🚀 NumPy for Everyone: From Basics to Advanced

🧠 What is NumPy?

NumPy (short for Numerical Python) is a powerful Python library used for mathematical and scientific computing. It provides a fast way to work with arrays (lists of numbers), matrices, and numerical functions.

✅ In simple terms: NumPy helps Python do fast maths with lots of numbers at once.

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🎯 Why Use NumPy?

• Python lists are slow for math operations.

• NumPy arrays are faster and memory efficient.

• NumPy lets you:

  • Add, subtract, multiply numbers easily

  • Create matrices, reshape them

  • Perform statistics and linear algebra

  • Work with images, signals, data science models, and much more

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🔧 Installation and Import

To use NumPy, first install it:

pip install numpy

Then import it in your Python file:

import numpy as np

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📦 NumPy Array (ndarray) – Core Object

The most important thing in NumPy is the ndarray object — this is what stores numbers in a fast, structured way.

Let’s create one:

import numpy as np

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

Now let’s understand the attributes of this array.

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🔍 Important Attributes of NumPy Array (with Examples)

Full Example:

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

print("Array:\n", arr)
print("Dimensions (ndim):", arr.ndim)
print("Shape:", arr.shape)
print("Total Elements (size):", arr.size)
print("Data Type (dtype):", arr.dtype)
print("Item Size (bytes):", arr.itemsize)
print("Total Bytes (nbytes):", arr.nbytes)
print("Transpose:\n", arr.T)

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🔨 Creating Arrays – Methods You’ll Love

np.zeros((2, 3))     # Array of all 0s
np.ones((2, 3))      # Array of all 1s
np.full((2, 2), 7)   # Filled with 7
np.eye(3)            # Identity matrix
np.arange(0, 10, 2)  # From 0 to 10 with step 2
np.linspace(1, 5, 4) # 4 equally spaced values from 1 to 5

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🔁 Array Operations

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

print(a + b)      # [5 7 9]
print(a * b)      # [ 4 10 18]
print(np.sin(a))  # Apply sin function to each element

NumPy automatically performs operations element-wise.

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🧬 Reshaping & Indexing

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

print(arr[1])        # [4 5 6]
print(arr[:, 1])     # [2 5] (2nd column)
print(arr[0, 2])     # 3

reshaped = arr.reshape(3, 2)

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🧠 Broadcasting

Even if two arrays have different shapes, NumPy can sometimes "stretch" them to work together:

a = np.array([[1, 2, 3], [4, 5, 6]])
b = np.array([1, 2, 3])
print(a + b)  # Adds b to each row of a

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🎲 Random Arrays

np.random.rand(2, 3)       # Random floats
np.random.randint(0, 10, 5) # Random integers between 0–9

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📂 Saving and Loading

np.save("myarray.npy", arr)
loaded = np.load("myarray.npy")

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🎓 Conclusion: Why Learn NumPy?

✅ Whether you’re into machine learning, data analysis, robotics, or just want to speed up your Python math, NumPy is the first library to learn.

🧪 Practice small examples. Try reshaping, slicing, and operating on arrays. You'll see why NumPy is powerful and fun!