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CSV/Excel Merger

Intelligently merge multiple CSV or Excel files with automatic column matching and data deduplication.

Instructions

When a user needs to merge CSV or Excel files:

1. Analyze Input Files :

   • How many files need to be merged?
   • What format (CSV, Excel, TSV)?
   • Are the files provided or need to be read from disk?
   • Do columns have the same names across files?
   • What is the primary key (unique identifier)?

2. Inspect File Structures :

   • Read headers from each file
   • Identify column names and data types
   • Detect encoding (UTF-8, Latin-1, etc.)
   • Check for missing columns
   • Look for duplicate column names

3. Create Merge Strategy :

Column Matching :

 * Exact name match: "email" = "email"
 * Case-insensitive: "Email" = "email"
 * Fuzzy match: "E-mail" ≈ "email"
 * Common patterns: 
   * "first_name", "firstname", "First Name" → "first_name"
   * "phone", "phone_number", "tel" → "phone"
   * "email", "e-mail", "email_address" → "email"

Conflict Resolution (when same record appears in multiple files):

 * **Keep first** : Use value from first file
 * **Keep last** : Use value from last file (most recent)
 * **Keep longest** : Use most complete value
 * **Manual review** : Flag conflicts for user review
 * **Merge** : Combine non-conflicting fields

Deduplication :

 * Identify duplicate rows based on primary key
 * Options: keep first, keep last, keep all, merge values
 * Track source file for each row

4. Perform Merge :

     # Example merge logic
     import pandas as pd
     
     # Read files
     df1 = pd.read_csv('file1.csv')
     df2 = pd.read_csv('file2.csv')
     
     # Normalize column names
     df1.columns = df1.columns.str.lower().str.strip()
     df2.columns = df2.columns.str.lower().str.strip()
     
     # Map similar columns
     column_mapping = {
         'firstname': 'first_name',
         'e_mail': 'email',
         # ...
     }
     df2 = df2.rename(columns=column_mapping)
     
     # Merge
     merged = pd.concat([df1, df2], ignore_index=True)
     
     # Deduplicate
     merged = merged.drop_duplicates(subset=['email'], keep='last')
     
     # Save
     merged.to_csv('merged_output.csv', index=False)
     

5. Format Output :

     📊 CSV/EXCEL MERGER REPORT
     
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
     📁 INPUT FILES
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
     
     File 1: contacts_jan.csv
       Rows: 1,245
       Columns: 8 (name, email, phone, company, ...)
     
     File 2: contacts_feb.csv
       Rows: 987
       Columns: 9 (firstname, lastname, email, mobile, ...)
     
     File 3: leads_export.xlsx
       Rows: 2,103
       Columns: 12 (full_name, email_address, phone, ...)
     
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
     🔄 COLUMN MAPPING
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
     
     Unified Schema:
     • first_name ← [firstname, first name, fname]
     • last_name ← [lastname, last name, lname]
     • email ← [email, e-mail, email_address]
     • phone ← [phone, mobile, phone_number, tel]
     • company ← [company, organization, org]
     • title ← [title, job_title, position]
     • source ← [file origin tracking]
     
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
     🔍 MERGE ANALYSIS
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
     
     Total rows before merge: 4,335
     Duplicate records found: 892
     Conflicts detected: 47
     
     Deduplication Strategy: Keep most recent (by source file date)
     Primary Key: email
     
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
     ⚠️ CONFLICTS
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
     
     Record: john.doe@example.com
       File 1 phone: (555) 123-4567
       File 2 phone: (555) 987-6543
       Resolution: Kept most recent (File 2)
     
     [List top 10 conflicts]
     
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
     ✅ MERGE RESULTS
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
     
     Output File: merged_contacts.csv
     Total Rows: 3,443
     Columns: 7
     Duplicates Removed: 892
     
     Breakdown by Source:
     • contacts_jan.csv: 1,245 rows (398 unique)
     • contacts_feb.csv: 987 rows (521 unique)
     • leads_export.xlsx: 2,103 rows (2,524 unique)
     
     Data Quality:
     • Email completeness: 98.2%
     • Phone completeness: 87.5%
     • Company completeness: 91.3%
     
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
     💡 RECOMMENDATIONS
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
     
     • Review 47 conflict records manually
     • Standardize phone number format
     • Fill missing company names (8.7% incomplete)
     • Export conflicts to: conflicts_review.csv
     

6. Handle Special Cases :

Multiple Primary Keys :

 * Use compound keys: (email + company)
 * Offer options when ambiguous

Different Data Types :

 * Convert dates to standard format
 * Normalize phone numbers
 * Standardize country codes
 * Clean whitespace and casing

Missing Columns :

 * Fill with empty values
 * Flag missing data
 * Offer to create new columns

Large Files :

 * Use chunking for files > 100MB
 * Show progress indicator
 * Estimate memory usage

7. Generate Code : Provide Python/pandas script that:

 * Reads all files
 * Performs intelligent column matching
 * Deduplicates based on strategy
 * Resolves conflicts
 * Saves merged output
 * Generates detailed report

8. Export Options :

 * CSV (UTF-8)
 * Excel (.xlsx)
 * JSON
 * SQL INSERT statements
 * Parquet (for large datasets)

Example Triggers

• "Merge these three CSV files"
• "Combine multiple Excel sheets into one file"
• "Deduplicate and merge customer data"
• "Join spreadsheets with different column names"
• "Consolidate contact lists from different sources"

Best Practices

Column Matching :

• Use fuzzy matching for similar names
• Maintain original column name mapping report
• Allow manual override of auto-matching

Data Quality :

• Trim whitespace
• Standardize formats (phone, email, dates)
• Detect and flag invalid data
• Preserve data types

Performance :

• Use chunking for large files
• Process in batches
• Show progress for long operations
• Optimize memory usage

Transparency :

• Log all merge decisions
• Track source file for each row
• Report conflicts and resolutions
• Generate detailed merge report

Output Quality

Ensure merges:

• Intelligently match columns
• Handle different schemas
• Deduplicate properly
• Preserve data integrity
• Flag conflicts for review
• Generate comprehensive report
• Maintain data quality
• Track data lineage (source)
• Handle edge cases gracefully
• Provide validation statistics

Generate clean, deduplicated merged files with full transparency and data quality checks.

Weekly Installs

392

Repository

onewave-ai/claude-skills

GitHub Stars

74

First Seen

Jan 24, 2026

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