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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