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.