import numpy as np

# Array aggregation overview
sales_data = np.array([[120, 135, 145, 160],
                       [98, 112, 125, 140],
                       [156, 167, 175, 185]])

print(f"Sales data: \n{sales_data}")

# Basic aggregations
print(f"Total sales: {np.sum(sales_data)}")
print(f"Average: {np.mean(sales_data):.1f}")
print(f"Best performance: {np.max(sales_data)}")
print(f"Worst performance: {np.min(sales_data)}")

import numpy as np

# Student test scores across subjects
test_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']

print(f"Test scores: \n{test_scores}")

# Different aggregation insights
print(f"Class average: {np.mean(test_scores):.1f}")
print(f"Top score: {np.max(test_scores)}")
print(f"Student averages: {np.mean(test_scores, axis=1).round(1)}")

import numpy as np

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

# Common reductions
print(f"Sales: {monthly_sales}")
print(f"Total: {np.sum(monthly_sales)}")
print(f"Average: {np.mean(monthly_sales):.0f}")
print(f"Best month: {np.max(monthly_sales)}")
print(f"Worst month: {np.min(monthly_sales)}")
print(f"Range: {np.ptp(monthly_sales)}")  # peak-to-peak

import numpy as np

response_times = np.array([245, 123, 456, 189, 334, 267, 198])

# Sorting operations
sorted_times = np.sort(response_times)
sort_indices = np.argsort(response_times)

print(f"Original: {response_times}")
print(f"Sorted: {sorted_times}")
print(f"Sort indices: {sort_indices}")
print(f"Fastest response: {sorted_times[0]}ms")
print(f"Slowest response: {sorted_times[-1]}ms")

import numpy as np

survey_responses = np.array([5, 3, 4, 5, 2, 4, 5, 3, 4, 1, 5, 4])

# Unique value analysis
unique_values, counts = np.unique(survey_responses, return_counts=True)

print(f"Responses: {survey_responses}")
print(f"Unique ratings: {unique_values}")
print(f"Response counts: {counts}")

# Most common response
most_common_idx = np.argmax(counts)
print(f"Most common rating: {unique_values[most_common_idx]} ({counts[most_common_idx]} times)")

import numpy as np

# Quarterly sales: 4 stores × 4 quarters
quarterly_sales = np.array([[120, 135, 145, 160],  # Store A
                           [98, 112, 125, 140],   # Store B
                           [156, 167, 175, 185],  # Store C
                           [89, 95, 105, 125]])   # Store D

stores = ['Store A', 'Store B', 'Store C', 'Store D']

# Different axis aggregations
store_totals = np.sum(quarterly_sales, axis=1)  # Sum across quarters
quarter_totals = np.sum(quarterly_sales, axis=0)  # Sum across stores

print(f"Store year totals: {store_totals}")
print(f"Quarterly totals: {quarter_totals}")

# Best performing store and quarter
best_store = np.argmax(store_totals)
best_quarter = np.argmax(quarter_totals)
print(f"Best store: {stores[best_store]} ({store_totals[best_store]})")
print(f"Best quarter: Q{best_quarter + 1} ({quarter_totals[best_quarter]})")

import numpy as np

# Website traffic data: daily visitors for 4 weeks
daily_traffic = np.array([[1200, 1350, 1180, 1420, 1290, 1100, 980],   # Week 1
                         [1250, 1400, 1220, 1380, 1340, 1150, 1020],   # Week 2
                         [1180, 1320, 1160, 1450, 1280, 1080, 950],    # Week 3
                         [1300, 1480, 1240, 1520, 1390, 1200, 1050]])  # Week 4

days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']

# Traffic analysis
weekly_totals = np.sum(daily_traffic, axis=1)
daily_averages = np.mean(daily_traffic, axis=0)

print(f"Weekly traffic totals: {weekly_totals}")
print(f"Average by day: {daily_averages.round(0).astype(int)}")

# Find patterns
best_day_idx = np.argmax(daily_averages)
worst_day_idx = np.argmin(daily_averages)
print(f"Best day: {days[best_day_idx]} ({daily_averages[best_day_idx]:.0f} avg)")
print(f"Worst day: {days[worst_day_idx]} ({daily_averages[worst_day_idx]:.0f} avg)")

import numpy as np

# Product sales data
product_sales = np.array([45, 67, 89, 34, 56, 78, 45, 23, 67, 89])
product_ids = np.array(['A', 'B', 'C', 'D', 'E', 'F', 'A', 'G', 'B', 'C'])

# Aggregation insights
total_revenue = np.sum(product_sales)
avg_sale = np.mean(product_sales)
best_sale = np.max(product_sales)

print(f"Total revenue: ${total_revenue}")
print(f"Average sale: ${avg_sale:.2f}")
print(f"Best single sale: ${best_sale}")

# Find top performers
top_sale_idx = np.argmax(product_sales)
print(f"Top performer: Product {product_ids[top_sale_idx]} (${product_sales[top_sale_idx]})")

import numpy as np

# Manufacturing measurements
measurements = np.array([10.2, 9.8, 10.1, 10.3, 9.9, 10.0, 10.2, 9.7, 10.4, 10.1])
tolerance = 0.3  # ±0.3 units from target (10.0)

# Quality metrics
mean_value = np.mean(measurements)
std_deviation = np.std(measurements)
within_tolerance = np.abs(measurements - 10.0) <= tolerance

print(f"Measurements: {measurements}")
print(f"Mean: {mean_value:.2f}")
print(f"Std deviation: {std_deviation:.3f}")
print(f"Within tolerance: {np.sum(within_tolerance)}/{len(measurements)}")
print(f"Quality rate: {np.mean(within_tolerance)*100:.1f}%")