Data Utilities

def as_pandas(cursor, coerce_float=False):
    """
    Convert cursor results to pandas DataFrame.
    
    This function converts the results from an executed cursor into a pandas
    DataFrame, enabling easy integration with the Python data science ecosystem
    including scikit-learn, matplotlib, and other analysis libraries.
    
    Parameters:
        cursor: Active cursor object with executed query results
        coerce_float (bool): Coerce numeric columns to float type. Default is False.
        
    Returns:
        pandas.DataFrame: Query results as a DataFrame with proper column names
        
    Raises:
        ImportError: If pandas is not installed
        ValueError: If cursor has no results or is not executed
    """

def force_drop_impala_database(cursor, database_name):
    """
    Force drop an Impala database and all its tables.
    
    This is a utility function for development and testing that forcibly
    drops a database even if it contains tables.
    
    Parameters:
        cursor: Active cursor object
        database_name (str): Name of the database to drop
        
    Warning:
        This operation is destructive and cannot be undone.
    """

def force_drop_hive_database(cursor, database_name):
    """
    Force drop a Hive database and all its tables.
    
    Similar to force_drop_impala_database but optimized for Hive-specific
    metadata handling.
    
    Parameters:
        cursor: Active cursor object  
        database_name (str): Name of the database to drop
        
    Warning:
        This operation is destructive and cannot be undone.
    """

def get_logger_and_init_null():
    """
    Get logger instance with null handler initialization.
    
    Returns a properly configured logger for impyla operations with
    a null handler to prevent unwanted log output unless explicitly
    configured by the user.
    
    Returns:
        logging.Logger: Configured logger instance
    """

def _random_id(prefix='', length=8):
    """
    Generate a random identifier.
    
    Used internally for generating unique identifiers for operations
    and temporary objects.
    
    Parameters:
        prefix (str): Optional prefix for the identifier
        length (int): Length of the random part (default 8)
        
    Returns:
        str: Random identifier string
    """

def _get_table_schema_hack(cursor, table):
    """
    Get table schema information using internal methods.
    
    This is an internal utility for extracting table schema information
    when standard methods are not available.
    
    Parameters:
        cursor: Active cursor object
        table (str): Name of the table
        
    Returns:
        list: Schema information as list of column descriptors
    """

def _gen_safe_random_table_name(cursor, prefix='tmp'):
    """
    Generate a safe random table name that doesn't conflict with existing tables.
    
    Parameters:
        cursor: Active cursor object
        prefix (str): Prefix for the table name (default 'tmp')
        
    Returns:
        str: Safe random table name
    """

def compute_result_schema(cursor, query_string):
    """
    Compute the result schema for a query without executing it.
    
    Parameters:
        cursor: Active cursor object
        query_string (str): SQL query to analyze
        
    Returns:
        list: Schema information for the query result
    """

def get_basic_credentials_for_request_headers(user, password):
    """
    Generate basic authentication credentials for HTTP request headers.
    
    Parameters:
        user (str): Username
        password (str): Password
        
    Returns:
        str: Base64 encoded credentials for Authorization header
    """

def cookie_matches_path(c, path):
    """
    Check if a cookie matches a given path.
    
    Parameters:
        c: Cookie object
        path (str): URL path to check
        
    Returns:
        bool: True if cookie matches the path
    """

def get_cookie_expiry(c):
    """
    Get the expiry time of a cookie.
    
    Parameters:
        c: Cookie object
        
    Returns:
        datetime or None: Cookie expiry time
    """

def get_cookies(resp_headers):
    """
    Extract cookies from HTTP response headers.
    
    Parameters:
        resp_headers: HTTP response headers
        
    Returns:
        list: List of cookie objects
    """

def get_all_cookies(path, resp_headers):
    """
    Get all cookies from HTTP response headers for a given path.
    
    Parameters:
        path (str): URL path
        resp_headers: HTTP response headers
        
    Returns:
        list: List of cookie objects matching the path
    """

def get_all_matching_cookies(cookie_names, path, resp_headers):
    """
    Get cookies matching specific names from HTTP response headers.
    
    Parameters:
        cookie_names (list): List of cookie names to match
        path (str): URL path
        resp_headers: HTTP response headers
        
    Returns:
        list: List of matching cookie objects
    """

def warn_protocol_param():
    """
    Issue a warning about deprecated protocol parameters.
    """

def warn_deprecate(functionality='This', alternative=None):
    """
    Issue a deprecation warning for functionality.
    
