Data Science Beispiele

# NumPy Fundamentals for Data Science

import numpy as np
import matplotlib.pyplot as plt

# 1. Creating Arrays
def array_creation():
    print("=== Array Creation ===")

    # From Python lists
    arr1 = np.array([1, 2, 3, 4, 5])
    print("1D array:", arr1)

    arr2 = np.array([[1, 2, 3], [4, 5, 6]])
    print("2D array:\n", arr2)

    # Using NumPy functions
    zeros = np.zeros((3, 4))
    print("Zeros array:\n", zeros)

    ones = np.ones((2, 3))
    print("Ones array:\n", ones)

    # Special arrays
    identity = np.eye(3)
    print("Identity matrix:\n", identity)

    # Range and linspace
    range_arr = np.arange(0, 10, 2)  # Start, stop, step
    print("Arange:", range_arr)

    linspace = np.linspace(0, 10, 5)  # Start, stop, num_points
    print("Linspace:", linspace)

    # Random arrays
    random_uniform = np.random.random((2, 3))
    print("Random uniform:\n", random_uniform)

    random_normal = np.random.randn(3, 3)
    print("Random normal:\n", random_normal)

# 2. Array Properties and Manipulation
def array_properties():
    print("\n=== Array Properties ===")

    arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])

    print("Array shape:", arr.shape)
    print("Array dimensions:", arr.ndim)
    print("Array size:", arr.size)
    print("Data type:", arr.dtype)

    # Reshaping
    reshaped = arr.reshape(2, 6)
    print("Reshaped array:\n", reshaped)

    # Flattening
    flattened = arr.flatten()
    print("Flattened array:", flattened)

    # Transpose
    transposed = arr.T
    print("Transposed array:\n", transposed)

# 3. Array Indexing and Slicing
def array_indexing():
    print("\n=== Array Indexing and Slicing ===")

    arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])

    # Indexing
    print("Element at [1,2]:", arr[1, 2])
    print("First row:", arr[0])
    print("Second column:", arr[:, 1])

    # Slicing
    print("First two rows:\n", arr[:2])
    print("Last two columns:\n", arr[:, 2:])
    print("Sub-array (rows 0-1, cols 1-2):\n", arr[:2, 1:3])

    # Boolean indexing
    mask = arr > 5
    print("Elements > 5:", arr[mask])

    # Fancy indexing
    row_indices = np.array([0, 2])
    col_indices = np.array([1, 3])
    print("Fancy indexing:", arr[row_indices, col_indices])

# 4. Mathematical Operations
def mathematical_operations():
    print("\n=== Mathematical Operations ===")

    arr1 = np.array([1, 2, 3, 4])
    arr2 = np.array([5, 6, 7, 8])

    # Element-wise operations
    print("Addition:", arr1 + arr2)
    print("Multiplication:", arr1 * arr2)
    print("Division:", arr2 / arr1)
    print("Power:", arr1 ** 2)

    # Universal functions (ufuncs)
    print("Square root:", np.sqrt(arr1))
    print("Exponential:", np.exp(arr1))
    print("Logarithm:", np.log(arr2))
    print("Sine:", np.sin(arr1))

    # Array operations
    matrix_a = np.array([[1, 2], [3, 4]])
    matrix_b = np.array([[5, 6], [7, 8]])

    print("Matrix multiplication:\n", np.dot(matrix_a, matrix_b))
    print("Element-wise multiplication:\n", matrix_a * matrix_b)

    # Statistical operations
    data = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
    print("Mean:", np.mean(data))
    print("Median:", np.median(data))
    print("Standard deviation:", np.std(data))
    print("Variance:", np.var(data))
    print("Min:", np.min(data))
    print("Max:", np.max(data))
    print("Sum:", np.sum(data))
    print("Product:", np.prod(data))

# 5. Linear Algebra Operations
def linear_algebra():
    print("\n=== Linear Algebra Operations ===")

    # Matrix operations
    A = np.array([[1, 2], [3, 4]])
    B = np.array([[5, 6], [7, 8]])

    print("Matrix A:\n", A)
    print("Matrix B:\n", B)

    # Determinant
    print("Determinant of A:", np.linalg.det(A))

    # Inverse
    try:
        inv_A = np.linalg.inv(A)
        print("Inverse of A:\n", inv_A)
    except np.linalg.LinAlgError:
        print("Matrix A is singular")

    # Eigenvalues and eigenvectors
    eigenvalues, eigenvectors = np.linalg.eig(A)
    print("Eigenvalues:", eigenvalues)
    print("Eigenvectors:\n", eigenvectors)

    # Solving linear equations
    # Ax = b
    A_eq = np.array([[2, 1], [1, 3]])
    b_eq = np.array([5, 6])
    x = np.linalg.solve(A_eq, b_eq)
    print("Solution to Ax = b:", x)

# 6. Data Aggregation and Reduction
def data_aggregation():
    print("\n=== Data Aggregation ===")

    # Create sample data
    data = np.random.randint(0, 100, (5, 4))
    print("Sample data:\n", data)

    # Row-wise operations
    print("Row-wise sum:", data.sum(axis=1))
    print("Row-wise mean:", data.mean(axis=1))
    print("Row-wise max:", data.max(axis=1))

