Pandas

by manutej1861cc9720d5No license62 starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated 3 months ago

Expert data analysis and manipulation for customer support operations using pandas

Instructions onlyData & Analytics

AI-generated overview

Guides pandas-based analysis of customer support ticket data, including SLA tracking, agent metrics, ETL and reporting.

What it does: This skill provides pandas patterns and code examples for customer support analytics: SLA compliance, ticket volume trends, agent performance metrics, data cleaning and validation, groupby aggregations, merges, time series resampling and pivot tables. It also covers PostgreSQL integration through SQLAlchemy and exporting reports to Excel and other formats. It is instruction-only and produces analysis code, DataFrames and report exports rather than a fixed artifact.
When to use it: Use it when analyzing support ticket datasets with pandas, for example to compute SLA compliance, agent or team performance, ticket trends or executive pivot reports. It also fits building ETL pipelines that load ticket data from PostgreSQL and export metrics to spreadsheets.
Requirements: Requires pandas, SQLAlchemy, psycopg2-binary, numpy, openpyxl and pytest, plus a PostgreSQL database and credentials for the database integration examples. Ships no scripts; it is instructions and code examples only.

pandas - Data Analysis and Manipulation for Customer Support

Overview

You are an expert in pandas, the powerful Python library for data analysis and manipulation, with specialized knowledge in customer support analytics, ticket management, SLA tracking, and performance reporting. Your expertise covers DataFrame operations, data transformation, time series analysis, database integration, and production-ready data pipelines for support operations.

Core Competencies

1. DataFrame Operations and Data Structures

DataFrame Creation and Initialization

Create DataFrames from various sources: dictionaries, lists, CSV files, databases, JSON, Excel
Understand DataFrame anatomy: index, columns, values, dtypes
Use appropriate data types for memory optimization (category, int32, datetime64)
Initialize DataFrames with proper indices for time series data

Data Selection and Indexing

Use .loc[] for label-based indexing (rows and columns by name)
Use .iloc[] for position-based indexing (integer positions)
Boolean indexing for filtering data based on conditions
Query method for SQL-like filtering: df.query('priority == "high" and status == "open"')
Multi-level indexing for hierarchical data (team > agent > ticket)

Column Operations

Select, rename, and reorder columns efficiently
Create calculated columns using vectorized operations
Apply functions to columns: .apply(), .map(), .transform()
Use .assign() for method chaining and creating new columns
Handle column data type conversions with .astype()

2. Customer Support Analytics Patterns

SLA Tracking and Compliance

python

# Calculate SLA compliance for support ticketsdef analyze_sla_compliance(tickets_df):    """    Analyze SLA compliance for customer support tickets.
    Args:        tickets_df: DataFrame with columns [ticket_id, created_at, first_response_at,                    resolved_at, priority, sla_target_hours]
    Returns:        DataFrame with SLA metrics and compliance flags    """    # Calculate response and resolution times    tickets_df['first_response_time'] = (        tickets_df['first_response_at'] - tickets_df['created_at']    ).dt.total_seconds() / 3600  # Convert to hours
    tickets_df['resolution_time'] = (        tickets_df['resolved_at'] - tickets_df['created_at']    ).dt.total_seconds() / 3600
    # Determine SLA compliance    tickets_df['response_sla_met'] = (        tickets_df['first_response_time'] <= tickets_df['sla_target_hours']    )
    tickets_df['resolution_sla_met'] = (        tickets_df['resolution_time'] <= tickets_df['sla_target_hours'] * 2    )
    # Calculate compliance rate by priority    compliance_by_priority = tickets_df.groupby('priority').agg({        'response_sla_met': ['sum', 'count', 'mean'],        'resolution_sla_met': ['sum', 'count', 'mean'],        'first_response_time': ['mean', 'median', 'std'],        'resolution_time': ['mean', 'median', 'std']    })
    return tickets_df, compliance_by_priority

# Calculate SLA compliance for support ticketsdef analyze_sla_compliance(tickets_df):    """    Analyze SLA compliance for customer support tickets.
    Args:        tickets_df: DataFrame with columns [ticket_id, created_at, first_response_at,                    resolved_at, priority, sla_target_hours]
    Returns:        DataFrame with SLA metrics and compliance flags    """    # Calculate response and resolution times    tickets_df['first_response_time'] = (        tickets_df['first_response_at'] - tickets_df['created_at']    ).dt.total_seconds() / 3600  # Convert to hours
    tickets_df['resolution_time'] = (        tickets_df['resolved_at'] - tickets_df['created_at']    ).dt.total_seconds() / 3600
    # Determine SLA compliance    tickets_df['response_sla_met'] = (        tickets_df['first_response_time'] <= tickets_df['sla_target_hours']    )
    tickets_df['resolution_sla_met'] = (        tickets_df['resolution_time'] <= tickets_df['sla_target_hours'] * 2    )
    # Calculate compliance rate by priority    compliance_by_priority = tickets_df.groupby('priority').agg({        'response_sla_met': ['sum', 'count', 'mean'],        'resolution_sla_met': ['sum', 'count', 'mean'],        'first_response_time': ['mean', 'median', 'std'],        'resolution_time': ['mean', 'median', 'std']    })
    return tickets_df, compliance_by_priority

Ticket Volume and Trend Analysis

python

# Time series analysis of ticket volumedef analyze_ticket_trends(tickets_df, frequency='D'):    """    Analyze ticket volume trends over time.
    Args:        tickets_df: DataFrame with created_at column        frequency: Resampling frequency ('D', 'W', 'M', 'Q')
    Returns:        DataFrame with aggregated metrics by time period    """    # Set datetime index    tickets_ts = tickets_df.set_index('created_at').sort_index()
    # Resample and aggregate    volume_trends = tickets_ts.resample(frequency).agg({        'ticket_id': 'count',        'priority': lambda x: (x == 'high').sum(),        'channel': lambda x: x.value_counts().to_dict(),        'customer_id': 'nunique'    }).rename(columns={        'ticket_id': 'total_tickets',        'priority': 'high_priority_count',        'customer_id': 'unique_customers'    })
    # Calculate rolling averages    volume_trends['7day_avg'] = volume_trends['total_tickets'].rolling(7).mean()    volume_trends['30day_avg'] = volume_trends['total_tickets'].rolling(30).mean()
    # Calculate percentage change    volume_trends['pct_change'] = volume_trends['total_tickets'].pct_change()
    return volume_trends

# Time series analysis of ticket volumedef analyze_ticket_trends(tickets_df, frequency='D'):    """    Analyze ticket volume trends over time.
    Args:        tickets_df: DataFrame with created_at column        frequency: Resampling frequency ('D', 'W', 'M', 'Q')
    Returns:        DataFrame with aggregated metrics by time period    """    # Set datetime index    tickets_ts = tickets_df.set_index('created_at').sort_index()
    # Resample and aggregate    volume_trends = tickets_ts.resample(frequency).agg({        'ticket_id': 'count',        'priority': lambda x: (x == 'high').sum(),        'channel': lambda x: x.value_counts().to_dict(),        'customer_id': 'nunique'    }).rename(columns={        'ticket_id': 'total_tickets',        'priority': 'high_priority_count',        'customer_id': 'unique_customers'    })
    # Calculate rolling averages    volume_trends['7day_avg'] = volume_trends['total_tickets'].rolling(7).mean()    volume_trends['30day_avg'] = volume_trends['total_tickets'].rolling(30).mean()
    # Calculate percentage change    volume_trends['pct_change'] = volume_trends['total_tickets'].pct_change()
    return volume_trends

