How do I join time-series CSVs from multiple financial data vendors when their date formats differ?

Normalize all source CSVs to ISO 8601 (YYYY-MM-DD) before joining. This tool rewrites date columns across all your source files to a single format, making date-key joins in Excel, pandas, or SQL reliable without per-file conversion formulas.

Will this fix European-formatted numeric columns where commas and periods are swapped?

Not reliably. Numeric normalization uses standard parsing, which does not handle European decimal conventions where commas serve as decimal separators. Values like '1.200,50' will likely be replaced with empty cells rather than converted. For European-format CSVs, explicitly specifying the column and reviewing the output is strongly recommended.

Does normalizing a CSV change anything other than the date and numeric columns I specify?

No. Only the columns you name, or those auto-detected as numeric or date, are rewritten. All other columns, column headers, and row order are preserved exactly.

Can I process multiple datasets — for example, price history and earnings CSVs — in one session?

Yes. Upload multiple CSV files in one session and each is processed independently with the same normalization settings applied.

Normalize CSV Exports from Financial Data Sources Before Modeling

Financial analysts pulling CSV exports from Bloomberg, FactSet, Refinitiv, or internal model outputs routinely encounter date and numeric format conflicts that break time-series joins and return calculation formulas. Deliteful's CSV Normalize Data Types tool standardizes these columns before the data enters your model, eliminating a manual cleaning step that compounds across every dataset refresh.

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A common modeling bottleneck: you pull price history from Bloomberg as YYYYMMDD integers, earnings data from an internal system as MM/DD/YYYY strings, and a third dataset with Unix timestamps — all representing the same date column. Joining them on a date key fails silently or requires a column of conversion formulas that break when formats change next quarter. These are not edge cases; they are the standard state of multi-source financial data.

This tool resolves date format conflicts at the source file level before data enters your model. Upload the raw CSVs, specify the date and numeric columns relevant to your analysis, and download files with a single consistent format throughout. ISO 8601 dates join cleanly on date keys in Excel, Python (pandas), or SQL. Note that numeric normalization uses standard parsing — values with non-standard formatting such as European decimal conventions may become empty cells rather than being converted, so reviewing output on first use is recommended.

How it works

1
Export source CSVs
Pull CSV exports from Bloomberg, FactSet, internal systems, or any other data source feeding your model.
2
Upload to Deliteful
Upload one or more CSV files for normalization in a single batch.
3
Specify date and numeric columns
Name the columns used as date keys or numeric inputs in your model, or use auto-detection.
4
Select ISO date output
Choose YYYY-MM-DD for clean date joins across datasets and compatibility with Python, SQL, and Excel date functions.
5
Download and load into your model
Replace raw source files with normalized versions and load into your spreadsheet or analysis environment without reformatting.

Frequently asked questions

How do I join time-series CSVs from multiple financial data vendors when their date formats differ?: Normalize all source CSVs to ISO 8601 (YYYY-MM-DD) before joining. This tool rewrites date columns across all your source files to a single format, making date-key joins in Excel, pandas, or SQL reliable without per-file conversion formulas.
Will this fix European-formatted numeric columns where commas and periods are swapped?: Not reliably. Numeric normalization uses standard parsing, which does not handle European decimal conventions where commas serve as decimal separators. Values like '1.200,50' will likely be replaced with empty cells rather than converted. For European-format CSVs, explicitly specifying the column and reviewing the output is strongly recommended.
Does normalizing a CSV change anything other than the date and numeric columns I specify?: No. Only the columns you name, or those auto-detected as numeric or date, are rewritten. All other columns, column headers, and row order are preserved exactly.
Can I process multiple datasets — for example, price history and earnings CSVs — in one session?: Yes. Upload multiple CSV files in one session and each is processed independently with the same normalization settings applied.

Create your free Deliteful account with Google and clean up your multi-source CSV exports before they enter your financial model — no card required.