    # Column-wise operations
    print("Column-wise sum:", data.sum(axis=0))
    print("Column-wise mean:", data.mean(axis=0))
    print("Column-wise max:", data.max(axis=0))

    # Cumulative operations
    print("Cumulative sum:", np.cumsum(data))
    print("Cumulative product:", np.cumprod(data))

    # Sorting
    print("Sorted array:", np.sort(data.flatten()))
    print("Sorted by rows:", np.sort(data, axis=1))
    print("Sorted by columns:", np.sort(data, axis=0))

# 7. Broadcasting and Shape Manipulation
def broadcasting_examples():
    print("\n=== Broadcasting Examples ===")

    # Basic broadcasting
    a = np.array([[1, 2, 3], [4, 5, 6]])
    b = np.array([10, 20, 30])

    print("Array a:\n", a)
    print("Array b:", b)
    print("Broadcasted addition:\n", a + b)

    # More complex broadcasting
    x = np.array([[1], [2], [3]])  # Shape (3,1)
    y = np.array([10, 20, 30, 40])  # Shape (4,)

    result = x + y  # Results in (3,4) array
    print("Complex broadcasting result:\n", result)

    # Outer product
    outer = np.outer(x.flatten(), y)
    print("Outer product:\n", outer)

# 8. Performance Comparison
def performance_comparison():
    print("\n=== Performance Comparison ===")

    import time

    # Large arrays
    size = 1000000
    list1 = list(range(size))
    list2 = list(range(size, 2*size))

    arr1 = np.arange(size)
    arr2 = np.arange(size, 2*size)

    # Python list operation
    start = time.time()
    result_list = [a + b for a, b in zip(list1, list2)]
    list_time = time.time() - start

    # NumPy operation
    start = time.time()
    result_array = arr1 + arr2
    numpy_time = time.time() - start

    print(f"Python list time: {list_time:.4f} seconds")
    print(f"NumPy array time: {numpy_time:.4f} seconds")
    print(f"Speedup: {list_time/numpy_time:.2f}x")

# 9. Real-world Data Analysis Example
def sales_data_analysis():
    print("\n=== Sales Data Analysis ===")

    # Simulate sales data (products x months)
    np.random.seed(42)
    products = ['Product_A', 'Product_B', 'Product_C', 'Product_D', 'Product_E']
    months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']

    # Random sales data
    sales_data = np.random.randint(100, 1000, (5, 6))

    print("Sales data (products x months):\n", sales_data)

    # Basic statistics
    print("\nTotal sales per product:", sales_data.sum(axis=1))
    print("Total sales per month:", sales_data.sum(axis=0))
    print("Best selling product:", products[np.argmax(sales_data.sum(axis=1))])
    print("Best month:", months[np.argmax(sales_data.sum(axis=0))])

    # Monthly growth rate
    monthly_totals = sales_data.sum(axis=0)
    growth_rate = (monthly_totals[1:] - monthly_totals[:-1]) / monthly_totals[:-1] * 100
    print("Monthly growth rates (%):", growth_rate)

    # Product performance categories
    product_totals = sales_data.sum(axis=1)
    avg_sales = np.mean(product_totals)

    high_performers = products[product_totals > avg_sales * 1.2]
    low_performers = products[product_totals < avg_sales * 0.8]

    print("High performers (>120% average):", high_performers)
    print("Low performers (<80% average):", low_performers)

# Run all examples
if __name__ == "__main__":
    print("NumPy Data Science Examples")
    print("=" * 40)

    array_creation()
    array_properties()
    array_indexing()
    mathematical_operations()
    linear_algebra()
    data_aggregation()
    broadcasting_examples()
    performance_comparison()
    sales_data_analysis()

# Pandas Data Analysis for Data Science

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime, timedelta

# Set styling
plt.style.use('default')
sns.set_palette("husl")

# 1. Creating and Loading Data
def data_creation_loading():
    print("=== Data Creation and Loading ===")

    # Create DataFrame from dictionary
    data = {
        'name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
        'age': [25, 30, 35, 28, 32],
        'city': ['New York', 'London', 'Paris', 'Tokyo', 'Sydney'],
        'salary': [70000, 80000, 90000, 75000, 85000],
        'department': ['IT', 'Finance', 'IT', 'Marketing', 'Finance']
    }

    df = pd.DataFrame(data)
    print("Created DataFrame:\n", df)

    # Create DataFrame from lists
    names = ['Product_A', 'Product_B', 'Product_C']
    prices = [100, 150, 200]
    quantities = [50, 30, 40]

    df_products = pd.DataFrame({
        'Product': names,
        'Price': prices,
        'Quantity': quantities
    })
    print("\nProducts DataFrame:\n", df_products)

    # Load CSV (simulated)
    # df_csv = pd.read_csv('data.csv')
    # print("Loaded from CSV:\n", df_csv.head())

    # Basic information
    print("\nDataFrame Info:")
    df.info()

    print("\nStatistical Summary:")
    print(df.describe())

