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引言 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登录后复制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登录后复制Out[2]: MultiIndex([('xiaoming', 22), ( 'guanyu', 25), ('zhangfei', 27)], )登录后复制登录后复制In [3]: type(m1) # 查看数据类型登录后复制通过type函数来查看数据类型,发现的确是:MultiIndex Out[3]: pandas.core.indexes.multi.MultiIndex登录后复制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登录后复制Out[4]: MultiIndex([('xiaoming', 'male'), ( 'guanyu', 'male'), ('zhangfei', 'female')], names=['name', 'sex'])登录后复制下面的例子是生成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登录后复制Out[5]: MultiIndex([('xiaoming', 'male', 22), ( 'guanyu', 'male', 25), ('zhangfei', 'female', 27)], names=['姓名', '性别', '年龄'])登录后复制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登录后复制Out[6]: MultiIndex([('xiaoming', 22), ( 'guanyu', 25), ('zhangfei', 27)], )登录后复制登录后复制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登录后复制Out[7]: MultiIndex([('xiaoming', 'male', 22), ( 'guanyu', 'male', 25), ('zhangfei', 'female', 27)], )登录后复制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登录后复制Out[8]: MultiIndex([('xiaoming', 'male', 18), ( 'guanyu', 'male', 35), ('zhangfei', 'female', 27)], names=['姓名', '性别', '年龄'] # 指定名字 )登录后复制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登录后复制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登录后复制Out[18]: MultiIndex([('xiaoming', 22), ('xiaoming', 25), ( 'guanyu', 22), ( 'guanyu', 25), ('zhangfei', 22), ('zhangfei', 25)], names=['name', 'number'])登录后复制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登录后复制Out[19]: MultiIndex([('a', 1), ('a', 2), ('b', 1), ('b', 2), ('c', 1), ('c', 2)], names=['alpha', 'number'])登录后复制登录后复制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登录后复制Out[20]: MultiIndex([('a', 1), ('a', 2), ('b', 1), ('b', 2), ('c', 1), ('c', 2)], names=['alpha', 'number'])登录后复制登录后复制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登录后复制Out[21]: MultiIndex([('a', 0), ('a', 1), ('a', 2), ('b', 0), ('b', 1), ('b', 2), ('c', 0), ('c', 1), ('c', 2)], names=['alpha', 'number'])登录后复制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登录后复制总个数是``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'])登录后复制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登录后复制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)登录后复制Out[24]: MultiIndex([('xiaoming', 23, 'male'), ( 'guanyu', 39, 'male'), ( 'zhaoyun', 34, 'female')], names=['name', 'age', 'sex'])登录后复制通过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"])登录后复制Out[25]: MultiIndex([('xiaoming', 23, 'male'), ( 'guanyu', 39, 'male'), ( 'zhaoyun', 34, 'female')], names=['col1', 'col2', 'col3'])登录后复制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登录后复制Out[26]: 查看数据的索引: In [28]: df2.index登录后复制Out[28]: MultiIndex([('a', 'x'), ('a', 'y'), ('b', 'x'), ('b', 'y'), ('b', 'z'), ('c', 'z')], names=['col1', 'col2'])登录后复制登录后复制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登录后复制In [30]: df3.index登录后复制Out[30]: MultiIndex([('a', 'x'), ('a', 'y'), ('b', 'x'), ('b', 'y'), ('b', 'z'), ('c', 'z')], names=['col1', 'col2'])登录后复制登录后复制以上就是如何使用Python的pandas库创建多层次索引(MultiIndex)?的详细内容,更多请关注php中文网其它相关文章! |
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