又是新的一周，最近上海的疫情似乎又开始严重起来了，小编所在的小区也被封了，身边很多包括同事、朋友所在的小区也都被封了。希望这个疫情可以尽快过去吧，生活能够重新回到正轨。今天我们来聊一下 Pandas当中的数据集中带有多重索引的数据分析实战
通常我们接触比较多的是单层索引（左图），而多级索引也就意味着数据集当中的行索引有多个层级（右图），具体的如下图所示
AUTUMN
导入数据
我们先导入数据与
pandas模块，源数据获取，公众号后台回复【 多重索引】就能拿到
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
import pandas as pd
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
## 导入数据集
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
df = pd.read_csv('dataset.csv')
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
df.head()
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
output
该数据集描述的是 英国部分城市在2019年7月1日至7月4日期间的全天天气状况，我们先来看一下当前的数据集的行索引有哪些？代码如下
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
df.index.names
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
output
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
FrozenList(['City', 'Date'])
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
数据集当中
City、 Date，这里的 City我们可以当作是 第一层级索引，而 Date则是 第二层级索引。
我们也可以通过调用
sort_index()方法来按照数据集的行索引来进行排序，代码如下
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
df_1 = df.sort_index()
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
df_1
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
output
要是我们想将这个多层索引去除掉，就调用
reset_index()方法，代码如下
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
df.reset_index()
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
下面我们就开始针对多层索引来对数据集进行一些分析的实战吧
第一层级的数据筛选
在
pandas当中数据筛选的方法，一般我们是调用 loc以及 iloc方法，同样地，在多层级索引的数据集当中数据的筛选也是调用该两种方法，例如筛选出伦敦白天的天气状况如何，代码如下
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
df_1.loc['London' , 'Day']
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
output
要是我们想针对所有的行，就可以这么来做
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
df_1.loc[:, 'Day']
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
output
同理针对所有的列，就可以这么来做
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
df_1.loc['London' , :]
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
output
多层级索引的数据筛选
要是我们想看伦敦2019年7月1日白天的天气状况，就可以这么来做
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
df.loc['London', 'Day'].loc['2019-07-01']
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
output
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
Weather Shower
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
Wind SW 16 mph
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
Max Temperature 28
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
Name: 2019-07-01, dtype: object
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
这里我们进行了两次数据筛选的操作，先是
df.loc['London', 'Day']，然后再此的基础之上再进行 loc['2019-07-01']操作，当然还有更加方便的步骤，代码如下
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
df.loc[('London', '2019-07-01'), 'Day']
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
output
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
Weather Shower
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
Wind SW 16 mph
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
Max Temperature 28
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
Name: 2019-07-01, dtype: object
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
除此之外我们要是想看一下伦敦2019年7月1日和7月2日两天白天的天气情况，就可以这么来做
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
df.loc[
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
('London' , ['2019-07-01','2019-07-02'] ) ,
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
'Day'
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
]
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
output
在此基础之上，我们想要看天气和风速这两列，我们也可以单独摘出来，代码如下
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
df.loc[
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
'London' ,
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
('Day', ['Weather', 'Wind'])
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
]
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
output
按照范围来筛选数据
对于第一层级的索引而言，我们同样还是调用
loc方法来实现
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
df.loc[
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
'Cambridge':'Oxford',
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
'Day'
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
]
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
output
但是对于第二层级的索引，要是用同样的方式来用就会报错，
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
df.loc[
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
('London', '2019-07-01': '2019-07-03'),
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
'Day'
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
]
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
output
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
SyntaxError: invalid syntax (, line 3)
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
正确的写法代码如下
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
df.loc[
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
('London','2019-07-01'):('London','2019-07-03'),
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
'Day'
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
]
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
output
筛选出所有全部的内容
对于单层索引而言，我们通过
:来筛选出所有的内容，但是在多层级的索引上面则并不适用，
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
# 出现语法错误
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
df.loc[
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
('London', :),
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
'Day'
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
]
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
# 出现语法错误
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
df.loc[
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
(: , '2019-07-04'),
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
'Day'
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
]
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
正确的做法如下所示
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
# 筛选出伦敦下面所有天数的白天天气情况
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
df.loc[
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
('London', slice(None)),
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
'Day'
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
]
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
output
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
# 筛选出2019年7月4日下所有城市的白天天气情况
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
df.loc[
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
(slice(None) , '2019-07-04'),
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
'Day'
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
]
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
output
当然这里还有更加简便的方法，我们通过调用
pandas当中 IndexSlice函数来实现，代码如下
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
from pandas import IndexSlice as idx
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
df.loc[
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
idx[: , '2019-07-04'],
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
'Day'
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
]
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
output
又或者是
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
rows = idx[: , '2019-07-01']
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
cols = idx['Day' , ['Max Temperature','Weather']]
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
df.loc[rows, cols]
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
output
xs() 方法的调用
对于多层级索引的数据集而言，调用
xs()方法能够更加方便地进行数据的筛选，例如我们想要筛选出日期是2019年7月4日的所有数据，代码如下
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
df.xs('2019-07-04', level='Date')
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
output
我们需要在
level参数上指定是哪个标签，例如我们想要筛选出伦敦2019年7月4日全天的天气情况，代码如下
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
df.xs(('London', '2019-07-04'), level=['City','Date'])
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
output
最后
xs方法可以和上面提到的 IndexSlice函数联用，针对多层级的数据集来进行数据的筛选，例如我们想要筛选出2019年7月2日至7月4日，伦敦全天的天气状况，代码如下
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
rows= (
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
idx['2019-07-02':'2019-07-04'],
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
'London'
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
)
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
df.xs(
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
rows ,
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
level = ['Date','City']
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
)
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"345.png")
![Pandas多层级索引的数据分析案例，超干货的！](https://johngo-pic.oss-cn-beijing.aliyuncs.com/articles/20230619/"312.png")
output
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Original: https://blog.csdn.net/weixin_43373042/article/details/123492444
Author: 欣一2002
Title: Pandas多层级索引的数据分析案例，超干货的！
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