[MySQL] mysql 에서 컬럼과 로우 바꾸기,

행과 열 바꾸기

How to Transpose Rows to Columns Dynamically in MySQL

Sometimes, your data might stored in rows and you might want to report it as columns. In such cases, you will need to transpose rows into columns. Sometimes, even these rows can be variable. So you might know how many columns you need. In such cases, you need to transpose rows to columns dynamically. Since there is no built-in function to do that in MySQL, you need to accomplish it using an SQL query. Here’s an SQL query to dynamically transpose rows to columns in MySQL.

 

How to Transpose Rows to Columns Dynamically in MySQL

Here’s how to create dynamic pivot tables in MySQL. Let’s say you have the following table

CREATE TABLE Meeting
(
    ID INT,
    Meeting_id INT,
    field_key VARCHAR(100),
    field_value VARCHAR(100)
);

INSERT INTO Meeting(ID,Meeting_id,field_key,field_value)
VALUES (1, 1,'first_name' , 'Alec');
INSERT INTO Meeting(ID,Meeting_id,field_key,field_value)
VALUES (2, 1,'last_name' , 'Jones');
INSERT INTO Meeting(ID,Meeting_id,field_key,field_value)
VALUES (3, 1,'occupation' , 'engineer');
INSERT INTO Meeting(ID,Meeting_id,field_key,field_value)
VALUES (4,2,'first_name' , 'John');
INSERT INTO Meeting(ID,Meeting_id,field_key,field_value)
VALUES (5,2,'last_name' , 'Doe');
INSERT INTO Meeting(ID,Meeting_id,field_key,field_value)
VALUES (6,2,'occupation' , 'engineer');

+------+------------+------------+-------------+
| ID   | Meeting_id | field_key  | field_value |
+------+------------+------------+-------------+
|    1 |          1 | first_name | Alec        |
|    2 |          1 | last_name  | Jones       |
|    3 |          1 | occupation | engineer    |
|    4 |          2 | first_name | John        |
|    5 |          2 | last_name  | Doe         |
|    6 |          2 | occupation | engineer    |
+------+------------+------------+-------------+

Let’s say you want to transpose rows to columns dynamically, such that a new column is created for each unique value in field_key column, that is (first_name, last_name, occupation)

 [출처] https://ubiq.co/database-blog/transpose-rows-columns-dynamically-mysql/

+------------+-------------+-------------+-------------+
| Meeting_id | first_name  |  last_name  |  occupation |
+------------+-------------+-------------+-------------+
|          1 |       Alec  | Jones       | engineer    |
|          2 |       John  | Doe         | engineer    |
+------------+-------------+-------------+-------------+

 

Transpose rows to columns dynamically

If you already know which columns you would be creating beforehand, you can simply use a CASE statement to create a pivot table.

Since we don’t know which columns to be created, we will have to dynamically transpose rows to columns using GROUP_CONCAT function, as shown below

SET @sql = NULL;
SELECT
  GROUP_CONCAT(DISTINCT
    CONCAT(
      'max(case when field_key = ''',
      field_key,
      ''' then field_value end) ',
      field_key
    )
  ) INTO @sql
FROM
  Meeting;
SET @sql = CONCAT('SELECT Meeting_id, ', @sql, ' 
                  FROM Meeting 
                   GROUP BY Meeting_id');

PREPARE stmt FROM @sql;
EXECUTE stmt;
DEALLOCATE PREPARE stmt;

GROUP_CONCAT allows you to concatenate field_key values from multiple rows into a single string. In the above query, we use GROUP_CONCAT to dynamically create CASE statements, based on the unique values in field_key column and store that string in @sql variable, which is then used to create our select query.

+------------+------------+-----------+------------+
| Meeting_id | first_name | last_name | occupation |
+------------+------------+-----------+------------+
|          1 | Alec       | Jones     | engineer   |
|          2 | John       | Doe       | engineer   |
+------------+------------+-----------+------------+

This is how you can automate pivot table queries in MySQL and transpose rows to columns dynamically.

 

You can customize the above query as per your requirements by adding WHERE clause or JOINS.

 

If you want to transpose only select row values as columns, you can add WHERE clause in your 1st select GROUP_CONCAT statement.

SELECT
  GROUP_CONCAT(DISTINCT
    CONCAT(
      'max(case when field_key = ''',
      field_key,
      ''' then field_value end) ',
      field_key
    )
  ) INTO @sql
FROM
  Meeting
WHERE <condition>;

Also read: How to Unpivot Table in MySQL

경축! 아무것도 안하여 에스천사게임즈가 새로운 모습으로 재오픈 하였습니다.
어린이용이며, 설치가 필요없는 브라우저 게임입니다.
https://s1004games.com

 

If you want to filter rows in your final pivot table, you can add the WHERE clause in your SET statement.