Agent Performance Metrics

python

# Calculate comprehensive agent performance metricsdef calculate_agent_metrics(tickets_df, agents_df):    """    Calculate detailed performance metrics for support agents.
    Args:        tickets_df: DataFrame with ticket data        agents_df: DataFrame with agent information
    Returns:        DataFrame with agent performance metrics    """    # Group by agent    agent_metrics = tickets_df.groupby('agent_id').agg({        'ticket_id': 'count',        'first_response_time': ['mean', 'median', 'std'],        'resolution_time': ['mean', 'median', 'std'],        'csat_score': ['mean', 'count'],        'response_sla_met': 'mean',        'resolution_sla_met': 'mean',        'reopened': 'sum'    })
    # Flatten multi-level columns    agent_metrics.columns = ['_'.join(col).strip() for col in agent_metrics.columns]
    # Calculate additional metrics    agent_metrics['tickets_per_day'] = (        agent_metrics['ticket_id_count'] /        (tickets_df['created_at'].max() - tickets_df['created_at'].min()).days    )
    agent_metrics['reopen_rate'] = (        agent_metrics['reopened_sum'] / agent_metrics['ticket_id_count']    )
    # Merge with agent details    agent_metrics = agent_metrics.merge(        agents_df[['agent_id', 'name', 'team', 'hire_date']],        left_index=True,        right_on='agent_id'    )
    return agent_metrics

# Calculate comprehensive agent performance metricsdef calculate_agent_metrics(tickets_df, agents_df):    """    Calculate detailed performance metrics for support agents.
    Args:        tickets_df: DataFrame with ticket data        agents_df: DataFrame with agent information
    Returns:        DataFrame with agent performance metrics    """    # Group by agent    agent_metrics = tickets_df.groupby('agent_id').agg({        'ticket_id': 'count',        'first_response_time': ['mean', 'median', 'std'],        'resolution_time': ['mean', 'median', 'std'],        'csat_score': ['mean', 'count'],        'response_sla_met': 'mean',        'resolution_sla_met': 'mean',        'reopened': 'sum'    })
    # Flatten multi-level columns    agent_metrics.columns = ['_'.join(col).strip() for col in agent_metrics.columns]
    # Calculate additional metrics    agent_metrics['tickets_per_day'] = (        agent_metrics['ticket_id_count'] /        (tickets_df['created_at'].max() - tickets_df['created_at'].min()).days    )
    agent_metrics['reopen_rate'] = (        agent_metrics['reopened_sum'] / agent_metrics['ticket_id_count']    )
    # Merge with agent details    agent_metrics = agent_metrics.merge(        agents_df[['agent_id', 'name', 'team', 'hire_date']],        left_index=True,        right_on='agent_id'    )
    return agent_metrics

3. Data Integration and ETL

PostgreSQL Integration with SQLAlchemy

python

# Load and save data to PostgreSQLfrom sqlalchemy import create_engine, textimport pandas as pd
def create_db_connection(host, database, user, password, port=5432):    """Create SQLAlchemy engine for PostgreSQL."""    connection_string = f"postgresql://{user}:{password}@{host}:{port}/{database}"    return create_engine(connection_string)
def load_tickets_from_db(engine, start_date, end_date):    """    Load ticket data from PostgreSQL with optimized query.
    Args:        engine: SQLAlchemy engine        start_date: Start date for filtering        end_date: End date for filtering
    Returns:        DataFrame with ticket data    """    query = text("""        SELECT            t.ticket_id,            t.created_at,            t.updated_at,            t.resolved_at,            t.first_response_at,            t.priority,            t.status,            t.channel,            t.category,            t.agent_id,            t.customer_id,            t.subject,            c.name as customer_name,            c.tier as customer_tier,            a.name as agent_name,            a.team as agent_team        FROM tickets t        LEFT JOIN customers c ON t.customer_id = c.customer_id        LEFT JOIN agents a ON t.agent_id = a.agent_id        WHERE t.created_at >= :start_date          AND t.created_at < :end_date        ORDER BY t.created_at DESC    """)
    # Load with proper data types    df = pd.read_sql(        query,        engine,        params={'start_date': start_date, 'end_date': end_date},        parse_dates=['created_at', 'updated_at', 'resolved_at', 'first_response_at']    )
    # Optimize data types    df['priority'] = df['priority'].astype('category')    df['status'] = df['status'].astype('category')    df['channel'] = df['channel'].astype('category')    df['customer_tier'] = df['customer_tier'].astype('category')
    return df
def save_metrics_to_db(df, table_name, engine, if_exists='replace'):    """    Save processed metrics to PostgreSQL.
    Args:        df: DataFrame to save        table_name: Target table name        engine: SQLAlchemy engine        if_exists: 'replace', 'append', or 'fail'    """    df.to_sql(        table_name,        engine,        if_exists=if_exists,        index=True,        method='multi',  # Faster multi-row insert        chunksize=1000    )

# Load and save data to PostgreSQLfrom sqlalchemy import create_engine, textimport pandas as pd
def create_db_connection(host, database, user, password, port=5432):    """Create SQLAlchemy engine for PostgreSQL."""    connection_string = f"postgresql://{user}:{password}@{host}:{port}/{database}"    return create_engine(connection_string)
def load_tickets_from_db(engine, start_date, end_date):    """    Load ticket data from PostgreSQL with optimized query.
    Args:        engine: SQLAlchemy engine        start_date: Start date for filtering        end_date: End date for filtering
    Returns:        DataFrame with ticket data    """    query = text("""        SELECT            t.ticket_id,            t.created_at,            t.updated_at,            t.resolved_at,            t.first_response_at,            t.priority,            t.status,            t.channel,            t.category,            t.agent_id,            t.customer_id,            t.subject,            c.name as customer_name,            c.tier as customer_tier,            a.name as agent_name,            a.team as agent_team        FROM tickets t        LEFT JOIN customers c ON t.customer_id = c.customer_id        LEFT JOIN agents a ON t.agent_id = a.agent_id        WHERE t.created_at >= :start_date          AND t.created_at < :end_date        ORDER BY t.created_at DESC    """)
    # Load with proper data types    df = pd.read_sql(        query,        engine,        params={'start_date': start_date, 'end_date': end_date},        parse_dates=['created_at', 'updated_at', 'resolved_at', 'first_response_at']    )
    # Optimize data types    df['priority'] = df['priority'].astype('category')    df['status'] = df['status'].astype('category')    df['channel'] = df['channel'].astype('category')    df['customer_tier'] = df['customer_tier'].astype('category')
    return df
def save_metrics_to_db(df, table_name, engine, if_exists='replace'):    """    Save processed metrics to PostgreSQL.
    Args:        df: DataFrame to save        table_name: Target table name        engine: SQLAlchemy engine        if_exists: 'replace', 'append', or 'fail'    """    df.to_sql(        table_name,        engine,        if_exists=if_exists,        index=True,        method='multi',  # Faster multi-row insert        chunksize=1000    )

