Array shapes and dimensions in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the shape of a NumPy array represent?

The shape of a NumPy array shows the size of the array in each dimension. It is a tuple where each number tells how many elements are in that dimension.

beginner

How many dimensions does a 1D array have?

A 1D array has exactly one dimension. It looks like a simple list of values.

beginner

What is the shape of this array? np.array([[1, 2, 3], [4, 5, 6]])

The shape is (2, 3) because there are 2 rows and 3 columns.

beginner

What does the ndim attribute of a NumPy array tell you?

The ndim attribute tells you how many dimensions the array has. For example, 1 for 1D, 2 for 2D, and so on.

intermediate

How can you change the shape of an array without changing its data?

You can use the reshape() method to change the shape of an array while keeping the same data.

What is the shape of a 3D array with dimensions 4, 3, and 2?

A (4, 2, 3)

B (3, 2, 4)

C (2, 3, 4)

D (4, 3, 2)

The shape tuple lists dimensions in order: (4, 3, 2).

If an array has shape (5,), what does this mean?

A It is a 2D array with 5 rows

B It is a 1D array with 5 elements

C It is a 3D array with 5 layers

D It is an empty array

Shape (5,) means one dimension with 5 elements.

Which NumPy attribute gives the number of dimensions of an array?

A shape

B size

C ndim

D dim

The ndim attribute tells the number of dimensions.

What happens if you try to reshape an array to a shape that does not match the total number of elements?

A It raises an error

B It reshapes anyway, filling missing values with zeros

C It truncates extra elements

D It duplicates elements to fill the shape

Reshape requires the total number of elements to stay the same, otherwise it raises an error.

What is the shape of np.array([[1, 2], [3, 4], [5, 6]])?

A (3, 2)

B (2, 3)

C (6,)

D (3,)

There are 3 rows and 2 columns, so shape is (3, 2).

Explain what array shape and dimensions mean in simple terms.

Think about rows and columns in a table.

Describe how you can change the shape of an array and what you must be careful about.

Imagine rearranging blocks without adding or removing any.

Practice

1. What does the shape of a NumPy array represent?

easy

A. The number of elements along each dimension

B. The total number of elements in the array

C. The data type of the elements

D. The memory size of the array

Solution

1. Step 1: Understand the meaning of shape

   The shape of an array is a tuple that shows how many elements are in each dimension.

2. Step 2: Differentiate shape from other properties

   Shape is not the total count, data type, or memory size, but the size per dimension.

3. Final Answer:

   The number of elements along each dimension -> Option A

4. Quick Check:

   Shape = elements per dimension [OK]

Hint: Shape = count of elements per axis [OK]

Common Mistakes:

• Confusing shape with total number of elements
• Mixing shape with data type
• Thinking shape is memory size

2. Which of the following is the correct way to get the shape of a NumPy array arr?

easy

A. arr.shape

B. arr.shape()

C. shape(arr)

D. arr.get_shape()

Solution

1. Step 1: Recall NumPy shape syntax

   In NumPy, shape is an attribute, not a method, so no parentheses are used.

2. Step 2: Check each option

   arr.shape uses arr.shape correctly; others use incorrect syntax or functions.

3. Final Answer:

   arr.shape -> Option A

4. Quick Check:

   Use attribute, no parentheses [OK]

Hint: Use arr.shape without parentheses [OK]

Common Mistakes:

• Adding parentheses after shape
• Using non-existent functions like get_shape()
• Trying to call shape as a function

3. Given the code:

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

What is the output?

medium

A. (3, 2)

B. (6,)

C. (2, 3)

D. (2,)

Solution

1. Step 1: Analyze the array structure

   The array has 2 rows and 3 columns, so shape is (2, 3).

2. Step 2: Confirm shape output

   Printing arr.shape returns the tuple (2, 3).

3. Final Answer:

   (2, 3) -> Option C

4. Quick Check:

   Rows=2, Columns=3 -> (2, 3) [OK]

Hint: Count rows then columns for shape [OK]

Common Mistakes:

• Swapping rows and columns in shape
• Thinking shape is total elements
• Confusing shape with array 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 statement syntax is incorrect

B. arr is not a NumPy array

C. The array is empty

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

Solution

1. Step 1: Identify the error source

   The code calls arr.shape() with parentheses, but shape is an attribute.

2. Step 2: Understand attribute vs method

   Attributes do not use parentheses; calling with () causes a TypeError.

3. Final Answer:

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

4. Quick Check:

   Attributes no parentheses [OK]

Hint: Remember: shape is attribute, no () [OK]

Common Mistakes:

• Using parentheses with shape
• Assuming array is empty causes error
• Misreading print syntax

5. You have a 3D NumPy array with shape (4, 3, 2). Which of the following operations will correctly reshape it into a 2D array with 4 rows and 6 columns?

hard

A. arr.reshape(12, 2)

B. arr.reshape(4, 6)

C. arr.reshape(6, 4)

D. arr.reshape(2, 12)

Solution

1. Step 1: Calculate total elements

   Original shape (4, 3, 2) means 4*3*2 = 24 elements total.

2. Step 2: Determine new shape dimensions

   New shape (4, 6) means 4*6 = 24 elements, matching total elements.

3. Step 3: Verify other options

   arr.reshape(12, 2), arr.reshape(6, 4), and arr.reshape(2, 12) do not produce 4 rows and 6 columns.

4. Final Answer:

   arr.reshape(4, 6) -> Option B

5. Quick Check:

   4*6=24 matches total elements [OK]

Hint: New shape must keep total elements same [OK]

Common Mistakes:

• Mixing up rows and columns in reshape
• Choosing shapes with wrong total elements
• Forgetting reshape must keep total size