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数据科学与SQL：组距分组分析 | 区间分布问题

news 2025/7/5 14:31:05

0 问题描述

1 数据准备

2 问题分析

3 小结

0 问题描述

绝对值分布分析也可以理解为组距分组分析。对于某个指标而言，一个记录对应的指标值的绝对值，肯定落在所有指标值的绝对值的最小值和最大值构成的区间内，根据一定的算法，在把这个区间划分为等距离的几个小区间，统计落入这些区间的指标值的绝对值的情况，决策者就可以得到指标值的绝对值在各个区间的分布情况。

以销售表为例，销售表如下：

销售信息样例表（Sales）
country	sale_month	sales_number	sales_value
USA	2008-01-01	1200	500000
USA	2008-02-01	1150	450000
USA	2008-03-01	1300	520000
USA	2008-04-01	1280	510000
USA	2008-05-01	1350	530000
USA	2008-06-01	1400	535000
USA	2008-07-01	1300	510000
USA	2008-08-01	1250	460000
USA	2008-09-01	1400	530000
USA	2008-10-01	1380	520000
USA	2008-11-01	1450	540000
USA	2008-12-01	1500	545000
USA	2009-01-01	1600	550000
USA	2009-02-01	1390	532000
USA	2009-03-01	1730	570000
USA	2009-04-01	1900	600000
USA	2009-05-01	1850	585000
USA	2009-06-01	3800	780000
USA	2009-07-01	1700	560000
USA	2009-08-01	1490	542000
USA	2009-09-01	1830	580000
USA	2009-10-01	2000	610000
USA	2009-11-01	1950	595000
USA	2009-12-01	1900	590000

1 数据准备

create table sales asselect 'USA' country, '2008-01-01' sale_month, '1200' sales_number, '500000' sales_value union allselect 'USA' country, '2008-02-01' sale_month, '1150' sales_number, '450000' sales_value union allselect 'USA' country, '2008-03-01' sale_month, '1300' sales_number, '520000' sales_value union allselect 'USA' country, '2008-04-01' sale_month, '1280' sales_number, '510000' sales_value union allselect 'USA' country, '2008-05-01' sale_month, '1350' sales_number, '530000' sales_value union allselect 'USA' country, '2008-06-01' sale_month, '1400' sales_number, '535000' sales_value union allselect 'USA' country, '2008-07-01' sale_month, '1300' sales_number, '510000' sales_value union allselect 'USA' country, '2008-08-01' sale_month, '1250' sales_number, '460000' sales_value union allselect 'USA' country, '2008-09-01' sale_month, '1400' sales_number, '530000' sales_value union allselect 'USA' country, '2008-10-01' sale_month, '1380' sales_number, '520000' sales_value union allselect 'USA' country, '2008-11-01' sale_month, '1450' sales_number, '540000' sales_value union allselect 'USA' country, '2008-12-01' sale_month, '1500' sales_number, '545000' sales_value union allselect 'USA' country, '2009-01-01' sale_month, '1600' sales_number, '550000' sales_value union allselect 'USA' country, '2009-02-01' sale_month, '1390' sales_number, '532000' sales_value union allselect 'USA' country, '2009-03-01' sale_month, '1730' sales_number, '570000' sales_value union allselect 'USA' country, '2009-04-01' sale_month, '1900' sales_number, '600000' sales_value union allselect 'USA' country, '2009-05-01' sale_month, '1850' sales_number, '585000' sales_value union allselect 'USA' country, '2009-06-01' sale_month, '3800' sales_number, '780000' sales_value union allselect 'USA' country, '2009-07-01' sale_month, '1700' sales_number, '560000' sales_value union allselect 'USA' country, '2009-08-01' sale_month, '1490' sales_number, '542000' sales_value union allselect 'USA' country, '2009-09-01' sale_month, '1830' sales_number, '580000' sales_value union allselect 'USA' country, '2009-10-01' sale_month, '2000' sales_number, '610000' sales_value union allselect 'USA' country, '2009-11-01' sale_month, '1950' sales_number, '595000' sales_value union allselect 'USA' country, '2009-12-01' sale_month, '1900' sales_number, '590000' sales_value
;

2 问题分析

第一步：按照给定的分组方法，计算区间开始，区间结束的值。计算区间范围维度表DIM

select group_num, min_num + group_step * pos       begin_num --区间开始, min_num + group_step * (pos + 1) end_num   --区间结束, pos
from (select pos, group_num, group_step, min_numfrom (select--分组方法CEIL(1 + LOG(10, count_num) / LOG(10, 2))                             group_num,--极差/组数 =组距CEIL((max_num - min_num) / CEIL(1 + LOG(10, count_num) / LOG(10, 2))) group_step,min_numfrom (SELECT MAX(sales_number) max_num,MIN(sales_number) min_num,COUNT(*)          COUNT_NUMFROM sales) t) tlateral view posexplode(split(space(cast(group_num as int) - 1), space(1))) tmp as pos, value) t

第二步：关联数据表SALES，计算落入区间范围的个数

with dim as (
select group_num, min_num + group_step * pos       begin_num --区间开始, min_num + group_step * (pos + 1) end_num   --区间结束, posfrom (select pos, group_num, group_step, min_numfrom (select--分组方法CEIL(1 + LOG(10, count_num) / LOG(10, 2))                             group_num,--极差/组数 =组距CEIL((max_num - min_num) / CEIL(1 + LOG(10, count_num) / LOG(10, 2))) group_step,min_numfrom (SELECT MAX(sales_number) max_num,MIN(sales_number) min_num,COUNT(*)          COUNT_NUMFROM sales) t) tlateral view posexplode(split(space(cast(group_num as int) - 1), space(1))) tmp as pos, value) t)select concat_ws('-', cast(b.begin_num as string), cast(b.end_num as string)) group_name, count(*)                                                           cnt
from dim bleft join sales a
WHERE a.sales_number >= b.begin_numAND a.sales_number < b.end_num
GROUP BY concat_ws('-', cast(b.begin_num as string), cast(b.end_num as string))

3 小结

组距分组是将全部变量值依次划分为若干个区间，并将这一区间的变量值作为一组。组距分组是数值型数据分组的基本形式。离散变量的整数值如果变动幅度较大，而且总体单位数N又很大，则也要进行组距分组。在组距分组中，各组之间的取值界限称为组限，一个组的最小值称为下限，最大值称为上限；上限与下限的差值称为组距；上限与下限值的平均数称为组中值，它是一组变量值的代表值。

具体步骤如下：

1. 确定组数。一组数据的组数一般与数据本身的特点及数据的多少有关。由于分组的目的之一是为了观察数据分布的特征，因此组数的多少应适中。如组数太少，数据的分布就会过于集中，组数太多，数据的分布就会过于分散，这都不便于观察数据分布的特征和规律。组数的确定应以能够显示数据的分布特征和规律为目的。

2.确定各组的组距。组距是一个组的上限与下限的差，可根据全部数据的最大值和最小值（即极差）及所分的组数来确定，即组距=（最大值－最小值）/组数。

3.根据分组整理成频数分布表。