【Python】SQL to Pandas 速查表（二）

摘要： 本篇將解構下面的SQL 查詢句式, 使用Pandas 進行實現 SQL 查詢句式 SELECT DISTINCT [欄位] FROM [表] JOIN [bin] ON [連線條件] WHERE [過濾條件] GROUP BY [欄位] HAVING [條...

本篇將解構下面的SQL 查詢句式, 使用Pandas 進行實現

SQL 查詢句式

SELECT DISTINCT [欄位] 
FROM [表] JOIN [bin] ON [連線條件] 
WHERE [過濾條件] 
GROUP BY [欄位] 
HAVING [條件] 
ORDER BY [欄位] DESC 
LIMIT [個數] OFFSET [個數]
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讀取評論資料

df_comments = pd.read_sql(sql="select * from comments", con=conn)
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資料預覽

df_comments
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	id	student_id	content
0	1	1	測試評論1
1	2	5	測試評論5
2	3	2	測試評論2
3	4	3	測試評論3
4	5	1	測試評論11
5	6	9	測試評論9

JOIN

(INNER) JOIN

SQL

SELECT
* 
FROM
student
INNER JOIN comments ON student.id = comments.student_id;
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Pandas

pd.merge(df, df_comments, left_on='id', right_on='student_id')
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LEFT (OUTER) JOIN

SQL

SELECT
* 
FROM
student
LEFT JOIN comments ON student.id = comments.student_id;
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Pandas

pd.merge(df, df_comments, left_on='id', right_on='student_id', how='left')
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RIGHT (OUTER) JOIN

SQL

SELECT
* 
FROM
student
RIGHT JOIN comments ON student.id = comments.student_id;
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Pandas

pd.merge(df, df_comments, left_on='id', right_on='student_id', how='right')
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UNION

SQL

SELECT * FROM student where city ='北京' 
UNION 
SELECT * FROM student where sex ='男';
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Pandas

pd.concat([df[df.city == '北京'], df[df.sex == '男']]).drop_duplicates().reset_index()
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UNION ALL

SQL

SELECT * FROM student where city ='北京' 
UNION ALL 
SELECT * FROM student where sex ='男';
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Pandas

pd.concat([df[df.city == '北京'], df[df.sex == '男']]).reset_index()
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本篇內容

本篇將解構下面的SQL 查詢句式, 使用Pandas 進行實現

SQL 建立句式

CREATE TABLE [表名] (
[列名] [型別],
[列名] [型別],
....
);
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SQL 插入句式

INSERT INTO [表名] VALUES ([值], [值], ...);
INSERT INTO [表名] ([列名],[列名] ...) VALUES ([值], [值], ...);
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SQL 更新句式

UPDATE [表名]
SET [列名] = [值], [列名] = [值]
WHERE [過濾條件];
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SQL 刪除句式

DELETE FROM [表名] WHERE [過濾條件];
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CREATE

SQL

CREATE TABLE student (
id INT ( 11 ) NOT NULL AUTO_INCREMENT,
name VARCHAR ( 10 ) COLLATE utf8mb4_general_ci DEFAULT NULL,
age date DEFAULT NULL,
sex VARCHAR ( 10 ) COLLATE utf8mb4_general_ci DEFAULT NULL,
city VARCHAR ( 255 ) CHARACTER 
SET utf8mb4 COLLATE utf8mb4_general_ci DEFAULT NULL,
money DOUBLE ( 255, 2 ) DEFAULT NULL,

);
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Pandas

pd.DataFrame(columns=['id', 'name', 'sex', 'city', 'money'])
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INSERT

SQL

INSERT INTO student (id, name, age, sex, city, money )
VALUES
(1, '張三', '2017-12-20', '女', '天津', 20.00 );
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Pandas

# 第一種
df.loc[-1] = [1, '張三', '女', '天津', 20.00]
df.index = df.index + 1
df = df.sort_index()

# 第二種
temp_pd = pd.DataFrame({'id': [1], 'name': ['張三'], 'sex': ['女'], 'city': ['天津'], 'money': [20.00]})
df = pd.concat([df,temp_pd], ignore_index=True)
df.reset_index()

# 第三種
temp_pd = pd.DataFrame([[1,'張三1', '女', '天津', 20.00]], columns=df.columns)
df = pd.concat([df, temp_pd])
df.reset_index()
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UPDATE

SQL

UPDATE student SET money = 300 WHERE id = 1;
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Pandas

df.loc[df.id == 1, 'money'] = 300
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