目录
- 引言
- pd.MultiIndex.from_arrays()
- pd.MultiIndex.from_tuples()
- 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。
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]:
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
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]:
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]:
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的资料请关注编程笔记其它相关文章！
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