# 2. Data Selection and Filtering
def data_selection_filtering():
    print("\n=== Data Selection and Filtering ===")

    # Sample data
    data = {
        'name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve', 'Frank', 'Grace'],
        'age': [25, 30, 35, 28, 32, 45, 29],
        'city': ['New York', 'London', 'Paris', 'Tokyo', 'Sydney', 'London', 'Paris'],
        'salary': [70000, 80000, 90000, 75000, 85000, 95000, 72000],
        'department': ['IT', 'Finance', 'IT', 'Marketing', 'Finance', 'IT', 'Marketing'],
        'join_date': pd.to_datetime(['2020-01-15', '2019-03-20', '2018-07-10',
                                   '2021-02-01', '2020-06-15', '2017-11-30', '2021-08-20'])
    }

    df = pd.DataFrame(data)

    # Column selection
    print("Name and Salary columns:\n", df[['name', 'salary']])

    # Row selection by position
    print("\nFirst 3 rows:\n", df.head(3))
    print("\nRows 2-4:\n", df.iloc[2:5])

    # Row selection by label
    print("\nRows with index 1-3:\n", df.loc[1:3])

    # Conditional filtering
    high_salary = df[df['salary'] > 80000]
    print("\nEmployees with salary > 80k:\n", high_salary)

    # Multiple conditions
    it_dept_30plus = df[(df['department'] == 'IT') & (df['age'] >= 30)]
    print("\nIT employees age 30+:\n", it_dept_30plus)

    # Using isin()
    target_cities = ['New York', 'London', 'Paris']
    eu_employees = df[df['city'].isin(target_cities)]
    print("\nEmployees in target cities:\n", eu_employees)

    # String methods
    print("\nNames starting with 'A':\n", df[df['name'].str.startswith('A')])

# 3. Data Cleaning and Preparation
def data_cleaning():
    print("\n=== Data Cleaning and Preparation ===")

    # Create messy data
    messy_data = {
        'name': ['Alice', 'Bob', None, 'Diana', 'Eve', '  Frank  ', 'Grace'],
        'age': [25, 30, 35, None, 32, 45, 29],
        'salary': [70000, 80000, 90000, 75000, None, 95000, 72000],
        'department': ['IT', 'Finance', 'IT', 'Marketing', 'FINANCE', 'IT', 'marketing'],
        'email': ['[email protected]', 'bob@email', '[email protected]',
                 None, '[email protected]', '[email protected]', '[email protected]']
    }

    df = pd.DataFrame(messy_data)
    print("Original messy data:\n", df)

    # Handle missing values
    print("\nMissing values count:\n", df.isnull().sum())

    # Drop rows with missing names
    df_clean = df.dropna(subset=['name'])

    # Fill missing ages with mean
    df_clean['age'] = df_clean['age'].fillna(df_clean['age'].mean())

    # Fill missing salaries with median
    df_clean['salary'] = df_clean['salary'].fillna(df_clean['salary'].median())

    print("\nAfter handling missing values:\n", df_clean)

    # Clean string data
    df_clean['name'] = df_clean['name'].str.strip()  # Remove whitespace
    df_clean['department'] = df_clean['department'].str.title()  # Title case
    df_clean['email'] = df_clean['email'].str.lower()  # Lowercase

    # Validate email format (simple check)
    email_pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
    df_clean['valid_email'] = df_clean['email'].str.match(email_pattern)

    print("\nAfter cleaning strings:\n", df_clean)

    # Remove duplicate rows
    df_clean = df_clean.drop_duplicates()
    print("\nAfter removing duplicates (if any):\n", df_clean)

# 4. Data Transformation and Feature Engineering
def data_transformation():
    print("\n=== Data Transformation ===")

    # Sample sales data
    sales_data = {
        'product': ['A', 'B', 'A', 'C', 'B', 'A', 'C', 'B', 'A'],
        'region': ['North', 'South', 'North', 'East', 'West', 'South', 'North', 'East', 'West'],
        'sales': [1000, 1500, 1200, 800, 2000, 900, 1100, 1800, 700],
        'date': pd.to_datetime(['2023-01-01', '2023-01-02', '2023-01-03',
                               '2023-01-04', '2023-01-05', '2023-01-06',
                               '2023-01-07', '2023-01-08', '2023-01-09'])
    }

    df = pd.DataFrame(sales_data)

    # Create new features
    df['sales_tax'] = df['sales'] * 0.08  # 8% tax
    df['total_revenue'] = df['sales'] + df['sales_tax']
    df['quarter'] = df['date'].dt.quarter
    df['month'] = df['date'].dt.month
    df['weekday'] = df['date'].dt.day_name()

    # Categorical encoding
    df['product_encoded'] = df['product'].astype('category').cat.codes
    df['region_encoded'] = df['region'].astype('category').cat.codes

    # One-hot encoding
    df_encoded = pd.get_dummies(df, columns=['product', 'region'], prefix=['prod', 'reg'])

    print("Original DataFrame:\n", df.head())
    print("\nOne-hot encoded DataFrame:\n", df_encoded.head())

    # Grouping and aggregation
    product_stats = df.groupby('product')['sales'].agg(['mean', 'sum', 'count', 'std'])
    print("\nProduct statistics:\n", product_stats)

    # Pivot table
    pivot_table = df.pivot_table(values='sales', index='product', columns='region', aggfunc='sum', fill_value=0)
    print("\nPivot table:\n", pivot_table)

