# normalize

### cuml.preprocessing.normalize(X, norm='l2', , axis=1, copy=True, return_norm=False)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/_thirdparty/sklearn/preprocessing/_data.py#L1789)

Scale input vectors individually to unit norm (vector length).

* **Parameters:**
  **X**
  : The data to normalize, element by element.
    Please provide CSC matrix to normalize on axis 0,
    conversely provide CSR matrix to normalize on axis 1

  **norm**
  : The norm to use to normalize each non zero sample (or each non-zero
    feature if axis is 0).

  **axis**
  : axis used to normalize the data along. If 1, independently normalize
    each sample, otherwise (if 0) normalize each feature.

  **copy**
  : Whether a forced copy will be triggered. If copy=False, a copy might
    be triggered by a conversion.

  **return_norm**
  : whether to return the computed norms
* **Returns:**
  **X**
  : Normalized input X.

  **norms**
  : An array of norms along given axis for X.
    When X is sparse, a NotImplementedError will be raised
    for norm ‘l1’ or ‘l2’.

#### SEE ALSO
[`Normalizer`](../cuml.preprocessing.Normalizer/index.html.md#cuml.preprocessing.Normalizer)
: Performs normalization using the `Transformer` API

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