np.min(arr) and arr.min() are equivalent, both scanning the array for its smallest value; with an axis argument on a multi-dimensional array, it instead returns the minimum along that axis, collapsing it into an array of minimums, one per remaining position. Unlike Python's built-in min(), which works on any iterable of comparable objects, np.min() is specifically optimized for numeric ndarrays and computes at C speed.

1Understanding np.min()

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For multiple arrays combined element-wise, use np.minimum(a, b) instead of np.min() — np.min() finds the smallest value within a single array, or along an axis, while np.minimum() compares two arrays element-wise and keeps the smaller value at each position.

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import numpy as np

arr = np.array([5, 2, 8, 1, 9])
print(np.min(arr))

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2Practical Example

Here is a real-world application of np.min() showing how it is used in production NumPy code.

editor.html

import numpy as np

a = np.array([1, 5, 3])
b = np.array([4, 2, 6])
print(np.minimum(a, b))

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3Best Practices

Follow these guidelines when working with np.min():

1. Use np.min()/arr.min() for the overall or per-axis smallest value within one array

2. Use np.minimum(a, b) instead when you need an element-wise comparison between two separate arrays

3. Use np.nanmin() when NaN values in the data should be ignored rather than causing the result to be NaN

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Tip: For multiple arrays combined element-wise, use np.minimum(a, b) instead of np.min() — np.min() finds the smallest value within a single array, or along an axis, while np.minimum() compares two arrays element-wise and keeps the smaller value at each position.

editor.html

import numpy as np

arr = np.array([5, 2, 8, 1, 9])
print(np.min(arr))

localhost:3000

Examples

Example 01Basic Usage

import numpy as np

arr = np.array([5, 2, 8, 1, 9])
print(np.min(arr))

Example 02Advanced Example

import numpy as np

a = np.array([1, 5, 3])
b = np.array([4, 2, 6])
print(np.minimum(a, b))

Best Practices

Use np.min()/arr.min() for the overall or per-axis smallest value within one array
Use np.minimum(a, b) instead when you need an element-wise comparison between two separate arrays
Use np.nanmin() when NaN values in the data should be ignored rather than causing the result to be NaN

Interview Question

What's the difference between np.min(arr) and np.minimum(a, b)?▼

Hint: Think about how many arrays each function operates on, and what kind of result each produces.

np.min() operates on a single array, or along one axis of it, and reduces it down to its smallest value, or an array of smallest values if reducing along one axis of a multi-dimensional input. np.minimum() instead takes two arrays of compatible shape and compares them element-wise, position by position, returning an array of the same shape where each position holds whichever of the two inputs was smaller at that position — it's a comparison between arrays, not a reduction within one.

Exercises

MediumPractice using np.min() in a real scenario.

View Solution

import numpy as np

arr = np.array([5, 2, 8, 1, 9])
print(np.min(arr))

Frequently Asked Questions

What's the difference between np.min(arr) and np.minimum(a, b)?▼

Related Functions

np-maxnp-argminnp-clip