# 5. Time Series Analysis
def time_series_analysis():
    print("\n=== Time Series Analysis ===")

    # Create time series data
    date_range = pd.date_range(start='2023-01-01', end='2023-12-31', freq='D')
    np.random.seed(42)

    # Simulate sales data with trend and seasonality
    trend = np.linspace(100, 200, len(date_range))
    seasonal = 50 * np.sin(2 * np.pi * np.arange(len(date_range)) / 30)  # Monthly seasonality
    noise = np.random.normal(0, 20, len(date_range))

    sales = trend + seasonal + noise
    sales[sales < 0] = 0  # Ensure non-negative

    df_ts = pd.DataFrame({
        'date': date_range,
        'sales': sales
    })

    df_ts.set_index('date', inplace=True)

    print("Time series data (first 10 days):\n", df_ts.head(10))

    # Resampling
    monthly_sales = df_ts.resample('M').sum()
    print("\nMonthly sales:\n", monthly_sales.head())

    weekly_sales = df_ts.resample('W').mean()
    print("\nWeekly average sales:\n", weekly_sales.head())

    # Moving averages
    df_ts['sales_7d_ma'] = df_ts['sales'].rolling(window=7).mean()
    df_ts['sales_30d_ma'] = df_ts['sales'].rolling(window=30).mean()

    print("\nWith moving averages (last 5 days):\n", df_ts.tail(5))

    # Date-based filtering
    q1_sales = df_ts['2023-01':'2023-03']
    print("\nQ1 2023 total sales:", q1_sales['sales'].sum())

    # Lag features
    df_ts['sales_lag_1'] = df_ts['sales'].shift(1)
    df_ts['sales_lag_7'] = df_ts['sales'].shift(7)

    print("\nWith lag features (last 3 days):\n",
          df_ts[['sales', 'sales_lag_1', 'sales_lag_7']].tail(3))

# 6. Data Visualization with Pandas
def pandas_visualization():
    print("\n=== Data Visualization ===")

    # Create comprehensive dataset
    np.random.seed(42)

    products = ['A', 'B', 'C', 'D', 'E']
    regions = ['North', 'South', 'East', 'West']
    months = pd.date_range('2023-01-01', '2023-06-30', freq='M')

    data = []
    for month in months:
        for product in products:
            for region in regions:
                base_sales = np.random.randint(500, 2000)
                seasonal_factor = 1 + 0.2 * np.sin(2 * np.pi * month.month / 12)
                sales = int(base_sales * seasonal_factor * np.random.normal(1, 0.1))

                data.append({
                    'date': month,
                    'product': product,
                    'region': region,
                    'sales': max(0, sales)
                })

    df_viz = pd.DataFrame(data)

    # Basic statistics
    print("Dataset shape:", df_viz.shape)
    print("Columns:", df_viz.columns.tolist())
    print("\nSales statistics:\n", df_viz['sales'].describe())

    # Product performance
    product_sales = df_viz.groupby('product')['sales'].sum().sort_values(ascending=False)
    print("\nTotal sales by product:\n", product_sales)

    # Region performance
    region_sales = df_viz.groupby('region')['sales'].sum().sort_values(ascending=False)
    print("\nTotal sales by region:\n", region_sales)

    # Monthly trends
    monthly_sales = df_viz.groupby(df_viz['date'].dt.strftime('%Y-%m'))['sales'].sum()
    print("\nMonthly sales trend:\n", monthly_sales)

    # Correlation analysis (if we had numeric variables)
    numeric_df = df_viz.select_dtypes(include=[np.number])
    if len(numeric_df.columns) > 1:
        print("\nCorrelation matrix:\n", numeric_df.corr())

# 7. Advanced Data Operations
def advanced_operations():
    print("\n=== Advanced Data Operations ===")

    # Merge operations
    employees = pd.DataFrame({
        'emp_id': [1, 2, 3, 4],
        'name': ['Alice', 'Bob', 'Charlie', 'Diana'],
        'dept_id': [101, 102, 101, 103]
    })

    departments = pd.DataFrame({
        'dept_id': [101, 102, 103, 104],
        'dept_name': ['IT', 'Finance', 'Marketing', 'HR'],
        'budget': [1000000, 800000, 600000, 400000]
    })

    # Inner join
    merged_inner = pd.merge(employees, departments, on='dept_id', how='inner')
    print("Inner join:\n", merged_inner)

    # Left join
    merged_left = pd.merge(employees, departments, on='dept_id', how='left')
    print("\nLeft join:\n", merged_left)

    # Concatenation
    q1_data = pd.DataFrame({
        'month': ['Jan', 'Feb', 'Mar'],
        'sales': [1000, 1200, 1100]
    })

    q2_data = pd.DataFrame({
        'month': ['Apr', 'May', 'Jun'],
        'sales': [1300, 1400, 1350]
    })

    half_year = pd.concat([q1_data, q2_data], ignore_index=True)
    print("\nConcatenated data:\n", half_year)

