# Input Data

Note

Scikit-learn patching functionality in daal4py was deprecated and moved to a separate package, Intel(R) Extension for Scikit-learn*. All future patches will be available only in Intel(R) Extension for Scikit-learn*. Use the scikit-learn-intelex package instead of daal4py for the scikit-learn acceleration.

All array arguments to compute functions and to algorithm constructors can be provided in different formats. daal4py will automatically do its best to work on the provided data with minimal overhead, most notably without copying the data.

## Numpy Arrays

daal4py can directly handle all types of numpy arrays with numerical data without copying the entire data. Arrays can be homogeneous (e.g. simple dtype) or heterogeneous (structured array) as well as contiguous or non-contiguous.

## Pandas DataFrames

daal4py directly accepts pandas DataFrames with columns of numerical data. No extra full copy is required.

## SciPy Sparse CSR Matrix

daal4py can directly handle matrices of type scipy.sparse.csr_matrix without copying the entire data.

Note: some algorithms can be configured to use an optimized compute path for CSR data. It is required to explicitly specify the CSR method, otherwise the default and less efficient method is used.

## CSV Files

The compute functions daal4py’s algorithms additionally accept CSV-filenames. Internally, daal4py will use DAAL’s fast CSV reader to create contiguous homogeneous tables.