=None)

参数说明：

• axis：排序的轴：axis=0表示行，axis=1表示列
• level：如果是多层索引的排序，表示根据指定的索引进行排序，可以是索引号，名称或者多个索引组成的列表
• ascending：排序规则，默认是升序
• inplace：表示是否原地修改；默认是False
• kind：表示选的排序算法
• na_position：空值的位置选择，first或者last。默认是last
• sort_remaining：

数据模拟

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import pandas as pd
import numpy as np

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df = pd.DataFrame({"name":["Jimmy","Ana","Tom","John"],
                   "age":[24,20,19,28],
                   "Math":[100,120,80,150],
                   "address":["beijing","shanghai","shenzhen","guangzhou"]
                  },
                 index=[np.nan,2,0,1])  # 存在空值

df

.dataframe tbody tr th:only-of-type { vertical-align: middle; } <pre><code>.dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </code></pre>

	name	age	Math	address
NaN	Jimmy	24	100	beijing
2.0	Ana	20	120	shanghai
0.0	Tom	19	80	shenzhen
1.0	John	28	150	guangzhou

参数axis

代码语言：javascript复制

# df.sort_index()  默认
df.sort_index(axis=0)

.dataframe tbody tr th:only-of-type { vertical-align: middle; } <pre><code>.dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </code></pre>

	name	age	Math	address
0.0	Tom	19	80	shenzhen
1.0	John	28	150	guangzhou
2.0	Ana	20	120	shanghai
NaN	Jimmy	24	100	beijing

默认是在axis=0轴上进行排序；且默认是升序排列

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df.sort_index(axis=1)

.dataframe tbody tr th:only-of-type { vertical-align: middle; } <pre><code>.dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </code></pre>

	Math	address	age	name
NaN	100	beijing	24	Jimmy
2.0	120	shanghai	20	Ana
0.0	80	shenzhen	19	Tom
1.0	150	guangzhou	28	John

axis=1表示在列方向上进行排序；上面的列字段全部是字母，则根据它们的ASCII码表的大小来排序

参数ignore_index

默认情况是保留原索引。如果是设置成True，则行索引变成0,1,2…N-1

代码语言：javascript复制

# 默认情况
df.sort_index(axis=1,ignore_index=False)

.dataframe tbody tr th:only-of-type { vertical-align: middle; } <pre><code>.dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </code></pre>

	Math	address	age	name
NaN	100	beijing	24	Jimmy
2.0	120	shanghai	20	Ana
0.0	80	shenzhen	19	Tom
1.0	150	guangzhou	28	John

代码语言：javascript复制

df.sort_index(axis=1,ignore_index=True)

.dataframe tbody tr th:only-of-type { vertical-align: middle; } <pre><code>.dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </code></pre>

	Math	address	age	name
0	100	beijing	24	Jimmy
1	120	shanghai	20	Ana
2	80	shenzhen	19	Tom
3	150	guangzhou	28	John

参数key

可选项，如果不是空值，则在排序之前现将key函数作用于指定的索引上，再进行排序。

代码语言：javascript复制

df.sort_index(axis=1)  # 默认axis=1

.dataframe tbody tr th:only-of-type { vertical-align: middle; } <pre><code>.dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </code></pre>

	Math	address	age	name
NaN	100	beijing	24	Jimmy
2.0	120	shanghai	20	Ana
0.0	80	shenzhen	19	Tom
1.0	150	guangzhou	28	John

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df.sort_index(axis=1, key=lambda x: x.str.lower())

.dataframe tbody tr th:only-of-type { vertical-align: middle; } <pre><code>.dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </code></pre>

	address	age	Math	name
NaN	beijing	24	100	Jimmy
2.0	shanghai	20	120	Ana
0.0	shenzhen	19	80	Tom
1.0	guangzhou	28	150	John

当指定了key函数：将列属性全部小写；此时Math变成了math。

后面排序的话，也就是根据全部小写的字段进行排序，所以Math会在name的前面。

参数ascending

代码语言：javascript复制

df.sort_index()

.dataframe tbody tr th:only-of-type { vertical-align: middle; } <pre><code>.dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </code></pre>

	name	age	Math	address
0.0	Tom	19	80	shenzhen
1.0	John	28	150	guangzhou
2.0	Ana	20	120	shanghai
NaN	Jimmy	24	100	beijing

代码语言：javascript复制

# df.sort_index()  默认情况：升序
df.sort_index(ascending=True)

.dataframe tbody tr th:only-of-type { vertical-align: middle; } <pre><code>.dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </code></pre>

	name	age	Math	address
0.0	Tom	19	80	shenzhen
1.0	John	28	150	guangzhou
2.0	Ana	20	120	shanghai
NaN	Jimmy	24	100	beijing

代码语言：javascript复制

df.sort_index(ascending=False)  # 设置成降序

.dataframe tbody tr th:only-of-type { vertical-align: middle; } <pre><code>.dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </code></pre>

	name	age	Math	address
2.0	Ana	20	120	shanghai
1.0	John	28	150	guangzhou
0.0	Tom	19	80	shenzhen
NaN	Jimmy	24	100	beijing

参数inplace

inplace的作用是用来直接修改原数据还是生成新的数据。

如果是True，则表示原地修改，即原数据直接改变。

为了演示的方便，先生成一个df的副本df1，对df1直接操作：

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df1 = df.copy()
df1

.dataframe tbody tr th:only-of-type { vertical-align: middle; } <pre><code>.dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </code></pre>

	name	age	Math	address
NaN	Jimmy	24	100	beijing
2.0	Ana	20	120	shanghai
0.0	Tom	19	80	shenzhen
1.0	John	28	150	guangzhou

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# 默认是False

df1.sort_index(inplace=False)