    # Apply custom functions
    performance_data = pd.DataFrame({
        'employee': ['Alice', 'Bob', 'Charlie', 'Diana'],
        'score': [85, 92, 78, 95],
        'projects': [5, 7, 3, 8]
    })

    def calculate_performance(row):
        score_weight = 0.7
        projects_weight = 0.3
        return (row['score'] * score_weight + row['projects'] * projects_weight * 10)

    performance_data['performance_score'] = performance_data.apply(calculate_performance, axis=1)
    print("\nPerformance data with calculated score:\n", performance_data)

    # Window functions
    sales_data = pd.DataFrame({
        'date': pd.date_range('2023-01-01', periods=10),
        'daily_sales': [100, 120, 110, 130, 125, 140, 135, 150, 145, 160]
    })

    sales_data['cumulative_sum'] = sales_data['daily_sales'].cumsum()
    sales_data['rolling_mean_3'] = sales_data['daily_sales'].rolling(window=3).mean()
    sales_data['rolling_std_3'] = sales_data['daily_sales'].rolling(window=3).std()

    print("\nSales data with window functions:\n", sales_data)

# Run all examples
if __name__ == "__main__":
    print("Pandas Data Science Examples")
    print("=" * 40)

    data_creation_loading()
    data_selection_filtering()
    data_cleaning()
    data_transformation()
    time_series_analysis()
    pandas_visualization()
    advanced_operations()

    print("\n" + "=" * 40)
    print("Examples completed successfully!")

# Matplotlib and Seaborn Data Visualization

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np

# Set style and figure parameters
plt.style.use('default')  # or 'seaborn-v0_8', 'ggplot', etc.
sns.set_palette("husl")
plt.rcParams['figure.figsize'] = (12, 8)
plt.rcParams['font.size'] = 12

# 1. Basic Plot Types
def basic_plots():
    print("=== Basic Plot Types ===")

    # Sample data
    x = np.linspace(0, 10, 100)
    y1 = np.sin(x)
    y2 = np.cos(x)
    y3 = np.exp(-x/5) * np.sin(x)

    # Line plots
    fig, axes = plt.subplots(2, 2, figsize=(15, 10))

    # Simple line plot
    axes[0, 0].plot(x, y1, 'b-', linewidth=2, label='sin(x)')
    axes[0, 0].plot(x, y2, 'r--', linewidth=2, label='cos(x)')
    axes[0, 0].set_title('Trigonometric Functions')
    axes[0, 0].set_xlabel('x')
    axes[0, 0].set_ylabel('y')
    axes[0, 0].legend()
    axes[0, 0].grid(True, alpha=0.3)

    # Scatter plot
    np.random.seed(42)
    x_scatter = np.random.randn(100)
    y_scatter = x_scatter * 0.5 + np.random.randn(100) * 0.3
    colors = np.random.rand(100)

    scatter = axes[0, 1].scatter(x_scatter, y_scatter, c=colors, alpha=0.7,
                                 cmap='viridis', s=50)
    axes[0, 1].set_title('Scatter Plot with Color Mapping')
    axes[0, 1].set_xlabel('X values')
    axes[0, 1].set_ylabel('Y values')
    plt.colorbar(scatter, ax=axes[0, 1])

    # Bar plot
    categories = ['A', 'B', 'C', 'D', 'E']
    values = [23, 45, 56, 78, 32]

    bars = axes[1, 0].bar(categories, values, color=['red', 'green', 'blue', 'orange', 'purple'])
    axes[1, 0].set_title('Bar Chart')
    axes[1, 0].set_xlabel('Categories')
    axes[1, 0].set_ylabel('Values')

    # Add value labels on bars
    for bar, value in zip(bars, values):
        axes[1, 0].text(bar.get_x() + bar.get_width()/2, bar.get_height() + 1,
                       str(value), ha='center', va='bottom')

    # Histogram
    data_hist = np.random.normal(100, 15, 1000)
    axes[1, 1].hist(data_hist, bins=30, alpha=0.7, color='skyblue', edgecolor='black')
    axes[1, 1].set_title('Histogram')
    axes[1, 1].set_xlabel('Value')
    axes[1, 1].set_ylabel('Frequency')
    axes[1, 1].axvline(data_hist.mean(), color='red', linestyle='--',
                       label=f'Mean: {data_hist.mean():.2f}')
    axes[1, 1].legend()

    plt.tight_layout()
    plt.show()

# 2. Advanced Plotting Techniques
def advanced_plots():
    print("\n=== Advanced Plotting Techniques ===")

    # Create sample dataset
    np.random.seed(42)
    n_points = 200

    # Multiple subplots with different types
    fig = plt.figure(figsize=(16, 12))

    # Create grid layout
    gs = fig.add_gridspec(3, 3, hspace=0.3, wspace=0.3)

    # 2D density plot
    ax1 = fig.add_subplot(gs[0, 0])
    x_2d = np.random.multivariate_normal([0, 0], [[1, 0.5], [0.5, 1]], 1000)
    hb = ax1.hexbin(x_2d[:, 0], x_2d[:, 1], gridsize=20, cmap='Blues')
    ax1.set_title('2D Hexbin Density Plot')
    ax1.set_xlabel('X')
    ax1.set_ylabel('Y')
    plt.colorbar(hb, ax=ax1)

