NumPy ndarray.min() Method

NumPy ndarray.min()

The numpy.ndarray.min() method returns the minimum value in a NumPy array. It can operate across the entire array or along a specified axis.

Syntax

ndarray.min(axis=None, out=None, keepdims=False, initial=<no value>, where=True)

Parameters

Parameter	Type	Description
axis	None, int, or tuple of ints, optional	Axis or axes along which the minimum is computed. If None, the minimum of the entire array is returned.
out	ndarray, optional	Alternative output array for storing the result. Must have the same shape as the expected output.
keepdims	bool, optional	If True, retains reduced dimensions as size one, allowing proper broadcasting.
initial	scalar, optional	Initial value to compare against when computing the minimum. If provided, the minimum is at least this value.
where	array_like of bool, optional	Specifies elements to include in the computation.

Return Value

Returns a scalar value if axis=None, or an array of minimum values if an axis is specified. The result is the smallest element (or smallest element along the specified axis).

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Examples

1. Finding the Minimum Value in an ndarray

This example demonstrates how to find the minimum value in a NumPy array.

import numpy as np

# Creating a 2D NumPy array
arr = np.array([[4, 7, 1],
                [9, 3, 6]])

# Finding the minimum value in the entire array
result = arr.min()

print("Minimum value in the array:", result)

Output:

Minimum value in the array: 1

2. Using the axis Parameter in ndarray.min()

Here, we compute the minimum along different axes.

import numpy as np

# Creating a 2D NumPy array
arr = np.array([[8, 2, 5],
                [1, 7, 3]])

# Finding the minimum value along axis 0 (column-wise)
result_axis0 = arr.min(axis=0)
print("Minimum along axis 0 (columns):", result_axis0)

# Finding the minimum value along axis 1 (row-wise)
result_axis1 = arr.min(axis=1)
print("Minimum along axis 1 (rows):", result_axis1)

Output:

Minimum along axis 0 (columns): [1 2 3]
Minimum along axis 1 (rows): [2 1]

3. Keeping Dimensions with keepdims=True in ndarray.min()

Using keepdims=True ensures the output retains the reduced dimension.

import numpy as np

# Creating a 2D NumPy array
arr = np.array([[5, 9, 2],
                [6, 4, 8]])

# Finding the minimum along axis 1 while keeping dimensions
result = arr.min(axis=1, keepdims=True)

print("Minimum values with kept dimensions:\n", result)

Output:

Minimum values with kept dimensions:
 [[2]
  [4]]

4. Using the initial Parameter in ndarray.min()

The initial parameter sets a minimum starting value.

import numpy as np

# Creating a 1D NumPy array
arr = np.array([10, 15, 3, 7])

# Finding the minimum value with an initial comparison value of 5
result = arr.min(initial=5)

print("Minimum value with initial=5:", result)

Output:

Minimum value with initial=5: 3

5. Using the where Parameter in ndarray.min()

The where parameter allows computing the minimum only for selected elements.

import numpy as np

# Creating a 1D NumPy array
arr = np.array([12, 7, 19, 5])

# Creating a boolean mask
mask = np.array([True, False, True, True])

# Finding the minimum only for elements where mask is True
result = arr.min(initial=100, where=mask)

print("Minimum value considering only masked elements:", result)

Output:

Minimum value considering only masked elements: 5