Numpy Tutorial Summary

Introduction

• Numpy is a powerful scientific computing library used in Python, essential for data science, linear algebra, statistics, and machine learning.
• Tutorial focuses on covering the majority of the Numpy library with practical examples.
• Uses Anaconda with Jupyter Notebook for demonstration.

Basics

• Start by importing Numpy using import numpy as np.
• Basic operations include creating arrays, performing mathematical operations, and manipulating data.

Creating Arrays

• Create a one-dimensional array:

  np.array([1, 2, 3])
  

• Multi-dimensional arrays can be created similarly, specifying rows and columns.

  np.array([[1, 2, 3], [4, 5, 6]])
  

• Use functions like np.zeros, np.ones, np.arange, and np.linspace to create arrays with specific properties.

Array Manipulation

• Slicing and Indexing: Access parts of the array using indices.
• Reshape Arrays: Change the shape of arrays using np.reshape or np.resize.
• Stacking and Splitting: Combine arrays using np.vstack, np.hstack; split using np.split or np.array_split.
• Copying Arrays: Use .copy() to create independent copies.

Mathematical Functions

• Perform basic operations: addition, subtraction, multiplication, division.
• Use functions like np.sum, np.min, np.max, np.mean, np.median, np.std for statistical calculations.
• Advanced operations: matrix multiplication (np.dot), eigenvalues (np.linalg.eig), etc.

Working with Files

• Read from and write to CSV files with np.genfromtxt and np.savetxt.
• Load and save Numpy arrays using np.save and np.load.

Statistics

• Calculate statistical values such as mean, median, variance, and standard deviation.
• Use np.corrcoef to find correlation coefficients.
• Linear regression and analysis using arrays.

Advanced Topics

• Linear Algebra: Extensive coverage of Numpy's linear algebra capabilities including solving systems of equations, computing determinants, and more.
• Trigonometric Functions: Perform operations using sine, cosine, and other trigonometric functions.

Financial Functions

• Compute financial calculations like future value, net present value using numpy_financial.

Comparison Functions

• Compare arrays element-wise using np.greater, np.less, np.equal, and related functions.

Conclusion

• Numpy is a foundational library for scientific computing in Python with extensive functionalities for data manipulation and mathematical operations.