Data Cleaning and Validation

python

# Comprehensive data cleaning for support datadef clean_ticket_data(df):    """    Clean and validate ticket data.
    Args:        df: Raw ticket DataFrame
    Returns:        Cleaned DataFrame with validation report    """    validation_report = {}
    # 1. Handle missing values    validation_report['missing_before'] = df.isnull().sum().to_dict()
    # Fill missing agent_id for unassigned tickets    df['agent_id'] = df['agent_id'].fillna('UNASSIGNED')
    # Fill missing categories    df['category'] = df['category'].fillna('UNCATEGORIZED')
    # Drop tickets with missing critical fields    critical_fields = ['ticket_id', 'created_at', 'customer_id']    df = df.dropna(subset=critical_fields)
    validation_report['missing_after'] = df.isnull().sum().to_dict()
    # 2. Remove duplicates    validation_report['duplicates_found'] = df.duplicated(subset=['ticket_id']).sum()    df = df.drop_duplicates(subset=['ticket_id'], keep='first')
    # 3. Validate data types and ranges    df['created_at'] = pd.to_datetime(df['created_at'], errors='coerce')    df['resolved_at'] = pd.to_datetime(df['resolved_at'], errors='coerce')
    # 4. Validate business logic    # Resolution time should be positive    invalid_resolution = df[        (df['resolved_at'].notna()) &        (df['resolved_at'] < df['created_at'])    ]    validation_report['invalid_resolution_times'] = len(invalid_resolution)
    # Fix by setting to None    df.loc[df['resolved_at'] < df['created_at'], 'resolved_at'] = None
    # 5. Standardize categorical values    priority_mapping = {        'CRITICAL': 'critical',        'HIGH': 'high',        'MEDIUM': 'medium',        'LOW': 'low',        'urgent': 'high',        'normal': 'medium'    }    df['priority'] = df['priority'].replace(priority_mapping)
    # 6. Outlier detection for response times    if 'first_response_time' in df.columns:        q1 = df['first_response_time'].quantile(0.25)        q3 = df['first_response_time'].quantile(0.75)        iqr = q3 - q1        outlier_threshold = q3 + (3 * iqr)
        validation_report['response_time_outliers'] = (            df['first_response_time'] > outlier_threshold        ).sum()
    validation_report['final_row_count'] = len(df)
    return df, validation_report

# Comprehensive data cleaning for support datadef clean_ticket_data(df):    """    Clean and validate ticket data.
    Args:        df: Raw ticket DataFrame
    Returns:        Cleaned DataFrame with validation report    """    validation_report = {}
    # 1. Handle missing values    validation_report['missing_before'] = df.isnull().sum().to_dict()
    # Fill missing agent_id for unassigned tickets    df['agent_id'] = df['agent_id'].fillna('UNASSIGNED')
    # Fill missing categories    df['category'] = df['category'].fillna('UNCATEGORIZED')
    # Drop tickets with missing critical fields    critical_fields = ['ticket_id', 'created_at', 'customer_id']    df = df.dropna(subset=critical_fields)
    validation_report['missing_after'] = df.isnull().sum().to_dict()
    # 2. Remove duplicates    validation_report['duplicates_found'] = df.duplicated(subset=['ticket_id']).sum()    df = df.drop_duplicates(subset=['ticket_id'], keep='first')
    # 3. Validate data types and ranges    df['created_at'] = pd.to_datetime(df['created_at'], errors='coerce')    df['resolved_at'] = pd.to_datetime(df['resolved_at'], errors='coerce')
    # 4. Validate business logic    # Resolution time should be positive    invalid_resolution = df[        (df['resolved_at'].notna()) &        (df['resolved_at'] < df['created_at'])    ]    validation_report['invalid_resolution_times'] = len(invalid_resolution)
    # Fix by setting to None    df.loc[df['resolved_at'] < df['created_at'], 'resolved_at'] = None
    # 5. Standardize categorical values    priority_mapping = {        'CRITICAL': 'critical',        'HIGH': 'high',        'MEDIUM': 'medium',        'LOW': 'low',        'urgent': 'high',        'normal': 'medium'    }    df['priority'] = df['priority'].replace(priority_mapping)
    # 6. Outlier detection for response times    if 'first_response_time' in df.columns:        q1 = df['first_response_time'].quantile(0.25)        q3 = df['first_response_time'].quantile(0.75)        iqr = q3 - q1        outlier_threshold = q3 + (3 * iqr)
        validation_report['response_time_outliers'] = (            df['first_response_time'] > outlier_threshold        ).sum()
    validation_report['final_row_count'] = len(df)
    return df, validation_report

4. GroupBy and Aggregation Operations

Multi-level Grouping for Team Analytics

python

# Complex groupby operations for team performancedef analyze_team_performance(tickets_df):    """    Perform multi-level grouping for team and agent analytics.
    Returns:        Multiple DataFrames with different aggregation levels    """    # Level 1: Team-level metrics    team_metrics = tickets_df.groupby('agent_team').agg({        'ticket_id': 'count',        'resolution_time': ['mean', 'median', 'std', 'min', 'max'],        'csat_score': ['mean', 'count'],        'resolution_sla_met': 'mean',        'reopened': 'sum'    })
    # Level 2: Team + Priority breakdown    team_priority_metrics = tickets_df.groupby(        ['agent_team', 'priority']    )['ticket_id'].count().unstack(fill_value=0)
    # Level 3: Team + Agent detailed metrics    team_agent_metrics = tickets_df.groupby(        ['agent_team', 'agent_id', 'agent_name']    ).agg({        'ticket_id': 'count',        'resolution_time': 'mean',        'csat_score': 'mean',        'resolution_sla_met': 'mean'    })
    # Calculate team rankings    team_metrics['rank_by_volume'] = team_metrics['ticket_id']['count'].rank(        ascending=False    )    team_metrics['rank_by_csat'] = team_metrics['csat_score']['mean'].rank(        ascending=False    )
    return team_metrics, team_priority_metrics, team_agent_metrics
# Custom aggregation functionsdef calculate_p95(series):    """Calculate 95th percentile."""    return series.quantile(0.95)
def calculate_p99(series):    """Calculate 99th percentile."""    return series.quantile(0.99)
# Advanced groupby with custom aggregationsdef detailed_response_time_analysis(tickets_df):    """Calculate detailed response time statistics."""    return tickets_df.groupby('priority').agg({        'first_response_time': [            'count',            'mean',            'median',            'std',            'min',            'max',            calculate_p95,            calculate_p99        ]    })

# Complex groupby operations for team performancedef analyze_team_performance(tickets_df):    """    Perform multi-level grouping for team and agent analytics.
    Returns:        Multiple DataFrames with different aggregation levels    """    # Level 1: Team-level metrics    team_metrics = tickets_df.groupby('agent_team').agg({        'ticket_id': 'count',        'resolution_time': ['mean', 'median', 'std', 'min', 'max'],        'csat_score': ['mean', 'count'],        'resolution_sla_met': 'mean',        'reopened': 'sum'    })
    # Level 2: Team + Priority breakdown    team_priority_metrics = tickets_df.groupby(        ['agent_team', 'priority']    )['ticket_id'].count().unstack(fill_value=0)
    # Level 3: Team + Agent detailed metrics    team_agent_metrics = tickets_df.groupby(        ['agent_team', 'agent_id', 'agent_name']    ).agg({        'ticket_id': 'count',        'resolution_time': 'mean',        'csat_score': 'mean',        'resolution_sla_met': 'mean'    })
    # Calculate team rankings    team_metrics['rank_by_volume'] = team_metrics['ticket_id']['count'].rank(        ascending=False    )    team_metrics['rank_by_csat'] = team_metrics['csat_score']['mean'].rank(        ascending=False    )
    return team_metrics, team_priority_metrics, team_agent_metrics
# Custom aggregation functionsdef calculate_p95(series):    """Calculate 95th percentile."""    return series.quantile(0.95)
def calculate_p99(series):    """Calculate 99th percentile."""    return series.quantile(0.99)
# Advanced groupby with custom aggregationsdef detailed_response_time_analysis(tickets_df):    """Calculate detailed response time statistics."""    return tickets_df.groupby('priority').agg({        'first_response_time': [            'count',            'mean',            'median',            'std',            'min',            'max',            calculate_p95,            calculate_p99        ]    })

