Python dictionary is a versatile data structure that allows a lot of operations to be done without any hassle. Calculating the standard deviation is shown below.
Example #1: Using numpy.std()
First, we create a dictionary. Then we store all the values in a list by iterating over it. After this using the NumPy we calculate the standard deviation of the list.
# importing numpy import numpy as np
# creating our test dictionary dicti = { 'a' : 20 , 'b' : 32 , 'c' : 12 , 'd' : 93 , 'e' : 84 }
# declaring an empty list listr = []
# appending all the values in the list for value in dicti.values():
listr.append(value)
# calculating standard deviation using np.std std = np.std(listr)
# printing results print (std)
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Output:
33.63569532505609
Example #2: Using list comprehension
First, we create a list of values from the dictionary using a loop. Then we calculate mean, variance and then the standard deviation.
# creating our test dictionary dicti = { 'a' : 20 , 'b' : 32 , 'c' : 12 , 'd' : 93 , 'e' : 84 }
# declaring an empty list listr = []
# appending all the values in the list for value in dicti.values():
listr.append(value)
# Standard deviation of list # Using sum() + list comprehension mean = sum (listr) / len (listr)
variance = sum ([((x - mean) * * 2 ) for x in listr]) / len (listr)
res = variance * * 0.5
print (res)
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Output:
33.63569532505609
Example #3: Using pstdev()
Pythons inbuilt statistics library provides a function to compute the standard deviation of a given list.
# importing the module import statistics
# creating the test dictionary dicti = { 'a' : 20 , 'b' : 32 , 'c' : 12 , 'd' : 93 , 'e' : 84 }
# declaring an empty list listr = []
# appending all the values in the list for value in dicti.values():
listr.append(value)
# Standard deviation of list # Using pstdev() res = statistics.pstdev(listr)
print (res)
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Output:
33.63569532505609