np.min() and np.max() in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does np.min() do in numpy?

np.min() finds the smallest value in an array or along a specified axis.

beginner

What is the purpose of np.max()?

np.max() returns the largest value in an array or along a specified axis.

intermediate

How do you find the minimum value in each column of a 2D numpy array?

Use np.min(array, axis=0). Axis 0 means down the rows, so it finds min per column.

intermediate

How to find the maximum value in each row of a 2D numpy array?

Use np.max(array, axis=1). Axis 1 means across columns, so it finds max per row.

advanced

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

It raises a ValueError because there is no data to find min or max.

What does np.min() return when used on a numpy array?

A The average value of the array

B The largest value in the array

C The smallest value in the array

D The sum of all values in the array

np.min() finds the smallest value in the array.

How do you find the maximum value in each row of a 2D numpy array named arr?

A <code>np.max(arr, axis=1)</code>

B <code>np.min(arr, axis=1)</code>

C <code>np.max(arr, axis=0)</code>

D <code>np.min(arr, axis=0)</code>

Axis 1 means across columns, so np.max(arr, axis=1) finds max per row.

What will np.min() do if the array is empty?

A Return 0

B Raise a ValueError

C Return infinity

D Return None

Calling np.min() on an empty array raises a ValueError.

Which axis should you use with np.min() to find the minimum value in each column of a 2D array?

A axis=0

B axis=1

C axis=2

D axis=None

Axis 0 means down the rows, so it finds min per column.

If you want the overall maximum value in a numpy array, which function call is correct?

A <code>array.min(axis=0)</code>

B <code>np.min(array)</code>

C <code>array.max(axis=1)</code>

D <code>np.max(array)</code>

np.max(array) returns the largest value in the whole array.

Explain how to use np.min() and np.max() to find minimum and maximum values along different axes in a 2D numpy array.

Think about rows and columns as directions to find min or max.

What error occurs if you try to find the minimum or maximum of an empty numpy array using np.min() or np.max()? Why?

Consider what happens when there is nothing to compare.

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