Exploring NumPy Arrays
Today I started with one of the most powerful Python libraries for data and scientific computing — NumPy 🔢
🔹 Creating Arrays
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(arr) # [1 2 3 4 5]
🔹 Indexing & Slicing
print(arr[0]) # 1
print(arr[1:4]) # [2 3 4]
🔹 Array Manipulation
arr2 = np.array([10, 20, 30, 40, 50])
print(arr + arr2) # [11 22 33 44 55]
print(arr * 2) # [ 2 4 6 8 10]
⚡ Interesting Facts
• NumPy arrays are faster and more memory efficient than Python lists
• They support vectorized operations (no need for loops!)
• Widely used in machine learning, AI, and data analytics
✨ Reflection
Working with NumPy arrays feels like unlocking Python’s superpower. I can already see why it’s a must-have for data analytics.
Next → I’ll explore more advanced NumPy operations 🚀
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