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Shreya Ghorui
Shreya Ghorui

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🔢NumPy Simplified

#python #pythonfordatascience #datasciencebasics #numpy

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)
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🔹 2D Array (Matrix) 

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

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

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

🔸 Using arange() 

np.arange(1, 11)
np.arange(1, 11, 2)
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🔸 Reshaping Arrays 

np.arange(1, 26).reshape(5, 5)
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🔸 Ones and Zeros 

np.ones((3, 4))
np.zeros((4, 5))
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🔸 Random Arrays 

np.random.random((3, 4))
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🔸 Linearly Spaced Values 

np.linspace(-10, 10, 10)
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🔸 Identity Matrix 

np.identity(3)
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🧠 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)
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📏 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
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🧪 Changing Data Type 

a3.astype(np.int32)
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➕ 5. Arithmetic Operations 

a1 * 2
a1 ** 2
a2 > 5
a1 + a2
a1 * a2
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📊 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)
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📌 Axis-wise Aggregation 

np.max(a2, axis=1)
np.mean(a2, axis=0)
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📐 7. Trigonometric & Dot Product 

np.sin(a1)              # Sine values
np.dot(a2, a3.reshape(4, 2))  # Matrix multiplication
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✂️ 8. Slicing and Indexing

🔸 Basic Indexing 

a1[2:5]
a1[6:9:2]
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🔸 Multidimensional Indexing 

a2[1, 2]
a2[:, 2]
a2[1:, 1:3]
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🔸 Tensor Access 

a3[1, 1, 0]
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🔀 9. Stacking and Splitting

🔸 Horizontal Stacking 

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

np.hsplit(a4, 4)
np.vsplit(a4, 3)
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🎾 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! 🚀📚

Top comments (2)

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anikchand461 profile image
Anik Chand •

useful blog. Thanks for it.

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shreya_ghorui profile image
Shreya Ghorui •

Thank You