Normalize Columns in JSON Files Online

Normalize numeric columns in JSON 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 JSON 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 (JSON)

[
  {
    "player": "Aoife",
    "reaction_ms": 180,
    "jump_cm": 40
  },
  {
    "player": "Bram",
    "reaction_ms": 220,
    "jump_cm": 52
  },
  {
    "player": "Chidi",
    "reaction_ms": 200,
    "jump_cm": 46
  },
  {
    "player": "Dana",
    "reaction_ms": 160,
    "jump_cm": 60
  },
  {
    "player": "Eli",
    "reaction_ms": 260,
    "jump_cm": 35
  }
]

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.

Working with JSON files

A key is offered for scaling when it holds numbers in every object that has it. Whole numbers and decimals can be mixed. A key that sometimes holds a quoted number such as "42" is loaded as a JSON-typed column and will not be listed. Neither will values nested inside an object, such as metrics.score. Flatten the file first to bring nested numbers up to the top level. Booleans are not treated as numbers, so a key holding true and false is not offered.

Objects that lack a key are treated as null for that column. They are ignored when the statistics are computed, and the scaled key is null in the output. In the downloaded JSON, each new key sits right after its source key inside every object. That keeps score and score_normalized together when you read a record. Values are written as JSON numbers, so a minimum becomes 0 and a maximum becomes 1.

Frequently Asked Questions

Can I normalize a nested JSON field such as stats.points?

Not directly, because only top-level keys are listed. Run Flatten first so stats.points becomes its own column, then normalize it.

What happens to JSON objects that don't have the key I normalize?

They get null for the new key. Missing values are not counted as zero and do not affect the min, max, mean or standard deviation.

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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