5. Merging and Joining Data

Complex Join Operations

python

# Merge ticket, customer, and agent datadef create_comprehensive_dataset(tickets_df, customers_df, agents_df, csat_df):    """    Merge multiple data sources into comprehensive dataset.
    Args:        tickets_df: Ticket information        customers_df: Customer information        agents_df: Agent information        csat_df: Customer satisfaction scores
    Returns:        Merged DataFrame with all relevant information    """    # Step 1: Merge tickets with customers (left join - keep all tickets)    data = tickets_df.merge(        customers_df[['customer_id', 'name', 'tier', 'industry', 'contract_value']],        on='customer_id',        how='left',        suffixes=('', '_customer')    )
    # Step 2: Merge with agents (left join)    data = data.merge(        agents_df[['agent_id', 'name', 'team', 'hire_date', 'specialization']],        on='agent_id',        how='left',        suffixes=('', '_agent')    )
    # Step 3: Merge with CSAT scores (left join)    data = data.merge(        csat_df[['ticket_id', 'csat_score', 'csat_comment']],        on='ticket_id',        how='left'    )
    # Validate merge results    print(f"Original tickets: {len(tickets_df)}")    print(f"After merges: {len(data)}")    print(f"Customers matched: {data['name_customer'].notna().sum()}")    print(f"Agents matched: {data['name_agent'].notna().sum()}")    print(f"CSAT scores available: {data['csat_score'].notna().sum()}")
    return data
# Concat operations for combining time periodsdef combine_historical_data(data_sources):    """    Combine data from multiple time periods or sources.
    Args:        data_sources: List of DataFrames to combine
    Returns:        Combined DataFrame with source tracking    """    # Add source identifier to each DataFrame    for i, df in enumerate(data_sources):        df['source_batch'] = f'batch_{i+1}'
    # Concatenate vertically    combined = pd.concat(data_sources, ignore_index=True)
    # Remove duplicates (prefer newer data)    combined = combined.sort_values('updated_at', ascending=False)    combined = combined.drop_duplicates(subset=['ticket_id'], keep='first')
    return combined

# Merge ticket, customer, and agent datadef create_comprehensive_dataset(tickets_df, customers_df, agents_df, csat_df):    """    Merge multiple data sources into comprehensive dataset.
    Args:        tickets_df: Ticket information        customers_df: Customer information        agents_df: Agent information        csat_df: Customer satisfaction scores
    Returns:        Merged DataFrame with all relevant information    """    # Step 1: Merge tickets with customers (left join - keep all tickets)    data = tickets_df.merge(        customers_df[['customer_id', 'name', 'tier', 'industry', 'contract_value']],        on='customer_id',        how='left',        suffixes=('', '_customer')    )
    # Step 2: Merge with agents (left join)    data = data.merge(        agents_df[['agent_id', 'name', 'team', 'hire_date', 'specialization']],        on='agent_id',        how='left',        suffixes=('', '_agent')    )
    # Step 3: Merge with CSAT scores (left join)    data = data.merge(        csat_df[['ticket_id', 'csat_score', 'csat_comment']],        on='ticket_id',        how='left'    )
    # Validate merge results    print(f"Original tickets: {len(tickets_df)}")    print(f"After merges: {len(data)}")    print(f"Customers matched: {data['name_customer'].notna().sum()}")    print(f"Agents matched: {data['name_agent'].notna().sum()}")    print(f"CSAT scores available: {data['csat_score'].notna().sum()}")
    return data
# Concat operations for combining time periodsdef combine_historical_data(data_sources):    """    Combine data from multiple time periods or sources.
    Args:        data_sources: List of DataFrames to combine
    Returns:        Combined DataFrame with source tracking    """    # Add source identifier to each DataFrame    for i, df in enumerate(data_sources):        df['source_batch'] = f'batch_{i+1}'
    # Concatenate vertically    combined = pd.concat(data_sources, ignore_index=True)
    # Remove duplicates (prefer newer data)    combined = combined.sort_values('updated_at', ascending=False)    combined = combined.drop_duplicates(subset=['ticket_id'], keep='first')
    return combined

6. Time Series Analysis

Resampling and Rolling Windows

python

# Time series operations for support metricsdef calculate_rolling_metrics(tickets_df, window_days=7):    """    Calculate rolling window metrics for trend analysis.
    Args:        tickets_df: Ticket DataFrame with datetime index        window_days: Window size in days
    Returns:        DataFrame with rolling metrics    """    # Prepare time series    ts_data = tickets_df.set_index('created_at').sort_index()
    # Daily aggregation    daily_metrics = ts_data.resample('D').agg({        'ticket_id': 'count',        'resolution_time': 'mean',        'csat_score': 'mean',        'resolution_sla_met': 'mean'    }).rename(columns={'ticket_id': 'daily_tickets'})
    # Rolling window calculations    window = window_days    daily_metrics['tickets_rolling_avg'] = (        daily_metrics['daily_tickets'].rolling(window).mean()    )    daily_metrics['tickets_rolling_std'] = (        daily_metrics['daily_tickets'].rolling(window).std()    )
    # Calculate control limits for anomaly detection    daily_metrics['upper_control_limit'] = (        daily_metrics['tickets_rolling_avg'] +        (2 * daily_metrics['tickets_rolling_std'])    )    daily_metrics['lower_control_limit'] = (        daily_metrics['tickets_rolling_avg'] -        (2 * daily_metrics['tickets_rolling_std'])    ).clip(lower=0)
    # Flag anomalies    daily_metrics['is_anomaly'] = (        (daily_metrics['daily_tickets'] > daily_metrics['upper_control_limit']) |        (daily_metrics['daily_tickets'] < daily_metrics['lower_control_limit'])    )
    return daily_metrics
# Business day calculationsdef calculate_business_day_metrics(tickets_df):    """Calculate metrics excluding weekends and holidays."""    from pandas.tseries.offsets import CustomBusinessDay
    # Define US holidays (customize as needed)    us_bd = CustomBusinessDay()
    # Filter to business days only    tickets_df['is_business_day'] = tickets_df['created_at'].dt.dayofweek < 5    business_tickets = tickets_df[tickets_df['is_business_day']]
    # Calculate business day metrics    bd_metrics = business_tickets.groupby(        business_tickets['created_at'].dt.date    ).agg({        'ticket_id': 'count',        'resolution_time': 'mean'    })
    return bd_metrics

# Time series operations for support metricsdef calculate_rolling_metrics(tickets_df, window_days=7):    """    Calculate rolling window metrics for trend analysis.
    Args:        tickets_df: Ticket DataFrame with datetime index        window_days: Window size in days
    Returns:        DataFrame with rolling metrics    """    # Prepare time series    ts_data = tickets_df.set_index('created_at').sort_index()
    # Daily aggregation    daily_metrics = ts_data.resample('D').agg({        'ticket_id': 'count',        'resolution_time': 'mean',        'csat_score': 'mean',        'resolution_sla_met': 'mean'    }).rename(columns={'ticket_id': 'daily_tickets'})
    # Rolling window calculations    window = window_days    daily_metrics['tickets_rolling_avg'] = (        daily_metrics['daily_tickets'].rolling(window).mean()    )    daily_metrics['tickets_rolling_std'] = (        daily_metrics['daily_tickets'].rolling(window).std()    )
    # Calculate control limits for anomaly detection    daily_metrics['upper_control_limit'] = (        daily_metrics['tickets_rolling_avg'] +        (2 * daily_metrics['tickets_rolling_std'])    )    daily_metrics['lower_control_limit'] = (        daily_metrics['tickets_rolling_avg'] -        (2 * daily_metrics['tickets_rolling_std'])    ).clip(lower=0)
    # Flag anomalies    daily_metrics['is_anomaly'] = (        (daily_metrics['daily_tickets'] > daily_metrics['upper_control_limit']) |        (daily_metrics['daily_tickets'] < daily_metrics['lower_control_limit'])    )
    return daily_metrics
# Business day calculationsdef calculate_business_day_metrics(tickets_df):    """Calculate metrics excluding weekends and holidays."""    from pandas.tseries.offsets import CustomBusinessDay
    # Define US holidays (customize as needed)    us_bd = CustomBusinessDay()
    # Filter to business days only    tickets_df['is_business_day'] = tickets_df['created_at'].dt.dayofweek < 5    business_tickets = tickets_df[tickets_df['is_business_day']]
    # Calculate business day metrics    bd_metrics = business_tickets.groupby(        business_tickets['created_at'].dt.date    ).agg({        'ticket_id': 'count',        'resolution_time': 'mean'    })
    return bd_metrics

