Learn NumPy in 10 Days: A Simple Daily Plan

Hey there!

I want to share my journey of learning NumPy in just 10 days. Every day, I spent 30 to 45 minutes learning. I followed a simple roadmap and practiced step by step. I wrote everything down in a Jupyter Notebook. You can find all my code here:

🔗 GitHub Repository:  
  FahimMuntashir/zero-to-ds - NumPy Folder

 What is NumPy?  
- NumPy is a Python library that makes working with numbers and arrays super easy and fast. If you're learning data science, machine learning, or even basic math with Python, you’ll love NumPy!

📅 My 10-Day NumPy Learning Plan

📘 Day 1: Introduction to NumPy & Basics

• ✅ Install NumPy (pip install numpy)
• ✅ Import and check version
• ✅ Learn about ndarray (NumPy arrays)
• ✅ Create arrays using array(), zeros(), ones(), full(), arange(), linspace()
• ✅ Check data types using .dtype
• ✅ Understand .shape, .size, .ndim

• ✅ Basic indexing and slicing

  📝 Practice: Create 1D, 2D, and 3D arrays and try slicing them.

📗 Day 2: Array Operations & Broadcasting

• ✅ Create arrays with random numbers
• ✅ Learn NumPy data types
• ✅ Perform arithmetic operations: +, -, *, /, **
• ✅ Use universal functions: np.add(), np.subtract(), etc.
• ✅ Try aggregation functions: np.sum(), np.mean(), np.std(), np.sin(), np.cos()
• ✅ Element-wise operations
• ✅ Learn broadcasting rules

📝 Practice: Add scalars to arrays and try element-wise multiplication.

📙 Day 3: Shape, Reshaping & Broadcasting

• ✅ Understand array shapes
• ✅ Use .reshape() to convert 1D to 2D, or vice versa
• ✅ Learn how broadcasting works with different shapes

📕 Day 4: Indexing and Slicing

• ✅ Indexing with boolean masks and integers
• ✅ Slice 1D, 2D, and 3D arrays
• ✅ Use np.where() for condition-based selection
• ✅ Flatten arrays with .ravel() and .flatten()
• ✅ Transpose arrays using .T
• ✅ Stack arrays with np.hstack(), np.vstack(), np.concatenate()

📒 Day 5: Iterating, Copy vs Views

• ✅ Iterate through arrays using np.nditer()
• ✅ Get index and data using np.ndenumerate()
• ✅ Understand the difference between copies and views of arrays

📓 Day 6: Joining & Splitting Arrays

• ✅ Join arrays using concatenate(), stack(), hstack(), vstack(), dstack()
• ✅ Split arrays using split(), array_split(), vsplit(), dsplit()

📔 Day 7: Search, Sort, Filter, Arithmetic

• ✅ Search with np.where()
• ✅ Use searchsorted() to find insert locations
• ✅ Sort arrays with np.sort()
• ✅ Filter arrays using conditions
• ✅ Try extra functions: np.shuffle(), np.unique(), np.resize(), np.flatten(), np.ravel()

🛠️ Day 8 to Day 10: Practice Time

After learning the basics in 7 days, I spent the last 3 days just practicing! Practice helped me become more confident with NumPy.

Here are two amazing resources I used:

• 🔗 NumPy 100 Exercises – GitHub by rougier
• 🔗 NumPy 50 Exercises with Solutions – Kaggle

If you want to start your data science journey, I think NumPy is a great first step. You don’t need to be a pro to begin. Just take small steps like I did.

Thanks for reading! 😊  
Let me know if you try this challenge too!