Introduction to Pandas & Numpy

NumPy (Numerical Python)

NumPy is a core Python library for numerical and scientific computing. It provides efficient data structures like ndarray for working with large arrays and matrices, along with a comprehensive suite of mathematical functions.

Key Features:

1. ndarray: Efficient N-dimensional array for numerical operations.
2. Mathematical functions: Extensive library for mathematical computations.
3. Efficiency: Optimized operations implemented in C for speed.
4. Integration: Foundation for other scientific libraries like Pandas and SciPy.

Pandas

Pandas is a powerful library for data manipulation and analysis, built on top of NumPy. It introduces high-level data structures like DataFrame and Series for handling labeled and relational data.

Key Features:

1. DataFrame: 2D labeled data structure resembling a table.
2. Series: 1D labeled array for time series and other data.
3. Data operations: Simplifies data alignment, grouping, and aggregation.
4. Time series functionality: Tools for working with time-related data.
5. Integration:

NumPy and Pandas are essential tools in the Python data science ecosystem, often used alongside libraries like Matplotlib for visualization and Scikit-learn for machine learning.