Preprocessing

Preprocessing of a dataset is a common requirement for many machine learning estimators and may involve scaling, centering, normalization, smoothing, binarization, and imputation methods.

Preprocessors

Binarizer([threshold, copy])	Binarize data (set feature values to 0 or 1) according to a threshold
Butterworth([width, order, analog])	Smooth time-series data using a low-pass, zero-delay Butterworth filter.
EWMA([com, span, halflife, min_periods, ...])	Smooth time-series data using an exponentially-weighted moving average filter
DoubleEWMA([com, span, halflife, ...])	Smooth time-series data using forward and backward exponentially-weighted moving average filters
Imputer([missing_values, strategy, axis, ...])	Imputation transformer for completing missing values.
KernelCenterer	Center a kernel matrix
LabelBinarizer([neg_label, pos_label, ...])	Binarize labels in a one-vs-all fashion
MultiLabelBinarizer([classes, sparse_output])	Transform between iterable of iterables and a multilabel format
MinMaxScaler([feature_range, copy])	Transforms features by scaling each feature to a given range.
MaxAbsScaler([copy])	Scale each feature by its maximum absolute value.
Normalizer([norm, copy])	Normalize samples individually to unit norm.
RobustScaler([with_centering, with_scaling, ...])	Scale features using statistics that are robust to outliers.
StandardScaler([copy, with_mean, with_std])	Standardize features by removing the mean and scaling to unit variance
PolynomialFeatures([degree, ...])	Generate polynomial and interaction features.