CH - 10 Libraries-and-modules

How to use standard libraries, create modules, and manage packages

One of Python's strengths is its extensive collection of libraries and modules. These are prewritten pieces of code that you can import into your programs to save time and effort.

• NumPy: This is a powerful library used for numerical computing. It makes working with large arrays and matrices much easier and more efficient. NumPy also provides mathematical functions for performing complex calculations, making it an essential tool in data science and machine learning.
• Vectorization: This is a technique used in NumPy that allows you to apply operations to entire arrays of data without writing explicit loops. This makes your code not only simpler but also faster, as vectorized operations are optimized for performance.
• SQLite3: It is a lightweight, disk-based database engine that doesn’t require a separate server process. It’s part of the Python Standard Library, allowing easy integration of SQL-based data management into Python applications. With SQLite3, you can create, read, update, and delete database records using SQL commands. The library provides a simple interface to interact with SQLite databases through Python's `sqlite3` module, which supports executing SQL queries, managing transactions, and handling database connections. This makes SQLite3 ideal for lightweight, embedded applications or as a temporary database for development and testing.
• File handling in Python: It can be efficiently managed using `with open()` and `writelines()`. The `with open()` statement serves as a context manager that opens a file and ensures it is properly closed after the block of code is executed, even if an error occurs, thereby simplifying resource management. Within this context, `writelines()` is used to write a list of strings to a file in one operation. Unlike `write()`, which handles single strings, `writelines()` writes multiple lines or strings from a list to the file sequentially without adding extra newlines. This approach is particularly useful for writing large amounts of text data efficiently.
• Pandas: It is a powerful data manipulation library that builds on file handling by providing high-level data structures and functions specifically designed for data analysis. It supports reading from and writing to various file formats, such as CSV, Excel, and SQL databases, using functions like read_csv(), read_excel(), to_csv(), and to_sql(). Pandas makes it easy to clean, transform, and analyse data using its DataFrame and Series objects, streamlining the data analysis process and enabling sophisticated data operations with minimal code.