What is std function in python?

numpy.std(arr, axis = None) : Compute the standard deviation of the given data (array elements) along the specified axis(if any)..

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What is STD in Python?
How do you calculate STD in Python?
Is there a standard deviation function in Python?
What is standard deviation in NumPy?

Standard Deviation (SD) is measured as the spread of data distribution in the given data set.

For example :

x = 1 1 1 1 1 
Standard Deviation = 0 . 

y = 9, 2, 5, 4, 12, 7, 8, 11, 9, 3, 7, 4, 12, 5, 4, 10, 9, 6, 9, 4 
Step 1 : Mean of distribution 4 = 7
Step 2 : Summation of (x - x.mean())**2 = 178
Step 3 : Finding Mean = 178 /20 = 8.9 
This Result is Variance.
Step 4 : Standard Deviation = sqrt(Variance) = sqrt(8.9) = 2.983..

Parameters :
arr : [array_like]input array.
axis : [int or tuples of int]axis along which we want to calculate the standard deviation. Otherwise, it will consider arr to be flattened (works on all the axis). axis = 0 means SD along the column and axis = 1 means SD along the row.
out : [ndarray, optional]Different array in which we want to place the result. The array must have the same dimensions as expected output.
dtype : [data-type, optional]Type we desire while computing SD.
Results : Standard Deviation of the array (a scalar value if axis is none) or array with standard deviation values along specified axis.

Code #1:

import numpy as np

arr = [20, 2, 7, 1, 34]

print("arr : ", arr)

print("std of arr : ", np.std(arr))

print ("\nMore precision with float32")

print("std of arr : ", np.std(arr, dtype = np.float32))

print ("\nMore accuracy with float64")

print("std of arr : ", np.std(arr, dtype = np.float64))

Output :

arr :  [20, 2, 7, 1, 34]
std of arr :  12.576167937809991

More precision with float32
std of arr :  12.576168

More accuracy with float64
std of arr :  12.576167937809991

Code #2:

import numpy as np

arr = [[2, 2, 2, 2, 2],

[15, 6, 27, 8, 2],

[23, 2, 54, 1, 2, ],

[11, 44, 34, 7, 2]]

print("\nstd of arr, axis = None : ", np.std(arr))

print("\nstd of arr, axis = 0 : ", np.std(arr, axis = 0))

print("\nstd of arr, axis = 1 : ", np.std(arr, axis = 1))

Output :

std of arr, axis = None :  15.3668474320532

std of arr, axis = 0 :  [ 7.56224173 17.68473918 18.59267329  3.04138127  0.        ]

std of arr, axis = 1 :  [ 0.          8.7772433  20.53874388 16.40243884]

numpy.std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=<no value>, *, where=<no value>)[source]#

Compute the standard deviation along the specified axis.

Returns the standard deviation, a measure of the spread of a distribution, of the array elements. The standard deviation is computed for the flattened array by default, otherwise over the specified axis.

Parametersaarray_like

Calculate the standard deviation of these values.

axisNone or int or tuple of ints, optional

Axis or axes along which the standard deviation is computed. The default is to compute the standard deviation of the flattened array.

New in version 1.7.0.

If this is a tuple of ints, a standard deviation is performed over multiple axes, instead of a single axis or all the axes as before.

dtypedtype, optional

Type to use in computing the standard deviation. For arrays of integer type the default is float64, for arrays of float types it is the same as the array type.

outndarray, optional

Alternative output array in which to place the result. It must have the same shape as the expected output but the type (of the calculated values) will be cast if necessary.

ddofint, optional

Means Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. By default ddof is zero.

keepdimsbool, optional

If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.

If the default value is passed, then keepdims will not be passed through to the std method of sub-classes of ndarray, however any non-default value will be. If the sub-class’ method does not implement keepdims any exceptions will be raised.

wherearray_like of bool, optional

Elements to include in the standard deviation. See reduce for details.

New in version 1.20.0.

Returnsstandard_deviationndarray, see dtype parameter above.

If out is None, return a new array containing the standard deviation, otherwise return a reference to the output array.

Notes

The standard deviation is the square root of the average of the squared deviations from the mean, i.e., std = sqrt(mean(x)), where x = abs(a - a.mean())**2.

The average squared deviation is typically calculated as x.sum() / N, where N = len(x). If, however, ddof is specified, the divisor N - ddof is used instead. In standard statistical practice, ddof=1 provides an unbiased estimator of the variance of the infinite population. ddof=0 provides a maximum likelihood estimate of the variance for normally distributed variables. The standard deviation computed in this function is the square root of the estimated variance, so even with ddof=1, it will not be an unbiased estimate of the standard deviation per se.

Note that, for complex numbers, std takes the absolute value before squaring, so that the result is always real and nonnegative.

For floating-point input, the std is computed using the same precision the input has. Depending on the input data, this can cause the results to be inaccurate, especially for float32 (see example below). Specifying a higher-accuracy accumulator using the dtype keyword can alleviate this issue.

Examples

>>> a = np.array([[1, 2], [3, 4]])
>>> np.std(a)
1.1180339887498949 # may vary
>>> np.std(a, axis=0)
array([1.,  1.])
>>> np.std(a, axis=1)
array([0.5,  0.5])

In single precision, std() can be inaccurate:

>>> a = np.zeros((2, 512*512), dtype=np.float32)
>>> a[0, :] = 1.0
>>> a[1, :] = 0.1
>>> np.std(a)
0.45000005

Computing the standard deviation in float64 is more accurate:

>>> np.std(a, dtype=np.float64)
0.44999999925494177 # may vary

Specifying a where argument:

>>> a = np.array([[14, 8, 11, 10], [7, 9, 10, 11], [10, 15, 5, 10]])
>>> np.std(a)
2.614064523559687 # may vary
>>> np.std(a, where=[[True], [True], [False]])
2.0

What is STD in Python?

std(arr, axis = None) : Compute the standard deviation of the given data (array elements) along the specified axis(if any).. Standard Deviation (SD) is measured as the spread of data distribution in the given data set. For example : x = 1 1 1 1 1 Standard Deviation = 0 .

How do you calculate STD in Python?

Coding a stdev() Function in Python sqrt() to take the square root of the variance. With this new implementation, we can use ddof=0 to calculate the standard deviation of a population, or we can use ddof=1 to estimate the standard deviation of a population using a sample of data.

Is there a standard deviation function in Python?

Statistics module in Python provides a function known as stdev() , which can be used to calculate the standard deviation. stdev() function only calculates standard deviation from a sample of data, rather than an entire population.

What is standard deviation in NumPy?

The standard deviation is the square root of the average of the squared deviations from the mean, i.e., std = sqrt(mean(x)) , where x = abs(a - a. mean())**2 . The average squared deviation is typically calculated as x. sum() / N , where N = len(x) .