环球微动态丨python pandas创建多层索引MultiIndex的6种方式
来源:脚本之家    时间:2022-07-29 17:59:47
目录
引言pd.MultiIndex.from_arrays()pd.MultiIndex.from_tuples()列表和元组是可以混合使用的pd.MultiIndex.from_product()pd.MultiIndex.from_frame()groupby()pivot_table()

引言

在上一篇文章中介绍了如何创建Pandas中的单层索引,今天给大家带来的是如何创建Pandas中的多层索引。


(资料图)

pd.MultiIndex,即具有多个层次的索引。通过多层次索引,我们就可以操作整个索引组的数据。本文主要介绍在Pandas中创建多层索引的6种方式:

pd.MultiIndex.from_arrays():多维数组作为参数,高维指定高层索引,低维指定低层索引。pd.MultiIndex.from_tuples():元组的列表作为参数,每个元组指定每个索引(高维和低维索引)。pd.MultiIndex.from_product():一个可迭代对象的列表作为参数,根据多个可迭代对象元素的笛卡尔积(元素间的两两组合)进行创建索引。pd.MultiIndex.from_frame:根据现有的数据框来直接生成groupby():通过数据分组统计得到pivot_table():生成透视表的方式来得到

pd.MultiIndex.from_arrays()

In [1]:

import pandas as pd
import numpy as np

通过数组的方式来生成,通常指定的是列表中的元素:

In [2]:

# 列表元素是字符串和数字
array1 = [["xiaoming","guanyu","zhangfei"], 
          [22,25,27]
         ]
m1 = pd.MultiIndex.from_arrays(array1)
m1

Out[2]:

MultiIndex([("xiaoming", 22),            (  "guanyu", 25),            ("zhangfei", 27)],
           )

In [3]:

type(m1)  # 查看数据类型

通过type函数来查看数据类型,发现的确是:MultiIndex

Out[3]:

pandas.core.indexes.multi.MultiIndex

在创建的同时可以指定每个层级的名字:

In [4]:

# 列表元素全是字符串
array2 = [["xiaoming","guanyu","zhangfei"],
          ["male","male","female"]
         ]
m2 = pd.MultiIndex.from_arrays(
	array2, 
  # 指定姓名和性别
  names=["name","sex"])
m2

Out[4]:

MultiIndex([("xiaoming",   "male"),            (  "guanyu",   "male"),            ("zhangfei", "female")],
           names=["name", "sex"])

下面的例子是生成3个层次的索引且指定名字:

In [5]:

array3 = [["xiaoming","guanyu","zhangfei"],
          ["male","male","female"],
          [22,25,27]
         ]
m3 = pd.MultiIndex.from_arrays(
	array3, 
	names=["姓名","性别","年龄"])
m3

Out[5]:

MultiIndex([("xiaoming",   "male", 22),            (  "guanyu",   "male", 25),            ("zhangfei", "female", 27)],
           names=["姓名", "性别", "年龄"])

pd.MultiIndex.from_tuples()

通过元组的形式来生成多层索引:

In [6]:

# 元组的形式
array4 = (("xiaoming","guanyu","zhangfei"), 
          (22,25,27)
         )
m4 = pd.MultiIndex.from_arrays(array4)
m4

Out[6]:

MultiIndex([("xiaoming", 22),            (  "guanyu", 25),            ("zhangfei", 27)],
           )

In [7]:

# 元组构成的3层索引
array5 = (("xiaoming","guanyu","zhangfei"),
          ("male","male","female"),
          (22,25,27))
m5 = pd.MultiIndex.from_arrays(array5)
m5

Out[7]:

MultiIndex([("xiaoming",   "male", 22),            (  "guanyu",   "male", 25),            ("zhangfei", "female", 27)],
           )

列表和元组是可以混合使用的

最外层是列表里面全部是元组

In [8]:

array6 = [("xiaoming","guanyu","zhangfei"),
          ("male","male","female"),
          (18,35,27)
         ]
# 指定名字
m6 = pd.MultiIndex.from_arrays(array6,names=["姓名","性别","年龄"])
m6

Out[8]:

MultiIndex([("xiaoming",   "male", 18),            (  "guanyu",   "male", 35),            ("zhangfei", "female", 27)],
           names=["姓名", "性别", "年龄"] # 指定名字
           )

pd.MultiIndex.from_product()

使用可迭代对象的列表作为参数,根据多个可迭代对象元素的笛卡尔积(元素间的两两组合)进行创建索引。

在Python中,我们使用 isinstance()函数 判断python对象是否可迭代:

# 导入 collections 模块的 Iterable 对比对象
from collections import Iterable

通过上面的例子我们总结:常见的字符串、列表、集合、元组、字典都是可迭代对象

下面举例子来说明:

