Normalize Columns in Arrow Files Online
Normalize numeric columns in Arrow files using min-max scaling (0–1) or z-score standardisation (mean=0, std=1). Adds new columns alongside the originals — no upload required.
How to normalize Columns in Arrow files
- Drop your file onto the upload area. It is loaded into the in-browser engine and the first 200 rows are shown.
- Choose a method: Min-Max (0–1), the default, or Z-Score (μ=0, σ=1).
- Tick the numeric columns to scale. Every numeric column is ticked to start with, and the table shows the output name for each one (_normalized or _zscore).
- Click Normalize. Each new column is placed directly after the column it was computed from, and the originals are kept.
- Download the result in the same format you uploaded.
Your file is processed locally in your browser and is never uploaded. The free limit is 50 MB per file; larger files work if your device has the memory for them.
Worked example
A coach compares five players on a reaction-time test in milliseconds and a vertical jump in centimetres. The units are different, so the raw numbers cannot be compared or combined directly.
Input (Arrow)
Arrow IPC file (binary, columnar) — shown as a table with its schema
| player | reaction_ms | jump_cm |
|---|---|---|
| Aoife | 180 | 40 |
| Bram | 220 | 52 |
| Chidi | 200 | 46 |
| Dana | 160 | 60 |
| Eli | 260 | 35 |
Schema: player VARCHAR, reaction_ms BIGINT, jump_cm BIGINT
Settings
- Method: Min-Max (0–1)
- Columns: reaction_ms and jump_cm (both ticked)
Result
| player | reaction_ms | reaction_ms_normalized | jump_cm | jump_cm_normalized |
|---|---|---|---|---|
| Aoife | 180 | 0.2 | 40 | 0.2 |
| Bram | 220 | 0.6 | 52 | 0.68 |
| Chidi | 200 | 0.4 | 46 | 0.44 |
| Dana | 160 | 0 | 60 | 1 |
| Eli | 260 | 1 | 35 | 0 |
Reaction times run from 160 to 260 ms, a range of 100, so Aoife's 180 becomes (180 − 160) / 100 = 0.2. Jumps run from 35 to 60 cm, so Bram's 52 becomes 17 / 25 = 0.68. The fastest reaction scores 0 because min-max does not know that lower is better here. Use Calculate Column with 1 − reaction_ms_normalized to flip it.
Frequently Asked Questions
Does z-score use the sample or population standard deviation?
The sample standard deviation, which divides by n − 1. Results therefore differ slightly from tools that use the population figure, such as scikit-learn's StandardScaler. The gap shrinks as the row count grows.
What happens if every value in a column is the same?
The range (for min-max) or standard deviation (for z-score) is zero. The tool returns NULL for that column instead of dividing by zero. Null input values also stay null.
Can I scale values within each group, such as per region?
No. Statistics are computed over the whole file. For per-group scaling, filter the file to one group at a time, or use the SQL Query tool with PARTITION BY in the window functions.
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