Python and RDBMS Crash Course | Jupyter Notebook | Python Data Science for beginners

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Python is a popular programming language that is widely used for a variety of tasks, including data analysis, machine learning, and web development. One of the key features of Python is its ability to interact with databases, including relational databases such as MySQL, PostgreSQL, and SQLite.

Relational databases, also known as RDBMS (Relational Database Management Systems), are a type of database that store data in tables, which are similar to Excel spreadsheets. Each table is made up of rows and columns, where each row represents a record and each column represents a field within that record.

Python can interact with RDBMS through a variety of libraries and modules. The most popular libraries used for this purpose are MySQLdb, psycopg2, and sqlite3. These libraries allow Python to connect to a database, execute SQL commands, and retrieve data from the database.

To connect to a database using one of these libraries, you need to provide the library with the necessary connection details such as the hostname, port, username, and password. Once connected, you can use the library to execute SQL commands and retrieve data from the database.

Python can also be used to manipulate data in a RDBMS. For instance, you can use it to insert, update and delete data from tables.

In addition to the libraries mentioned above, there are other libraries and frameworks that are built on top of the RDBMS libraries, such as SQLAlchemy, Django ORM, and Peewee, that provide a higher-level abstraction for working with databases. These libraries can simplify the process of connecting to a database and executing SQL commands, making it easier to work with data in Python.

In summary, Python is a powerful programming language that can be used to interact with RDBMS, allowing you to retrieve and manipulate data in a variety of ways. With the help of libraries such as MySQLdb, psycopg2, and sqlite3, you can easily connect to a database, execute SQL commands and retrieve data, making it an ideal choice for data analysis and other data-related tasks.

 

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in Python programming: Python and RDBMS Crash Course.

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Python and RDBMS Crash Course:



 

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