import numpy as np
"""
This script demonstrates various statistical operations on NumPy arrays, including mean, median, variance, and standard deviation calculations for both 1D and 2D arrays.
Features:
- Calculates mean and median for samples with odd and even numbers of elements.
- Sorts arrays for display purposes.
- Computes sample variance (using Bessel's correction) and standard deviation.
- Performs mean calculations on 2D arrays, both flattened and along specific axes.
- Simulates exam scores for two students and computes per-student mean, overall median, variance, and standard deviation.
Dependencies:
- numpy
Usage:
Run the script to see printed outputs of statistical calculations for various sample arrays and a simulated exam scores dataset.
"""

sample = np.array([10, 25, 15, 30, 20, 10, 2])
print(f'Original sample: {sample}, Odd # of elements:')
# Calculating the mean
sample_mean = np.mean(sample)
print(f'Sorted sample: {np.sort(sample)}')
# Calculating the median
sample_median = np.median(sample)
print(f'Mean: {sample_mean}, median: {sample_median}')


sample = np.array([1, 2, 8, 10, 15, 20, 25, 30])
print(f'\nOriginal sample: {sample}, Even # of elements:')
sample_mean = np.mean(sample)
sample_median = np.median(sample)
# Sorting the sample
print(f'Sorted sample: {np.sort(sample)}')
print(f'Mean: {sample_mean}, median: {sample_median}')


sample = np.array([10, 25, 15, 30, 20, 10, 2])
print(f'\nOriginal sample: {sample}, Odd # of elements:')
# Calculating the variance
sample_variance = np.var(sample, ddof=1)
# Calculating the standard deviation
sample_std = np.std(sample)
print(f'Variance: {sample_variance}, Standard Deviation: {sample_std}')

#Higher dimensional array calculations
array_2d = np.array([[1, 2, 3], [4, 5, 6]])
print(f'\n2D Array:\n{array_2d}')
# Calculating the mean in a flattened array
print(f'Mean (flattened): {np.mean(array_2d)}')
# Calculating the mean along axis 0
print(f'Mean (axis 0): {np.mean(array_2d, axis=0)}')
# Calculating the mean along axis 1
print(f'Mean (axis 1): {np.mean(array_2d, axis=1)}')

# Task: Simulated test scores of 2 students for five different exams
exam_scores = np.array([[85, 90, 78, 92, 88], [72, 89, 65, 78, 92]])
print(f'\nExam Scores:\n{exam_scores}')

# Calculate the mean score for each student
mean_scores = np.mean(exam_scores, axis=1)
print(f'Mean score for each student: {mean_scores}')
# Calculate the median score of all scores
median_score = np.median(exam_scores)
print(f'Median score of all exams: {median_score}')

# Calculate the median score of all scores
median_score = np.median(exam_scores)
print(f'Median score for all scores: {median_score}')
# Calculate the variance of all scores
scores_variance = np.var(exam_scores)
print(f'Variance for all scores: {scores_variance}')
# Calculate the standard deviation of all scores
scores_std = np.std(exam_scores)
print(f'Standard deviation for all scores: {scores_std}')