深入解析：板凳-------Mysql cookbook学习（九--2）

mysql>
show
databases
;
+--------------------+
|
Database |
+--------------------+
| cookbook |
| employees |
| information_schema |
| mysql |
| performance_schema |
| sys |
+--------------------+
6
rows in
set (0.01 sec)
mysql>
use employees;
Database changed

#–基本SQL语句练习

基本联表查询（获取当前薪资）

sql
SELECT e.first_name, s.salary, s.salary+1000
AS increased_salary
FROM employees e
JOIN salaries s ON e.emp_no = s.emp_no
WHERE e.emp_no IN (
SELECT emp_no FROM dept_emp WHERE dept_no = 'd005' -- 假设 department_id 50 对应 dept_no 'd005'
)
AND s.to_date = '9999-01-01'
;
-- 获取当前有效薪资
2. 如果你需要按部门查询（假设部门50对应 dept_no 'd005'）：
sql
SELECT e.first_name, s.salary, s.salary+1000
AS increased_salary
FROM employees e
JOIN salaries s ON e.emp_no = s.emp_no
JOIN dept_emp de ON e.emp_no = de.emp_no
WHERE de.dept_no = 'd005' -- 部门编号可能需要调整
AND s.to_date = '9999-01-01'
AND de.to_date = '9999-01-01'
;
-- 当前部门任职
3. 如果不知道部门编号对应关系：
sql
SELECT *
FROM departments;
-- 先查看部门编号对应关系
mysql>
SELECT
-> d.dept_no,
-> d.dept_name,
->
COUNT(
DISTINCT e.emp_no)
AS employee_count,
->
SUM(s.salary)
AS total_salary,
->
AVG(s.salary)
AS avg_salary
->
FROM
-> departments d
->
JOIN
-> dept_emp de ON d.dept_no = de.dept_no
->
JOIN
-> employees e ON de.emp_no = e.emp_no
->
JOIN
-> salaries s ON e.emp_no = s.emp_no
->
WHERE
-> de.to_date = '9999-01-01'
->
AND s.to_date = '9999-01-01'
->
GROUP
BY
-> d.dept_no, d.dept_name;
+---------+--------------------+----------------+--------------+------------+
| dept_no | dept_name | employee_count | total_salary | avg_salary |
+---------+--------------------+----------------+--------------+------------+
| d001 | Marketing | 14842 | 1188233434 | 80058.8488 |
| d002 | Finance | 12437 | 977049936 | 78559.9370 |
| d003 | Human Resources | 12898 | 824464664 | 63921.8998 |
| d004 | Production | 53304 | 3616319369 | 67843.3020 |
| d005 | Development | 61386 | 4153249050 | 67657.9196 |
| d006 | Quality Management | 14546 | 951919236 | 65441.9934 |
| d007 | Sales | 37701 | 3349845802 | 88852.9695 |
| d008 | Research | 15441 | 1048650423 | 67913.3750 |
| d009 | Customer Service | 17569 | 1182134209 | 67285.2302 |
+---------+--------------------+----------------+--------------+------------+
9
rows in
set (3.55 sec)

