## Introduction

The term slicing in programming usually refers to obtaining a substring, sub-tuple, or sublist from a string, tuple, or list respectively.

Python offers an array of straightforward ways to slice not only these three but *any iterable*. An *iterable* is, as the name suggests, any object that can be iterated over.

In this article, we'll go over everything you need to know about *Slicing Numpy Arrays in Python*.

## NumPy Array slicing

The most common way to slice a NumPy array is by using the `:`

operator with the following syntax:

```
array[start:end]
array[start:end:step]
```

The `start`

parameter represents the starting index, `end`

is the ending index, and `step`

is the number of items that are "stepped" over.

NumPy is a free Python package that offers, among other things, n-dimensional arrays.

Slicing 1D (one dimensional) arrays in NumPy can be done with the same notation as slicing regular lists in Python:

```
import numpy as np
arr = np.array([1,2,3,4])
print(arr[1:3:2])
print(arr[:3])
print(arr[::2])
```

Output:

```
[2]
[1 2 3]
[1 3]
```

### 2D NumPy Array Slicing

A 2D array in NumPy is an array of arrays, a 3D array is an array of arrays of arrays and so forth. A 2D array can be represented as a matrix like so:

```
import numpy
arr = numpy.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
print(arr)
```

Let's print this matrix out:

```
[[ 1 2 3 4]
[ 5 6 7 8]
[ 9 10 11 12]]
```

Slicing a 2D array can either result in an array or a matrix. The syntax that results in a matrix would be:

```
arr[startx:endx:stepx, starty:endy:stepy]
```

The syntax that results in an array:

```
arr[startx:endx:stepx, const]
arr[const, starty:endy:stepy]
```

## Free eBook: Git Essentials

Check out our hands-on, practical guide to learning Git, with best-practices, industry-accepted standards, and included cheat sheet. Stop Googling Git commands and actually *learn* it!

Utilizing this syntax results in a matrix whose elements are the columns in the range from `startx`

to `endx`

on the x-axis, and rows in the range from `starty`

to `endy`

on the y-axis of the original matrix:

Let's take a look at how we can slice this matrix and what the slicing results in:

```
import numpy
arr = numpy.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]])
print("The original matrix:")
print(arr)
print("A sliced submatrix:")
print(arr[1:4,2:4])
print("A sliced subarray:")
print(arr[1,:])
print("A sliced submatrix:")
print(arr[:,3:])
print("A sliced subarray:")
print(arr[:,3])
```

This code segment prints out:

```
The original matrix:
[[ 1 2 3 4]
[ 5 6 7 8]
[ 9 10 11 12]
[13 14 15 16]]
A sliced submatrix:
[[ 7 8]
[11 12]
[15 16]]
A sliced subarray:
[5 6 7 8]
A sliced submatrix:
[[ 4]
[ 8]
[12]
[16]]
A sliced subarray:
[ 4 8 12 16]
```

## Conclusion

Slicing any sequence in Python is easy, simple, and intuitive. Negative indexing offers an easy way to acquire the first or last few elements of a sequence, or reverse its order.

In this article, we've covered how to slice Python's NumPy arrays.