.dataframe tbody tr th:only-of-type { vertical-align: middle; } <pre><code>.dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </code></pre>

	name	age	Math	address
0.0	Tom	19	80	shenzhen
1.0	John	28	150	guangzhou
2.0	Ana	20	120	shanghai
NaN	Jimmy	24	100	beijing

此时df1是没有改变的：

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df1

.dataframe tbody tr th:only-of-type { vertical-align: middle; } <pre><code>.dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </code></pre>

	name	age	Math	address
NaN	Jimmy	24	100	beijing
2.0	Ana	20	120	shanghai
0.0	Tom	19	80	shenzhen
1.0	John	28	150	guangzhou

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df1.sort_index(inplace=True)  # 原地修改

如果设置成True，此时df1已经完成了排序工作：

代码语言：javascript复制

df1

.dataframe tbody tr th:only-of-type { vertical-align: middle; } <pre><code>.dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </code></pre>

	name	age	Math	address
0.0	Tom	19	80	shenzhen
1.0	John	28	150	guangzhou
2.0	Ana	20	120	shanghai
NaN	Jimmy	24	100	beijing

参数kind

kind表示排序选择的算法：{‘quicksort’, ‘mergesort’, ‘heapsort’}，默认是’quicksort‘。

• ‘quicksort’：快速排序
• ‘mergesort’：合并排序
• ‘heapsort’：堆排序

代码语言：javascript复制

df.sort_index()

.dataframe tbody tr th:only-of-type { vertical-align: middle; } <pre><code>.dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </code></pre>

	name	age	Math	address
0.0	Tom	19	80	shenzhen
1.0	John	28	150	guangzhou
2.0	Ana	20	120	shanghai
NaN	Jimmy	24	100	beijing

代码语言：javascript复制

df.sort_index(kind="mergesort")

.dataframe tbody tr th:only-of-type { vertical-align: middle; } <pre><code>.dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </code></pre>

	name	age	Math	address
0.0	Tom	19	80	shenzhen
1.0	John	28	150	guangzhou
2.0	Ana	20	120	shanghai
NaN	Jimmy	24	100	beijing

参数na_position

空值的位置选择，first或者last。默认是last

代码语言：javascript复制

df.sort_index()

.dataframe tbody tr th:only-of-type { vertical-align: middle; } <pre><code>.dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </code></pre>

	name	age	Math	address
0.0	Tom	19	80	shenzhen
1.0	John	28	150	guangzhou
2.0	Ana	20	120	shanghai
NaN	Jimmy	24	100	beijing

代码语言：javascript复制

df.sort_index(na_position="first")

.dataframe tbody tr th:only-of-type { vertical-align: middle; } <pre><code>.dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </code></pre>

	name	age	Math	address
NaN	Jimmy	24	100	beijing
0.0	Tom	19	80	shenzhen
1.0	John	28	150	guangzhou
2.0	Ana	20	120	shanghai

参数sort_remaining

如果为 true 且按级别和索引排序是多层，则按指定级别排序后也按其他级别(按顺序)排序

代码语言：javascript复制

# 一个来自官网的例子

arrays = [np.array(['qux', 'qux', 'foo', 'foo',
                    'baz', 'baz', 'bar', 'bar']),
          np.array(['two', 'one', 'two', 'one',
                    'two', 'one', 'two', 'one'])]
s = pd.Series([1, 2, 3, 4, 5, 6, 7, 8], index=arrays)
s

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qux  two    1
     one    2
foo  two    3
     one    4
baz  two    5
     one    6
bar  two    7
     one    8
dtype: int64

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s.sort_index(level=1, sort_remaining=True)  # 默认True

代码语言：javascript复制

bar  one    8
baz  one    6
foo  one    4
qux  one    2
bar  two    7
baz  two    5
foo  two    3
qux  two    1
dtype: int64

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s.sort_index(level=1, sort_remaining=False)

代码语言：javascript复制

qux  one    2
foo  one    4
baz  one    6
bar  one    8
qux  two    1
foo  two    3
baz  two    5
bar  two    7
dtype: int64

参数level

代码语言：javascript复制

df = pd.DataFrame({"name":["Jimmy","Ana","Tom","John"],
                   "age":[24,20,19,28],
                   "Math":[100,120,80,150],
                   "address":["beijing","shanghai","shenzhen","guangzhou"]
                  },
                 index=[[np.nan,2,0,1],  # 创建多层索引的DataFrame
                        [4,5,8,1]
                       ])

df

.dataframe tbody tr th:only-of-type { vertical-align: middle; } <pre><code>.dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </code></pre>

		name	age	Math	address
NaN	4	Jimmy	24	100	beijing
2	5	Ana	20	120	shanghai
0	8	Tom	19	80	shenzhen
1	1	John	28	150	guangzhou

可以看到df是多层索引：

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df.index

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MultiIndex([(nan, 4),
            (2.0, 5),
            (0.0, 8),
            (1.0, 1)],
           )

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df.sort_index(level=0)

.dataframe tbody tr th:only-of-type { vertical-align: middle; } <pre><code>.dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </code></pre>

		name	age	Math	address
NaN	4	Jimmy	24	100	beijing
0	8	Tom	19	80	shenzhen
1	1	John	28	150	guangzhou
2	5	Ana	20	120	shanghai

代码语言：javascript复制

df.sort_index(level=1)

.dataframe tbody tr th:only-of-type { vertical-align: middle; } <pre><code>.dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </code></pre>

		name	age	Math	address
1	1	John	28	150	guangzhou
NaN	4	Jimmy	24	100	beijing
2	5	Ana	20	120	shanghai
0	8	Tom	19	80	shenzhen

code dataframe pandas 排序 索引

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