NumPy Introduction

🧠 NumPy Introduction

NumPy stands for Numerical Python and is a popular open-source Python library used for:

✔ Scientific Computing
✔ Working with arrays
✔ Mathematical operations
✔ Data analysis
✔ Machine learning and AI
✔ Linear algebra & Fourier transform
✔ Working with large datasets efficiently

NumPy is the foundation of libraries like Pandas, SciPy, TensorFlow, and Scikit-Learn.

🔥 Why NumPy?

Python’s built-in lists are slow and inefficient for numeric processing. NumPy solves this by offering:

Feature Python List NumPy Array
Speed ❌ Slow ✅ Very Fast
Memory Usage ❌ High ✅ Low
Supports Vectorized Operations ❌ No ✅ Yes
Suitable for ML/Data Science ❌ No ✅ Yes

📦 Installing NumPy

If not installed, use:

Or for Anaconda users:

🧩 Importing NumPy

Standard practice:



 

📌 NumPy Array

NumPy works with a special data type called ndarray (n-dimensional array).

👉 Creating an array:


 

Output:

[1 2 3 4 5]
<class 'numpy.ndarray'>

🎯 Multidimensional Array



 

Output:

[[1 2 3]
[4 5 6]]

📏 Array Properties



 

⚙ Creating Special Arrays



 

🔥 Fast Vectorized Operations


 

➗ Mathematical Functions



 

🔄 Array Indexing & Slicing


 

🧮 Reshaping Arrays



 

📊 Conclusion

NumPy is a powerful and essential library for:

  • Data Science

  • Machine Learning

  • Deep Learning

  • Scientific and mathematical calculations

It makes handling numeric data faster, easier, and more memory-efficient.

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