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NumPy arrays can be indexed and sliced in a similar way to regular Python lists, but with some additional features. In this article, we’ll explore the indexing and slicing operations available in NumPy.


NumPy arrays can be indexed using integers, slices, and boolean arrays.

import numpy as np

# 1D array
a = np.array([1, 2, 3, 4, 5])

# Indexing with integers
print(a[0]) # Output: 1
print(a[-1]) # Output: 5

# Indexing with slices
print(a[1:3]) # Output: [2 3]
print(a[:3]) # Output: [1 2 3]
print(a[3:]) # Output: [4 5]

# Indexing with boolean arrays
b = np.array([True, False, True, False, True])
print(a[b]) # Output: [1 3 5]

In the above example, we have created a 1D array a and used various indexing methods to extract elements from it. The first and last elements are accessed using integer indices. Slicing is used to extract a range of elements from the array. Finally, we create a boolean array b with the same shape as a and use it to extract elements where the corresponding value in b is True.


NumPy arrays can be sliced in a similar way to Python lists. The basic syntax for slicing is start:stop:step, where start is the index of the first element to include, stop is the index of the first element to exclude, and step is the step size.

import numpy as np

# 2D array
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

# Slicing rows and columns
print(a[1]) # Output: [4 5 6]
print(a[:, 1]) # Output: [2 5 8]
print(a[1:, :2]) # Output: [[4 5]
# [7 8]]

In the above example, we have created a 2D array a and used slicing to extract rows and columns from it. The first slice a[1] extracts the second row of the array. The second slice a[:, 1] extracts the second column of the array. Finally, the third slice a[1:, :2] extracts the sub-array consisting of the last two rows and the first two columns.


NumPy arrays can be indexed and sliced using a variety of methods. Integer indexing, slicing, and boolean indexing are all useful tools for extracting elements from an array. Slicing is particularly useful for extracting subsets of an array. By mastering these indexing and slicing techniques, you can effectively manipulate NumPy arrays for scientific computing and data analysis.

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