🔢NumPy Simplified

Shreya Ghorui — Sun, 13 Jul 2025 13:54:16 +0000

From Grocery Lists to Rocket Launches: Why Numbers Rule the World?

From the price of your favorite snack to the trajectory of a Mars rover – numbers silently shape our world. 📈🛰️ Whether it's your fitness tracker counting steps or the traffic lights syncing to rush hour chaos, there's a mathematical brain behind it all.🌍

Curious how it all works under the hood? Let’s peel back the curtain and explore the toolkit that helps decode the universe of data.

In this guide, we’ll unravel the magic of NumPy. From creating simple vectors to handling high-dimensional tensors, you'll learn how to manipulate data like a pro. Whether you're a beginner or brushing up your skills, this blog is your entry point into the world of efficient, high-performance computing! 💻📊

🧱 1. Creating NumPy Arrays

🔹 1D Array (Vector)

import numpy as np

a = np.array([1, 2, 3])
print(a)

🔹 2D Array (Matrix)

b = np.array([[1, 2, 3], [4, 5, 6]])
print(b)

🔹 3D Array (Tensor)

c = np.array([[[1, 2, 3], [4, 7, 9], [12, 6, 3]]])
print(c)

🎯 2. Array Data Types

np.array([1, 2, 3], dtype=float)    # Float array
np.array([1, 2, 3], dtype=complex)  # Complex array

🔁 3. Array Creation Routines

🔸 Using `arange()`

np.arange(1, 11)
np.arange(1, 11, 2)

🔸 Reshaping Arrays

np.arange(1, 26).reshape(5, 5)

🔸 Ones and Zeros

np.ones((3, 4))
np.zeros((4, 5))

🔸 Random Arrays

np.random.random((3, 4))

🔸 Linearly Spaced Values

np.linspace(-10, 10, 10)

🔸 Identity Matrix

np.identity(3)

🧠 4. Array Properties

a1 = np.arange(10, dtype=np.int32)
a2 = np.arange(12, dtype=float).reshape(3, 4)
a3 = np.arange(8).reshape(2, 2, 2)

📏 Shape, Type, Size, and More

a2.ndim       # Number of dimensions
a1.shape      # Shape of the array
a2.size       # Total elements
a1.itemsize   # Bytes per item
a1.dtype      # Data type

🧪 Changing Data Type

a3.astype(np.int32)

➕ 5. Arithmetic Operations

a1 * 2
a1 ** 2
a2 > 5
a1 + a2
a1 * a2

📊 6. Aggregation Functions

np.max(a1)
np.min(a1)
np.sum(a1)
np.prod(a1)
np.mean(a1)
np.median(a1)
np.std(a1)
np.var(a1)

📌 Axis-wise Aggregation

np.max(a2, axis=1)
np.mean(a2, axis=0)

📐 7. Trigonometric & Dot Product

np.sin(a1)              # Sine values
np.dot(a2, a3.reshape(4, 2))  # Matrix multiplication

✂️ 8. Slicing and Indexing

🔸 Basic Indexing

a1[2:5]
a1[6:9:2]

🔸 Multidimensional Indexing

a2[1, 2]
a2[:, 2]
a2[1:, 1:3]

🔸 Tensor Access

a3[1, 1, 0]

🔀 9. Stacking and Splitting

🔸 Horizontal Stacking

a4 = np.ones((3, 4))
a5 = np.zeros((3, 4))
np.hstack((a4, a5))

🔸 Splitting Arrays

np.hsplit(a4, 4)
np.vsplit(a4, 3)

🎾 Wrap-Up

With NumPy, working with arrays becomes intuitive and efficient. Mastering these basics will set a strong foundation for data science, AI, and scientific computing! 🚀📚

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