Compute Statistical properties of NumPy array with SciPy

Last update on March 27 2025 09:13:33 (UTC/GMT +8 hours)

1. Random Array Statistical Properties

Write a NumPy program that creates a NumPy array of random numbers and uses SciPy to compute the statistical properties (mean, median, variance) of the array.

Sample Solution:

Python Code:

# Import necessary libraries
import numpy as np
from scipy import stats

# Create a NumPy array of random numbers
data = np.random.rand(100)

# Compute the mean of the array using SciPy
mean = stats.tmean(data)

# Compute the median of the array using SciPy
median = np.median(data)

# Compute the variance of the array using SciPy
variance = stats.tvar(data)

# Print the statistical properties
print("Mean:", mean)
print("Median:", median)
print("Variance:", variance)

Output:

Mean: 0.4879592058849749
Median: 0.49297261150369925
Variance: 0.09259159781167492

Explanation:

• Import the necessary libraries:
• Import NumPy and SciPy's "stats" module.
• Create a NumPy array of random numbers:
• Generate an array of 100 random numbers between 0 and 1.
• Compute the mean:
• Use SciPy's tmean function to calculate the mean of the array.
• Compute the median:
• Use NumPy's median function to calculate the median of the array.
• Compute the variance:
• Use SciPy's tvar function to calculate the variance of the array.
• Finally display the mean, median, and variance.

For more Practice: Solve these Related Problems:

• Write a Numpy program to create a random array and compute its skewness and kurtosis using SciPy's stats module.
• Write a Numpy program to generate a random array and compare the results of np.mean with SciPy's describe function.
• Write a Numpy program to produce a random array and compute its confidence interval for the mean using SciPy.
• Write a Numpy program to create a random array and perform a normality test (e.g., Shapiro-Wilk) using SciPy's stats module.

Python-Numpy Code Editor:

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