# Variance Estimator
A [variance estimator](https://skfolio.org/api.html.md#variance-ref) estimates the variance vector of the
assets.
It follows the same API as scikit-learn’s `estimator`: the `fit` method takes `X` as
the assets returns and stores the variances in its `variance_` attribute.
Variance estimators are useful when only marginal volatility is needed, for example
when modelling idiosyncratic risk or working with orthogonalized return series.
`X` can be any array-like structure (numpy array, pandas DataFrame, etc.)
Available estimators are:
: * [`EmpiricalVariance`](https://skfolio.org/generated/skfolio.moments.EmpiricalVariance.html.md#skfolio.moments.EmpiricalVariance)
* [`EWVariance`](https://skfolio.org/generated/skfolio.moments.EWVariance.html.md#skfolio.moments.EWVariance)
* [`RegimeAdjustedEWVariance`](https://skfolio.org/generated/skfolio.moments.RegimeAdjustedEWVariance.html.md#skfolio.moments.RegimeAdjustedEWVariance)
For online learning and streaming workflows, [`EWVariance`](https://skfolio.org/generated/skfolio.moments.EWVariance.html.md#skfolio.moments.EWVariance) and
[`RegimeAdjustedEWVariance`](https://skfolio.org/generated/skfolio.moments.RegimeAdjustedEWVariance.html.md#skfolio.moments.RegimeAdjustedEWVariance) support incremental updates with
`partial_fit`. They also support NaN-aware updates with `active_mask`, which
helps distinguish assets that belong to the universe but have missing returns (e.g.
holidays), from assets outside the universe (e.g. pre-listing or post-delisting
periods).
See [Missing Data and Changing Universes](https://skfolio.org/user_guide/data_representation.html.md#missing-data) for the full convention
on NaNs, universe membership, estimator warmup and investability.
**Example:**
```python
from skfolio.datasets import load_sp500_dataset
from skfolio.moments import EmpiricalVariance
from skfolio.preprocessing import prices_to_returns
prices = load_sp500_dataset()
X = prices_to_returns(prices)
model = EmpiricalVariance()
model.fit(X)
print(model.variance_)
```