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.