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.

Practice

1. Which numpy attribute tells you the total number of elements in an array?

easy

A. size

B. shape

C. ndim

D. dtype

Solution

1. Step 1: Understand the meaning of each attribute

   shape shows dimensions, dtype shows data type, ndim shows number of dimensions, and size shows total elements.

2. Step 2: Identify the attribute for total elements

   The attribute size gives the total number of elements in the array.

3. Final Answer:

   size -> Option A

4. Quick Check:

   Total elements = size [OK]

Hint: Total elements? Use size attribute [OK]

Common Mistakes:

• Confusing shape with total elements
• Thinking ndim gives total elements
• Mixing dtype with size

2. Which of the following is the correct way to get the number of dimensions of a numpy array arr?

easy

A. arr.size()

B. arr.ndim

C. arr.shape()

D. arr.dtype

Solution

1. Step 1: Recall attribute vs method syntax in numpy

   Attributes like ndim and dtype are accessed without parentheses, methods like size() do not exist.

2. Step 2: Identify correct attribute for number of dimensions

   arr.ndim gives the number of dimensions directly.

3. Final Answer:

   arr.ndim -> Option B

4. Quick Check:

   Number of dimensions = arr.ndim [OK]

Hint: Use attribute without () for ndim, size, dtype [OK]

Common Mistakes:

• Using parentheses with attributes
• Confusing shape with ndim
• Trying to call size() as method

3. Given the code:

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

What is the output?

medium

A. (2, 3) float64 2 6

B. (3, 2) int64 2 6

C. (2, 3) int64 2 6

D. (2, 3) int32 2 6

Solution

1. Step 1: Analyze the array shape and data type

   The array has 2 rows and 3 columns, so shape is (2, 3). The default integer type on many systems is int64.

2. Step 2: Check number of dimensions and total size

   It is a 2D array, so ndim = 2. Total elements = 2*3 = 6.

3. Final Answer:

   (2, 3) int64 2 6 -> Option C

4. Quick Check:

   shape=(2,3), dtype=int64, ndim=2, size=6 [OK]

Hint: Shape = rows x columns, size = product of shape [OK]

Common Mistakes:

• Mixing rows and columns in shape
• Assuming dtype is int32 by default
• Confusing ndim with size

4. What is wrong with this code snippet?

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

medium

A. print cannot display tuples

B. arr is not defined

C. shape returns a number, not a tuple

D. shape is an attribute, not a method; remove parentheses

Solution

1. Step 1: Identify usage of shape

   shape is an attribute, so it should be accessed without parentheses.

2. Step 2: Correct the code

   Change arr.shape() to arr.shape to fix the error.

3. Final Answer:

   shape is an attribute, not a method; remove parentheses -> Option D

4. Quick Check:

   Attributes have no () [OK]

Hint: Attributes like shape have no () [OK]

Common Mistakes:

• Using parentheses with attributes
• Thinking shape returns a single number
• Assuming print can't show tuples

5. You have a numpy array arr with shape (4, 5, 6). Which attribute would you use to find the total number of elements, and what is that number?

hard

A. arr.size, 120

B. arr.ndim, 3

C. arr.shape, (4, 5, 6)

D. arr.dtype, depends on data

Solution

1. Step 1: Understand the attributes

   size gives total elements, ndim gives number of dimensions, shape gives dimensions, dtype gives data type.

2. Step 2: Calculate total elements from shape

   Total elements = 4 * 5 * 6 = 120, so arr.size = 120.

3. Final Answer:

   arr.size, 120 -> Option A

4. Quick Check:

   Total elements = product of shape = 120 [OK]

Hint: Multiply shape dimensions or use size [OK]

Common Mistakes:

• Confusing ndim with total elements
• Using shape instead of size
• Ignoring that dtype is unrelated to size