python pandas創建多層索引MultiIndex的6種方式

引言

在上一篇文章中介紹瞭如何創建Pandas中的單層索引,今天給大傢帶來的是如何創建Pandas中的多層索引。

pd.MultiIndex,即具有多個層次的索引。通過多層次索引,我們就可以操作整個索引組的數據。本文主要介紹在Pandas中創建多層索引的6種方式:

  • pd.MultiIndex.from_arrays():多維數組作為參數,高維指定高層索引,低維指定低層索引。
  • pd.MultiIndex.from_tuples():元組的列表作為參數,每個元組指定每個索引(高維和低維索引)。
  • pd.MultiIndex.from_product():一個可迭代對象的列表作為參數,根據多個可迭代對象元素的笛卡爾積(元素間的兩兩組合)進行創建索引。
  • pd.MultiIndex.from_frame:根據現有的數據框來直接生成
  • groupby():通過數據分組統計得到
  • pivot_table():生成透視表的方式來得到

pd.MultiIndex.from_arrays()

In [1]:

import pandas as pd
import numpy as np

通過數組的方式來生成,通常指定的是列表中的元素:

In [2]:

# 列表元素是字符串和數字
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)  # 查看數據類型

通過type函數來查看數據類型,發現的確是:MultiIndex

Out[3]:

pandas.core.indexes.multi.MultiIndex

在創建的同時可以指定每個層級的名字:

In [4]:

# 列表元素全是字符串
array2 = [["xiaoming","guanyu","zhangfei"],
          ["male","male","female"]
         ]
m2 = pd.MultiIndex.from_arrays(
	array2, 
  # 指定姓名和性別
  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個層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","性別","年齡"])
m3

Out[5]:

MultiIndex([('xiaoming',   'male', 22),            (  'guanyu',   'male', 25),            ('zhangfei', 'female', 27)],
           names=['姓名', '性別', '年齡'])

pd.MultiIndex.from_tuples()

通過元組的形式來生成多層索引:

In [6]:

# 元組的形式
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]:

# 元組構成的3層索引
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)],
           )

列表和元組是可以混合使用的

  • 最外層是列表
  • 裡面全部是元組

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","性別","年齡"])
m6

Out[8]:

MultiIndex([('xiaoming',   'male', 18),            (  'guanyu',   'male', 35),            ('zhangfei', 'female', 27)],
           names=['姓名', '性別', '年齡'] # 指定名字
           )

pd.MultiIndex.from_product()

使用可迭代對象的列表作為參數,根據多個可迭代對象元素的笛卡爾積(元素間的兩兩組合)進行創建索引。

在Python中,我們使用 isinstance()函數 判斷python對象是否可迭代:

# 導入 collections 模塊的 Iterable 對比對象
from collections import Iterable

通過上面的例子我們總結:常見的字符串、列表、集合、元組、字典都是可迭代對象

下面舉例子來說明:

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]:

# 需要展開成列表形式
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]:

# 使用元組形式
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函數
strings = ("a","b","c")  # 3個元素
lists = range(3)  # 0,1,2  3個元素
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函數
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  # 總個數 3*3*2=18

總個數是“332=18`個:

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()

通過現有的DataFrame直接來生成多層索引:

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

直接生成瞭多層索引,名字就是現有數據框的列字段:

In [24]:

pd.MultiIndex.from_frame(df)

Out[24]:

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

通過names參數來指定名字:

In [25]:

# 可以自定義名字
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()

通過groupby函數的分組功能計算得到:

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

查看數據的索引:

In [28]:

df2.index

Out[28]:

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

pivot_table()

通過數據透視功能得到:

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'])

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