NumPy ndarray.std() Method

NumPy ndarray.std()

The numpy.ndarray.std() method computes the standard deviation of elements in a NumPy array. It can calculate the standard deviation across the entire array or along a specified axis.

Syntax

ndarray.std(axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True)

Parameters

Parameter	Type	Description
axis	None, int, or tuple of ints, optional	Axis or axes along which the standard deviation is computed. If None, the standard deviation is computed for the entire array.
dtype	dtype, optional	Data type for the computation. Defaults to float64 for integer arrays and the same type as the input array for floating-point arrays.
out	ndarray, optional	Alternative output array where the result is stored. Must have the same shape as the expected output.
ddof	int or float, optional	Delta Degrees of Freedom. The divisor used is N - ddof, where N is the number of elements. Defaults to 0.
keepdims	bool, optional	If True, the reduced dimensions are retained as size one, enabling proper broadcasting.
where	array_like of bool, optional	Specifies elements to include in the standard deviation calculation.

Return Value

Returns a float value if axis=None, or an array of standard deviation values if an axis is specified.

---

Examples

1. Calculating Standard Deviation for an Entire Array

Computing the standard deviation for all elements in a NumPy array.

import numpy as np

# Creating a 1D array
arr = np.array([1, 2, 3, 4, 5])

# Calculating the standard deviation of the entire array
result = arr.std()
print(result)  # Output: Standard deviation of all elements

Output:

1.4142135623730951

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

Computing the standard deviation along different axes in a 2D array.

import numpy as np

arr = np.array([[1, 2, 3], 
                [4, 5, 6]])

# Standard deviation along axis 0 (columns)
result_axis0 = arr.std(axis=0)
print(result_axis0)

# Standard deviation along axis 1 (rows)
result_axis1 = arr.std(axis=1)
print(result_axis1)

Output:

[1.5 1.5 1.5]
[0.81649658 0.81649658]

3. Using the ddof Parameter in ndarray.std()

Modifying the degrees of freedom for standard deviation calculation.

import numpy as np

arr = np.array([1, 2, 3, 4, 5])

# Calculating standard deviation with ddof=1 (sample standard deviation)
result_ddof1 = arr.std(ddof=1)
print(result_ddof1)

Output:

1.5811388300841898

4. Keeping Dimensions with keepdims=True in ndarray.std()

Using keepdims=True to retain the reduced dimension as size one.

import numpy as np

arr = np.array([[1, 2, 3], 
                [4, 5, 6]])

# Keeping dimensions after standard deviation computation
result = arr.std(axis=1, keepdims=True)
print(result)

Output:

[[0.81649658]
 [0.81649658]]

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

Calculating the standard deviation by selecting specific elements using a mask.

import numpy as np

arr = np.array([1, 2, 3, 4, 5])
mask = np.array([True, False, True, False, True])

# Computing standard deviation for selected elements
result = arr.std(where=mask)
print(result)

Output:

1.632993161855452