SET @sql = CONCAT('SELECT Meeting_id, ', @sql, ' 
                  FROM Meeting WHERE <condition>
                   GROUP BY Meeting_id');

Similarly, you can also apply JOINS in your SQL query while you transpose rows to columns dynamically in MySQL.

Here’s an example of pivot table created using Ubiq.

transpose rows to columns dynamically

 

 

[출처] https://ubiq.co/database-blog/transpose-rows-columns-dynamically-mysql/

 

 

 

 

 

 

본 웹사이트는 광고를 포함하고 있습니다.
광고 클릭에서 발생하는 수익금은 모두 웹사이트 서버의 유지 및 관리, 그리고 기술 콘텐츠 향상을 위해 쓰여집니다.
번호 제목 글쓴이 날짜 조회 수
공지 오라클 기본 샘플 데이터베이스 졸리운_곰 2014.01.02 86040
공지 [SQL컨셉] 서적 "SQL컨셉"의 샘플 데이타 베이스 SAMPLE DATABASE of ORACLE 가을의 곰을... 2013.02.10 78568
공지 [G_SQL] Sample Database 가을의 곰을... 2012.05.20 95292
33 [spark][sparksql][odbc][jdbc] JDBC and ODBC drivers and configuration parameters file 졸리운_곰 2021.04.14 4127
32 [spark][pyspark][php] Natively Connect to Spark Data in PHP 졸리운_곰 2021.04.14 1700
31 [SPARK][Python][pySpark][아콘 소프트][나무기술] Real-world Python workloads on Spark: Standalone clusters : 스파크 예제 논란, driver-host 불필요 file 졸리운_곰 2021.04.03 1690
30 [Spark] Apache Spark Cluster(Standalone) 스파크 클러스터 스텐드 얼론 구축 졸리운_곰 2021.03.28 1522
29 [Spark][머신러닝] Apache Spark-Python vs Scala 성능 비교 file 졸리운_곰 2021.03.21 1152
28 [Spark][MSA] Apache Spark - Key/Value Paris (Pair RDD) 졸리운_곰 2021.03.21 1583
27 [Spark][머신러닝] Apache Spark - RDD (Resilient Distributed DataSet) Persistence file 졸리운_곰 2021.03.21 1312
26 [Spark][머신러닝] Apache Spark - RDD (Resilient Distributed DataSet) 이해하기 - #2 file 졸리운_곰 2021.03.21 1085
25 [Spark][머신러닝] Apache Spark - RDD (Resilient Distributed DataSet) 이해하기 - #1 file 졸리운_곰 2021.03.21 1677
24 [Spark][머신러닝] Apache Spark 소개 - 스파크 스택 구조 file 졸리운_곰 2021.03.21 1318
23 [Spark] cache()와 persist()의 차이 file 졸리운_곰 2021.03.16 1550
22 [Spark] Spark - RDD vs Dataframes vs Datasets 우리는 언제, 왜 RDD, Dataframes, Datasets를 사용해야 할까? file 졸리운_곰 2021.03.15 1317
21 [Spark & Oracle] Reading Data From Oracle Database With Apache Spark file 졸리운_곰 2021.03.15 1147
20 [spark][pySpark] 스파크 튜토리얼 - 스파크 SQL file 졸리운_곰 2021.03.15 1856
19 [spark][flask][python] Machine learning at Scale using Pyspark & deployment using AzureML/Flask file 졸리운_곰 2021.03.14 2034
18 [pySpark, 파이썬 spark] Best Practices Writing Production-Grade PySpark Jobs file 졸리운_곰 2021.03.14 1689
17 [apache spark] 아파치 스파크 Data Sharing between multiple Spark Jobs in Databricks file 졸리운_곰 2021.03.13 1792
16 [Apache Spark] Spark SQL 아파치 스파크 SQL 개요 졸리운_곰 2021.03.13 1282
15 [spark] Apache Livy: A REST Interface for Apache Spark file 졸리운_곰 2021.03.12 1354
14 [spark] Spark 및 Oracle 데이터베이스 file 졸리운_곰 2021.03.06 1409
대표 김성준 주소 : 경기 용인 분당수지 U타워 등록번호 : 142-07-27414
통신판매업 신고 : 제2012-용인수지-0185호 출판업 신고 : 수지구청 제 123호 개인정보보호최고책임자 : 김성준 sjkim70@stechstar.com
대표전화 : 010-4589-2193 [fax] 02-6280-1294 COPYRIGHT(C) stechstar.com ALL RIGHTS RESERVED