Lecture on Numpy Functions

Introduction

• Previous topics covered: numpy slicing and various numpy functions
• Agenda: demonstrating usage of Jupyter Notebook for numpy functions in this session

Setting Up Jupyter Notebook

• Open Command Prompt as Administrator
• Change directory using cd command
• Launch Jupyter Notebook by typing jupyter notebook
• Select or create a new notebook and rename it for simplicity

Numpy Basics

• Import numpy as np using import numpy as np
• Create a numpy array:

  arr1 = np.array([[1, 2, 3], [4, 5, 6]])
  

Axis in Numpy

• axis=0: column-wise operations
• axis=1: row-wise operations
• Example: sum function

  arr1.sum(axis=0)  # Column wise sum
  arr1.sum(axis=1)  # Row wise sum
  

Dot Product

• Dot product of two matrices requires compatible dimensions
• Example and troubleshooting with Transpose function

  np.dot(arr1, arr2.T)  # Transpose second matrix
  

• Search functionality for finding functions

Cross Product

• Use np.cross for cross product of vectors

  np.cross(arr1, arr2)
  

Sorting in Numpy

• Create numpy array and sort

  np.sort(arr1)
  np.sort(arr1, axis=0)  # Column-wise sort
  

• Specifying sorting algorithm using kind parameter

  np.sort(arr1, kind='mergesort')
  

• Available algorithms: 'quicksort', 'heapsort', 'mergesort'

Best Practices and Tips

• Understanding numpy's reliance on precompiled C libraries
• Memory allocation considerations while working with numpy
• Keep updated with numpy documentation for latest changes and updates

Conclusion

• Encouragement to share videos and subscribe to the channel
• Upcoming topics: Data analysis techniques in numpy
• Questionnaire on experience with numpy package