NumPy Examples - Consolidated & Deduplicated

This directory contains a comprehensive collection of NumPy tutorials, exercises, and examples, consolidated from multiple GitHub repositories and deduplicated for clarity.

📁 Directory Structure

01-basics/

Beginner-friendly tutorials covering fundamental NumPy concepts

Files from python-bootcamp-numpy:

  • 01-NumPy Arrays.ipynb - Array creation and basic operations

  • 01-Python Crash Course.ipynb - Python fundamentals

  • 02-Numpy Indexing and Selection.ipynb - Indexing, slicing, and selection

  • 02-Python Crash Course Exercises.ipynb - Python practice exercises

  • 03-Numpy Operations.ipynb - Mathematical operations and broadcasting

Files from NumPy tutorial:

  • NumPy Tutorial.ipynb - Comprehensive basics tutorial

Topics Covered:

  • Array creation (from lists, built-in methods)

  • Array attributes (shape, dtype, size, ndim)

  • Indexing and slicing

  • Basic operations (arithmetic, broadcasting)

  • Array manipulation

Recommended Order: Start with 01-NumPy Arrays.ipynb, then NumPy Tutorial.ipynb

02-intermediate/

Intermediate topics for deepening NumPy knowledge

Files from Intro_To_Numpy:

  • intro.ipynb - Introduction to NumPy concepts

  • intro2.ipynb - Additional introductory material

  • copyview.ipynb - Understanding copy vs view

  • filtering.ipynb - Boolean indexing and filtering

  • fun.ipynb - Fun NumPy tricks and patterns

  • it.ipynb - Array iteration techniques

  • s.ipynb - Stacking and splitting arrays

  • searcher.ipynb - Searching in arrays

  • shaper.ipynb - Reshaping and transposing

  • slice.ipynb - Advanced slicing techniques

  • slice2d.ipynb - 2D array slicing

Topics Covered:

  • Memory management (copy vs view)

  • Boolean indexing and fancy indexing

  • Array searching and filtering

  • Reshaping, stacking, splitting

  • Advanced iteration patterns

Note: numpy_youtube directory was a duplicate of this content and has been excluded.

03-exercises/

Practice exercises to test and improve your NumPy skills

From numpy-100:

  • 100_Numpy_exercises.ipynb - 100 curated NumPy exercises

Description: A collection of 100 exercises from easy (★☆☆) to hard (★★★), covering all aspects of NumPy. Great for systematic practice.

From numpy_exercises:

Topic-based exercise sets with solutions:

  • 1_Array_creation_routines.ipynb / 1_Array_creation_routines_Solution.ipynb

  • 2_Array_manipulation_routines.ipynb / 2_Array_manipulation_routines_Solution.ipynb

  • 3_Binary_operations.ipynb / 3_Binary_operations_Solution.ipynb

  • 4_String_operations.ipynb / 4_String_operations_Solution.ipynb

  • 5_Mathematical_functions.ipynb / 5_Mathematical_functions_Solutions.ipynb

  • 6_Arithmetic_operations.ipynb / 6_Arithmetic_operations_Solutions.ipynb

  • 7_Statistics.ipynb / 7_Statistics_Solutions.ipynb

  • 8_Linear_Algebra.ipynb / 8_Linear_Algebra_Solutions.ipynb

  • 9_Sorting_and_searching.ipynb / 9_Sorting_and_searching_Solutions.ipynb

  • 10_Random_sampling.ipynb / 10_Random_sampling_Solutions.ipynb

  • 11_Set_routines.ipynb / 11_Set_routines_Solutions.ipynb

Recommended Approach:

  1. Start with 100_Numpy_exercises.ipynb for breadth

  2. Use topic-based exercises for depth in specific areas

04-advanced/

Advanced tutorials and real-world applications

numpy-tutorials/

Advanced tutorials from the official NumPy tutorials repository:

Markdown Tutorials:

  • mooreslaw-tutorial.md - Data analysis with Moore’s Law

  • pairing.md - Pairing algorithms

  • save-load-arrays.md - Saving and loading NumPy arrays

  • tutorial-air-quality-analysis.md - Real-world data analysis

  • tutorial-deep-learning-on-mnist.md - Deep learning with NumPy

  • tutorial-deep-reinforcement-learning-with-pong-from-pixels.md - RL implementation

