Array attributes (shape, dtype, ndim, size) in NumPy

Introduction

Array attributes help us understand the structure and type of data inside an array. They tell us the shape, data type, number of dimensions, and total size of the array.

When you want to check how many rows and columns your data has.

When you need to know what type of data (like integers or floats) is stored in the array.

When you want to find out how many dimensions your data has, like 1D, 2D, or 3D.

When you want to know the total number of elements in the array.

Syntax

NumPy

import numpy as np

array = np.array([...])

# Get the shape (rows, columns, ...)
array.shape

# Get the data type of elements
array.dtype

# Get the number of dimensions
array.ndim

# Get the total number of elements
array.size

shape returns a tuple showing the size in each dimension.

dtype tells the type of data stored, like int32 or float64.

Examples

Empty 1D array has shape (0,) and zero size.

NumPy

import numpy as np

empty_array = np.array([])
print(empty_array.shape)  # (0,) means empty 1D array
print(empty_array.size)   # 0 elements

Array with one element shows shape (1,) and 1 dimension.

NumPy

import numpy as np

one_element_array = np.array([42])
print(one_element_array.shape)  # (1,) means 1 element in 1D
print(one_element_array.ndim)   # 1 dimension
print(one_element_array.size)   # 1 element

2D array with 2 rows and 3 columns.

NumPy

import numpy as np

matrix = np.array([[1, 2, 3], [4, 5, 6]])
print(matrix.shape)  # (2, 3) means 2 rows and 3 columns
print(matrix.ndim)   # 2 dimensions
print(matrix.size)   # 6 elements total

Shows the data type of elements in the array.

NumPy

import numpy as np

float_array = np.array([1.5, 2.5, 3.5])
print(float_array.dtype)  # float64 means floating point numbers

Sample Program

This program creates a 2D array and prints its shape, data type, number of dimensions, and total size.

NumPy

import numpy as np

# Create a 2D array (matrix)
number_matrix = np.array([[10, 20, 30], [40, 50, 60]])

print("Array:")
print(number_matrix)

# Print array attributes
print("Shape of array:", number_matrix.shape)
print("Data type of elements:", number_matrix.dtype)
print("Number of dimensions:", number_matrix.ndim)
print("Total number of elements:", number_matrix.size)

Important Notes

Time complexity: Accessing these attributes is very fast, O(1), because they are stored properties.

Space complexity: No extra space is used when accessing these attributes.

Common mistake: Confusing shape with size. Shape shows dimensions, size shows total elements.

Use shape to understand layout, size to know total data points, dtype to check data type, and ndim to know how many dimensions your data has.

Summary

Array attributes help you understand your data's structure and type.

shape shows the size in each dimension.

dtype tells the type of data stored.

ndim gives the number of dimensions.

size gives the total number of elements.

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