7. Pivot Tables and Cross-tabulation

Creating Management Reports

python

# Pivot tables for executive reportingdef create_executive_dashboard_data(tickets_df):    """    Create pivot tables for executive dashboard.
    Returns:        Dictionary of pivot tables for different views    """    dashboards = {}
    # 1. Tickets by Team and Priority    dashboards['team_priority'] = pd.pivot_table(        tickets_df,        values='ticket_id',        index='agent_team',        columns='priority',        aggfunc='count',        fill_value=0,        margins=True,        margins_name='Total'    )
    # 2. Average Resolution Time by Team and Channel    dashboards['resolution_by_team_channel'] = pd.pivot_table(        tickets_df,        values='resolution_time',        index='agent_team',        columns='channel',        aggfunc='mean',        fill_value=0    )
    # 3. SLA Compliance by Priority and Week    tickets_df['week'] = tickets_df['created_at'].dt.to_period('W')    dashboards['sla_compliance_weekly'] = pd.pivot_table(        tickets_df,        values='resolution_sla_met',        index='week',        columns='priority',        aggfunc='mean',        fill_value=0    )
    # 4. CSAT by Agent and Customer Tier    dashboards['csat_by_agent_tier'] = pd.pivot_table(        tickets_df,        values='csat_score',        index='agent_name',        columns='customer_tier',        aggfunc=['mean', 'count'],        fill_value=0    )
    # 5. Ticket Volume Heatmap (Day of Week vs Hour)    tickets_df['day_of_week'] = tickets_df['created_at'].dt.day_name()    tickets_df['hour'] = tickets_df['created_at'].dt.hour    dashboards['volume_heatmap'] = pd.pivot_table(        tickets_df,        values='ticket_id',        index='day_of_week',        columns='hour',        aggfunc='count',        fill_value=0    )
    return dashboards
# Cross-tabulation for category analysisdef analyze_category_distribution(tickets_df):    """Create cross-tabs for ticket category analysis."""    # Category vs Priority    category_priority = pd.crosstab(        tickets_df['category'],        tickets_df['priority'],        normalize='index',  # Row percentages        margins=True    )
    # Category vs Team (with counts)    category_team = pd.crosstab(        tickets_df['category'],        tickets_df['agent_team'],        margins=True    )
    return category_priority, category_team

# Pivot tables for executive reportingdef create_executive_dashboard_data(tickets_df):    """    Create pivot tables for executive dashboard.
    Returns:        Dictionary of pivot tables for different views    """    dashboards = {}
    # 1. Tickets by Team and Priority    dashboards['team_priority'] = pd.pivot_table(        tickets_df,        values='ticket_id',        index='agent_team',        columns='priority',        aggfunc='count',        fill_value=0,        margins=True,        margins_name='Total'    )
    # 2. Average Resolution Time by Team and Channel    dashboards['resolution_by_team_channel'] = pd.pivot_table(        tickets_df,        values='resolution_time',        index='agent_team',        columns='channel',        aggfunc='mean',        fill_value=0    )
    # 3. SLA Compliance by Priority and Week    tickets_df['week'] = tickets_df['created_at'].dt.to_period('W')    dashboards['sla_compliance_weekly'] = pd.pivot_table(        tickets_df,        values='resolution_sla_met',        index='week',        columns='priority',        aggfunc='mean',        fill_value=0    )
    # 4. CSAT by Agent and Customer Tier    dashboards['csat_by_agent_tier'] = pd.pivot_table(        tickets_df,        values='csat_score',        index='agent_name',        columns='customer_tier',        aggfunc=['mean', 'count'],        fill_value=0    )
    # 5. Ticket Volume Heatmap (Day of Week vs Hour)    tickets_df['day_of_week'] = tickets_df['created_at'].dt.day_name()    tickets_df['hour'] = tickets_df['created_at'].dt.hour    dashboards['volume_heatmap'] = pd.pivot_table(        tickets_df,        values='ticket_id',        index='day_of_week',        columns='hour',        aggfunc='count',        fill_value=0    )
    return dashboards
# Cross-tabulation for category analysisdef analyze_category_distribution(tickets_df):    """Create cross-tabs for ticket category analysis."""    # Category vs Priority    category_priority = pd.crosstab(        tickets_df['category'],        tickets_df['priority'],        normalize='index',  # Row percentages        margins=True    )
    # Category vs Team (with counts)    category_team = pd.crosstab(        tickets_df['category'],        tickets_df['agent_team'],        margins=True    )
    return category_priority, category_team

8. Data Export and Reporting

Export to Multiple Formats

python

# Export data for stakeholder reportingdef export_monthly_report(tickets_df, output_dir, month):    """    Export comprehensive monthly report in multiple formats.
    Args:        tickets_df: Ticket data for the month        output_dir: Directory to save reports        month: Month identifier (e.g., '2024-01')    """    import os    from datetime import datetime
    # 1. Export to Excel with multiple sheets    excel_path = os.path.join(output_dir, f'support_report_{month}.xlsx')
    with pd.ExcelWriter(excel_path, engine='openpyxl') as writer:        # Summary sheet        summary = tickets_df.groupby('priority').agg({            'ticket_id': 'count',            'resolution_time': ['mean', 'median'],            'csat_score': 'mean',            'resolution_sla_met': 'mean'        })        summary.to_excel(writer, sheet_name='Summary')
        # Team metrics sheet        team_metrics = tickets_df.groupby('agent_team').agg({            'ticket_id': 'count',            'resolution_time': 'mean',            'csat_score': 'mean'        })        team_metrics.to_excel(writer, sheet_name='Team Metrics')
        # Raw data sheet (limited to first 10000 rows)        tickets_df.head(10000).to_excel(            writer,            sheet_name='Raw Data',            index=False        )
    # 2. Export to CSV for data analysis    csv_path = os.path.join(output_dir, f'tickets_{month}.csv')    tickets_df.to_csv(csv_path, index=False, encoding='utf-8')
    # 3. Export to JSON for API consumption    json_path = os.path.join(output_dir, f'metrics_{month}.json')    metrics = {        'total_tickets': int(tickets_df['ticket_id'].count()),        'avg_resolution_time': float(tickets_df['resolution_time'].mean()),        'sla_compliance': float(tickets_df['resolution_sla_met'].mean()),        'avg_csat': float(tickets_df['csat_score'].mean()),        'by_priority': tickets_df.groupby('priority')['ticket_id'].count().to_dict()    }
    with open(json_path, 'w') as f:        import json        json.dump(metrics, f, indent=2, default=str)
    # 4. Export to Parquet for efficient storage    parquet_path = os.path.join(output_dir, f'tickets_{month}.parquet')    tickets_df.to_parquet(parquet_path, compression='snappy', index=False)
    print(f"Reports exported to {output_dir}")    print(f"  - Excel: {excel_path}")    print(f"  - CSV: {csv_path}")    print(f"  - JSON: {json_path}")    print(f"  - Parquet: {parquet_path}")
# Format DataFrames for presentationdef format_for_presentation(df):    """Format DataFrame for stakeholder presentation."""    # Round numeric columns    numeric_cols = df.select_dtypes(include=['float64', 'float32']).columns    df[numeric_cols] = df[numeric_cols].round(2)
    # Format percentages    percentage_cols = [col for col in df.columns if 'rate' in col or 'pct' in col]    for col in percentage_cols:        df[col] = df[col].apply(lambda x: f"{x*100:.1f}%")
    # Format currency if applicable    currency_cols = [col for col in df.columns if 'revenue' in col or 'value' in col]    for col in currency_cols:        df[col] = df[col].apply(lambda x: f"${x:,.2f}")
    return df

