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pandas

Analyze customer-support ticket data for SLA compliance and agent performance.

61|15|Updated Oct 18, 2025
Search Tags:#reporting#pandas#data-analysis#etl#customer-support#ticket-analytics#sla-tracking
One-click install
npx skills add https://github.com/manutej/luxor-claude-marketplace --skill pandas
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pandas
Source: https://github.com/manutej/luxor-claude-marketplace/tree/main/plugins/luxor-database-pro/skills/pandas
Command: npx skills add https://github.com/manutej/luxor-claude-marketplace --skill pandas

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas>=2.2.0, sqlalchemy>=2.0.0, psycopg2-binary>=2.9.0, numpy>=1.26.0, openpyxl>=3.1.0, pytest>=8.0.0, pyarrow>=15.0.0.

What problem does it solve?

This Skill enables data analysis and manipulation for customer support operations, turning raw ticket data into actionable insights for SLA tracking, agent performance, and trend analysis.

Core Features & Use Cases

  • Data Loading & Cleaning: Import tickets, CSAT scores, and customer data; clean and validate data quality.
  • SLA Tracking & Metrics: Compute response times, resolution times, and SLA compliance rates.
  • Agent & Team Analytics: Aggregate by team, agent, channel, and priority to measure performance.
  • Time Series & Trends: Analyze ticket volume trends and seasonality.
  • Automated Reporting: Prepare dashboards or export for stakeholders (Excel/CSV/Parquet).

Quick Start

Run a pandas script to compute average response time from a tickets dataframe and export a summary to CSV.

Frequently Asked Questions about pandas

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I calculate SLA compliance and response times from customer support ticket data?▼

SLA compliance and response times are computed by analyzing ticket timestamps in pandas DataFrames. Load your ticket data from PostgreSQL or CSV, calculate time deltas between ticket creation and agent response, then aggregate by SLA thresholds to produce compliance rates and average response metrics.

Can I analyze ticket data stored in PostgreSQL and export results to Excel or CSV?▼

Yes. Use SQLAlchemy to connect to PostgreSQL, load ticket data into pandas DataFrames, perform your analysis, then export results to Excel, CSV, JSON, or Parquet formats. The Skill handles memory-efficient operations and multi-format output for stakeholder reporting.

How do I track agent performance and team metrics from support tickets?▼

Aggregate ticket data by agent, team, and channel using pandas groupby operations to measure metrics like resolution time, ticket volume, and CSAT scores. Compare performance across dimensions to identify trends and coaching opportunities.

What's the best way to identify ticket volume trends and seasonality patterns?▼

Time-series analysis on ticket data reveals volume trends and seasonality. Parse ticket timestamps, resample to daily or weekly intervals, and visualize trends to forecast demand, plan staffing, and detect anomalies in support workload.

Do I need to validate and clean support ticket data before analysis?▼

Yes. Data validation and cleaning are essential preprocessing steps. Handle missing CSAT scores, deduplicate ticket records, standardize date formats, and remove invalid entries to ensure accurate SLA calculations and agent metrics downstream.

Can I use this approach for large-scale ticket datasets across multiple years?▼

Yes. pandas DataFrames with SQLAlchemy PostgreSQL integration and Parquet export support production-grade performance on large datasets. Memory-efficient operations and chunked processing enable analysis of multi-year ticket histories at scale.

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