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# atoti.math.erf()

### atoti.math.erf(measure, /)

Return the error function of the input measure.

This can be used to compute traditional statistical measures such as the cumulative standard normal distribution.

For more information read:

* Python’s built-in [`math.erf()`](https://docs.python.org/3/library/math.html#math.erf)
* [scipy.special.erf](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.erf.html)
* [The Wikipedia page](https://en.wikipedia.org/wiki/Error_function#Numerical_approximations)

### Example

```pycon theme={"languages":{"custom":["/engine/python-sdk/0.9/languages/pycon.tmLanguage.json"]}}
>>> df = pd.DataFrame(
...     columns=["City", "A", "B", "C", "D"],
...     data=[
...         ("Berlin", 15.0, 10.0, 10.1, 1.0),
...         ("London", 24.0, 16.0, 20.5, 3.14),
...         ("New York", -27.0, 15.0, 30.7, 10.0),
...         ("Paris", 0.0, 0.0, 0.0, 0.0),
...     ],
... )
>>> table = session.read_pandas(df, keys={"City"}, table_name="Math")
>>> cube = session.create_cube(table)
>>> l, m = cube.levels, cube.measures
>>> m["erf"] = tt.math.erf(m["D.SUM"])
>>> m["erf"].formatter = "DOUBLE[#,##0.000000]"
>>> cube.query(m["D.SUM"], m["erf"], levels=[l["City"]])
          D.SUM       erf
City
Berlin     1.00  0.842701
London     3.14  0.999991
New York  10.00  1.000000
Paris       .00  0.000000
```

* **Parameters:**
  **measure** (*VariableMeasureConvertible*)
* **Return type:**
  *MeasureDefinition*