How to Access Databases Using Python

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  1. Benefits of Using Python for Database Access:

    • Python ecosystem provides rich tools for data science, including NumPy, Pandas, matplotlib, and SciPy.
    • Python is easy to learn and has a simple syntax.
    • It's open-source and portable to many platforms.
    • Python supports relational database systems, and accessing databases is made easier with the Python Database API (DB API).

  2. Jupyter Notebooks:

    • Jupyter notebooks are popular in data science for creating and sharing documents containing live code, equations, visualizations, and narrative text.
    • Advantages of Jupyter notebooks include support for over 40 programming languages, easy sharing, and rich interactive output.

  3. Accessing Databases with Python:

    • Python programs communicate with database management systems (DBMS) using API calls.
    • SQL API consists of library function calls for the DBMS.
    • The basic operation of a typical SQL API involves connecting to the DBMS, sending SQL statements, retrieving query results, checking status, and disconnecting from the database.

  4. Proprietary APIs for SQL-based DBMS Systems:

    • Popular SQL-based DBMS systems have their own proprietary APIs.
    • Examples include MySQL C API, psycopg2 for PostgreSQL, IBM_DB for IBM DB2, dblib for SQL Server, ODBC for Microsoft Windows OS, OCI for Oracle, and JDBC for Java applications.

By understanding these concepts, you'll be able to connect Python applications to databases, execute SQL queries, and analyze data effectively.

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