Course Content

# NumPy in a Nutshell

1. Getting Started with NumPy

2. Dimensions in Arrays

3. Indexing and Slicing

4. Important Functions

NumPy in a Nutshell

## Access 2-D and 3-D Arrays

Let's have a look at an example of a 2-D array with axis numbering:

Let's have a look at an example of indexing (both positive and negative) in 2-D arrays:

Let's examine the syntax of slicing: `array[start_row: end_row: step_row, start_column: end_column: step_column]`

, where:

- start_row is the index from which row slicing begins;
- end_row is the index where row slicing stops (note that this index is not included);
- step_row is the parameter that specifies the intervals between row indices;
- start_column is the index from which column slicing starts;
- end_column is the index where column slicing ends (note that this index is not included);
- step_column is the parameter that determines the intervals between column indices.

Now, let's refer to the following image:

Let's have a look at an example of a 3-D array with axis numbering:

# Task

Consider the following array: `[[6, 5, 7, 8], [65, 2, 7, 9]]`

.

- Retrieve the fourth element from the first part of the array
`[6, 5, 7, 8]`

, and the first element from the second part of the array`[65, 2, 7, 9]`

. - Multiply the obtained elements together.
- Display the product of the obtained elements.

Everything was clear?

Section 3. Chapter 3