What are Modules ? 🤔

Python modules are essentially Python scripts that contain function definitions, variables, and other Python code. Modules are useful because they allow you to break up your code into smaller, more manageable pieces that can be imported into other scripts or programs. This makes it easier to reuse code and build larger, more complex programs.

To use a module in your code, you must first import it using the import statement. For example, to import the math module, you would use the following code:

import math

Once you have imported the module, you can access its functions and variables using the dot notation. For example, to use the sqrt function from the math module, you would use the following code:

import math

result = math.sqrt(4)
print(result)  # Output: 2.0

Types of Modules: 💻

There are two main types of modules in Python: built-in modules and third-party modules. Built-in modules are modules that are included with the Python interpreter and are available to all Python programs. Some examples of built-in modules include math, os, and sys.

Why we use PIP ?

Third-party modules are modules that are developed by external organizations and are not included with the Python interpreter. To use a third-party module, you must first install it using a package manager such as pip.

pip is a package manager for Python that makes it easy to install and manage third-party modules. To use pip, you must first install it onto your system. Once pip is installed, you can use it to install third-party modules by running the following command:

pip install <module name>

For example, to install the requests module, you would run the following command:

pip install requests

After installing a module using pip, you can import it into your Python code just like any other module.

In summary, modules are a useful way to organize and reuse your code in Python. pip is a package manager that makes it easy to install and manage third-party modules in Python.

Some Python modules and their use:

1. NumPy - a library for working with large, multi-dimensional arrays and matrices of numerical data, used for scientific computing and data analysis.

2. Pandas - a library for data manipulation and analysis, designed to work with data stored in tables (e.g. in a DataFrame).

3. Matplotlib - a 2D plotting library used to create static, animated, and interactive visualizations in Python.

4. Seaborn - a library for statistical data visualization based on Matplotlib, used for creating attractive and informative statistical graphics.

5. Scikit-learn - a library for machine learning in Python, providing simple and efficient tools for data mining and data analysis.

6. TensorFlow - a library for numerical computation and large-scale machine learning, used for training and deploying machine learning models.

7. PyTorch - a library for deep learning and optimization, used for building and training neural networks.

8. requests - a library for making HTTP requests in Python, used for accessing web pages and API endpoints.

9. Beautiful Soup - a library for parsing and navigating HTML and XML documents, used for web scraping.

10. Django - a web framework for building web applications in Python, with a focus on simplicity and flexibility.