Extract Numbers
Infoveave Data Automation — Text & String
When a notes field reads Order 1234 Amount 45.67, Extract Numbers pulls out 1234 and 45.67 as separate numeric columns — no regex, no custom parsing, no manual data cleaning required.
Data from legacy systems, unstructured text exports, free-text notes, and formula-generated strings frequently embeds numeric values alongside text characters in a single column. Order numbers glued to order descriptions, amounts inside notes fields, measurements written as 1.5K or 2M compact notation — all require numeric extraction before any calculation or aggregation can be performed. Extract Numbers handles the complete extraction across four modes: pulling each number into its own column for row-level analysis, packaging all numbers as a JSON array for programmatic processing, expanding compact notation like 1K to 1000 for calculation, and correcting regional decimal separator formats for consistent aggregation.
What Extract Numbers does
Extract numeric values from mixed text columns in Infoveave across four modes — several numbers, JSON array, expand notation like 1K to 1000, and decimal separator handling. Recover numeric data from unstructured descriptions, codes, and notes fields without manual parsing.
When to use Extract Numbers
- You have a mixed text column — order descriptions, product notes, measurement strings, or comments — where numeric values are embedded within characters and you need to extract those numbers to perform aggregation, comparison, or calculation
- You have data with compact notation like 1K, 2.5M, or 3B — commonly used in financial reporting, market sizing data, or marketing metric exports — and need to expand these values to their full numeric representation before performing calculations
- You are processing data exported from regional systems that use regional decimal separators — commas instead of periods or periods instead of commas — and need to standardize the number format before importing into an analytics system that expects a specific decimal format
- You have free-text notes or descriptions where multiple numeric values appear per record — for example order amount, quantity, and discount percentage all mentioned in a single description field — and need each number extracted into its own column
When to avoid it
- Your numeric values are already stored in properly typed numeric columns — Extract Numbers is designed for columns where numbers are embedded within text characters, not for columns that already hold clean numeric data
- You only need to extract a specific pattern from text — like product codes, part numbers, or reference ID patterns — rather than any numeric value present; use Find Text with a regex pattern for structure-specific extraction
- Your column combines a number with a currency code and you need to separate the amount from the currency — use Split Currencies which handles the monetary amount-currency pair structure specifically
Where it fits in your Infoveave automation
Extract Numbers is one step inside a multi-step Infoveave workflow. Chain it with other activities — no code, no manual hand-offs.
Build this workflow visually in Infoveave Data Automation — drag, connect, and schedule with no infrastructure setup.
How teams use Extract Numbers
Real scenarios where this transformation saves hours of manual work.
Expand Market Data Compact Notation into Full Numeric Values
A financial data team processes market sizing and valuation data exported from a research tool where figures are formatted in compact notation — 1.2B, 450M, 3.5T — stored in a text column alongside qualifiers. Extract Numbers with Expand Notation mode converts each compact notation value to its full numeric equivalent — 1200000000, 450000000, 3500000000000 — in the output column. The team can then aggregate, compare, and calculate with the full numeric values in dashboards and financial models.
Extract Order Amount and Quantity from Order Description Strings
A retail operations team processes order data from a legacy system where each record has a single description field containing multiple numeric values embedded in text — Order 1234 Qty 5 Amount 89.50 Discount 10. Extract Numbers with Several mode pulls each number into its own output column, producing columns for 1234, 5, 89.50, and 10 across each row. The team can then calculate net amounts, sum quantities, and aggregate discount totals from structured numeric columns.
Standardize Measurement Values from Multi-Region Quality Records
A manufacturing data team processes quality inspection records from plants in different regions where measurement values use different decimal separator conventions — European records use commas as decimal separators while US records use periods. Extract Numbers with Decimal Separator mode and the comma separator configuration normalizes European-format numbers — 1.234,56 — into the standard periode-based format — 1234.56 — so all measurement values aggregate correctly in cross-plant quality dashboards.
See Extract Numbers in action
Input data (left) is transformed using the configuration below. The output table (right) is ready for dashboards or downstream steps.
DescriptionSeveralNumbersYesInput Data
| Order ID | Description |
|---|---|
| 001 | Order #1234, Amount: $45.67, Qty: 3 |
| 002 | Invoice No. 5678 for 12 items at $9.99 each |
| 003 | Ref: 9012 Total: $250.00 with $25.00 discount |
Output Data
| Order ID | Description | Numbers_1 | Numbers_2 | Numbers_3 |
|---|---|---|---|---|
| 001 | Order #1234, Amount: $45.67, Qty: 3 | 1234 | 45.67 | 3 |
| 002 | Invoice No. 5678 for 12 items at $9.99 each | 5678 | 12 | 9.99 |
| 003 | Ref: 9012 Total: $250.00 with $25.00 discount | 9012 | 250.00 | 25.00 |
Configuration
Key fields to configure in the Infoveave workflow builder. Full reference available in the documentation.
Column
Select the text column from which numbers should be extracted. The column should contain natural language text or structured strings where numeric values appear mixed with other characters. Purely numeric columns do not need this transformation — it is designed for mixed text-and-number content.
Extract Mode
Choose from four extraction modes. Several extracts each number from the text into a separate output column numbered sequentially — Numbers_1, Numbers_2, Numbers_3 — one column per distinct number found in the row. JSON Array packages all extracted numbers into a JSON array in a single output column for programmatic processing. Expand Notation converts compact numbers like 1K, 2.5M, and 3B to their full numeric values in a single output column. Decimal Separator corrects regional decimal separator formats to produce standard decimal notation in a single output column.
Output Column
Specify the base name for the output column or columns. In Several mode, columns are named as the base name plus a sequential number suffix — Numbers_1, Numbers_2. In JSON Array, Expand Notation, and Decimal Separator modes, a single column is created with exactly the base name you specify.
Include Original
Choose whether to keep the original text column alongside the extracted number columns. Keep it on for debugging or when the full description text is needed for display. Disable it to keep the output schema clean once the numeric values have been successfully extracted.
Separator (Decimal Separator mode only)
When using Decimal Separator mode, specify the decimal separator character used in your source data — a comma for European-format numbers or a period for US-format numbers. Infoveave uses this to correctly identify the decimal position in the number string before converting to the standard output format.
Frequently asked questions
Everything you need to know about Extract Numbers in Infoveave.
Also in Text & String — and what runs before & after
Transformations in the same family as Extract Numbers, often chained together in the same Infoveave workflow.
Part of Infoveave Data Automation
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Extract Numbers is one of over 80 transformation activities available inside Infoveave workflows. Chain transformations together — no code, no exports, no waiting for IT.
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