    # Box plot with multiple groups
    ax2 = fig.add_subplot(gs[0, 1])
    data_box = [np.random.normal(0, std, 100) for std in range(1, 4)]
    bp = ax2.boxplot(data_box, patch_artist=True, labels=['Group 1', 'Group 2', 'Group 3'])
    colors = ['lightblue', 'lightgreen', 'lightpink']
    for patch, color in zip(bp['boxes'], colors):
        patch.set_facecolor(color)
    ax2.set_title('Box Plot Comparison')
    ax2.set_ylabel('Values')

    # Violin plot
    ax3 = fig.add_subplot(gs[0, 2])
    data_violin = [np.random.normal(0, std, 100) for std in range(1, 4)]
    vp = ax3.violinplot(data_violin, positions=[1, 2, 3], showmeans=True)
    ax3.set_title('Violin Plot')
    ax3.set_xticks([1, 2, 3])
    ax3.set_xticklabels(['Group 1', 'Group 2', 'Group 3'])
    ax3.set_ylabel('Values')

    # Error bar plot
    ax4 = fig.add_subplot(gs[1, 0])
    x_err = np.arange(5)
    y_err = np.array([20, 35, 30, 45, 40])
    y_err_values = np.array([2, 3, 4, 2, 3])
    ax4.errorbar(x_err, y_err, yerr=y_err_values, fmt='o-',
                capsize=5, capthick=2, linewidth=2)
    ax4.set_title('Error Bar Plot')
    ax4.set_xlabel('Category')
    ax4.set_ylabel('Mean ± Error')

    # Stacked area plot
    ax5 = fig.add_subplot(gs[1, 1])
    x_area = np.arange(10)
    y1_area = np.random.randint(1, 5, 10)
    y2_area = np.random.randint(1, 5, 10)
    y3_area = np.random.randint(1, 5, 10)

    ax5.stackplot(x_area, y1_area, y2_area, y3_area,
                 labels=['Series 1', 'Series 2', 'Series 3'],
                 colors=['skyblue', 'lightgreen', 'lightcoral'])
    ax5.set_title('Stacked Area Plot')
    ax5.set_xlabel('X')
    ax5.set_ylabel('Cumulative Values')
    ax5.legend()

    # Polar plot
    ax6 = fig.add_subplot(gs[1, 2], projection='polar')
    theta = np.linspace(0, 2*np.pi, 100)
    r = np.abs(np.sin(theta) * np.cos(2*theta))
    ax6.plot(theta, r, 'b-', linewidth=2)
    ax6.fill(theta, r, alpha=0.3)
    ax6.set_title('Polar Plot')

    # Heatmap
    ax7 = fig.add_subplot(gs[2, :2])
    data_heatmap = np.random.randn(10, 12)
    im = ax7.imshow(data_heatmap, cmap='coolwarm', aspect='auto')
    ax7.set_title('Heatmap')
    ax7.set_xlabel('Columns')
    ax7.set_ylabel('Rows')
    plt.colorbar(im, ax=ax7)

    # Pie chart
    ax8 = fig.add_subplot(gs[2, 2])
    sizes = [30, 25, 20, 15, 10]
    labels = ['A', 'B', 'C', 'D', 'E']
    colors_pie = ['gold', 'lightcoral', 'lightskyblue', 'lightgreen', 'plum']
    explode = (0.1, 0, 0, 0, 0)  # explode first slice

    wedges, texts, autotexts = ax8.pie(sizes, explode=explode, labels=labels, colors=colors_pie,
                                       autopct='%1.1f%%', shadow=True, startangle=90)
    ax8.set_title('Pie Chart')

    # Enhance text appearance
    for autotext in autotexts:
        autotext.set_color('white')
        autotext.set_weight('bold')

    plt.suptitle('Advanced Visualization Techniques', fontsize=16, y=1.02)
    plt.show()

# 3. Seaborn Statistical Plots
def seaborn_plots():
    print("\n=== Seaborn Statistical Plots ===")

    # Create sample datasets
    np.random.seed(42)

    # Dataset for regression plots
    x_reg = np.linspace(0, 10, 100)
    y_reg = 2 * x_reg + 1 + np.random.normal(0, 2, 100)

    # Dataset for categorical plots
    categories = ['A', 'B', 'C', 'D']
    data_cat = []
    for cat in categories:
        data_cat.extend([(cat, np.random.normal(100 + categories.index(cat) * 10, 15))
                        for _ in range(50)])

    df_cat = pd.DataFrame(data_cat, columns=['Category', 'Value'])

    # Dataset for distribution plots
    data_dist = np.random.gamma(2, 2, 1000)

    # Create comprehensive seaborn plot
    fig, axes = plt.subplots(2, 3, figsize=(18, 12))

    # Regression plot with confidence interval
    sns.regplot(x=x_reg, y=y_reg, ax=axes[0, 0],
                scatter_kws={'alpha': 0.6}, line_kws={'color': 'red'})
    axes[0, 0].set_title('Regression Plot with Confidence Interval')

    # Box plot with swarm overlay
    sns.boxplot(x='Category', y='Value', data=df_cat, ax=axes[0, 1])
    sns.swarmplot(x='Category', y='Value', data=df_cat, ax=axes[0, 1],
                  color='black', alpha=0.5, size=4)
    axes[0, 1].set_title('Box Plot with Swarm Overlay')

