transpose() for swapping axes in NumPy - Mini Project: Build & Apply

Transpose a NumPy Array to Swap Axes

📖 Scenario: Imagine you have a table of data representing sales numbers for different products across several months. The data is stored in a 2D array where rows are products and columns are months. Sometimes, you want to switch the rows and columns to analyze the data from a different perspective.

🎯 Goal: You will create a NumPy array with sales data, then use the transpose() function to swap its axes. Finally, you will print the transposed array to see the switched rows and columns.

📋 What You'll Learn

Create a 2D NumPy array named sales with the exact values [[10, 20, 30], [40, 50, 60], [70, 80, 90]]

Create a variable named swapped_axes to hold the transposed array using sales.transpose()

Print the swapped_axes array to display the result

💡 Why This Matters

🌍 Real World

Swapping axes of data arrays is common in data science when you want to change the perspective of your data, such as switching rows and columns in tables or images.

💼 Career

Understanding how to manipulate array shapes and axes is essential for data cleaning, feature engineering, and preparing data for machine learning models.

1

Create the sales data array

Import NumPy as np and create a 2D NumPy array called sales with these exact values: [[10, 20, 30], [40, 50, 60], [70, 80, 90]].

NumPy

import numpy as np
# Create the sales array with the exact values
# Your code here

Hint

Use np.array() to create the array with the exact nested list.

2

Create a variable for the transposed array

Create a variable called swapped_axes and assign it the transposed version of the sales array using sales.transpose().

NumPy

import numpy as np
sales = np.array([[10, 20, 30], [40, 50, 60], [70, 80, 90]])
# Create swapped_axes by transposing sales
# Your code here

Hint

Use the transpose() method on the sales array.

3

Print the transposed array

Print the variable swapped_axes to display the transposed array.

NumPy

import numpy as np
sales = np.array([[10, 20, 30], [40, 50, 60], [70, 80, 90]])
swapped_axes = sales.transpose()
# Print swapped_axes
# Your code here

Hint

Use print(swapped_axes) to show the transposed array.

4

Verify the shape change after transpose

Print the shape of the original sales array and the shape of the swapped_axes array using print(sales.shape) and print(swapped_axes.shape).

NumPy

import numpy as np
sales = np.array([[10, 20, 30], [40, 50, 60], [70, 80, 90]])
swapped_axes = sales.transpose()
print(swapped_axes)
# Print the shapes of sales and swapped_axes
# Your code here

Hint

Use print(sales.shape) and print(swapped_axes.shape) to see the shapes.

Practice

1. What does the numpy.transpose() function do to an array?

easy

A. It removes duplicate elements from the array.

B. It adds new elements to the array.

C. It sorts the array elements in ascending order.

D. It swaps or flips the axes of the array.

Solution

1. Step 1: Understand the purpose of transpose()

   The transpose() function changes the order of axes in an array, effectively flipping or swapping them.

2. Step 2: Compare with other options

   Adding elements, sorting, or removing duplicates are unrelated to axis swapping.

3. Final Answer:

   It swaps or flips the axes of the array. -> Option D

4. Quick Check:

   transpose() = swaps axes [OK]

Hint: Remember: transpose flips axes, not values or elements [OK]

Common Mistakes:

• Thinking transpose sorts or changes values
• Confusing transpose with adding or removing elements
• Assuming transpose only works on 2D arrays

2. Which of the following is the correct syntax to transpose a 2D NumPy array named arr?

easy

A. transpose(arr)

B. arr.transpose

C. arr.transpose()

D. arr.transpose[]

Solution

1. Step 1: Recall method call syntax in Python

   To call a method, parentheses are required: arr.transpose().

2. Step 2: Check other options

   transpose(arr) is incorrect because transpose is a method, not a standalone function here; arr.transpose misses parentheses; arr.transpose[] is invalid syntax.

3. Final Answer:

   arr.transpose() -> Option C

4. Quick Check:

   Method call needs parentheses [OK]

Hint: Use parentheses to call transpose method: arr.transpose() [OK]

Common Mistakes:

• Omitting parentheses when calling transpose
• Using transpose as a standalone function without import
• Using invalid syntax like brackets after method

3. What is the output of the following code?

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

medium

A. [[1 4]
[2 5]
[3 6]]

B. [[1 2 3]
[4 5 6]]

C. [[1 4 2]
[3 5 6]]

D. [[6 5 4]
[3 2 1]]

Solution

1. Step 1: Understand the original array shape

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

2. Step 2: Apply transpose()

   Transpose swaps axes, so shape becomes (3, 2). Rows become columns and vice versa. The first column becomes the first row: [1,4], second: [2,5], third: [3,6].

3. Final Answer:

   [[1 4] [2 5] [3 6]] -> Option A

4. Quick Check:

   Transpose swaps rows and columns [OK]

Hint: Transpose flips rows and columns in 2D arrays [OK]

Common Mistakes:

• Confusing transpose with reversing elements
• Expecting original shape after transpose
• Mixing up rows and columns in output

4. The following code throws an error. What is the mistake?

import numpy as np
arr = np.array([[[1,2],[3,4]], [[5,6],[7,8]]])
print(arr.transpose(1, 0))

medium

A. transpose() requires all axes to be specified for 3D arrays.

B. The array is not a NumPy array.

C. transpose() cannot be used on 3D arrays.

D. The axes argument must be a list, not a tuple.

Solution

1. Step 1: Check array dimensions

   The array has 3 dimensions (shape (2, 2, 2)).

2. Step 2: Understand transpose axes argument

   For 3D arrays, transpose requires all three axes to be specified. Passing only two axes (1, 0) causes an error.

3. Final Answer:

   transpose() requires all axes to be specified for 3D arrays. -> Option A

4. Quick Check:

   All axes must be given for 3D transpose [OK]

Hint: Specify all axes when transposing multi-dimensional arrays [OK]

Common Mistakes:

• Passing fewer axes than array dimensions
• Thinking transpose can't handle 3D arrays
• Confusing tuple and list for axes argument

5. Given a 3D NumPy array arr with shape (2, 3, 4), which transpose() call will swap the first and last axes, resulting in shape (4, 3, 2)?

hard

A. arr.transpose(2, 0, 1)

B. arr.transpose(2, 1, 0)

C. arr.transpose(0, 2, 1)

D. arr.transpose(1, 0, 2)

Solution

1. Step 1: Identify original axes order

   Original shape (2, 3, 4) corresponds to axes (0, 1, 2).

2. Step 2: Determine desired axes order

   Swapping first and last axes means new order is (2, 1, 0), resulting in shape (4, 3, 2).

3. Final Answer:

   arr.transpose(2, 1, 0) -> Option B

4. Quick Check:

   Swap axes 0 and 2: (2,1,0) [OK]

Hint: List axes in desired order to swap them correctly [OK]

Common Mistakes:

• Mixing up axes order in transpose
• Not specifying all axes for 3D arrays
• Assuming transpose swaps only two axes automatically