Indexing, slicing, iterating, shape manipulation, and stacking

Question 1

What is slicing in multi-dimensional arrays?

Answer 1

Slicing allows you to access specific rows, columns, or subarrays. For example, b[0:5, 1] accesses rows from index 0 to index 4, in the second column.

Question 2

Why can’t you reshape a 1D array into a 3x3 array?

Answer 2

You cannot reshape if the total number of elements doesn’t match. A 3x3 array requires 9 elements.

Question 3

What happens when using -1 in reshape?

Answer 3

Using -1 allows NumPy to automatically calculate the missing dimension based on the total number of elements.

Question 4

What is iterating in programming?

Answer 4

Iterating means going through each element of a collection (like an array) one at a time, often using loops.

Question 5

How can you access a specific element in a 2D array?

Answer 5

Use indexing with two indices, e.g., array[row_index, column_index].

Question 6

What does b[:, 1] do?

Answer 6

It selects all rows in the second column of the array b.

Question 7

How can you flatten a multi-dimensional array?

Answer 7

Use the ravel() method to convert a multi-dimensional array into a 1D array. It returns a flattened view of the array without altering the original data structure, allowing for easy manipulation and analysis of the elements in a single line.

Question 8

What does the reshape function do?

Answer 8

The reshape function changes the shape of an array without altering its data.

Question 9

What does the T attribute do for arrays?

Answer 9

The T attribute transposes the array, swapping rows and columns.