Csv Excel Merger

• "first_name", "firstname", "First Name" → "first_name"
• "phone", "phone_number", "tel" → "phone"
• "email", "e-mail", "email_address" → "email"

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)

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: [email protected]
  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

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

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

Export Options:

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