mysql>
SELECT
-> d.dept_no,
-> d.dept_name,
->
COUNT(
DISTINCT e.emp_no)
AS employee_count,
->
SUM(s.salary)
AS total_salary,
->
AVG(s.salary)
AS avg_salary,
-> CONCAT(FORMAT(SUM(s.salary)/10000
, 2
)
, '万'
)
AS total_salary_wan,
-> CONCAT(
->
SUM(
CASE
WHEN s.salary <
50000
THEN 1
ELSE 0
END
)
, '人'
,
->
'('
, ROUND(SUM(
CASE
WHEN s.salary <
50000
THEN 1
ELSE 0
END
)/COUNT(*
)*100
, 0
)
, '%)'
->
)
AS low_salary_stats,
-> CONCAT(
->
SUM(
CASE
WHEN s.salary BETWEEN 50000 AND 100000
THEN 1
ELSE 0
END
)
, '人'
,
->
'('
, ROUND(SUM(
CASE
WHEN s.salary BETWEEN 50000 AND 100000
THEN 1
ELSE 0
END
)/COUNT(*
)*100
, 0
)
, '%)'
->
)
AS medium_salary_stats
->
FROM
-> departments d
->
JOIN
-> dept_emp de ON d.dept_no = de.dept_no
->
JOIN
-> employees e ON de.emp_no = e.emp_no
->
JOIN
-> salaries s ON e.emp_no = s.emp_no
->
WHERE
-> de.to_date = '9999-01-01'
->
AND s.to_date = '9999-01-01'
->
GROUP
BY
-> d.dept_no, d.dept_name;
+---------+--------------------+----------------+--------------+------------+------------------+------------------+---------------------+
| dept_no | dept_name | employee_count | total_salary | avg_salary | total_salary_wan | low_salary_stats | medium_salary_stats |
+---------+--------------------+----------------+--------------+------------+------------------+------------------+---------------------+
| d001 | Marketing | 14842 | 1188233434 | 80058.8488 | 118
,823.34万 | 481人(3%
) | 12369人(83%
) |
| d002 | Finance | 12437 | 977049936 | 78559.9370 | 97
,704.99万 | 456人(4%
) | 10560人(85%
) |
| d003 | Human Resources | 12898 | 824464664 | 63921.8998 | 82
,446.47万 | 1825人(14%
) | 10927人(85%
) |
| d004 | Production | 53304 | 3616319369 | 67843.3020 | 361
,631.94万 | 5292人(10%
) | 46794人(88%
) |
| d005 | Development | 61386 | 4153249050 | 67657.9196 | 415
,324.91万 | 6229人(10%
) | 53805人(88%
) |
| d006 | Quality Management | 14546 | 951919236 | 65441.9934 | 95
,191.92万 | 1821人(13%
) | 12546人(86%
) |
| d007 | Sales | 37701 | 3349845802 | 88852.9695 | 334
,984.58万 | 397人(1%
) | 27240人(72%
) |
| d008 | Research | 15441 | 1048650423 | 67913.3750 | 104
,865.04万 | 1584人(10%
) | 13487人(87%
) |
| d009 | Customer Service | 17569 | 1182134209 | 67285.2302 | 118
,213.42万 | 2220人(13%
) | 14594人(83%
) |
+---------+--------------------+----------------+--------------+------------+------------------+------------------+---------------------+
9
rows in
set (4.37 sec)

mysql>
SHOW VARIABLES LIKE 'secure_file_priv'
;
+------------------+---------------------------------+
| Variable_name |
Value |
+------------------+---------------------------------+
| secure_file_priv | D:\software\MySql\Data\Uploads\ |
+------------------+---------------------------------+
1
row in
set
, 1 warning (0.10 sec)
SELECT
d.dept_name,
COUNT(*
)
AS employee_count,
AVG(s.salary)
AS avg_salary,
SUM(s.salary)
AS total_salary
FROM
departments d
JOIN
dept_emp de ON d.dept_no = de.dept_no
JOIN
salaries s ON de.emp_no = s.emp_no
WHERE
de.to_date = '9999-01-01'
AND s.to_date = '9999-01-01'
GROUP
BY
d.dept_name
INTO
OUTFILE 'D:\\software\\MySql\\Data\\Uploads\\department_salary.csv'
FIELDS
TERMINATED
BY ','
ENCLOSED
BY '"'
LINES
TERMINATED
BY '\n'
;

在 MySQL 中，确保使用 utf8mb4 字符集导出数据：

SELECT
'dept_name'
AS dept_name,
'employee_count'
AS employee_count,
'avg_salary'
AS avg_salary,
'total_salary'
AS total_salary
UNION
ALL
SELECT
d.dept_name,
COUNT(*
)
AS employee_count,
AVG(s.salary)
AS avg_salary,
SUM(s.salary)
AS total_salary
FROM
departments d
JOIN
dept_emp de ON d.dept_no = de.dept_no
JOIN
salaries s ON de.emp_no = s.emp_no
WHERE
de.to_date = '9999-01-01'
AND s.to_date = '9999-01-01'
GROUP
BY
d.dept_name
INTO
OUTFILE 'D:\\software\\MySql\\Data\\Uploads\\department_salary.csv'
CHARACTER
SET utf8mb4 -- 显式指定字符集
FIELDS
TERMINATED
BY ','
ENCLOSED
BY '"'
LINES
TERMINATED
BY '\n'
;

import pandas as pd
import matplotlib.pyplot as plt
# 方法1：使用 utf-8-sig 编码（自动处理 BOM 头）
df = pd.read_csv(
'D:/software/MySql/Data/Uploads/department_salary.csv'
,
encoding='utf-8-sig'
, # 推荐使用，兼容带 BOM 的 UTF-8 文件
header=None,
names=['dept_name'
, 'employee_count'
, 'avg_salary'
, 'total_salary']
)
# 方法2：使用 utf-8 编码（如果文件无 BOM 头）
df = pd.read_csv(
'D:/software/MySql/Data/Uploads/department_salary.csv'
,
encoding='utf-8'
,
header=None,
names=['dept_name'
, 'employee_count'
, 'avg_salary'
, 'total_salary']
)
# 验证中文显示
print(df.head(
)
)
# 绘图代码（保持不变）
plt.figure(figsize=(12
, 6
)
)
plt.bar(df['dept_name']
, df['avg_salary']
, color='skyblue'
)
plt.xticks(rotation=45
, ha='right'
)
plt.title('各部门平均薪资对比'
, fontsize=14
)
plt.xlabel('部门名称'
, fontsize=12
)
plt.ylabel('平均薪资'
, fontsize=12
)
plt.grid(axis='y'
, linestyle='--'
, alpha=0.7
)
plt.tight_layout(
)
plt.
show(
)