  • tutorial-ma.md - Moving averages

  • tutorial-nlp-from-scratch.md - NLP fundamentals

  • tutorial-plotting-fractals.md - Fractal generation

  • tutorial-static_equilibrium.md - Physics simulation

  • tutorial-style-guide.md - NumPy coding best practices

Data Files:

  • air-quality-data.csv - Air quality dataset

  • transistor_data.csv - Transistor data for Moore’s Law

Topics Covered:

  • Real-world data analysis

  • Scientific computing (physics, engineering)

  • Deep learning from scratch

  • Reinforcement learning

  • NLP fundamentals

  • Data visualization

  • Performance optimization

05-ml-applications/

Machine learning algorithms implemented from scratch with NumPy

numpy-ml/

A complete library of ML algorithms implemented in pure NumPy.

Repository: https://github.com/ddbourgin/numpy-ml

Contents:

  • Neural networks (MLP, CNNs, RNNs, LSTMs, attention mechanisms)

  • Reinforcement learning (DQN, A3C, DDPG)

  • Decision trees and ensemble methods

  • Linear models (regression, classification)

  • Gaussian processes

  • Hidden Markov Models

  • Bayesian models

  • Preprocessing utilities

Use Cases:

  • Understanding ML algorithms at a low level

  • Educational implementations

  • Reference for algorithm details

  • Building custom ML pipelines

Note: This is a complete library. See its README.md for detailed documentation.

🎯 Learning Path

Beginner (0-2 weeks)

  1. Start with 01-basics/01-NumPy Arrays.ipynb

  2. Work through 01-basics/NumPy Tutorial.ipynb

  3. Practice with first 30 exercises in 03-exercises/100_Numpy_exercises.ipynb

Intermediate (2-6 weeks)

  1. Complete all notebooks in 02-intermediate/

  2. Finish remaining exercises in 03-exercises/100_Numpy_exercises.ipynb

  3. Work through topic-based exercises in 03-exercises/ (with solutions)

Advanced (6+ weeks)

  1. Study tutorials in 04-advanced/numpy-tutorials/

  2. Implement projects from the tutorials

  3. Explore 05-ml-applications/numpy-ml/ for ML implementations

📊 Content Statistics

  • Total Notebooks: ~50+ unique notebooks

  • Total Exercises: 100+ (numpy-100) + 11 topic sets

  • Advanced Tutorials: 10+ real-world applications

  • ML Implementations: Complete library with 100+ algorithms

🗑️ Excluded Content (Duplicates)

The following directories were excluded because they contained duplicate content:

numpy_youtube/

  • Status: Duplicate of Intro_To_Numpy/

  • Reason: Contains identical files with same filenames

  • Files: copyview.ipynb, filtering.ipynb, fun.ipynb, intro.ipynb, it.ipynb, s.ipynb, searcher.ipynb, shaper.ipynb, slice.ipynb, slice2d.ipynb

📚 Source Repositories

This consolidated collection was created from the following GitHub repositories:

  1. python-bootcamp-numpy - Pierian Data Python for Data Science Bootcamp

  2. NumPy Tutorial - Basic NumPy tutorial

  3. Intro_To_Numpy - YouTube-based NumPy course

  4. numpy-100 - https://github.com/rougier/numpy-100

  5. numpy_exercises - Topic-based NumPy exercises

  6. numpy-tutorials - https://github.com/numpy/numpy-tutorials

  7. numpy-ml - https://github.com/ddbourgin/numpy-ml

🚀 Getting Started

# Install NumPy
pip install numpy

# Import NumPy
import numpy as np

# Verify installation
print(np.__version__)

💡 Tips for Success

  1. Practice Daily: Work through 3-5 exercises per day

  2. Type Code: Don’t copy-paste; type out examples to build muscle memory

  3. Experiment: Modify examples to test your understanding

  4. Read Docs: Use np.array? in Jupyter to access documentation

  5. Debug: Use print() statements to understand array shapes and values

  6. Performance: Use %timeit to compare different approaches

🔗 Additional Resources

📝 Contributing

If you find errors or have improvements:

  1. Note the specific file and line number

  2. Describe the issue or improvement

  3. Submit corrections or enhancements

📄 License

Individual directories may have their own licenses. Please refer to original repository licenses for attribution and usage rights.

Last Updated: December 2024 Consolidated By: Automated deduplication process Total Files Before: 158 files across 8 directories Total Files After: ~60 unique files (62% reduction)