# foundryts.functions.scale

## foundryts.functions.scale(factor)

Returns a function that multiplies each value in a single time series by the specified factor.

For a source time series with points `(timestamp, value)`, upon scaling by `factor`,
the resulting scaled time series will have points `(timestamp, value * factor)`.

* **Parameters:**
  **factor** ([*float*](https://docs.python.org/3/library/functions.html#float)) – The scaling factor that is multiplied with the value of each point.
* **Returns:**
  A function that accepts a single time series as input and returns the scaled time series.
* **Return type:**
  (`FunctionNode`) -> `FunctionNode`

## Dataframe schema

| Column name   | Type             | Description               |
|---------------|------------------|---------------------------|
| timestamp     | pandas.Timestamp | Timestamp of the point    |
| value         | float            | Scaled value of the point |

:::callout{theme="success" title="See Also"}
[`timestamp_scale()`](../functions-timestamp-scale/index.md#foundryts.functions.timestamp_scale), [`value_shift()`](../functions-value-shift/index.md#foundryts.functions.value_shift)
:::

:::callout{theme="warning" title="Note"}
This function is only applicable to numeric series.
:::

## Examples

```pycon
>>> series = F.points(
...     (100, 0.0),
...     (200, float("inf")),
...     (300, 3.14159),
...     (2147483647, 1.0),
...     name="series"
... )
>>> series.to_pandas()
                      timestamp    value
0 1970-01-01 00:00:00.000000100  0.00000
1 1970-01-01 00:00:00.000000200      inf
2 1970-01-01 00:00:00.000000300  3.14159
3 1970-01-01 00:00:02.147483647  1.00000
```

```pycon
>>> scaled = F.scale(1.5)(series)
>>> scaled.to_pandas()
                      timestamp     value
0 1970-01-01 00:00:00.000000100  0.000000
1 1970-01-01 00:00:00.000000200       inf
2 1970-01-01 00:00:00.000000300  4.712385
3 1970-01-01 00:00:02.147483647  1.500000
```