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, warn_on_dtype=False, estimator=None)
[source]

Input validation on an array, list, sparse matrix or similar.
By default, the input is converted to an at least 2D numpy array. 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 : string, boolean or list/tuple of strings (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. Deprecated since version 0.19: Passing ‘None’ to parameter accept_sparse in methods is deprecated in version 0.19 “and will be removed in 0.21. Use accept_sparse=False instead. 
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 32bit dtype. 
dtype : string, type, list of types 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 cstyle. 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 : boolean (default=False) 
Whether a forced copy will be triggered. If copy=False, a copy might be triggered by a conversion. 
force_all_finite : boolean or ‘allownan’, (default=True) 
Whether to raise an error on np.inf and np.nan in X. The possibilities are:  True: Force all values of X to be finite.
 False: accept both np.inf and np.nan in X.
 ‘allownan’: accept only np.nan values in X. Values cannot be infinite.
New in version 0.20: force_all_finite accepts the string 'allownan' . 
ensure_2d : boolean (default=True) 
Whether to raise a value error if X is not 2d. 
allow_nd : boolean (default=False) 
Whether to allow X.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. 
warn_on_dtype : boolean (default=False) 
Raise DataConversionWarning if the dtype of the input data structure does not match the requested dtype, causing a memory copy. 
estimator : str or estimator instance (default=None) 
If passed, include the name of the estimator in warning messages. 
Returns: 

X_converted : object 
The converted and validated X. 