Extra Hacks

Research two other Python Libraries NOT DISCUSSED DURING LESSON and make a markdown post, explaining their function and how it helps programmers code.

1. SciPy SciPy is another Python library for scientific computing. It builds on NumPy and provides additional functions for working with numerical data. This includes functions for optimization, linear algebra, signal and image processing, and more. SciPy is often used in combination with NumPy to perform more advanced scientific computing tasks in Python.

2. Seaborn Seaborn is a Python library for statistical data visualization. It is built on top of Matplotlib and provides a high-level interface for creating attractive and informative visualizations of data. Seaborn includes many advanced statistical plotting functions, such as regression plots and pair plots, which allow for the exploration and analysis of complex datasets. It is often used in combination with other scientific computing libraries, such as NumPy and Pandas, to create meaningful visualizations of data.

3. Matplotlib Matplotlib is a Python library for creating visualizations of data. It provides a wide range of plotting functions for creating static, animated, and interactive visualizations of data. This allows for the creation of complex visualizations, such as scatter plots, histograms, and heatmaps, to help gain insights from data. Matplotlib is often used in combination with other scientific computing libraries, such as NumPy and Pandas, to create informative visualizations of data.