# ee.FeatureCollection.randomColumn

Adds a column of deterministic pseudorandom numbers to a collection. The outputs are double-precision floating point numbers. When using the 'uniform' distribution (default), outputs are in the range of \[0, 1). Using the 'normal' distribution, outputs have μ=0, σ=1, but have no explicit limits.

<br />

| Usage | Returns |
|---|---|
| `FeatureCollection.randomColumn(*columnName*, *seed*, *distribution*, *rowKeys*)` | FeatureCollection |

| Argument | Type | Details |
|---|---|---|
| this: `collection` | FeatureCollection | The input collection to which to add a random column. |
| `columnName` | String, default: "random" | The name of the column to add. |
| `seed` | Long, default: 0 | A seed used when generating the random numbers. |
| `distribution` | String, default: "uniform" | The distribution type of random numbers to produce; one of 'uniform' or 'normal'. |
| `rowKeys` | List, optional | A list of properties that should uniquely and repeatably identify an element of the collection, used to generate the random number. Defaults to \[system:index\]. |

## Examples

### Code Editor (JavaScript)

```javascript
// FeatureCollection of power plants in Belgium.
var fc = ee.FeatureCollection('WRI/GPPD/power_plants')
            .filter('country_lg == "Belgium"');
print('N features in collection', fc.size());

// Add a uniform distribution random value column to the FeatureCollection.
fc = fc.randomColumn();

// Randomly split the collection into two sets, 30% and 70% of the total.
var randomSample30 = fc.filter('random < 0.3');
print('N features in 30% sample', randomSample30.size());

var randomSample70 = fc.filter('random >= 0.3');
print('N features in 70% sample', randomSample70.size());
```
Python setup

See the [Python Environment](https://developers.google.com/earth-engine/guides/python_install) page for information on the Python API and using
`geemap` for interactive development.

```python
import ee
import geemap.core as geemap
```

### Colab (Python)

```python
# FeatureCollection of power plants in Belgium.
fc = ee.FeatureCollection('WRI/GPPD/power_plants').filter(
    'country_lg == "Belgium"')
display('N features in collection:', fc.size())

# Add a uniform distribution random value column to the FeatureCollection.
fc = fc.randomColumn()

# Randomly split the collection into two sets, 30% and 70% of the total.
random_sample_30 = fc.filter('random < 0.3')
display('N features in 30% sample:', random_sample_30.size())

random_sample_70 = fc.filter('random >= 0.3')
display('N features in 70% sample:', random_sample_70.size())
```