SQL Syntax in 5 minutes. aka. DML, DDL, DCL...

When you look at the SQL Syntax you may notice that at least most simple things will be tackled quite similar all the time. Of course you still have freedom to do things a bit different in SQL. But I would claim that chances are much higher for two people to write the same SQL code for a given task compared to them writting the same Pandas code.

SQL is split into three different categories:

• DDL (Data Definition Language): CREATE, ALTER, and DROP
• DML (Data Manipulation Language): SELECT, INSERT, UPDATE, and DELETE
• DCL (Data Control Language): GRANT and REVOKE

1.Creating a Table:

SQLPandas

CREATE TABLE Employees (
    ID INT,
    Name VARCHAR(50),
    Age INT,
    Group VARCHAR(50)
);

import pandas as pd
df = pd.DataFrame(columns=['ID', 'Name', 'Age', 'Group'])

2.Inserting Data:

SQLPandas

INSERT INTO Employees (ID, Name, Age, Group)
VALUES (1, 'John', 30, 'A'),
      (2, 'Jane', 25, 'B'),
      (3, 'Smith', 35, 'A');

data = {'ID': [1, 2, 3],
        'Name': ['John', 'Jane', 'Smith'],
        'Age': [30, 25, 35]}
        'Group': ['A', 'B', 'A']}
df = pd.DataFrame(data)

3.Querying Data:

SQLPandas

SELECT * FROM Employees;

print(df)

4. Aggregations

SQLPandas

SELECT AVG(Age) as AverageAge, MAX(Age) as MaxAge
FROM Employees
GROUP BY Group;

grouped_df = df.groupby('Group').mean()

5. Permissions

SQL

GRANT SELECT, INSERT, UPDATE, DELETE ON employees TO user1;

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