csv-excel-merger

CSV/Excel Merger

Merge multiple CSV or Excel files with automatic column matching, deduplication, and conflict resolution.

Contents

• Workflow — the step-by-step merge process
• Verification — confirm the merge before handing it back
• Special cases — encoding, compound keys, large files
• Guidelines — quality and transparency standards
• Example triggers
• references/merge_strategies.md — column matching, conflict resolution, and dedup options
• references/output_template.md — the merge-report format

Workflow

1. Inspect the inputs. Determine file count, format (CSV / Excel / TSV), and whether the files are attached or read from disk. Read each header; identify column names, data types, and encoding (UTF-8, Latin-1). Note the candidate primary key.

2. Plan the merge. Match columns across files to one unified schema, choose a conflict-resolution rule, and pick a deduplication strategy. See references/merge_strategies.md for the matching heuristics and the full set of options.

3. Execute the merge with pandas:

   import pandas as pd
   
   df1 = pd.read_csv("file1.csv")
   df2 = pd.read_csv("file2.csv")
   
   # Normalize, then map column names onto the unified schema
   for df in (df1, df2):
       df.columns = df.columns.str.lower().str.strip()
   df2 = df2.rename(columns={"firstname": "first_name", "e_mail": "email"})
   
   merged = pd.concat([df1, df2], ignore_index=True)
   merged = merged.drop_duplicates(subset=["email"], keep="last")
   merged.to_csv("merged_output.csv", index=False)
   

4. Verify the result before reporting — see Verification.

5. Report using the layout in references/output_template.md, then offer export options: CSV (UTF-8), Excel (.xlsx), JSON, SQL INSERT statements, or Parquet for large datasets.

Verification

Never hand back a merge without checking it. After merging, assert the row math holds and the key is actually unique: