import pandas as pd

import numpy as np

from datetime import datetime, timedelta

import random

# Set random seed for reproducible data

np.random.seed(42)

random.seed(42)

print("Creating Stock Market and Other Datasets for Visualization Examples...")

print("=" * 70)

# 1. STOCK MARKET DATA

print("\n1. Creating Stock Market Dataset...")

# Generate 2 years of daily stock data for 5 companies

start_date = datetime(2022, 1, 1)

end_date = datetime(2024, 1, 1)

date_range = pd.date_range(start=start_date, end=end_date, freq='D')

# Filter out weekends (stock markets closed)

business_days = [d for d in date_range if d.weekday() < 5]

companies = {

'RELIANCE': {'initial_price': 2400, 'volatility': 0.02},

'TCS': {'initial_price': 3200, 'volatility': 0.025},

'INFY': {'initial_price': 1500, 'volatility': 0.03},

'HDFC': {'initial_price': 2800, 'volatility': 0.022},

'ICICI': {'initial_price': 800, 'volatility': 0.028}

}

stock_data = []

for company, params in companies.items():

price = params['initial_price']

vol = params['volatility']

for date in business_days:

# Simulate realistic stock price movement

daily_return = np.random.normal(0.0005, vol) # Small positive drift

price = price * (1 + daily_return)

# Create OHLC data

open_price = price

high_price = open_price * (1 + abs(np.random.normal(0, vol/2)))

low_price = open_price * (1 - abs(np.random.normal(0, vol/2)))

close_price = low_price + (high_price - low_price) * np.random.random()

# Volume (higher volume on high volatility days)

base_volume = np.random.randint(50000, 200000)

volatility_multiplier = 1 + abs(daily_return) * 10

volume = int(base_volume * volatility_multiplier)

stock_data.append({

'Date': date.strftime('%Y-%m-%d'),

'Company': company,

'Open': round(open_price, 2),

'High': round(high_price, 2),

'Low': round(low_price, 2),

'Close': round(close_price, 2),

'Volume': volume,

'Daily_Return': round(daily_return * 100, 3)

})

price = close_price # Update price for next day

# Save stock market data

stock_df = pd.DataFrame(stock_data)

stock_df.to_csv('Day14/stock_market_data.csv', index=False)

print(f"✓ Created stock_market_data.csv with {len(stock_df)} records")

# 2. SALES DATA

print("\n2. Creating Sales Dataset...")

months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']

products = ['Laptops', 'Smartphones', 'Tablets', 'Headphones', 'Watches']

regions = ['North', 'South', 'East', 'West', 'Central']

years = [2022, 2023]

sales_data = []

for year in years:

for month in months:

for product in products:

for region in regions:

# Seasonal trends

seasonal_multiplier = 1.0

if month in ['Nov', 'Dec']: # Holiday season

seasonal_multiplier = 1.4

elif month in ['Jun', 'Jul']: # Summer

seasonal_multiplier = 0.8

# Product popularity

product_multiplier = {

'Smartphones': 1.5, 'Laptops': 1.2, 'Tablets': 0.8,

'Headphones': 1.0, 'Watches': 0.9

}

base_sales = np.random.randint(100, 500)

sales = int(base_sales * seasonal_multiplier * product_multiplier[product])

revenue = sales * np.random.randint(200, 1500)

sales_data.append({

'Year': year,

'Month': month,

'Product': product,

'Region': region,

'Sales_Units': sales,

'Revenue': revenue,

'Profit_Margin': round(np.random.uniform(0.15, 0.35), 3)

})

sales_df = pd.DataFrame(sales_data)

sales_df.to_csv('Day14/sales_data.csv', index=False)

print(f"✓ Created sales_data.csv with {len(sales_df)} records")

# 3. STUDENT GRADES DATA

print("\n3. Creating Student Grades Dataset...")

students_data = []

subjects = ['Math', 'Physics', 'Chemistry', 'Biology', 'English', 'History']

classes = ['10A', '10B', '11A', '11B', '12A', '12B']

genders = ['Male', 'Female']

student_id = 1000

for class_name in classes:

for i in range(35): # 35 students per class

student_id += 1

name = f"Student_{student_id}"

gender = random.choice(genders)

# Base performance level for student

base_performance = np.random.normal(75, 15) # Mean 75, std 15

for subject in subjects:

# Subject difficulty adjustment

subject_adjustment = {

'Math': -5, 'Physics': -3, 'Chemistry': -2,

'Biology': 2, 'English': 3, 'History': 5

}[subject]

marks = base_performance + subject_adjustment + np.random.normal(0, 8)

marks = max(35, min(100, marks)) # Clamp between 35-100

students_data.append({

'Student_ID': student_id,

'Name': name,

'Class': class_name,

'Gender': gender,

'Subject': subject,

'Marks': round(marks, 1),

'Grade': 'A' if marks >= 90 else 'B' if marks >= 75 else 'C' if marks >= 60 else 'D',

'Attendance': round(np.random.uniform(0.7, 1.0), 3)

})

students_df = pd.DataFrame(students_data)

students_df.to_csv('Day14/student_grades.csv', index=False)

print(f"✓ Created student_grades.csv with {len(students_df)} records")

# 4. WEATHER DATA

print("\n4. Creating Weather Dataset...")

cities = ['Mumbai', 'Delhi', 'Bangalore', 'Chennai', 'Kolkata', 'Pune']

weather_data = []

for city in cities:

# City-specific base temperatures

base_temps = {

'Mumbai': 28, 'Delhi': 25, 'Bangalore': 23,

'Chennai': 30, 'Kolkata': 27, 'Pune': 25

}

base_temp = base_temps[city]

for i in range(365): # 1 year of data

date = start_date + timedelta(days=i)

# Seasonal temperature variation

seasonal_factor = 5 * np.sin(2 * np.pi * i / 365) # ±5°C seasonal variation

daily_temp = base_temp + seasonal_factor + np.random.normal(0, 3)

# Humidity (higher in coastal cities)

base_humidity = 70 if city in ['Mumbai', 'Chennai', 'Kolkata'] else 60

humidity = base_humidity + np.random.normal(0, 10)

humidity = max(30, min(95, humidity))

# Rainfall (monsoon season effect)

rainfall = 0

if 6 <= date.month <= 9: # Monsoon months

rainfall = max(0, np.random.exponential(2))

else:

rainfall = max(0, np.random.exponential(0.5))

weather_data.append({

'Date': date.strftime('%Y-%m-%d'),

'City': city,

'Temperature': round(daily_temp, 1),

'Humidity': round(humidity, 1),

'Rainfall': round(rainfall, 2),

'Wind_Speed': round(np.random.uniform(5, 25), 1),

'Pressure': round(np.random.uniform(1008, 1018), 1)

})

weather_df = pd.DataFrame(weather_data)

weather_df.to_csv('Day14/weather_data.csv', index=False)

print(f"✓ Created weather_data.csv with {len(weather_df)} records")

# 5. DEMOGRAPHICS DATA

print("\n5. Creating Demographics Dataset...")

states = ['Maharashtra', 'Karnataka', 'Tamil Nadu', 'Delhi', 'Gujarat', 'Rajasthan',

'West Bengal', 'Uttar Pradesh', 'Kerala', 'Punjab']

demographics_data = []

for state in states:

# Generate age group distribution

age_groups = ['0-14', '15-29', '30-44', '45-59', '60+']

total_population = np.random.randint(50000000, 200000000)

# Age distribution percentages

age_dist = [0.25, 0.30, 0.25, 0.15, 0.05] # Typical developing country distribution

age_dist = np.random.dirichlet(np.array(age_dist) * 10) # Add some variation

for i, age_group in enumerate(age_groups):

population = int(total_population * age_dist[i])