在这里插入图片描述

mysql>
SELECT
-> dept_name,
->
COUNT(*
)
AS total_employees,
-> CONCAT(ROUND(SUM(salary <
40000
)/COUNT(*
)*100
)
, '%'
)
AS "低薪(<4万)"
,
-> CONCAT(ROUND(SUM(salary BETWEEN 40000 AND 80000
)/COUNT(*
)*100
)
, '%'
)
AS "中薪(4-8万)"
,
-> CONCAT(ROUND(SUM(salary >
80000
)/COUNT(*
)*100
)
, '%'
)
AS "高薪(>8万)"
->
FROM (
->
SELECT
-> d.dept_name, s.salary
->
FROM
-> salaries s
->
JOIN
-> dept_emp de ON s.emp_no = de.emp_no
->
JOIN
-> departments d ON de.dept_no = d.dept_no
->
WHERE
-> s.to_date = '9999-01-01'
->
AND de.to_date = '9999-01-01'
->
)
AS current_salaries
->
GROUP
BY dept_name
->
ORDER
BY SUM(salary >
80000
)/COUNT(*
)
DESC
;
-- 按高薪比例排序
+--------------------+-----------------+------------+-------------+------------+
| dept_name | total_employees | 低薪(<
4万) | 中薪(4-8万) | 高薪(>
8万) |
+--------------------+-----------------+------------+-------------+------------+
| Sales | 37701 | 0% | 32% | 68% |
| Marketing | 14842 | 0% | 52% | 48% |
| Finance | 12437 | 0% | 55% | 45% |
| Production | 53304 | 0% | 81% | 19% |
| Research | 15441 | 0% | 81% | 19% |
| Development | 61386 | 0% | 81% | 19% |
| Customer Service | 17569 | 0% | 81% | 18% |
| Quality Management | 14546 | 0% | 86% | 14% |
| Human Resources | 12898 | 0% | 89% | 11% |
+--------------------+-----------------+------------+-------------+------------+
9
rows in
set (7.02 sec)

mysql>
WITH ranked_salaries AS (
->
SELECT
-> d.dept_name,
-> s.salary,
-> ROW_NUMBER(
)
OVER (
->
PARTITION
BY d.dept_no
->
ORDER
BY s.salary
->
)
AS row_num,
->
COUNT(*
)
OVER (
PARTITION
BY d.dept_no)
AS total_employees
->
FROM departments d
->
JOIN dept_emp de ON d.dept_no = de.dept_no
->
JOIN salaries s ON de.emp_no = s.emp_no
->
WHERE de.to_date = '9999-01-01'
->
AND s.to_date = '9999-01-01'
->
)
->
SELECT
-> dept_name,
->
AVG(salary)
AS avg_salary,
->
MAX(
CASE
WHEN row_num = CEIL(total_employees/2.0
)
THEN salary END
)
AS median_salary
->
FROM ranked_salaries
->
GROUP
BY dept_name;
+--------------------+------------+---------------+
| dept_name | avg_salary | median_salary |
+--------------------+------------+---------------+
| Marketing | 80058.8488 | 79125 |
| Finance | 78559.9370 | 77858 |
| Human Resources | 63921.8998 | 62810 |
| Production | 67843.3020 | 66725 |
| Development | 67657.9196 | 66449 |
| Quality Management | 65441.9934 | 64381 |
| Sales | 88852.9695 | 88516 |
| Research | 67913.3750 | 66667 |
| Customer Service | 67285.2302 | 65149 |
+--------------------+------------+---------------+
9
rows in
set (11.74 sec)

posted on 2025-10-06 21:22 slgkaifa 阅读(8) 评论(0) 收藏举报

刷新页面返回顶部

slgkaifa

深入解析：板凳-------Mysql cookbook学习（九--2）

导航

公告

slgkaifa

深入解析：板凳-------Mysql cookbook学习 （九--2）

导航

公告

深入解析：板凳-------Mysql cookbook学习（九--2）