- Welcome to the NumPy Learning Repository : a beginner-friendly collection of Jupyter notebooks covering the most important NumPy concepts. Each topic is explained in simple words with examples, so you can learn step by step.
- Introduction to NumPy
- NumPy Functions
- Random Numbers in NumPy
- Data Types in NumPy
- Arithmetic Operations
- Arithmetic Functions
- Shape & Reshape
- Broadcasting
- Indexing & Slicing
- Iteration with nditer
- Copy vs View
- Concatenate, Stack & Split
- Searching Elements
- Shuffle & Unique
- Insert & Delete
- Matrix Functions
Learn what NumPy is and why it’s used for scientific computing. Understand arrays and how they are better than Python lists.
Explore built-in functions for creating arrays (zeros, ones, arange, linspace). Learn how these functions make coding easier.
Generate random integers, floats, and samples. Useful for simulations, testing, and machine learning.
Learn different data types (int, float, bool, complex). How to check and change the type of an array.
Perform basic math (+, -, *, /) on arrays. Fast and vectorized compared to Python loops.
Use functions like sum, mean, sqrt, exp. Perform advanced math easily with NumPy.
Check the size/shape of arrays. Reshape arrays into different dimensions (e.g., 1D → 2D).
Learn how NumPy handles operations between arrays of different shapes. Very powerful for reducing code.
Access elements using indices. Slice arrays to get subarrays.
Loop through arrays efficiently. Work with multi-dimensional arrays in simple ways.
Understand the difference between copying an array and creating a view. Important for memory management.
Combine arrays (concatenate, stack). Break arrays into smaller parts (split).
Find elements using conditions. Learn functions like where, searchsorted.
Shuffle elements randomly. Find unique values in arrays.
Insert new elements into arrays. Delete elements from arrays.
Work with matrices using functions like dot, transpose, inv. Essential for linear algebra and machine learning.
open terminal write the below command -> pip install numpy
- Build a strong foundation in NumPy.
- Understand arrays and mathematical operations.
- Be ready to use NumPy in data science, AI, and machine learning projects.
Want to improve this repo? Feel free to fork the project, add your notebooks, and make a pull request. Contributions are always welcome!
👤 Muhammad Sayyad Khan 📧 msswati43215@gmail.come 🔗 [https://www.linkedin.com/in/sayyad-khan-16250a377?utm_source=share&utm_campaign=share_via&utm_content=profile&utm_medium=android_app]