Array attributes (shape, dtype, ndim, size) in NumPy - Step-by-Step Execution

Concept Flow - Array attributes (shape, dtype, ndim, size)

Create numpy array

↓

Access .shape

↓

Access .dtype

↓

Access .ndim

↓

Access .size

↓

Done

Create a numpy array, then check its shape, data type, number of dimensions, and total size step-by-step.

Execution Sample

NumPy

import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
print(arr.shape)
print(arr.dtype)
print(arr.ndim)
print(arr.size)

Create a 2D numpy array and print its shape, data type, number of dimensions, and total number of elements.

Execution Table

Step	Action	Expression Evaluated	Result
1	Create array	arr = np.array([[1, 2, 3], [4, 5, 6]])	[[1 2 3] [4 5 6]]
2	Access shape	arr.shape	(2, 3)
3	Access dtype	arr.dtype	int32
4	Access ndim	arr.ndim	2
5	Access size	arr.size	6
6	End	No more attributes to access	Execution stops

💡 All four attributes accessed; execution ends.

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	After Step 5
arr	undefined	[[1 2 3] [4 5 6]]	[[1 2 3] [4 5 6]]	[[1 2 3] [4 5 6]]	[[1 2 3] [4 5 6]]	[[1 2 3] [4 5 6]]
arr.shape	undefined	undefined	(2, 3)	(2, 3)	(2, 3)	(2, 3)
arr.dtype	undefined	undefined	undefined	int32	int32	int32
arr.ndim	undefined	undefined	undefined	undefined	2	2
arr.size	undefined	undefined	undefined	undefined	undefined	6

Key Moments - 3 Insights

Why does arr.shape return a tuple like (2, 3)?

Because arr has 2 rows and 3 columns, shape shows the size in each dimension as a tuple. See execution_table step 2.

What does arr.dtype tell us about the array?

It tells the type of data stored in the array elements, here int32 means 32-bit integers. See execution_table step 3.

Why is arr.ndim equal to 2?

Because the array has two dimensions: rows and columns. See execution_table step 4.

Visual Quiz - 3 Questions

Look at the execution table, what is the value of arr.size at step 5?

A 2

B 6

C (2, 3)

D int64

💡 Hint

Check the 'Result' column at step 5 in the execution_table.

At which step does arr.dtype get evaluated?

A Step 2

B Step 4

C Step 3

D Step 5

💡 Hint

Look for 'Access dtype' in the 'Action' column of execution_table.

If the array was 3D, which attribute would show the number of dimensions?

A arr.ndim

B arr.shape

C arr.size

D arr.dtype

💡 Hint

Check the meaning of arr.ndim in key_moments and execution_table step 4.

Concept Snapshot

Array attributes in numpy:
- shape: tuple showing size in each dimension
- dtype: data type of elements
- ndim: number of dimensions
- size: total number of elements
Use these to understand array structure quickly.

Full Transcript

We create a numpy array with two rows and three columns. Then we check its shape, which is (2, 3), meaning 2 rows and 3 columns. Next, we look at dtype, which tells us the data type of the elements, here int32. Then ndim shows the number of dimensions, which is 2 for this 2D array. Finally, size gives the total number of elements, which is 6. This helps us understand the array's structure and contents clearly.