Python:NumPy .std()

The NumPy .std() function calculates the NumPy standard deviation of given data along a specified axis. Standard deviation is a statistical measure that indicates how spread out the values in a dataset are, represented by an array, along a specified axis.

NumPy .std() Syntax

np.std(a, axis, dtype, out, ddof, keepdims, where)

Parameters:

• a: Array of elements used to find the standard deviation.
• axis(Optional): The axis along which the standard deviation will be computed. By default, the array is flattened before computation.
  • If 0, calculates the standard deviation along the vertical axis.
  • If 1, calculates the standard deviation along the horizontal axis.
  • If a tuple of integers, calculates the standard deviation along multiple specified axes.
• dtype (Optional): Type used in computing the standard deviation, if specified. By default, for arrays of integer type, it is float64, while for arrays of float types, it matches the array type.
• out (Optional): Specifies an alternative output array to contain the result. This array must have the same shape as the expected output.
• ddof (Optional): It stands for Delta Degrees of Freedom. It helps adjust the calculation of standard deviation for samples.
• keepdims (Optional): It accepts a boolean value and is used to determine whether to retain the dimensions of the given array in the output. By default, it is set to False.
• where (Optional): It accepts boolean arrays or conditions where True values indicate the indices, or elements within the array for which the standard deviation should be calculated.

Return value:

If the out parameter is None, the NumPy .std() function returns a new array containing the standard deviation. Otherwise, it assigns the result to the specified output array and returns its reference.

Notes:

1. For floating-point inputs, the standard deviation is calculated with the same precision as the input data. This may cause inaccuracies, especially with np.float32 data type.
2. For complex numbers, std takes the absolute value before squaring for a real, non-negative result.

Example 1: Basic Usage of NumPy .std()

In this example, the NumPy .std() function calculates the standard deviation of the given data:

import numpy as np

data = [10, 13, 23, 23, 16, 23, 21, 16]

std_dev = np.std(data)

print("Standard Deviation:", std_dev)

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The output of this code is:

Standard Deviation: 4.7549316504025585

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Example 2: Using NumPy .std() with axis

In this example, the axis parameter is used with the NumPy .std() function to calculate the standard deviation of the given data:

import numpy as np

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

col_std = np.std(arr, axis=0)
row_std = np.std(arr, axis=1)

print("Column-wise STD:", col_std)
print("Row-wise STD:", row_std)

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The output of this code is:

Column-wise STD: [1.5 1.5 1.5]
Row-wise STD: [0.81649658 0.81649658]

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Codebyte Example: Using NumPy .std() with ddof

In this codebyte example, the ddof parameter is used with the NumPy .std() function to calculate the standard deviation of the given data:

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Frequently Asked Questions

1. What is the difference between NumPy .std() and stdev()?

NumPy .std() works on NumPy arrays and is optimized for performance, supporting multi-dimensional data and the axis parameter. Python’s built-in statistics.stdev() works on standard Python iterables, calculates the sample standard deviation by default, and doesn’t support multi-dimensional arrays or the axis argument.

2. Is NumPy .std() population or sample?

By default, NumPy .std() calculates the population standard deviation (ddof=0). To compute the sample standard deviation, set ddof=1.

3. What is the difference between NumPy .std() and Pandas .std()?

NumPy .std() calculates standard deviation on NumPy arrays and defaults to population standard deviation (ddof=0). Pandas .std() works on Series and DataFrame objects, automatically excludes NaN values, and defaults to the sample standard deviation (ddof=1).