import numpy as np

# Statistical operations overview
test_scores = np.array([85, 92, 78, 96, 89, 74, 88, 93, 82, 90])
print(f"Test scores: {test_scores}")

# Basic statistics
print(f"Mean: {np.mean(test_scores):.1f}")
print(f"Median: {np.median(test_scores):.1f}")
print(f"Std Dev: {np.std(test_scores):.2f}")
print(f"Range: {np.ptp(test_scores)}")  # peak-to-peak

import numpy as np

grades = np.array([78, 85, 92, 88, 79, 94, 87, 91, 83, 89])

# Measures of central tendency
mean_grade = np.mean(grades)
median_grade = np.median(grades)

print(f"Grades: {grades}")
print(f"Mean (average): {mean_grade:.1f}")
print(f"Median (middle): {median_grade:.1f}")

# Find mode (most frequent)
unique, counts = np.unique(grades, return_counts=True)
mode_idx = np.argmax(counts)
print(f"Most common: {unique[mode_idx]} (appears {counts[mode_idx]} times)")

import numpy as np

sales = np.array([1200, 1350, 1180, 1420, 1290, 1380])

# Spread measures
std_dev = np.std(sales)
variance = np.var(sales)
range_val = np.ptp(sales)  # max - min

print(f"Sales: {sales}")
print(f"Standard deviation: {std_dev:.1f}")
print(f"Variance: {variance:.1f}")
print(f"Range: {range_val}")
print(f"Min/Max: {np.min(sales)}, {np.max(sales)}")

import numpy as np

response_times = np.array([120, 150, 180, 200, 230, 250, 280, 320, 350, 400])

# Quartile analysis
q1 = np.percentile(response_times, 25)
q2 = np.percentile(response_times, 50)  # median
q3 = np.percentile(response_times, 75)

print(f"Response times: {response_times}")
print(f"Q1 (25th): {q1}")
print(f"Q2 (50th): {q2}")  
print(f"Q3 (75th): {q3}")
print(f"IQR: {q3 - q1}")

import numpy as np

# Student scores: rows=students, cols=subjects
scores = np.array([[85, 92, 78, 88],   # Alice
                   [79, 85, 91, 82],   # Bob
                   [94, 89, 96, 93],   # Carol
                   [72, 78, 74, 76]])  # David

students = ['Alice', 'Bob', 'Carol', 'David']
subjects = ['Math', 'Science', 'English', 'History']

# Statistics by student (axis=1)
student_avgs = np.mean(scores, axis=1)
print(f"Student averages: {student_avgs.round(1)}")

# Statistics by subject (axis=0)  
subject_avgs = np.mean(scores, axis=0)
print(f"Subject averages: {subject_avgs.round(1)}")

import numpy as np

scores = np.array([[85, 92, 78, 88],
                   [79, 85, 91, 82], 
                   [94, 89, 96, 93],
                   [72, 78, 74, 76]])

students = ['Alice', 'Bob', 'Carol', 'David']

# Best overall student
totals = np.sum(scores, axis=1)
best_idx = np.argmax(totals)
print(f"Top student: {students[best_idx]} ({totals[best_idx]} total)")

# Subject averages
subject_avgs = np.mean(scores, axis=0)
best_subject = np.argmax(subject_avgs)
subjects = ['Math', 'Science', 'English', 'History']
print(f"Best subject: {subjects[best_subject]} ({subject_avgs[best_subject]:.1f})")

import numpy as np

# Study hours vs test scores
study_hours = np.array([2, 4, 3, 6, 5, 8, 7, 9, 1, 10])
test_scores = np.array([65, 75, 70, 85, 80, 95, 90, 98, 60, 100])

# Calculate correlation
correlation = np.corrcoef(study_hours, test_scores)[0, 1]

print(f"Study hours: {study_hours}")
print(f"Test scores: {test_scores}")
print(f"Correlation: {correlation:.3f}")

if correlation > 0.7:
    print("Strong positive correlation!")
elif correlation > 0.3:
    print("Moderate positive correlation")
else:
    print("Weak correlation")

import numpy as np

# Multiple variables: height, weight, age
data = np.array([[170, 65, 25],   # Person 1
                 [175, 70, 30],   # Person 2  
                 [165, 60, 22],   # Person 3
                 [180, 80, 35],   # Person 4
                 [160, 55, 20]])  # Person 5

# Correlation matrix
corr_matrix = np.corrcoef(data.T)

print(f"Correlation matrix:")
variables = ['Height', 'Weight', 'Age']
for i, var in enumerate(variables):
    print(f"{var}: {corr_matrix[i].round(3)}")

import numpy as np

exam_scores = np.array([65, 67, 70, 72, 75, 78, 80, 82, 85, 87,
                       89, 90, 92, 94, 95, 96, 98, 85, 88, 91])

# Create histogram
hist, bin_edges = np.histogram(exam_scores, bins=5)

print(f"Score distribution:")
for i in range(len(hist)):
    range_str = f"{bin_edges[i]:.0f}-{bin_edges[i+1]:.0f}"
    print(f"{range_str}: {hist[i]} students")

# Skewness indicator
skew = np.mean(exam_scores) - np.median(exam_scores)
print(f"Skewness indicator: {skew:.2f}")

import numpy as np

# Data with outliers
data = np.array([120, 115, 130, 125, 140, 135, 128, 2000, 122, 138])

# IQR method
q1 = np.percentile(data, 25)
q3 = np.percentile(data, 75)
iqr = q3 - q1

# Outlier boundaries
lower = q1 - 1.5 * iqr
upper = q3 + 1.5 * iqr

# Find outliers
outliers = (data < lower) | (data > upper)

print(f"Data: {data}")
print(f"Outlier bounds: [{lower:.1f}, {upper:.1f}]")
print(f"Outliers: {data[outliers]}")

import numpy as np

# Grades with weights
grades = np.array([85, 92, 78, 96])
weights = np.array([0.2, 0.3, 0.3, 0.2])  # Quiz, Midterm, Final, Project

# Weighted average
weighted_avg = np.average(grades, weights=weights)

print(f"Grades: {grades}")
print(f"Weights: {weights}")
print(f"Weighted average: {weighted_avg:.1f}")
print(f"Regular average: {np.mean(grades):.1f}")

import numpy as np

daily_sales = np.array([120, 150, 130, 180, 160, 200, 175])

# Cumulative operations
cumsum = np.cumsum(daily_sales)
running_avg = cumsum / np.arange(1, len(daily_sales) + 1)

print(f"Daily sales: {daily_sales}")
print(f"Cumulative total: {cumsum}")
print(f"Running average: {running_avg.round(1)}")

# Growth rate
growth = np.diff(daily_sales) / daily_sales[:-1] * 100
print(f"Daily growth %: {growth.round(1)}")

import numpy as np

# Quarterly revenue comparison
q1_revenue = np.array([50000, 45000, 60000, 55000, 48000])
q2_revenue = np.array([55000, 50000, 65000, 60000, 52000])

# Compare quarters
q1_mean, q1_std = np.mean(q1_revenue), np.std(q1_revenue)
q2_mean, q2_std = np.mean(q2_revenue), np.std(q2_revenue)

print(f"Q1 - Mean: ${q1_mean:,.0f}, Std: ${q1_std:,.0f}")
print(f"Q2 - Mean: ${q2_mean:,.0f}, Std: ${q2_std:,.0f}")

# Growth analysis
growth = ((q2_mean - q1_mean) / q1_mean) * 100
print(f"Quarter-over-quarter growth: {growth:.1f}%")