32. The table below shows the age of each employee at a cardboard box factory ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 AGE 51 39 34 27 23 43 41 55 24 25 38 17 21 37 35 38 31 24 Calculate the following summary statistics for the age feature using python (num py) code snippet: a. Minimum,Maximum, Range b.Mean and Medianc. Variance and Statnard deviation
Question
- The table below shows the age of each employee at a cardboard box factory ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 AGE 51 39 34 27 23 43 41 55 24 25 38 17 21 37 35 38 31 24 Calculate the following summary statistics for the age feature using python (num py) code snippet: a. Minimum,Maximum, Range b.Mean and Medianc. Variance and Statnard deviation
Solution
Sure, here is the Python code using numpy to calculate the summary statistics for the age feature:
import numpy as np
# Given data
ages = np.array([51, 39, 34, 27, 23, 43, 41, 55, 24, 25, 38, 17, 21, 37, 35, 38, 31, 24])
# a. Minimum, Maximum, Range
min_age = np.min(ages)
max_age = np.max(ages)
range_age = np.ptp(ages) # ptp() function returns the range (maximum-minimum) of values along an axis
# b. Mean and Median
mean_age = np.mean(ages)
median_age = np.median(ages)
# c. Variance and Standard deviation
variance_age = np.var(ages)
std_dev_age = np.std(ages)
# Print the results
print("Minimum age: ", min_age)
print("Maximum age: ", max_age)
print("Range of ages: ", range_age)
print("Mean age: ", mean_age)
print("Median age: ", median_age)
print("Variance of ages: ", variance_age)
print("Standard deviation of ages: ", std_dev_age)
This code first imports the numpy library, then defines the ages array. It then calculates and prints the minimum, maximum, range, mean, median, variance, and standard deviation of the ages.
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