csv-excel-merger

# CSV/Excel Merger Merge multiple CSV or Excel files with automatic column matching, deduplication, and conflict resolution. ## Contents - [Workflow](#workflow) — the step-by-step merge process - [Verification](#verification) — confirm the merge before handing it back - [Special cases](#special-cases) — encoding, compound keys, large files - [Guidelines](#guidelines) — quality and transparency standards - [Example triggers](#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: ```python 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],...