np.min() and np.max() in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to find the minimum value in the array.

NumPy

import numpy as np
arr = np.array([3, 7, 1, 9, 5])
min_value = np.[1](arr)
print(min_value)

Drag options to blanks, or click blank then click option'

Amax

Bmean

Csum

Dmin

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.max() instead of np.min()

Trying to use Python's built-in min() without numpy

The np.min() function returns the smallest value in the array.

2fill in blank

medium

Complete the code to find the maximum value in the 2D array.

NumPy

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

Drag options to blanks, or click blank then click option'

Amax

Bmin

Cmedian

Daverage

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.min() instead of np.max()

Trying to use Python's built-in max() without numpy

The np.max() function returns the largest value in the array.

3fill in blank

hard

Fix the error in the code to correctly find the minimum value along axis 0.

NumPy

import numpy as np
arr = np.array([[10, 20], [5, 15]])
min_values = np.min(arr, [1]=0)
print(min_values)

Drag options to blanks, or click blank then click option'

Aaxis

Baxes

Cdim

Ddirection

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'axes' instead of 'axis'

Using 'dim' or 'direction' which are invalid parameters

The correct parameter name to specify the axis is axis.

4fill in blank

hard

Fill both blanks to create a dictionary of max values for each column in the array.

NumPy

import numpy as np
arr = np.array([[3, 6, 9], [2, 8, 5]])
max_dict = {i: np.[1](arr, [2]=0)[i] for i in range(arr.shape[1])}
print(max_dict)

Drag options to blanks, or click blank then click option'

Amax

Bmin

Caxis

Ddim

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.min() instead of np.max()

Using 'dim' instead of 'axis' as parameter

Use np.max() to find max values and axis to specify the dimension.

5fill in blank

hard

Fill all three blanks to create a dictionary of min values for each row in the array.

NumPy

import numpy as np
arr = np.array([[7, 4, 1], [3, 9, 6]])
min_dict = {i: np.[1](arr, [2]=1)[[3]] for i in range(arr.shape[0])}
print(min_dict)

Drag options to blanks, or click blank then click option'

Amax

Bmin

Caxis

Di

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.max() instead of np.min()

Using wrong parameter name instead of 'axis'

Using a fixed number instead of the loop variable

Use np.min() to find minimum values, axis to specify dimension, and i as the axis index.

Practice

1. What does the function np.min() do when applied to a NumPy array?

easy

A. Calculates the average of the array

B. Finds the largest value in the array

C. Finds the smallest value in the array

D. Counts the number of elements in the array

Solution

1. Step 1: Understand the purpose of np.min()

   The function np.min() is designed to find the minimum value in a NumPy array.

2. Step 2: Compare with other options

   Options B, C, and D describe different functions: max, mean, and size respectively, which are not what np.min() does.

3. Final Answer:

   Finds the smallest value in the array -> Option C

4. Quick Check:

   np.min() = smallest value [OK]

Hint: np.min() always returns the smallest number [OK]

Common Mistakes:

• Confusing np.min() with np.max()
• Thinking np.min() calculates average
• Assuming np.min() counts elements

2. Which of the following is the correct NumPy function syntax to find the maximum value in a NumPy array named arr?

easy

A. max(arr)

B. np.max(arr)

C. np.maximum(arr)

D. arr.max()

Solution

1. Step 1: Identify correct function usage

   The function np.max(arr) correctly finds the maximum value in the array arr.

2. Step 2: Analyze other options

   arr.max() is valid but uses method syntax, not function syntax. np.maximum(arr) is incorrect because np.maximum() compares two arrays element-wise. max(arr) is Python's built-in max, which works on lists but not efficiently on NumPy arrays.

3. Final Answer:

   np.max(arr) -> Option B

4. Quick Check:

   np.max(arr) = max value [OK]

Hint: Use np.max(array) to get max value quickly [OK]

Common Mistakes:

• Using np.maximum() instead of np.max()
• Confusing method and function syntax
• Using Python max() on NumPy arrays

3. What is the output of the following code?

import numpy as np
arr = np.array([[3, 7, 2], [5, 1, 9]])
print(np.min(arr, axis=0))

medium

A. [3 1 2]

B. [3 7 2]

C. [5 1 9]

D. [1 2 3]

Solution

1. Step 1: Understand axis=0 in np.min()

   Using axis=0 means finding the minimum value down each column.

2. Step 2: Calculate minimum per column

   Column 1: min(3,5) = 3
   Column 2: min(7,1) = 1
   Column 3: min(2,9) = 2

3. Final Answer:

   [3 1 2] -> Option A

4. Quick Check:

   np.min(arr, axis=0) = [3 1 2] [OK]

Hint: axis=0 finds min down columns, axis=1 across rows [OK]

Common Mistakes:

• Mixing axis=0 and axis=1
• Confusing min with max
• Expecting a single number instead of array

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

import numpy as np
arr = np.array([4, 8, 6])
print(np.min(arr, axis=1))

medium

A. The array is 1D, axis=1 is invalid

B. np.min() does not accept axis argument

C. np.min() requires a list, not an array

D. The array contains non-numeric values

Solution

1. Step 1: Check array dimensions

   The array arr is one-dimensional (shape (3,)).

2. Step 2: Understand axis parameter

   Axis=1 means operate along columns of a 2D or higher array. For 1D arrays, axis=1 is invalid and causes an error.

3. Final Answer:

   The array is 1D, axis=1 is invalid -> Option A

4. Quick Check:

   1D array + axis=1 = error [OK]

Hint: Use axis only within array dimension range [OK]

Common Mistakes:

• Using axis=1 on 1D arrays
• Thinking np.min() can't take axis
• Assuming array has non-numeric data

5. Given a 2D NumPy array data representing daily temperatures for a week (rows = days, columns = cities), how would you find the highest temperature recorded in each city?

hard

A. np.max(data, axis=1)

B. np.min(data, axis=1)

C. np.min(data, axis=0)

D. np.max(data, axis=0)

Solution

1. Step 1: Understand data layout

   Rows represent days, columns represent cities. To find max per city, we want max down each column.

2. Step 2: Use axis=0 for column-wise operation

   Using np.max(data, axis=0) finds the maximum temperature for each city across all days.

3. Final Answer:

   np.max(data, axis=0) -> Option D

4. Quick Check:

   Max per city = np.max(data, axis=0) [OK]

Hint: axis=0 aggregates down columns (cities) [OK]

Common Mistakes:

• Using axis=1 to find max per city
• Using np.min() instead of np.max()
• Confusing rows and columns