# Gender split (slightly more males in some age groups)

male_ratio = 0.52 if age_group in ['0-14', '15-29'] else 0.50

male_population = int(population * male_ratio)

female_population = population - male_population

demographics_data.extend([

{

'State': state,

'Age_Group': age_group,

'Gender': 'Male',

'Population': male_population,

'Literacy_Rate': round(np.random.uniform(0.65, 0.95), 3),

'Employment_Rate': round(np.random.uniform(0.40, 0.75), 3)

},

{

'State': state,

'Age_Group': age_group,

'Gender': 'Female',

'Population': female_population,

'Literacy_Rate': round(np.random.uniform(0.55, 0.90), 3),

'Employment_Rate': round(np.random.uniform(0.25, 0.50), 3)

}

])

demographics_df = pd.DataFrame(demographics_data)

demographics_df.to_csv('Day14/demographics_data.csv', index=False)

print(f"✓ Created demographics_data.csv with {len(demographics_df)} records")

# 6. E-COMMERCE DATA

print("\n6. Creating E-commerce Dataset...")

categories = ['Electronics', 'Clothing', 'Books', 'Home & Garden', 'Sports', 'Beauty']

customer_segments = ['Premium', 'Regular', 'Budget']

ecommerce_data = []

for i in range(5000): # 5000 transactions

transaction_id = f"TXN_{1000 + i}"

date = start_date + timedelta(days=np.random.randint(0, 730)) # 2 years

category = random.choice(categories)

segment = random.choice(customer_segments)

# Price based on category and segment

base_prices = {

'Electronics': 500, 'Clothing': 100, 'Books': 30,

'Home & Garden': 150, 'Sports': 200, 'Beauty': 80

}

segment_multiplier = {'Premium': 2.0, 'Regular': 1.0, 'Budget': 0.6}

base_price = base_prices[category]

price = base_price * segment_multiplier[segment] * np.random.uniform(0.5, 2.0)

quantity = np.random.randint(1, 5)

total_amount = price * quantity

# Customer satisfaction (higher for premium)

base_satisfaction = {'Premium': 4.2, 'Regular': 3.8, 'Budget': 3.5}

satisfaction = base_satisfaction[segment] + np.random.normal(0, 0.5)

satisfaction = max(1, min(5, satisfaction))

ecommerce_data.append({

'Transaction_ID': transaction_id,

'Date': date.strftime('%Y-%m-%d'),

'Category': category,

'Customer_Segment': segment,

'Quantity': quantity,

'Unit_Price': round(price, 2),

'Total_Amount': round(total_amount, 2),

'Customer_Satisfaction': round(satisfaction, 1),

'Return_Status': 'Yes' if np.random.random() < 0.08 else 'No', # 8% return rate

'Payment_Method': random.choice(['Credit Card', 'Debit Card', 'UPI', 'Cash on Delivery'])

})

ecommerce_df = pd.DataFrame(ecommerce_data)

ecommerce_df.to_csv('Day14/ecommerce_data.csv', index=False)

print(f"✓ Created ecommerce_data.csv with {len(ecommerce_df)} records")

print("\n" + "=" * 70)

print("DATASET CREATION COMPLETED!")

print("=" * 70)

datasets_created = [

('stock_market_data.csv', len(stock_df), 'OHLCV data for 5 companies over 2 years'),

('sales_data.csv', len(sales_df), 'Sales data by product, region, and time'),

('student_grades.csv', len(students_df), 'Student performance across subjects'),

('weather_data.csv', len(weather_df), 'Weather data for 6 Indian cities'),

('demographics_data.csv', len(demographics_df), 'Population demographics by state'),

('ecommerce_data.csv', len(ecommerce_df), 'E-commerce transactions and customer data')

]

for filename, records, description in datasets_created:

print(f"✓ {filename}: {records:,} records - {description}")

print(f"\nAll datasets saved in Day14/ folder and ready for visualization examples!")