Sorting along axes in NumPy - Mini Project: Build & Apply

Sorting Along Axes with NumPy

📖 Scenario: Imagine you work in a small bakery. You have a list of daily sales for different types of bread over a week. You want to organize this data to see which days had the lowest to highest sales for each bread type and also which bread types sold the least to most on each day.

🎯 Goal: You will create a 2D NumPy array representing sales data, then sort the sales along rows and columns to better understand sales patterns.

📋 What You'll Learn

Create a 2D NumPy array called sales with exact values

Create a variable called axis_row set to 1

Create a variable called axis_col set to 0

Sort the sales array along rows using axis_row

Sort the sales array along columns using axis_col

Print both sorted arrays exactly as specified

💡 Why This Matters

🌍 Real World

Sorting sales data helps businesses quickly identify trends, like which products sell best on certain days or which days have the highest sales.

💼 Career

Data scientists and analysts often sort data along different axes to prepare it for reports, visualizations, or further analysis.

1

Create the sales data array

Create a 2D NumPy array called sales with these exact values: [[20, 35, 30], [25, 32, 34], [22, 30, 35]]

NumPy

import numpy as np
# Create the 2D array called sales with the given values
# Your code here

Hint

Use np.array() to create the 2D array with the exact nested list.

2

Set axis variables for sorting

Create a variable called axis_row and set it to 1. Then create a variable called axis_col and set it to 0.

NumPy

import numpy as np
sales = np.array([[20, 35, 30], [25, 32, 34], [22, 30, 35]])
# Create axis_row and axis_col variables
# Your code here

Hint

Remember, axis=1 means sorting along rows, and axis=0 means sorting along columns.

3

Sort the sales array along rows and columns

Use np.sort() to sort the sales array along rows using axis_row and store it in sorted_rows. Then sort the sales array along columns using axis_col and store it in sorted_cols.

NumPy

import numpy as np
sales = np.array([[20, 35, 30], [25, 32, 34], [22, 30, 35]])
axis_row = 1
axis_col = 0
# Sort sales along rows and columns
# Your code here

Hint

Use np.sort() with the axis parameter set to the axis variables.

4

Print the sorted arrays

Print the sorted_rows array first, then print the sorted_cols array.

NumPy

import numpy as np
sales = np.array([[20, 35, 30], [25, 32, 34], [22, 30, 35]])
axis_row = 1
axis_col = 0
sorted_rows = np.sort(sales, axis=axis_row)
sorted_cols = np.sort(sales, axis=axis_col)
# Print sorted_rows and sorted_cols
# Your code here

Hint

Use two print() statements, one for each sorted array.

Practice

1. What does the axis parameter control in numpy.sort?

easy

A. It decides whether to sort rows or columns in an array.

B. It sets the sorting algorithm type.

C. It changes the data type of the array before sorting.

D. It specifies the order of sorting (ascending or descending).

Solution

1. Step 1: Understand the role of axis in sorting

   The axis parameter tells numpy which direction to sort: 0 means sort each column, 1 means sort each row.

2. Step 2: Differentiate from other parameters

   Sorting algorithm type and order are controlled by other parameters, not axis.

3. Final Answer:

   It decides whether to sort rows or columns in an array. -> Option A

4. Quick Check:

   axis controls direction = A [OK]

Hint: Remember axis=0 sorts columns, axis=1 sorts rows [OK]

Common Mistakes:

• Confusing axis with sorting order
• Thinking axis changes data type
• Assuming axis sets sorting algorithm

2. Which of the following is the correct syntax to sort a 2D numpy array arr along rows?

easy

A. numpy.sort(arr, axis=0)

B. numpy.sort(arr, axis=None)

C. numpy.sort(arr, axis=1)

D. numpy.sort(arr, axis=2)

Solution

1. Step 1: Identify axis for sorting rows

   In a 2D array, axis=1 means sorting each row individually.

2. Step 2: Check other options for validity

   Axis=0 sorts columns, axis=None flattens the array into 1D before sorting, axis=2 is invalid for 2D arrays.

3. Final Answer:

   numpy.sort(arr, axis=1) -> Option C

4. Quick Check:

   axis=1 sorts rows = B [OK]

Hint: Use axis=1 to sort rows in 2D arrays [OK]

Common Mistakes:

• Using axis=0 to sort rows
• Using invalid axis like 2 for 2D arrays
• Using axis=None which flattens the array

3. What is the output of the following code?

import numpy as np
arr = np.array([[3, 1, 2], [6, 4, 5]])
sorted_arr = np.sort(arr, axis=0)
print(sorted_arr)

medium

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

B. [[3 1 2]
[6 4 5]] sorted by columns

C. [[3 1 2]
[6 4 5]] (unchanged)

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

Solution

1. Step 1: Understand sorting along axis=0

   Sorting with axis=0 sorts each column independently in ascending order.

2. Step 2: Sort each column of the array

   Columns: [3,6] -> [3,6], [1,4] -> [1,4], [2,5] -> [2,5]. Since columns are already sorted, array remains the same.

3. Final Answer:

   [[3 1 2] [6 4 5]] sorted by columns -> Option B

4. Quick Check:

   axis=0 sorts columns = A [OK]

Hint: axis=0 sorts columns top to bottom [OK]

Common Mistakes:

• Assuming sorting rearranges rows
• Confusing axis=0 with axis=1
• Expecting full array sort instead of column-wise

4. The code below throws an error. What is the problem?

import numpy as np
arr = np.array([[1, 3], [2, 4]])
sorted_arr = np.sort(arr, axis=2)
print(sorted_arr)

medium

A. Axis 2 does not exist for a 2D array.

B. The array contains non-numeric data.

C. The sort function requires axis to be None.

D. The array must be 1D to sort.

Solution

1. Step 1: Check array dimensions

   The array is 2D with shape (2,2), so valid axes are 0 and 1 only.

2. Step 2: Validate axis parameter

   Using axis=2 is invalid and causes an IndexError because axis 2 does not exist.

3. Final Answer:

   Axis 2 does not exist for a 2D array. -> Option A

4. Quick Check:

   Axis must be within array dimensions = C [OK]

Hint: Axis must be less than array dimensions [OK]

Common Mistakes:

• Using axis out of range
• Assuming sort only works on 1D arrays
• Confusing axis with array shape

5. Given a 3D numpy array arr with shape (2, 2, 3), how would you sort the array along the last axis for each 2D slice?

hard

A. np.sort(arr, axis=-2)

B. np.sort(arr, axis=1)

C. np.sort(arr, axis=0)

D. np.sort(arr, axis=2)

Solution

1. Step 1: Identify the last axis in a 3D array

   For shape (2, 2, 3), axes are 0, 1, 2. The last axis is 2.

2. Step 2: Use axis=2 to sort along the last axis

   Sorting with axis=2 sorts each 2D slice along the last dimension (length 3).

3. Final Answer:

   np.sort(arr, axis=2) -> Option D

4. Quick Check:

   Last axis is 2, so axis=2 sorts last dimension [OK]

Hint: Use axis=-1 or axis=2 for last axis in 3D arrays [OK]

Common Mistakes:

• Using axis=0 or 1 instead of last axis
• Confusing negative axis indexing
• Not matching axis to array shape