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02 Data Science1 Numpy Examples

NumPy Examples

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

Use this folder to build array intuition, shape awareness, and vectorized thinking. The goal is not to finish every notebook. The goal is to become comfortable enough with arrays that pandas, embeddings, and model tensors stop feeling mysterious.

📁 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

How To Use This Folder Well

  • Complete a small basics pass before spending time on the advanced archive.
  • Prioritize indexing, broadcasting, reshaping, and reduction operations because those transfer everywhere else.
  • Use the exercise sets to check fluency, not just recognition.
  • Return to this folder whenever later phases expose confusion about shapes, similarity math, or vectorized computation.

📊 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

What Comes Next

📝 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)

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