    # Violin plot
    sns.violinplot(x='Category', y='Value', data=df_cat, ax=axes[0, 2])
    axes[0, 2].set_title('Violin Plot by Category')

    # Distribution plot (histogram + KDE)
    sns.histplot(data_dist, kde=True, ax=axes[1, 0])
    axes[1, 0].set_title('Distribution Plot with KDE')

    # Pair plot (requires 2D data)
    # Creating correlation data
    np.random.seed(42)
    n_samples = 200
    corr_data = {
        'Variable1': np.random.normal(0, 1, n_samples),
        'Variable2': np.random.normal(0, 1, n_samples),
        'Variable3': np.random.normal(0, 1, n_samples)
    }
    # Add some correlation
    corr_data['Variable2'] = 0.7 * corr_data['Variable1'] + 0.3 * corr_data['Variable2']

    df_corr = pd.DataFrame(corr_data)

    # Correlation heatmap
    correlation_matrix = df_corr.corr()
    sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0,
                square=True, ax=axes[1, 1])
    axes[1, 1].set_title('Correlation Heatmap')

    # Count plot
    count_data = np.random.choice(categories, 100)
    sns.countplot(x=count_data, ax=axes[1, 2])
    axes[1, 2].set_title('Count Plot')
    axes[1, 2].set_xlabel('Category')

    plt.tight_layout()
    plt.show()

# 4. Real-world Data Visualization
def real_world_visualization():
    print("\n=== Real-world Data Visualization ===")

    # Create realistic sales dataset
    np.random.seed(42)

    # Time series sales data
    dates = pd.date_range('2022-01-01', '2023-12-31', freq='D')
    base_sales = 1000
    trend = np.linspace(0, 500, len(dates))
    seasonal = 200 * np.sin(2 * np.pi * np.arange(len(dates)) / 365.25)
    noise = np.random.normal(0, 50, len(dates))

    sales = base_sales + trend + seasonal + noise
    sales[sales < 0] = 0  # Ensure non-negative

    df_sales = pd.DataFrame({
        'date': dates,
        'sales': sales
    })

    # Create monthly aggregations
    df_monthly = df_sales.set_index('date').resample('M').agg({
        'sales': ['sum', 'mean', 'std']
    }).reset_index()
    df_monthly.columns = ['date', 'total_sales', 'avg_sales', 'sales_std']

    # Create comprehensive dashboard
    fig = plt.figure(figsize=(20, 15))
    gs = fig.add_gridspec(3, 3, hspace=0.3, wspace=0.3)

    # 1. Time series plot with trend
    ax1 = fig.add_subplot(gs[0, :])
    ax1.plot(df_sales['date'], df_sales['sales'], alpha=0.7, linewidth=1, label='Daily Sales')

    # Add moving average
    df_sales['MA30'] = df_sales['sales'].rolling(window=30).mean()
    ax1.plot(df_sales['date'], df_sales['MA30'], 'r-', linewidth=2, label='30-day MA')

    ax1.set_title('Daily Sales with Moving Average', fontsize=14, fontweight='bold')
    ax1.set_xlabel('Date')
    ax1.set_ylabel('Sales')
    ax1.legend()
    ax1.grid(True, alpha=0.3)

    # 2. Monthly sales bar chart
    ax2 = fig.add_subplot(gs[1, 0])
    monthly_totals = df_sales.set_index('date').resample('M').sum()
    ax2.bar(range(len(monthly_totals)), monthly_totals['sales'],
            color=plt.cm.viridis(np.linspace(0, 1, len(monthly_totals))))
    ax2.set_title('Monthly Total Sales')
    ax2.set_xlabel('Month')
    ax2.set_ylabel('Total Sales')
    ax2.set_xticks(range(0, len(monthly_totals), 3))
    ax2.set_xticklabels([month.strftime('%Y-%m') for month in monthly_totals.index[::3]],
                       rotation=45)

    # 3. Sales distribution
    ax3 = fig.add_subplot(gs[1, 1])
    ax3.hist(df_sales['sales'], bins=50, alpha=0.7, color='skyblue', edgecolor='black')
    ax3.axvline(df_sales['sales'].mean(), color='red', linestyle='--',
               label=f'Mean: {df_sales["sales"].mean():.0f}')
    ax3.axvline(df_sales['sales'].median(), color='orange', linestyle='--',
               label=f'Median: {df_sales["sales"].median():.0f}')
    ax3.set_title('Sales Distribution')
    ax3.set_xlabel('Sales')
    ax3.set_ylabel('Frequency')
    ax3.legend()

    # 4. Monthly statistics
    ax4 = fig.add_subplot(gs[1, 2])
    months = [month.strftime('%Y-%m') for month in monthly_totals.index]
    ax4.plot(monthly_totals.index, monthly_totals['sales'], 'o-', label='Monthly Sales')
    ax4.fill_between(monthly_totals.index, monthly_totals['sales'], alpha=0.3)
    ax4.set_title('Monthly Sales Trend')
    ax4.set_xlabel('Month')
    ax4.set_ylabel('Sales')
    ax4.set_xticks(range(0, len(monthly_totals), 3))
    ax4.set_xticklabels([months[i] for i in range(0, len(monthly_totals), 3)],
                       rotation=45)
    ax4.legend()