# Export data for stakeholder reportingdef export_monthly_report(tickets_df, output_dir, month):    """    Export comprehensive monthly report in multiple formats.
    Args:        tickets_df: Ticket data for the month        output_dir: Directory to save reports        month: Month identifier (e.g., '2024-01')    """    import os    from datetime import datetime
    # 1. Export to Excel with multiple sheets    excel_path = os.path.join(output_dir, f'support_report_{month}.xlsx')
    with pd.ExcelWriter(excel_path, engine='openpyxl') as writer:        # Summary sheet        summary = tickets_df.groupby('priority').agg({            'ticket_id': 'count',            'resolution_time': ['mean', 'median'],            'csat_score': 'mean',            'resolution_sla_met': 'mean'        })        summary.to_excel(writer, sheet_name='Summary')
        # Team metrics sheet        team_metrics = tickets_df.groupby('agent_team').agg({            'ticket_id': 'count',            'resolution_time': 'mean',            'csat_score': 'mean'        })        team_metrics.to_excel(writer, sheet_name='Team Metrics')
        # Raw data sheet (limited to first 10000 rows)        tickets_df.head(10000).to_excel(            writer,            sheet_name='Raw Data',            index=False        )
    # 2. Export to CSV for data analysis    csv_path = os.path.join(output_dir, f'tickets_{month}.csv')    tickets_df.to_csv(csv_path, index=False, encoding='utf-8')
    # 3. Export to JSON for API consumption    json_path = os.path.join(output_dir, f'metrics_{month}.json')    metrics = {        'total_tickets': int(tickets_df['ticket_id'].count()),        'avg_resolution_time': float(tickets_df['resolution_time'].mean()),        'sla_compliance': float(tickets_df['resolution_sla_met'].mean()),        'avg_csat': float(tickets_df['csat_score'].mean()),        'by_priority': tickets_df.groupby('priority')['ticket_id'].count().to_dict()    }
    with open(json_path, 'w') as f:        import json        json.dump(metrics, f, indent=2, default=str)
    # 4. Export to Parquet for efficient storage    parquet_path = os.path.join(output_dir, f'tickets_{month}.parquet')    tickets_df.to_parquet(parquet_path, compression='snappy', index=False)
    print(f"Reports exported to {output_dir}")    print(f"  - Excel: {excel_path}")    print(f"  - CSV: {csv_path}")    print(f"  - JSON: {json_path}")    print(f"  - Parquet: {parquet_path}")
# Format DataFrames for presentationdef format_for_presentation(df):    """Format DataFrame for stakeholder presentation."""    # Round numeric columns    numeric_cols = df.select_dtypes(include=['float64', 'float32']).columns    df[numeric_cols] = df[numeric_cols].round(2)
    # Format percentages    percentage_cols = [col for col in df.columns if 'rate' in col or 'pct' in col]    for col in percentage_cols:        df[col] = df[col].apply(lambda x: f"{x*100:.1f}%")
    # Format currency if applicable    currency_cols = [col for col in df.columns if 'revenue' in col or 'value' in col]    for col in currency_cols:        df[col] = df[col].apply(lambda x: f"${x:,.2f}")
    return df

9. Performance Optimization

Memory Optimization Techniques

python

# Optimize DataFrame memory usagedef optimize_dataframe_memory(df):    """    Reduce DataFrame memory footprint.
    Args:        df: DataFrame to optimize
    Returns:        Optimized DataFrame with memory usage report    """    initial_memory = df.memory_usage(deep=True).sum() / 1024**2
    # Optimize integer columns    int_cols = df.select_dtypes(include=['int64']).columns    for col in int_cols:        col_min = df[col].min()        col_max = df[col].max()
        if col_min >= 0:            if col_max < 255:                df[col] = df[col].astype('uint8')            elif col_max < 65535:                df[col] = df[col].astype('uint16')            elif col_max < 4294967295:                df[col] = df[col].astype('uint32')        else:            if col_min > -128 and col_max < 127:                df[col] = df[col].astype('int8')            elif col_min > -32768 and col_max < 32767:                df[col] = df[col].astype('int16')            elif col_min > -2147483648 and col_max < 2147483647:                df[col] = df[col].astype('int32')
    # Optimize float columns    float_cols = df.select_dtypes(include=['float64']).columns    df[float_cols] = df[float_cols].astype('float32')
    # Convert object columns to category if cardinality is low    object_cols = df.select_dtypes(include=['object']).columns    for col in object_cols:        num_unique = df[col].nunique()        num_total = len(df[col])
        if num_unique / num_total < 0.5:  # Less than 50% unique values            df[col] = df[col].astype('category')
    final_memory = df.memory_usage(deep=True).sum() / 1024**2    reduction = (1 - final_memory/initial_memory) * 100
    print(f"Memory usage reduced from {initial_memory:.2f} MB to {final_memory:.2f} MB")    print(f"Reduction: {reduction:.1f}%")
    return df
# Chunked processing for large datasetsdef process_large_dataset_in_chunks(file_path, chunk_size=10000):    """    Process large CSV files in chunks to avoid memory issues.
    Args:        file_path: Path to large CSV file        chunk_size: Number of rows per chunk
    Returns:        Aggregated results from all chunks    """    # Initialize aggregation containers    total_tickets = 0    priority_counts = {}
    # Process in chunks    for chunk in pd.read_csv(file_path, chunksize=chunk_size):        # Process each chunk        chunk = clean_ticket_data(chunk)[0]
        # Aggregate metrics        total_tickets += len(chunk)
        chunk_priority = chunk['priority'].value_counts().to_dict()        for priority, count in chunk_priority.items():            priority_counts[priority] = priority_counts.get(priority, 0) + count
    return {        'total_tickets': total_tickets,        'priority_distribution': priority_counts    }

# Optimize DataFrame memory usagedef optimize_dataframe_memory(df):    """    Reduce DataFrame memory footprint.
    Args:        df: DataFrame to optimize
    Returns:        Optimized DataFrame with memory usage report    """    initial_memory = df.memory_usage(deep=True).sum() / 1024**2
    # Optimize integer columns    int_cols = df.select_dtypes(include=['int64']).columns    for col in int_cols:        col_min = df[col].min()        col_max = df[col].max()
        if col_min >= 0:            if col_max < 255:                df[col] = df[col].astype('uint8')            elif col_max < 65535:                df[col] = df[col].astype('uint16')            elif col_max < 4294967295:                df[col] = df[col].astype('uint32')        else:            if col_min > -128 and col_max < 127:                df[col] = df[col].astype('int8')            elif col_min > -32768 and col_max < 32767:                df[col] = df[col].astype('int16')            elif col_min > -2147483648 and col_max < 2147483647:                df[col] = df[col].astype('int32')
    # Optimize float columns    float_cols = df.select_dtypes(include=['float64']).columns    df[float_cols] = df[float_cols].astype('float32')
    # Convert object columns to category if cardinality is low    object_cols = df.select_dtypes(include=['object']).columns    for col in object_cols:        num_unique = df[col].nunique()        num_total = len(df[col])
        if num_unique / num_total < 0.5:  # Less than 50% unique values            df[col] = df[col].astype('category')
    final_memory = df.memory_usage(deep=True).sum() / 1024**2    reduction = (1 - final_memory/initial_memory) * 100
    print(f"Memory usage reduced from {initial_memory:.2f} MB to {final_memory:.2f} MB")    print(f"Reduction: {reduction:.1f}%")
    return df
# Chunked processing for large datasetsdef process_large_dataset_in_chunks(file_path, chunk_size=10000):    """    Process large CSV files in chunks to avoid memory issues.
    Args:        file_path: Path to large CSV file        chunk_size: Number of rows per chunk
    Returns:        Aggregated results from all chunks    """    # Initialize aggregation containers    total_tickets = 0    priority_counts = {}
    # Process in chunks    for chunk in pd.read_csv(file_path, chunksize=chunk_size):        # Process each chunk        chunk = clean_ticket_data(chunk)[0]
        # Aggregate metrics        total_tickets += len(chunk)
        chunk_priority = chunk['priority'].value_counts().to_dict()        for priority, count in chunk_priority.items():            priority_counts[priority] = priority_counts.get(priority, 0) + count
    return {        'total_tickets': total_tickets,        'priority_distribution': priority_counts    }

