Numpy

README.md

Numpy Introduction and Performance Comparison

This notebook demonstrates:

• How to use numpy for array operations.
• A comparison of performance between native Python lists and numpy arrays.

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Contents

• Importing libraries: Initializes numpy and time.
• Zipping two lists: Shows how zip can be used to combine two lists into pairs.
• Performance comparison: Measures and prints the execution time for adding two large lists and two large numpy arrays.
• Conclusion: Highlights that numpy performs faster than native Python lists for large array operations.

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Requirements

• Python 3.x
• Numpy

pip install numpy

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How to use

1. Open this notebook in Jupyter Notebook or JupyterLab.
2. Step through the cells and execute them in order.
3. The output will show:
   • The result of zipping two small lists.
   • The time taken to add large native Python lists.
   • The time taken to add large numpy arrays.

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Summary

This notebook illustrates: ✅ The ease of performing vectorized operations with numpy. ✅ The performance benefits of numpy over pure Python for large datasets.