    Parameters:
        functionality (str): Description of deprecated functionality
        alternative (str): Suggested alternative (optional)
    """

def warn_nontls_jwt():
    """
    Issue a warning about using JWT without TLS.
    """

from impala.dbapi import connect
from impala.util import as_pandas

# Connect and execute query
conn = connect(host='impala-host', port=21050)
cursor = conn.cursor()

cursor.execute("""
    SELECT 
        customer_id,
        order_date,
        total_amount,
        status
    FROM orders 
    WHERE order_date >= '2023-01-01'
    LIMIT 1000
""")

# Convert to pandas DataFrame
df = as_pandas(cursor)

print(f"DataFrame shape: {df.shape}")
print("\nColumn types:")
print(df.dtypes)

print("\nFirst few rows:")
print(df.head())

cursor.close()
conn.close()

from impala.dbapi import connect
from impala.util import as_pandas
import matplotlib.pyplot as plt

conn = connect(host='impala-host', port=21050)
cursor = conn.cursor()

# Query sales data
cursor.execute("""
    SELECT 
        DATE_TRUNC('month', order_date) as month,
        SUM(total_amount) as monthly_sales,
        COUNT(*) as order_count,
        AVG(total_amount) as avg_order_value
    FROM orders 
    WHERE order_date >= '2023-01-01'
    GROUP BY DATE_TRUNC('month', order_date)
    ORDER BY month
""")

# Convert to DataFrame
df = as_pandas(cursor, coerce_float=True)

# Perform analysis
print("Monthly Sales Summary:")
print(df.describe())

# Plot monthly trends
plt.figure(figsize=(12, 6))
plt.subplot(1, 2, 1)
plt.plot(df['month'], df['monthly_sales'])
plt.title('Monthly Sales')
plt.xticks(rotation=45)

plt.subplot(1, 2, 2)
plt.plot(df['month'], df['avg_order_value'])
plt.title('Average Order Value')
plt.xticks(rotation=45)

plt.tight_layout()
plt.show()

cursor.close()
conn.close()

from impala.dbapi import connect
from impala.util import as_pandas
import json

conn = connect(host='impala-host', port=21050)
cursor = conn.cursor()

# Query with complex data types (arrays, structs)
cursor.execute("""
    SELECT 
        user_id,
        profile.name,
        profile.email,
        tags,
        metadata
    FROM user_profiles 
    LIMIT 100
""")

df = as_pandas(cursor)

# Handle complex types
print("Data types:")
print(df.dtypes)

# Work with array columns
if 'tags' in df.columns:
    # Convert string representation of arrays to Python lists
    df['tags_list'] = df['tags'].apply(
        lambda x: json.loads(x) if x and x != 'NULL' else []
    )
    
    # Analyze tag frequency
    all_tags = []
    for tags in df['tags_list']:
        all_tags.extend(tags)
    
    from collections import Counter
    tag_counts = Counter(all_tags)
    print("\nTop 10 most common tags:")
    for tag, count in tag_counts.most_common(10):
        print(f"{tag}: {count}")

cursor.close()
conn.close()

from impala.dbapi import connect
from impala.util import force_drop_impala_database

# Connect with administrative privileges
conn = connect(
    host='impala-host', 
    port=21050,
    auth_mechanism='GSSAPI'  # Admin access
)
cursor = conn.cursor()

# Create test database
cursor.execute("CREATE DATABASE IF NOT EXISTS test_analytics")

# Create some test tables
cursor.execute("""
    CREATE TABLE IF NOT EXISTS test_analytics.sample_data (
        id INT,
        name STRING,
        value DOUBLE
    )
""")

cursor.execute("""
    INSERT INTO test_analytics.sample_data 
    VALUES (1, 'test', 123.45)
""")

# List tables in the database
cursor.execute("SHOW TABLES IN test_analytics")
tables = cursor.fetchall()
print(f"Tables in test_analytics: {[t[0] for t in tables]}")

# Force drop the entire database (use with caution!)
try:
    force_drop_impala_database(cursor, 'test_analytics')
    print("Database test_analytics dropped successfully")
except Exception as e:
    print(f"Error dropping database: {e}")

cursor.close()
conn.close()

from impala.dbapi import connect
from impala.util import as_pandas
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier

def create_ml_pipeline(host, query, target_column):
    """Create a machine learning pipeline from Impala data."""
    
    # Connect and fetch data
    conn = connect(host=host, port=21050)
    cursor = conn.cursor()
    
    cursor.execute(query)
    df = as_pandas(cursor, coerce_float=True)
    
    print(f"Loaded {len(df)} rows from Impala")
    
    # Prepare features and target
    feature_columns = [col for col in df.columns if col != target_column]
    X = df[feature_columns]
    y = df[target_column]
    
    # Handle missing values
    X = X.fillna(X.mean())
    
    # Scale features
    scaler = StandardScaler()
    X_scaled = scaler.fit_transform(X)
    
    # Train model
    model = RandomForestClassifier(n_estimators=100, random_state=42)
    model.fit(X_scaled, y)
    
    print(f"Model trained with accuracy: {model.score(X_scaled, y):.3f}")
    
    # Feature importance
    feature_importance = pd.DataFrame({
        'feature': feature_columns,
        'importance': model.feature_importances_
    }).sort_values('importance', ascending=False)
    
    print("\nTop 5 important features:")
    print(feature_importance.head())
    
    cursor.close()
    conn.close()
    
    return model, scaler, feature_importance

# Usage
ml_query = """
    SELECT 
        age, income, education_years, experience_years,
        CASE WHEN salary > 50000 THEN 1 ELSE 0 END as high_earner
    FROM employee_data 
    WHERE age IS NOT NULL 
    AND income IS NOT NULL
"""

model, scaler, importance = create_ml_pipeline(
    'impala-host', 
    ml_query, 
    'high_earner'
)