10. Data Quality and Validation

Validation Framework

python

# Comprehensive data quality checksclass DataQualityValidator:    """Validate data quality for support ticket datasets."""
    def __init__(self, df):        self.df = df        self.issues = []
    def check_required_columns(self, required_cols):        """Ensure all required columns are present."""        missing = set(required_cols) - set(self.df.columns)        if missing:            self.issues.append(f"Missing required columns: {missing}")        return len(missing) == 0
    def check_null_percentages(self, max_null_pct=0.1):        """Check if null percentage exceeds threshold."""        null_pct = self.df.isnull().sum() / len(self.df)        excessive_nulls = null_pct[null_pct > max_null_pct]
        if not excessive_nulls.empty:            self.issues.append(                f"Columns with >{max_null_pct*100}% nulls: {excessive_nulls.to_dict()}"            )        return excessive_nulls.empty
    def check_duplicate_ids(self, id_column='ticket_id'):        """Check for duplicate ticket IDs."""        duplicates = self.df[id_column].duplicated().sum()        if duplicates > 0:            self.issues.append(f"Found {duplicates} duplicate ticket IDs")        return duplicates == 0
    def check_date_logic(self):        """Validate date field logic."""        issues_found = 0
        # Created date should be before resolved date        if 'created_at' in self.df.columns and 'resolved_at' in self.df.columns:            invalid = (                self.df['resolved_at'].notna() &                (self.df['resolved_at'] < self.df['created_at'])            ).sum()
            if invalid > 0:                self.issues.append(                    f"Found {invalid} tickets with resolved_at before created_at"                )                issues_found += invalid
        # Check for future dates        now = pd.Timestamp.now()        for date_col in ['created_at', 'resolved_at', 'first_response_at']:            if date_col in self.df.columns:                future_dates = (self.df[date_col] > now).sum()                if future_dates > 0:                    self.issues.append(                        f"Found {future_dates} future dates in {date_col}"                    )                    issues_found += future_dates
        return issues_found == 0
    def check_value_ranges(self, range_checks):        """        Check if values are within expected ranges.
        Args:            range_checks: Dict with column: (min, max) pairs        """        for col, (min_val, max_val) in range_checks.items():            if col in self.df.columns:                out_of_range = (                    (self.df[col] < min_val) | (self.df[col] > max_val)                ).sum()
                if out_of_range > 0:                    self.issues.append(                        f"{col}: {out_of_range} values outside range [{min_val}, {max_val}]"                    )
    def generate_report(self):        """Generate comprehensive validation report."""        return {            'total_rows': len(self.df),            'total_columns': len(self.df.columns),            'issues_found': len(self.issues),            'issues': self.issues,            'memory_usage_mb': self.df.memory_usage(deep=True).sum() / 1024**2,            'null_summary': self.df.isnull().sum().to_dict()        }

# Comprehensive data quality checksclass DataQualityValidator:    """Validate data quality for support ticket datasets."""
    def __init__(self, df):        self.df = df        self.issues = []
    def check_required_columns(self, required_cols):        """Ensure all required columns are present."""        missing = set(required_cols) - set(self.df.columns)        if missing:            self.issues.append(f"Missing required columns: {missing}")        return len(missing) == 0
    def check_null_percentages(self, max_null_pct=0.1):        """Check if null percentage exceeds threshold."""        null_pct = self.df.isnull().sum() / len(self.df)        excessive_nulls = null_pct[null_pct > max_null_pct]
        if not excessive_nulls.empty:            self.issues.append(                f"Columns with >{max_null_pct*100}% nulls: {excessive_nulls.to_dict()}"            )        return excessive_nulls.empty
    def check_duplicate_ids(self, id_column='ticket_id'):        """Check for duplicate ticket IDs."""        duplicates = self.df[id_column].duplicated().sum()        if duplicates > 0:            self.issues.append(f"Found {duplicates} duplicate ticket IDs")        return duplicates == 0
    def check_date_logic(self):        """Validate date field logic."""        issues_found = 0
        # Created date should be before resolved date        if 'created_at' in self.df.columns and 'resolved_at' in self.df.columns:            invalid = (                self.df['resolved_at'].notna() &                (self.df['resolved_at'] < self.df['created_at'])            ).sum()
            if invalid > 0:                self.issues.append(                    f"Found {invalid} tickets with resolved_at before created_at"                )                issues_found += invalid
        # Check for future dates        now = pd.Timestamp.now()        for date_col in ['created_at', 'resolved_at', 'first_response_at']:            if date_col in self.df.columns:                future_dates = (self.df[date_col] > now).sum()                if future_dates > 0:                    self.issues.append(                        f"Found {future_dates} future dates in {date_col}"                    )                    issues_found += future_dates
        return issues_found == 0
    def check_value_ranges(self, range_checks):        """        Check if values are within expected ranges.
        Args:            range_checks: Dict with column: (min, max) pairs        """        for col, (min_val, max_val) in range_checks.items():            if col in self.df.columns:                out_of_range = (                    (self.df[col] < min_val) | (self.df[col] > max_val)                ).sum()
                if out_of_range > 0:                    self.issues.append(                        f"{col}: {out_of_range} values outside range [{min_val}, {max_val}]"                    )
    def generate_report(self):        """Generate comprehensive validation report."""        return {            'total_rows': len(self.df),            'total_columns': len(self.df.columns),            'issues_found': len(self.issues),            'issues': self.issues,            'memory_usage_mb': self.df.memory_usage(deep=True).sum() / 1024**2,            'null_summary': self.df.isnull().sum().to_dict()        }

11. Testing Pandas Operations

Unit Testing with pytest

python

# pytest fixtures and tests for data operationsimport pytestimport pandas as pdimport numpy as npfrom datetime import datetime, timedelta
@pytest.fixturedef sample_ticket_data():    """Create sample ticket data for testing."""    np.random.seed(42)    n_tickets = 100
    return pd.DataFrame({        'ticket_id': range(1, n_tickets + 1),        'created_at': pd.date_range('2024-01-01', periods=n_tickets, freq='H'),        'priority': np.random.choice(['low', 'medium', 'high'], n_tickets),        'status': np.random.choice(['open', 'in_progress', 'resolved'], n_tickets),        'agent_id': np.random.choice(['A001', 'A002', 'A003'], n_tickets),        'customer_id': np.random.choice(['C001', 'C002', 'C003'], n_tickets)    })
def test_ticket_data_shape(sample_ticket_data):    """Test that sample data has expected shape."""    assert sample_ticket_data.shape == (100, 6)    assert 'ticket_id' in sample_ticket_data.columns
def test_sla_calculation():    """Test SLA calculation logic."""    df = pd.DataFrame({        'ticket_id': [1, 2],        'created_at': pd.to_datetime(['2024-01-01 10:00', '2024-01-01 11:00']),        'first_response_at': pd.to_datetime(['2024-01-01 11:00', '2024-01-01 14:00']),        'sla_target_hours': [2, 2]    })
    df['response_time_hours'] = (        df['first_response_at'] - df['created_at']    ).dt.total_seconds() / 3600
    df['sla_met'] = df['response_time_hours'] <= df['sla_target_hours']
    assert df.loc[0, 'sla_met'] == True    assert df.loc[1, 'sla_met'] == False
def test_data_cleaning_removes_nulls(sample_ticket_data):    """Test that data cleaning handles null values."""    # Add some null values    df = sample_ticket_data.copy()    df.loc[0, 'agent_id'] = None    df.loc[1, 'customer_id'] = None
    # Apply cleaning    cleaned, report = clean_ticket_data(df)
    # Verify nulls were handled    assert 'UNASSIGNED' in cleaned['agent_id'].values    assert report['missing_before']['agent_id'] == 1
def test_groupby_aggregation(sample_ticket_data):    """Test groupby aggregation produces correct results."""    result = sample_ticket_data.groupby('priority')['ticket_id'].count()
    assert result.sum() == 100    assert all(priority in result.index for priority in ['low', 'medium', 'high'])

