np.min() and np.max() in NumPy - Step-by-Step Execution

Concept Flow - np.min() and np.max()

Start with array

↓

Call np.min()

↓

Scan all elements

↓

Find smallest value

↓

Return min value

↓

Call np.max()

↓

Scan all elements

↓

Find largest value

↓

Return max value

np.min() scans the array to find the smallest number and returns it. np.max() scans to find the largest number and returns it.

Execution Sample

NumPy

import numpy as np
arr = np.array([3, 7, 1, 9, 4])
min_val = np.min(arr)
max_val = np.max(arr)
print(min_val, max_val)

This code finds the smallest and largest numbers in the array and prints them.

Execution Table

Step	Function Call	Array Elements	Operation	Result
1	np.min(arr)	[3, 7, 1, 9, 4]	Scan all elements to find smallest	1
2	np.max(arr)	[3, 7, 1, 9, 4]	Scan all elements to find largest	9
3	Print results	min_val=1, max_val=9	Output values	1 9

💡 All elements scanned; min and max values found and printed.

Variable Tracker

Variable	Start	After np.min()	After np.max()	Final
arr	[3,7,1,9,4]	[3,7,1,9,4]	[3,7,1,9,4]	[3,7,1,9,4]
min_val	undefined	1	1	1
max_val	undefined	undefined	9	9

Key Moments - 2 Insights

Why does np.min(arr) return 1 and not the first element 3?

np.min() checks every element in the array (see execution_table step 1) and returns the smallest value found, which is 1, not just the first element.

Does np.max() change the original array?

No, np.max() only reads the array to find the largest value (execution_table step 2). It does not modify the array.

Visual Quiz - 3 Questions

Look at the execution table, what is the result of np.min(arr) at step 1?

A 9

B 3

C 1

D 7

💡 Hint

Check the 'Result' column in execution_table row for step 1.

At which step does the code print the min and max values?

A Step 3

B Step 2

C Step 1

D No print step

💡 Hint

Look for the step with 'Print results' in the 'Function Call' column.

If the array was [5, 5, 5], what would np.max(arr) return?

A 0

B 5

C 15

D Error

💡 Hint

np.max() returns the largest element; check variable_tracker for max_val.

Concept Snapshot

np.min(array) returns the smallest value in the array.
np.max(array) returns the largest value.
Both scan all elements without changing the array.
Useful to quickly find range boundaries.
Syntax: min_val = np.min(arr), max_val = np.max(arr)

Full Transcript

This lesson shows how np.min() and np.max() work by scanning an array to find the smallest and largest values. We start with an array of numbers. Calling np.min() checks each number and returns the smallest one. Calling np.max() does the same but returns the largest. The original array stays the same. We track variables min_val and max_val as they get assigned. The execution table shows each step clearly: first finding min, then max, then printing results. Common confusions include thinking np.min() returns the first element or that np.max() changes the array. The quiz tests understanding of these steps and results.

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