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Ticket Analysis Script

Python script for analyzing support tickets to identify common patterns and keywords in LIMS-related issues.

Requirements

pip install pandas

Usage

1. Place your CSV file named tickets.csv in the same directory
2. Run the script:

python ticket_analysis.py

What It Does

• Loads ticket data from CSV file
• Extracts common LIMS error keywords from descriptions
• Counts frequency of each keyword/pattern
• Shows top 5 most common issues with example tickets

Keywords Detected

• sample
• login
• slow
• error
• batch
• test
• patient
• report
• connection
• timeout

Script Code

import pandas as pd
from collections import Counter
import re

# Load your CSV
df = pd.read_csv('tickets.csv')

# Extract keywords from descriptions
def extract_keywords(text):
    # Common LIMS error patterns
    keywords = ['sample', 'login', 'slow', 'error', 'batch', 'test', 'patient', 'report', 'connection', 'timeout']
    found = []
    text_lower = str(text).lower()
    for keyword in keywords:
        if keyword in text_lower:
            found.append(keyword)
    return found

# Apply to descriptions
df['keywords'] = df['description'].apply(extract_keywords)

# Count patterns
all_keywords = [keyword for sublist in df['keywords'] for keyword in sublist]
keyword_counts = Counter(all_keywords)
print(keyword_counts)

# Show tickets for top keywords
for keyword, count in keyword_counts.most_common(5):
    print(f"\n=== {keyword.upper()} ({count} tickets) ===")
    matching_tickets = df[df['keywords'].apply(lambda x: keyword in x)]
    for idx, row in matching_tickets.head(3).iterrows():
        print(f"- {row['short_description']}")

Expected CSV Format

Your tickets.csv should have columns:
- description: Full ticket description
- short_description: Brief ticket summary

Last updated: 2025-08-26 20:00 UTC