Learn Numerical Python

What Will You Learn?

• NumPy Basics: Understanding what NumPy is and its importance in scientific computing and data analysis. Installing NumPy and setting up the development environment.
• NumPy Arrays: Creating NumPy arrays using various methods. Slicing, indexing, reshaping, and combining arrays.
• Basic Operations with NumPy: Performing mathematical operations (element-wise operations, arithmetic operations, etc.) on arrays. Applying universal functions provided by NumPy.
• Broadcasting: Understanding how NumPy handles operations between arrays of different shapes.
• NumPy and Linear Algebra: Performing matrix operations: matrix multiplication, determinant, inverse, solving linear equations. Eigenvalues and eigenvectors.
• Advanced NumPy Features: Working with structured arrays. Using masked arrays for handling missing or invalid data. Understanding NumPy's datetime and timedelta support.
• File I/O with NumPy: Reading and writing data from/to files using NumPy.
• Data Processing and Analysis: Using NumPy for data manipulation tasks like filtering, sorting, and aggregation. Statistical functions: mean, median, standard deviation, variance, etc.
• NumPy and Integration with Pandas: Using NumPy arrays with Pandas DataFrames for data analysis and manipulation.
• NumPy Performance Optimization: Techniques to improve performance, such as vectorization and avoiding unnecessary copying of data.
• NumPy in Machine Learning: Using NumPy arrays as input data for machine learning models implemented in libraries like scikit-learn, TensorFlow, or PyTorch.