公众号：尤而小屋
作者：Peter
编辑：Peter

大家好，我是Peter~

本文记录的是Pandas中10种单层索引的常用属性，文末有汇总的常见属性，建议收藏！

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10种索引

快速回顾Pandas中10种单层索引的创建：

pd.Index

In [1]:

import pandas as pd
import numpy as np

In [2]:

# 指定类型和名称

s1 = pd.Index([1,2,3,4,5,6,7], 
         dtype="int",
         name="Peter")

s1

Out[2]:

Int64Index([1, 2, 3, 4, 5, 6, 7], dtype='int64', name='Peter')

pd.RangeIndex

指定整数范围内的不可变索引

In [3]:

s2 = pd.RangeIndex(0,20,2)
s2

Out[3]:

RangeIndex(start=0, stop=20, step=2)

pd.Int64Index

64位整数型索引

In [4]:

s3 = pd.Int64Index([1,2,3,4,5,6,7,8],name="Peter")
s3

Out[4]:

Int64Index([1, 2, 3, 4, 5, 6, 7, 8], dtype='int64', name='Peter')

pd.UInt64Index

无符号整数索引

In [5]:

s4 = pd.UInt64Index([1, 2.0, 3, 4],name="Tom")
s4

Out[5]:

UInt64Index([1, 2, 3, 4], dtype='uint64', name='Tom')

pd.Float64Index

64位浮点型的索引

In [6]:

s5 = pd.Float64Index([1.5, 2.4, 3.7, 4.9],name="peter")
s5

Out[6]:

Float64Index([1.5, 2.4, 3.7, 4.9], dtype='float64', name='peter')

pd.IntervalIndex

新的间隔索引 IntervalIndex 通常使用 interval_range()函数来进行构造，它使用的是数据或者数值区间，基本用法：

In [7]:

s6 = pd.interval_range(start=0, end=6, closed="left")
s6

Out[7]:

IntervalIndex([[0, 1), [1, 2), [2, 3), [3, 4), [4, 5), [5, 6)],
              closed='left',
              dtype='interval[int64]')

pd.CategoricalIndex

In [8]:

s7 = pd.CategoricalIndex(
    # 待排序的数据
    ["S","M","L","XS","M","L","S","M","L","XL"],
    # 指定分类顺序
    categories=["XS","S","M","L","XL"],
    # 排需
    ordered=True,
    # 索引名字
    name="category"
)

s7

Out[8]:

CategoricalIndex(['S', 'M', 'L', 'XS', 'M', 'L', 'S', 'M', 'L', 'XL'], categories=['XS', 'S', 'M', 'L', 'XL'], ordered=True, name='category', dtype='category')

pd.DatetimeIndex

以时间和日期作为索引，通过date_range函数来生成，具体例子为：

In [9]:

# 日期作为索引，D代表天

s8 = pd.date_range("2022-01-01",periods=6, freq="D")
s8

Out[9]:

DatetimeIndex(['2022-01-01', '2022-01-02', '2022-01-03', '2022-01-04',
               '2022-01-05', '2022-01-06'],
              dtype='datetime64[ns]', freq='D')

pd.PeriodIndex

pd.PeriodIndex是一个专门针对周期性数据的索引，方便针对具有一定周期的数据进行处理，具体用法如下：

In [10]:

s9 = pd.PeriodIndex(['2022-01-01', '2022-01-02', '2022-01-03', '2022-01-04'], freq = '2H')
s9

Out[10]:

PeriodIndex(['2022-01-01 00:00', '2022-01-02 00:00', '2022-01-03 00:00',
             '2022-01-04 00:00'],
            dtype='period[2H]', freq='2H')

pd.TimedeltaIndex

In [11]:

data = pd.timedelta_range(start='1 day', end='3 days', freq='6H')
data

Out[11]:

TimedeltaIndex(['1 days 00:00:00', '1 days 06:00:00', '1 days 12:00:00',
                '1 days 18:00:00', '2 days 00:00:00', '2 days 06:00:00',
                '2 days 12:00:00', '2 days 18:00:00', '3 days 00:00:00'],
               dtype='timedelta64[ns]', freq='6H')

In [12]:

s10 = pd.TimedeltaIndex(data)
s10

Out[12]:

TimedeltaIndex(['1 days 00:00:00', '1 days 06:00:00', '1 days 12:00:00',
                '1 days 18:00:00', '2 days 00:00:00', '2 days 06:00:00',
                '2 days 12:00:00', '2 days 18:00:00', '3 days 00:00:00'],
               dtype='timedelta64[ns]', freq='6H')

属性1：name

如果有的话，返回索引的名称

In [13]:

s1.name

Out[13]:

'Peter'

In [14]:

s4.name

Out[14]:

'Tom'

属性2：dtype

返回索引的数据类型

In [15]:

s1.dtype

Out[15]:

dtype('int64')

In [16]:

s8.dtype

Out[16]:

dtype('<M8[ns]')

In [17]:

s10.dtype

Out[17]:

dtype('<m8[ns]')

属性3：array

返回索引组成的数组：

In [18]:

s1.array

Out[18]:

<PandasArray>
[1, 2, 3, 4, 5, 6, 7]
Length: 7, dtype: int64

In [19]:

s5.array

Out[19]:

<PandasArray>
[1.5, 2.4, 3.7, 4.9]
Length: 4, dtype: float64

In [20]:

s8.array

Out[20]:

<DatetimeArray>
['2022-01-01 00:00:00', '2022-01-02 00:00:00', '2022-01-03 00:00:00',
 '2022-01-04 00:00:00', '2022-01-05 00:00:00', '2022-01-06 00:00:00']
Length: 6, dtype: datetime64[ns]

属性4：shape

返回索引的形状：几行几列

In [21]:

s1.shape

Out[21]:

(7,)

In [22]:

s4.shape

Out[22]:

(4,)

In [23]:

s8.shape

Out[23]:

(6,)

属性5：size

返回索引的总个数：行数乘以列数

In [24]:

s1.size

Out[24]:

7

In [25]:

s2.size

Out[25]:

10

In [26]:

s5.size

Out[26]:

4

In [27]:

s10.size

Out[27]:

9

属性6：empty

返回索引是否为空

In [28]:

s1.empty

Out[28]:

False

In [29]:

s4.empty

Out[29]:

False

In [30]:

s10.empty

Out[30]:

False

属性7：ndim

返回索引的维度

In [31]:

s1.ndim

Out[31]:

1

In [32]:

s4.ndim

Out[32]:

1

属性8：T

将索引进行转置操作

In [33]:

s1.T

Out[33]:

Int64Index([1, 2, 3, 4, 5, 6, 7], dtype='int64', name='Peter')

In [34]:

s3.T

Out[34]:

Int64Index([1, 2, 3, 4, 5, 6, 7, 8], dtype='int64', name='Peter')

In [35]:

s6.T

Out[35]:

IntervalIndex([[0, 1), [1, 2), [2, 3), [3, 4), [4, 5), [5, 6)],
              closed='left',
              dtype='interval[int64]')

属性9：argmax

返回最大索引所在的位置

In [36]:

s1.argmax()  # 最大索引所在的位置

Out[36]:

6

In [37]:

s5.argmax()

Out[37]:

3

属性10：is_integer

判断索引是否为整数型

In [38]:

s1.is_integer()

Out[38]:

True

In [39]:

s2.is_integer()

Out[39]:

True

In [40]:

s6.is_integer()

Out[40]:

False

属性汇总

对Pandas的常用属性进行一下简单的汇总。需要注意的是针对行索引的属性同样适用于列属性columns，因为它们二者都是同属于Pandas中的index对象。

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