    # 5. Seasonal pattern
    ax5 = fig.add_subplot(gs[2, 0])
    df_sales['month'] = df_sales['date'].dt.month
    monthly_avg = df_sales.groupby('month')['sales'].mean()
    ax5.bar(monthly_avg.index, monthly_avg.values, color='lightgreen')
    ax5.set_title('Average Sales by Month')
    ax5.set_xlabel('Month')
    ax5.set_ylabel('Average Sales')
    ax5.set_xticks(range(1, 13))
    ax5.set_xticklabels(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
                        'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])

    # 6. Day of week analysis
    ax6 = fig.add_subplot(gs[2, 1])
    df_sales['day_of_week'] = df_sales['date'].dt.day_name()
    dow_avg = df_sales.groupby('day_of_week')['sales'].mean()
    dow_order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
    dow_avg = dow_avg.reindex(dow_order)

    bars = ax6.bar(dow_avg.index, dow_avg.values, color='coral')
    ax6.set_title('Average Sales by Day of Week')
    ax6.set_xlabel('Day of Week')
    ax6.set_ylabel('Average Sales')
    ax6.tick_params(axis='x', rotation=45)

    # Add value labels on bars
    for bar, value in zip(bars, dow_avg.values):
        ax6.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 10,
                f'{value:.0f}', ha='center', va='bottom')

    # 7. Sales heatmap (by month and day of week)
    ax7 = fig.add_subplot(gs[2, 2])
    df_sales['year_month'] = df_sales['date'].dt.to_period('M')
    heatmap_data = df_sales.groupby(['year_month', 'day_of_week'])['sales'].mean().unstack()
    heatmap_data = heatmap_data.reindex(columns=dow_order)

    sns.heatmap(heatmap_data.T, cmap='YlOrRd', ax=ax7, cbar_kws={'label': 'Average Sales'})
    ax7.set_title('Sales Heatmap (Month vs Day of Week)')
    ax7.set_xlabel('Month')
    ax7.set_ylabel('Day of Week')

    plt.suptitle('Sales Analytics Dashboard', fontsize=16, fontweight='bold', y=1.02)
    plt.show()

# 5. Publication Quality Plots
def publication_quality():
    print("\n=== Publication Quality Plots ===")

    # Create high-quality scientific plot
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))

    # Plot 1: Comparison of methods with error bars
    methods = ['Method A', 'Method B', 'Method C', 'Method D']
    means = [85.2, 78.9, 92.1, 88.7]
    stds = [3.2, 4.1, 2.8, 3.5]

    colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']
    bars = ax1.bar(methods, means, yerr=stds, capsize=5,
                   color=colors, alpha=0.8, edgecolor='black', linewidth=1.2)

    ax1.set_title('Algorithm Performance Comparison', fontsize=14, fontweight='bold')
    ax1.set_ylabel('Accuracy (%)', fontsize=12)
    ax1.set_ylim(0, 100)
    ax1.grid(True, alpha=0.3, axis='y')

    # Add significance markers
    significance = ['***', 'ns', '****', '**']
    for i, (bar, sig) in enumerate(zip(bars, significance)):
        ax1.text(bar.get_x() + bar.get_width()/2, bar.get_height() + stds[i] + 2,
                sig, ha='center', va='bottom', fontsize=12, fontweight='bold')

    # Plot 2: ROC curves
    fpr = np.linspace(0, 1, 100)

    # Simulate ROC curves for different models
    tpr_model1 = 1 - np.exp(-3 * fpr)
    tpr_model2 = fpr**0.5
    tpr_model3 = np.sqrt(fpr)

    ax2.plot(fpr, tpr_model1, 'b-', linewidth=2, label=f'Model 1 (AUC = 0.91)')
    ax2.plot(fpr, tpr_model2, 'r-', linewidth=2, label=f'Model 2 (AUC = 0.82)')
    ax2.plot(fpr, tpr_model3, 'g-', linewidth=2, label=f'Model 3 (AUC = 0.76)')
    ax2.plot([0, 1], [0, 1], 'k--', linewidth=1, label='Random')

    ax2.set_title('ROC Curves Comparison', fontsize=14, fontweight='bold')
    ax2.set_xlabel('False Positive Rate', fontsize=12)
    ax2.set_ylabel('True Positive Rate', fontsize=12)
    ax2.legend(loc='lower right')
    ax2.grid(True, alpha=0.3)

    # Style improvements for publication
    for ax in [ax1, ax2]:
        ax.spines['top'].set_visible(False)
        ax.spines['right'].set_visible(False)
        ax.spines['left'].set_linewidth(1.2)
        ax.spines['bottom'].set_linewidth(1.2)
        ax.tick_params(axis='both', which='major', labelsize=10)

    plt.tight_layout()
    plt.show()

# Main execution
if __name__ == "__main__":
    print("Matplotlib and Seaborn Visualization Examples")
    print("=" * 50)

    basic_plots()
    advanced_plots()
    seaborn_plots()
    real_world_visualization()
    publication_quality()

    print("\n" + "=" * 50)
    print("All visualization examples completed successfully!")