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🎯 How to Create a NumPy Array in Python?

Mastering NumPy is the first step toward efficient numerical computing in Python—whether you're doing data analysis, ML, or AI.

🧠 Here's a quick cheat sheet for creating different types of arrays using NumPy:

import numpy as np

# Basic array
array1 = np.array([1, 2, 3])

# Zero matrix
array2 = np.zeros((3, 3))

# Ones matrix
array3 = np.ones((2, 2))

# Constant array
array4 = np.full((2, 2), 7)

# Range of numbers
array5 = np.arange(0, 10, 2)

# Evenly spaced values
array6 = np.linspace(0, 1, 5)

# Random float array between 0 and 1
array7 = np.random.rand(1, 100)

# Random integers
array8 = np.random.randint(1, 100, 2)
📌 Whether you're building data pipelines or training ML models—NumPy makes array operations seamless and optimized.

✅ Question for you:
Which NumPy function do you use most often?
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