np.min() and np.max() in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of np.min() on a 2D array with axis parameter

What is the output of the following code?

import numpy as np
arr = np.array([[3, 7, 5], [1, 6, 9]])
result = np.min(arr, axis=0)
print(result)

NumPy

import numpy as np
arr = np.array([[3, 7, 5], [1, 6, 9]])
result = np.min(arr, axis=0)
print(result)

A [1 6 5]

B [1 7 5]

C [3 7 5]

D [1 6 9]

💡 Hint

Think about what axis=0 means for a 2D array.

np.min with axis=0 finds the minimum value in each column. For columns: [3,1], [7,6], [5,9], the minimums are 1, 6, and 5 respectively.

❓ data_output

intermediate

Number of elements in np.max() result with keepdims=True

Given the code below, how many elements are in the resulting array?

import numpy as np
arr = np.array([[4, 2, 8], [7, 1, 5]])
result = np.max(arr, axis=1, keepdims=True)
print(result)
print(result.size)

NumPy

import numpy as np
arr = np.array([[4, 2, 8], [7, 1, 5]])
result = np.max(arr, axis=1, keepdims=True)
print(result)
print(result.size)

A 3

B 6

C 2

D 1

💡 Hint

Keepdims keeps the dimension, but does not change the number of rows.

np.max with axis=1 and keepdims=True returns a 2x1 array. So total elements = 2.

🔧 Debug

advanced

Identify the error in np.min() usage

What error does the following code raise?

import numpy as np
arr = np.array([1, 2, 3])
result = np.min(arr, axis=1)
print(result)

NumPy

import numpy as np
arr = np.array([1, 2, 3])
result = np.min(arr, axis=1)
print(result)

A TypeError: 'int' object is not iterable

B AxisError: axis 1 is out of bounds for array of dimension 1

C ValueError: operands could not be broadcast together

D No error, output is 1

💡 Hint

Check the shape of the array and the axis parameter.

The array is 1D, so axis=1 is invalid and raises AxisError.

🚀 Application

advanced

Find the maximum value ignoring NaNs

Which code correctly finds the maximum value in the array ignoring NaN values?

import numpy as np
arr = np.array([3, np.nan, 7, 2])

NumPy

import numpy as np
arr = np.array([3, np.nan, 7, 2])

A np.max(arr, where=~np.isnan(arr))

B np.max(arr)

C np.max(arr[~np.isnan(arr)])

D np.nanmax(arr)

💡 Hint

Look for a numpy function that ignores NaNs automatically.

np.nanmax returns the max ignoring NaNs. np.max returns NaN if any NaN present. Option D works but is less direct. Option D is invalid syntax.

🧠 Conceptual

expert

Effect of np.min() and np.max() on empty arrays

What happens when you call np.min() or np.max() on an empty numpy array?

import numpy as np
empty_arr = np.array([])
min_val = np.min(empty_arr)
max_val = np.max(empty_arr)

NumPy

import numpy as np
empty_arr = np.array([])
min_val = np.min(empty_arr)
max_val = np.max(empty_arr)

A Both calls raise a ValueError: zero-size array to reduction operation minimum/maximum which has no identity

B min_val is 0, max_val is 0

C Both min_val and max_val are np.inf and -np.inf respectively

D min_val and max_val are None

💡 Hint

Think about what minimum or maximum means for no elements.

Calling np.min or np.max on an empty array raises ValueError because there is no identity value to return.

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