Indexing & Slicing in NumPy

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Indexing & Slicing in NumPy

Indexing and slicing are fundamental operations in NumPy that allow users to access, retrieve, and manipulate elements of an array efficiently. These operations make NumPy suitable for scientific computing, data analysis, and machine learning.

1.Basic Indexing

Basic indexing in NumPy refers to the process of accessing individual elements of an array using their index position. NumPy follows zero-based indexing, where the first element of the array is accessed using index 0.

Explanation

Each element in an array has a unique index.
Indexing returns a single element (scalar value).

Example

```

import numpy as np

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

print(arr[0])

print(arr[2])

```

2.Negative Indexing

Negative indexing is a method in NumPy that allows accessing elements from the end of an array using negative index values, where -1 represents the last element of the array.

Explanation

Useful when array length is unknown
Provides backward access to elements

Example

```

print(arr[-1])

print(arr[-2])

```

3. Slicing

Slicing in NumPy is the technique of extracting a subset of elements from an array using a specified range defined by start index, stop index, and step size.

Syntax

```

Hide

array[start : stop : step]

```

Explanation

start → starting index (inclusive)
stop → ending index (exclusive)
step → interval between elements

Examples

```

arr = np.array([0, 1, 2, 3, 4, 5])

print(arr[1:4])

print(arr[:3])

print(arr[::2])

print(arr[::-1])

```

Important Note

Slicing in NumPy returns a view of the original array, not a copy.

4.Indexing in 2D Arrays

Indexing in a two-dimensional NumPy array involves accessing elements using two indices, where the first index represents the row number and the second index represents the column number.

Example

```

mat = np.array([[1, 2, 3],

[4, 5, 6]])

print(mat)

```

Accessing Single Element

```

print(mat[0, 1])

```

Row and Column Access

```

print(mat[0])

print(mat[:, 1])

```

Slicing in 2D Array

```

print(mat[0:2, 1:3])

```

5.Indexing in 3D Arrays

Indexing in a three-dimensional NumPy array involves accessing elements using three indices, commonly representing depth, row, and column.

Example

```

arr3d = np.array([

[[1, 2], [3, 4]],

[[5, 6], [7, 8]]

])

print(arr3d)

```

Accessing Single Element

```

print(arr3d[0, 1, 1])

```

Accessing a 2D Slice

```

print(arr3d[1])

```

Slicing in 3D Array

```

print(arr3d[:, :, 0])

```

Difference Between Indexing and Slicing

Feature	Indexing	Slicing
Purpose	Access single element	Extract multiple elements
Output	Scalar value	Sub-array
Syntax	arr[i]	arr[start:stop]
Memory	Independent value	View of original array

Indexing & Slicing in NumPy