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# `sklearn.utils`.check_array

sklearn.utils.check_array(array, accept_sparse=False, *, accept_large_sparse=True, dtype='numeric', order=None, copy=False, force_all_finite=True, ensure_2d=True, allow_nd=False, ensure_min_samples=1, ensure_min_features=1, estimator=None, input_name='')

  Input validation on an array, list, sparse matrix or similar.

  By default, the input is checked to be a non-empty 2D array containing only finite values. If the dtype of the array is object, attempt converting to float, raising on failure.

  Parameters:
    array object

      Input object to check / convert.

    accept_sparse str, bool or list/tuple of str, default=False

      String[s] representing allowed sparse matrix formats, such as ‘csc’, ‘csr’, etc. If the input is sparse but not in the allowed format, it will be converted to the first listed format. True allows the input to be any format. False means that a sparse matrix input will raise an error.

    accept_large_sparse bool, default=True

      If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by accept_sparse, accept_large_sparse=False will cause it to be accepted only if its indices are stored with a 32-bit dtype.

      New in version 0.20.

    dtype‘numeric’, type, list of type or None, default=’numeric’

      Data type of result. If None, the dtype of the input is preserved. If “numeric”, dtype is preserved unless array.dtype is object. If dtype is a list of types, conversion on the first type is only performed if the dtype of the input is not in the list.

    order{‘F’, ‘C’} or None, default=None

      Whether an array will be forced to be fortran or c-style. When order is None (default), then if copy=False, nothing is ensured about the memory layout of the output array; otherwise (copy=True) the memory layout of the returned array is kept as close as possible to the original array.

    copy bool, default=False

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

    force_all_finite bool or ‘allow-nan’, default=True

      Whether to raise an error on np.inf, np.nan, pd.NA in array. The possibilities are:

      - True: Force all values of array to be finite.

      - False: accepts np.inf, np.nan, pd.NA in array.

      - ‘allow-nan’: accepts only np.nan and pd.NA values in array. Values cannot be infinite.

      New in version 0.20: `force_all_finite` accepts the string `'allow-nan'`.

      Changed in version 0.23: Accepts `pd.NA` and converts it into `np.nan`

    ensure_2d bool, default=True

      Whether to raise a value error if array is not 2D.

    allow_nd bool, default=False

      Whether to allow array.ndim > 2.

    ensure_min_samples int, default=1

      Make sure that the array has a minimum number of samples in its first axis (rows for a 2D array). Setting to 0 disables this check.

    ensure_min_features int, default=1

      Make sure that the 2D array has some minimum number of features (columns). The default value of 1 rejects empty datasets. This check is only enforced when the input data has effectively 2 dimensions or is originally 1D and `ensure_2d` is True. Setting to 0 disables this check.

    estimator str or estimator instance, default=None

      If passed, include the name of the estimator in warning messages.

    input_name str, default=””

      The data name used to construct the error message. In particular if `input_name` is “X” and the data has NaN values and allow_nan is False, the error message will link to the imputer documentation.

      New in version 1.1.0.

  Returns:
    array_converted object

      The converted and validated array.