Combine monthly reports
Join spreadsheets from different reporting periods into one dataset with consistent column names.
PYTHON · EXCEL · XLSX · DATA MERGING
Merge Excel files with Python
Combine spreadsheets from different departments, periods or systems into one consistent dataset. Python workflows can align columns, normalize values, identify duplicate records and prepare a validated Excel result.
Common Excel merging tasks
Align different columns
Map columns with different names or positions to an agreed output structure.
Check duplicate records
Identify repeated identifiers and apply explicitly agreed deduplication rules.
Prepare consolidated exports
Generate an XLSX dataset with validation results and unresolved issues.
How Excel consolidation works
Review source files
Inspect worksheets, column names, identifiers, formats and missing values.
Define mapping rules
Agree how source columns correspond to the consolidated structure.
Merge and validate
Combine records and check duplicates, required fields and conflicting values.
Deliver results
Export the merged workbook with processing statistics and validation findings.
Example: combining two Excel reports
Illustrative example using fictional order records, not a completed client project.
Two source reports
| Source | Order ID | Customer | Amount |
|---|---|---|---|
| A | A-101 | Alpha LLC | 1200 |
| A | A-102 | Beta Ltd | 850 |
| B | A-102 | Beta Ltd | 850 |
| B | B-201 | Delta LLC | 640 |
Consolidated result
| Order ID | Customer | Amount |
|---|---|---|
| A-101 | Alpha LLC | 1200 |
| A-102 | Beta Ltd | 850 |
| B-201 | Delta LLC | 640 |
The preview shows selected records. The downloadable workbook contains all six input records, five unique output records and a validation sheet. The duplicate A-102 is identical across the source reports.
Download the Excel merging example
The XLSX workbook contains Source_A, Source_B, Merged_Result and Validation worksheets. All records are fictional.
Inputs, validation and deliverables
Before development, we review representative files, sheet layouts, column mappings, identifiers and expected output. Conflicting records are flagged or handled according to agreed rules rather than silently overwritten.
Depending on scope, deliverables may include a reusable Python script, a consolidated XLSX file, duplicate-check results, validation statistics and run instructions. Processing rules and acceptance criteria are agreed in advance.
Discuss an Excel merging project
Sample files, expected number of records, matching rules, output columns, update frequency, acceptance criteria, schedule and price are confirmed before implementation.
Discuss Excel automation