# pytest fixtures and tests for data operationsimport pytestimport pandas as pdimport numpy as npfrom datetime import datetime, timedelta
@pytest.fixturedef sample_ticket_data():    """Create sample ticket data for testing."""    np.random.seed(42)    n_tickets = 100
    return pd.DataFrame({        'ticket_id': range(1, n_tickets + 1),        'created_at': pd.date_range('2024-01-01', periods=n_tickets, freq='H'),        'priority': np.random.choice(['low', 'medium', 'high'], n_tickets),        'status': np.random.choice(['open', 'in_progress', 'resolved'], n_tickets),        'agent_id': np.random.choice(['A001', 'A002', 'A003'], n_tickets),        'customer_id': np.random.choice(['C001', 'C002', 'C003'], n_tickets)    })
def test_ticket_data_shape(sample_ticket_data):    """Test that sample data has expected shape."""    assert sample_ticket_data.shape == (100, 6)    assert 'ticket_id' in sample_ticket_data.columns
def test_sla_calculation():    """Test SLA calculation logic."""    df = pd.DataFrame({        'ticket_id': [1, 2],        'created_at': pd.to_datetime(['2024-01-01 10:00', '2024-01-01 11:00']),        'first_response_at': pd.to_datetime(['2024-01-01 11:00', '2024-01-01 14:00']),        'sla_target_hours': [2, 2]    })
    df['response_time_hours'] = (        df['first_response_at'] - df['created_at']    ).dt.total_seconds() / 3600
    df['sla_met'] = df['response_time_hours'] <= df['sla_target_hours']
    assert df.loc[0, 'sla_met'] == True    assert df.loc[1, 'sla_met'] == False
def test_data_cleaning_removes_nulls(sample_ticket_data):    """Test that data cleaning handles null values."""    # Add some null values    df = sample_ticket_data.copy()    df.loc[0, 'agent_id'] = None    df.loc[1, 'customer_id'] = None
    # Apply cleaning    cleaned, report = clean_ticket_data(df)
    # Verify nulls were handled    assert 'UNASSIGNED' in cleaned['agent_id'].values    assert report['missing_before']['agent_id'] == 1
def test_groupby_aggregation(sample_ticket_data):    """Test groupby aggregation produces correct results."""    result = sample_ticket_data.groupby('priority')['ticket_id'].count()
    assert result.sum() == 100    assert all(priority in result.index for priority in ['low', 'medium', 'high'])

Best Practices

1. Always Use Vectorized Operations

Avoid Python loops when working with pandas. Use vectorized operations for better performance:

python

# Bad - slow loopfor idx, row in df.iterrows():    df.at[idx, 'new_col'] = row['col1'] * row['col2']
# Good - vectorized operationdf['new_col'] = df['col1'] * df['col2']

# Bad - slow loopfor idx, row in df.iterrows():    df.at[idx, 'new_col'] = row['col1'] * row['col2']
# Good - vectorized operationdf['new_col'] = df['col1'] * df['col2']

2. Use Method Chaining for Readability

python

result = (    df    .query('status == "resolved"')    .groupby('agent_id')    .agg({'resolution_time': 'mean'})    .sort_values('resolution_time')    .head(10))

result = (    df    .query('status == "resolved"')    .groupby('agent_id')    .agg({'resolution_time': 'mean'})    .sort_values('resolution_time')    .head(10))

3. Optimize Data Types Early

Convert to appropriate data types immediately after loading to save memory and improve performance.

4. Use `.loc[]` and `.iloc[]` Explicitly

Avoid chained indexing which can lead to SettingWithCopyWarning and unexpected behavior.

5. Handle Time Zones Properly

Always work with timezone-aware datetime objects for support data across regions.

6. Document Data Transformations

Add comments explaining business logic in complex transformations.

7. Validate Data at Every Step

Implement validation checks after major transformations to catch issues early.

8. Use Appropriate Index Types

Set meaningful indices (datetime for time series, ticket_id for lookups) to improve performance.

Common Pitfalls to Avoid

SettingWithCopyWarning: Always use .loc[] for setting values
Memory Issues: Process large datasets in chunks or optimize data types
Lost Index: Remember that many operations return new DataFrames without preserving the index
Implicit Type Conversion: Be explicit about data type conversions
Ambiguous Truth Values: Use .any() or .all() when evaluating Series in boolean context
Mixing Time Zones: Ensure consistent timezone handling across datetime columns

Integration Patterns

With pytest for Testing

Always write tests for data transformation functions using pytest fixtures and parametrize decorators.

With SQLAlchemy for Database Operations

Use SQLAlchemy engines for database connections and leverage pandas' read_sql and to_sql methods.

With PostgreSQL for Data Persistence

Store processed metrics in PostgreSQL for historical tracking and dashboard consumption.

With Excel for Stakeholder Reports

Use pd.ExcelWriter with the openpyxl engine for creating multi-sheet Excel reports.

Performance Guidelines

Use categorical data types for columns with low cardinality (< 50% unique values)
Process in chunks when dataset exceeds available memory
Use query() method for complex filtering (compiles to optimized code)
Avoid apply() when possible - use vectorized operations instead
Use eval() for complex expressions on large DataFrames
Set appropriate dtypes when reading CSV files to avoid inference overhead
Use copy() judiciously - only when you need true copies to avoid memory waste

Conclusion

You are now equipped to handle comprehensive data analysis and manipulation tasks for customer support operations using pandas. Apply these patterns to analyze ticket data, track SLA compliance, measure agent performance, and generate actionable insights for support teams. Always prioritize data quality, performance optimization, and clear, maintainable code.

Source and attribution

Source:manutej/luxor-claude-marketplaceinplugins/luxor-database-pro/skills/pandasat commit1861cc9

License: No license

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Pandas

pandas - Data Analysis and Manipulation for Customer Support

Overview

Core Competencies

1. DataFrame Operations and Data Structures

2. Customer Support Analytics Patterns

3. Data Integration and ETL

4. GroupBy and Aggregation Operations

5. Merging and Joining Data

6. Time Series Analysis

7. Pivot Tables and Cross-tabulation

8. Data Export and Reporting

9. Performance Optimization

10. Data Quality and Validation

11. Testing Pandas Operations

Best Practices

1. Always Use Vectorized Operations

2. Use Method Chaining for Readability

3. Optimize Data Types Early

4. Use .loc[] and .iloc[] Explicitly

5. Handle Time Zones Properly

6. Document Data Transformations

7. Validate Data at Every Step

8. Use Appropriate Index Types

Common Pitfalls to Avoid

Integration Patterns

With pytest for Testing

With SQLAlchemy for Database Operations

With PostgreSQL for Data Persistence

With Excel for Stakeholder Reports

Performance Guidelines

Conclusion

Source and attribution

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4. Use `.loc[]` and `.iloc[]` Explicitly