Basics of Numpy, Pandas , Matplotlib, Seaborn | Projects
1. Why Numpy? NumPy arrays are faster and more compact than Python lists. An array consumes less memory and is convenient to use. NumPy uses much less memory to store data and it provides a mechanism of specifying the data types.
2. Why Pandas? Pandas is a Python library for data analysis. Pandas is built on top of two core Python libraries—matplotlib for data visualization and NumPy for mathematical operations. Pandas acts as a wrapper over these libraries, allowing you to access many of matplotlib's and NumPy's methods with less code.
3. Why Matplotlib? Matplotlib is a multi-platform data visualization library built on NumPy arrays.
4. Why Seaborn? Seaborn is a library in Python predominantly used for making statistical graphics. Seaborn is a data visualization library built on top of matplotlib and closely integrated with pandas data structures in Python. Visualization is the central part of Seaborn which helps in the exploration and understanding of data.
PROJECT: Pandas - Project on Superstore Data
The project will focus on fundamental operations for understanding the structure of the dataset, inspecting data types, and calculating basic statistical measures.The goal is to develop a foundational understanding of the data, which can later be used for deeper analysis.
PROJECT: Pandas - Exploring Countries Economic Data
This project involved the process of importing, exploring, and analyzing a dataset using Python and its powerful libraries like pandas and seaborn. The workflow included data cleaning, manipulation, and visualization to extract insights from the data.
PROJECT: Matplotlib - Data Visualization
This project explores Matplotlib, covering its object hierarchy, architecture, and plotting techniques. It discusses various plot types, subplots, and customization methods like styles, grids, labels, and color adjustments.