Basic Operations and Advanced Features of NumPy

NumPy goes beyond simple array operations, offering a variety of essential functions for data analysis and machine learning.

In this lesson, we’ll explore basic array operations, broadcasting, and random number generation.

NumPy arrays support basic arithmetic operations, which are performed element-wise as shown below:

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import numpy as np

arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])

print(arr1 + arr2)  # [5 7 9]
print(arr1 * arr2)  # [ 4 10 18]
print(arr1 - arr2)  # [-3 -3 -3]
print(arr1 / arr2)  # [0.25 0.4  0.5 ]

Unlike Python lists, NumPy arrays perform operations element-wise automatically.

NumPy makes it simple to change the shape of an array.

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

# Convert to 2x3 matrix
reshaped_arr = arr.reshape(2, 3)

print(reshaped_arr)

The ability to reshape arrays dynamically is especially helpful in data preprocessing.

Specific elements in NumPy arrays can be selected similarly to Python lists.

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arr = np.array([10, 20, 30, 40, 50])

print(arr[0])  # 10
print(arr[1:4])  # [20 30 40]

Elements in multi-dimensional arrays can also be specifically selected by row and column.

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

print(matrix[0, 1])  # 2 (second element in the first row)
print(matrix[:, 1])  # [2 5] (second column of every row)

Broadcasting is a key NumPy feature that enables operations between arrays of different sizes.

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arr1 = np.array([[1, 2, 3], [4, 5, 6]])
arr2 = np.array([10, 20, 30])

# `arr2` is automatically expanded across each row
result = arr1 + arr2

print(result)

In the above example, although arr2 has a shape of (1,3), NumPy automatically expands it to (2,3) for the operation.

This allows for efficient operations even when array shapes don't initially match.

NumPy makes it easy to extract data using conditions.

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arr = np.array([10, 20, 30, 40, 50])

# Select values greater than 30
filtered = arr[arr > 30]

print(filtered)

NumPy provides random number generation functions, which are useful for sampling and simulations.

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# Generate 5 random numbers between 0 and 1
random_arr = np.random.rand(5)

print(random_arr)

NumPy is a versatile library essential for AI and data science.

With NumPy, you can quickly create arrays, perform operations, and carry out mathematical computations with ease.

• NumPy Official Documentation