Data

Combine CSV files into an Excel workbook

Get batch workbook entities: one CSV per sheet, source filename, headers, rows, delimiter, encoding, and typed or string-safe cells.

Output: Excel XLSX
Free: 5 conversions a day · 5 MB each
Deleted after 24 h · never used for training

Before you start

What to expect

Browser-capable conversions run locally where supported; larger batches may still be queued and retained only for the product retention window.
Uploads are not used to train models, and retained files are deleted after 24 hours unless your account policy says otherwise.
True multi-file merge into one table is not supported yet; this workflow is for batch conversion and sheet-per-file review.

What the output contains

A batch workflow that converts each CSV into XLSX form, ideally as one sheet per file when batch packaging is available.
Source filename context preserved so teams can inspect which export produced each sheet.
No true row-level merge, union-column stacking, or deduplication across multiple CSV files is promised yet.
Use DIY csvstack or pandas concat when you need one combined table rather than sheet-per-file review.

A real run

Monthly sales CSV batch

Input: 12 monthly CSV exports, 42 columns each, January and February had columns in a different order, and three files used semicolon delimiters.
Output: One batch workbook package with 12 sheets for review; no cross-file merge was performed, avoiding a bad union of mismatched month schemas.

Known failure modes and how they're handled

User expects one merged CSV table: The product does not merge multiple files into one table yet. It converts each CSV for workbook review and documents DIY merge commands.
Different column orders across files: Sheet-per-file output avoids unsafe column union. Use pandas/csvstack with explicit schema decisions for stacking.
Repeated header rows inside each CSV: Headers are preserved as supplied. Remove duplicate embedded headers before downstream analysis.
Combined data too large for Excel: Excel row limits still apply. For large datasets, convert to Parquet or load into a database instead of stacking into XLSX.

Command line

Do it yourself

csvstack

bash

csvstack jan.csv feb.csv mar.csv > combined.csv

Use only when files share the same schema and delimiter assumptions.

pandas concat

bash

python -c 'import pandas as pd, glob; pd.concat((pd.read_csv(f).assign(source=f) for f in glob.glob("*.csv")), ignore_index=True).to_csv("combined.csv", index=False)'

Safer for adding source filenames and explicit schema cleanup before writing one table.

Questions about this workflow

Can FileType Converters merge many CSVs into one CSV?

Not yet. It can batch-convert CSV files to XLSX form, but row-level merging is a DIY step today.

Can each CSV become its own sheet?

That is the honest target for this workflow: one source CSV per sheet/package item for review.

What if the headers differ?

Do not stack them blindly. Keep separate sheets or use pandas with an explicit union-column plan.

What should I use for very large CSV sets?

Use Parquet, DuckDB, or a database instead of trying to force everything into Excel.

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