In [18]:

names = ["xiaoming","guanyu","zhangfei"]
numbers = [22,25]
m7 = pd.MultiIndex.from_product(
    [names, numbers], 
    names=["name","number"]) # 指定名字
m7

Out[18]:

MultiIndex([("xiaoming", 22),            ("xiaoming", 25),            (  "guanyu", 22),            (  "guanyu", 25),            ("zhangfei", 22),            ("zhangfei", 25)],
           names=["name", "number"])

In [19]:

# 需要展开成列表形式
strings = list("abc") 
lists = [1,2]
m8 = pd.MultiIndex.from_product(
	[strings, lists],
	names=["alpha","number"])
m8

Out[19]:

MultiIndex([("a", 1),            ("a", 2),            ("b", 1),            ("b", 2),            ("c", 1),            ("c", 2)],
           names=["alpha", "number"])

In [20]:

# 使用元组形式
strings = ("a","b","c") 
lists = [1,2]
m9 = pd.MultiIndex.from_product(
	[strings, lists],
	names=["alpha","number"])
m9

Out[20]:

MultiIndex([("a", 1),            ("a", 2),            ("b", 1),            ("b", 2),            ("c", 1),            ("c", 2)],
           names=["alpha", "number"])

In [21]:

# 使用range函数
strings = ("a","b","c")  # 3个元素
lists = range(3)  # 0,1,2  3个元素
m10 = pd.MultiIndex.from_product(
	[strings, lists],
	names=["alpha","number"])
m10

Out[21]:

MultiIndex([("a", 0),            ("a", 1),            ("a", 2),            ("b", 0),            ("b", 1),            ("b", 2),            ("c", 0),            ("c", 1),            ("c", 2)],
           names=["alpha", "number"])

In [22]:

# 使用range函数
strings = ("a","b","c") 
list1 = range(3)  # 0,1,2
list2 = ["x","y"]
m11 = pd.MultiIndex.from_product(
	[strings, list1, list2],
  names=["name","l1","l2"]
  )
m11  # 总个数 3*3*2=18

总个数是``332=18`个:

Out[22]:

MultiIndex([("a", 0, "x"),            ("a", 0, "y"),            ("a", 1, "x"),            ("a", 1, "y"),            ("a", 2, "x"),            ("a", 2, "y"),            ("b", 0, "x"),            ("b", 0, "y"),            ("b", 1, "x"),            ("b", 1, "y"),            ("b", 2, "x"),            ("b", 2, "y"),            ("c", 0, "x"),            ("c", 0, "y"),            ("c", 1, "x"),            ("c", 1, "y"),            ("c", 2, "x"),            ("c", 2, "y")],
           names=["name", "l1", "l2"])

pd.MultiIndex.from_frame()

通过现有的DataFrame直接来生成多层索引:

df = pd.DataFrame({"name":["xiaoming","guanyu","zhaoyun"],
                  "age":[23,39,34],
                  "sex":["male","male","female"]})
df

直接生成了多层索引,名字就是现有数据框的列字段:

In [24]:

pd.MultiIndex.from_frame(df)

Out[24]:

MultiIndex([("xiaoming", 23,   "male"),            (  "guanyu", 39,   "male"),            ( "zhaoyun", 34, "female")],
           names=["name", "age", "sex"])

通过names参数来指定名字:

In [25]:

# 可以自定义名字
pd.MultiIndex.from_frame(df,names=["col1","col2","col3"])

Out[25]:

MultiIndex([("xiaoming", 23,   "male"),            (  "guanyu", 39,   "male"),            ( "zhaoyun", 34, "female")],
           names=["col1", "col2", "col3"])

groupby()

通过groupby函数的分组功能计算得到:

In [26]:

df1 = pd.DataFrame({"col1":list("ababbc"),
                   "col2":list("xxyyzz"),
                   "number1":range(90,96),
                   "number2":range(100,106)})
df1

Out[26]:

df2 = df1.groupby(["col1","col2"]).agg({"number1":sum,
                                        "number2":np.mean})
df2

查看数据的索引:

In [28]:

df2.index

Out[28]:

MultiIndex([("a", "x"),            ("a", "y"),            ("b", "x"),            ("b", "y"),            ("b", "z"),            ("c", "z")],
           names=["col1", "col2"])

pivot_table()

通过数据透视功能得到:

In [29]:

df3 = df1.pivot_table(values=["col1","col2"],index=["col1","col2"])
df3

In [30]:

df3.index

Out[30]:

MultiIndex([("a", "x"),            ("a", "y"),            ("b", "x"),            ("b", "y"),            ("b", "z"),            ("c", "z")],
           names=["col1", "col2"])

以上就是python pandas创建多层索引MultiIndex的6种方式的详细内容,更多关于python pandas多层索引MultiIndex的资料请关注脚本之家其它相关文章!

关键词: 数据类型 笛卡尔积 混合使用 相关文章

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