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.
To calculate standard deviation of an entire population, another function known as pstdev() is used.
Standard Deviation is a measure of spread in Statistics. It is used to quantify the measure of spread, variation of a set of data values. It is very much similar to variance, gives the measure of deviation whereas variance provides the squared value.
A low measure of Standard Deviation indicates that the data are less spread out, whereas a high value of Standard Deviation shows that the data in a set are spread apart from their mean average values. A useful property of the standard deviation is that, unlike the variance, it is expressed in the same units as the data.
Standard Deviation is calculated by : where x1, x2, x3.....xn are observed values in sample data, is the mean value of observations and N is the number of sample observations.
Syntax :stdev( [data-set], xbar )
[data] : An iterable with real valued numbers.
xbar (Optional): Takes actual mean of data-set as value.
Returnype :Returns the actual standard deviation of the values passed as parameter.
StatisticsError is raised for data-set less than 2 values passed as parameter.
Impossible/precision-less values when the value provided as xbar doesn’t match actual mean of the data-set.
Code #1 :
Standard Deviation of the sample is 1.5811388300841898
Code #2 : Demonstrate stdev() on a varying set of data types
The Standard Deviation of Sample1 is 3.9761191895520196 The Standard Deviation of Sample2 is 1.8708286933869707 The Standard Deviation of Sample3 is 7.8182478855559445 The Standard Deviation of Sample4 is 0.41967844833872525
Code #3 :Demonstrate the difference between results of variance() and stdev()
Standard Deviation of the sample is 1.5811388300841898 Variance of the sample is 2.5
Code #4 : Demomstrate the use of xbar parameter
Standard Deviation of Sample set is 0.6047037842337906
Code #5 : Demonstrates StatisticsError
Traceback (most recent call last): File "/home/f921f9269b061f1cc4e5fc74abf6ce10.py", line 12, in print(statistics.stdev(sample)) File "/usr/lib/python3.5/statistics.py", line 617, in stdev var = variance(data, xbar) File "/usr/lib/python3.5/statistics.py", line 555, in variance raise StatisticsError('variance requires at least two data points') statistics.StatisticsError: variance requires at least two data points
- Standard Deviation is highly essential in the field of statistical maths and statistical study. It is commonly used to measure confidence in statistical calculations. For example, the margin of error in calculating marks of an exam is determined by calculating the expected standard deviation in the results if the same exam were to be conducted multiple times.
- It is very useful in the field of financial studies as well as it helps to determine the margin of profit and loss. The standard deviation is also important, where the standard deviation on the rate of return on an investment is a measure of the volatility of the investment.
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- Python statistics | pvariance()
- Python statistics | median_grouped()
- Python statistics | mean() function
- Python statistics | variance()
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- Python statistics | median_high()
- Python - Rayleigh Distribution in Statistics
- Python - R-distributed Distribution in Statistics
- Python - Uniform Distribution in Statistics
- Python - Von Mises Distribution in Statistics
- Python - Wald Distribution in Statistics
- Python statistics | mode function
- Python - Mielke Distribution in Statistics
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Improved By : nidhi_biet