Python Stddev() Example | Standard Deviation In Python Tutorial is today’s topic. Standard Deviation is the measure of spread in the Statistics. It is used to quantify the measure of spread, variation of the set of data values.

*Originally published by **Krunal** at** appdividend.com*

It is very much similar to the variance, gives the measure of deviation, whereas variance provides a squared value. In this blog, we have already covered Python mean(), Python median(), Python mode(), and Python variance() function.

**Content Overview**

- 1 Python Stddev() Example
- 2 #Pass the xbar parameter
- 3 #Python standard deviation example using pstdev
- 4 #Use stdev() on a varying set of data types
- 5 #Python standard deviation example using numpy
- 6 #Difference between variance() and stddev()
- 7 #StatisticsError

There are two ways to calculate a standard deviation in Python.

- Using stdev or pstdev functions of statistics package.
- Using std function of numpy package.

The stdev is used when the data is just a sample of the entire dataset.

The pstdev is used when the data represents the whole population. Note that statistics is a lightweight module added in Python 3.x.

The process of finding standard deviation requires you to know whether the data you have is the entire dataset or it is a sample of a group.

Let’s see the syntax of stddev() function.

stdev([data-set], xbar)

See the following parameters.

**[data]:** An iterable with real-valued numbers.

**xbar (Optional):** Takes actual mean of the data-set as value.

See the following code example.

# app.pyimport statistics

dataset = [1, 2, 3, 4, 5]

print("Standard Deviation of a dataset is % s " % (statistics.stdev(dataset)))

See the following output.

➜ pyt python3 app.py Standard Deviation of a dataset is 1.5811388300841898 ➜ pyt

Let’s take another example.

# app.pyimport statistics

dataset = [11, 21, 18, 19, 46]

print("Standard Deviation of dataset is % s " % (statistics.stdev(dataset)))

See the following output.

➜ pyt python3 app.py Standard Deviation of dataset is 13.397761006974262 ➜ pyt

Okay, let’s take the list and now while finding the stddev, we pass the second parameter to the function called xbar and see the output.

# app.pyimport statistics

dataset = [11, 21, 18, 19, 46]

meanValue = statistics.mean(dataset)

print("Standard Deviation of the dataset is % s " % (statistics.stdev(dataset, xbar=meanValue)))

See the output.

➜ pyt python3 app.py Standard Deviation of the dataset is 13.397761006974262 ➜ pyt

Let’s take an example using Python Statistics pstdev() function.

# app.pyimport statistics

dataset = [11, 21, 18, 19, 46]

print("Standard Deviation of a dataset is % s " % (statistics.pstdev(dataset)))

See the following output.

➜ pyt python3 app.py Standard Deviation of a dataset is 11.983321743156194 ➜ pyt

See the following code.

# app.pyfrom statistics import stdev

from fractions import Fraction as fr

sample1 = (21, 19, 11, 14, 18, 19, 46)

sample2 = (-21, -19, -11, -14, -18, -19, -46)

sample3 = (-9, -1, -0, 2, 1, 3, 4, 19)

sample4 = (21.23, 19.45, 29.1, 11.2, 18.9)

print("The Standard Deviation of Sample1 is % s" % (stdev(sample1)))

print("The Standard Deviation of Sample2 is % s" % (stdev(sample2)))

print("The Standard Deviation of Sample3 is % s" % (stdev(sample3)))

print("The Standard Deviation of Sample4 is % s" % (stdev(sample4)))

See the following output.

➜ pyt python3 app.py The Standard Deviation of Sample1 is 11.480832888319723 The Standard Deviation of Sample2 is 11.480832888319723 The Standard Deviation of Sample3 is 7.8182478855559445 The Standard Deviation of Sample4 is 6.388906792245447 ➜ pyt

We can execute numpy.std() to calculate standard deviation. First, we need to import numpy library.

See the following output.

# app.pyimport numpy as np

num = [21, 19, 11, 14, 18, 19, 46]

print("The Standard Deviation of Numpy Data is % s" % (np.std(num)))

See the following output.

➜ pyt python3 app.py The Standard Deviation of Numpy Data is 10.629185850136157 ➜ pyt

Okay, let’s take a simple Python List and get its variance() and stddev().

# app.pyimport statistics

dataset = [11, 21, 18, 19, 46]

print("Standard Deviation of the dataset is % s " % (statistics.stdev(dataset))) print("Variance of the dataset is % s" % (statistics.variance(dataset)))

See the following output.

➜ pyt python3 app.py Standard Deviation of the dataset is 13.397761006974262 Variance of the dataset is 179.5 ➜ pyt

Okay, now if we only pass the one data point, then it will raise the StatisticsError because the stddev() function requires a minimum of two data points. See the following code.

# app.pyimport statistics

dataset = [11]

print("Standard Deviation of the dataset is % s " % (statistics.stdev(dataset)))

See the following output.

➜ pyt python3 app.py Traceback (most recent call last): File "app.py", line 6, in <module> % (statistics.stdev(dataset))) File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/statistics.py", line 650, in stdev var = variance(data, xbar) File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/statistics.py", line 588, in variance raise StatisticsError('variance requires at least two data points') statistics.StatisticsError: variance requires at least two data points ➜ pyt

Finally, Python stddev() Example | Standard Deviation In Python Tutorial is over.

*Originally published b**y **Krunal** at** appdividend.com*

** **===================================================================

Thanks for reading :heart: If you liked this post, share it with all of your programming buddies! Follow me on **Facebook** | **Twitter**

☞ Complete Python Bootcamp: Go from zero to hero in Python 3

☞ Python for Time Series Data Analysis

☞ The complete beginner’s guide to JSON

☞ The Complete Guide to JSON Web Tokens

☞ Python Programming For Beginners From Scratch

☞ Python Network Programming | Network Apps & Hacking Tools

☞ Intro To SQLite Databases for Python Programming

☞ Ethical Hacking With Python, JavaScript and Kali Linux

☞ Beginner’s guide on Python: Learn python from scratch! (New)

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

Today you're going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates. We gonna use Python OS remove( ) method to remove the duplicates on our drive. Well, that's simple you just call remove ( ) with a parameter of the name of the file you wanna remove done.

In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.

Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc.. You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like __init__, __call__, __str__ etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).

The OS module is a python module that provides the interface for interacting with the underlying operating system that Python is running.