How to Slice NumPy Arrays for Machine Learning Using Python

NumPy array slicing

Installation and import

Use the following command to install NumPy on your computer :

To import the library, use:

Note: np in the command is used as an alias.

Types of arrays

There are three types of arrays that are commonly used: one-dimensional arrays, two-dimensional arrays, and three-dimensional arrays. Let’s examine them briefly.

• One-dimensional array

This is a type of array where elements are stored linearly, i.e., elements are just one row of values and can be accesssed by specifying the index of the element.

• Two-dimensional array

In this type of array, elements are stored in rows and columns which represent a matrix.

• Three-dimensional array

This type of array comprises 2-D matrices as its elements.

Slicing 1-D arrays

When slicing an array, you pass the starting index and the ending index which are separated by a full colon. You use the square brackets as shown below.

arr[start:end]

arr is the variable name of the array.

Here’s an example for better understanding:

The starting index is 1 and the ending index is 5. Therefore, you will slice from the second element since indexing in the array starts from 0 up to the fourth element.

Note: The result above includes the start index and excludes the end index.

Negative slicing

The minus operator is used to refer to an index from the end of an array; you slice an array from the end instead of from the start.

Example: Slice from index 4 (from the end) to index 2 (from the end).

Slicing 2-D arrays

To slice a 2-D array in NumPy, you have to specify row index and column index which are separated using a comma as shown below.

arr[1, 1:4]

The part before the comma represents the row while the part after the comma represents the column.

Example: Slice elements from index 1 to 4 from the second row.

The last index is excluded.

NumPy array indexing

Array indexing refers to accessing an array element using an index number that starts from 0. The difference between indexing and slicing is that with the former, you simply access the element. With slicing, you extract specific elements from the array.

NumPy 1-D array indexing

You need to pass the index of that element as shown below, to access the 1-D array.

The output shows 10 is at index 0.

NumPy 2-D array indexing

To access the 2-D array, you need to use commas to separate the integers which represent the dimension and the index of the element. The first integer represents the row and the other represents the column.

Conclusion

NumPy is an important library that’s widely used in Python to work with arrays. As seen, to use NumPy, you need to install the library and then import it. It can be used in many ways, such as to slice and index arrays. It can slice either 1-D or 2-D arrays by extracting elements from the original array. It can also access the elements in an array, either from the start or from the end of the array.