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

Calculate SLA compliance for support tickets

def 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

Time series analysis of ticket volume

def 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

Calculate comprehensive agent performance metrics

def 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

Load and save data to PostgreSQL

from sqlalchemy import create_engine, text import 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

Comprehensive data cleaning for support data

def 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

Complex groupby operations for team performance

def 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 functions

def 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 aggregations

def 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

Merge ticket, customer, and agent data

def 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 periods

def 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

Time series operations for support metrics

def 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 calculations

def 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

Pivot tables for executive reporting

def 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 analysis

def 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

Export data for stakeholder reporting

def 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 presentation

def 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

Optimize DataFrame memory usage

def 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 datasets

def 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

Comprehensive data quality checks

class 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

pytest fixtures and tests for data operations

import pytest import pandas as pd import numpy as np from datetime import datetime, timedelta

@pytest.fixture def 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:

Bad - slow loop

for idx, row in df.iterrows(): df.at[idx, 'new_col'] = row['col1'] * row['col2']

Good - vectorized operation

df['new_col'] = df['col1'] * df['col2']

2. Use Method Chaining for Readability

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.